diff --git a/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-json.py b/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-json.py
new file mode 100644
index 0000000..153a84c
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-json.py
@@ -0,0 +1,35 @@
+import csv
+
+fields = None
+with open('income-scraped.csv') as csvfile:
+
+ reader = csv.reader(csvfile, delimiter=",")
+ for row in reader:
+
+ # Save header variables
+ if fields == None:
+ fields = row
+
+ else:
+
+ # Start JSON string
+ if row[0] == "1":
+ famunit = '['
+ else:
+ famunit = ''
+
+ # Add family unit
+ famunit += '{'
+ famunit += '"' + fields[0] + '":' + row[0] + ',' # famindex
+ famunit += '"' + fields[1] + '":' + row[1] + ',' # AGE
+ famunit += '"' + fields[2] + '":' + row[2] # FTOTINC
+ famunit += '}'
+
+ # Close set if at end
+ if (row[0] == "200"):
+ famunit += ']'
+ else:
+ famunit += ','
+
+ # Print family unit
+ print(famunit)
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-xml.py b/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-xml.py
new file mode 100644
index 0000000..c2cdd35
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/convert-csv-to-xml.py
@@ -0,0 +1,26 @@
+import csv
+
+fields = None
+with open('income-scraped.csv') as csvfile:
+
+ reader = csv.reader(csvfile, delimiter=",")
+ for row in reader:
+
+ # Save header variables
+ if fields == None:
+ fields = row
+ print('')
+ print("")
+ else:
+
+ # Add family unit
+ famunit = ''
+ famunit += '<' + fields[0] + '>' + row[0] + '' # famindex
+ famunit += '<' + fields[1] + '>' + row[1] + '' # AGE
+ famunit += '<' + fields[2] + '>' + row[2] + '' # FTOTINC
+ famunit += '
'
+
+ # Print family unit
+ print(famunit)
+
+ print("")
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch3-codedata-dir/convert-xml-to-csv.py b/VT2-examples/vt2-ch3-codedata-dir/convert-xml-to-csv.py
new file mode 100644
index 0000000..85ae89b
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/convert-xml-to-csv.py
@@ -0,0 +1,20 @@
+from bs4 import BeautifulSoup
+
+fields = None
+with open('income-scraped-to-xml.xml') as xmlfile:
+
+ xmlread = xmlfile.read()
+
+ soup = BeautifulSoup(xmlread, features='xml')
+
+ # Start file
+ f = open('xml-back-to-csv.csv', 'w')
+
+ # Write rows to file
+ f.write(",".join(['famindex', 'AGE', 'FTOTINC'])+'\n')
+ rows = soup.findAll('row')
+ for row in rows:
+ f.write(','.join([row.famindex.string, row.AGE.string, row.FTOTINC.string])+'\n')
+
+ # Close file
+ f.close()
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch3-codedata-dir/csv-to-json.json b/VT2-examples/vt2-ch3-codedata-dir/csv-to-json.json
new file mode 100644
index 0000000..2247a0d
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/csv-to-json.json
@@ -0,0 +1,200 @@
+[{"famindex":1,"AGE":45,"FTOTINC":114000},
+{"famindex":2,"AGE":37,"FTOTINC":280000},
+{"famindex":3,"AGE":60,"FTOTINC":184000},
+{"famindex":4,"AGE":70,"FTOTINC":250100},
+{"famindex":5,"AGE":79,"FTOTINC":33040},
+{"famindex":6,"AGE":60,"FTOTINC":168000},
+{"famindex":7,"AGE":56,"FTOTINC":557000},
+{"famindex":8,"AGE":69,"FTOTINC":84500},
+{"famindex":9,"AGE":54,"FTOTINC":128800},
+{"famindex":10,"AGE":72,"FTOTINC":28500},
+{"famindex":11,"AGE":26,"FTOTINC":85000},
+{"famindex":12,"AGE":60,"FTOTINC":52000},
+{"famindex":13,"AGE":54,"FTOTINC":24000},
+{"famindex":14,"AGE":51,"FTOTINC":316600},
+{"famindex":15,"AGE":35,"FTOTINC":261590},
+{"famindex":16,"AGE":62,"FTOTINC":41200},
+{"famindex":17,"AGE":28,"FTOTINC":11000},
+{"famindex":18,"AGE":66,"FTOTINC":15500},
+{"famindex":19,"AGE":75,"FTOTINC":37100},
+{"famindex":20,"AGE":57,"FTOTINC":137000},
+{"famindex":21,"AGE":69,"FTOTINC":80000},
+{"famindex":22,"AGE":78,"FTOTINC":376100},
+{"famindex":23,"AGE":47,"FTOTINC":10000},
+{"famindex":24,"AGE":84,"FTOTINC":31400},
+{"famindex":25,"AGE":75,"FTOTINC":40000},
+{"famindex":26,"AGE":23,"FTOTINC":600},
+{"famindex":27,"AGE":43,"FTOTINC":59000},
+{"famindex":28,"AGE":87,"FTOTINC":26000},
+{"famindex":29,"AGE":66,"FTOTINC":22000},
+{"famindex":30,"AGE":62,"FTOTINC":103600},
+{"famindex":31,"AGE":25,"FTOTINC":18100},
+{"famindex":32,"AGE":45,"FTOTINC":25000},
+{"famindex":33,"AGE":65,"FTOTINC":106000},
+{"famindex":34,"AGE":63,"FTOTINC":111000},
+{"famindex":35,"AGE":68,"FTOTINC":13700},
+{"famindex":36,"AGE":28,"FTOTINC":160400},
+{"famindex":37,"AGE":49,"FTOTINC":206600},
+{"famindex":38,"AGE":58,"FTOTINC":67500},
+{"famindex":39,"AGE":74,"FTOTINC":115000},
+{"famindex":40,"AGE":70,"FTOTINC":107800},
+{"famindex":41,"AGE":79,"FTOTINC":28100},
+{"famindex":42,"AGE":22,"FTOTINC":36000},
+{"famindex":43,"AGE":69,"FTOTINC":92920},
+{"famindex":44,"AGE":27,"FTOTINC":91000},
+{"famindex":45,"AGE":60,"FTOTINC":89000},
+{"famindex":46,"AGE":69,"FTOTINC":309400},
+{"famindex":47,"AGE":34,"FTOTINC":151500},
+{"famindex":48,"AGE":35,"FTOTINC":71000},
+{"famindex":49,"AGE":42,"FTOTINC":94000},
+{"famindex":50,"AGE":64,"FTOTINC":94300},
+{"famindex":51,"AGE":40,"FTOTINC":148000},
+{"famindex":52,"AGE":95,"FTOTINC":1400},
+{"famindex":53,"AGE":39,"FTOTINC":75000},
+{"famindex":54,"AGE":63,"FTOTINC":89000},
+{"famindex":55,"AGE":54,"FTOTINC":317000},
+{"famindex":56,"AGE":53,"FTOTINC":57200},
+{"famindex":57,"AGE":57,"FTOTINC":49000},
+{"famindex":58,"AGE":44,"FTOTINC":7500},
+{"famindex":59,"AGE":87,"FTOTINC":50100},
+{"famindex":60,"AGE":60,"FTOTINC":8000},
+{"famindex":61,"AGE":55,"FTOTINC":48000},
+{"famindex":62,"AGE":32,"FTOTINC":24500},
+{"famindex":63,"AGE":87,"FTOTINC":25000},
+{"famindex":64,"AGE":67,"FTOTINC":68200},
+{"famindex":65,"AGE":90,"FTOTINC":32400},
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+{"famindex":200,"AGE":60,"FTOTINC":70800}]
diff --git a/VT2-examples/vt2-ch3-codedata-dir/income-scraped-to-xml.xml b/VT2-examples/vt2-ch3-codedata-dir/income-scraped-to-xml.xml
new file mode 100644
index 0000000..80e2877
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/income-scraped-to-xml.xml
@@ -0,0 +1,203 @@
+
+
+145114000
+237280000
+360184000
+470250100
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diff --git a/VT2-examples/vt2-ch3-codedata-dir/income-scraped.csv b/VT2-examples/vt2-ch3-codedata-dir/income-scraped.csv
new file mode 100644
index 0000000..b2a147f
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/income-scraped.csv
@@ -0,0 +1,201 @@
+famindex,AGE,FTOTINC
+1,45,114000
+2,37,280000
+3,60,184000
+4,70,250100
+5,79,33040
+6,60,168000
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+111,45,337000
+112,31,100000
+113,67,63400
+114,64,141600
+115,86,30100
+116,83,44300
+117,61,122000
+118,58,43000
+119,79,104000
+120,61,9600
+121,44,66100
+122,26,38100
+123,32,103000
+124,22,59000
+125,36,56000
+126,84,114300
+127,24,68300
+128,76,84600
+129,57,161400
+130,46,18800
+131,53,45300
+132,26,85000
+133,67,40600
+134,36,88000
+135,57,134300
+136,30,54000
+137,41,45600
+138,74,43210
+139,22,10000
+140,51,160000
+141,51,130400
+142,47,45000
+143,25,34000
+144,37,78000
+145,62,18500
+146,44,169000
+147,41,86000
+148,66,446000
+149,40,55000
+150,63,41400
+151,41,29000
+152,22,38000
+153,50,83460
+154,69,16400
+155,29,181600
+156,70,23800
+157,65,132200
+158,32,217000
+159,48,32800
+160,47,55000
+161,60,88000
+162,75,46300
+163,55,31500
+164,73,8100
+165,40,45000
+166,65,47000
+167,84,11700
+168,71,9800
+169,83,55400
+170,51,77000
+171,40,34100
+172,39,130000
+173,27,82000
+174,36,54000
+175,81,29630
+176,35,93800
+177,59,122900
+178,26,65000
+179,66,108000
+180,46,2500
+181,28,42000
+182,25,77000
+183,66,9000
+184,58,80500
+185,56,84000
+186,61,85000
+187,32,156000
+188,86,359400
+189,60,99000
+190,26,42000
+191,62,56000
+192,32,78000
+193,26,35000
+194,50,27000
+195,28,70000
+196,32,38000
+197,59,120000
+198,64,9600
+199,33,107200
+200,60,70800
diff --git a/VT2-examples/vt2-ch3-codedata-dir/process-income.py b/VT2-examples/vt2-ch3-codedata-dir/process-income.py
new file mode 100644
index 0000000..6ad00f0
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/process-income.py
@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+
+
+# Load CSV
+people = pd.read_csv('income-scraped.csv')
+
+# See first rows.
+people.head()
+
+# Subset.
+over40 = people[people.AGE > 40]
+over40.head()
+
+# New column.
+people['isover40'] = people['AGE'] > 40
+people['agegrp'] = (people['AGE'] / 10).apply(np.floor) * 10
+
+
+# Median.
+people['FTOTINC'].median()
+
+
+# Grouped median.
+people.groupby(['isover40'])['FTOTINC'].median()
+people.groupby(['agegrp'])['FTOTINC'].median()
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch3-codedata-dir/scrape-income.py b/VT2-examples/vt2-ch3-codedata-dir/scrape-income.py
new file mode 100644
index 0000000..9476d18
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/scrape-income.py
@@ -0,0 +1,34 @@
+from bs4 import BeautifulSoup
+import urllib.request
+
+base_url = 'http://book.flowingdata.com/vt2/ch3/scraping/'
+
+# Header
+print("famindex,AGE,FTOTINC")
+
+for i in range(1, 21):
+
+ # Page URL, based on page number
+ page_url = base_url + 'family-income-page'+str(i)+'.html'
+
+ # Open the page
+ req = urllib.request.Request(page_url)
+ with urllib.request.urlopen(req) as response:
+
+ # Save page contents.
+ the_page = response.read()
+
+ # Parse.
+ soup = BeautifulSoup(the_page, features="html.parser")
+
+ # Get the table rows
+ rows = soup.find_all("tr")
+
+ # Save each row of data
+ for j in range(1, len(rows)):
+
+ # Get the cells in a row
+ cells = rows[j].findAll("td")
+
+ # Comma-delimited row
+ print(cells[0].string + "," + cells[1].string + "," + cells[2].string)
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch3-codedata-dir/xml-back-to-csv.csv b/VT2-examples/vt2-ch3-codedata-dir/xml-back-to-csv.csv
new file mode 100644
index 0000000..b2a147f
--- /dev/null
+++ b/VT2-examples/vt2-ch3-codedata-dir/xml-back-to-csv.csv
@@ -0,0 +1,201 @@
+famindex,AGE,FTOTINC
+1,45,114000
+2,37,280000
+3,60,184000
+4,70,250100
+5,79,33040
+6,60,168000
+7,56,557000
+8,69,84500
+9,54,128800
+10,72,28500
+11,26,85000
+12,60,52000
+13,54,24000
+14,51,316600
+15,35,261590
+16,62,41200
+17,28,11000
+18,66,15500
+19,75,37100
+20,57,137000
+21,69,80000
+22,78,376100
+23,47,10000
+24,84,31400
+25,75,40000
+26,23,600
+27,43,59000
+28,87,26000
+29,66,22000
+30,62,103600
+31,25,18100
+32,45,25000
+33,65,106000
+34,63,111000
+35,68,13700
+36,28,160400
+37,49,206600
+38,58,67500
+39,74,115000
+40,70,107800
+41,79,28100
+42,22,36000
+43,69,92920
+44,27,91000
+45,60,89000
+46,69,309400
+47,34,151500
+48,35,71000
+49,42,94000
+50,64,94300
+51,40,148000
+52,95,1400
+53,39,75000
+54,63,89000
+55,54,317000
+56,53,57200
+57,57,49000
+58,44,7500
+59,87,50100
+60,60,8000
+61,55,48000
+62,32,24500
+63,87,25000
+64,67,68200
+65,90,32400
+66,23,41100
+67,64,77000
+68,32,86700
+69,49,15200
+70,54,573000
+71,69,69000
+72,64,147000
+73,57,111600
+74,53,31200
+75,40,151500
+76,58,52300
+77,42,13500
+78,75,20900
+79,33,133800
+80,39,83000
+81,24,80000
+82,59,16000
+83,48,60000
+84,45,162200
+85,83,3000
+86,76,147900
+87,40,78300
+88,61,240000
+89,35,85000
+90,46,80000
+91,46,112200
+92,30,130000
+93,25,19550
+94,65,148000
+95,57,129400
+96,39,94000
+97,21,31100
+98,56,100000
+99,38,40000
+100,76,63310
+101,59,128000
+102,25,45000
+103,43,136800
+104,62,125000
+105,59,94400
+106,81,86300
+107,39,150100
+108,28,34000
+109,34,79640
+110,60,35000
+111,45,337000
+112,31,100000
+113,67,63400
+114,64,141600
+115,86,30100
+116,83,44300
+117,61,122000
+118,58,43000
+119,79,104000
+120,61,9600
+121,44,66100
+122,26,38100
+123,32,103000
+124,22,59000
+125,36,56000
+126,84,114300
+127,24,68300
+128,76,84600
+129,57,161400
+130,46,18800
+131,53,45300
+132,26,85000
+133,67,40600
+134,36,88000
+135,57,134300
+136,30,54000
+137,41,45600
+138,74,43210
+139,22,10000
+140,51,160000
+141,51,130400
+142,47,45000
+143,25,34000
+144,37,78000
+145,62,18500
+146,44,169000
+147,41,86000
+148,66,446000
+149,40,55000
+150,63,41400
+151,41,29000
+152,22,38000
+153,50,83460
+154,69,16400
+155,29,181600
+156,70,23800
+157,65,132200
+158,32,217000
+159,48,32800
+160,47,55000
+161,60,88000
+162,75,46300
+163,55,31500
+164,73,8100
+165,40,45000
+166,65,47000
+167,84,11700
+168,71,9800
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+170,51,77000
+171,40,34100
+172,39,130000
+173,27,82000
+174,36,54000
+175,81,29630
+176,35,93800
+177,59,122900
+178,26,65000
+179,66,108000
+180,46,2500
+181,28,42000
+182,25,77000
+183,66,9000
+184,58,80500
+185,56,84000
+186,61,85000
+187,32,156000
+188,86,359400
+189,60,99000
+190,26,42000
+191,62,56000
+192,32,78000
+193,26,35000
+194,50,27000
+195,28,70000
+196,32,38000
+197,59,120000
+198,64,9600
+199,33,107200
+200,60,70800
diff --git a/VT2-examples/vt2-ch4-codedata-dir/cycles.R b/VT2-examples/vt2-ch4-codedata-dir/cycles.R
new file mode 100644
index 0000000..71b5327
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/cycles.R
@@ -0,0 +1,77 @@
+#
+# Multi-line chart
+#
+
+# Load data
+marstat <- read.csv("data/mar_w_age_all.tsv", sep="\t")
+head(marstat)
+unique(marstat$MARST)
+
+# Subset married
+married <- marstat[marstat$MARST == 1,]
+
+# Line chart for 2021
+married2021 <- married[married$YEAR == 2021,]
+par(las=1)
+plot(married2021$AGE, married2021$prop*100,
+ ylim=c(0, 100),
+ type="n",
+ bty="n", xaxt="n", yaxt="n",
+ xlab="", ylab="",
+ main="Proportion of people married, by age")
+axis(1, tick=FALSE)
+axis(2,
+ at=seq(0, 100, by=20),
+ labels=paste0(seq(0, 100, by=20), "%"),
+ tick=FALSE)
+grid()
+lines(married2021$AGE, married2021$prop*100,
+ lwd=2,
+ col="#b31dc2")
+
+
+
+# All available years
+plot(married2021$AGE, married2021$prop*100,
+ ylim=c(0, 100),
+ type="n",
+ bty="n", xaxt="n", yaxt="n",
+ xlab="", ylab="",
+ main="Proportion of people married, by age")
+axis(1, tick=FALSE)
+axis(2,
+ at=seq(0, 100, by=20),
+ labels=paste0(seq(0, 100, by=20), "%"),
+ tick=FALSE)
+grid()
+abline(h=0, lwd=2)
+years <- unique(married$YEAR)
+
+# Multiple lines with a for-loop
+for (yr in years) {
+ curr <- married[married$YEAR == yr,]
+ lines(curr$AGE, curr$prop*100,
+ col="#b31dc2")
+}
+
+# Change line width for each year
+for (yr in years) {
+ curr <- married[married$YEAR == yr,]
+
+ if (yr == 1900) {
+ lines(curr$AGE, curr$prop*100,
+ lwd=3,
+ col="green")
+ } else {
+ lines(curr$AGE, curr$prop*100,
+ lwd=min(c(3, 15*1/(2021-yr+.5))),
+ col="#b31dc2")
+ }
+
+
+}
+
+
+
+
+
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/EGOT-winners.csv b/VT2-examples/vt2-ch4-codedata-dir/data/EGOT-winners.csv
new file mode 100644
index 0000000..f057a5b
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/EGOT-winners.csv
@@ -0,0 +1,19 @@
+name,emmy_year,emmy_title,emmy_desc,grammy_year,grammy_title,grammy_desc,oscar_year,oscar_title,oscar_desc,tony_year,tony_title,tony_desc,completion_year,category
+Viola Davis,2015,,,2023,,,2017,,,2001,,,2023,
+Jennifer Hudson,2021,,,2009,,,2007,,,2022,,,2022,
+Alan Menken,2020,,,1991,,,1990,,,2012,,,2020,
+John Legend,2018,Outstanding Variety Special (Live),Jesus Christ Superstar Live in Concert,2006,Best R&B Album,Get Lifted,2015,Best Original Song ,"""Glory"" from Selma",2017,Best Revival of a Play,Jitney,2018,Singer/Composer/Producer
+Andrew Lloyd Webber,2018,Outstanding Variety Special (Live),Jesus Christ Superstar Live in Concert,1980,Best Cast Show Album,Evita,1996,Best Original Song,"""You Must Love Me"" from Evita",1980,Best Original Score,Evita,2018,Composer/Producer
+Tim Rice,2018,Outstanding Variety Special (Live),Jesus Christ Superstar Live in Concert,1980,Best Cast Show Album,Evita,1993,Best Original Song,"""A Whole New World"" from Aladdin",1980,Best Original Score,Evita,2018,Lyricist/Producer
+Robert Lopez,2008,Outstanding Achievement in Music Direction and Composition,Wonder Pets,2012,Best Musical Theater Album,The Book of Mormon: Original Broadway Cast Recording,2014,Best Original Song,"""Let It Go"" from Frozen",2004,Best Original Score,Avenue Q,2014,Composer
+Scott Rudin,1984,Outstanding Children's Program,He Makes Me Feel Like Dancin',2012,Best Musical Theater Album,The Book of Mormon: Original Broadway Cast Recording,2008,Best Picture,No Country For Old Men,1994,Best Musical,Passion,2012,Producer
+Whoopi Goldberg,2002,Outstanding Special Class Special,Beyond Tara: The Extraordinary Life of Hattie McDaniel (Host),1986,Best Comedy Recording,Whoopi Goldberg: Original Broadway Show Recording,1991,Best Actress in a Supporting Role,Ghost,2002,Best Musical,Thoroughly Modern Millie,2002,Comedian/Actress/Host
+Mel Brooks,1967,Outstanding Writing Achievement in Variety ,"The Sid Caesar, Imogene Coca, Carl Reiner, Howard Morris Special",1998,Best Spoken Comedy Album,The 2000 Year Old Man in the Year 2000,1968,Best Writing (Screenplay Written Directly for the Screen),The Producers,2001,Best Book of a Musical ,The Producers,2001,Writer/Composer/Actor
+Mike Nichols,2001,"Outstanding Directing for a Miniseries, Movie or a Special",Wit,1961,Best Comedy Performance,An Evening with Mike Nichols and Elaine May,1967,Best Director,The Graduate,1964,"Best Director, Dramatic",Barefoot in the Park,2001,Director/Comedian
+Jonathan Tunick,1982,Outstanding Achievement in Music Direction,Night of 100 Stars,1988,Best Instrumental Arrangement Accompanying Vocals,"""No One is Alone,"" Cleo Laine",1977,"Best Music, Original Song Score and Its Adaptation or Best Adaptation Score ",A Little Night Music,1997,Best Orchestrations,Titanic,1997,Composer/Conductor
+Marvin Hamlisch,1995,Outstanding Individual Achievement in Music Direction,Barbra: The Concert,1974,Song of the Year,"""The Way We Were""",1973,"Best Music, Original Dramatic Score",The Way We Were,1976,Best Musical Score ,A Chorus Line,1995,Composer
+Audrey Hepburn,1993,"Outstanding Individual Achievement, Informational Programming",Gardens of the World with Audrey Hepburn,1994,Best Spoken Word Album for Children,Audrey Hepburn's Enchanted Tales,1953,Best Actress in a Leading Role,Roman Holiday,1954,Best Actress in a Play,Ondine,1994,Actress
+John Gielgud,1991,Outstanding Lead Actor in a Miniseries or Special,Summer's Lease,1979,"Best Spoken Word, Documentary or Drama Recording",Ages of Man,1981,Best Actor in a Supporting Role,Arthur,1961,Outstanding Foreign Company,The Importance of Being Earnest,1991,Actor/Director
+Helen Hayes,1953,Best Actress,"Schlitz Playhouse of Stars for the episode ""Not a Chance""",1977,Best Spoken Word Recording,Great American Documents,1932,Best Actress in a Leading Role,The Sin of Madelon Claudet,1947,"Best Actress, Dramatic",Happy Birthday,1977,Actress
+Rita Moreno,1977,Outstanding Continuing or Single Performance by a Supporting Actress in Variety or Music,The Muppet Show,1972,Best Recording for Children,The Electric Company,1961,Best Actress in a Supporting Role,West Side Story,1975,Best Featured or Supporting Actress in a Play,The Ritz,1977,Actress/Singer
+Richard Rodgers,1962,Outstanding Achievement in Original Music Composed,Winston Churchill: The Valiant Years,1960,Best Show Album (Original Cast),The Sound of Music,1945,Best Song,"""It Might as Well Be Spring"" from State Fair",1950,Best Musical,South Pacific,1962,Composer
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.csv b/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.csv
new file mode 100644
index 0000000..0eb222d
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.csv
@@ -0,0 +1,45 @@
+year,winner,hotdogs,country,record
+1980,Paul Siederman & Joe Baldini,9.75,United States,0
+1981,Thomas DeBerry ,11,United States,1
+1982,Steven Abrams ,11.1,United States,1
+1983,Emil Gomez,10.5,Mexico,0
+1984,Birgit Felden ,9.5,Germany,0
+1985,Oscar Rodriguez ,11.75,United States,1
+1986,Mark Heller ,15.5,United States,1
+1987,Don Wolfman ,13.5,United States,0
+1988,Jay Green ,10,United States,0
+1989,Jay Green ,15.5,United States,0
+1990,Mike DeVito & Jay Green,15,United States,0
+1991,Frank Dellarosa ,21,United States,1
+1992,Frank Dellarosa ,19,United States,0
+1993,Mike DeVito ,17,United States,0
+1994,Mike DeVito ,20,United States,0
+1995,Edward Krachie ,19.5,United States,0
+1996,Edward Krachie ,22,United States,1
+1997,Hirofumi Nakajima ,24.5,Japan,1
+1998,Hirofumi Nakajima ,19,Japan,0
+1999,Steve Keiner ,21.5,United States,0
+2000,"Kazutoyo ""The Rabbit"" Arai ",25.13,Japan,1
+2001,Takeru Kobayashi ,50,Japan,1
+2002,Takeru Kobayashi ,50.5,Japan,1
+2003,Takeru Kobayashi ,44.5,Japan,0
+2004,Takeru Kobayashi ,53.5,Japan,1
+2005,Takeru Kobayashi ,49,Japan,0
+2006,"Takeru ""Tsunami"" Kobayashi ",53.75,Japan,1
+2007,Joey Chestnut ,66,United States,1
+2008,Joey Chestnut ,59,United States,0
+2009,Joey Chestnut ,68,United States,1
+2010,Joey Chestnut ,54,United States,0
+2011,Joey Chestnut ,62,United States,0
+2012,Joey Chestnut ,68,United States,0
+2013,Joey Chestnut ,69,United States,1
+2014,Joey Chestnut ,61,United States,0
+2015,Matt Stonie,62,United States,0
+2016,Joey Chestnut ,70,United States,1
+2017,Joey Chestnut ,72,United States,1
+2018,Joey Chestnut ,74,United States,1
+2019,Joey Chestnut ,71,United States,0
+2020,Joey Chestnut ,75,United States,1
+2021,Joey Chestnut ,76,United States,1
+2022,Joey Chestnut ,63,United States,0
+2023,Joey Chestnut ,62,United States,0
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.xls b/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.xls
new file mode 100644
index 0000000..0193b6c
Binary files /dev/null and b/VT2-examples/vt2-ch4-codedata-dir/data/hot-dog-contest-winners.xls differ
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/mar_w_age_all.tsv b/VT2-examples/vt2-ch4-codedata-dir/data/mar_w_age_all.tsv
new file mode 100644
index 0000000..4a9762b
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/mar_w_age_all.tsv
@@ -0,0 +1,6290 @@
+"YEAR" "AGE" "MARST" "n" "prop"
+1900 0 6 1916975.4 1
+1900 1 6 1750945.51 1
+1900 2 6 1825203.12 1
+1900 3 6 1825021.11 1
+1900 4 6 1826842.31 1
+1900 5 6 1804254.6 1
+1900 6 6 1814506.72 1
+1900 7 6 1773167 1
+1900 8 6 1768972.46 1
+1900 9 6 1662598.91 1
+1900 10 6 1737072.59 1
+1900 11 6 1566160.49 1
+1900 12 1 19.96 0.0000121561677289022
+1900 12 2 78.73 0.0000479486515679593
+1900 12 4 58.87 0.0000358533864829895
+1900 12 5 78.99 0.0000481069984421835
+1900 12 6 1641728.29 0.999855934795778
+1900 13 1 694.45 0.000449948335473652
+1900 13 2 552.12 0.000357729822135089
+1900 13 4 98.18 0.0000636128268079821
+1900 13 5 177.82 0.000115213209034379
+1900 13 6 1541876.85 0.999013495806549
+1900 14 1 2956.08 0.00188632604921909
+1900 14 2 940.15 0.000599926062614452
+1900 14 4 96.14 0.0000613486057115922
+1900 14 5 119.46 0.0000762295032068525
+1900 14 6 1562997.95 0.997376169779248
+1900 15 1 9838.87 0.00644593490906018
+1900 15 2 1159.77 0.000759823224565496
+1900 15 4 275.68 0.000180611730384659
+1900 15 5 591.29 0.000387383597138512
+1900 15 6 1514502.58 0.992226246538851
+1900 16 1 30981.44 0.0199770617505416
+1900 16 2 3170.57 0.00204440699575018
+1900 16 4 356.19 0.000229673947528759
+1900 16 5 850.45 0.000548376452668052
+1900 16 6 1515492.04 0.977200480853512
+1900 17 1 62903.61 0.0425681676038198
+1900 17 2 6489.54 0.00439160528929409
+1900 17 4 431.82 0.000292221481957577
+1900 17 5 1850.55 0.00125230527404149
+1900 17 6 1406039.25 0.951495700350887
+1900 18 1 126942.08 0.0823477053802549
+1900 18 2 11197.08 0.00726357914537988
+1900 18 4 900.17 0.000583942960066072
+1900 18 5 2950.51 0.00191400462479814
+1900 18 6 1399547.71 0.907890767889501
+1900 19 1 188820.83 0.13154403165609
+1900 19 2 15554.63 0.010836297780911
+1900 19 4 987.91 0.000688237967778068
+1900 19 5 4579.97 0.00319068462236896
+1900 19 6 1225475.87 0.853740747972852
+1900 20 1 300927.52 0.196814404455685
+1900 20 2 20956.43 0.0137060488517875
+1900 20 4 1866.97 0.00122104681116114
+1900 20 5 8999.91 0.00588617460711059
+1900 20 6 1196240.51 0.782372325274256
+1900 21 1 356285.79 0.252845015514537
+1900 21 2 22785.76 0.0161703497653121
+1900 21 4 2669.27 0.00189429843542873
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diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct.tsv b/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct.tsv
new file mode 100644
index 0000000..0d67e17
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct.tsv
@@ -0,0 +1,289 @@
+"MONTH" "HOUR" "n_all" "n_drunk_all" "n_ped_all" "n_weather_all" "n_rural_all" "n_drunk_rural" "n_ped_rural" "n_weather_rural" "n_urban" "n_drunk_urban" "n_ped_urban" "n_weather_urban" "pct_drunk_all" "pct_ped_all" "pct_weather_all" "pct_drunk_rural" "pct_ped_rural" "pct_weather_rural" "pct_drunk_urban" "pct_ped_urban" "pct_weather_urban"
+1 0 86 49 17 25 35 23 2 13 51 26 15 12 0.57 0.198 0.291 0.657 0.057 0.371 0.51 0.294 0.235
+1 2 103 58 23 33 36 22 3 12 67 36 20 21 0.563 0.223 0.32 0.611 0.083 0.333 0.537 0.299 0.313
+1 3 70 36 12 29 28 16 2 11 42 20 10 18 0.514 0.171 0.414 0.571 0.071 0.393 0.476 0.238 0.429
+1 4 57 27 11 20 17 6 2 3 40 21 9 17 0.474 0.193 0.351 0.353 0.118 0.176 0.525 0.225 0.425
+1 5 69 10 23 33 34 1 5 17 35 9 18 16 0.145 0.333 0.478 0.029 0.147 0.5 0.257 0.514 0.457
+1 6 110 13 37 50 55 9 7 31 55 4 30 19 0.118 0.336 0.455 0.164 0.127 0.564 0.073 0.545 0.345
+1 7 107 11 22 53 58 5 2 28 49 6 20 25 0.103 0.206 0.495 0.086 0.034 0.483 0.122 0.408 0.51
+1 8 80 8 7 36 43 4 1 22 37 4 6 14 0.1 0.088 0.45 0.093 0.023 0.512 0.108 0.162 0.378
+1 9 67 3 7 28 37 1 1 21 30 2 6 7 0.045 0.104 0.418 0.027 0.027 0.568 0.067 0.2 0.233
+1 11 69 6 8 27 28 3 2 15 41 3 6 12 0.087 0.116 0.391 0.107 0.071 0.536 0.073 0.146 0.293
+1 12 77 8 8 28 40 2 2 19 37 6 6 9 0.104 0.104 0.364 0.05 0.05 0.475 0.162 0.162 0.243
+1 13 102 10 16 45 57 6 3 26 45 4 13 19 0.098 0.157 0.441 0.105 0.053 0.456 0.089 0.289 0.422
+1 14 108 12 17 33 55 7 5 21 53 5 12 12 0.111 0.157 0.306 0.127 0.091 0.382 0.094 0.226 0.226
+1 15 79 16 14 21 39 11 2 13 40 5 12 8 0.203 0.177 0.266 0.282 0.051 0.333 0.125 0.3 0.2
+1 16 108 21 12 39 67 13 7 32 41 8 5 7 0.194 0.111 0.361 0.194 0.104 0.478 0.195 0.122 0.171
+1 17 132 27 43 48 62 14 3 23 70 13 40 25 0.205 0.326 0.364 0.226 0.048 0.371 0.186 0.571 0.357
+1 18 192 35 77 52 70 16 9 25 122 19 68 27 0.182 0.401 0.271 0.229 0.129 0.357 0.156 0.557 0.221
+1 19 132 38 63 41 55 21 16 22 77 17 47 19 0.288 0.477 0.311 0.382 0.291 0.4 0.221 0.61 0.247
+1 20 123 37 45 34 43 18 6 14 80 19 39 20 0.301 0.366 0.276 0.419 0.14 0.326 0.238 0.488 0.25
+1 21 114 38 33 37 49 22 8 20 65 16 25 17 0.333 0.289 0.325 0.449 0.163 0.408 0.246 0.385 0.262
+1 22 114 45 35 35 48 24 7 14 66 21 28 21 0.395 0.307 0.307 0.5 0.146 0.292 0.318 0.424 0.318
+1 23 88 39 28 31 29 16 3 12 59 23 25 19 0.443 0.318 0.352 0.552 0.103 0.414 0.39 0.424 0.322
+2 0 66 38 15 20 31 20 4 13 35 18 11 7 0.576 0.227 0.303 0.645 0.129 0.419 0.514 0.314 0.2
+2 1 76 51 15 18 28 19 2 6 48 32 13 12 0.671 0.197 0.237 0.679 0.071 0.214 0.667 0.271 0.25
+2 2 82 49 20 23 29 19 1 7 53 30 19 16 0.598 0.244 0.28 0.655 0.034 0.241 0.566 0.358 0.302
+2 3 59 37 7 22 28 15 3 9 31 22 4 13 0.627 0.119 0.373 0.536 0.107 0.321 0.71 0.129 0.419
+2 4 38 14 7 15 18 6 2 7 20 8 5 8 0.368 0.184 0.395 0.333 0.111 0.389 0.4 0.25 0.4
+2 5 62 19 16 24 27 11 2 11 35 8 14 13 0.306 0.258 0.387 0.407 0.074 0.407 0.229 0.4 0.371
+2 6 80 20 17 30 42 11 2 15 38 9 15 15 0.25 0.212 0.375 0.262 0.048 0.357 0.237 0.395 0.395
+2 7 76 11 7 33 48 7 3 20 28 4 4 13 0.145 0.092 0.434 0.146 0.062 0.417 0.143 0.143 0.464
+2 8 60 6 8 23 39 5 4 16 21 1 4 7 0.1 0.133 0.383 0.128 0.103 0.41 0.048 0.19 0.333
+2 9 62 8 6 26 34 4 1 17 28 4 5 9 0.129 0.097 0.419 0.118 0.029 0.5 0.143 0.179 0.321
+2 12 86 12 10 21 42 8 1 8 44 4 9 13 0.14 0.116 0.244 0.19 0.024 0.19 0.091 0.205 0.295
+2 13 81 5 8 24 51 4 4 19 30 1 4 5 0.062 0.099 0.296 0.078 0.078 0.373 0.033 0.133 0.167
+2 14 93 12 10 27 59 10 3 21 34 2 7 6 0.129 0.108 0.29 0.169 0.051 0.356 0.059 0.206 0.176
+2 15 107 18 12 24 68 14 1 15 39 4 11 9 0.168 0.112 0.224 0.206 0.015 0.221 0.103 0.282 0.231
+2 16 96 18 12 31 56 12 2 23 40 6 10 8 0.188 0.125 0.323 0.214 0.036 0.411 0.15 0.25 0.2
+2 17 91 23 15 33 57 12 4 23 34 11 11 10 0.253 0.165 0.363 0.211 0.07 0.404 0.324 0.324 0.294
+2 18 125 25 58 36 45 11 12 10 80 14 46 26 0.2 0.464 0.288 0.244 0.267 0.222 0.175 0.575 0.325
+2 19 121 27 48 29 43 16 6 14 78 11 42 15 0.223 0.397 0.24 0.372 0.14 0.326 0.141 0.538 0.192
+2 20 115 31 43 32 52 20 10 21 63 11 33 11 0.27 0.374 0.278 0.385 0.192 0.404 0.175 0.524 0.175
+2 21 99 40 37 27 50 22 10 15 49 18 27 12 0.404 0.374 0.273 0.44 0.2 0.3 0.367 0.551 0.245
+2 22 78 24 27 21 34 14 3 10 44 10 24 11 0.308 0.346 0.269 0.412 0.088 0.294 0.227 0.545 0.25
+2 23 71 34 14 24 43 23 4 18 28 11 10 6 0.479 0.197 0.338 0.535 0.093 0.419 0.393 0.357 0.214
+3 0 98 47 21 32 37 17 4 16 61 30 17 16 0.48 0.214 0.327 0.459 0.108 0.432 0.492 0.279 0.262
+3 3 72 45 13 26 34 19 7 11 38 26 6 15 0.625 0.181 0.361 0.559 0.206 0.324 0.684 0.158 0.395
+3 4 57 26 8 18 28 14 1 9 29 12 7 9 0.456 0.14 0.316 0.5 0.036 0.321 0.414 0.241 0.31
+3 5 68 15 18 32 26 8 1 15 42 7 17 17 0.221 0.265 0.471 0.308 0.038 0.577 0.167 0.405 0.405
+3 6 102 19 29 46 51 13 5 25 51 6 24 21 0.186 0.284 0.451 0.255 0.098 0.49 0.118 0.471 0.412
+3 7 91 14 11 41 51 7 2 23 40 7 9 18 0.154 0.121 0.451 0.137 0.039 0.451 0.175 0.225 0.45
+3 8 83 3 11 38 50 2 1 25 33 1 10 13 0.036 0.133 0.458 0.04 0.02 0.5 0.03 0.303 0.394
+3 9 71 9 9 27 38 4 3 16 33 5 6 11 0.127 0.127 0.38 0.105 0.079 0.421 0.152 0.182 0.333
+3 10 85 7 15 33 43 4 2 21 42 3 13 12 0.082 0.176 0.388 0.093 0.047 0.488 0.071 0.31 0.286
+3 11 86 7 8 27 42 5 1 17 44 2 7 10 0.081 0.093 0.314 0.119 0.024 0.405 0.045 0.159 0.227
+3 12 108 12 11 37 57 3 2 24 51 9 9 13 0.111 0.102 0.343 0.053 0.035 0.421 0.176 0.176 0.255
+3 13 101 13 13 29 42 6 1 17 59 7 12 12 0.129 0.129 0.287 0.143 0.024 0.405 0.119 0.203 0.203
+3 15 118 16 11 33 61 12 2 14 57 4 9 19 0.136 0.093 0.28 0.197 0.033 0.23 0.07 0.158 0.333
+3 16 112 27 13 32 50 17 1 16 62 10 12 16 0.241 0.116 0.286 0.34 0.02 0.32 0.161 0.194 0.258
+3 17 114 38 10 31 62 25 2 20 52 13 8 11 0.333 0.088 0.272 0.403 0.032 0.323 0.25 0.154 0.212
+3 18 115 34 17 32 62 21 4 16 53 13 13 16 0.296 0.148 0.278 0.339 0.065 0.258 0.245 0.245 0.302
+3 19 127 34 45 41 58 20 8 18 69 14 37 23 0.268 0.354 0.323 0.345 0.138 0.31 0.203 0.536 0.333
+3 20 178 54 75 38 72 31 14 16 106 23 61 22 0.303 0.421 0.213 0.431 0.194 0.222 0.217 0.575 0.208
+3 21 119 39 37 37 55 21 7 22 64 18 30 15 0.328 0.311 0.311 0.382 0.127 0.4 0.281 0.469 0.234
+3 22 106 47 37 26 32 16 6 11 74 31 31 15 0.443 0.349 0.245 0.5 0.188 0.344 0.419 0.419 0.203
+3 23 90 42 19 23 36 17 2 12 54 25 17 11 0.467 0.211 0.256 0.472 0.056 0.333 0.463 0.315 0.204
+4 0 77 38 17 29 34 19 4 13 43 19 13 16 0.494 0.221 0.377 0.559 0.118 0.382 0.442 0.302 0.372
+4 1 92 48 19 25 35 15 5 12 57 33 14 13 0.522 0.207 0.272 0.429 0.143 0.343 0.579 0.246 0.228
+4 2 83 54 13 30 25 19 1 11 58 35 12 19 0.651 0.157 0.361 0.76 0.04 0.44 0.603 0.207 0.328
+4 3 73 38 10 22 35 20 2 10 38 18 8 12 0.521 0.137 0.301 0.571 0.057 0.286 0.474 0.211 0.316
+4 4 63 25 13 21 27 10 5 11 36 15 8 10 0.397 0.206 0.333 0.37 0.185 0.407 0.417 0.222 0.278
+4 5 80 19 14 19 40 10 2 9 40 9 12 10 0.238 0.175 0.238 0.25 0.05 0.225 0.225 0.3 0.25
+4 6 67 9 14 26 40 3 5 16 27 6 9 10 0.134 0.209 0.388 0.075 0.125 0.4 0.222 0.333 0.37
+4 7 83 14 9 28 49 10 2 17 34 4 7 11 0.169 0.108 0.337 0.204 0.041 0.347 0.118 0.206 0.324
+4 8 50 6 9 17 25 5 1 10 25 1 8 7 0.12 0.18 0.34 0.2 0.04 0.4 0.04 0.32 0.28
+4 9 56 3 5 20 29 2 1 12 27 1 4 8 0.054 0.089 0.357 0.069 0.034 0.414 0.037 0.148 0.296
+4 10 72 7 12 18 33 5 3 6 39 2 9 12 0.097 0.167 0.25 0.152 0.091 0.182 0.051 0.231 0.308
+4 11 83 9 8 24 50 7 2 13 33 2 6 11 0.108 0.096 0.289 0.14 0.04 0.26 0.061 0.182 0.333
+4 12 90 11 8 16 49 7 3 13 41 4 5 3 0.122 0.089 0.178 0.143 0.061 0.265 0.098 0.122 0.073
+4 13 119 18 14 36 58 7 3 19 61 11 11 17 0.151 0.118 0.303 0.121 0.052 0.328 0.18 0.18 0.279
+4 15 126 17 11 34 75 12 2 22 51 5 9 12 0.135 0.087 0.27 0.16 0.027 0.293 0.098 0.176 0.235
+4 16 132 18 9 30 75 9 1 19 57 9 8 11 0.136 0.068 0.227 0.12 0.013 0.253 0.158 0.14 0.193
+4 17 142 42 14 32 83 28 5 20 59 14 9 12 0.296 0.099 0.225 0.337 0.06 0.241 0.237 0.153 0.203
+4 19 114 41 15 23 59 26 1 13 55 15 14 10 0.36 0.132 0.202 0.441 0.017 0.22 0.273 0.255 0.182
+4 20 179 60 62 50 75 39 8 22 104 21 54 28 0.335 0.346 0.279 0.52 0.107 0.293 0.202 0.519 0.269
+4 21 144 50 45 37 55 23 8 10 89 27 37 27 0.347 0.312 0.257 0.418 0.145 0.182 0.303 0.416 0.303
+4 22 133 62 40 42 45 25 5 14 88 37 35 28 0.466 0.301 0.316 0.556 0.111 0.311 0.42 0.398 0.318
+4 23 118 60 21 36 60 38 5 18 58 22 16 18 0.508 0.178 0.305 0.633 0.083 0.3 0.379 0.276 0.31
+5 0 125 61 28 29 55 32 6 13 70 29 22 16 0.488 0.224 0.232 0.582 0.109 0.236 0.414 0.314 0.229
+5 1 110 69 18 35 48 31 4 15 62 38 14 20 0.627 0.164 0.318 0.646 0.083 0.312 0.613 0.226 0.323
+5 2 105 65 15 28 52 34 2 14 53 31 13 14 0.619 0.143 0.267 0.654 0.038 0.269 0.585 0.245 0.264
+5 3 91 57 13 29 36 24 1 11 55 33 12 18 0.626 0.143 0.319 0.667 0.028 0.306 0.6 0.218 0.327
+5 4 70 32 9 22 37 19 3 13 33 13 6 9 0.457 0.129 0.314 0.514 0.081 0.351 0.394 0.182 0.273
+5 5 91 27 18 30 51 16 5 18 40 11 13 12 0.297 0.198 0.33 0.314 0.098 0.353 0.275 0.325 0.3
+5 7 97 15 9 27 56 8 2 17 41 7 7 10 0.155 0.093 0.278 0.143 0.036 0.304 0.171 0.171 0.244
+5 8 83 6 10 25 45 4 3 18 38 2 7 7 0.072 0.12 0.301 0.089 0.067 0.4 0.053 0.184 0.184
+5 9 85 11 5 22 48 9 1 14 37 2 4 8 0.129 0.059 0.259 0.188 0.021 0.292 0.054 0.108 0.216
+5 10 91 9 7 22 54 4 4 15 37 5 3 7 0.099 0.077 0.242 0.074 0.074 0.278 0.135 0.081 0.189
+5 11 98 16 13 25 59 8 6 17 39 8 7 8 0.163 0.133 0.255 0.136 0.102 0.288 0.205 0.179 0.205
+5 13 132 18 14 34 76 12 6 24 56 6 8 10 0.136 0.106 0.258 0.158 0.079 0.316 0.107 0.143 0.179
+5 15 143 20 17 27 78 10 2 18 65 10 15 9 0.14 0.119 0.189 0.128 0.026 0.231 0.154 0.231 0.138
+5 16 170 39 23 42 103 28 5 30 67 11 18 12 0.229 0.135 0.247 0.272 0.049 0.291 0.164 0.269 0.179
+5 17 158 47 11 36 90 30 1 22 68 17 10 14 0.297 0.07 0.228 0.333 0.011 0.244 0.25 0.147 0.206
+5 18 146 54 13 45 89 40 3 29 57 14 10 16 0.37 0.089 0.308 0.449 0.034 0.326 0.246 0.175 0.281
+5 19 126 55 11 29 69 28 3 21 57 27 8 8 0.437 0.087 0.23 0.406 0.043 0.304 0.474 0.14 0.14
+5 20 155 57 49 39 69 34 10 16 86 23 39 23 0.368 0.316 0.252 0.493 0.145 0.232 0.267 0.453 0.267
+5 21 177 59 57 38 77 36 12 18 100 23 45 20 0.333 0.322 0.215 0.468 0.156 0.234 0.23 0.45 0.2
+5 22 146 52 44 41 58 26 7 16 88 26 37 25 0.356 0.301 0.281 0.448 0.121 0.276 0.295 0.42 0.284
+5 23 111 53 22 28 55 31 5 17 56 22 17 11 0.477 0.198 0.252 0.564 0.091 0.309 0.393 0.304 0.196
+6 0 119 65 29 35 62 39 6 22 57 26 23 13 0.546 0.244 0.294 0.629 0.097 0.355 0.456 0.404 0.228
+6 1 104 60 21 29 41 24 5 12 63 36 16 17 0.577 0.202 0.279 0.585 0.122 0.293 0.571 0.254 0.27
+6 2 107 65 24 33 45 26 7 12 62 39 17 21 0.607 0.224 0.308 0.578 0.156 0.267 0.629 0.274 0.339
+6 3 71 32 23 24 29 16 7 11 42 16 16 13 0.451 0.324 0.338 0.552 0.241 0.379 0.381 0.381 0.31
+6 4 69 29 14 20 26 12 4 10 43 17 10 10 0.42 0.203 0.29 0.462 0.154 0.385 0.395 0.233 0.233
+6 5 74 13 11 20 50 9 3 16 24 4 8 4 0.176 0.149 0.27 0.18 0.06 0.32 0.167 0.333 0.167
+6 6 92 20 10 20 49 15 2 11 43 5 8 9 0.217 0.109 0.217 0.306 0.041 0.224 0.116 0.186 0.209
+6 7 75 14 10 28 42 9 1 20 33 5 9 8 0.187 0.133 0.373 0.214 0.024 0.476 0.152 0.273 0.242
+6 8 70 4 12 15 42 3 3 9 28 1 9 6 0.057 0.171 0.214 0.071 0.071 0.214 0.036 0.321 0.214
+6 9 89 8 9 15 50 5 2 6 39 3 7 9 0.09 0.101 0.169 0.1 0.04 0.12 0.077 0.179 0.231
+6 10 89 5 10 18 57 4 2 12 32 1 8 6 0.056 0.112 0.202 0.07 0.035 0.211 0.031 0.25 0.188
+6 11 99 9 14 20 50 8 3 11 49 1 11 9 0.091 0.141 0.202 0.16 0.06 0.22 0.02 0.224 0.184
+6 12 130 17 16 37 84 14 3 25 46 3 13 12 0.131 0.123 0.285 0.167 0.036 0.298 0.065 0.283 0.261
+6 13 108 9 12 19 64 5 4 11 44 4 8 8 0.083 0.111 0.176 0.078 0.062 0.172 0.091 0.182 0.182
+6 14 150 16 17 44 89 10 3 31 61 6 14 13 0.107 0.113 0.293 0.112 0.034 0.348 0.098 0.23 0.213
+6 15 162 23 20 42 85 14 5 24 77 9 15 18 0.142 0.123 0.259 0.165 0.059 0.282 0.117 0.195 0.234
+6 16 135 25 16 31 81 18 5 19 54 7 11 12 0.185 0.119 0.23 0.222 0.062 0.235 0.13 0.204 0.222
+6 17 123 26 17 31 73 15 7 19 50 11 10 12 0.211 0.138 0.252 0.205 0.096 0.26 0.22 0.2 0.24
+6 18 152 51 18 30 84 28 6 16 68 23 12 14 0.336 0.118 0.197 0.333 0.071 0.19 0.338 0.176 0.206
+6 19 109 42 11 23 61 24 1 12 48 18 10 11 0.385 0.101 0.211 0.393 0.016 0.197 0.375 0.208 0.229
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+6 22 151 59 47 35 61 27 12 11 90 32 35 24 0.391 0.311 0.232 0.443 0.197 0.18 0.356 0.389 0.267
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+7 3 71 44 14 17 27 20 2 7 44 24 12 10 0.62 0.197 0.239 0.741 0.074 0.259 0.545 0.273 0.227
+7 4 62 16 21 15 25 8 7 5 37 8 14 10 0.258 0.339 0.242 0.32 0.28 0.2 0.216 0.378 0.27
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+7 9 83 5 11 16 44 3 2 10 39 2 9 6 0.06 0.133 0.193 0.068 0.045 0.227 0.051 0.231 0.154
+7 11 135 14 7 30 85 10 1 19 50 4 6 11 0.104 0.052 0.222 0.118 0.012 0.224 0.08 0.12 0.22
+7 12 108 10 10 17 69 7 2 6 39 3 8 11 0.093 0.093 0.157 0.101 0.029 0.087 0.077 0.205 0.282
+7 13 141 18 17 35 80 14 5 23 61 4 12 12 0.128 0.121 0.248 0.175 0.062 0.288 0.066 0.197 0.197
+7 14 151 18 15 35 105 16 6 26 46 2 9 9 0.119 0.099 0.232 0.152 0.057 0.248 0.043 0.196 0.196
+7 15 172 24 11 45 108 16 4 31 64 8 7 14 0.14 0.064 0.262 0.148 0.037 0.287 0.125 0.109 0.219
+7 16 144 27 12 21 83 19 3 11 61 8 9 10 0.188 0.083 0.146 0.229 0.036 0.133 0.131 0.148 0.164
+7 17 155 39 16 35 105 29 6 21 50 10 10 14 0.252 0.103 0.226 0.276 0.057 0.2 0.2 0.2 0.28
+7 18 162 55 18 29 97 36 5 17 65 19 13 12 0.34 0.111 0.179 0.371 0.052 0.175 0.292 0.2 0.185
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+7 20 156 71 27 29 75 39 5 15 81 32 22 14 0.455 0.173 0.186 0.52 0.067 0.2 0.395 0.272 0.173
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+1 1 92 58 9 26 42 26 0 16 50 32 9 10 0.63 0.098 0.283 0.619 0 0.381 0.64 0.18 0.2
+10 12 97 8 10 28 55 5 0 20 42 3 10 8 0.082 0.103 0.289 0.091 0 0.364 0.071 0.238 0.19
+5 14 137 21 8 34 74 16 0 20 63 5 8 14 0.153 0.058 0.248 0.216 0 0.27 0.079 0.127 0.222
+3 1 94 58 23 25 30 22 0 9 64 36 23 16 0.617 0.245 0.266 0.733 0 0.3 0.562 0.359 0.25
+4 14 122 16 14 36 60 6 0 18 62 10 14 18 0.131 0.115 0.295 0.1 0 0.3 0.161 0.226 0.29
+11 8 82 12 8 27 45 6 0 14 37 6 8 13 0.146 0.098 0.329 0.133 0 0.311 0.162 0.216 0.351
+11 13 114 15 9 43 51 6 0 25 63 9 9 18 0.132 0.079 0.377 0.118 0 0.49 0.143 0.143 0.286
+3 2 81 54 10 21 32 23 0 8 49 31 10 13 0.667 0.123 0.259 0.719 0 0.25 0.633 0.204 0.265
+5 12 99 10 9 25 51 7 0 19 48 3 9 6 0.101 0.091 0.253 0.137 0 0.373 0.062 0.188 0.125
+12 13 103 7 9 34 55 5 0 20 48 2 9 14 0.068 0.087 0.33 0.091 0 0.364 0.042 0.188 0.292
+7 10 102 9 6 21 62 8 0 15 40 1 6 6 0.088 0.059 0.206 0.129 0 0.242 0.025 0.15 0.15
+3 14 93 19 5 25 53 11 0 17 40 8 5 8 0.204 0.054 0.269 0.208 0 0.321 0.2 0.125 0.2
+5 6 84 20 2 26 58 12 0 17 26 8 2 9 0.238 0.024 0.31 0.207 0 0.293 0.308 0.077 0.346
+4 18 113 40 12 26 59 22 0 15 54 18 12 11 0.354 0.106 0.23 0.373 0 0.254 0.333 0.222 0.204
+10 10 108 9 10 28 61 6 0 17 47 3 10 11 0.083 0.093 0.259 0.098 0 0.279 0.064 0.213 0.234
+2 11 67 8 5 25 40 3 0 18 27 5 5 7 0.119 0.075 0.373 0.075 0 0.45 0.185 0.185 0.259
+1 10 71 5 5 28 37 5 0 14 34 0 5 14 0.07 0.07 0.394 0.135 0 0.378 0 0.147 0.412
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct_matrix.csv b/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct_matrix.csv
new file mode 100644
index 0000000..28927df
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/month_hour_pct_matrix.csv
@@ -0,0 +1,13 @@
+month,"V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24"
+"Jan.",0.57,0.63,0.563,0.514,0.474,0.145,0.118,0.103,0.1,0.045,0.07,0.087,0.104,0.098,0.111,0.203,0.194,0.205,0.182,0.288,0.301,0.333,0.395,0.443
+"Feb.",0.576,0.671,0.598,0.627,0.368,0.306,0.25,0.145,0.1,0.129,0.048,0.119,0.14,0.062,0.129,0.168,0.188,0.253,0.2,0.223,0.27,0.404,0.308,0.479
+"March",0.48,0.617,0.667,0.625,0.456,0.221,0.186,0.154,0.036,0.127,0.082,0.081,0.111,0.129,0.204,0.136,0.241,0.333,0.296,0.268,0.303,0.328,0.443,0.467
+"April",0.494,0.522,0.651,0.521,0.397,0.238,0.134,0.169,0.12,0.054,0.097,0.108,0.122,0.151,0.131,0.135,0.136,0.296,0.354,0.36,0.335,0.347,0.466,0.508
+"May",0.488,0.627,0.619,0.626,0.457,0.297,0.238,0.155,0.072,0.129,0.099,0.163,0.101,0.136,0.153,0.14,0.229,0.297,0.37,0.437,0.368,0.333,0.356,0.477
+"June",0.546,0.577,0.607,0.451,0.42,0.176,0.217,0.187,0.057,0.09,0.056,0.091,0.131,0.083,0.107,0.142,0.185,0.211,0.336,0.385,0.396,0.383,0.391,0.449
+"July",0.469,0.542,0.504,0.62,0.258,0.236,0.244,0.154,0.059,0.06,0.088,0.104,0.093,0.128,0.119,0.14,0.188,0.252,0.34,0.373,0.455,0.378,0.411,0.32
+"Aug.",0.489,0.64,0.683,0.63,0.425,0.286,0.198,0.146,0.065,0.092,0.084,0.126,0.068,0.143,0.134,0.196,0.203,0.265,0.373,0.404,0.367,0.38,0.412,0.543
+"Sept.",0.565,0.541,0.66,0.568,0.31,0.14,0.109,0.096,0.125,0.063,0.077,0.111,0.1,0.168,0.094,0.147,0.184,0.259,0.287,0.269,0.29,0.275,0.383,0.427
+"Oct.",0.54,0.46,0.588,0.506,0.492,0.241,0.108,0.104,0.053,0.082,0.083,0.1,0.082,0.164,0.147,0.19,0.161,0.228,0.224,0.212,0.228,0.349,0.325,0.446
+"Nov.",0.491,0.602,0.593,0.488,0.466,0.268,0.142,0.152,0.146,0.106,0.143,0.143,0.119,0.132,0.113,0.149,0.205,0.169,0.188,0.268,0.218,0.318,0.308,0.44
+"Dec.",0.474,0.556,0.649,0.544,0.353,0.292,0.092,0.167,0.127,0.057,0.081,0.071,0.059,0.068,0.12,0.124,0.182,0.143,0.176,0.246,0.215,0.348,0.387,0.431
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/nathan-beatrice.tsv b/VT2-examples/vt2-ch4-codedata-dir/data/nathan-beatrice.tsv
new file mode 100644
index 0000000..7f8bd75
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/nathan-beatrice.tsv
@@ -0,0 +1,277 @@
+"year" "sex" "name" "n" "prop"
+1880 "M" "Nathan" 114 0.00096284
+1881 "M" "Nathan" 91 0.0008404
+1882 "M" "Nathan" 113 0.00092599
+1883 "M" "Nathan" 93 0.00082684
+1884 "M" "Nathan" 100 0.00081474
+1885 "M" "Nathan" 103 0.00088835
+1886 "M" "Nathan" 136 0.00114246
+1887 "M" "Nathan" 106 0.00096969
+1888 "M" "Nathan" 129 0.00099303
+1889 "M" "Nathan" 121 0.00101653
+1890 "M" "Nathan" 110 0.00091896
+1891 "M" "Nathan" 106 0.00097012
+1892 "M" "Nathan" 144 0.00109545
+1893 "M" "Nathan" 114 0.00094183
+1894 "M" "Nathan" 127 0.00101687
+1895 "M" "Nathan" 119 0.00093965
+1896 "M" "Nathan" 110 0.00085224
+1897 "M" "Nathan" 139 0.00113989
+1898 "M" "Nathan" 99 0.00074941
+1899 "M" "Nathan" 112 0.00097228
+1900 "M" "Nathan" 186 0.00114721
+1901 "M" "Nathan" 131 0.00113327
+1902 "M" "Nathan" 143 0.00107721
+1903 "M" "Nathan" 146 0.00112893
+1904 "M" "Nathan" 131 0.00094579
+1905 "M" "Nathan" 168 0.00117287
+1906 "M" "Nathan" 148 0.00102727
+1907 "M" "Nathan" 176 0.0011098
+1908 "M" "Nathan" 178 0.00106992
+1909 "M" "Nathan" 209 0.00118168
+1910 "M" "Nathan" 245 0.00117494
+1911 "M" "Nathan" 348 0.00144162
+1912 "M" "Nathan" 581 0.00128694
+1913 "M" "Nathan" 615 0.00114686
+1914 "M" "Nathan" 848 0.00124099
+1915 "M" "Nathan" 916 0.0010398
+1916 "M" "Nathan" 904 0.00097914
+1917 "M" "Nathan" 881 0.00091835
+1918 "M" "Nathan" 945 0.00090114
+1919 "M" "Nathan" 849 0.00083618
+1920 "M" "Nathan" 815 0.00074036
+1921 "M" "Nathan" 751 0.00065996
+1922 "M" "Nathan" 749 0.00066562
+1923 "M" "Nathan" 739 0.00065264
+1924 "M" "Nathan" 680 0.00058166
+1925 "M" "Nathan" 615 0.00053411
+1926 "M" "Nathan" 669 0.00058402
+1927 "M" "Nathan" 636 0.0005474
+1928 "M" "Nathan" 548 0.00048021
+1929 "M" "Nathan" 513 0.00046319
+1930 "M" "Nathan" 540 0.00047812
+1931 "M" "Nathan" 442 0.00041325
+1932 "M" "Nathan" 486 0.00045239
+1933 "M" "Nathan" 413 0.0004049
+1934 "M" "Nathan" 469 0.00044173
+1935 "M" "Nathan" 468 0.00043763
+1936 "M" "Nathan" 464 0.00043602
+1937 "M" "Nathan" 434 0.0003969
+1938 "M" "Nathan" 516 0.0004541
+1939 "M" "Nathan" 468 0.000413
+1940 "M" "Nathan" 497 0.00041905
+1941 "M" "Nathan" 528 0.00042083
+1942 "M" "Nathan" 522 0.00037072
+1943 "M" "Nathan" 582 0.00040019
+1944 "M" "Nathan" 507 0.00036502
+1945 "M" "Nathan" 488 0.00035587
+1946 "M" "Nathan" 558 0.00033814
+1947 "M" "Nathan" 617 0.00033219
+1948 "M" "Nathan" 619 0.00034725
+1949 "M" "Nathan" 660 0.00036629
+1950 "M" "Nathan" 753 0.00041395
+1951 "M" "Nathan" 870 0.00045506
+1952 "M" "Nathan" 850 0.00043055
+1953 "M" "Nathan" 810 0.00040475
+1954 "M" "Nathan" 961 0.00046461
+1955 "M" "Nathan" 949 0.00045416
+1956 "M" "Nathan" 1095 0.00051059
+1957 "M" "Nathan" 1227 0.00056092
+1958 "M" "Nathan" 1242 0.00057686
+1959 "M" "Nathan" 1374 0.00063427
+1960 "M" "Nathan" 1627 0.00075126
+1961 "M" "Nathan" 1490 0.00069111
+1962 "M" "Nathan" 1534 0.00072976
+1963 "M" "Nathan" 1543 0.00074715
+1964 "M" "Nathan" 1500 0.00073989
+1965 "M" "Nathan" 1419 0.00074873
+1966 "M" "Nathan" 1422 0.00078222
+1967 "M" "Nathan" 1434 0.00080572
+1968 "M" "Nathan" 1656 0.0009324
+1969 "M" "Nathan" 1909 0.00104318
+1970 "M" "Nathan" 2548 0.0013371
+1971 "M" "Nathan" 3050 0.00167732
+1972 "M" "Nathan" 3522 0.00210309
+1973 "M" "Nathan" 4749 0.00294199
+1974 "M" "Nathan" 5849 0.00358672
+1975 "M" "Nathan" 7064 0.00435236
+1976 "M" "Nathan" 8616 0.00527545
+1977 "M" "Nathan" 9894 0.00578644
+1978 "M" "Nathan" 9891 0.00578726
+1979 "M" "Nathan" 10741 0.00599445
+1980 "M" "Nathan" 11144 0.00600747
+1981 "M" "Nathan" 11261 0.00604645
+1982 "M" "Nathan" 10803 0.00572458
+1983 "M" "Nathan" 10556 0.00566512
+1984 "M" "Nathan" 10612 0.00565604
+1985 "M" "Nathan" 10650 0.00553586
+1986 "M" "Nathan" 9995 0.00520313
+1987 "M" "Nathan" 9908 0.0050824
+1988 "M" "Nathan" 10148 0.00507063
+1989 "M" "Nathan" 10906 0.0052046
+1990 "M" "Nathan" 10936 0.00508377
+1991 "M" "Nathan" 10494 0.00495189
+1992 "M" "Nathan" 10146 0.00483461
+1993 "M" "Nathan" 10192 0.00493569
+1994 "M" "Nathan" 10382 0.00509434
+1995 "M" "Nathan" 10304 0.00512383
+1996 "M" "Nathan" 10283 0.00513285
+1997 "M" "Nathan" 10441 0.00522746
+1998 "M" "Nathan" 11584 0.00571447
+1999 "M" "Nathan" 12534 0.0061491
+2000 "M" "Nathan" 13036 0.00624512
+2001 "M" "Nathan" 12645 0.00611631
+2002 "M" "Nathan" 13220 0.00640029
+2003 "M" "Nathan" 13659 0.00650412
+2004 "M" "Nathan" 14627 0.00692504
+2005 "M" "Nathan" 14356 0.00675248
+2006 "M" "Nathan" 14273 0.00651486
+2007 "M" "Nathan" 13589 0.00613936
+2008 "M" "Nathan" 13253 0.00608367
+2009 "M" "Nathan" 12119 0.00571933
+2010 "M" "Nathan" 11372 0.00554158
+2011 "M" "Nathan" 10501 0.0051765
+2012 "M" "Nathan" 10417 0.00514194
+2013 "M" "Nathan" 9709 0.00481423
+2014 "M" "Nathan" 8967 0.00438618
+2015 "M" "Nathan" 8581 0.00420982
+2016 "M" "Nathan" 7970 0.00394987
+2017 "M" "Nathan" 7390 0.00376409
+1880 "F" "Beatrice" 87 0.00089135
+1881 "F" "Beatrice" 87 0.00088008
+1882 "F" "Beatrice" 123 0.00106314
+1883 "F" "Beatrice" 126 0.00104948
+1884 "F" "Beatrice" 167 0.00121379
+1885 "F" "Beatrice" 188 0.00132443
+1886 "F" "Beatrice" 192 0.0012489
+1887 "F" "Beatrice" 262 0.00168573
+1888 "F" "Beatrice" 316 0.00166803
+1889 "F" "Beatrice" 352 0.00186028
+1890 "F" "Beatrice" 417 0.00206783
+1891 "F" "Beatrice" 485 0.00246736
+1892 "F" "Beatrice" 541 0.00240537
+1893 "F" "Beatrice" 564 0.00250408
+1894 "F" "Beatrice" 684 0.00289866
+1895 "F" "Beatrice" 814 0.00329413
+1896 "F" "Beatrice" 870 0.00345248
+1897 "F" "Beatrice" 942 0.00379418
+1898 "F" "Beatrice" 1122 0.00409274
+1899 "F" "Beatrice" 1061 0.00428704
+1900 "F" "Beatrice" 1382 0.0043493
+1901 "F" "Beatrice" 1183 0.00465329
+1902 "F" "Beatrice" 1429 0.00509749
+1903 "F" "Beatrice" 1439 0.00517261
+1904 "F" "Beatrice" 1549 0.00529689
+1905 "F" "Beatrice" 1661 0.0053604
+1906 "F" "Beatrice" 1679 0.00535669
+1907 "F" "Beatrice" 1858 0.00550626
+1908 "F" "Beatrice" 1997 0.00563279
+1909 "F" "Beatrice" 2105 0.00571862
+1910 "F" "Beatrice" 2459 0.00586138
+1911 "F" "Beatrice" 2522 0.00570823
+1912 "F" "Beatrice" 3205 0.00546265
+1913 "F" "Beatrice" 3567 0.00544652
+1914 "F" "Beatrice" 4232 0.00531245
+1915 "F" "Beatrice" 5342 0.00521743
+1916 "F" "Beatrice" 5586 0.00514497
+1917 "F" "Beatrice" 5839 0.00519624
+1918 "F" "Beatrice" 5907 0.00491282
+1919 "F" "Beatrice" 5856 0.00498532
+1920 "F" "Beatrice" 5804 0.00466545
+1921 "F" "Beatrice" 5968 0.00466355
+1922 "F" "Beatrice" 5476 0.00438938
+1923 "F" "Beatrice" 5464 0.00436261
+1924 "F" "Beatrice" 5269 0.00406651
+1925 "F" "Beatrice" 4991 0.00395147
+1926 "F" "Beatrice" 4519 0.00367353
+1927 "F" "Beatrice" 4240 0.00342932
+1928 "F" "Beatrice" 3921 0.0032799
+1929 "F" "Beatrice" 3572 0.00308591
+1930 "F" "Beatrice" 3392 0.00290804
+1931 "F" "Beatrice" 2842 0.00257515
+1932 "F" "Beatrice" 2787 0.00251939
+1933 "F" "Beatrice" 2410 0.00230416
+1934 "F" "Beatrice" 2408 0.00222505
+1935 "F" "Beatrice" 2241 0.0020622
+1936 "F" "Beatrice" 1956 0.00181533
+1937 "F" "Beatrice" 1986 0.00180257
+1938 "F" "Beatrice" 1911 0.00167432
+1939 "F" "Beatrice" 1780 0.00156961
+1940 "F" "Beatrice" 1729 0.0014637
+1941 "F" "Beatrice" 1665 0.00133643
+1942 "F" "Beatrice" 1771 0.00127374
+1943 "F" "Beatrice" 1757 0.00122416
+1944 "F" "Beatrice" 1579 0.00115554
+1945 "F" "Beatrice" 1359 0.00100961
+1946 "F" "Beatrice" 1463 0.00090709
+1947 "F" "Beatrice" 1466 0.00080646
+1948 "F" "Beatrice" 1432 0.00082177
+1949 "F" "Beatrice" 1340 0.00076332
+1950 "F" "Beatrice" 1270 0.00072216
+1951 "F" "Beatrice" 1283 0.00069454
+1952 "F" "Beatrice" 1223 0.00064291
+1953 "F" "Beatrice" 1093 0.00056659
+1954 "F" "Beatrice" 1085 0.000545
+1955 "F" "Beatrice" 1033 0.00051529
+1956 "F" "Beatrice" 1010 0.00049047
+1957 "F" "Beatrice" 981 0.00046769
+1958 "F" "Beatrice" 879 0.00042566
+1959 "F" "Beatrice" 882 0.00042434
+1960 "F" "Beatrice" 909 0.00043705
+1961 "F" "Beatrice" 766 0.00036891
+1962 "F" "Beatrice" 736 0.0003631
+1963 "F" "Beatrice" 718 0.00036118
+1964 "F" "Beatrice" 650 0.0003321
+1965 "F" "Beatrice" 639 0.00034968
+1966 "F" "Beatrice" 602 0.00034291
+1967 "F" "Beatrice" 503 0.000293
+1968 "F" "Beatrice" 494 0.00028897
+1969 "F" "Beatrice" 437 0.00024791
+1970 "F" "Beatrice" 424 0.00023146
+1971 "F" "Beatrice" 427 0.00024367
+1972 "F" "Beatrice" 309 0.00019163
+1973 "F" "Beatrice" 327 0.00021042
+1974 "F" "Beatrice" 316 0.00020177
+1975 "F" "Beatrice" 322 0.00020631
+1976 "F" "Beatrice" 282 0.0001794
+1977 "F" "Beatrice" 284 0.00017265
+1978 "F" "Beatrice" 292 0.00017764
+1979 "F" "Beatrice" 280 0.00016249
+1980 "F" "Beatrice" 287 0.0001612
+1981 "F" "Beatrice" 338 0.00018901
+1982 "F" "Beatrice" 289 0.00015932
+1983 "F" "Beatrice" 257 0.00014363
+1984 "F" "Beatrice" 235 0.00013035
+1985 "F" "Beatrice" 294 0.00015927
+1986 "F" "Beatrice" 258 0.00013983
+1987 "F" "Beatrice" 209 0.00011154
+1988 "F" "Beatrice" 286 0.00014876
+1989 "F" "Beatrice" 303 0.00015212
+1990 "F" "Beatrice" 290 0.00014119
+1991 "F" "Beatrice" 306 0.00015051
+1992 "F" "Beatrice" 286 0.00014269
+1993 "F" "Beatrice" 266 0.00013494
+1994 "F" "Beatrice" 224 0.00011493
+1995 "F" "Beatrice" 198 0.00010306
+1996 "F" "Beatrice" 215 0.00011216
+1997 "F" "Beatrice" 182 0.00009535
+1998 "F" "Beatrice" 220 0.00011352
+1999 "F" "Beatrice" 196 0.00010071
+2000 "F" "Beatrice" 218 0.00010928
+2001 "F" "Beatrice" 218 0.0001101
+2002 "F" "Beatrice" 190 0.00009625
+2003 "F" "Beatrice" 210 0.00010472
+2004 "F" "Beatrice" 234 0.00011604
+2005 "F" "Beatrice" 213 0.00010502
+2006 "F" "Beatrice" 272 0.0001302
+2007 "F" "Beatrice" 308 0.00014564
+2008 "F" "Beatrice" 338 0.00016243
+2009 "F" "Beatrice" 327 0.00016164
+2010 "F" "Beatrice" 321 0.00016392
+2011 "F" "Beatrice" 396 0.00020465
+2012 "F" "Beatrice" 404 0.00020865
+2013 "F" "Beatrice" 485 0.0002521
+2014 "F" "Beatrice" 489 0.00025056
+2015 "F" "Beatrice" 541 0.0002781
+2016 "F" "Beatrice" 557 0.00028883
+2017 "F" "Beatrice" 544 0.00029015
diff --git a/VT2-examples/vt2-ch4-codedata-dir/data/us-postage.csv b/VT2-examples/vt2-ch4-codedata-dir/data/us-postage.csv
new file mode 100644
index 0000000..2b6532d
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/data/us-postage.csv
@@ -0,0 +1,36 @@
+"Year","Price"
+1863,0.06
+1883,0.04
+1885,0.02
+1917,0.03
+1919,0.02
+1932,0.03
+1958,0.04
+1963,0.05
+1968,0.06
+1971,0.08
+1974,0.10
+1975,0.13
+1978,0.15
+1981,0.20
+1985,0.22
+1988,0.25
+1991,0.29
+1995,0.32
+1999,0.33
+2001,0.34
+2002,0.37
+2006,0.39
+2007,0.41
+2008,0.42
+2009,0.44
+2012,0.45
+2013,0.46
+2014,0.49
+2016,0.47
+2017,0.49
+2018,0.50
+2019,0.55
+2021,0.58
+2022,0.60
+2023,0.66
diff --git a/VT2-examples/vt2-ch4-codedata-dir/egot-winners-raw-dots.pdf b/VT2-examples/vt2-ch4-codedata-dir/egot-winners-raw-dots.pdf
new file mode 100644
index 0000000..8f10780
Binary files /dev/null and b/VT2-examples/vt2-ch4-codedata-dir/egot-winners-raw-dots.pdf differ
diff --git a/VT2-examples/vt2-ch4-codedata-dir/events.R b/VT2-examples/vt2-ch4-codedata-dir/events.R
new file mode 100644
index 0000000..4c58932
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/events.R
@@ -0,0 +1,182 @@
+#
+# Timeline
+#
+
+# Load data
+egots <- read.csv("data/EGOT-winners.csv")
+
+# Dimensions of data frame
+dim(egots)
+
+# Column names
+colnames(egots)
+
+# Range of completion years
+range(egots$completion_year)
+
+# Blank plot
+plot(NA,
+ xlim=range(egots$completion_year), ylim=c(0,2),
+ xlab="Completion Year", ylab="",
+ yaxt="n", bty="n", type="n")
+
+# Baseline
+abline(h=1)
+
+# Points
+points(egots$completion_year,
+ rep(1, dim(egots)[1]))
+
+# e.g. 2001
+egots[egots$completion_year == 2001, c("completion_year", "name")]
+
+# Jitter
+points(jitter(egots$completion_year),
+ jitter(rep(1, dim(egots)[1]), factor=2),
+ pch=21, bg="#efe2aa", cex=2)
+
+# Last rows of data frame
+tail(egots)
+
+# Annotation
+anntext <- paste0(egots$name[18], "\ncompletes first \nEGOT in ",
+ egots$completion_year[18])
+text(egots$completion_year[18]-1,
+ 1.5,
+ anntext,
+ pos=4)
+
+
+
+#
+# Dot plot
+#
+
+# Load data
+egots <- read.csv("data/EGOT-winners.csv")
+
+# Awards and colors
+year_cols <- c("emmy_year", "grammy_year", "oscar_year", "tony_year")
+award_colors <- c("#3F78E7", "#cb84cc", "#BDB63B", "#83c4b3")
+
+# Blank plot
+par(las=1)
+plot(NA, xlim=c(1930, 2023),
+ ylim=c(0, dim(egots)[1]),
+ type="n", xlab="", ylab="")
+
+# Points
+for (i in 1:length(year_cols)) {
+ points(egots[,year_cols[i]], 1:dim(egots)[1], pch=19)
+}
+
+
+# With color
+plot(NA, xlim=c(1930, 2023),
+ ylim=c(0, dim(egots)[1]),
+ type="n", xlab="", ylab="")
+for (i in 1:length(year_cols)) {
+ points(egots[,year_cols[i]], 1:dim(egots)[1],
+ pch=21, cex=1.5,
+ col="black",
+ bg=award_colors[i])
+}
+
+
+# Connect
+plot(NA, xlim=c(1930, 2023),
+ ylim=c(0, dim(egots)[1]),
+ type="n", xlab="", ylab="")
+for (j in 1:dim(egots)[1]) {
+ endyrs <- range(egots[j, year_cols])
+ lines(endyrs, c(j, j))
+}
+for (i in 1:length(year_cols)) {
+ points(egots[,year_cols[i]], 1:dim(egots)[1],
+ pch=21, cex=1.5,
+ col="black",
+ bg=award_colors[i])
+}
+
+### With Labels
+
+# Blank plot, margin
+par(las=1, mar=c(4,12,2,2))
+plot(NA, xlim=c(1930, 2023),
+ ylim=c(0, dim(egots)[1]),
+ yaxt="n", bty="n",
+ type="n", xlab="", ylab="")
+
+# Axis and grid
+axis(side=2, at=1:dim(egots)[1], labels = egots$name,
+ tick=FALSE)
+grid(NULL, 0, lty=1, lwd=.5)
+
+# Lines and points
+for (j in 1:dim(egots)[1]) {
+ endyrs <- range(egots[j, year_cols])
+ lines(endyrs, c(j, j))
+}
+for (i in 1:length(year_cols)) {
+ points(egots[,year_cols[i]], 1:dim(egots)[1],
+ pch=21, cex=1.5,
+ col="black",
+ bg=award_colors[i])
+}
+
+
+
+
+
+#
+# Bar chart, highlighted
+#
+
+# Load data
+winners <- read.csv("data/hot-dog-contest-winners.csv")
+
+# Bar chart
+barplot(winners$hotdogs,
+ names.arg=winners$year,
+ border=NA,
+ space=.1,
+ xlab="Year",
+ ylab="Hot dogs and buns (HDB) eaten")
+
+# Record years
+head(winners)
+
+# Bar chart, color
+barplot(winners$hotdogs,
+ names.arg=winners$year,
+ col="lightblue",
+ border=NA,
+ space=.1,
+ xlab="Year",
+ ylab="Hot dogs and buns (HDB) eaten")
+
+
+# Bar chart, some color
+winners$barcolor <- NA
+head(winners)
+winners$barcolor[winners$record == 1] <- "#04b8c8"
+winners$barcolor[winners$record != 1] <- "lightgray"
+barplot(winners$hotdogs,
+ names.arg=winners$year,
+ col=winners$barcolor,
+ border=NA,
+ space=.1,
+ xlab="Year",
+ ylab="Hot dogs and buns (HDB) eaten")
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/VT2-examples/vt2-ch4-codedata-dir/heatmap-scripts.zip b/VT2-examples/vt2-ch4-codedata-dir/heatmap-scripts.zip
new file mode 100644
index 0000000..4af4b49
Binary files /dev/null and b/VT2-examples/vt2-ch4-codedata-dir/heatmap-scripts.zip differ
diff --git a/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.R b/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.R
new file mode 100644
index 0000000..ad45064
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.R
@@ -0,0 +1,73 @@
+library(readr)
+library(dplyr)
+library(extrafont)
+
+# Load data.
+accidents <- read_tsv("data/month_hour_pct.tsv")
+
+# Color scale.
+getCol <- function(p) {
+ if (p <= .1) {
+ col <- "#dff5ee"
+ } else if (p <= .2) {
+ col <- "#92dee9"
+ } else if (p <= .3) {
+ col <- "#3fc1f9"
+ } else if (p <= .4) {
+ col <- "#5598fd"
+ } else if (p <= .5) {
+ col <- "#a75bd8"
+ } else {
+ col <- "#d4007f"
+ }
+ return(col)
+}
+
+
+# Setup blank plot.
+par(mar=c(0,0,0,0), bg="white", las=1, family="Inconsolata")
+plot(0:1, 0:1, type="n", xlab="", ylab="", axes=FALSE, asp=1, bty="n")
+
+
+par(new=TRUE, plt=c(0.33, .66, 0.1, .12))
+symbols(1:6, rep(1, 6), squares=rep(1, 6),
+ bg=sapply(c(.1, .2, .3, .4, .5, .6), getCol),
+ fg="white",
+ inches=FALSE, bty="n",
+ xaxt="n", yaxt="n", ylab="", xlab="")
+axis(1, at=c(2,3,4,5,6)-.5,
+ labels=paste0(c(.1, .2, .3, .4, .5)*100, "%"), lwd=0, lwd.ticks=.5)
+
+par(new=TRUE, plt=c(0.1, .99, 0.25, .95))
+plot(NA, xlim=c(0, 24), ylim=c(12,1), type="n", asp="1",
+ xlab="", ylab="",
+ xaxt="n", yaxt="n", bty="n", cex.main=1.7,
+ main="")
+axis(2, at=1:12,
+ labels=c("Jan.", "Feb.", "March", "April", "May", "June", "July", "Aug.", "Sept.", "Oct.", "Nov.", "Dec."),
+ lwd=0, lwd.ticks=.8)
+axis(1, at=c(0, 6, 12, 18, 23),
+ labels=c("12am", "6am", "noon", "6pm", "11pm"),
+ lwd=0, lwd.ticks=.8)
+
+
+
+# Get color.
+squarecol <- sapply(accidents$pct_drunk_all, getCol)
+
+# Add squares.
+symbols(accidents$HOUR, accidents$MONTH,
+ squares=rep(1, dim(accidents)[1]),
+ inches=FALSE,
+ bg=squarecol,
+ fg="#ffffff",
+ add=TRUE)
+
+# Make a matrix.
+pmat <- matrix(NA, nrow=12, ncol=24)
+for (i in 1:dim(accidents)[1]) {
+ pmat[accidents$MONTH[i], accidents$HOUR[i]+1] <- accidents$pct_drunk_all[i]
+}
+# pmat[accidents$MONTH, accidents$HOUR+1] <- accidents$pct_drunk_all
+row.names(pmat) <- c("Jan.", "Feb", "March", "April", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec")
+write.table(pmat, file="month_hour_pct_matrix.csv", sep=",")
diff --git a/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.py b/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.py
new file mode 100644
index 0000000..2810b3d
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/make-heatmap.py
@@ -0,0 +1,42 @@
+import seaborn as sns
+import pandas as pd
+import matplotlib.colors as mc
+
+# For defining color map
+def NonLinCdict(steps, hexcol_array):
+ cdict = {'red': (), 'green': (), 'blue': ()}
+ for s, hexcol in zip(steps, hexcol_array):
+ rgb = mc.hex2color(hexcol)
+ cdict['red'] = cdict['red'] + ((s, rgb[0], rgb[0]),)
+ cdict['green'] = cdict['green'] + ((s, rgb[1], rgb[1]),)
+ cdict['blue'] = cdict['blue'] + ((s, rgb[2], rgb[2]),)
+ return cdict
+
+# Load data
+thedata = pd.read_csv("data/month_hour_pct_matrix.csv")
+mns = thedata.iloc[0:12, 0]
+pmat = thedata.iloc[0:12, 1:25]
+
+# Set figure size
+sns.set(rc={'figure.figsize':(11,4.5), 'pdf.fonttype': 42})
+
+# Make color map
+# hc = ['#dff5ee', '#92dee9', '#3fc1f9', '#5598fd', '#a75bd8', '#d4007f']
+hc = ['#f0f0f0', '#c3dde3', '#acc6cc', '#aeaaaa', '#c0877c', '#da5241']
+th = [0, 0.1, 0.2, 0.3, .4, 1]
+cdict = NonLinCdict(th, hc)
+cm = mc.LinearSegmentedColormap('custom', cdict)
+
+# Heatmap
+p1 = sns.heatmap(
+ pmat,
+ square=True,
+ linewidths=0.1,
+ yticklabels=mns,
+ cmap=cm)
+p1.set_xticks([0.5,6.5,12.5,18.5,23.5])
+p1.set_xticklabels(["12am", "6am", "noon", "6pm", "11pm"])
+fig = p1.get_figure()
+
+# Save file
+fig.savefig("pmat-heatmap.pdf")
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch4-codedata-dir/multi-line-chart-raw.pdf b/VT2-examples/vt2-ch4-codedata-dir/multi-line-chart-raw.pdf
new file mode 100644
index 0000000..6fac240
Binary files /dev/null and b/VT2-examples/vt2-ch4-codedata-dir/multi-line-chart-raw.pdf differ
diff --git a/VT2-examples/vt2-ch4-codedata-dir/pmat-heatmap.pdf b/VT2-examples/vt2-ch4-codedata-dir/pmat-heatmap.pdf
new file mode 100644
index 0000000..d533310
Binary files /dev/null and b/VT2-examples/vt2-ch4-codedata-dir/pmat-heatmap.pdf differ
diff --git a/VT2-examples/vt2-ch4-codedata-dir/trends.R b/VT2-examples/vt2-ch4-codedata-dir/trends.R
new file mode 100644
index 0000000..9978d07
--- /dev/null
+++ b/VT2-examples/vt2-ch4-codedata-dir/trends.R
@@ -0,0 +1,158 @@
+#
+# Bar chart
+#
+
+# Load data
+winners <- read.csv("data/hot-dog-contest-winners.csv")
+head(winners)
+
+# Default bar chart
+barplot(winners$hotdogs)
+
+# Year labels
+barplot(winners$hotdogs, names.arg=winners$year)
+
+# Axis labels
+barplot(winners$hotdogs,
+ names.arg=winners$year,
+ border=NA,
+ space=.1,
+ xlab="Year",
+ ylab="Hot dogs and buns (HDB) eaten",
+ main="Contest Winners")
+
+
+
+
+#
+# Line chart
+#
+
+# Graphical parameter to make axis labels horizontal
+par(las=1)
+
+# Line chart with plot()
+plot(winners$year, winners$hotdogs,
+ type="l",
+ xlab="",
+ ylab="Hot dogs and buns (HDB) eaten",
+ main="Nathan's Hot Dog Eating Contest, Winning Results, 1980-2023")
+
+par(mfrow=c(3,2), las=1)
+for (currtype in c("p", "l", "b", "c", "o", "n")) {
+ plot(winners$year, winners$hotdogs, type=currtype,
+ xlab="", ylab="",
+ main=paste0("type='", currtype, "'"))
+}
+
+# Text
+text(2001, 50,
+ "In 2001, Takeru Kobayashi nearly doubled the previous record with 50 HDBs.",
+ pos=2)
+
+# Text with line breaks
+text(2001, 48,
+ "In 2001, Takeru Kobayashi
+ nearly doubled the previous
+ record with 50 HDBs.",
+ pos=2)
+
+# Find documentation
+?text
+
+# Wrapped text
+anno <- "In 2001, Takeru Kobayashi nearly doubled the previous record with 50 HDBs."
+text(2001, 48,
+ paste(strwrap(anno, 30), collapse="\n"),
+ pos=2)
+
+
+#
+# Dot plot
+#
+
+par(las=1)
+plot(winners$year, winners$hotdogs,
+ type="p",
+ bty="n",
+ xlab="",
+ ylab="Hot dogs and buns (HDB) eaten",
+ main="Nathan's Hot Dog Eating Contest, Winning Results, 1980-2023")
+
+
+
+
+#
+# Step chart
+#
+
+# Load data
+postage <- read.csv("data/us-postage.csv")
+
+# Summary
+head(postage)
+summary(postage)
+
+# Plot as regular time series
+par(las=1)
+plot(postage$Year, postage$Price, type="l",
+ xlab="Year", ylab="Postage Rate (Dollars)",
+ main="US Postage Rates for Letters, First Ounce, 1863-2023")
+
+# Default step chart
+plot(postage$Year, postage$Price, type="s")
+
+# Add labels
+par(las=1)
+plot(postage$Year, postage$Price, type="s",
+ xlab="Year", ylab="Postage Rate (Dollars)",
+ main="US Postage Rates for Letters, First Ounce, 1863-2023")
+
+# Change limits ("zoom")
+postagesub <- postage[postage$Year >= 1991,]
+head(postagesub)
+
+par(las=1)
+plot(postagesub$Year, postagesub$Price, type="s",
+ xlab="Year", ylab="Postage Rate (Dollars)",
+ main="US Postage Rates for Letters, First Ounce, 1991-2023")
+
+
+
+
+#
+# Smoothing
+#
+
+# Load data.
+# install.packages("babynames")
+library(babynames)
+nathan <- babynames[babynames$name == "Nathan" & babynames$sex == "M", ]
+beatrice <- babynames[babynames$name == "Beatrice" & babynames$sex == "F",]
+
+# Line chart, no smoothing
+par(las=1, mar=c(4,5,3,2))
+plot(nathan$year, nathan$prop,
+ type="l",
+ xlab="", ylab="Proportion")
+lines(beatrice$year, beatrice$prop,
+ lwd=2)
+
+# Splines
+natcoords <- spline(nathan$year, nathan$prop)
+beacoords <- spline(beatrice$year, beatrice$prop)
+par(las=1, mar=c(4,5,3,2))
+plot(natcoords,
+ col="#ac5c5a",
+ type="l",
+ xlab="", ylab="Proportion")
+lines(beacoords, lwd=2, col="#176572")
+
+natcoords <- spline(nathan$year, nathan$prop, n=20)
+beacoords <- spline(beatrice$year, beatrice$prop, n=20)
+par(las=1, mar=c(4,5,3,2))
+plot(natcoords,
+ col="#ac5c5a",
+ type="l",
+ xlab="", ylab="Proportion")
+lines(beacoords, lwd=2, col="#176572")
diff --git a/VT2-examples/vt2-ch5-codedata-dir/bar-mar.pdf b/VT2-examples/vt2-ch5-codedata-dir/bar-mar.pdf
new file mode 100644
index 0000000..3074920
Binary files /dev/null and b/VT2-examples/vt2-ch5-codedata-dir/bar-mar.pdf differ
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/household-types.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/household-types.tsv
new file mode 100644
index 0000000..4440a07
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/household-types.tsv
@@ -0,0 +1,323 @@
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+2010 "nuclear-married-children" 28212122.1 0.238044102240456
+2010 "nuclear-married-nochildren" 25540672.52 0.21550333715027
+2010 "nuclear-mother" 8797920.66 0.0742338033866819
+2010 "single" 31602430.03 0.266650344785945
+2011 "composite" 11987785.45 0.0999028865393945
+2011 "extended" 10554704.76 0.0879599886478688
+2011 "nuclear-father" 1829748.99 0.0152486217330109
+2011 "nuclear-married-children" 27741032.61 0.231186088947102
+2011 "nuclear-married-nochildren" 25883881.52 0.215709105695722
+2011 "nuclear-mother" 8935779.14 0.0744683105389168
+2011 "single" 33061452.73 0.275524997897985
+2012 "composite" 12227450.05 0.100915055882802
+2012 "extended" 10410830.86 0.0859222138489376
+2012 "nuclear-father" 1887319.43 0.015576342161966
+2012 "nuclear-married-children" 27640615.09 0.228122315367167
+2012 "nuclear-married-nochildren" 26463618.49 0.218408378513843
+2012 "nuclear-mother" 9293508.56 0.0767007783180191
+2012 "single" 33242423.55 0.274354915907265
+2013 "composite" 12848231.4 0.104841230811202
+2013 "extended" 10740467.89 0.0876419359224646
+2013 "nuclear-father" 1926537.52 0.0157204955695895
+2013 "nuclear-married-children" 27969500.34 0.228230388255612
+2013 "nuclear-married-nochildren" 26354238.98 0.215049897977068
+2013 "nuclear-mother" 9094804.68 0.0742133673463169
+2013 "single" 33615633.2 0.274302684117747
+2014 "composite" 24486978.73 0.0991060915758774
+2014 "extended" 22116766.3 0.0895131363676436
+2014 "nuclear-father" 3870159.48 0.0156636873853376
+2014 "nuclear-married-children" 56221156.52 0.22754375490785
+2014 "nuclear-married-nochildren" 53422034.48 0.216214874841138
+2014 "nuclear-mother" 18405813.22 0.0744938046715825
+2014 "single" 68555533.58 0.277464650250571
+2015 "composite" 12766210.56 0.102352983882629
+2015 "extended" 11096943.91 0.0889696528056223
+2015 "nuclear-father" 1883948.25 0.015104538967275
+2015 "nuclear-married-children" 28386300.82 0.227586923830036
+2015 "nuclear-married-nochildren" 26848404.8 0.215256855654487
+2015 "nuclear-mother" 8763640.55 0.0702624130905221
+2015 "single" 34981843.62 0.280466631769429
+2016 "composite" 12950225.76 0.102724489386347
+2016 "extended" 10825457.21 0.0858702840305541
+2016 "nuclear-father" 1975140.39 0.0156673166776592
+2016 "nuclear-married-children" 28214026.71 0.223800846489452
+2016 "nuclear-married-nochildren" 27380345.12 0.217187871763709
+2016 "nuclear-mother" 9113049.74 0.0722870317971879
+2016 "single" 35609315.37 0.282462159855092
+2017 "composite" 12831835.37 0.101421934334673
+2017 "extended" 11396121.89 0.0900741548399015
+2017 "nuclear-father" 2042132.35 0.0161408720679691
+2017 "nuclear-married-children" 27829541.1 0.21996275736261
+2017 "nuclear-married-nochildren" 28026002.2 0.221515572233513
+2017 "nuclear-mother" 8881544.64 0.0701991111399789
+2017 "single" 35512154.33 0.280685598021354
+2018 "composite" 13630453.86 0.106755566845541
+2018 "extended" 11336580.16 0.0887896363174157
+2018 "nuclear-father" 1989927.08 0.0155853792976115
+2018 "nuclear-married-children" 27984330.86 0.219177082028028
+2018 "nuclear-married-nochildren" 28186771.08 0.220762621340248
+2018 "nuclear-mother" 8735020.99 0.0684138713775119
+2018 "single" 35816008.15 0.280515842793643
+2019 "composite" 13332222.8 0.103612014327867
+2019 "extended" 11564485.39 0.0898739575461556
+2019 "nuclear-father" 1893593.99 0.0147161572804681
+2019 "nuclear-married-children" 27918305.71 0.216968463145867
+2019 "nuclear-married-nochildren" 28919282.67 0.224747604004613
+2019 "nuclear-mother" 8500056.56 0.0660586006770303
+2019 "single" 36546539.97 0.284023203017999
+2020 "composite" 13110723.7 0.101958995681941
+2020 "extended" 11639529.63 0.0905178675441837
+2020 "nuclear-father" 1901185.67 0.014785071057374
+2020 "nuclear-married-children" 28271692.12 0.219862259379643
+2020 "nuclear-married-nochildren" 29049611.21 0.225911952055129
+2020 "nuclear-mother" 8305042.62 0.0645863511432945
+2020 "single" 36310414.77 0.282377503138435
+2021 "composite" 14105790.72 0.108487098946671
+2021 "extended" 11643889.72 0.0895527121628647
+2021 "nuclear-father" 2142912.65 0.0164810595385484
+2021 "nuclear-married-children" 27565701.82 0.212006762346236
+2021 "nuclear-married-nochildren" 28881925.41 0.22212978782412
+2021 "nuclear-mother" 8638090.98 0.0664352285851585
+2021 "single" 37044436.63 0.284907350596401
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/how-met-2010s.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/how-met-2010s.tsv
new file mode 100644
index 0000000..5c9053c
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/how-met-2010s.tsv
@@ -0,0 +1,20 @@
+waymet category year p
+Met Through Family Friends and Family 2010 0.0638
+Met Through Friend Friends and Family 2010 0.1722
+Met Through Neighbors Friends and Family 2010 0.0304
+Met Through Coworker Work 2010 0.0853
+Met Online Online 2010 0.189
+Primary or Secondary School School 2010 0.0461
+College School 2010 0.0398
+Military Work 2010 0.0051
+Church Group Setting 2010 0.0318
+Volunteering Group Setting 2010 0.0345
+Customer Work 2010 0.0343
+Bar or Restaurant Group Setting 2010 0.1528
+Party Group Setting 2010 0.0693
+Public Group Setting 2010 0.0236
+Blind Date Dating 2010 0.0068
+Vacation Group Setting 2010 0.0085
+Singles Service Dating 2010 0.0024
+Business Trip Work 2010 0.0002
+Work Neighbors Work 2010 0.0039
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/how_met_decade.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/how_met_decade.tsv
new file mode 100644
index 0000000..06b84ed
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/how_met_decade.tsv
@@ -0,0 +1,115 @@
+"waymet" "year" "p"
+"met_through_family" 1960 0.1353
+"met_through_friend" 1960 0.1919
+"met_through_as_nghbrs" 1960 0.0477
+"met_as_through_cowork" 1960 0.0799
+"met_online" 1960 0.0042
+"school" 1960 0.1224
+"college" 1960 0.0417
+"mil" 1960 0.048
+"church" 1960 0.0322
+"vol_org" 1960 0.032
+"customer" 1960 0.046
+"bar_restaurant" 1960 0.0871
+"party" 1960 0.0514
+"public" 1960 0.0288
+"blind_date" 1960 0.0393
+"vacation" 1960 0.0102
+"single_serve_nonint" 1960 0
+"business_trip" 1960 0
+"work_neighbors" 1960 0.0019
+"met_through_family" 1970 0.1285
+"met_through_friend" 1970 0.2097
+"met_through_as_nghbrs" 1970 0.0391
+"met_as_through_cowork" 1970 0.1007
+"met_online" 1970 0.0032
+"school" 1970 0.0973
+"college" 1970 0.0646
+"mil" 1970 0.019
+"church" 1970 0.0455
+"vol_org" 1970 0.0342
+"customer" 1970 0.0312
+"bar_restaurant" 1970 0.1008
+"party" 1970 0.0589
+"public" 1970 0.0196
+"blind_date" 1970 0.0351
+"vacation" 1970 0.007
+"single_serve_nonint" 1970 0.0023
+"business_trip" 1970 0
+"work_neighbors" 1970 0.0033
+"met_through_family" 1980 0.1055
+"met_through_friend" 1980 0.1769
+"met_through_as_nghbrs" 1980 0.0374
+"met_as_through_cowork" 1980 0.1639
+"met_online" 1980 0.0002
+"school" 1980 0.0532
+"college" 1980 0.039
+"mil" 1980 0.0096
+"church" 1980 0.0415
+"vol_org" 1980 0.0406
+"customer" 1980 0.0569
+"bar_restaurant" 1980 0.1381
+"party" 1980 0.0576
+"public" 1980 0.0181
+"blind_date" 1980 0.0257
+"vacation" 1980 0.0087
+"single_serve_nonint" 1980 0.006
+"business_trip" 1980 0.0013
+"work_neighbors" 1980 0.02
+"met_through_family" 1990 0.1025
+"met_through_friend" 1990 0.1755
+"met_through_as_nghbrs" 1990 0.0355
+"met_as_through_cowork" 1990 0.1524
+"met_online" 1990 0.0153
+"school" 1990 0.0671
+"college" 1990 0.0511
+"mil" 1990 0.0134
+"church" 1990 0.0432
+"vol_org" 1990 0.0349
+"customer" 1990 0.0524
+"bar_restaurant" 1990 0.1171
+"party" 1990 0.0597
+"public" 1990 0.0267
+"blind_date" 1990 0.0189
+"vacation" 1990 0.007
+"single_serve_nonint" 1990 0.0171
+"business_trip" 1990 0.0047
+"work_neighbors" 1990 0.0053
+"met_through_family" 2000 0.0808
+"met_through_friend" 2000 0.1918
+"met_through_as_nghbrs" 2000 0.0439
+"met_as_through_cowork" 2000 0.1022
+"met_online" 2000 0.0949
+"school" 2000 0.0746
+"college" 2000 0.055
+"mil" 2000 0.0028
+"church" 2000 0.0445
+"vol_org" 2000 0.0357
+"customer" 2000 0.033
+"bar_restaurant" 2000 0.1065
+"party" 2000 0.0739
+"public" 2000 0.0242
+"blind_date" 2000 0.0107
+"vacation" 2000 0.0092
+"single_serve_nonint" 2000 0.0052
+"business_trip" 2000 0.002
+"work_neighbors" 2000 0.0092
+"met_through_family" 2010 0.0638
+"met_through_friend" 2010 0.1722
+"met_through_as_nghbrs" 2010 0.0304
+"met_as_through_cowork" 2010 0.0853
+"met_online" 2010 0.189
+"school" 2010 0.0461
+"college" 2010 0.0398
+"mil" 2010 0.0051
+"church" 2010 0.0318
+"vol_org" 2010 0.0345
+"customer" 2010 0.0343
+"bar_restaurant" 2010 0.1528
+"party" 2010 0.0693
+"public" 2010 0.0236
+"blind_date" 2010 0.0068
+"vacation" 2010 0.0085
+"single_serve_nonint" 2010 0.0024
+"business_trip" 2010 0.0002
+"work_neighbors" 2010 0.0039
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/htypes_long.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/htypes_long.tsv
new file mode 100644
index 0000000..32d4368
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/htypes_long.tsv
@@ -0,0 +1,8 @@
+"1976" "1977" "1978" "1979" "1980" "1981" "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989" "1990" "1991" "1992" "1993" "1994" "1995" "1996" "1997" "1998" "1999" "2000" "2001" "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010" "2011" "2012" "2013" "2014" "2015" "2016" "2017" "2018" "2019" "2020" "2021" "waymet"
+0.0359171491054953 0.0399304190012852 0.0428525973962617 0.0474735256922412 0.0501995587151689 0.0520402621932969 0.0528187007039854 0.0551498353045369 0.0572437966280052 0.0586315936717927 0.0598521155387804 0.062224080653711 0.0631306965698012 0.0651619692730516 0.068110148768524 0.0693664548034937 0.0719078532368738 0.0723608210010821 0.0778308390078657 0.0764573886371595 0.0799532380844666 0.0837161827210115 0.0825555129486877 0.0844332209772884 0.0896421842307627 0.089011172432275 0.0872071033449697 0.087084876736775 0.0874503363035857 0.0881810599961824 0.0899257664205084 0.0888063800538201 0.0910911600605037 0.0913819783626526 0.100582419516824 0.0999028865393945 0.100915055882802 0.104841230811202 0.0991060915758774 0.102352983882629 0.102724489386347 0.101421934334673 0.106755566845541 0.103612014327867 0.101958995681941 0.108487098946671 "composite"
+0.0737172522934185 0.0727315168805551 0.0758147976700633 0.0725050284854784 0.0696998821253608 0.0711682499584102 0.0729168455795199 0.0747292387483697 0.0716615668446401 0.070910970842229 0.071909536214278 0.0728586786060707 0.0717726521571869 0.0708002037832075 0.0709242242065303 0.0727560901295643 0.0717638124880954 0.0717016297654046 0.0745430075238214 0.0746001173455608 0.0747371137602061 0.076569609540085 0.0744529816145868 0.0746909091583756 0.076240937711205 0.0774338345864903 0.0767508931215668 0.0790837429433754 0.0813482328770508 0.0831172036767798 0.0832350756039985 0.0819442329802185 0.0824417240946155 0.0864541032954844 0.0903518305756296 0.0879599886478688 0.0859222138489376 0.0876419359224646 0.0895131363676436 0.0889696528056223 0.0858702840305541 0.0900741548399015 0.0887896363174157 0.0898739575461556 0.0905178675441837 0.0895527121628647 "extended"
+0.00843526602633583 0.00850777847249008 0.00884128546216286 0.00871238039369094 0.00922124889540031 0.00987548349927263 0.00960289954449856 0.0106284058870679 0.0102764563447354 0.0112867726707859 0.011555395836625 0.0124407426126183 0.0118775315029513 0.0113591248195951 0.011721090640924 0.0113157698518771 0.0125250964427845 0.0121601944899692 0.011573801401964 0.0128370964906525 0.0134572734542054 0.0140098263037266 0.0140129438941686 0.0147048558020019 0.0137413309623923 0.0139710385316971 0.0149264246775303 0.0147760706802127 0.0146864548917721 0.0140035077964467 0.0146879125443847 0.0143576939406037 0.0150004931202757 0.0140200670210693 0.014634162344193 0.0152486217330109 0.015576342161966 0.0157204955695895 0.0156636873853376 0.015104538967275 0.0156673166776592 0.0161408720679691 0.0155853792976115 0.0147161572804681 0.014785071057374 0.0164810595385484 "nuclear_father"
+0.372174728684127 0.366009606933288 0.352582350888287 0.348515951295164 0.342222592291195 0.336966780988604 0.327345839367472 0.32787275641601 0.325873219510477 0.320055671882903 0.31862095882471 0.315356497567858 0.311822005730658 0.307263329800481 0.302119228956253 0.297220222869942 0.29400495589387 0.296479963951474 0.292422540950275 0.287746743042085 0.282043268008802 0.280014032685759 0.278712383274044 0.271665484967356 0.269134929713948 0.264903331259992 0.261454684974571 0.258867573340172 0.255771008698044 0.255803453387787 0.253384188789247 0.251752808876081 0.244648611819688 0.242872298724713 0.238044102240456 0.231186088947102 0.228122315367167 0.228230388255612 0.22754375490785 0.227586923830036 0.223800846489452 0.21996275736261 0.219177082028028 0.216968463145867 0.219862259379643 0.212006762346236 "nuclear_married_children"
+0.23105684786821 0.228954282036918 0.22329750766847 0.224119880842422 0.225846957601157 0.22135769441088 0.223916958826045 0.220601349815541 0.217861625896122 0.21882945420392 0.215480962401521 0.218608367665754 0.21875105108323 0.217709050818948 0.218907078467126 0.215957299648124 0.21568954060959 0.217827736525544 0.214057839784099 0.215663527922776 0.2170987060378 0.21039489234554 0.212875331750921 0.21663463206885 0.218541833656927 0.218737758466502 0.218729639405749 0.218260971113627 0.220897183013127 0.216408047669538 0.2148945881855 0.217177208841511 0.216167236296419 0.220568720227114 0.21550333715027 0.215709105695722 0.218408378513843 0.215049897977068 0.216214874841138 0.215256855654487 0.217187871763709 0.221515572233513 0.220762621340248 0.224747604004613 0.225911952055129 0.22212978782412 "nuclear_married_nochildren"
+0.0703327451860424 0.0714186357959817 0.0742946512462897 0.0735824943473564 0.0759507046717957 0.0770169587728375 0.0799098718683007 0.0789965491244178 0.0805888866834837 0.0807817520787598 0.0812876597110068 0.0802089343571296 0.0799268937514529 0.0813341026419157 0.0802360531198163 0.0815682384508434 0.0818201817192453 0.083095800845789 0.0856379149973299 0.0823594543606017 0.0824567385962726 0.083657903336338 0.0803903329188037 0.08142048088052 0.0772322724130535 0.0749738847046828 0.0772441657965296 0.0771011479253706 0.0753316307578279 0.0759517714215265 0.0770067534616388 0.0771879174525132 0.0750564120935269 0.0743081502178945 0.0742338033866819 0.0744683105389168 0.0767007783180191 0.0742133673463169 0.0744938046715825 0.0702624130905221 0.0722870317971879 0.0701991111399789 0.0684138713775119 0.0660586006770303 0.0645863511432945 0.0664352285851585 "nuclear_mother"
+0.20836601083637 0.212447760879482 0.222316809668466 0.225090738943647 0.226859055699923 0.231574570176698 0.233488884110178 0.232021864704056 0.236494448092536 0.23950378464961 0.241293371473079 0.238302698536858 0.242719169204719 0.246372218862801 0.247982175840827 0.251815924246156 0.252288559609541 0.246373853420737 0.243934056334644 0.250335672201165 0.250253662058248 0.251637553067539 0.257000513598788 0.256450416145608 0.255466511311711 0.26096898001836 0.263687088679084 0.264825617260468 0.264515153458592 0.26653495605174 0.266865714994722 0.268773757855253 0.275594362514971 0.270394682151072 0.266650344785945 0.275524997897985 0.274354915907265 0.274302684117747 0.277464650250571 0.280466631769429 0.282462159855092 0.280685598021354 0.280515842793643 0.284023203017999 0.282377503138435 0.284907350596401 "single"
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/htypes_rectangular.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/htypes_rectangular.tsv
new file mode 100644
index 0000000..2df0b7a
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/htypes_rectangular.tsv
@@ -0,0 +1,47 @@
+"year" "p_composite" "p_extended" "p_nuclear_father" "p_nuclear_married_children" "p_nuclear_married_nochildren" "p_nuclear_mother" "p_single"
+1976 0.0359171491054953 0.0737172522934185 0.00843526602633583 0.372174728684127 0.23105684786821 0.0703327451860424 0.20836601083637
+1977 0.0399304190012852 0.0727315168805551 0.00850777847249008 0.366009606933288 0.228954282036918 0.0714186357959817 0.212447760879482
+1978 0.0428525973962617 0.0758147976700633 0.00884128546216286 0.352582350888287 0.22329750766847 0.0742946512462897 0.222316809668466
+1979 0.0474735256922412 0.0725050284854784 0.00871238039369094 0.348515951295164 0.224119880842422 0.0735824943473564 0.225090738943647
+1980 0.0501995587151689 0.0696998821253608 0.00922124889540031 0.342222592291195 0.225846957601157 0.0759507046717957 0.226859055699923
+1981 0.0520402621932969 0.0711682499584102 0.00987548349927263 0.336966780988604 0.22135769441088 0.0770169587728375 0.231574570176698
+1982 0.0528187007039854 0.0729168455795199 0.00960289954449856 0.327345839367472 0.223916958826045 0.0799098718683007 0.233488884110178
+1983 0.0551498353045369 0.0747292387483697 0.0106284058870679 0.32787275641601 0.220601349815541 0.0789965491244178 0.232021864704056
+1984 0.0572437966280052 0.0716615668446401 0.0102764563447354 0.325873219510477 0.217861625896122 0.0805888866834837 0.236494448092536
+1985 0.0586315936717927 0.070910970842229 0.0112867726707859 0.320055671882903 0.21882945420392 0.0807817520787598 0.23950378464961
+1986 0.0598521155387804 0.071909536214278 0.011555395836625 0.31862095882471 0.215480962401521 0.0812876597110068 0.241293371473079
+1987 0.062224080653711 0.0728586786060707 0.0124407426126183 0.315356497567858 0.218608367665754 0.0802089343571296 0.238302698536858
+1988 0.0631306965698012 0.0717726521571869 0.0118775315029513 0.311822005730658 0.21875105108323 0.0799268937514529 0.242719169204719
+1989 0.0651619692730516 0.0708002037832075 0.0113591248195951 0.307263329800481 0.217709050818948 0.0813341026419157 0.246372218862801
+1990 0.068110148768524 0.0709242242065303 0.011721090640924 0.302119228956253 0.218907078467126 0.0802360531198163 0.247982175840827
+1991 0.0693664548034937 0.0727560901295643 0.0113157698518771 0.297220222869942 0.215957299648124 0.0815682384508434 0.251815924246156
+1992 0.0719078532368738 0.0717638124880954 0.0125250964427845 0.29400495589387 0.21568954060959 0.0818201817192453 0.252288559609541
+1993 0.0723608210010821 0.0717016297654046 0.0121601944899692 0.296479963951474 0.217827736525544 0.083095800845789 0.246373853420737
+1994 0.0778308390078657 0.0745430075238214 0.011573801401964 0.292422540950275 0.214057839784099 0.0856379149973299 0.243934056334644
+1995 0.0764573886371595 0.0746001173455608 0.0128370964906525 0.287746743042085 0.215663527922776 0.0823594543606017 0.250335672201165
+1996 0.0799532380844666 0.0747371137602061 0.0134572734542054 0.282043268008802 0.2170987060378 0.0824567385962726 0.250253662058248
+1997 0.0837161827210115 0.076569609540085 0.0140098263037266 0.280014032685759 0.21039489234554 0.083657903336338 0.251637553067539
+1998 0.0825555129486877 0.0744529816145868 0.0140129438941686 0.278712383274044 0.212875331750921 0.0803903329188037 0.257000513598788
+1999 0.0844332209772884 0.0746909091583756 0.0147048558020019 0.271665484967356 0.21663463206885 0.08142048088052 0.256450416145608
+2000 0.0896421842307627 0.076240937711205 0.0137413309623923 0.269134929713948 0.218541833656927 0.0772322724130535 0.255466511311711
+2001 0.089011172432275 0.0774338345864903 0.0139710385316971 0.264903331259992 0.218737758466502 0.0749738847046828 0.26096898001836
+2002 0.0872071033449697 0.0767508931215668 0.0149264246775303 0.261454684974571 0.218729639405749 0.0772441657965296 0.263687088679084
+2003 0.087084876736775 0.0790837429433754 0.0147760706802127 0.258867573340172 0.218260971113627 0.0771011479253706 0.264825617260468
+2004 0.0874503363035857 0.0813482328770508 0.0146864548917721 0.255771008698044 0.220897183013127 0.0753316307578279 0.264515153458592
+2005 0.0881810599961824 0.0831172036767798 0.0140035077964467 0.255803453387787 0.216408047669538 0.0759517714215265 0.26653495605174
+2006 0.0899257664205084 0.0832350756039985 0.0146879125443847 0.253384188789247 0.2148945881855 0.0770067534616388 0.266865714994722
+2007 0.0888063800538201 0.0819442329802185 0.0143576939406037 0.251752808876081 0.217177208841511 0.0771879174525132 0.268773757855253
+2008 0.0910911600605037 0.0824417240946155 0.0150004931202757 0.244648611819688 0.216167236296419 0.0750564120935269 0.275594362514971
+2009 0.0913819783626526 0.0864541032954844 0.0140200670210693 0.242872298724713 0.220568720227114 0.0743081502178945 0.270394682151072
+2010 0.100582419516824 0.0903518305756296 0.014634162344193 0.238044102240456 0.21550333715027 0.0742338033866819 0.266650344785945
+2011 0.0999028865393945 0.0879599886478688 0.0152486217330109 0.231186088947102 0.215709105695722 0.0744683105389168 0.275524997897985
+2012 0.100915055882802 0.0859222138489376 0.015576342161966 0.228122315367167 0.218408378513843 0.0767007783180191 0.274354915907265
+2013 0.104841230811202 0.0876419359224646 0.0157204955695895 0.228230388255612 0.215049897977068 0.0742133673463169 0.274302684117747
+2014 0.0991060915758774 0.0895131363676436 0.0156636873853376 0.22754375490785 0.216214874841138 0.0744938046715825 0.277464650250571
+2015 0.102352983882629 0.0889696528056223 0.015104538967275 0.227586923830036 0.215256855654487 0.0702624130905221 0.280466631769429
+2016 0.102724489386347 0.0858702840305541 0.0156673166776592 0.223800846489452 0.217187871763709 0.0722870317971879 0.282462159855092
+2017 0.101421934334673 0.0900741548399015 0.0161408720679691 0.21996275736261 0.221515572233513 0.0701991111399789 0.280685598021354
+2018 0.106755566845541 0.0887896363174157 0.0155853792976115 0.219177082028028 0.220762621340248 0.0684138713775119 0.280515842793643
+2019 0.103612014327867 0.0898739575461556 0.0147161572804681 0.216968463145867 0.224747604004613 0.0660586006770303 0.284023203017999
+2020 0.101958995681941 0.0905178675441837 0.014785071057374 0.219862259379643 0.225911952055129 0.0645863511432945 0.282377503138435
+2021 0.108487098946671 0.0895527121628647 0.0164810595385484 0.212006762346236 0.22212978782412 0.0664352285851585 0.284907350596401
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/met-online-2010s.txt b/VT2-examples/vt2-ch5-codedata-dir/data/met-online-2010s.txt
new file mode 100644
index 0000000..2cd3906
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/met-online-2010s.txt
@@ -0,0 +1,3 @@
+waymet,p
+"Met Online",18.9
+"Met Offline",81.1
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/met-online.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/met-online.tsv
new file mode 100644
index 0000000..9c23f8a
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/met-online.tsv
@@ -0,0 +1,7 @@
+"decade" "ponline" "poffline"
+1960 0.0042 0.9958
+1970 0.0032 0.9968
+1980 0.0002 0.9998
+1990 0.0153 0.9847
+2000 0.0949 0.9051
+2010 0.189 0.811
diff --git a/VT2-examples/vt2-ch5-codedata-dir/data/physician-marry.tsv b/VT2-examples/vt2-ch5-codedata-dir/data/physician-marry.tsv
new file mode 100644
index 0000000..58a619e
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/data/physician-marry.tsv
@@ -0,0 +1,442 @@
+"code" "occname" "p" "n"
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+3620 "Physical therapist assistants and aides" 0.000144968855857467 150
+2060 "Religious workers, all other" 0.000764469099888374 791
+4150 "Hosts and hostesses, restaurant, lounge, and coffee shop" 0.00030540105633973 316
+4435 "Other entertainment attendants and related workers" 0.000224218497059548 232
+9810 "First-line enlisted military supervisors" 0.000448436994119097 464
+5330 "Loan interviewers and clerks" 0.0000618533784991858 64
+520 "Wholesale and retail buyers, except farm products" 0.000804093920489415 832
+6410 "Painters and paperhangers" 0.000922001923253488 954
+2400 "Archivists, curators, and museum technicians" 0.000501592241266835 519
+1410 "Electrical and electronics engineers" 0.00232046815275852 2401
+3402 "Paramedics" 0.000850483954363804 880
+8600 "Power plant operators, distributors, and dispatchers" 0.000451336371236246 467
+4340 "Animal trainers" 0.00012950551123267 134
+8130 "Tool and die makers" 0.000179761381263259 186
+4350 "Animal caretakers" 0.000408812173518056 423
+5630 "Weighers, measurers, checkers, and samplers, recordkeeping" 0.000318931482886427 330
+3321 "Cardiovascular technologists and technicians" 0.000154633446247964 160
+4110 "Waiters and waitresses" 0.00160142262770548 1657
+9720 "Refuse and recyclable material collectors" 0.0000589540013820364 61
+8610 "Stationary engineers and boiler operators" 0.000363388598682716 376
+3245 "Other therapists" 0.00258044563426291 2670
+4040 "Bartenders" 0.000335361286550273 347
+1650 "Other life scientists" 0.00708607767431297 7332
+2025 "Other community and social service specialists" 0.000991586974065072 1026
+8320 "Sewing machine operators" 0.000480330142407739 497
+102 "Facilities managers" 0.000553781029375522 573
+2105 "Judicial law clerks" 0.00056344561976602 583
+4521 "Manicurists and pedicurists" 0.000534451848594527 553
+3250 "Veterinarians" 0.00243451031936639 2519
+1400 "Computer hardware engineers" 0.00122933589767132 1272
+1825 "Other psychologists" 0.00656998854746039 6798
+6120 "Forest and conservation workers" 0.000078283182163032 81
+1031 "Web developers" 0.000455202207392445 471
+7510 "Coin, vending, and amusement machine servicers and repairers" 0.0000657192146553849 68
+5510 "Couriers and messengers" 0.000295736465949232 306
+7560 "Riggers" 0.0000183627217419458 19
+3150 "Occupational therapists" 0.00276503931072141 2861
+7950 "Cutting, punching, and press machine setters, operators, and tenders, metal and plastic" 0.000133371347388869 138
+3649 "Phlebotomists" 0.000134337806427919 139
+3515 "Medical records specialists" 0.00095679444865928 990
+3647 "Pharmacy aides" 0.000182660758380408 189
+6005 "First-line supervisors of farming, fishing, and forestry workers" 0.0000376919025229413 39
+8620 "Water and wastewater treatment plant and system operators" 0.0000753838050458826 78
+2015 "Probation officers and correctional treatment specialists" 0.000149801151052715 155
+2180 "Legal support workers, all other" 0.000836953527817107 866
+7020 "Radio and telecommunications equipment installers and repairers" 0.0000773167231239822 80
+7905 "Computer numerically controlled tool operators and programmers" 0.0000947129858268782 98
+640 "Compensation, benefits, and job analysis specialists" 0.000515122667813531 533
+1240 "Other mathematical science occupations" 0.00122933589767132 1272
+3802 "Correctional officers and jailers" 0.0000531552471477378 55
+7040 "Electric motor, power tool, and related repairers" 0.000122740297959322 127
+9141 "Shuttle drivers and chauffeurs" 0.000504491618383984 522
+5140 "Payroll and timekeeping clerks" 0.000292837088832083 303
+3655 "Other healthcare support workers" 0.00129408865328765 1339
+3648 "Veterinary assistants and laboratory animal caretakers" 0.00008698131351448 90
+8800 "Packaging and filling machine operators and tenders" 0.000223252038020499 231
+2004 "Mental health counselors" 0.00220449306807254 2281
+3424 "Veterinary technologists and technicians" 0.000384650697541811 398
+930 "Tax examiners and collectors, and revenue agents" 0.0000463900338743893 48
+7800 "Bakers" 0.00117231481436738 1213
+2752 "Musicians and singers" 0.0015028438057224 1555
+1821 "Clinical and counseling psychologists" 0.00082632247838756 855
+9142 "Taxi drivers" 0.000979023006557425 1013
+3160 "Physical therapists" 0.00581711695604061 6019
+5165 "Other financial clerks" 0.0013066526207953 1352
+3220 "Respiratory therapists" 0.000436839485650499 452
+1600 "Agricultural and food scientists" 0.000427174895260002 442
+4000 "Chefs and head cooks" 0.00104280930313471 1079
+4820 "Securities, commodities, and financial services sales agents" 0.00131535075214675 1361
+6515 "Roofers" 0.000254178727270091 263
+425 "Emergency management directors" 0.0000637862965772853 66
+101 "Administrative services managers" 0.000306367515378779 317
+1440 "Marine engineers and naval architects" 0.000105344035256426 109
+4055 "Fast food and counter workers" 0.000125639675076471 130
+8465 "Other textile, apparel, and furnishings workers" 0.000052188788108688 54
+3423 "Surgical technologists" 0.000139170101623168 144
+2751 "Music directors and composers" 0.00039141591081516 405
+8300 "Laundry and dry-cleaning workers" 0.000148834692013666 154
+2050 "Directors, religious activities and education" 0.00095582798962023 989
+2016 "Social and human service assistants" 0.000528653094360228 547
+2825 "Public relations specialists" 0.00111239435394629 1151
+7260 "Miscellaneous vehicle and mobile equipment mechanics, installers, and repairers" 0.0000309266892495929 32
+3801 "Bailiffs" 0.0000570210833039369 59
+6115 "Fishing and hunting workers" 0.0000202956398200453 21
+4740 "Counter and rental clerks" 0.000373053189073214 386
+960 "Other financial specialists" 0.000776066608356971 803
+1900 "Agricultural and food science technicians" 0.000122740297959322 127
+3330 "Nuclear medicine technologists and medical dosimetrists" 0.0000405912796400907 42
+6660 "Construction and building inspectors" 0.000316032105769277 327
+726 "Fundraisers" 0.000883343561691497 914
+3820 "Detectives and criminal investigators" 0.00133661285100584 1383
+3200 "Radiation therapists" 0.000180727840302308 187
+2710 "Producers and directors" 0.000889142315925795 920
+9430 "Other transportation workers" 0.0000763502640849324 79
+5420 "Other information and records clerks" 0.000582774800547016 603
+3960 "Other protective service workers" 0.000513189749735432 531
+3610 "Occupational therapy assistants and aides" 0.0000724844279287333 75
+8650 "Crushing, grinding, polishing, mixing, and blending workers" 0.000147868232974616 153
+5350 "Correspondence clerks and order clerks" 0.000176862004146109 183
+6530 "Structural iron and steel workers" 0.0000357589844448418 37
+860 "Insurance underwriters" 0.0000966459039049777 100
+845 "Financial and investment analysts" 0.00294576715102372 3048
+2011 "Child, family, and school social workers" 0.000616600866913758 638
+7010 "Computer, automated teller, and office machine repairers" 0.000321830860003576 333
+3030 "Dietitians and nutritionists" 0.00312262915516983 3231
+7430 "Precision instrument and equipment repairers" 0.000228084333215747 236
+2805 "Broadcast announcers and radio disc jockeys" 0.0000289937711714933 30
+1200 "Actuaries" 0.000637862965772853 660
+8640 "Chemical processing machine setters, operators, and tenders" 0.000069585050811584 72
+830 "Credit analysts" 0.000171063249911811 177
+8500 "Cabinetmakers and bench carpenters" 0.0000628198375382355 65
+2865 "Media and communication workers, all other" 0.000134337806427919 139
+1305 "Architects, except landscape and naval" 0.00185656781401462 1921
+500 "Agents and business managers of artists, performers, and athletes" 0.00056537853784412 585
+1545 "Other drafters" 0.000358556303487467 371
+8350 "Tailors, dressmakers, and sewers" 0.000441671780845748 457
+3601 "Home health aides" 0.000453269289314346 469
+1860 "Other social scientists" 0.000628198375382355 650
+6825 "Surface mining machine operators and earth drillers" 0.000142069478740317 147
+9630 "Machine feeders and offbearers" 0.000155599905287014 161
+9410 "Transportation inspectors" 0.00026191039958249 271
+2634 "Graphic designers" 0.00153860279016725 1592
+4252 "Tree trimmers and pruners" 0.0000647527556163351 67
+7540 "Locksmiths and safe repairers" 0.00004349065675724 45
+4830 "Travel agents" 0.000404946337361857 419
+1700 "Astronomers and physicists" 0.000123706756998372 128
+3324 "Magnetic resonance imaging technologists" 0.000445537617001947 461
+4525 "Other personal appearance workers" 0.0000773167231239822 80
+7100 "Other electrical and electronic equipment mechanics, installers, and repairers" 0.000124673216037421 129
+6250 "Cement masons, concrete finishers, and terrazzo workers" 0.0000512223290696382 53
+3940 "Crossing guards and flaggers" 0.000420409681986653 435
+1420 "Environmental engineers" 0.000158499282404164 164
+8255 "Printing press operators" 0.000297669384027331 308
+9825 "Military enlisted tactical operations and air/weapons specialists and crew members" 0.000159465741443213 165
+7855 "Food processing workers, all other" 0.000190392430692806 197
+9050 "Flight attendants" 0.000460034502587694 476
+3140 "Audiologists" 0.000342126499823621 354
+6130 "Logging workers" 0.0000318931482886427 33
+5310 "Interviewers, except eligibility and loan" 0.000466799715861043 483
+9240 "Railroad conductors and yardmasters" 0.000183627217419458 190
+1910 "Biological technicians" 0.0000976123629440275 101
+940 "Tax preparers" 0.000977090088479325 1011
+9650 "Pumping station operators" 0.0000135304265466969 14
+5530 "Meter readers, utilities" 0.000052188788108688 54
+3261 "Acupuncturists" 0.000309266892495929 320
+8250 "Prepress technicians and workers" 0.000244514136879594 253
+2013 "Mental health and substance abuse social workers" 0.00034599233597982 358
+3646 "Medical transcriptionists" 0.000237748923606245 246
+9800 "Military officer special and tactical operations leaders" 0.000290904170753983 301
+3110 "Physician assistants" 0.00601814043616296 6227
+510 "Buyers and purchasing agents, farm products" 0.000144968855857467 150
+4420 "Ushers, lobby attendants, and ticket takers" 0.0000541217061867875 56
+6220 "Brickmasons, blockmasons, stonemasons, and reinforcing iron and rebar workers" 0.000263843317660589 273
+3945 "Transportation security screeners" 0.000158499282404164 164
+5320 "Library assistants, clerical" 0.000482263060485839 499
+4330 "Supervisors of personal care and service workers" 0.00017396262702896 180
+4522 "Skincare specialists" 0.000312166269613078 323
+5300 "Hotel, motel, and resort desk clerks" 0.000245480595918643 254
+5020 "Telephone operators" 0.000127572593154571 132
+4140 "Dishwashers" 0.000233883087450046 242
+3040 "Optometrists" 0.00103797700793946 1074
+1640 "Conservation scientists and foresters" 0.000345025876940771 357
+1822 "School psychologists" 0.000730643033521632 756
+1350 "Chemical engineers" 0.000471632011056291 488
+1032 "Web and digital interface designers" 0.000506424536462083 524
+4900 "Models, demonstrators, and product promoters" 0.000288971252675883 299
+60 "Public relations and fundraising managers" 0.000465833256821993 482
+3422 "Psychiatric technicians" 0.000247413513996743 256
+8830 "Photographic process workers and processing machine operators" 0.0000724844279287333 75
+6460 "Plasterers and stucco masons" 0.0000280273121324435 29
+2721 "Athletes and sports competitors" 0.0000318931482886427 33
+2861 "Interpreters and translators" 0.000722911361209234 748
+4940 "Telemarketers" 0.000276407285168236 286
+8750 "Jewelers and precious stone and metal workers" 0.0000193291807809955 20
+3210 "Recreational therapists" 0.000158499282404164 164
+5920 "Statistical assistants" 0.000278340203246336 288
+6400 "Insulation workers" 0.000163331577599412 169
+2631 "Commercial and industrial designers" 0.000078283182163032 81
+5230 "Credit authorizers, checkers, and clerks" 0.0000115975084685973 12
+2440 "Library technicians" 0.000101478199100227 105
+5040 "Communications equipment operators, all other" 0.0000773167231239822 80
+1520 "Petroleum, mining and geological engineers, including mining safety engineers" 0.000331495450394074 343
+4930 "Sales engineers" 0.0000241614759762444 25
+2755 "Disc jockeys, except radio" 0.0000231950169371947 24
+5360 "Human resources assistants, except payroll and timekeeping" 0.000235816005528146 244
+360 "Natural sciences managers" 0.000119840920842172 124
+1005 "Computer and information research scientists" 0.000461000961626744 477
+3946 "School bus monitors" 0.0000541217061867875 56
+2920 "Television, video, and film camera operators and editors" 0.0000831154773582809 86
+3120 "Podiatrists" 0.000445537617001947 461
+2632 "Fashion designers" 0.000588573554781315 609
+1306 "Landscape architects" 0.00026191039958249 271
+1840 "Urban and regional planners" 0.000387550074658961 401
+1340 "Biomedical and agricultural engineers" 0.000224218497059548 232
+2770 "Entertainers and performers, sports and related workers, all other" 0.0000657192146553849 68
+900 "Financial examiners" 0.00025901102246534 268
+1800 "Economists" 0.000192325348770906 199
+4530 "Baggage porters, bellhops, and concierges" 0.000183627217419458 190
+2700 "Actors" 0.000467766174900092 484
+8450 "Upholsterers" 0.000155599905287014 161
+2723 "Umpires, referees, and other sports officials" 0.0000802161002411315 83
+8850 "Adhesive bonding machine operators and tenders" 0.00026191039958249 271
diff --git a/VT2-examples/vt2-ch5-codedata-dir/make-bar-chart.py b/VT2-examples/vt2-ch5-codedata-dir/make-bar-chart.py
new file mode 100644
index 0000000..2b72853
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/make-bar-chart.py
@@ -0,0 +1,23 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+# Load data
+marjobs = pd.read_csv("data/physician-marry.tsv", delimiter="\t")
+
+# Sort by most common
+marsorted = marjobs.sort_values(by=['p'], ascending=False)
+
+# Set figure size and margin
+sns.set(rc={'figure.figsize':(8,8), 'pdf.fonttype': 42 })
+subs, axes = plt.subplots(1, 1)
+plt.subplots_adjust(left=.5)
+
+p1 = sns.barplot(data=marsorted[0:30],
+ x="p", y="occname", orient="h")
+fig = p1.get_figure()
+
+# Save as PDF
+fig.savefig("bar-mar.pdf")
+
+
diff --git a/VT2-examples/vt2-ch5-codedata-dir/make-pies.R b/VT2-examples/vt2-ch5-codedata-dir/make-pies.R
new file mode 100644
index 0000000..7e22fd0
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/make-pies.R
@@ -0,0 +1,27 @@
+#
+# Pie charts
+#
+
+# Load data
+htypes <- read.csv("data/household-types.tsv", sep="\t")
+h2021 <- htypes[htypes$year == 2021,]
+h2021_alpha <- h2021[order(h2021$htype, decreasing=TRUE),]
+h2021_num <- h2021[order(h2021$p),]
+
+
+#
+# Pie chart, sorted and unsorted
+#
+
+par(mfrow=c(1,2))
+pie(h2021_alpha$p, labels=h2021_alpha$htype, init.angle=90, col = "gray")
+pie(h2021_num$p, labels=h2021_num$htype, init.angle=90, col = "gray")
+
+
+#
+# Bar chart, sorted and unsorted
+#
+
+par(mfrow=c(1,2))
+barplot(h2021_alpha$p, names.arg=h2021_alpha$htype, horiz=TRUE)
+barplot(h2021_num$p, names.arg=h2021_num$htype, horiz=TRUE)
diff --git a/VT2-examples/vt2-ch5-codedata-dir/make-symbols-chart.py b/VT2-examples/vt2-ch5-codedata-dir/make-symbols-chart.py
new file mode 100644
index 0000000..792b9b9
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/make-symbols-chart.py
@@ -0,0 +1,39 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+# import seaborn as sns
+# import math
+
+# Load data
+marjobs = pd.read_csv("data/physician-marry.tsv", delimiter="\t")
+marsorted = marjobs.sort_values(by=['p'],
+ ascending=False)[0:10] # Top 10.
+
+# Prepare data for circles using bubble plot
+mardf = pd.DataFrame({
+ "x": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ "y": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
+ "r": list(marsorted.p * 50000)
+})
+
+# sns.set(rc={'pdf.fonttype': 42 })
+
+# p1 = sns.relplot(x="x", y="y", size="r", sizes=(40,2000), data=mardf)
+# fig = p1.get_figure()
+# p1.savefig("symbols-chart-mar.pdf")
+
+
+# Figure size
+plt.rcParams['figure.figsize'] = (7, 10)
+plt.rcParams['pdf.fonttype'] = 42
+
+# Plot
+plt.scatter("x", "y", s="r", data=mardf)
+
+# Text
+for i in range(0, len(marsorted)):
+ thelabel = str(round(100*marsorted.iloc[i].p)) + "%, "
+ thelabel += marsorted.iloc[i].occname
+ plt.text(1, 10-i, thelabel)
+
+# Save as PDF
+plt.savefig("symbols-chart-mar.pdf")
diff --git a/VT2-examples/vt2-ch5-codedata-dir/make-symbols-grid.py b/VT2-examples/vt2-ch5-codedata-dir/make-symbols-grid.py
new file mode 100644
index 0000000..3e477bc
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/make-symbols-grid.py
@@ -0,0 +1,43 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+# import seaborn as sns
+
+# Load data
+marjobs = pd.read_csv("data/physician-marry.tsv", delimiter="\t")
+marsorted = marjobs.sort_values(by=['p'], ascending=False)[0:49]
+
+# Grid coordinates for a 7x7 grid
+x = []
+for i in range(0, 7):
+ x = x + list(range(0, 7))
+y = list(reversed(np.repeat(np.arange(0,7), 7)))
+
+# Prepare data for circles using bubble plot
+mardf = pd.DataFrame({
+ "x" : x,
+ "y" : y,
+ "r" : list(marsorted.p * 50000),
+ "occname" : list(marsorted.occname),
+ "p" : list(marsorted.p * 100)
+})
+
+
+# Figure size
+plt.rcParams['figure.figsize'] = (13, 13)
+plt.rcParams['pdf.fonttype'] = 42
+
+# Plot
+plt.scatter("x", "y", s="r", data=mardf)
+
+# Text
+plt.rcParams['font.size'] = 8
+plt.rcParams['pdf.fonttype'] = 42
+for i in range(0, len(mardf)):
+ thelabel = str(round(mardf.p[i], 2)) + "%\n "
+ thelabel += mardf.occname[i]
+ plt.text(mardf.x[i], mardf.y[i], thelabel,
+ horizontalalignment="center")
+
+# Save as PDF
+plt.savefig("symbols-grid-mar.pdf")
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch5-codedata-dir/make-treemap-howmet.R b/VT2-examples/vt2-ch5-codedata-dir/make-treemap-howmet.R
new file mode 100644
index 0000000..86636eb
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/make-treemap-howmet.R
@@ -0,0 +1,24 @@
+# Install package if have not yet
+# install.packages("treemap")
+
+# Load package.
+library(treemap)
+
+#
+# Load data.
+#
+howmet2010 <- read.csv("data/how-met-2010s.tsv", sep="\t")
+
+
+#
+# Draw treemap.
+#
+
+treemap(howmet2010,
+ index = c("category", "waymet"),
+ vSize = "p",
+ algorithm = "squarified",
+ title = "How People Met, 2010s")
+
+
+
diff --git a/VT2-examples/vt2-ch5-codedata-dir/prepare-household-types.R b/VT2-examples/vt2-ch5-codedata-dir/prepare-household-types.R
new file mode 100644
index 0000000..d089d07
--- /dev/null
+++ b/VT2-examples/vt2-ch5-codedata-dir/prepare-household-types.R
@@ -0,0 +1,48 @@
+#
+# Load data.
+#
+
+htypes <- read.csv("data/household-types.tsv", sep="\t")
+head(htypes)
+
+
+#
+# Reformat data.
+#
+
+years <- unique(htypes$year)
+uhtypes <- unique(htypes$htype)
+
+# Start data frame
+htypes_new <- data.frame(year = years)
+
+# Add columns
+for (uht in uhtypes) {
+ curr <- htypes[htypes$htype == uht,]
+ curr_name <- paste0("p_", gsub("-", "_", uht))
+ htypes_new[, curr_name] <- curr$p[match(htypes_new$year, curr$year)]
+}
+
+# Save formatted data
+write.table(htypes_new,
+ file="data/htypes_rectangular.tsv",
+ sep="\t",
+ row.names=FALSE)
+
+
+# Transpose?
+htypes_long <- as.data.frame(t(htypes_new[,-1]))
+colnames(htypes_long) <- htypes_new$year
+htypes_long$waymet <- gsub("p_", "", row.names(htypes_long))
+write.table(htypes_long, file="data/htypes_long.tsv", sep="\t", row.names=FALSE)
+
+
+
+#
+# Alluvial diagram
+#
+
+# install.packages("alluvial")
+library(alluvial)
+alluvial_ts(htypes[,c("htype", "year", "p")],
+ wave = .3, lwd = .5, ygap=1)
diff --git a/VT2-examples/vt2-ch5-codedata-dir/symbols-chart-mar.pdf b/VT2-examples/vt2-ch5-codedata-dir/symbols-chart-mar.pdf
new file mode 100644
index 0000000..8ca565b
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diff --git a/VT2-examples/vt2-ch5-codedata-dir/symbols-grid-mar.pdf b/VT2-examples/vt2-ch5-codedata-dir/symbols-grid-mar.pdf
new file mode 100644
index 0000000..dba4dd9
Binary files /dev/null and b/VT2-examples/vt2-ch5-codedata-dir/symbols-grid-mar.pdf differ
diff --git a/VT2-examples/vt2-ch5-codedata-dir/treemap-howmet.pdf b/VT2-examples/vt2-ch5-codedata-dir/treemap-howmet.pdf
new file mode 100644
index 0000000..dfcb733
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diff --git a/VT2-examples/vt2-ch6-codedata-dir/data/nba-top50scorers-2022-23.csv b/VT2-examples/vt2-ch6-codedata-dir/data/nba-top50scorers-2022-23.csv
new file mode 100644
index 0000000..f04d8d8
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/data/nba-top50scorers-2022-23.csv
@@ -0,0 +1,51 @@
+Rk,Player,Pos,Age,Tm,G,GS,MP,FG,FGA,FGpct,3P,3PA,3Ppct,2P,2PA,2Ppct,eFGpct,FT,FTA,FTpct,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS,Player-additional
+1,Joel Embiid,C,28,PHI,66,66,34.6,11.0,20.1,.548,1.0,3.0,.330,10.0,17.1,.587,.573,10.0,11.7,.857,1.7,8.4,10.2,4.2,1.0,1.7,3.4,3.1,33.1,embiijo01
+2,Luka DonÄić,PG,23,DAL,66,66,36.2,10.9,22.0,.496,2.8,8.2,.342,8.1,13.8,.588,.560,7.8,10.5,.742,0.8,7.8,8.6,8.0,1.4,0.5,3.6,2.5,32.4,doncilu01
+3,Damian Lillard,PG,32,POR,58,58,36.3,9.6,20.7,.463,4.2,11.3,.371,5.4,9.4,.574,.564,8.8,9.6,.914,0.8,4.0,4.8,7.3,0.9,0.3,3.3,1.9,32.2,lillada01
+4,Shai Gilgeous-Alexander,PG,24,OKC,68,68,35.5,10.4,20.3,.510,0.9,2.5,.345,9.5,17.8,.533,.531,9.8,10.9,.905,0.9,4.0,4.8,5.5,1.6,1.0,2.8,2.8,31.4,gilgesh01
+5,Giannis Antetokounmpo,PF,28,MIL,63,63,32.1,11.2,20.3,.553,0.7,2.7,.275,10.5,17.6,.596,.572,7.9,12.3,.645,2.2,9.6,11.8,5.7,0.8,0.8,3.9,3.1,31.1,antetgi01
+6,Jayson Tatum,SF,24,BOS,74,74,36.9,9.8,21.1,.466,3.2,9.3,.350,6.6,11.8,.558,.543,7.2,8.4,.854,1.1,7.7,8.8,4.6,1.1,0.7,2.9,2.2,30.1,tatumja01
+7,Stephen Curry,PG,34,GSW,56,56,34.7,10.0,20.2,.493,4.9,11.4,.427,5.1,8.8,.579,.614,4.6,5.0,.915,0.7,5.4,6.1,6.3,0.9,0.4,3.2,2.1,29.4,curryst01
+8,Kevin Durant,PF,34,TOT,47,47,35.6,10.3,18.3,.560,2.0,4.9,.404,8.3,13.4,.617,.614,6.5,7.1,.919,0.4,6.3,6.7,5.0,0.7,1.4,3.3,2.1,29.1,duranke01
+9,LeBron James,PF,38,LAL,55,54,35.5,11.1,22.2,.500,2.2,6.9,.321,8.9,15.3,.580,.549,4.6,5.9,.768,1.2,7.1,8.3,6.8,0.9,0.6,3.2,1.6,28.9,jamesle01
+10,Donovan Mitchell,SG,26,CLE,68,68,35.8,10.0,20.6,.484,3.6,9.3,.386,6.4,11.3,.566,.572,4.7,5.4,.867,0.9,3.3,4.3,4.4,1.5,0.4,2.6,2.5,28.3,mitchdo01
+11,Devin Booker,SG,26,PHO,53,53,34.6,9.9,20.1,.494,2.1,6.0,.351,7.8,14.2,.554,.546,5.8,6.8,.855,0.9,3.7,4.5,5.5,1.0,0.3,2.7,3.0,27.8,bookede01
+12,Kyrie Irving,PG-SG,30,TOT,60,60,37.4,9.9,20.1,.494,3.1,8.3,.379,6.8,11.8,.574,.572,4.1,4.6,.905,1.0,4.1,5.1,5.5,1.1,0.8,2.1,2.8,27.1,irvinky01
+13,Jaylen Brown,SF,26,BOS,67,67,35.9,10.1,20.6,.491,2.4,7.3,.335,7.7,13.4,.576,.550,3.9,5.1,.765,1.2,5.7,6.9,3.5,1.1,0.4,2.9,2.6,26.6,brownja02
+14,Ja Morant,PG,23,MEM,61,59,31.9,9.3,19.9,.466,1.5,4.9,.307,7.8,15.0,.519,.504,6.1,8.1,.748,1.0,4.9,5.9,8.1,1.1,0.3,3.4,1.6,26.2,moranja01
+15,Trae Young,PG,24,ATL,73,73,34.8,8.2,19.0,.429,2.1,6.3,.335,6.1,12.7,.476,.485,7.8,8.8,.886,0.8,2.2,3.0,10.2,1.1,0.1,4.1,1.4,26.2,youngtr01
+16,Zion Williamson,PF,22,NOP,29,29,33.0,9.8,16.2,.608,0.2,0.7,.368,9.6,15.5,.618,.615,6.1,8.6,.714,2.0,5.0,7.0,4.6,1.1,0.6,3.4,2.2,26.0,willizi01
+17,Anthony Davis,C,29,LAL,56,54,34.0,9.7,17.2,.563,0.3,1.3,.257,9.3,15.9,.589,.573,6.2,7.9,.784,3.5,9.1,12.5,2.6,1.1,2.0,2.2,2.6,25.9,davisan02
+18,Lauri Markkanen,PF,25,UTA,66,66,34.4,8.7,17.3,.499,3.0,7.7,.391,5.6,9.6,.585,.586,5.3,6.0,.875,2.0,6.7,8.6,1.9,0.6,0.6,1.9,2.1,25.6,markkla01
+19,Julius Randle,PF,28,NYK,77,77,35.5,8.5,18.6,.459,2.8,8.3,.343,5.7,10.3,.553,.536,5.2,6.9,.757,1.8,8.1,10.0,4.1,0.6,0.3,2.8,3.0,25.1,randlju01
+20,De'Aaron Fox,PG,25,SAC,73,73,33.4,9.3,18.2,.512,1.6,5.0,.324,7.7,13.2,.584,.557,4.7,6.0,.780,0.5,3.6,4.2,6.1,1.1,0.3,2.5,2.4,25.0,foxde01
+21,Zach LaVine,SG,27,CHI,77,77,35.9,8.7,18.0,.485,2.6,7.1,.375,6.1,11.0,.556,.558,4.7,5.6,.848,0.5,3.9,4.5,4.2,0.9,0.2,2.5,2.1,24.8,lavinza01
+22,Brandon Ingram,SF,25,NOP,45,45,34.2,9.0,18.6,.484,1.4,3.6,.390,7.6,14.9,.507,.522,5.3,6.0,.882,0.5,5.0,5.5,5.8,0.7,0.4,3.3,2.6,24.7,ingrabr01
+23,Anthony Edwards,SG,21,MIN,79,79,36.0,8.9,19.5,.459,2.7,7.3,.369,6.3,12.2,.513,.528,4.0,5.3,.756,0.6,5.2,5.8,4.4,1.6,0.7,3.3,2.4,24.6,edwaran01
+24,DeMar DeRozan,SF,33,CHI,74,74,36.2,8.9,17.6,.504,0.6,1.9,.324,8.3,15.7,.526,.522,6.2,7.1,.872,0.5,4.2,4.6,5.1,1.1,0.5,2.1,2.5,24.5,derozde01
+25,Nikola Jokić,C,27,DEN,69,69,33.7,9.4,14.8,.632,0.8,2.2,.383,8.5,12.7,.675,.660,4.9,6.0,.822,2.4,9.4,11.8,9.8,1.3,0.7,3.6,2.5,24.5,jokicni01
+26,Pascal Siakam,PF,28,TOR,71,71,37.4,8.9,18.5,.480,1.3,4.0,.324,7.6,14.5,.523,.515,5.2,6.7,.774,1.8,6.0,7.8,5.8,0.9,0.5,2.4,3.2,24.2,siakapa01
+27,Jalen Brunson,PG,26,NYK,68,68,35.0,8.6,17.6,.491,2.0,4.7,.416,6.7,12.8,.519,.547,4.8,5.8,.829,0.6,3.0,3.5,6.2,0.9,0.2,2.1,2.2,24.0,brunsja01
+28,Paul George,SF,32,LAC,56,56,34.6,8.2,17.9,.457,2.8,7.6,.371,5.4,10.3,.521,.536,4.6,5.3,.871,0.8,5.3,6.1,5.1,1.5,0.4,3.1,2.8,23.8,georgpa01
+29,Kawhi Leonard,SF,31,LAC,52,50,33.6,8.6,16.8,.512,2.0,4.8,.416,6.6,11.9,.551,.572,4.7,5.4,.871,1.1,5.4,6.5,3.9,1.4,0.5,1.7,1.6,23.8,leonaka01
+30,LaMelo Ball,PG,21,CHO,36,36,35.2,8.2,20.0,.411,4.0,10.6,.376,4.2,9.4,.450,.510,2.8,3.4,.836,1.2,5.3,6.4,8.4,1.3,0.3,3.6,3.3,23.3,ballla01
+31,Kristaps Porziņģis,C,27,WAS,65,65,32.6,7.8,15.7,.498,2.1,5.5,.385,5.7,10.2,.559,.565,5.4,6.4,.851,1.8,6.6,8.4,2.7,0.9,1.5,2.1,3.0,23.2,porzikr01
+32,Bradley Beal,SG,29,WAS,50,50,33.5,8.9,17.6,.506,1.6,4.4,.365,7.3,13.2,.552,.551,3.8,4.6,.842,0.8,3.1,3.9,5.4,0.9,0.7,2.9,2.1,23.2,bealbr01
+33,Jimmy Butler,PF,33,MIA,64,64,33.4,7.5,13.9,.539,0.6,1.6,.350,6.9,12.3,.564,.560,7.4,8.7,.850,2.2,3.7,5.9,5.3,1.8,0.3,1.6,1.3,22.9,butleji01
+34,Jalen Green,SG,20,HOU,76,76,34.2,7.4,17.9,.416,2.5,7.3,.338,5.0,10.6,.471,.485,4.8,6.1,.786,0.6,3.2,3.7,3.7,0.8,0.2,2.6,1.7,22.1,greenja05
+35,Keldon Johnson,SF,23,SAS,63,63,32.7,8.0,17.7,.452,2.1,6.5,.329,5.9,11.2,.523,.512,3.9,5.2,.749,0.9,4.2,5.0,2.9,0.7,0.2,2.1,2.0,22.0,johnske04
+36,Klay Thompson,SF,32,GSW,69,69,33.0,7.9,18.1,.436,4.4,10.6,.412,3.6,7.6,.470,.556,1.7,1.9,.879,0.6,3.6,4.1,2.4,0.7,0.4,1.8,1.9,21.9,thompkl01
+37,Bojan Bogdanović,PF,33,DET,59,59,32.1,7.3,14.9,.488,2.5,6.0,.411,4.8,9.0,.539,.570,4.5,5.1,.884,0.6,3.2,3.8,2.6,0.6,0.1,2.3,1.9,21.6,bogdabo02
+38,Darius Garland,PG,23,CLE,69,69,35.5,7.6,16.4,.462,2.4,6.0,.410,5.1,10.4,.492,.537,4.0,4.7,.863,0.4,2.3,2.7,7.8,1.2,0.1,2.9,2.1,21.6,garlada01
+39,Desmond Bane,SG,24,MEM,58,58,31.7,7.8,16.2,.479,2.9,7.0,.408,4.9,9.2,.534,.568,3.1,3.5,.883,0.7,4.3,5.0,4.4,1.0,0.4,2.2,2.6,21.5,banede01
+40,Kyle Kuzma,PF,27,WAS,64,64,35.0,8.0,17.8,.448,2.5,7.5,.333,5.5,10.3,.533,.518,2.7,3.7,.730,0.9,6.4,7.2,3.7,0.6,0.5,3.0,2.3,21.2,kuzmaky01
+41,Terry Rozier,SG,28,CHO,63,63,35.3,7.8,18.9,.415,2.6,8.0,.327,5.2,10.8,.480,.484,2.8,3.5,.809,0.8,3.3,4.1,5.1,1.2,0.3,2.1,1.9,21.1,roziete01
+42,Anfernee Simons,SG,23,POR,62,62,35.0,7.5,16.9,.447,3.4,9.1,.377,4.1,7.8,.528,.548,2.6,2.9,.894,0.3,2.3,2.6,4.1,0.7,0.2,2.1,2.3,21.1,simonan01
+43,James Harden,PG,33,PHI,58,58,36.8,6.4,14.5,.441,2.8,7.2,.385,3.6,7.3,.495,.536,5.4,6.2,.867,0.7,5.4,6.1,10.7,1.2,0.5,3.4,1.9,21.0,hardeja01
+44,CJ McCollum,SG,31,NOP,75,75,35.3,7.8,17.9,.437,2.8,7.2,.389,5.0,10.7,.469,.515,2.4,3.2,.769,0.7,3.6,4.4,5.7,0.9,0.5,2.4,2.0,20.9,mccolcj01
+45,Jordan Clarkson,SG,30,UTA,61,61,32.6,7.5,16.9,.444,2.5,7.5,.338,5.0,9.4,.530,.519,3.3,4.0,.816,1.2,2.9,4.0,4.4,0.5,0.2,3.0,2.0,20.8,clarkjo01
+46,Karl-Anthony Towns,PF,27,MIN,29,29,33.0,7.3,14.8,.495,2.1,5.7,.366,5.2,9.1,.576,.565,4.1,4.7,.874,1.7,6.5,8.1,4.8,0.7,0.6,3.0,3.8,20.8,townska01
+47,Tyrese Haliburton,PG,22,IND,56,56,33.6,7.4,15.0,.490,2.9,7.2,.400,4.5,7.8,.572,.586,3.1,3.6,.871,0.6,3.1,3.7,10.4,1.6,0.4,2.5,1.2,20.7,halibty01
+48,Jerami Grant,PF,28,POR,63,63,35.7,6.9,14.5,.475,2.3,5.7,.401,4.6,8.8,.523,.554,4.4,5.4,.813,0.8,3.7,4.5,2.4,0.8,0.8,1.8,2.4,20.5,grantje01
+49,Dejounte Murray,SG,26,ATL,74,74,36.4,8.3,17.8,.464,1.8,5.2,.344,6.5,12.6,.514,.514,2.1,2.6,.832,0.7,4.5,5.3,6.1,1.5,0.3,2.2,1.4,20.5,murrade01
+50,Bam Adebayo,C,25,MIA,75,75,34.6,8.0,14.9,.540,0.0,0.2,.083,8.0,14.7,.545,.541,4.3,5.4,.806,2.5,6.7,9.2,3.2,1.2,0.8,2.5,2.8,20.4,adebaba01
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch6-codedata-dir/data/occ_codes.tsv b/VT2-examples/vt2-ch6-codedata-dir/data/occ_codes.tsv
new file mode 100644
index 0000000..3b07cd9
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/data/occ_codes.tsv
@@ -0,0 +1,570 @@
+"code" "name"
+"0" "N/A (not applicable)"
+"10" "Chief executives"
+"20" "General and operations managers"
+"30" "Legislators"
+"40" "Advertising and promotions managers"
+"50" "Marketing and sales managers"
+"60" "Public relations managers"
+"100" "Administrative services managers"
+"110" "Computer and information systems managers"
+"120" "Financial managers"
+"135" "Compensation and Benefits Managers"
+"136" "Human Resources Managers"
+"137" "Training and Development Managers"
+"140" "Industrial production managers"
+"150" "Purchasing managers"
+"160" "Transportation, storage, and distribution managers"
+"205" "Farmers, Ranchers, and Other Agricultural Managers"
+"220" "Construction managers"
+"230" "Education administrators"
+"300" "Engineering managers"
+"310" "Food service managers"
+"325" "Funeral service managers"
+"330" "Gaming managers"
+"340" "Lodging managers"
+"350" "Medical and health services managers"
+"360" "Natural sciences managers"
+"400" "Postmasters and mail superintendents"
+"410" "Property, real estate, and community association managers"
+"420" "Social and community service managers"
+"425" "Emergency Management Directors"
+"430" "Managers, all other"
+"500" "Agents and business managers of artists, performers, and athletes"
+"510" "Purchasing agents and buyers, farm products"
+"520" "Wholesale and retail buyers, except farm products"
+"530" "Purchasing agents, except wholesale, retail, and farm products"
+"540" "Claims adjusters, appraisers, examiners, and investigators"
+"550" "Not used"
+"565" "Compliance officers"
+"600" "Cost estimators"
+"610" "Not used"
+"630" "Human resource workers"
+"640" "Compensation, benefits, and job analysis specialists"
+"650" "Training and development specialists"
+"700" "Logisticians"
+"710" "Management analysts"
+"725" "Meeting, convention, and event planners"
+"726" "Fundraisers"
+"735" "Market research analysts and marketing specialists"
+"740" "Business operations specialists, all other"
+"800" "Accountants and auditors"
+"810" "Appraisers and assessors of real estate"
+"820" "Budget analysts"
+"830" "Credit analysts"
+"840" "Financial analysts"
+"850" "Personal financial advisors"
+"860" "Insurance underwriters"
+"900" "Financial examiners"
+"910" "Loan counselors and officers"
+"920" "Not used"
+"930" "Tax examiners, collectors, and revenue agents"
+"940" "Tax preparers"
+"950" "Financial specialists, all other"
+"1000" "Computer scientists and systems analysts"
+"1005" "Computer and information research scientists"
+"1006" "Computer systems analysts"
+"1007" "Information security analysts"
+"1010" "Computer programmers"
+"1020" "Software developers, applications and systems software"
+"1030" "Web developers"
+"1050" "Computer support specialists"
+"1060" "Database administrators"
+"1105" "Network and computer systems administrators"
+"1106" "Computer network architects"
+"1107" "Computer occupations, all other"
+"1200" "Actuaries"
+"1210" "Mathematicians"
+"1220" "Operations research analysts"
+"1230" "Statisticians"
+"1240" "Miscellaneous mathematical science occupations"
+"1300" "Architects, except naval"
+"1310" "Surveyors, cartographers, and photogrammetrists"
+"1320" "Aerospace engineers"
+"1330" "Agricultural engineers"
+"1340" "Biomedical engineers"
+"1350" "Chemical engineers"
+"1360" "Civil engineers"
+"1400" "Computer hardware engineers"
+"1410" "Electrical and electronics engineers"
+"1420" "Environmental engineers"
+"1430" "Industrial engineers, including health and safety"
+"1440" "Marine engineers and naval architects"
+"1450" "Materials engineers"
+"1460" "Mechanical engineers"
+"1500" "Mining and geological engineers, including mining safety engineers"
+"1510" "Nuclear engineers"
+"1520" "Petroleum engineers"
+"1530" "Engineers, all other"
+"1540" "Drafters"
+"1550" "Engineering technicians, except drafters"
+"1560" "Surveying and mapping technicians"
+"1600" "Agricultural and food scientists"
+"1610" "Biological scientists"
+"1640" "Conservation scientists and foresters"
+"1650" "Medical scientists"
+"1660" "Life scientists, all other"
+"1700" "Astronomers and physicists"
+"1710" "Atmospheric and space scientists"
+"1720" "Chemists and materials scientists"
+"1730" "Not used"
+"1740" "Environmental scientists and geoscientists"
+"1750" "Not used"
+"1760" "Physical scientists, all other"
+"1800" "Economists"
+"1815" "Survey researchers"
+"1820" "Psychologists"
+"1830" "Sociologists"
+"1840" "Urban and regional planners"
+"1850" "Not used"
+"1860" "Miscellaneous social scientists and related workers"
+"1900" "Agricultural and food science technicians"
+"1910" "Biological technicians"
+"1920" "Chemical technicians"
+"1930" "Geological and petroleum technicians"
+"1940" "Nuclear technicians"
+"1950" "Social science research assistants"
+"1965" "Miscellaneous life, physical, and social science technicians"
+"2000" "Counselors"
+"2010" "Social workers"
+"2015" "Probation officers and correctional treatment specialists"
+"2016" "Social and human service assistants"
+"2025" "Miscellaneous community and social service specialists, including health educators and community health workers"
+"2030" "Not used"
+"2040" "Clergy"
+"2050" "Directors, religious activities and education"
+"2060" "Religious workers, all other"
+"2100" "Lawyers, Judges, magistrates, and other judicial workers"
+"2105" "Judicial law clerks"
+"2110" "Judges, magistrates, and other judicial workers"
+"2145" "Paralegals and legal assistants"
+"2160" "Miscellaneous legal support workers"
+"2200" "Postsecondary teachers"
+"2300" "Preschool and kindergarten teachers"
+"2310" "Elementary and middle school teachers"
+"2320" "Secondary school teachers"
+"2330" "Special education teachers"
+"2340" "Other teachers and instructors"
+"2400" "Archivists, curators, and museum technicians"
+"2430" "Librarians"
+"2440" "Library technicians"
+"2540" "Teacher assistants"
+"2550" "Other education, training, and library workers"
+"2600" "Artists and related workers"
+"2630" "Designers"
+"2700" "Actors"
+"2710" "Producers and directors"
+"2720" "Athletes, coaches, umpires, and related workers"
+"2730" "Not used"
+"2740" "Dancers and choreographers"
+"2750" "Musicians, singers, and related workers"
+"2760" "Entertainers and performers, sports and related workers, all other"
+"2800" "Announcers"
+"2810" "News analysts, reporters, and correspondents"
+"2825" "Public relations specialists"
+"2830" "Editors"
+"2840" "Technical writers"
+"2850" "Writers and authors"
+"2860" "Miscellaneous media and communications workers"
+"2900" "Broadcast and sound engineering technicians and radio operators"
+"2910" "Photographers"
+"2920" "Television, video, and motion picture camera operators and editors"
+"2960" "Media and communications workers, all other"
+"3000" "Chiropractors"
+"3010" "Dentists"
+"3020" "Not used"
+"3030" "Dietitians and nutritionists"
+"3040" "Optometrists"
+"3050" "Pharmacists"
+"3060" "Physicians and surgeons"
+"3110" "Physician assistants"
+"3120" "Podiatrists"
+"3140" "Audiologists"
+"3150" "Occupational therapists"
+"3160" "Physical therapists"
+"3200" "Radiation therapists"
+"3210" "Recreational therapists"
+"3220" "Respiratory therapists"
+"3230" "Speech-language pathologists"
+"3235" "Exercise Physiologists"
+"3245" "Therapists, all other"
+"3250" "Veterinarians"
+"3255" "Registered nurses"
+"3256" "Nurse anesthetists"
+"3257" "Nurse midwives"
+"3258" "Nurse practitioners"
+"3260" "Health diagnosing and treating practitioners, all other"
+"3300" "Clinical laboratory technologists and technicians"
+"3310" "Dental hygienists"
+"3320" "Diagnostic related technologists and technicians"
+"3400" "Emergency medical technicians and paramedics"
+"3420" "Health practitioner support technologists and technicians "
+"3500" "Licensed practical and licensed vocational nurses"
+"3510" "Medical records and health information technicians"
+"3520" "Opticians, dispensing"
+"3535" "Miscellaneous health technologists and technicians"
+"3540" "Other healthcare practitioners and technical occupations"
+"3600" "Nursing, psychiatric, and home health aides"
+"3610" "Occupational therapist assistants and aides"
+"3620" "Physical therapist assistants and aides"
+"3630" "Massage therapists"
+"3640" "Dental assistants"
+"3645" "Medical assistants"
+"3646" "Medical transcriptionists"
+"3647" "Pharmacy aides"
+"3648" "Veterinary assistants and labratory animal caretakers"
+"3649" "Phlebotmists"
+"3655" "Miscellaneous healthcare support occupations, including medical equipment preparers"
+"3700" "First-line supervisors/managers of correctional officers"
+"3710" "First-line supervisors/managers of police and detectives"
+"3720" "First-line supervisors/managers of fire fighting and preventions workers"
+"3730" "Supervisors, protective service workers, all other"
+"3740" "Fire fighters"
+"3750" "Fire inspectors"
+"3800" "Bailiffs, correctional officers, and jailers"
+"3810" "Not used"
+"3820" "Detectives and criminal investigators"
+"3830" "Fish and game wardens"
+"3840" "Parking enforcement workers"
+"3850" "Police and sheriff's patrol officers"
+"3860" "Transit and railroad police"
+"3900" "Animal control workers"
+"3910" "Private detectives and investigators"
+"3930" "Security guards and gaming surveillance officers"
+"3940" "Crossing guards"
+"3945" "Transportation security screeners"
+"3955" "Lifeguards and other recreational, and all other protective service workers"
+"4000" "Chefs and head cooks"
+"4010" "First-line supervisors/managers of food preparation and serving workers"
+"4020" "Cooks"
+"4030" "Food preparation workers"
+"4040" "Bartenders"
+"4050" "Combined food preparation and serving workers, including fast food"
+"4060" "Counter attendants, cafeteria, food concession, and coffee shop"
+"4110" "Waiters and waitresses"
+"4120" "Food servers, nonrestaurant"
+"4130" "Dining room, cafeteria attendants, and misc. food preparation workers"
+"4140" "Dishwashers"
+"4150" "Hosts and hostesses, restaurant, lounge, and coffee shop"
+"4160" "Food preparation and serving related workers, all other"
+"4200" "First-line supervisors/managers of housekeeping and janitorial workers"
+"4210" "First-line supervisors/managers of landscaping workers"
+"4220" "Janitors and building cleaners"
+"4230" "Maids and housekeeping cleaners"
+"4240" "Pest control workers"
+"4250" "Grounds maintenance workers"
+"4300" "First-line supervisors/managers of gaming workers"
+"4310" "Not used"
+"4320" "First-line supervisors/managers of personal service workers"
+"4330" "Not used"
+"4340" "Animal trainers"
+"4350" "Nonfarm animal caretakers"
+"4400" "Gaming services workers"
+"4410" "Motion picture projectionists"
+"4420" "Ushers, lobby attendants, and ticket takers"
+"4430" "Miscellaneous entertainment attendants and related workers"
+"4460" "Funeral service workers "
+"4465" "Morticians, undertakers, and funeral directors (2011 forward)"
+"4500" "Barbers"
+"4510" "Hairdressers, hairstylists, and cosmetologists"
+"4520" "Miscellaneous personal appearance workers"
+"4530" "Baggage porters, bellhops, and concierges"
+"4540" "Tour and travel guides"
+"4550" "Transportation attendants"
+"4600" "Child care workers"
+"4610" "Personal and home care aides"
+"4620" "Recreation and fitness workers"
+"4630" "Not used"
+"4640" "Residential advisors"
+"4650" "Personal care and service workers, all other"
+"4700" "First-line supervisors/managers of retail sales workers"
+"4710" "First-line supervisors/managers of non-retail sales workers"
+"4720" "Cashiers"
+"4730" "Not used"
+"4740" "Counter and rental clerks"
+"4750" "Parts salespersons"
+"4760" "Retail salespersons"
+"4800" "Advertising sales agents"
+"4810" "Insurance sales agents"
+"4820" "Securities, commodities, and financial services sales agents"
+"4830" "Travel agents"
+"4840" "Sales representatives, services, all other"
+"4850" "Sales representatives, wholesale and manufacturing"
+"4900" "Models, demonstrators, and product promoters"
+"4910" "Not used"
+"4920" "Real estate brokers and sales agents"
+"4930" "Sales engineers"
+"4940" "Telemarketers"
+"4950" "Door-to-door sales workers, news and street vendors, and related workers"
+"4965" "Sales and related workers, all other"
+"5000" "First-line supervisors/managers of office and administrative support workers"
+"5010" "Switchboard operators, including answering service"
+"5020" "Telephone operators"
+"5030" "Communications equipment operators, all other"
+"5100" "Bill and account collectors"
+"5110" "Billing and posting clerks and machine operators"
+"5120" "Bookkeeping, accounting, and auditing clerks"
+"5130" "Gaming cage workers"
+"5140" "Payroll and timekeeping clerks"
+"5150" "Procurement clerks"
+"5160" "Tellers"
+"5165" "Financial clerks, all other"
+"5200" "Brokerage clerks"
+"5210" "Correspondence clerks"
+"5220" "Court, municipal, and license clerks"
+"5230" "Credit authorizers, checkers, and clerks"
+"5240" "Customer service representatives"
+"5250" "Eligibility interviewers, government programs"
+"5260" "File clerks"
+"5300" "Hotel, motel, and resort desk clerks"
+"5310" "Interviewers, except eligibility and loan"
+"5320" "Library assistants, clerical"
+"5330" "Loan interviewers and clerks"
+"5340" "New accounts clerks"
+"5350" "Order clerks"
+"5360" "Human resources assistants, except payroll and timekeeping"
+"5400" "Receptionists and information clerks"
+"5410" "Reservation and transportation ticket agents and travel clerks"
+"5420" "Information and record clerks, all other"
+"5500" "Cargo and freight agents"
+"5510" "Couriers and messengers"
+"5520" "Dispatchers"
+"5530" "Meter readers, utilities"
+"5540" "Postal service clerks"
+"5550" "Postal service mail carriers"
+"5560" "Postal service mail sorters, processors, and processing machine operators"
+"5600" "Production, planning and expediting clerks"
+"5610" "Shipping, receiving, and traffic clerks"
+"5620" "Stock clerks and order fillers"
+"5630" "Weighers, measurers, checkers, and samplers, record keeping"
+"5700" "Secretaries and administrative assistants"
+"5800" "Computer operators"
+"5810" "Data entry keyers"
+"5820" "Word processors and typists"
+"5830" "Desktop publishers"
+"5840" "Insurance claims and policy processing clerks"
+"5850" "Mail clerks and mail machine operators, except postal service"
+"5860" "Office clerks, general"
+"5900" "Office machine operators, except computer"
+"5910" "Proofreaders and copy markers"
+"5920" "Statistical assistants"
+"5940" "Office and administrative support workers, all other"
+"6005" "First-line supervisors of farming, fishing, and forestry workers"
+"6010" "Agricultural inspectors"
+"6020" "Animal breeders"
+"6030" "Not used"
+"6040" "Graders and sorters, agricultural products"
+"6050" "Miscellaneous agricultural workers"
+"6100" "Fishers and related fishing workers"
+"6110" "Hunters and trappers"
+"6120" "Forest and conservation workers"
+"6130" "Logging workers"
+"6200" "First-line supervisors/managers of construction trades and extraction workers"
+"6210" "Boilermakers"
+"6220" "Brickmasons, blockmasons, and stonemasons"
+"6230" "Carpenters"
+"6240" "Carpet, floor, and tile installers and finishers"
+"6250" "Cement masons, concrete finishers, and terrazzo workers"
+"6260" "Construction laborers"
+"6300" "Paving, surfacing, and tamping equipment operators"
+"6310" "Pile-driver operators"
+"6320" "Operating engineers and other construction equipment operators"
+"6330" "Drywall installers, ceiling tile installers, and tapers"
+"6340" "Not used"
+"6355" "Electricians"
+"6360" "Glaziers"
+"6400" "Insulation workers"
+"6410" "Not used"
+"6420" "Painters, construction and maintenance"
+"6430" "Paperhangers"
+"6440" "Pipelayers, plumbers, pipefitters, and steamfitters"
+"6450" "Not used"
+"6460" "Plasterers and stucco masons"
+"6500" "Reinforcing iron and rebar workers"
+"6515" "Roofers"
+"6520" "Sheet metal workers"
+"6530" "Solar Photovoltaic Installers"
+"6540" "Structural iron and steel workers"
+"6600" "Helpers, construction trades"
+"6660" "Construction and building inspectors"
+"6700" "Elevator installers and repairers"
+"6710" "Fence erectors"
+"6720" "Hazardous materials removal workers"
+"6730" "Highway maintenance workers"
+"6740" "Rail-track laying and maintenance equipment operators"
+"6750" "Septic tank servicers and sewer pipe cleaners"
+"6765" "Miscellaneous construction and related workers"
+"6800" "Derrick, rotary drill, and service unit operators, oil, gas, and mining"
+"6810" "Not used"
+"6820" "Earth drillers, except oil and gas"
+"6830" "Explosives workers, ordnance handling experts, and blasters"
+"6840" "Mining machine operators"
+"6910" "Roof bolters, mining"
+"6920" "Roustabouts, oil and gas"
+"6930" "Helpers -- extraction workers"
+"6940" "Other extraction workers"
+"7000" "First-line supervisors/managers of mechanics, installers, and repairers"
+"7010" "Computer, automated teller, and office machine repairers"
+"7020" "Radio and telecommunications equipment installers and repairers"
+"7030" "Avionics technicians"
+"7040" "Electric motor, power tool, and related repairers"
+"7050" "Electrical and electronics installers and repairers, transportation equipment"
+"7100" "Electrical and electronics repairers, industrial and utility"
+"7110" "Electronic equipment installers and repairers, motor vehicles"
+"7120" "Electronic home entertainment equipment installers and repairers"
+"7130" "Security and fire alarm systems installers"
+"7140" "Aircraft mechanics and service technicians"
+"7150" "Automotive body and related repairers"
+"7160" "Automotive glass installers and repairers"
+"7200" "Automotive service technicians and mechanics"
+"7210" "Bus and truck mechanics and diesel engine specialists"
+"7220" "Heavy vehicle and mobile equipment service technicians and mechanics"
+"7230" "Not used"
+"7240" "Small engine mechanics"
+"7250" "Not used"
+"7260" "Miscellaneous vehicle and mobile equipment mechanics, installers, and repairers"
+"7300" "Control and valve installers and repairers"
+"7315" "Heating, air conditioning, and refrigeration mechanics and installers"
+"7320" "Home appliance repairers"
+"7330" "Industrial and refractory machinery mechanics"
+"7340" "Maintenance and repair workers, general"
+"7350" "Maintenance workers, machinery"
+"7360" "Millwrights"
+"7410" "Electrical power-line installers and repairers"
+"7420" "Telecommunications line installers and repairers"
+"7430" "Precision instrument and equipment repairers"
+"7440" "Wind turbine service technicians"
+"7510" "Coin, vending, and amusement machine servicers and repairers"
+"7520" "Commercial divers"
+"7530" "Not used"
+"7540" "Locksmiths and safe repairers"
+"7550" "Manufactured building and mobile home installers"
+"7560" "Riggers"
+"7600" "Signal and track switch repairers"
+"7610" "Helpers--installation, maintenance, and repair workers"
+"7630" "Other installation, maintenance, and repair workers"
+"7700" "First-line supervisors/managers of production and operating workers"
+"7710" "Aircraft structure, surfaces, rigging, and systems assemblers"
+"7720" "Electrical, electronics, and electromechanical assemblers"
+"7730" "Engine and other machine assemblers"
+"7740" "Structural metal fabricators and fitters"
+"7750" "Miscellaneous assemblers and fabricators"
+"7800" "Bakers"
+"7810" "Butchers and other meat, poultry, and fish processing workers"
+"7820" "Not used"
+"7830" "Food and tobacco roasting, baking, and drying machine operators and tenders"
+"7840" "Food batchmakers"
+"7850" "Food cooking machine operators and tenders"
+"7855" "Food processing workers, all other"
+"7900" "Computer control programmers and operators"
+"7910" "Not used"
+"7920" "Extruding and drawing machine setters, operators, and tenders, metal and plastic"
+"7930" "Forging machine setters, operators, and tenders, metal and plastic"
+"7940" "Rolling machine setters, operators, and tenders, metal and plastic"
+"7950" "Cutting, punching, and press machine setters, operators, and tenders, metal and plastic"
+"7960" "Drilling and boring machine tool setters, operators, and tenders, metal and plastic"
+"8000" "Grinding, lapping, polishing, and buffing machine tool setters, operators, and tenders, metal and plastic"
+"8010" "Lathe and turning machine tool setters, operators, and tenders, metal and plastic"
+"8020" "Milling and planing machine setters, operators, and tenders, metal and plastic"
+"8030" "Machinists"
+"8040" "Metal furnace and kiln operators and tenders"
+"8050" "Not used"
+"8060" "Model makers and patternmakers, metal and plastic"
+"8100" "Molders and molding machine setters, operators, and tenders, metal and plastic"
+"8110" "Not used"
+"8120" "Multiple machine tool setters, operators, and tenders, metal and plastic"
+"8130" "Tool and die makers"
+"8140" "Welding, soldering, and brazing workers"
+"8150" "Heat treating equipment setters, operators, and tenders, metal and plastic"
+"8160" "Lay-out workers, metal and plastic"
+"8200" "Plating and coating machine setters, operators, and tenders, metal and plastic"
+"8210" "Tool grinders, filers, and sharpeners"
+"8220" "Metalworkers and plastic workers,all others"
+"8250" "Prepress technicians and workers"
+"8255" "Printing machine operators"
+"8256" "Print binding and finishing workers"
+"8300" "Laundry and dry-cleaning workers"
+"8310" "Pressers, textile, garment, and related materials"
+"8320" "Sewing machine operators"
+"8330" "Shoe and leather workers and repairers"
+"8340" "Shoe machine operators and tenders"
+"8350" "Tailors, dressmakers, and sewers"
+"8360" "Textile bleaching and dyeing machine operators and tenders"
+"8400" "Textile cutting machine setters, operators, and tenders"
+"8410" "Textile knitting and weaving machine setters, operators, and tenders"
+"8420" "Textile winding, twisting, and drawing out machine setters, operators, and tenders"
+"8430" "Extruding and forming machine setters, operators, and tenders, synthetic and glass fibers"
+"8440" "Fabric and apparel patternmakers"
+"8450" "Upholsterers"
+"8460" "Textile, apparel, and furnishings workers, all others"
+"8500" "Cabinetmakers and bench carpenters"
+"8510" "Furniture finishers"
+"8520" "Model makers and patternmakers, wood"
+"8530" "Sawing machine setters, operators, and tenders, wood"
+"8540" "Woodworking machine setters, operators, and tenders, except sawing"
+"8550" "Woodworkers, all others"
+"8600" "Power plant operators, distributors, and dispatchers"
+"8610" "Stationary engineers and boiler operators"
+"8620" "Water and liquid waste treatment plant and system operators"
+"8630" "Miscellaneous plant and system operators"
+"8640" "Chemical processing machine setters, operators, and tenders"
+"8650" "Crushing, grinding, polishing, mixing, and blending workers"
+"8710" "Cutting workers"
+"8720" "Extruding, forming, pressing, and compacting machine setters, operators, and tenders"
+"8730" "Furnace, kiln, oven, drier, and kettle operators and tenders"
+"8740" "Inspectors, testers, sorters, samplers, and weighers"
+"8750" "Jewelers and precious stone and metal workers"
+"8760" "Medical, dental, and ophthalmic laboratory technicians"
+"8800" "Packaging and filling machine operators and tenders"
+"8810" "Painting workers"
+"8820" "Not used"
+"8830" "Photographic process workers and processing machine operators"
+"8840" "Semiconductor processors"
+"8850" "Cementing and gluing machine operators and tenders"
+"8860" "Cleaning, washing, and metal pickling equipment operators and tenders"
+"8900" "Cooling and freezing equipment operators and tenders"
+"8910" "Etchers and engravers"
+"8920" "Molders, shapers, and casters, except metal and plastic"
+"8930" "Paper goods machine setters, operators, and tenders"
+"8940" "Tire builders"
+"8950" "Helpers--production workers"
+"8965" "Production workers, all other"
+"9000" "Supervisors, transportation and material moving workers"
+"9030" "Aircraft pilots and flight engineers"
+"9040" "Air traffic controllers and airfield operations specialists"
+"9050" "Flight attendants"
+"9110" "Ambulance drivers and attendants, except emergency medical technicians"
+"9120" "Bus drivers"
+"9130" "Driver/sales workers and truck drivers"
+"9140" "Taxi drivers and chauffeurs"
+"9150" "Motor vehicle operators, all other"
+"9200" "Locomotive engineers and operators"
+"9230" "Railroad brake, signal, and switch operators"
+"9240" "Railroad conductors and yardmasters"
+"9250" "Not used"
+"9260" "Subway, streetcar, and other rail transportation workers"
+"9300" "Sailors and marine oilers"
+"9310" "Ship and boat captains and operators"
+"9320" "Not used"
+"9330" "Ship engineers"
+"9340" "Bridge and lock tenders"
+"9350" "Parking lot attendants"
+"9360" "Service station attendants"
+"9410" "Transportation inspectors"
+"9415" "Transportation attendants, except flight attendants"
+"9420" "Other transportation workers"
+"9500" "Conveyor operators and tenders"
+"9510" "Crane and tower operators"
+"9520" "Dredge, excavating, and loading machine operators"
+"9560" "Hoist and winch operators"
+"9600" "Industrial truck and tractor operators"
+"9610" "Cleaners of vehicles and equipment"
+"9620" "Laborers and freight, stock, and material movers, hand"
+"9630" "Machine feeders and offbearers"
+"9640" "Packers and packagers, hand"
+"9650" "Pumping station operators"
+"9720" "Refuse and recyclable material collectors"
+"9730" "Shuttle car operators"
+"9740" "Tank car, truck, and ship loaders"
+"9750" "Material moving workers, all other"
+"9840" "Armed forces"
diff --git a/VT2-examples/vt2-ch6-codedata-dir/data/occlinks.csv b/VT2-examples/vt2-ch6-codedata-dir/data/occlinks.csv
new file mode 100644
index 0000000..1f2d914
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/data/occlinks.csv
@@ -0,0 +1,14223 @@
+"source","target","value"
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+"4720","4760",1304895.52
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+"4220","4230",720824.6
+"4020","4000",540510.28
+"9130","9620",700463.78
+"5700","5860",502701.52
+"4030","4110",524783.94
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+"4010","4020",508491.24
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+"4760","4850",466129.2
+"5240","5400",409483.38
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+"4600","4510",325448.66
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+"9130","9600",231325.17
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+"3600","3640",254372.74
+"4110","4050",259494.85
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+"9620","9600",234294.35
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+"9120","9620",218536.57
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+"710","630",159597.25
+"4720","4920",169044.59
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+"4720","5240",163775.08
+"6260","6240",174297.58
+"4110","4120",157959.88
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+"5240","5160",170549.3
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+"4300","4320",179547.4
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+"9130","9610",148126.88
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+"6420","6440",182098.19
+"4250","9620",105242.53
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+"7200","7315",116843.13
+"4110","4150",156270.88
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+"4250","4230",167897.39
+"6420","6515",113346.6
+"4110","4760",163809.19
+"4200","4210",167236.99
+"2040","2000",142148.94
+"5700","5610",119376.17
+"5120","800",142612.7
+"3060","3050",162728.83
+"5700","5840",103833.02
+"4760","4820",118881.96
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+"4720","3600",131597.91
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+"6260","6330",155077.23
+"5700","5810",133361.82
+"3600","3649",117199.96
+"2630","2750",115521.45
+"5240","4760",128503.62
+"4720","4700",139008.6
+"4720","4600",126912.54
+"1020","1010",124005.84
+"2540","2430",138591.52
+"5400","5110",133835.62
+"4800","4840",85646.78
+"4010","4000",100110.91
+"5550","5540",129167.03
+"9620","9140",133073.51
+"2300","2340",144917.01
+"8140","7750",131911.3
+"6260","6230",115062.84
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+"1020","1006",129562.66
+"3500","3300",128301.49
+"4720","4840",131347.92
+"3820","3800",134165.07
+"4850","4920",142355.26
+"8740","7750",144128.42
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+"1050","1107",118863.63
+"4810","4920",144054
+"6040","6050",108861.33
+"4720","5700",96127.18
+"4620","4510",108340.19
+"2630","2710",100838.1
+"9640","9120",120487.3
+"4510","4520",124469.07
+"4030","4050",132830.91
+"4720","5620",131442.98
+"4600","4620",136542.23
+"5120","5110",95965.85
+"5240","5260",117619.56
+"140","220",134646
+"4720","4965",127737.5
+"4720","5400",102877.31
+"5940","5860",132747.88
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+"4720","4230",109276.28
+"430","340",110142.52
+"4110","5400",90155.46
+"3150","3255",125485.11
+"2010","2016",95106.59
+"1550","1540",127921.13
+"2300","2320",106284.93
+"9130","6260",125588.09
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+"5620","9620",106454.64
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+"4760","5620",89725.12
+"9130","6230",82109.1
+"3255","3230",88763.63
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+"5700","5600",82491.5
+"630","530",107207.04
+"1006","1107",98498.41
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+"8140","9360",192.4
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+"2025","1860",184.78
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+"6820","6320",127.38
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diff --git a/VT2-examples/vt2-ch6-codedata-dir/data/occverts.csv b/VT2-examples/vt2-ch6-codedata-dir/data/occverts.csv
new file mode 100644
index 0000000..fafcd1b
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/data/occverts.csv
@@ -0,0 +1,521 @@
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+"4700","Sales and Related",27463.5
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+"4710","Sales and Related",9713.4
+"4220","Building and Grounds Cleaning and Maintenance",20360.4
+"4020","Food Preparation and Serving",18940.5
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+"9130","Transportation and Material Moving",28642.3
+"9620","Transportation and Material Moving",17218.6
+"5700","Office and Administrative Support",25750.9
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+"4010","Food Preparation and Serving",4952
+"20","Management",8041.3
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+"4250","Building and Grounds Cleaning and Maintenance",12301.9
+"10","Management",12449.8
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+"4610","Personal Care and Service",11144.6
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+"4600","Personal Care and Service",11797.6
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+"5400","Office and Administrative Support",11378.5
+"3645","Healthcare Support",3868.9
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+"2000","Community and Social Services",6475
+"5620","Office and Administrative Support",14086.5
+"6320","Construction",3297.8
+"205","Management",7331.7
+"4510","Personal Care and Service",6357.5
+"120","Management",10195.4
+"3255","Healthcare Practitioners and Technicians",24520.9
+"2300","Education, Training, and Library",6052.8
+"9640","Transportation and Material Moving",5039.9
+"50","Management",8098.4
+"230","Management",7283.6
+"4810","Sales and Related",5092.2
+"4920","Sales and Related",7038.5
+"5120","Office and Administrative Support",10748.2
+"6260","Construction",14730.5
+"410","Management",5415.9
+"220","Management",6818
+"4040","Food Preparation and Serving",3625.9
+"350","Management",4901.2
+"3655",NA,1392.3
+"9120","Transportation and Material Moving",4999.3
+"710","Business Operations",7044.1
+"110","Management",5328.8
+"1020","Computer and Mathematical",10129.6
+"4050","Food Preparation and Serving",3378
+"9600","Transportation and Material Moving",4966
+"4840","Sales and Related",3958.6
+"3640","Healthcare Support",2458
+"2630","Arts, Design, Entertainment, Sports, and Media",7155.8
+"5610","Office and Administrative Support",4996.7
+"3500","Healthcare Practitioners and Technicians",5085
+"4300","Personal Care and Service",1325.7
+"2160",NA,1642
+"2550","Education, Training, and Library",1546.4
+"6420","Construction",4952.5
+"2145","Legal",3457.2
+"2540","Education, Training, and Library",8392.3
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+"9140","Transportation and Material Moving",3630
+"4200","Building and Grounds Cleaning and Maintenance",2445.7
+"2040","Community and Social Services",3602.3
+"3930","Protective Service",8288.2
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+"2600","Arts, Design, Entertainment, Sports, and Media",1676.9
+"3050","Healthcare Practitioners and Technicians",2358.6
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+"4140","Food Preparation and Serving",2639.2
+"4800","Sales and Related",2046.9
+"5550","Office and Administrative Support",2781.5
+"8140","Production",5152.8
+"5940","Office and Administrative Support",4557.6
+"6330","Construction",1330.6
+"3820","Protective Service",1339.6
+"8740","Production",6374.1
+"8965","Production",7954.9
+"1050","Computer and Mathematical",4327.2
+"3630","Healthcare Support",1467.3
+"7750","Production",8789.7
+"6040","Farming, Fishing, and Forestry",1109.7
+"5160","Office and Administrative Support",3023.8
+"4150","Food Preparation and Serving",2631.9
+"4060","Food Preparation and Serving",2374.7
+"140","Management",2236.8
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+"6240","Construction",1351.1
+"3150","Healthcare Practitioners and Technicians",1030.5
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+"4965","Sales and Related",2138.6
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+"910","Financial Specialists",2857.5
+"2430","Education, Training, and Library",1586.9
+"3800","Protective Service",3359.1
+"630","Business Operations",5179.6
+"2330","Education, Training, and Library",2946.8
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+"840","Financial Specialists",1634.1
+"4520","Personal Care and Service",2435.5
+"3160","Healthcare Practitioners and Technicians",1969.3
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+"1530","Architecture and Engineering",3418.6
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+"800","Financial Specialists",14715.4
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+"2830","Arts, Design, Entertainment, Sports, and Media",1529
+"3245","Healthcare Practitioners and Technicians",1428.5
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+"9000","Transportation and Material Moving",1639.7
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+"3300","Healthcare Practitioners and Technicians",2980.9
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+"5540","Office and Administrative Support",1025.8
+"7340","Installation, Maintenance, and Repair",4101.2
+"5810","Office and Administrative Support",2809.2
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+"4120","Food Preparation and Serving",1768.2
+"5520","Office and Administrative Support",2280.5
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+"5260","Office and Administrative Support",2305.7
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+"6050","Farming, Fishing, and Forestry",7606.1
+"5300","Office and Administrative Support",1098.8
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+"100","Management",1287.4
+"9150","Transportation and Material Moving",613.4
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+"1360","Architecture and Engineering",2969.5
+"1460","Architecture and Engineering",2555.7
+"1560","Technicians",597
+"3730","Protective Service",743.5
+"5000","Office and Administrative Support",11775.6
+"6250","Construction",547.4
+"1030","Computer and Mathematical",1682.3
+"540","Business Operations",2732.2
+"6230","Construction",11326.7
+"300","Management",916
+"2050","Community and Social Services",569
+"740",NA,2138.5
+"9840",NA,6353.6
+"3400","Healthcare Practitioners and Technicians",1695.4
+"1105","Computer and Mathematical",1854.6
+"2825","Arts, Design, Entertainment, Sports, and Media",1237.8
+"735",NA,2133.8
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+"2850","Arts, Design, Entertainment, Sports, and Media",1930
+"6130","Farming, Fishing, and Forestry",633.8
+"8030","Production",3262.8
+"1965","Life, Physical, and Social Science",1383.5
+"3649","Healthcare Support",875.9
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+"5360","Office and Administrative Support",714.1
+"4740","Sales and Related",1018.5
+"8800","Production",2236.2
+"1920","Life, Physical, and Social Science",575
+"4820","Sales and Related",2275.4
+"4430","Personal Care and Service",1903.7
+"4350","Personal Care and Service",1654.3
+"4950","Sales and Related",1567.9
+"2750","Arts, Design, Entertainment, Sports, and Media",1641.9
+"3647","Healthcare Support",444.4
+"136","Management",2048.1
+"6800","Extraction",428.6
+"565","Business Operations",1792.6
+"5840","Office and Administrative Support",2291.6
+"810","Financial Specialists",659.2
+"4940","Sales and Related",794.5
+"6515","Construction",1953.9
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+"4400","Personal Care and Service",865.6
+"2810","Arts, Design, Entertainment, Sports, and Media",695
+"2200","Education, Training, and Library",11838.2
+"5560","Office and Administrative Support",571.3
+"5140","Office and Administrative Support",1215.9
+"9350","Transportation and Material Moving",707.4
+"2400","Education, Training, and Library",468.8
+"3955",NA,1850.6
+"2710","Arts, Design, Entertainment, Sports, and Media",1349
+"7220","Installation, Maintenance, and Repair",1709.6
+"1650","Life, Physical, and Social Science",1275.4
+"725","Business Operations",1228.3
+"8320","Production",1670.9
+"3510","Healthcare Practitioners and Technicians",1110.7
+"3220","Healthcare Practitioners and Technicians",773.9
+"7315","Installation, Maintenance, and Repair",3203.5
+"9420","Transportation and Material Moving",261.5
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+"3310","Healthcare Practitioners and Technicians",1459.7
+"3535","Healthcare Practitioners and Technicians",956.2
+"9050","Transportation and Material Moving",713.4
+"8640","Production",469.3
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+"1760","Life, Physical, and Social Science",1723
+"3230","Healthcare Practitioners and Technicians",1206.3
+"4750","Sales and Related",850
+"6460","Construction",272.1
+"7630","Installation, Maintenance, and Repair",1709.3
+"6100","Farming, Fishing, and Forestry",376.1
+"860","Financial Specialists",865.9
+"7010","Installation, Maintenance, and Repair",2290.4
+"9720","Transportation and Material Moving",726.1
+"6520","Construction",1018.4
+"2015","Community and Social Services",811.9
+"5850","Office and Administrative Support",764.2
+"1240","Computer and Mathematical",623.2
+"8300","Production",1540.3
+"2910","Arts, Design, Entertainment, Sports, and Media",1450.2
+"2900","Arts, Design, Entertainment, Sports, and Media",929.8
+"1106","Computer and Mathematical",918
+"7020","Installation, Maintenance, and Repair",1302.8
+"3258","Healthcare Practitioners and Technicians",1148.4
+"9030","Transportation and Material Moving",895.3
+"150","Management",1733.2
+"4900","Sales and Related",717
+"3648","Healthcare Support",387.7
+"5820","Office and Administrative Support",873.1
+"1430","Architecture and Engineering",1439.2
+"3720","Protective Service",423.4
+"1610","Life, Physical, and Social Science",840.5
+"600","Business Operations",1004.9
+"4650","Personal Care and Service",1066.1
+"9510","Transportation and Material Moving",584.3
+"7840","Production",821.8
+"2860","Arts, Design, Entertainment, Sports, and Media",858.8
+"650","Business Operations",1131.2
+"940","Financial Specialists",1295.5
+"6840","Extraction",549.8
+"4240","Building and Grounds Cleaning and Maintenance",581.3
+"3620","Healthcare Support",602.8
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+"2920","Arts, Design, Entertainment, Sports, and Media",579.6
+"2700","Arts, Design, Entertainment, Sports, and Media",525
+"4500","Personal Care and Service",868.4
+"8810","Production",1214.5
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+"4530","Personal Care and Service",655.4
+"1220","Computer and Mathematical",1086.8
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+"2100","Legal",8809.5
+"6710","Construction",333.2
+"520","Business Operations",844.3
+"3110","Healthcare Practitioners and Technicians",795.3
+"6730","Construction",1014.1
+"1060","Computer and Mathematical",904.4
+"1350","Architecture and Engineering",571.7
+"3840","Protective Service",117.7
+"5410","Office and Administrative Support",871.7
+"4540","Personal Care and Service",453.2
+"7855","Production",974.3
+"9750","Transportation and Material Moving",451.4
+"1820","Life, Physical, and Social Science",1644.8
+"8650","Production",688.3
+"1007","Computer and Mathematical",483.5
+"1310","Architecture and Engineering",327.3
+"6400","Construction",392.7
+"8710","Production",633.9
+"6200","Construction",5730.6
+"1720","Life, Physical, and Social Science",851.5
+"5020","Office and Administrative Support",295.1
+"4830","Sales and Related",622.6
+"1320","Architecture and Engineering",1148.7
+"6355","Construction",6414.2
+"5800","Office and Administrative Support",727.1
+"5420","Office and Administrative Support",1132.5
+"5330","Office and Administrative Support",1111.1
+"2440","Education, Training, and Library",370.5
+"8750","Production",406.1
+"1200","Computer and Mathematical",195.8
+"7410","Installation, Maintenance, and Repair",964.7
+"7260","Installation, Maintenance, and Repair",837.6
+"8760","Production",754.5
+"1400","Architecture and Engineering",678.2
+"8255","Production",1541
+"4640","Personal Care and Service",459.5
+"8620","Production",643.1
+"2760","Arts, Design, Entertainment, Sports, and Media",348.6
+"4340","Personal Care and Service",320.4
+"3940","Protective Service",653.5
+"820","Financial Specialists",459.9
+"40","Management",499.8
+"8500","Production",473.9
+"7360","Installation, Maintenance, and Repair",514.2
+"7140","Installation, Maintenance, and Repair",1216.4
+"726","Business Operations",603.5
+"1420","Architecture and Engineering",389
+"3646","Healthcare Support",423.9
+"5220","Office and Administrative Support",775.2
+"7150","Installation, Maintenance, and Repair",1187.4
+"5500","Office and Administrative Support",187.2
+"5165","Office and Administrative Support",516.7
+"8350","Production",827.8
+"6220","Construction",1284.3
+"6820","Extraction",215.5
+"2840","Arts, Design, Entertainment, Sports, and Media",513.5
+"9360","Transportation and Material Moving",819.6
+"640","Business Operations",569.9
+"7120","Installation, Maintenance, and Repair",392.1
+"6765","Construction",371
+"930","Financial Specialists",582.6
+"6600","Construction",498.8
+"1005","Computer and Mathematical",178.9
+"1900","Life, Physical, and Social Science",206
+"6120","Farming, Fishing, and Forestry",207.8
+"4420","Personal Care and Service",483.6
+"2060","Community and Social Services",636.7
+"60","Management",541.4
+"6360","Construction",389.4
+"7040","Installation, Maintenance, and Repair",240.3
+"8130","Production",548.9
+"7830","Production",160.1
+"1520","Architecture and Engineering",302.5
+"8600","Production",366
+"3945","Protective Service",296.3
+"5900","Office and Administrative Support",386
+"1840","Life, Physical, and Social Science",210.3
+"1740","Life, Physical, and Social Science",802.7
+"9040","Transportation and Material Moving",286.6
+"8000","Production",456.7
+"7000","Installation, Maintenance, and Repair",2368.6
+"5320","Office and Administrative Support",792.5
+"6720","Construction",271.9
+"6210","Construction",193.6
+"3520","Healthcare Practitioners and Technicians",404
+"7110","Installation, Maintenance, and Repair",182.3
+"3040","Healthcare Practitioners and Technicians",331.1
+"6530","Construction",598.9
+"7950","Production",723.1
+"8830","Production",345.8
+"7350","Installation, Maintenance, and Repair",259.3
+"7700","Production",6384.5
+"7240","Installation, Maintenance, and Repair",405.6
+"8930","Production",326.2
+"500","Business Operations",370.3
+"700","Business Operations",716.3
+"5630","Office and Administrative Support",586.3
+"1700","Life, Physical, and Social Science",172.5
+"7130","Installation, Maintenance, and Repair",504.4
+"137","Management",370.3
+"7710","Production",108.1
+"8400","Production",60
+"7900","Production",670.1
+"5250","Office and Administrative Support",636
+"900","Financial Specialists",128
+"6830","Extraction",98.4
+"7320","Installation, Maintenance, and Repair",404.5
+"8730","Production",97.2
+"8310","Production",379.6
+"1340","Architecture and Engineering",120.3
+"5920","Office and Administrative Support",202.3
+"4930","Sales and Related",374.2
+"5010","Office and Administrative Support",234.2
+"3750","Protective Service",120.7
+"7430","Installation, Maintenance, and Repair",522.1
+"6660","Construction",749.9
+"7850","Production",144.1
+"8100","Production",344.8
+"7540","Installation, Maintenance, and Repair",214.8
+"6010","Farming, Fishing, and Forestry",201
+"9415","Transportation and Material Moving",252.6
+"9310","Transportation and Material Moving",365.9
+"3850","Protective Service",5540.5
+"8630","Production",322.5
+"1600","Life, Physical, and Social Science",336.2
+"3140","Healthcare Practitioners and Technicians",129
+"3610","Healthcare Support",163.1
+"3740","Protective Service",2370.3
+"1440","Architecture and Engineering",94.1
+"7510","Installation, Maintenance, and Repair",333.9
+"360","Management",126.3
+"6740","Construction",79.2
+"1930","Life, Physical, and Social Science",204.8
+"9630","Transportation and Material Moving",212.5
+"7100","Installation, Maintenance, and Repair",172
+"4465","Personal Care and Service",255.7
+"2105","Legal",98.7
+"7610","Installation, Maintenance, and Repair",153.5
+"8450","Production",316.2
+"6300","Construction",130.6
+"6700","Construction",241.2
+"8530","Production",221.9
+"3000","Healthcare Practitioners and Technicians",377.2
+"8540","Production",214.6
+"1640","Life, Physical, and Social Science",219.7
+"330","Management",140.4
+"5530","Office and Administrative Support",214.2
+"9240","Transportation and Material Moving",432.3
+"1450","Architecture and Engineering",297.7
+"5200","Office and Administrative Support",34.2
+"9200","Transportation and Material Moving",336.8
+"9300","Transportation and Material Moving",249.6
+"8720","Production",273
+"8610","Production",687.5
+"8040","Production",199.3
+"8940","Production",100.4
+"8256","Production",151.2
+"3260","Healthcare Practitioners and Technicians",197.2
+"5340","Office and Administrative Support",186.2
+"8410","Production",133.1
+"3900","Protective Service",75.7
+"1300","Architecture and Engineering",1645.6
+"9520","Transportation and Material Moving",313
+"5230","Office and Administrative Support",349.1
+"425","Management",47.9
+"8250","Production",262.7
+"7160","Installation, Maintenance, and Repair",181.3
+"8330","Production",97.9
+"7730","Production",131.9
+"8420","Production",96
+"7920","Production",112.3
+"135","Management",174.9
+"8200","Production",154.4
+"8510","Production",113.1
+"9650","Transportation and Material Moving",167.1
+"8920","Production",322.8
+"5030","Office and Administrative Support",90.4
+"9110","Transportation and Material Moving",120.7
+"6005","Farming, Fishing, and Forestry",429.5
+"7300","Installation, Maintenance, and Repair",194.5
+"9560","Transportation and Material Moving",89.8
+"830","Financial Specialists",227.5
+"7740","Production",236.9
+"7550","Installation, Maintenance, and Repair",52.6
+"8550","Production",225
+"9410","Transportation and Material Moving",328.1
+"2740","Arts, Design, Entertainment, Sports, and Media",143.8
+"5150","Office and Administrative Support",223.8
+"3250","Healthcare Practitioners and Technicians",648.1
+"5910","Office and Administrative Support",70.2
+"1800","Life, Physical, and Social Science",284.1
+"7940","Production",111.7
+"8860","Production",65.1
+"5130","Office and Administrative Support",82.5
+"8020","Production",11.8
+"1710","Life, Physical, and Social Science",90.7
+"3200","Healthcare Practitioners and Technicians",136.9
+"7560","Installation, Maintenance, and Repair",112.4
+"1910","Life, Physical, and Social Science",180.4
+"8910","Production",93.3
+"6920","Extraction",91.2
+"8850","Production",63.5
+"4460","Personal Care and Service",120
+"8460","Production",209.4
+"8060","Production",15.8
+"7030","Installation, Maintenance, and Repair",59
+"4410","Personal Care and Service",43.5
+"8010","Production",99.4
+"6500","Construction",54.3
+"8340","Production",19.8
+"7960","Production",16.3
+"6750","Construction",59.4
+"7440","Installation, Maintenance, and Repair",4
+"1500","Architecture and Engineering",98.9
+"1230","Computer and Mathematical",78.7
+"7930","Production",15
+"6540","Construction",9.9
+"5210","Office and Administrative Support",19
+"3210","Healthcare Practitioners and Technicians",80.1
+"3256","Healthcare Practitioners and Technicians",196.9
+"8210","Production",42.9
+"3830","Protective Service",6.8
+"510","Business Operations",121
+"7050","Installation, Maintenance, and Repair",11.5
+"9740","Transportation and Material Moving",8.2
+"9260","Transportation and Material Moving",94.7
+"1330","Architecture and Engineering",8
+"4160",NA,6
+"6020","Farming, Fishing, and Forestry",12
+"6430","Construction",2.4
+"325","Management",32.4
+"3120","Healthcare Practitioners and Technicians",13.3
+"8900","Production",5.3
+"3257","Healthcare Practitioners and Technicians",6.1
+"1830","Life, Physical, and Social Science",3.8
+"5830","Office and Administrative Support",1.9
+"6930","Extraction",9.4
+"9340","Transportation and Material Moving",9.3
+"9500","Transportation and Material Moving",9.6
+"1950","Life, Physical, and Social Science",0.8
+"8120","Production",4.2
+"1510","Architecture and Engineering",70
+"9230","Transportation and Material Moving",21.5
+"8440","Production",5.8
+"8840","Production",9.5
+"9330","Transportation and Material Moving",17.8
+"6910","Extraction",5.7
+"8360","Production",6.2
+"7520","Installation, Maintenance, and Repair",1.2
+"7600","Installation, Maintenance, and Repair",9.1
+"1815","Life, Physical, and Social Science",12.7
diff --git a/VT2-examples/vt2-ch6-codedata-dir/guides/index.html b/VT2-examples/vt2-ch6-codedata-dir/guides/index.html
new file mode 100644
index 0000000..e69de29
diff --git a/VT2-examples/vt2-ch6-codedata-dir/guides/setup-mac-environment.html b/VT2-examples/vt2-ch6-codedata-dir/guides/setup-mac-environment.html
new file mode 100644
index 0000000..e69de29
diff --git a/VT2-examples/vt2-ch6-codedata-dir/guides/setup-windows-environment.html b/VT2-examples/vt2-ch6-codedata-dir/guides/setup-windows-environment.html
new file mode 100644
index 0000000..e69de29
diff --git a/VT2-examples/vt2-ch6-codedata-dir/make-heatmap-multivariate.R b/VT2-examples/vt2-ch6-codedata-dir/make-heatmap-multivariate.R
new file mode 100644
index 0000000..3bbcc16
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/make-heatmap-multivariate.R
@@ -0,0 +1,42 @@
+
+#
+# Load data
+#
+
+players <- read.csv("data/nba-top50scorers-2022-23.csv")
+head(players)
+
+play20 <- players[1:20,]
+
+#
+# Heatmap
+#
+
+# Four columns
+heatmap(as.matrix(play20[,c("MP", "FG", "FGpct", "PTS")]),
+ scale = "column",
+ Rowv = NA, Colv = NA)
+
+# Names
+heatmap(as.matrix(play20[,c("MP", "FG", "FGpct", "PTS")]),
+ scale = "column",
+ labRow = play20$Player,
+ Rowv = NA, Colv = NA)
+
+# Change order
+playrev <- play20[order(play20$PTS, decreasing = FALSE),]
+heatmap(as.matrix(playrev[,c("MP", "FG", "FGpct", "PTS")]),
+ scale = "column",
+ labRow = playrev$Player,
+ Rowv = NA, Colv = NA)
+
+
+# Color
+playrev <- play20[order(play20$PTS, decreasing = FALSE),]
+heatmap(as.matrix(playrev[,c("MP", "FG", "FGpct", "X2Ppct", "X3Ppct", "FTpct", "PTS")]),
+ scale = "column",
+ labRow = playrev$Player,
+ cexCol = 1,
+ col = rev(hcl.colors(20, palette = "Blues 3")),
+ Rowv = NA, Colv = NA)
+
diff --git a/VT2-examples/vt2-ch6-codedata-dir/make-network-graph.R b/VT2-examples/vt2-ch6-codedata-dir/make-network-graph.R
new file mode 100644
index 0000000..cae4254
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/make-network-graph.R
@@ -0,0 +1,98 @@
+# install.packages("igraph")
+library(igraph)
+
+# Empty graph
+g <- make_empty_graph(directed=FALSE)
+
+#
+# Add vertices
+#
+
+g <- g %>% add_vertices(1, color="#a3b8a3", size=50)
+g <- g %>% add_vertices(1, color = "#d7c668", size=50)
+g <- g %>% add_vertices(10, color = "#cccccc", size=20)
+plot(g)
+
+
+
+#
+# Add edges (to connect vertices)
+#
+
+# Connect the two first vertices
+g <- g %>% add_edges(c(1,2), weight=0.01, color="#cccccc")
+plot(g)
+
+# Connect 8 to 10 and 7
+g <- g %>% add_edges(c(8,10, 8,7), weight=0.01, color="#cccccc")
+plot(g)
+
+# Connect 3, 4, and 5 to 1
+g <- g %>% add_edges(c(3,1, 4,1, 5,1), weight=0.01, color="#cccccc")
+plot(g)
+
+# Connect 11 and 12 to 2 with more weight
+g <- g %>% add_edges(c(12,2, 11,2), weight=1, color="#cccccc")
+plot(g)
+
+# Layouts
+par(mfrow=c(2,2), mar=c(2,0,4,0))
+plot(g, vertex.label=NA, layout=layout_with_fr, main="Force-directed")
+plot(g, vertex.label=NA, layout=layout_on_grid, main="Grid")
+plot(g, vertex.label=NA, layout=layout_in_circle, main="Circle")
+plot(g, vertex.label=NA, layout=layout_nicely, main="Nicely")
+
+
+
+
+
+
+
+#
+# Example with more data: Job Switching Network
+#
+
+# Load data
+occs <- read.csv("data/occ_codes.tsv", sep="\t")
+occverts <- read.csv("data/occverts.csv")
+occlinks <- read.csv("data/occlinks.csv")
+occgrps <- unique(occverts$grp)
+
+head(occverts)
+head(occlinks)
+
+# Subsetting and weights
+occlinks$relwt <- occlinks$value / max(occlinks$value)
+occlinks_sub <- occlinks[occlinks$relwt >= .01,]
+occverts_sub <- occverts[occverts$id %in% occlinks_sub$source | occverts$id %in% occlinks_sub$target,]
+
+# Vertices colors
+occverts_sub$color <- colors()[match(occverts_sub$grp, occgrps)*2]
+
+# Form the graph
+g <- make_empty_graph(directed=FALSE)
+g <- g %>%
+ add_vertices(dim(occverts_sub)[1], color=occverts_sub$color, size=sqrt(occverts_sub$nwt)/10) %>%
+ set_vertex_attr("occid", value = occverts_sub$id) %>%
+ set_vertex_attr("grp", value = occverts_sub$grp)
+
+# Get id of added vertices
+occverts_sub$vid <- match(occverts_sub$id, V(g)$occid)
+occverts_sub$name <- occs$name[match(occverts_sub$id, occs$code)]
+g <- g %>% set_vertex_attr("name", value = occverts_sub$name)
+
+# Join ids
+occlinks_sub$vid_source <- occverts_sub$vid[match(occlinks_sub$source, occverts_sub$id)]
+occlinks_sub$vid_target <- occverts_sub$vid[match(occlinks_sub$target, occverts_sub$id)]
+
+# Add edges
+for (i in 1:dim(occlinks_sub)[1]) {
+ g <- g %>% add_edges(c(occlinks_sub$vid_source[i], occlinks_sub$vid_target[i]),
+ weight=occlinks_sub$relwt[i]*20,
+ color="#f0f0f0")
+}
+
+# Plot
+plot(g, vertex.label = NA)
+
+
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch6-codedata-dir/make-parallel-coord.R b/VT2-examples/vt2-ch6-codedata-dir/make-parallel-coord.R
new file mode 100644
index 0000000..f5fe673
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/make-parallel-coord.R
@@ -0,0 +1,40 @@
+# install.packages("MASS")
+library(MASS)
+
+
+#
+# Load data
+#
+
+players <- read.csv("data/nba-top50scorers-2022-23.csv")
+head(players)
+
+
+#
+# Parallel coordinates
+#
+
+fnames <- c("MP", "FG", "FGpct", "X2Ppct", "X3Ppct", "FTpct", "PTS")
+psub <- players[,fnames]
+
+# Parallel coordinates
+parcoord(psub)
+
+# Pick out player
+curr_players <- c("Joel Embiid", "Giannis Antetokounmpo",
+ "Stephen Curry", "LeBron James")
+par(mfrow=c(2,2))
+for (pl in curr_players) {
+ col <- rep("gray", dim(players)[1])
+ lwd <- rep(.5, dim(players)[1])
+ i <- which(players$Player == pl)
+ col[i] <- "blue"
+ lwd[i] <- 3
+ parcoord(psub, col=col, lwd=lwd, main=pl)
+}
+
+
+
+# Scatterplot Matrix
+par(las=1)
+plot(psub)
diff --git a/VT2-examples/vt2-ch6-codedata-dir/web/barbell/data/hh-activities-withkids.tsv b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/data/hh-activities-withkids.tsv
new file mode 100644
index 0000000..c36bfae
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/data/hh-activities-withkids.tsv
@@ -0,0 +1,16 @@
+"code" "pctnokidsf" "pctkidsf" "pctnokidsm" "pctkidsm" "desc" "durmedkidsf" "durmednokidsf" "durmedkidsm" "durmednokidsm"
+30201 0.2 20.7 0.2 10.3 "Homework" 45 0 45 0
+30101 0.9 71.2 0.6 46.8 "Physical care" 50 0 31 0
+30103 0.8 21.6 0.3 21.4 "Playing, not sports" 70 0 60 0
+30112 0.9 48 0.3 27.2 "Picking up/dropping off" 10 0 10 0
+30102 0.2 14.5 0 9.1 "Reading to/with" 20 0 20 0
+30302 0.1 3 0 0.6 "Obtaining medical care" 60 0 50 0
+30301 0.1 3 0 0.8 "Providing medical care" 15 0 30 0
+30111 0.2 11.7 0.2 4.9 "Waiting for/with" 15 0 15 0
+30202 0 1.7 0 0.5 "Meetings and school conferences" 25 0 30 0
+30106 0.3 13.8 0.2 5.7 "Talking with/listening" 25 0 25 0
+30110 0.1 4.8 0 2.6 "Attending events" 71 0 85 0
+30109 0.3 12.1 0.1 5.1 "Looking after (as a pri. activity)" 40 0 45 0
+30105 0 1.3 0 1 "Playing sports" 43 0 60 0
+30108 0 6.4 0 1.6 "Organization and planning" 10 0 15 0
+30303 0.1 1.4 0 0.3 "Waiting for children's health" 20 0 30 0
diff --git a/VT2-examples/vt2-ch6-codedata-dir/web/barbell/index.html b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/index.html
new file mode 100644
index 0000000..63bcb55
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/index.html
@@ -0,0 +1,14 @@
+
+
+
+Who Takes Care of the Kids
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/barbell.js b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/barbell.js
new file mode 100644
index 0000000..8b35da0
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/barbell.js
@@ -0,0 +1,114 @@
+// Dimensions of chart.
+let margin = { top: 20, right: 20, bottom: 50, left: 290 },
+ // width = parseInt(d3.select('#chart').style('width'), 10) - margin.left - margin.right,
+ width = 650 - margin.left - margin.right,
+ height = 550 - margin.top - margin.bottom;
+
+// Start SVG
+let svg = d3.select("#chart").append("svg")
+ .attr("width", width + margin.left + margin.right)
+ .attr("height", height + margin.top + margin.bottom)
+ .append("g")
+ .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
+
+// Scales: x and y
+let x = d3.scaleLinear()
+ .domain([0, 80])
+ .range([0, width]);
+let y = d3.scaleBand()
+ // .domain([3, 6])
+ .range([height, 0]);
+
+
+let activities;
+let xAxis, yAxis, xAxisEl, yAxisEl;
+let barbell;
+
+
+// Load data
+const activitiesData = Promise.all([
+ d3.tsv("data/hh-activities-withkids.tsv", d3.autoType)
+]);
+activitiesData.then(function(data) {
+ activities = data[0].sort((a, b) => a.pctkidsf - b.pctkidsf);
+ // console.log(activities);
+
+ // Initialize chart now that data is loaded.
+ initChart();
+});
+
+
+function initChart() {
+
+ // x-axis
+ xAxis = d3.axisBottom(x)
+ .tickFormat(d => d + "%")
+ .ticks(5)
+ .tickSize(-height-8)
+ .tickPadding(5);
+ xAxisEl = svg.append("g")
+ .attr("class", "x axis bottom")
+ .attr("transform", "translate(0,"+(height+8)+")");
+ xAxisEl.append("text")
+ .attr("class", "axistitle")
+ .attr("text-anchor", "start")
+ .attr("x", 0).attr("y", 0)
+ .attr("dx", "-.75em").attr("dy", "2.5em")
+ .text("Engaged in Activity");
+ xAxisEl.call(xAxis);
+
+
+ // y-axis
+ y.domain(activities.map(d => d.desc));
+ yAxis = d3.axisLeft(y)
+ .tickSize(0);
+ yAxisEl = svg.append("g")
+ .attr("class", "y axis left")
+ .attr("transform", "translate("+(-8)+",0)");
+ yAxisEl.call(yAxis);
+
+
+ // barbells
+ barbell = svg.selectAll(".barbell")
+ .data(activities)
+ .join("g")
+ .attr("class", "barbell")
+ .attr("transform", d =>
+ "translate(0,"+(y(d.desc)+(y.bandwidth()/2))+")")
+ .attr("id", d => "act"+d.code);
+
+ // connecting lines
+ barbell.append("line")
+ .attr("x1", d => x(d.pctkidsm))
+ .attr("x2", d => x(d.pctkidsf))
+ .attr("y1", 0).attr("y2", 0)
+ .attr("stroke", "#000000");
+
+ // circles
+ barbell.append("circle")
+ .attr("class", "men")
+ .attr("cx", d => x(d.pctkidsm))
+ .attr("cy", 0)
+ .attr("r", 6);
+ barbell.append("circle")
+ .attr("class", "women")
+ .attr("cx", d => x(d.pctkidsf))
+ .attr("cy", 0)
+ .attr("r", 6);
+
+ // labels
+ svg.append("text")
+ .attr("x", x(activities[14].pctkidsm))
+ .attr("y", y(activities[14].desc))
+ .attr("text-anchor", "middle")
+ .text("MEN");
+ svg.append("text")
+ .attr("x", x(activities[14].pctkidsf))
+ .attr("y", y(activities[14].desc))
+ .attr("text-anchor", "middle")
+ .text("WOMEN");
+}
+
+
+
+
diff --git a/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/d3.v7.min.js b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/d3.v7.min.js
new file mode 100644
index 0000000..c6db253
--- /dev/null
+++ b/VT2-examples/vt2-ch6-codedata-dir/web/barbell/js/d3.v7.min.js
@@ -0,0 +1,2 @@
+// https://d3js.org v7.4.0 Copyright 2010-2022 Mike Bostock
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Tm(t,n){return[(n=+n)*Math.cos(t-=Math.PI/2),n*Math.sin(t)]}mm.prototype={areaStart:function(){this._curve.areaStart()},areaEnd:function(){this._curve.areaEnd()},lineStart:function(){this._curve.lineStart()},lineEnd:function(){this._curve.lineEnd()},point:function(t,n){this._curve.point(n*Math.sin(t),n*-Math.cos(t))}};class Sm{constructor(t,n){this._context=t,this._x=n}areaStart(){this._line=0}areaEnd(){this._line=NaN}lineStart(){this._point=0}lineEnd(){(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line}point(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;default:this._x?this._context.bezierCurveTo(this._x0=(this._x0+t)/2,this._y0,this._x0,n,t,n):this._context.bezierCurveTo(this._x0,this._y0=(this._y0+n)/2,t,this._y0,t,n)}this._x0=t,this._y0=n}}class Em{constructor(t){this._context=t}lineStart(){this._point=0}lineEnd(){}point(t,n){if(t=+t,n=+n,0==this._point++)this._x0=t,this._y0=n;else{const e=Tm(this._x0,this._y0),r=Tm(this._x0,this._y0=(this._y0+n)/2),i=Tm(t,this._y0),o=Tm(t,n);this._context.moveTo(...e),this._context.bezierCurveTo(...r,...i,...o)}}}function km(t){return new Sm(t,!0)}function Nm(t){return new Sm(t,!1)}function Cm(t){return new Em(t)}function Pm(t){return t.source}function zm(t){return t.target}function Dm(t){let n=Pm,e=zm,r=dm,i=pm,o=null,a=null;function u(){let u;const c=fm.call(arguments),f=n.apply(this,c),s=e.apply(this,c);if(null==o&&(a=t(u=ma())),a.lineStart(),c[0]=f,a.point(+r.apply(this,c),+i.apply(this,c)),c[0]=s,a.point(+r.apply(this,c),+i.apply(this,c)),a.lineEnd(),u)return a=null,u+""||null}return u.source=function(t){return arguments.length?(n=t,u):n},u.target=function(t){return arguments.length?(e=t,u):e},u.x=function(t){return arguments.length?(r="function"==typeof 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e=.87559*Wb(n-Gb(n/7,2));t.moveTo(-e,0),t.lineTo(e,0),t.moveTo(0,e),t.lineTo(0,-e)}},jm={draw(t,n){const e=Wb(n),r=-e/2;t.rect(r,r,e,e)}},$m={draw(t,n){const e=.4431*Wb(n);t.moveTo(e,e),t.lineTo(e,-e),t.lineTo(-e,-e),t.lineTo(-e,e),t.closePath()}};const Hm=Vb(Kb/10)/Vb(7*Kb/10),Xm=Vb(Jb/10)*Hm,Gm=-Hb(Jb/10)*Hm;var Vm={draw(t,n){const e=Wb(.8908130915292852*n),r=Xm*e,i=Gm*e;t.moveTo(0,-e),t.lineTo(r,i);for(let n=1;n<5;++n){const o=Jb*n/5,a=Hb(o),u=Vb(o);t.lineTo(u*e,-a*e),t.lineTo(a*r-u*i,u*r+a*i)}t.closePath()}};const Wm=Wb(3);var Zm={draw(t,n){const e=-Wb(n/(3*Wm));t.moveTo(0,2*e),t.lineTo(-Wm*e,-e),t.lineTo(Wm*e,-e),t.closePath()}};const Km=Wb(3);var Qm={draw(t,n){const e=.6824*Wb(n),r=e/2,i=e*Km/2;t.moveTo(0,-e),t.lineTo(i,r),t.lineTo(-i,r),t.closePath()}};const Jm=-.5,tx=Wb(3)/2,nx=1/Wb(12),ex=3*(nx/2+1);var rx={draw(t,n){const e=Wb(n/ex),r=e/2,i=e*nx,o=r,a=e*nx+e,u=-o,c=a;t.moveTo(r,i),t.lineTo(o,a),t.lineTo(u,c),t.lineTo(Jm*r-tx*i,tx*r+Jm*i),t.lineTo(Jm*o-tx*a,tx*o+Jm*a),t.lineTo(Jm*u-tx*c,tx*u+Jm*c),t.lineTo(Jm*r+tx*i,Jm*i-tx*r),t.lineTo(Jm*o+tx*a,Jm*a-tx*o),t.lineTo(Jm*u+tx*c,Jm*c-tx*u),t.closePath()}},ix={draw(t,n){const e=.6189*Wb(n-Gb(n/6,1.7));t.moveTo(-e,-e),t.lineTo(e,e),t.moveTo(-e,e),t.lineTo(e,-e)}};const ox=[qm,Om,Bm,jm,Vm,Zm,rx],ax=[qm,Lm,ix,Qm,Fm,$m,Ym];function ux(){}function cx(t,n,e){t._context.bezierCurveTo((2*t._x0+t._x1)/3,(2*t._y0+t._y1)/3,(t._x0+2*t._x1)/3,(t._y0+2*t._y1)/3,(t._x0+4*t._x1+n)/6,(t._y0+4*t._y1+e)/6)}function fx(t){this._context=t}function sx(t){this._context=t}function lx(t){this._context=t}function hx(t,n){this._basis=new fx(t),this._beta=n}fx.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){switch(this._point){case 3:cx(this,this._x1,this._y1);case 2:this._context.lineTo(this._x1,this._y1)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2;break;case 2:this._point=3,this._context.lineTo((5*this._x0+this._x1)/6,(5*this._y0+this._y1)/6);default:cx(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},sx.prototype={areaStart:ux,areaEnd:ux,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._y0=this._y1=this._y2=this._y3=this._y4=NaN,this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x2,this._y2),this._context.closePath();break;case 2:this._context.moveTo((this._x2+2*this._x3)/3,(this._y2+2*this._y3)/3),this._context.lineTo((this._x3+2*this._x2)/3,(this._y3+2*this._y2)/3),this._context.closePath();break;case 3:this.point(this._x2,this._y2),this.point(this._x3,this._y3),this.point(this._x4,this._y4)}},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._x2=t,this._y2=n;break;case 1:this._point=2,this._x3=t,this._y3=n;break;case 2:this._point=3,this._x4=t,this._y4=n,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+n)/6);break;default:cx(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},lx.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var e=(this._x0+4*this._x1+t)/6,r=(this._y0+4*this._y1+n)/6;this._line?this._context.lineTo(e,r):this._context.moveTo(e,r);break;case 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gx(t,n){this._context=t,this._k=(1-n)/6}gx.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:px(this,this._x1,this._y1)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,n):this._context.moveTo(t,n);break;case 1:this._point=2,this._x1=t,this._y1=n;break;case 2:this._point=3;default:px(this,t,n)}this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var yx=function t(n){function e(t){return new gx(t,n)}return e.tension=function(n){return t(+n)},e}(0);function 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+
+
+Activities, Happiness, and Meaning
+
+
+
+
+
Meaningfulness with Happiness
+
+