Add VT2-examples: all 6 chapters of 'Visualize This' (110 files) — data scraping, ETL, trends, categorical, multivariate, spatial, distribution analysis

VT2-examples/vt2-ch8-codedata-dir/make-histogram.R

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+library(readr)
+library(dplyr)
+
+
+#
+# Load data
+#
+
+players <- read_csv("data/nba-players-2013-14.csv")
+
+
+#
+# Histogram
+#
+
+# Default histogram
+par(las = 1)
+hist(players$Ht_inches,
+     xlab="inches",
+     main="NBA Player Heights")
+
+
+# Smaller breaks
+hist(players$Ht_inches, 
+     breaks=seq(65, 90, 1),
+     xlab="inches",
+     main="NBA Player Heights")
+
+# Vary bin sizes
+par(mfrow=c(1,3), mar=c(5,3,3,3))
+hist(players$Ht_inches, 
+     breaks=seq(65, 90, 1),
+     xlab="inches", main="One-inch bins")
+hist(players$Ht_inches,
+     breaks=seq(65, 90, 3),
+     xlab="inches", main="Three-inch bins")
+hist(players$Ht_inches,
+     breaks=seq(65, 90, 6),
+     xlab="inches", main="Six-inch bins")
+
+
+#
+# Comparison between positions
+#
+
+ppos <- unique(players$POS)
+par(mfrow=c(5,1), mar=c(2,3,3,2), oma=c(2,2,2,2))
+for (pos in ppos) {
+    curr <- players %>% filter(POS == pos)
+    hist(curr$Ht_inches, 
+         breaks=seq(65, 90, 1),
+         xlab="inches", main=paste0(pos, " Heights"))
+}
+
+
+ppos2 <- c("G", "G/F", "F", "F/C", "C")
+par(mfrow=c(5,1))
+for (pos in ppos2) {
+    curr <- players %>% filter(POS == pos)
+    curr_mean <- mean(curr$Ht_inches)
+    
+    hist(curr$Ht_inches, 
+         breaks=seq(65, 90, 1),
+         border = "white",
+         xlab="inches", 
+         main=paste0(pos, " Heights"))
+    abline(v = curr_mean, 
+           col = "blue", 
+           lwd = 2)
+}