ds: r -> rmd

ds/25-1/r/2.rmd

@@ -0,0 +1,61 @@
+---
+title: "Lab2: Introduction to R, exploring the box-plot"
+author: "Vladislav Litvinov <vlad@sek1ro>"
+output:
+  pdf_document:
+  toc_float: TRUE
+---
+# Data preparation
+```{r}
+df = read.csv("./zipIncome.txt", sep = "|")
+colnames(df) <- c("zipCode", "income")
+
+summary(df)
+mean(df$income)
+if (any(is.na(df[,2]))) {
+  na = which(is.na(df[,2]))
+  df = df[-na,]
+}
+
+mean(df$income)
+median(df$income, na.rm=TRUE)
+```
+# Histograms and box-plot "whiskers"
+```{r}
+plot(y=df$income, x=df$zipCode, xlab="income", ylab="zipCode")
+df$incomelog = log10(df$income)
+hist(df$incomelog, breaks=80)
+print(min_incomelog <- log10(7e3))
+print(max_incomelog <- log10(2e5))
+print(avg_incomelog <- median(df$incomelog))
+
+df <- subset(df, 7e3 < df$income & df$income < 2e5)
+hist(df$incomelog, breaks=80)
+summary(df)
+
+boxplot(incomelog ~ zipCode, data=df, main="boxplot", xlab="zipCode", ylab="incomelog")
+
+library(ggplot2)
+
+ggplot(df, aes(x=zipCode, y=income, color=zipCode)) +
+  geom_point(
+    position = position_jitter(width = 0.2),
+    alpha = 0.2,
+  ) +
+  geom_boxplot(
+    alpha = 0.5,
+    outlier.shape = NA,
+    width = 0.6,
+    fill = "white",
+    color = "black"
+  ) +
+  scale_y_log10(
+    breaks = c(1e4, 25e3, 5e4, 1e5, 2e5, 5e5)
+  ) +
+  labs(
+    title = "Income distribution by ZIP codes",
+    subtitle = "Scatter plot jitter",
+  ) +
+  theme_minimal()
+  
+```