Visualization of Reading Level Frequency by Congressional Bill Stage
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Here’s a fun example of how you might use my data on Congressional bill length and complexity. Imagine you want to understand the empirical distribution of Flesch-Kincaid reading level for Congressional bills and how this distribution is related to bill stage. A first step might be to visualize this relationship.
Based on this visualization, you might infer that engrossed bills tend to have less right-skew and have a lower mean reading level. The story behind this might be that Senators and Representatives are less likely to accept legislation they do not understand. To test this, you might run a simple KS test to see if the introduced bill reading levels are greater than engrossed bill reading levels.
> ks.test(introduced, engrossed, alternative="less") Two-sample Kolmogorov-Smirnov test data: introduced and engrossed D^- = 0.094, p-value = 0.006299 alternative hypothesis: the CDF of x lies below that of y
Sample source below.
# Clear and load libraries | |
rm(list=ls()) | |
library(ggplot2) | |
library(stats) | |
# Read data | |
data <- read.csv("bills-all.csv", comment.char='') | |
# Plot and save. | |
ggplot(data) + | |
geom_bar(aes(x=Reading.Level, fill=Stage), alpha=0.75, binwidth=1) + | |
scale_x_continuous("Reading Level") + | |
scale_y_continuous("Count") + | |
opts(title="Reading Level and Bill Stage") | |
ggsave(file="reading_level_bill_stage_20120415.jpg", width=8, height=6) | |
# Run KS test. | |
introduced <- (data$Reading.Level[which(data$Stage %in% c("Introduced-in-House", "Introduced-in-Senate"))]) | |
engrossed <- (data$Reading.Level[which(data$Stage %in% c("Engrossed-in-House", "Engrossed-in-Senate"))]) | |
print(ks.test(introduced, engrossed, alternative="less")) |
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