154 lines
6.5 KiB
R
154 lines
6.5 KiB
R
library(tidyverse)
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library(plyr)
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library(gridExtra)
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library(ggpubr)
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# script for the analysis of document readability metrics
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# readability metrics will be studied controlled by their length
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# gaughan@u.northwestern.edu
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# loading in the data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../text_analysis/dwo_readability_readme.csv")
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contributing_df <- read_csv("../text_analysis/dwo_readability_contributing.csv")
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head(readme_df)
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quantile(readme_df$reading_time)
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quantile(contributing_df$reading_time)
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aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
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aggregate(contributing_df[, 3:10], list(contributing_df$subdir), median)
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#getting basic stats for the readme readability
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median(readme_df$flesch_reading_ease)
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median(readme_df$linsear_write_formula)
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median(readme_df$mcalpine_eflaw)
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median(readme_df$reading_time)
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# establishing the color scheme
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subdirColors <-
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setNames( c('#31449c', '#4a7c85', '#c5db68')
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, levels(contributing_df$subdir) )
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readmeSubdirColors <-
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setNames( c('#4a7c85', '#c5db68')
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, levels(readme_df$subdir) )
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#plotting linsear scoring
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readme_linsear_plot <- ggplot(readme_df, aes(x=linsear_write_formula, group=as.factor(subdir))) +
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geom_density(aes(color = as.factor(subdir), fill=as.factor(subdir)), alpha=0.35, position="identity") +
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xlim(-30, 30) +
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theme_bw()
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#plotting readme reading ease
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readme_reading_ease <- ggplot(readme_df, aes(x=flesch_reading_ease, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), position="fill") +
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scale_fill_manual(values = readmeSubdirColors) +
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xlim(-5, 90) +
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labs(x= "Flesch Reading Ease", y= "README Density")+
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guides(fill="none", color="none")+
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theme_bw()
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readme_reading_ease
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#plotting readme reading time
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readme_reading_time_plot <- ggplot(readme_df, aes(x=reading_time, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), position="fill") +
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scale_fill_manual(values = readmeSubdirColors) +
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xlim(-5, 90) +
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labs(x= "Reading Time (s)", y= NULL)+
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guides(fill="none", color="none")+
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theme_bw()
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readme_reading_time_plot
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readme_reading_time_no_group <- ggplot(readme_df, aes(x=reading_time)) +
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geom_histogram(fill='forestgreen') +
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xlim(-5, 190) +
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ylab("Count of README Files") +
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xlab("Reading Time (s)") +
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ggtitle("Reading Time for README files from FLOSS Projects (n=2280)")+
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guides(fill="none", color="none")+
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theme_bw()
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readme_reading_time_no_group
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readme_linsear_plot <- ggplot(readme_df, aes(x=linsear_write_formula, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), alpha=0.35, position="identity") +
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scale_fill_manual(values = readmeSubdirColors) +
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xlim(-5, 90) +
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labs(x= "Linsear Write Score", y= NULL)+
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guides(fill="none", color="none")+
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theme_bw()
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readme_linsear_plot
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readme_mcalpine_eflaw <- ggplot(readme_df, aes(x=mcalpine_eflaw, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), alpha=0.35, position="identity") +
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scale_fill_manual(values = readmeSubdirColors) +
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xlim(-5, 90) +
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labs(x= "McAlpine EFLAW", y= NULL)+
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guides(fill="none", color="none")+
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theme_bw()
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#theme(axis.title.y=element_blank())
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#plot of reading_ease
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#readme_df <- readme_df |>
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# mutate(coef_grouping <- as.factor(subdir))
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#test_lm <- lm(mcalpine_eflaw ~ word_count + as.factor(subdir),data=readme_df)
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#summary(test_lm)
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aggregate(contributing_df[, 3:10], list(contributing_df$subdir), median)
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#basic stats for the contributing readability
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median(contributing_df$flesch_reading_ease)
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median(contributing_df$mcalpine_eflaw)
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median(contributing_df$reading_time)
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median(contributing_df$linsear_write_formula)
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# plotting contributing linsear writing formula
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contributing_linsear_plot <- ggplot(contributing_df, aes(x=linsear_write_formula, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), alpha=0.35, position="identity") +
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scale_fill_manual(values = subdirColors, labels=c('CI < 0', '0 in CI', '0 < CI')) +
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labs(x= NULL, y= NULL, fill="RE Grouping")+
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xlim(-5, 90) +
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theme_bw() +
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guides(fill="none", color="none")
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# plotting contributing reading time
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contributing_reading_time_plot <- ggplot(contributing_df, aes(x=reading_time, group=as.factor(subdir))) +
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scale_fill_manual(values = subdirColors, labels=c('CI < 0', '0 in CI', '0 < CI')) +
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geom_density(aes(fill=as.factor(subdir)), position="fill") +
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xlim(-5, 90) +
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labs(x= NULL, y= NULL, fill="RE Grouping")+
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theme_bw() +
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theme(legend.position = "inside",
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legend.position.inside = c(.93, .93),
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legend.justification = c("right", "top"),
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legend.direction = "horizontal",
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legend.margin = margin(6, 6, 6, 6))
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contributing_reading_time_plot
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# plotting contributing mcalpine eflaw
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contributing_mcalpine_eflaw <- ggplot(contributing_df, aes(x=mcalpine_eflaw, group=as.factor(subdir))) +
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scale_fill_manual(values = subdirColors, labels=c('CI < 0', '0 in CI', '0 < CI')) +
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geom_density(aes(fill=as.factor(subdir)), alpha=0.35, position="identity") +
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xlim(-5, 90) +
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labs(x= NULL, y= NULL, fill="RE Grouping")+
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theme_bw() +
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theme(legend.position = "inside",
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legend.position.inside = c(.93, .93),
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legend.justification = c("right", "top"),
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legend.direction = "vertical",
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legend.margin = margin(6, 6, 6, 6))
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# plotting contributing reading ease
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contributing_reading_ease <- ggplot(contributing_df, aes(x=flesch_reading_ease, group=as.factor(subdir))) +
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geom_density(aes(fill=as.factor(subdir)), position="fill") +
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scale_fill_manual(values = subdirColors, labels=c('CI < 0', '0 in CI', '0 < CI')) +
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labs(x= NULL, y="CONTRIBUTING Density", fill="RE Grouping")+
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xlim(-5, 90) +
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theme_bw() +
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guides(fill="none", color="none")
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contributing_reading_ease
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grid.arrange(contributing_reading_ease, contributing_reading_time_plot, readme_reading_ease, readme_reading_time_plot, nrow = 2)
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doctypeColors <-
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setNames( c('#5da2d8', '#c7756a')
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, c("CONTRIBUTING", "README"))
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readme_df$type = "README"
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contributing_df$type = "CONTRIBUTING"
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all_df = rbind(readme_df, contributing_df)
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length_plot_all <- ggplot(all_df, aes(x=word_count, group=as.factor(type))) +
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geom_density(aes(fill = as.factor(type)), color = NA, alpha=0.6, position="identity")+
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scale_fill_manual(values = doctypeColors) +
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xlim(-10, 500) +
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labs(
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x = "Word Count",
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y = "Density Across Documents",
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fill="Document Type"
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) +
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theme_bw() +
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theme(legend.position = "top")
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length_plot_all
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#grid.arrange(contributing_reading_time_plot, readme_reading_time_plot, nrow = 1)
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