Diversity Plots

diversity.R

library(data.table); library(dplyr); library(tidyr)
library(ggplot2); library(RColorBrewer)

## IMPORT AND TIDY DATA
demo_data <- read.csv(file.choose(),header=T)
names(demo_data) <- c("datetime","gender","ethnicity","region","age_range","department")

demo_data$ethnicity <- gsub("White","Caucasian",demo_data$ethnicity)
demo_data$ethnicity <- gsub("Southeast Asian","Asian",demo_data$ethnicity)


## GROUP BY DEPARTMENT AND GENDER
department_and_gender <- demo_data %>%
  group_by(department,gender) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n),department_size=sum(n))


gender_bar_plot <- ggplot(department_and_gender, aes(x=reorder(department,-department_size), y=n, fill=gender)) +
  geom_bar(stat="identity") + scale_y_continuous(breaks=seq(0,10,2)) + scale_fill_brewer(palette="Pastel1") +
  labs(x="Area at Buffer",y="Team Members", title="Gender Breakdown of Buffer Across Areas") +
  theme_minimal()


## GROUP BY DEPARTMENT AND ETHNICITY
department_and_ethnicity <- demo_data %>%
  group_by(department,ethnicity) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n),department_size=sum(n))


ethnicity_bar_plot <- ggplot(department_and_ethnicity, aes(x=reorder(department,department_size), y=n, fill=ethnicity)) +
  geom_bar(stat="identity") + scale_y_continuous(breaks=seq(0,10,2)) + coord_flip() +
  labs(x="Area at Buffer",y="Team Members", title="Ethnicity Breakdown of Buffer Across Areas") +
  scale_fill_brewer(palette="Pastel1") + theme_minimal()


## PIE CHART OF ETHNICITIES
by_ethnicity <- demo_data %>%
  group_by(department,ethnicity) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n))

total_ethnicity_row <- demo_data %>%
  group_by(ethnicity) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n),department="Total")


total_ethnicity_breakdown <- rbind(by_ethnicity,total_ethnicity_row)


ethnicity_pies <- ggplot(total_ethnicity_breakdown,aes(x=factor(1),y=percent,fill=ethnicity)) +
  geom_bar(stat="identity",width=1) + 
  facet_wrap(~department) +
  coord_polar(theta="y") + 
  scale_fill_brewer(palette="Pastel1") +
  theme_minimal() +
  theme(axis.ticks = element_blank(), axis.text.y = element_blank(), axis.text.x = element_blank()) +
  labs(x="",y="",title="Ethnicity Breakdown of Buffer Team :)")



## PIE CHART OF GENDER BY DEPARTMENT
department_and_gender <- demo_data %>%
  group_by(department,gender) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n))

total_row <- demo_data %>%
  group_by(gender) %>%
  summarise(n=n()) %>%
  mutate(percent=n/sum(n),department="Total")

total_gender_breakdown <- rbind(department_and_gender,total_row)
total_gender_breakdown$gender <- factor(total_gender_breakdown$gender,levels=rev(levels(total_gender_breakdown$gender)))


gender_pies <- ggplot(total_gender_breakdown,aes(x=factor(1),y=percent,fill=gender)) +
  geom_bar(stat="identity",width=1) + 
  facet_wrap(~department) +
  coord_polar(theta="y") + 
  scale_fill_brewer(palette="Pastel1") +
  theme_minimal() +
  theme(axis.ticks = element_blank(), axis.text.y = element_blank(), axis.text.x = element_blank()) +
  labs(x="",y="",title="Gender Breakdown of Buffer Team :)")