Quick gender analysis of Louisville UCR data

ucr_by_gender.R

## Load Packages
library(tidyverse)

## Set Theme
theme_set(
  theme_bw(base_family = 'Segoe UI', base_size = 12) +
    theme(
      plot.title = element_text(face = 'bold', hjust = 0),
      text = element_text(colour = '#445359'),
      panel.background = element_rect('#ffffff'),
      strip.background = element_rect('#ffffff', colour = 'white'),
      plot.background = element_rect('#ffffff'),
      panel.border = element_rect(colour = '#ffffff'),
      panel.grid.major.x = element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank(),
      legend.background = element_rect('#ffffff'),
      legend.title = element_blank(),
      legend.position = 'right',
      legend.direction = 'vertical',
      legend.key = element_blank(),
      strip.text = element_text(face = 'bold', size = 10),
      axis.text = element_text(face = 'bold', size = 9),
      axis.title = element_blank(),
      axis.ticks = element_blank()
    )
)


## Read in Data
ucr_data <- read_csv("https://data.louisvilleky.gov/sites/default/files/UniformCitationData.csv")

## Manipulate and Plot
crime_data_by_gender <- ucr_data %>% 
  group_by(PERSONS_SEX,
           UCR_DESC) %>%
  summarise(total = n()) %>% 
  spread(key = PERSONS_SEX,
         value = total) %>% 
  mutate(total = rowSums(.[2:5], na.rm = TRUE)) %>% 
  select(UCR_DESC,
         F,
         M,
         total) %>% 
  mutate(female_odds = round((F / sum(F, na.rm = TRUE)) / (total / sum(total, na.rm = TRUE)), 2),
         male_odds = round((M / sum(M, na.rm = TRUE)) / (total / sum(total, na.rm = TRUE)), 2)) %>% 
  group_by(UCR_DESC) %>% 
  summarise(female_odds = mean(female_odds),
            male_odds = mean(male_odds)) %>% 
  mutate(more_likely_gender = case_when(
    ifelse(is.na(female_odds), 0, female_odds) > ifelse(is.na(male_odds), 0, male_odds) ~ "female",
    TRUE ~ "male"
  )) %>% 
  gather(key = "gender",
         value = "log_odds",
         -UCR_DESC,
         -more_likely_gender) %>% 
  arrange(gender,
          desc(log_odds)) %>% 
  ggplot(aes(x = reorder(UCR_DESC, log_odds), 
             y = log_odds, 
             fill = gender,
             group = more_likely_gender)) + 
  geom_bar(stat = "identity",
           position = "dodge") + 
  facet_wrap(~more_likely_gender, 
             ncol = 2,
             scales = "free") + 
  theme(axis.text.x = element_text(size = 6, 
                                   angle = 90, 
                                   hjust = 1),
        legend.position = "none")