In RobWiederstein/KeyWestMopeds: Explore Motorcyle and Moped Crashes in Florida

knitr::opts_chunk$set(
  collapse = TRUE,
  echo = F,
  warning = F,
  comment = "#>",
  fig.height=4,
  fig.width = 6,
  fig.align = "center"
)

library(KeyWestMopeds)
library(kableExtra)
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)
# Suppress summarise info
options(dplyr.summarise.inform = FALSE)
library(magrittr)
library(scales)

# load all crashes and state-wide population data
data(mv_crashes_all)
data(fl_state_pop)

Total Crashes

df.1 <-
  mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, motorcycle_involved) %>%
        summarize(total_crashes = n()) 

p1 <-
  df.1 %>%
        ggplot() +
        aes(calendar_year, total_crashes, group = motorcycle_involved, color = motorcycle_involved) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Motorcycle") +
        labs(title = ("Total Number of Crashes")) +
        theme_light()
p1
# filename <- "../../blogdown-default/content/post/2021-08-09-motorcycle-and-moped-accidents-in-florida/total-number-of-crashes.jpg"
# ggsave(filename = filename, p1, width = 6, height = 4, units = "in", dpi = 300, bg = NULL)

Total Crashes Per Capita

df.2 <-
  mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, motorcycle_involved) %>%
        summarize(total_crashes = n()) %>%
        dplyr::left_join(., fl_state_pop, by = 'calendar_year') %>%
        dplyr::filter(calendar_year %in% c(2012:2019)) %>%
        mutate(per_100k = divide_by(total_crashes, pop) * 1e5) %>%
        mutate(per_100k = per_100k %>% round(1)) 

p2 <-
  df.2 %>%
        ggplot() +
        aes(calendar_year, per_100k, group = motorcycle_involved, color = motorcycle_involved) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Motorcycle") +
        labs(title = ("Total Number of Crashes Per 100,000 Residents")) +
        theme_light()
# filename <- "../../blogdown-default/content/post/2021-08-09-motorcycle-and-moped-accidents-in-florida/crashes-per-100k-residents.jpg"
#  ggsave(filename = filename, p2, width = 6, height = 4, units = "in", dpi = 300)

Total Persons Killed

df.3 <-
  mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, motorcycle_involved) %>%
        summarize(total_killed = sum(number_of_killed)) 
p3 <-
  df.3 %>%
        ggplot() +
        aes(calendar_year, total_killed, group = motorcycle_involved, color = motorcycle_involved) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Motorcycle") +
        labs(title = "Total Persons Killed") +
        theme_light()
filename <- "../../blogdown-default/content/post/2021-08-09-motorcycle-and-moped-accidents-in-florida/total-fatalities.jpg"
 ggsave(filename = filename, p3, width = 6, height = 4, units = "in", dpi = 300)

Total Persons Killed Per Capita

mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, motorcycle_involved) %>%
        summarize(total_killed = sum(number_of_killed)) %>%
        dplyr::left_join(. , fl_state_pop, by = "calendar_year") %>%
        dplyr::filter(calendar_year %in% c(2012:2019)) %>%
        mutate(per_100k = divide_by(total_killed, pop) * 1e5) %>%
        mutate(per_100k = per_100k %>% round(1)) %>%
        ggplot() +
        aes(calendar_year, per_100k, group = motorcycle_involved, color = motorcycle_involved) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Motorcycle") +
        labs(title = "Total Persons Killed Per 100,000 Residents") +
        theme_minimal()

Total Crashes Disaggregated by Impaired Driver

mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, impaired_driver) %>%
        summarize(total_crashes = n()) %>%
        ggplot() +
        aes(calendar_year, total_crashes, group = impaired_driver, color = impaired_driver) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Impaired Driver") +
        labs(title = "Total Crashes") +
        theme_minimal()

Total Crashes Disaggregated by Injury Severity

mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, injsever) %>%
        summarize(total_crashes = n()) %>%
        tidyr::drop_na() %>% 
        ggplot() +
        aes(calendar_year, total_crashes, group = injsever, color = injsever) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "Injury Severity") +
        labs(title = "Total Crashes by Injury Severity") +
        theme_minimal()

Total Crashes Disaggregated by Location

mv_crashes_all %>%
        mutate(calendar_year = calendar_year %>% as.integer) %>% 
        group_by(calendar_year, in_town_flag) %>%
        summarize(total_crashes = n()) %>%
        tidyr::drop_na() %>% 
        ggplot() +
        aes(calendar_year, total_crashes, group = in_town_flag, color = in_town_flag) +
        geom_line() +
        geom_point() +
        scale_y_continuous(label = comma, name = "") +
        scale_x_continuous(name = "") +
        scale_color_discrete(name = "In Town") +
        labs(title = "Total Crashes by Location") +
        theme_minimal()

Total Crashes Per Day 2015 - 2019

mv_crashes_all %>% 
  mutate(calendar_year = calendar_year %>% as.integer) %>% 
  dplyr::filter(calendar_year %in% c(2015:2019)) %>% 
  group_by(date, calendar_year) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(date, total_crashes) + 
  geom_line(alpha = .5) + 
  facet_wrap(.~calendar_year, scales = "free_x", ncol = 1, strip.position = "right") +
  geom_smooth(span = .1, method = 'loess', formula = 'y ~ x') +
  labs(title = "Total Crashes by Day") +
  theme_bw()

Total Crashes by Day of Week

mv_crashes_all %>% 
  group_by(dayoweek) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(dayoweek, total_crashes) + 
  geom_point(size = 3, color = "blue") +
  scale_y_continuous(name = "", labels = comma, limits=c(0, 500000)) +
  scale_x_discrete(name = "") +
  labs(title = "Total Crashes by Day of Week") +
  theme_minimal()

Total Motorcycle Crashes Per Day 2015 - 2019

mv_crashes_all %>% 
  mutate(calendar_year = calendar_year %>% as.integer) %>% 
  dplyr::filter(calendar_year %in% c(2015:2019)) %>% 
  dplyr::filter(motorcycle_involved == "Yes") %>% 
  group_by(date, calendar_year) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(date, total_crashes) + 
  geom_line(alpha = .5) + 
  facet_wrap(.~calendar_year, scales = "free_x", ncol = 1, strip.position = "right") +
  geom_smooth(span = .1, method = 'loess', formula = 'y ~ x') +
  labs(title = "Total Motorcycle Crashes by Day") +
  theme_bw()

Total Impaired Driver Crashes Per Day 2015 - 2019

mv_crashes_all %>% 
  mutate(calendar_year = calendar_year %>% as.integer) %>% 
  dplyr::filter(calendar_year %in% c(2015:2019)) %>% 
  dplyr::filter(impaired_driver == "Yes") %>% 
  group_by(date, calendar_year) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(date, total_crashes) + 
  geom_line(alpha = .5) + 
  facet_wrap(.~calendar_year, scales = "free_x", ncol = 1, strip.position = "right") +
  geom_smooth(span = .1, method = 'loess', formula = 'y ~ x') +
  labs(title = "Total Impaired Driver Crashes by Day") +
  theme_bw()

Total Crashes by Impairment and Day of Week

mv_crashes_all %>% 
  #dplyr::filter(impaired_driver == "Yes") %>% 
  group_by(dayoweek, impaired_driver) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(dayoweek, total_crashes, color = impaired_driver, group = impaired_driver) + 
  geom_point(size = 3) +
  facet_grid(impaired_driver ~ ., scales = "free")+
  scale_y_continuous(name = "", labels = comma) +
  scale_x_discrete(name = "") +
  labs(title = "Total by Impairment and Day of Week") +
  theme_minimal() +
  theme(legend.position = "none")

Total Impaired Driver/Motorcycle Crashes Per Day 2015 - 2019

mv_crashes_all %>% 
  mutate(calendar_year = calendar_year %>% as.integer) %>% 
  dplyr::filter(calendar_year %in% c(2015:2019)) %>% 
  dplyr::filter(impaired_driver == "Yes") %>% 
  dplyr::filter(motorcycle_involved == "Yes") %>% 
  group_by(date, calendar_year) %>% 
  summarize(total_crashes = n()) %>% 
  ggplot() +
  aes(date, total_crashes) + 
  geom_line(alpha = .5) + 
  facet_wrap(.~calendar_year, scales = "free_x", ncol = 1, strip.position = "right") +
  geom_smooth(span = .1, method = 'loess', formula = 'y ~ x') +
  labs(title = "Total Impaired Driver Motorcycle Crashes by Day") +
  theme_bw()

RobWiederstein/KeyWestMopeds documentation built on Dec. 18, 2021, 10:53 a.m.