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@jmuyskens jmuyskens/shootings.R Secret
Created Jul 8, 2018

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library(tidyverse)
library(ggbeeswarm)
library(gglabeller)
library(scales)
data <- read_csv("schoolshootingsdata FINAL - data.csv",
col_types = cols(date = col_date(format = "%m/%d/%Y")))
tidy.shooter.age <- gather(data, "shooter_i", "shooter.age", c("age_shooter", "shooter 2 age")) %>%
filter(is.numeric(shooter.age))
p <- ggplot(tidy.shooter.age, aes(shooter.age)) +
geom_dotplot(binwidth=1) +
theme_minimal() +
ggtitle('Distribution of shooter age')
ggsave('shooter_age_histogram.pdf', plot=p, useDingbats=FALSE)
theeeeme <- theme_minimal() + theme(
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.line.y=element_blank(),
panel.grid=element_blank())
ggplot(data, aes(date, 0, size=adjust)) +
geom_beeswarm(method = "frowney", groupOnX=FALSE, dodge.width=0.5) +
scale_size_area(max_size = 20) +
scale_x_date(date_breaks="1 year", labels=date_format("%Y")) +
theeeeme
ggsave('shooting_dots_timeline.pdf', width=50, height=5, limitsize=FALSE)
ggplot(data, aes(date, 0, size=adjust)) +
geom_beeswarm(method = "frowney", groupOnX=FALSE, dodge.width=0.5, alpha=0.5) +
scale_size_area(max_size = 20) +
scale_x_date(date_breaks="1 year", labels=date_format("%Y"), limits=as.Date(c('1998-04-01', '2023-04-01'))) +
scale_y_continuous(limits=c(-20, 10)) +
coord_polar(direction = -1) +
theeeeme
ggsave('shooting_timeline.pdf', width=40, height=40)
years <- c("1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018")
startvalues <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
start.frame <- data.frame(year=years, sum=startvalues, dayofyear=startvalues, school_name=rep(list(""), 19), stringsAsFactors=FALSE)
cumulative <- data %>%
mutate(year = format(date, '%Y'), dayofyear=as.numeric(format(date, '%j'))) %>%
group_by(year) %>%
arrange(date) %>%
mutate(sum=cumsum(adjust)) %>%
ungroup() %>%
select(sum, dayofyear, year, school_name) %>%
rbind(start.frame)
endofyear <- cumulative %>%
group_by(year) %>%
summarise(sum=max(sum)) %>%
filter(year!=2018) %>%
mutate(dayofyear=365, school_name="end of year")
cumulative %>% rbind(endofyear) %>% rbind(start.frame) %>% arrange(year) %>%
ggplot(aes(dayofyear, sum, group=year)) +
geom_step(alpha=0.4) +
geom_text(data=endofyear, aes(dayofyear, sum, label=year), hjust = 0, vjust = 0.5) +
geom_text()
theme_minimal() +
ggtitle("Cumulative number of students in school")
ggsave('cumulative_adjust.pdf')
cumulative.casualties <- data %>%
mutate(year = format(date, '%Y'), dayofyear=as.numeric(format(date, '%j'))) %>%
group_by(year) %>%
arrange(date) %>%
mutate(sum=cumsum(ifelse(is.na(casualties), 0, casualties))) %>%
ungroup() %>%
select(sum, dayofyear, year) %>%
rbind(start.frame)
endofyear.casualties <- cumulative.casualties %>%
group_by(year) %>%
summarise(sum=max(sum)) %>%
filter(year!=2018) %>%
mutate(dayofyear=365)
cumulative.casualties %>% rbind(endofyear.casualties) %>% rbind(start.frame) %>% arrange(year) %>%
ggplot(aes(dayofyear, sum, group=year)) +
geom_step(alpha=0.4) +
geom_text(data=endofyear.casualties, aes(dayofyear, sum, label=year), hjust = 0, vjust = 0.5) +
theme_minimal() +
ggtitle("Cumulative causualties")
ggsave('cumulative_causalties.pdf')
cumulative.count <- data %>%
mutate(year = format(date, '%Y'), dayofyear=as.numeric(format(date, '%j')), one=1) %>%
group_by(year) %>%
arrange(date) %>%
mutate(sum=cumsum(one)) %>%
ungroup() %>%
select(sum, dayofyear, year) %>%
rbind(start.frame)
endofyear.count <- cumulative.count %>%
group_by(year) %>%
summarise(sum=max(sum)) %>%
filter(year!=2018) %>%
mutate(dayofyear=365)
cumulative.count %>% rbind(endofyear.count) %>% rbind(start.frame) %>% arrange(year) %>%
ggplot(aes(dayofyear, sum, group=year)) +
geom_step(alpha=0.4) +
geom_text(data=endofyear.count, aes(dayofyear, sum, label=year), hjust = 0, vjust = 0.5) +
theme_minimal() +
ggtitle("Cumulative count")
ggsave('cumulative_count.pdf')
data %>%
mutate(year = format(date, '%Y')) %>%
ggplot(aes(year)) + geom_bar()
ggsave('years.pdf')
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