After some data munging, I have made some graphs with ggplot to explore the data.

I’ve put a compilation of the 2018 data on Github to facilitate a little visualisation

data <- readr::read_csv("https://raw.githubusercontent.com/brennanpincardiff/tg_data_working/master/TDOR_for_R_20181113.csv")
## Parsed with column specification:
## cols(
##   Name = col_character(),
##   Age = col_integer(),
##   Photo = col_character(),
##   `Photo source` = col_character(),
##   Date = col_date(format = ""),
##   `TGEU ref` = col_character(),
##   Location = col_character(),
##   Country = col_character(),
##   Latitude = col_double(),
##   Longitude = col_double(),
##   `Cause of death` = col_character(),
##   Description = col_character(),
##   Permalink = col_character(),
##   Age_high = col_integer()
## )

Try plotting some data

Deaths across the year

library(ggplot2)
ggplot(data, aes(Date)) + geom_bar() +
    ggtitle("Deaths across the year")

Deaths by age

ggplot(data, aes(Age)) + geom_bar() +
    ggtitle("Deaths by age")
## Warning: Removed 101 rows containing non-finite values (stat_count).

Deaths by country

ggplot2::ggplot(data, aes(Country)) + geom_bar() + 
    ggtitle("Deaths by country") +
    theme(axis.text.x = element_text(angle=45, hjust=1))