## Code to accept any of the offence_descriptions found in Offence Level and
## will accept a 2-element vector of postcodes.
#' Postcode correlation ggplot
#' \code{crime_file} : Gets crime data of year 2012 to 2017, filter it and generates ggplot between 2 postcode
#' @param crime_data A data.table object with the following columns:
#' "date" (POSIXct), "suburb" (chr), "postcode" (chr), "offence_level_1" (chr),
#' "offence_level_2" (chr), "offence_level_3" (chr), "offence_count" (num).
#' @param offence_description A character string of "offence_level_1"
#' @param postcodes A two-element character vector. Each element is an SA postcode.
#' @export
#' @return A ggplot object showing the correlation in offence count between the two input postcodes.
#' @examples
#' crime_file(data1,"ACTS INTENDED TO CAUSE INJURY", c(5000, 5006))
crime_file <- function(crime_data, offence_description, postcodes) {
require(data.table)
require(ggplot2)
# Error catching: Test to ensure the input `postcodes` vector has two elements
if (length(postcodes) != 2) {
stop("Please enter two postcodes")
}
expected_colnames <- c("date", "suburb", "postcode", "offence_level_1", "offence_level_2",
"offence_level_3", "offence_count")
#Get names of actual table
actual_colnames <- names(crime_data)
#Test to see if the input table has the right column names
if (!all.equal(expected_colnames, actual_colnames)) {
stop(paste("Input table columns need to match: ",
paste(expected_colnames, collapse = ", ")))
}
# Check that the input postcode and offence description exist in crime_data
if (any(!postcodes %in% crime_data$postcode) |
!offence_description %in% crime_data$offence_level_1) {
stop("Input postcode and offence description does not exist in crime_data")
}
# Make a data table for plotting using data.table transformations
# You will need to filter, summarise and group by
# Expect cols: "date", "postcode", "total_offence_count"
plot_data <- crime_data[postcode %in% c(postcodes[1], postcodes[2]) & offence_level_1 %in% c(offence_description),
list(total_offence_count = sum(offence_count),postcode),
by = date]
# These lines will transform the plot_data structure to allow us to plot
# correlations. Try them out
plot_data[, postcode := plyr::mapvalues(postcode, postcodes, c("x", "y"))]
plot_data <- dcast(plot_data, date ~ postcode, fun = sum,
fill = 0, value.var = "total_offence_count")
# Generate the plot
ggplot(plot_data, aes(x , y , group = month(date))) +
geom_count() +
labs(x = postcodes[1],
y = postcodes[2])
}
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