heat_map_discrete: Create a heat map of Chicago with discrete shading.
In dmwelgus/MapChi: Tools for making maps of Chicago

Description Usage Arguments Value Examples

Create a heat map of Chicago with discrete shading.

1
2
3

heat_map_discrete(regions, summary_df, regions_var, fill_var, legend_name,
  palette, na_replace = NA, lines = "black", title = NULL,
  title_size = 15, region_labels = FALSE)

`regions`	The type of region to plot. Options include: "CAs", "tracts," "districts," and "zips."
`summary_df`	A data frame with summary statistics by region. Should have one row for each region.
`regions_var`	The variable in summary_df that identifies the region. For example, if type = "CAs" a common merge_var.y would be "Community.Areas." In general, regions_var should be numeric (e.g. use community area numbers, not names.).
`fill_var`	The variable that determines color. For example, if the map shows homicides by police district, the fill_var would be the field that contains homicide totals.
`legend_name`	Self Explanatory. Should be a character vector of length one.
`palette`	The color palette. Current options are: green, blue, red, organge, and purple.
`na_replace`	What value should replace NAs in the fill_var. This arises most commonly in maps of homicides, where regions with zero homicides are sometimes missing from df.y. Add the lowest discrete value for your scale.
`lines`	Color of border lines. Default is black.
`title`	Title.
`title_size`	Title size.
`region_labels`	Option to add labels for CAs or districts. Default is false

A map of Chicago. More specifically, an object of type "ggplot."

data(hom_14)
library(dplyr)
hom_sum <- dplyr::summarise(group_by(hom_14, Community.Area), homicides = n())

There is now an na_replace option so disregard the following: 
# No na_replace option for discrete maps. Need to manually add zero homicide CAs.
additions <- c(1:77)[which(!c(1:77) %in% hom_sum$Community.Area)]
df_additions <- data.frame(Community.Area = additions, homicides = 0)
hom_sum <- rbind(hom_sum, df_additions)
hom_sum$range <- cut(hom_sum$homicides, c(0, 5, 10, 15, 20, 30, 40),
                     labels = c("0-4", "5-9", "10-14", "15-19",
                               "20-29", "30-39"), right = FALSE)

library(RColorBrewer)
colors <- brewer.pal(n = 6, name = "BuPu")
hm_dis <- heat_map_discrete(regions = "CAs", summary_df = hom_sum, regions_var = "Community.Area",
                            fill_var = "range", legend_name = "Total Homicides",
                            palette = colors, title = "Homicides by Community Area: 2014")