GHRexplore (version 0.2.1)

plot_map: Choropleth map

Description

Plots a choropleth map of covariates, case counts, or incidence rates.

Usage

plot_map(
  data,
  var,
  time,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  area = NULL,
  map = NULL,
  map_area = NULL,
  by_year = NULL,
  aggregate_time = "year",
  aggregate_time_fun = "mean",
  transform = "identity",
  title = NULL,
  var_label = NULL,
  palette = NULL,
  centering = NULL,
  bins = NULL,
  bins_method = "quantile",
  bins_label = NULL,
  ...
)

Value

A ggplot2 choropleth map.

Arguments

data

Data frame containing equally spaced (daily, weekly, monthly) covariate or case observations for one or multiple locations.

var

Name of the column identifying the variable to be plotted.

time

Name of the variable that identifies the temporal dimension of the data frame. Its values must be in date format ("yyyy-mm-dd") representing the day of observation for daily data, the first day of the week for weekly, or the first day of the month for monthly observations.

type

Character that specifies the type of variable in var. Possible values include 'cov' (covariate, default), 'counts' (case counts), and 'inc' (case incidence). If type='inc', pop is required.

pop

Character identifying the variable name for population. Only needed if type='inc'.

pt

Scale of the person-time (default 100,000) for incidence rates.

area

Name of variable that identifies the different locations (e.g., areal units) for which a time series is available.

map

Name of the sf object corresponding to the spatial unit specified in 'area'.

map_area

Name of the variable that identifies the different locations (e.g., areal units) in the map object. If not specified, it assumes the same name as in area.

by_year

Deprecated. Use 'aggregate_time' instead.

aggregate_time

Temporal scale for visualization and aggregation. Options include "all" (across all time points) and "year" (default).

aggregate_time_fun

Character indicating the function to be used in the aggregation over time for type="cov". Options are "mean" (default), "median", "sum". For case counts and incidence, "sum" is always applied.

transform

Character, defaults to "identity" (i.e., no transformation). Transforms the color ramp for better visualization. Useful options include "log10p1" log10(x+1) for case counts and incidence with 0s, or any of the in-built ggplot2 options such as "log10" log10(x), "log1p" log(x+1), and "sqrt" sqrt(x) (check all possible options using ?scale_y_continuous).

title

Optional title of the plot.

var_label

Character with a custom name for the case or covariate variable.

palette

GHR, RColorBrewer or colorspace palette. Use "-" before the palette name (e.g., "-Reds") to reverse it.

centering

Numerical or "median", defaults to NULL. If set, it centers the palette on that value.

bins

Number of bins for categorization of numerical variables. Defaults to NULL (no binning).

bins_method

Method to compute the bins, only used when bins is not NULL. Possible values are "quantile" (default) and "equal".

bins_label

Optional labels for the bins. They must have the same length as the number of bins. Defaults to NULL (default interval labels).

...

Additional arguments to be passed to geom_sf. Possible values include colour (e.g., colour="black"), linewidth (e.g., linewidth=0.1), linetype (e.g., linetype=2), and alpha (e.g., alpha=0.8).

Examples

# Load data
library("sf")
data("dengue_MS")
data("map_MS")

# Temporal average of a covariate
plot_map(data = dengue_MS, 
         var = "tmin",  
         time = "date",       
         type = "cov",
         area = "micro_code",  
         map = map_MS,         
         map_area = "code",   
         aggregate_time = "all",
         aggregate_time_fun = "mean",         
         palette ="Reds",
         var_label= "Min Temp.")

# Categorical covariate
plot_map(data = dengue_MS, 
         var = "biome_name",        
         time = "date",      
         area = "micro_code", 
         aggregate_time = "all",
         map = map_MS,       
         map_area = "code",  
         palette ="Viridis",
         var_label= "Biome")

# Case counts by year (log)
dengue_MS |>
  plot_map(var = "dengue_cases",    
           time = "date",    
           type = "counts",
           area = "micro_code", 
           pop = "population",
           map = map_MS,   
           map_area = "code",
           palette = "Reds",
           transform = "log10p1")

# Case incidence by year, binned
plot_map(dengue_MS,
         var = "dengue_cases", 
         type = "inc",
         time = "date",
         area = "micro_code",
         pop = "population",
         pt = 1000,
         map = map_MS, 
         map_area = "code",
         bins = 5,   
         palette = "Viridis")