Heatmap plot

Description

Plots temporal heatmaps of covariates, case counts, or incidence rates.

plot_heatmap(
  data,
  var,
  time,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  area = NULL,
  aggregate_space = NULL,
  aggregate_time = "month",
  aggregate_space_fun = "mean",
  aggregate_time_fun = "mean",
  transform = "identity",
  title = NULL,
  var_label = NULL,
  ylab = NULL,
  xlab = NULL,
  palette = NULL,
  centering = NULL
)

Arguments

• data: Data frame containing equally spaced (daily, weekly, monthly) covariate or disease 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: Numerical only used for type='inc'. It represents the scale of the person-time (default 100,000) for incidence rates.
• area: Name of variable that identifies the different locations (i.e., areal units) for which a time series is available.
• aggregate_space: Name of variable used to define spatial aggregation groups.
• aggregate_time: Temporal scale used to perform temporal aggregation. Options are: “week” (ISO 8601), “month”, “year”.
• aggregate_space_fun: Character indicating the function to be used in the aggregation over space for type="cov". Options are “mean” (default), “median”, “sum”. For case counts and incidence, “sum” is always applied.
• 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) useful 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.
• ylab: Label for the y-axis.
• xlab: Label for the x-axis.
• 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.

Returns

A ggplot2 heatmap plot.

Examples

# Load data
data("dengue_MS")

# Covariate heatmap with space aggregation
plot_heatmap(dengue_MS,
             var = "tmin",
             time = "date",
             var_label = "Minimum\ntemp.",
             type = "cov",
             area = "micro_code",
             aggregate_space = "meso_code",  
             palette = "Blue-Red")

# Case count heatmap with log scale
plot_heatmap(dengue_MS,
             var = "dengue_cases", 
             time = "date",  
             type = "counts",
             area = "micro_code",  
             palette = "Reds", 
             title = "Dengue counts", 
             var_label = "Dengue \ncounts",
             transform = "log10p1")  
             
# Case incidence (for 1,000 persons) heatmap with space aggregation
plot_heatmap(dengue_MS,
             var = "dengue_cases", 
             time = "date",          
             type = "inc",
             pop = "population",
             pt = 1000,
             area = "micro_code", 
             aggregate_space = "meso_code", 
             palette = "Purp")