Package {GHRexplore}

Title:	Exploratory Analysis of Temporal and Spatio-Temporal Health Data
Version:	0.3.0
Description:	A collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://gitlab.earth.bsc.es/ghr/ghrexplore, https://bsc-es.github.io/GHRtools/docs/GHRexplore/GHRexplore
BugReports:	https://gitlab.earth.bsc.es/ghr/ghrexplore/-/work_items
Depends:	R (≥ 4.1.0)
Imports:	colorspace, cowplot, dplyr, ggplot2 (≥ 3.5.0), grDevices, RColorBrewer, rlang, stats, tidyr, lubridate
Suggests:	knitr, rmarkdown, gganimate, magick, sf, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-11-04 08:15:41 UTC; cmilagar
Author:	Carles Milà [aut, cre], Giovenale Moirano [aut], Anna B. Kawiecki [aut], Rachel Lowe [aut, cph]
Maintainer:	Carles Milà <carles.milagarcia@bsc.es>
Repository:	CRAN
Date/Publication:	2026-11-06 15:50:02 UTC

GHRexplore: Exploratory Analysis of Temporal and Spatio-Temporal Health Data

Description

A collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Author(s)

Maintainer: Carles Milà carles.milagarcia@bsc.es (ORCID)

Authors:

• Carles Milà carles.milagarcia@bsc.es (ORCID)

• Giovenale Moirano (ORCID)

• Anna B. Kawiecki (ORCID)

• Rachel Lowe (ORCID) [copyright holder]

See Also

Useful links:

• https://gitlab.earth.bsc.es/ghr/ghrexplore

• https://bsc-es.github.io/GHRtools/docs/GHRexplore/GHRexplore

• Report bugs at https://gitlab.earth.bsc.es/ghr/ghrexplore/-/work_items

Generate GHR color palettes

Description

Generates color palettes including custom, ColorBrewer and colorspace palettes.

Creates a visualization of all custom GHR palettes.

Usage

GHR_palette(palette, ncols = 30)

GHR_palettes()

Arguments

Details

See all available options by running GHR_palettes(), RColorBrewer::display.brewer.all() and colorspace::hcl_palettes(plot=TRUE).

Value

GHR_palette() returns the function that generates the color palette.
GHR_palettes() returns a plot with the custom GHR palettes.

Examples

GHR_palette("IDE1", 5)(5)

GHR_palettes()

Aggregate cases

Description

Aggregates a data frame containing disease cases in space and/or time and provides case counts and incidence aggregations.

Usage

aggregate_cases(
  data,
  cases,
  pop,
  time,
  unit = NULL,
  pt = 1e+05,
  aggregate_space = NULL,
  aggregate_time = NULL,
  area = NULL
)

Arguments

Details

The temporal aggregation requires a number of time points depending on the time scale, e.g. from days to weeks it will require 7 points. Otherwise, it will return NA.

Value

A data frame with the aggregated cases and disease incidence.

Examples

# Load data
data("dengue_MS")

# Aggregate cases spatially from 'micro' to 'meso'
aggregate_cases(
  dengue_MS,
  time = "date",
  cases = "dengue_cases",
  pop = "population",
  unit = "micro_code",
  aggregate_space = "meso_name"
)

Aggregate covariates

Description

Aggregates a data frame containing a covariate of interest in space and/or time.

Usage

aggregate_cov(
  data,
  var,
  time,
  unit = NULL,
  aggregate_space = NULL,
  aggregate_time = NULL,
  aggregate_space_fun = "mean",
  aggregate_time_fun = "mean",
  area = NULL
)

Arguments

Details

The temporal aggregation requires a number of time points depending on the time scale, e.g. from days to weeks it will require 7 points. Otherwise, it will return NA.

Value

A data frame with the aggregated covariate.

Examples

# Load data
data("dengue_SP")

# Weekly to monthly total precipitation
aggregate_cov(
  dengue_SP,
  var = "precip_tot",
  time = "date",
  aggregate_time = "month",
  aggregate_time_fun = "sum"
)

Choropleth map animation

Description

Produces an animation of a choropleth map optionally complemented by a synchronised time series plot.

Usage

animate_map(
  data,
  var,
  time,
  unit = NULL,
  map,
  filename,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  map_unit = NULL,
  aggregate_time = NULL,
  aggregate_time_fun = "mean",
  transform = "identity",
  title = NULL,
  var_label = NULL,
  palette = NULL,
  centering = NULL,
  bins = NULL,
  bins_method = "quantile",
  bins_label = NULL,
  fps = 1,
  out_width = 600,
  out_height = 600,
  ts_include = FALSE,
  ts_color = "#168c81",
  ts_title = NULL,
  coordgrid = FALSE,
  ...,
  area = NULL,
  map_area = NULL
)

Arguments

Details

The magick package is used to merge the two gifs when ts_include=TRUE, which may run into memory issues. Please reduce the number of time points, perform a temporal aggregation, or reduce the gif size if that happens.

Value

Invisibly returns the path to the saved gif file; called for its side effect of writing the animation to disk.

Examples

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

# Animation of monthly minimum temperature in 2002
animate_map(
  data = dengue_MS[dengue_MS$year == 2002, ],
  var = "tmin",
  time = "date",
  unit = "micro_code",
  map = map_MS,
  map_unit = "code",
  type = "cov",
  var_label = "Min Temp",
  filename = tempfile(fileext = ".gif")
)

check_consecutive

Description

Verifies whether time values in a dataset are consecutive based on the detected time interval (e.g., daily, weekly, monthly, yearly). If a unit column is specified, the check is performed within each unit.

Usage

check_consecutive(data, time, unit = NULL, strict = FALSE, area = NULL)

Arguments

Details

Different time intervals per unit are not allowed.

Value

If strict = FALSE, returns a logical value: TRUE if all time values are consecutive, and FALSE otherwise. If strict = TRUE, the function throws an error if the check is not met.

Examples

df <- data.frame(
  date = seq(as.Date("2020-01-01"), by = "day", length.out = 10),
  cases = 1:10
)
check_consecutive(df, time = "date")

Convert a date to epiyearweek format

Description

The conversion is done following the ISO 8601 standard, whereby the first week of the year is the one that contains at least four days of the new year. This coincides with the approach taken by lubridate::epiweek and lubridate::isoweek but may not coincide with other definitions of epidemiological weeks.

Usage

date_to_epiyw(date, weekday = "Monday")

Arguments

Value

The transformed dates to epiyearweek.

Examples

# Convert dates in 'dengue_SP' in epiyw
data(dengue_SP)
head(dengue_SP$date)
dengue_SP$epiyw <- date_to_epiyw(as.Date(dengue_SP$date), "Sunday")
head(dengue_SP$epiyw)

Dengue cases from the "Mato Grosso do Sul" state of Brazil

Description

The dengue_MS example data set contains monthly counts of notified dengue cases by microregion, along with a range of spatial and spatiotemporal covariates (e.g., environmental, socio-economic and meteo-climatic factors). This data set represents a subset of a larger national data set that covers the entire territory of Brazil. The subset focuses on a specific region, Mato Grosso do Sul, for the purposes of illustration and computational efficiency. See the source below for access to the complete data set.

Usage

dengue_MS

Format

A data frame with 2,640 rows and 28 columns:

micro_code

Unique ID number to each micro region (11 units)

micro_name

Name of each micro region

micro_name_ibge

Name of each micro region following IBGE

meso_code

Unique ID number to each meso region (4 units)

meso_name

Name of each meso region

state_code

Unique ID number to each state (1 unit)

state_name

Name of each state

region_code

Unique ID number given to each Brazilian Region, In this data frame all observations come from the "Centre-West"

region_name

Name of each Brazilian Region, In this data frame all observations come from the "Centre-West"

biome_code

Biome code

biome_name

Biome name

ecozone_code

Ecozone code

ecozone_name

Ecozone name

main_climate

Most prevalent climate regime in the microregion. Based on Koppen Geiger climate regimes

month

Calendar month index, 1 = January, 12 = December

year

Year 2000 - 2019

time

Time index starting at 1 for January 2000

dengue_cases

Number of notified dengue cases registered in the notifiable diseases system in Brazil (SINAN) in the microregion of reference, at the month of first symptoms

population

Estimated population, based on projections calculated using the 2000 and 2010 censuses, and counts taken in 2007 and 2017

pop_density

Population density (number of people per km2)

tmax

Monthly average daily maximum temperature; gridded values (at a 0.5 deg resolution) averaged across each microregion

tmin

Monthly average daily minimum temperature; gridded values (at a 0.5 deg resolution) averaged across each microregion

pdsi

Self-calibrated Palmer drought severity index for each microregion. It measures how wet or dry a region is relative to usual conditions. Negative values represent periods of drought, positive values represent wetter periods. Calculated by taking the mean value within each microregion

urban

Percentage of inhabitants living in urban areas (2010 census)

water_network

Percentage of inhabitants with access to the piped water network according to the 2010 census

water_shortage

Frequency of reported water shortages per microregion between 2000 - 2016

date

First day of the Month, in date format ("%Y-%m-%d")

pop100000

Population divided by 100000.

Source

source code on GitHub; source code on Zenodo;

Dengue cases from the "São Paulo" state of Brazil

Description

The dengue_SP example data set reports the weekly number of notified dengue cases in the municipality of São Paulo together with climatic covariates. Data was sourced from Infodengue (see source below).

Usage

dengue_SP

Format

A data frame with 624 rows and 9 columns:

date

First day of the week, in date format ("%Y-%m-%d")

geocode

IBGE municipality code for the city of São Paulo

cases

Number of notified dengue cases

year

Year 2010 - 2021

temp_med

Weekly average daily mean temperature

precip_tot

Weekly cumulative precipitation

enso

El Niño-Southern Oscillation Index

pop

Number of inhabitants

pop100000

Population divided by 100000.

Source

Infodengue API

Convert a epiyearweek to date format

Description

The conversion is done following the ISO 8601 standard, whereby the first week of the year is the one that contains at least four days of the new year. This coincides with the approach taken by lubridate::epiweek and lubridate::isoweek but may not coincide with other definitions of epidemiological weeks.

Usage

epiyw_to_date(epiyw, weekday = "Monday")

Arguments

Value

The dates corresponding to each epiyearweek.

Examples

epiyw <- c("2025-01", "2025-10", "2025-20", "2025-30")
epiyw_to_date(epiyw)

get_time_interval

Description

This function determines the time interval (e.g., daily, weekly, monthly, yearly) of a dataset based on the differences between the first two time points.

Usage

get_time_interval(data, time, unit = NULL, area = NULL)

Arguments

Value

A character string indicating the time interval of the dataset. Possible values: "day", "week", "month", "year", or "unknown".

Examples

df <- data.frame(
  date = seq(as.Date("2020-01-01"), by = "week", length.out = 10),
  cases = 1:10
)
get_time_interval(df, time = "date")

log10p1_breaks_like

Description

Breaks and label generator for log10p1_trans

Usage

log10p1_breaks_like(x)

Arguments

Value

A numeric vector of breaks (powers of 10, preceded by 0 if x contains values below 1).

Examples

log10p1_breaks_like(c(0, 5, 2000))
log10p1_breaks_like(c(3, 50, 800))

library(ggplot2)
df <- data.frame(x = 1:5, y = c(0, 3, 30, 300, 3000))
ggplot(df, aes(x, y)) +
  geom_point() +
  scale_y_continuous(trans = log10p1_trans, breaks = log10p1_breaks_like)

log10p1_trans

Description

Transform to execute a log10(x+1) scale transformation

Usage

log10p1_trans

Value

A transformation object of class transform (see scales::new_transform()) with transform and inverse functions, to be used in the trans or transform argument of ggplot2 scales.

Examples

log10p1_trans$transform(c(0, 9, 99))
log10p1_trans$inverse(c(0, 1, 2))

library(ggplot2)
df <- data.frame(x = 1:5, y = c(0, 3, 30, 300, 3000))
ggplot(df, aes(x, y)) +
  geom_point() +
  scale_y_continuous(trans = log10p1_trans)

Administrative boundaries of Mato Grosso do Sul

Description

Administrative boundaries (polygon geometries) of the 11 municipalities in Mato Grosso do Sul (Brazil) to be used with the dengue_MS data set.

Usage

data(map_MS)

Format

A simple feature (sf) object including 11 rows and 2 columns:

code

Unique ID number to each micro region (11 units).

geometry

Geometries of the sf multipolygon.

Bivariate plot

Description

Plots a bivariate graph to visually assess associations. It will be a scatterplot if both variables are numeric and grouped boxplots if one of them is categorical.

Usage

plot_bivariate(
  data,
  var,
  unit = NULL,
  facet = FALSE,
  free_x_scale = FALSE,
  free_y_scale = FALSE,
  title = NULL,
  var_label = NULL,
  legend = NULL,
  palette = NULL,
  ...,
  area = NULL
)

Arguments

Value

A ggplot2 scatterplot or boxplot graph.

Examples

# Load data
data("dengue_MS")

# Scatter (two numeric variables) - No grouping
plot_bivariate(dengue_MS, var = c("pop_density", "tmin"), palette = "#d04a2d")

# Scatter (two numeric variables) - Grouping in the same graph
plot_bivariate(
  dengue_MS,
  var = c("pop_density", "tmin"),
  var_label = c("Pop. density", "Min temp."),
  unit = "micro_code"
)

# Scatter  (two numeric variables) - Grouping in facets
plot_bivariate(
  dengue_MS,
  var = c("pop_density", "tmin"),
  var_label = c("Pop. density", "Min temp."),
  unit = "micro_code",
  facet = TRUE,
  free_x_scale = TRUE
)

# Boxplots (one numeric, one categorical) - No grouping
plot_bivariate(
  dengue_MS,
  var = c("pop_density", "biome_name"),
  var_label = c("Pop. density", "Biome"),
  palette = "royalblue"
)

# Boxplots (one numeric, one categorical) - Grouping
plot_bivariate(
  dengue_MS,
  var = c("biome_name", "tmin"),
  unit = "meso_code",
  palette = "Accent"
)

Combine plots

Description

Combines plots, each representing one variable, into a single plot.

Usage

plot_combine(
  plot_list,
  combine_legend = FALSE,
  combine_xaxis = FALSE,
  ncol = 1,
  align = "v",
  ...,
  ncol_l = 2,
  nrow_l = NULL,
  rel_widths_l = c(3, 1),
  rel_heights_l = c(1, 1),
  ncol_legend = 1
)

Arguments

Details

This function takes any input from the cowplot::plot_grid function to customize the organization of the plots.

Value

A single (cow)plot including the provided multiple plots.

See Also

plot_compare, plot_multiple

Examples

# Load data
data("dengue_MS")

# Multiple time series plot
plots <- plot_multiple(
  plot_function = plot_timeseries,
  data = dengue_MS,
  var = c("tmax", "tmin", "pdsi"),
  type = c("cov", "cov", "cov"),
  aggregate_space = "meso_code",
  pop = "population",
  var_label = c("Max Temp", "Min Temp", "PDSI"),
  time = "date",
  unit = "micro_code"
)

# Combine them with a shared legend
plot_combine(
  plot_list = plots,
  ncol = 1,
  align = "v",
  combine_legend = TRUE,
  combine_xaxis = TRUE,
  rel_widths_l = c(7, 1)
)

Compare plots

Description

Combines multiple plots of several variables in a single graph.

Usage

plot_compare(plot_function, data, var, type, ...)

Arguments

Details

This function takes any input arguments from plot_combine() and plot_multiple() to customize the plots and their organization in a grid.

Value

A single (cow)plot containing plots of several variables.

See Also

plot_multiple, plot_combine

Examples

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

# Comparing time series plots
plot_compare(
  plot_function = plot_timeseries,
  data = dengue_MS,
  var = c("dengue_cases", "pdsi"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_code",
  var_label = c("Dengue inc", "PDSI"),
  combine_legend = TRUE,
  ncol_legend = 1,
  ncol = 1,
  align = "h"
)

# Comparing heatmaps plots
plot_compare(
  plot_function = plot_heatmap,
  data = dengue_MS,
  var = c("dengue_cases", "pdsi"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_code",
  var_label = c("Dengue Cases", "Min Temp"),
  palette = c("Reds", "Blues"),
  ncol_legend = 1,
  combine_xaxis = TRUE
)

# Comparing map plots
plot_compare(
  plot_function = plot_map,
  data = dengue_MS,
  var = c("dengue_cases", "tmax"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_code",
  var_label = c("Dengue Incidence", "Max Temperature"),
  palette = c("Reds", "Blues"),
  map = map_MS,
  map_unit = "code",
  aggregate_time = "all",
  ncol_legend = 1,
  combine_xaxis = TRUE
)

Correlation plot

Description

Plots a correlation matrix of a series of variables.

Usage

plot_correlation(
  data,
  var,
  var_label = NULL,
  method = "pearson",
  plot_type = c("circle", "number"),
  scale = 1,
  title = NULL,
  palette = "IDE1"
)

Arguments

Value

A plot of the correlation matrix.

Examples

# Load data
data("dengue_MS")

# Pearson correlation plot
plot_correlation(
  dengue_MS,
  method = "pearson",
  var = c(
    "dengue_cases",
    "pop_density",
    "tmax",
    "tmin",
    "pdsi",
    "urban",
    "water_network",
    "water_shortage"
  ),
  var_label = c(
    "dengue cases",
    "pop. density",
    "max temp",
    "min temp",
    "drought index",
    "urbanization",
    "water network",
    "water shortage"
  ),
  title = "Correlation matrix"
)

# Spearman correlation plot of type 'raster' and 'number'
plot_correlation(
  dengue_MS,
  method = "spearman",
  var = c(
    "dengue_cases",
    "pop_density",
    "tmax",
    "tmin",
    "pdsi",
    "urban",
    "water_network",
    "water_shortage"
  ),
  var_label = c(
    "dengue cases",
    "pop. density",
    "max temp",
    "min temp",
    "drought index",
    "urbanization",
    "water network",
    "water shortage"
  ),
  plot_type = c("raster", "number"),
  palette = "-Blue-Red 3"
)

Cross-correlation function (CCF) plot for two time series

Description

Plots the correlation between one time series and lagged versions of another across a range of lags.

Usage

plot_crosscorrelation(
  data,
  var,
  time,
  type = c("cov", "cov"),
  method = "pearson",
  lags = 0:9,
  pop = NULL,
  unit = NULL,
  aggregate_space = NULL,
  aggregate_space_fun = "mean",
  facet = FALSE,
  title = NULL,
  var_label = NULL,
  legend = NULL,
  ylab = NULL,
  xlab = NULL,
  palette = NULL,
  area = NULL
)

Arguments

Details

Correlations are computed after deleting missing rows in the lag data so that every correlation is computed with the same observations.

Value

A ggplot2 cross-correlation plot.

Examples

# Load data
data("dengue_MS")
data("dengue_SP")

# Cross-correlation (Pearson) between dengue counts and enso (single unit)
plot_crosscorrelation(
  data = dengue_SP,
  var = c("cases", "enso"),
  type = c("counts", "cov"),
  time = "date"
)

# Cross-correlation (Spearman) between dengue inc and tmax for multiple areas
plot_crosscorrelation(
  data = dengue_MS,
  var = c("dengue_cases", "tmax"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_name",
  method = "spearman"
)

# Cross-correlation (Kendall) between dengue inc and pdsi for multiple areas
# aggregated by mesoregion, faceted, with custom labels
plot_crosscorrelation(
  data = dengue_MS,
  var = c("dengue_cases", "pdsi"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_name",
  method = "kendall",
  aggregate_space = "meso_name",
  facet = TRUE,
  title = "Cross-correlation for meso areas"
)

Heatmap plot

Description

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

Usage

plot_heatmap(
  data,
  var,
  time,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  unit = 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,
  area = NULL
)

Arguments

Value

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",
  unit = "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",
  unit = "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,
  unit = "micro_code",
  aggregate_space = "meso_code",
  palette = "Purp"
)

Choropleth map

Description

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

Usage

plot_map(
  data,
  var,
  time,
  unit = NULL,
  map,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  map_unit = 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,
  coordgrid = FALSE,
  ...,
  area = NULL,
  map_area = NULL,
  by_year = NULL
)

Arguments

Value

A ggplot2 choropleth map.

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",
  unit = "micro_code",
  map = map_MS,
  map_unit = "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",
  unit = "micro_code",
  aggregate_time = "all",
  map = map_MS,
  map_unit = "code",
  palette = "Viridis",
  var_label = "Biome"
)

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

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

Multiple plot

Description

Produces a list of multiple plots of the same type, each representing one variable.

Usage

plot_multiple(plot_function, ...)

Arguments

Details

Variable names, types, labels, palette and transform can be customized for each plot, the rest of parameters will be the same for all variables (options depend on the chosen plot type).

Value

A list of the different generated plots.

See Also

plot_compare, plot_combine

Examples

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

# Multiple time series plots
plots <- plot_multiple(
  plot_function = plot_timeseries,
  data = dengue_MS,
  var = c("dengue_cases", "dengue_cases", "tmax"),
  type = c("counts", "inc", "cov"),
  pop = "population",
  var_label = c("Dengue Cases", "Dengue inc", "Max Temp"),
  palette = c("blue", "red", "darkgreen"),
  time = "date",
  unit = "micro_code",
  facet = TRUE
)

# Acess individual plots
print(plots[[1]])

# Multiple heatmap plots
plots <- plot_multiple(
  plot_function = plot_heatmap,
  data = dengue_MS,
  var = c("dengue_cases", "dengue_cases", "tmax"),
  type = c("counts", "inc", "cov"),
  pop = "population",
  var_label = c("Dengue Cases", "Dengue inc", "Max Temp"),
  palette = c("Blues", "Reds", "BrBG"),
  time = "date",
  unit = "micro_code"
)

# Multiple seasonality plots
plots <- plot_multiple(
  plot_function = plot_seasonality,
  data = dengue_MS,
  var = c("dengue_cases", "dengue_cases", "tmax"),
  type = c("counts", "inc", "cov"),
  pop = "population",
  var_label = c("Dengue Cases", "Dengue inc", "Max Temp"),
  palette = c("Blues", "Reds", "BrBG"),
  time = "date",
  unit = "micro_code"
)

# Multiple map plots
plots <- plot_multiple(
  plot_function = plot_map,
  data = dengue_MS,
  var = c("dengue_cases", "dengue_cases", "tmax"),
  type = c("counts", "inc", "cov"),
  pop = "population",
  var_label = c("Dengue Cases", "Dengue inc", "Max Temp"),
  palette = c("Reds", "Blues", "Viridis"),
  map = map_MS,
  map_unit = "code",
  time = "date",
  unit = "micro_code"
)

Seasonality plot

Description

Plots yearly time series of covariates, case counts, or incidence rates to explore seasonality patterns.

Usage

plot_seasonality(
  data,
  var,
  time,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  unit = 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,
  free_y_scale = FALSE,
  palette = "IDE1",
  area = NULL
)

Arguments

Value

A ggplot2 seasonality plot.

Examples

# Load data
data("dengue_MS")

# Seasonality plot of a covariate with space aggregation
plot_seasonality(
  dengue_MS,
  var = "tmax",
  time = "date",
  var_label = "Max temp.",
  type = "cov",
  unit = "micro_code",
  aggregate_space = "region_code"
)

# Plot case counts (log scale) with space aggregation
plot_seasonality(
  dengue_MS,
  var = "dengue_cases",
  time = "date",
  type = "counts",
  unit = "micro_code",
  aggregate_space = "meso_code",
  transform = "log10p1",
  var_label = "Monthly Dengue Cases",
  xlab = "Month",
  ylab = "Number of cases",
  free_y_scale = TRUE
)

# Seasonality plot of incidence
plot_seasonality(
  dengue_MS,
  var = "dengue_cases",
  time = "date",
  type = "inc",
  pop = "population",
  unit = "micro_code",
  pt = 1000,
  title = "Monthly Dengue Incidence",
  palette = "Reds"
)

Time series plot

Description

Plots time series of covariates, case counts, or incidence rates.

Usage

plot_timeseries(
  data,
  var,
  time,
  type = "cov",
  pop = NULL,
  pt = 1e+05,
  unit = NULL,
  aggregate_space = NULL,
  aggregate_time = NULL,
  aggregate_space_fun = "mean",
  aggregate_time_fun = "mean",
  facet = FALSE,
  highlight = NULL,
  transform = "identity",
  title = NULL,
  var_label = NULL,
  legend = NULL,
  ylab = NULL,
  xlab = NULL,
  free_y_scale = FALSE,
  palette = NULL,
  area = NULL
)

Arguments

Value

A ggplot2 time series plot.

See Also

plot_timeseries2 for dual-axis time series plots.

Examples

# Load data
data("dengue_MS")

# Plotting a covariate, all units in a single graph
plot_timeseries(
  dengue_MS,
  var = "tmin",
  time = "date",
  type = "cov",
  unit = "micro_code",
  title = "Minimun Temperature"
)

# Plotting a covariate with space aggregation and different facets
plot_timeseries(
  dengue_MS,
  var = "tmin",
  time = "date",
  type = "cov",
  unit = "micro_code",
  aggregate_space = "meso_code",
  aggregate_space_fun = "mean",
  facet = TRUE,
  var_label = "Minimum Temperature",
  palette = "violetred"
)

# Plotting counts, highlight a single unit
plot_timeseries(
  dengue_MS,
  var = "dengue_cases",
  time = "date",
  type = "counts",
  pop = "population",
  unit = "micro_code",
  title = "Dengue cases",
  highlight = "50001"
)

# Plot disease counts (log scale) with temporal and spatial aggregation
plot_timeseries(
  dengue_MS,
  var = "dengue_cases",
  time = "date",
  type = "counts",
  unit = "micro_code",
  aggregate_space = "meso_code",
  aggregate_time = "year",
  title = "Yearly Cases",
  transform = "log10"
)

# Plot incidence for 1,000 people and log y axis
plot_timeseries(
  dengue_MS,
  var = "dengue_cases",
  time = "date",
  type = "inc",
  pop = "population",
  unit = "micro_code",
  pt = 1000,
  transform = "log10p1"
)

Time series plot of two variables in two different axes

Description

Plots dual-axis time series of two covariates, case counts, or incidence rates.

Usage

plot_timeseries2(
  data,
  var,
  time,
  type = c("cov", "cov"),
  pop = NULL,
  pt = 1e+05,
  unit = NULL,
  aggregate_space = NULL,
  aggregate_time = NULL,
  aggregate_space_fun = "mean",
  aggregate_time_fun = "mean",
  align = "min",
  title = NULL,
  var_label = NULL,
  legend = "Variable",
  ylab = NULL,
  xlab = NULL,
  palette = c("#168c81", "#B98AFB"),
  alpha = 0.9,
  area = NULL
)

Arguments

Value

A dual-axis ggplot2 time series plot.

See Also

plot_timeseries for single-axis time series plots.

Examples

# Load data
data("dengue_MS")
data("dengue_SP")

# Plotting two covariates with temporal aggregation, align using the mean
plot_timeseries2(
  dengue_SP,
  var = c("temp_med", "precip_tot"),
  time = "date",
  align = "mean",
  aggregate_time = "month"
)

# Plotting case incidence and a covariate with temporal aggregation
# and customized colours and labels
plot_timeseries2(
  dengue_SP,
  var = c("cases", "precip_tot"),
  type = c("inc", "cov"),
  var_label = c("Incidence", "Precipitation"),
  title = "Precipitation and dengue incidence in Sao Paulo",
  time = "date",
  pop = "pop",
  aggregate_time = "month",
  palette = c("darkgreen", "royalblue"),
  alpha = 0.8
)

# Plotting case incidence and a covariate with spatial aggregation
plot_timeseries2(
  dengue_MS,
  var = c("dengue_cases", "pdsi"),
  type = c("inc", "cov"),
  pop = "population",
  time = "date",
  unit = "micro_code",
  aggregate_space = "meso_code"
)