GHRexplore (version 0.2.1)

plot_timeseries: 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,
  area = 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
)

Value

A ggplot2 time series plot.

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 (e.g., 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.

facet

If TRUE a separate time series for each space unit is plotted in different facets.

highlight

ID of the area to be highlighted. Using this option will only color the selected spatial unit and set all the rest to grey.

transform

Character, defaults to "identity" (i.e., no transformation). Transforms the y-axis 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.

legend

Character with a custom name for the legend.

ylab

Label for the y-axis.

xlab

Label for the x-axis.

free_y_scale

Logical, default FALSE. Allows different scales in the y_axis when facets are used.

palette

GHR, RColorBrewer or colorspace palette (e.g. "Purp"). Single R colors in colors() or hex codes can be used for single time series or facets. Use "-" before the palette name (e.g., "-Reds") to reverse it. Defaults to a dark green when area is NULL, when facet is TRUE or when highlight is used (i.e. single time series), otherwise defaults to the "IDE2" palette.

See Also

plot_timeseries2 for dual-axis time series plots.

Examples

# Load data
data("dengue_MS")

# Plotting a covariate, all areas in a single graph
plot_timeseries(dengue_MS,
                var = "tmin",
                time = "date",          
                type = "cov",
                area = "micro_code",   
                title = "Minimun Temperature") 
                
# Plotting a covariate with space aggregation and different facets
plot_timeseries(dengue_MS,
                var = "tmin",
                time = "date",
                type = "cov",
                area = "micro_code",
                aggregate_space = "meso_code",
                aggregate_space_fun = "mean",
                facet = TRUE,           
                var_label= "Minimum Temperature",
                palette = "violetred")
                
# Plotting counts, highlight a single area 
plot_timeseries(dengue_MS,
                var = "dengue_cases", 
                time = "date",         
                type = "counts",
                pop = "population",
                area = "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",
                area = "micro_code",   
                aggregate_space = "meso_code",
                aggregate_time = "year",
                title = "Yearly Cases",
                transform = "log10") 
               
# Plot incidence for 1,000 people with a Brewer palette and log y axis
plot_timeseries(dengue_MS,
                var = "dengue_cases",
                time = "date",          
                type = "inc",
                pop = "population",
                area = "micro_code",  
                pt = 1000,
                transform = "log10p1")