Time series plot of two variables in two different axes

Description

Plots time series of two covariates, case counts, or incidence rates in two different axes.

plot_timeseries2(
  data,
  var,
  time,
  type = c("cov", "cov"),
  pop = NULL,
  pt = 1e+05,
  area = 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,
  free_y_scale = FALSE,
  palette = c("#168c81", "#B98AFB"),
  alpha = 0.9
)

Arguments

  • data: Data frame containing equally spaced (daily, weekly, monthly) covariate or disease case observations for one or multiple locations.
  • var: A character vector of length 2 (left axis, right axis) identifying the variables 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: A character vector of length 2 (left axis, right axis) that specifies the types 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.
  • align: Options to align the two plots. Defaults to “min”, which forces the minimum of the two variables to be aligned. Other options include “mean” and “median”.
  • title: Optional title of the plot.
  • var_label: A character vector of length 2 (left axis, right axis) with custom names for the case or covariate variable.
  • legend: Character with a custom name for the legend.
  • ylab: A character vector of length 2 (left, right) for the y-axes.
  • xlab: Label for the x-axis.
  • free_y_scale: Logical, default FALSE. Allows different scales in the y_axis when facets are used.
  • palette: A character vector of length 2 (left axis, right axis) indicating the colours (R or hex codes) to use for each of the two variables).
  • alpha: Numerical between 0 and 1 determining the transparency of the lines.

Returns

A 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",
                 area = "micro_code",
                 aggregate_space = "meso_code")