ggyearday: ggplot2 heatmap for diurnal-yearly time series

In Ostluft/rOstluft.plot: Ploting air quality data

ggplot2 heatmap for diurnal-yearly time series

Description

creates a heatmap with date on x-axis and time of day on y-axis; z values as fill scale.

Usage

ggyearday(
  data,
  time,
  z,
  date_breaks = "1 month",
  date_labels = "%b",
  ybreaks = seq(6, 18, 6),
  ylabels = format_sprintf("%02d:00"),
  fill_scale = scale_fill_viridis_c(direction = -1, na.value = NA, option = "A"),
  ...
)

Arguments

data	a data.frame or tibble with input data (containing a POSIXct variable as time parameter).
time	symbol giving time column
z	symbol giving z column used as fill
date_breaks	character string as input for ggplot2::scale_x_date(), e.g. '1 month', defines date breaks on x-axis.
date_labels	character string as input for ggplot2::scale_x_date(), formatter for date labels on x-axis.
ybreaks	numeric vector, specifies y-axis breaks.
ylabels	function, format function for y-axis labels.
fill_scale	ggplot2 continuous fill scale, e.g. scale_fill_gradient().
...	other arguments passed on to ggplot2::geom_raster().

Value

ggplot

Examples

library(ggplot2)

fn <- rOstluft.data::f("Zch_Stampfenbachstrasse_2010-2014.csv")

# only 4 years for smaller plot size in examples
df <-
  rOstluft::read_airmo_csv(fn) %>%
  dplyr::filter(starttime < lubridate::ymd(20140101)) %>%
  rOstluft::rolf_to_openair()

ggyearday(df, time = "date", z = "O3")

# data with outliers / extreme values => not very informative...
ggyearday(df, time = date, z = PM10)

# ...use a custom scale and squish the outliers / extreme values
fill_scale <- scale_fill_viridis_squished(breaks=c(0, 25, 50, 75), limits = c(0, 75),
                                          direction = -1, na.value = NA, option = "A")

ggyearday(df, time = date, z = PM10, fill_scale = fill_scale)

Ostluft/rOstluft.plot documentation built on Jan. 26, 2025, 1:05 a.m.