Function reference • ggopenair

Data

In-built data to demonstrate ggopenair functions.

marylebone: Hourly Air Quality and Met Data for Marylebone Road, London

Directional Analysis

Polar Analysis

Examine the relationship between wind speed, wind direction and pollutant concentrations. Functions are split into “roses” with categorical colour axes and “polar” plots with continuous axes.

polar_annulus(): Bivariate polarAnnulus plot

polar_cluster(): K-means clustering of bivariate polar plots

polar_diff(): Polar plots considering changes in concentrations between two time periods

polar_freq(): Function to plot wind speed/direction frequencies and other statistics

polar_plot(): Function for plotting bivariate polar plots with smoothing.

rose_metbias(): Bias Rose

rose_percentile(): Function to plot percentiles by wind direction

rose_pollution(): Traditional wind rose plot

rose_wind(): Pollution rose variation of the traditional wind rose

Time Series and Trends

Examine how pollutant concentrations change with time.

trend_calendar(): Plot time series values in a conventional calendar format

trend_level(): Trend Heat Map

trend_prop(): Time series plot with categories shown as a stacked bar chart

trend_variation(): Diurnal, day of the week and monthly variation

Plot Utilities

Themes

theme_polar(): Default Polar Plot Theme

Scales

scale_opencolours_d() scale_opencolours_c() scale_opencolours_b(): Various colour scales from openair

scale_y_limitval() scale_x_limitval(): Position scales for continuous data (x & y) with labelled vertical/horizontal markers

Annotations

annotate_polar_axis(): Annotate a Polar Plot with Axis Labels

annotate_polar_wedge(): Annotate a Polar Plot with a Coloured Wedge

annotate_rose_text(): Annotate a wind or pollution rose with further information

annotate_calendar_text(): Annotate a Calendar Plot with Text

annotate_calendar_wd(): Annotate a Calendar Plot with Wind Direction Arrows

Other

quick_text(): Automatic text formatting for ggopenair

Data Utilities

Tools for manipulating and summarising air quality data.

time_average(): Function to calculate time averages for data frames

cut_wd(): Discretise wind speed into sectors

cut_date(): Discretise date-times into categories