Skip to contents

Computes spatial empirical means using a dataframe or a stars object

spatial_means.Rd

This function computes spatial empirical means by latitude and longitude averaged over time. This function can take either a stars object or a dataframe. Input arguments differ for each case. The autoplot function can plot this object.

The variations are * 'spatial_means.data.frame()' if the input is a dataframe * 'spatial_means.stars()' if the input is a stars object * 'autoplot.spatialmeans()' to plot the outputs.

Usage

spatial_means(x, ...)

# S3 method for data.frame
spatial_means(x, lat_col, lon_col, t_col, z_col, ...)

# S3 method for stars
spatial_means(x, ...)

# S3 method for spatialmeans
autoplot(
  object,
  ylab = "Mean Value",
  xlab1 = "Latitude",
  xlab2 = "Longitude",
  title = "Spatial Empirical Means",
  ...
)

Arguments

x

A stars object or a dataframe. Arguments differ according to the input type.

...

Other arguments currently ignored.

lat_col

For dataframes: the column or the column name giving the latitude. The y coordinate can be used instead of latitude.

lon_col

For dataframes: the column or the column name giving the longitude. The x coordinate can be used instead of longitude.

t_col

For dataframes: the time column. Time must be a set of discrete integer values.

z_col

For dataframes: the The quantity of interest that will be plotted. Eg. temperature.

object

For autoplot: the output from the `spatial_means' function.

ylab

For autoplot: the ylabel.

xlab1

For autoplot: The xlabel for the first plot.

xlab2

For autuoplot: The xlabel for the second plot.

title

The graph title.

Value

A spatialmeans object contaiing spatial averages and the original data.

Examples

# dataframe example
data(NOAA_df_1990)
library(dplyr)
Tmax <- filter(NOAA_df_1990,                      # subset the data
              proc == "Tmax" &                   # extract max temperature
                month %in% 5:9 &                 # May to July
                year == 1993)                    # year 1993
Tmax$t <- Tmax$julian - min(Tmax$julian) + 1      # create a new time variable starting at 1
sp_df <- spatial_means(Tmax,
       lat_col = "lat",
       lon_col = "lon",
       t_col = "t",
       z_col = "z")
autoplot(sp_df)

#> TableGrob (2 x 2) "arrange": 3 grobs
#>   z     cells    name                 grob
#> 1 1 (2-2,1-1) arrange       gtable[layout]
#> 2 2 (2-2,2-2) arrange       gtable[layout]
#> 3 3 (1-1,1-2) arrange text[GRID.text.1127]

# stars examples
library(stars)
tif = system.file("tif/olinda_dem_utm25s.tif", package = "stars")
x <- read_stars(tif)
sp_means <- spatial_means(x)
#> Only one observation of each location is present. The mean is the original value itself!
autoplot(sp_means)

#> TableGrob (3 x 2) "arrange": 4 grobs
#>   z     cells    name                 grob
#> 1 1 (2-2,1-1) arrange       gtable[layout]
#> 2 2 (2-2,2-2) arrange       gtable[layout]
#> 3 3 (3-3,1-2) arrange       gtable[layout]
#> 4 4 (1-1,1-2) arrange text[GRID.text.1251]