malariaAtlas (version 1.6.3)

extractRaster: Extract pixel values from MAP rasters using point coordinates.

Description

extractRaster extracts pixel values from MAP rasters at user-specified point locations (without downloading the entire raster).

Usage

extractRaster(
  df,
  csv_path = NULL,
  surface = NULL,
  year = NULL,
  dataset_id = NULL
)

Value

extractRaster returns the input dataframe (df), with the following columns appended, providing values for each raster, location and year.

1. layerName dataset id corresponding to extracted raster values for a given row, check listRaster for raster metadata.

2. year the year for which raster values were extracted (time-varying rasters only; static rasters do not have this column).

3. value the raster value for the pixel in which a given point location falls.

Arguments

df

data.frame containing coordinates of input point locations, must contain columns named 'latitude'/'lat'/'x' AND 'longitude'/'long'/'y')

csv_path

(optional) user-specified path to which extractRaster coordinates and results are stored.

surface

deprecated argument. Please remove it from your code.

year

for time-varying rasters: if downloading a single surface for one or more years, year should be a vector specifying the desired year(s). if downloading more than one surface, use a list the same length as surface, providing the desired year-range for each time-varying surface in surface or NA for static rasters.

dataset_id

A character string specifying the dataset ID(s) of one or more rasters. These dataset ids can be found in the data.frame returned by listRaster, in the dataset_id column e.g. c('Malaria__202206_Global_Pf_Mortality_Count', 'Malaria__202206_Global_Pf_Parasite_Rate')

See Also

autoplot method for quick mapping of PR point locations (autoplot.pr.points).

Examples

#Download PfPR data for Nigeria and Cameroon and map the locations of these points using autoplot
if (FALSE) {
# Get some data and remove rows with NAs in location or examined or positive columns.
NGA_CMR_PR <- getPR(country = c("Nigeria", "Cameroon"), species = "Pf")
complete <- complete.cases(NGA_CMR_PR[, c(4, 5, 16, 17)])
NGA_CMR_PR <- NGA_CMR_PR[complete, ]

# Extract PfPR data at those locations.
data <- extractRaster(df = NGA_CMR_PR[, c('latitude', 'longitude')],
                      dataset_id = 'Malaria__202206_Global_Pf_Parasite_Rate',
                      year=2020)

# Some rasters are stored with NA encoded as -9999
data$value[data$value == -9999] <- NA

# We can quickly plot a summary
plot((NGA_CMR_PR$positive / NGA_CMR_PR$examined) ~ data$value)
}