plot_exposure: Plot exposure to applied pesticides at a location.

In leighseverson/purexposure: Pull and Calculate Exposure to CA Pesticide Use Registry Records

Description

plot_exposure returns a plot of pesticide application in the PLS units intersected by a buffer for each combination of time period, applied active ingredients, and application method relevant for the exposure values returned from calculate_exposure.

Usage

plot_exposure(
  exposure_list,
  color_by = "amount",
  buffer_or_county = "county",
  percentile = c(0.25, 0.5, 0.75),
  fill = "viridis",
  alpha = 0.7,
  pls_labels = FALSE,
  pls_labels_size = 4
)

Arguments

exposure_list	A list returned from calculate_exposure.
color_by	Either "amount" (the default) or "percentile". Specifies whether you would like application amounts to be colored according to amount, resulting in a gradient legend, or by the percentile that they fall into for the given data set and date range. You can specify percentile cutpoints with the percentile argument.
buffer_or_county	Either "county" (the default) or "buffer". Specifies whether you would like colors to be scaled according to the limits of application within the buffer, or in the county for the same time period, chemicals, and method of application.
percentile	A numeric vector in (0, 1) specifying percentile cutpoints if color_by = "percentile". The default is c(0.25, 0.5, 0.75), which results in four categories: < 25th percentile, >= 25th to < 50th, >= 50th to < 75th, and >= 75th.
fill	A palette from the colormap package. The default is "viridis". To see colormap palette options, visit https://bhaskarvk.github.io/colormap/ or run colormap::colormaps.
alpha	A number in [0,1] specifying the transparency of fill colors. Numbers closer to 0 will result in more transparency. The default is 0.7.
pls_labels	TRUE / FALSE for whether you would like sections or townships to be labeled with their PLS ID. The default is FALSE.
pls_labels_size	A number specifying the size of PLS labels. The default is 4.

Value

A list with the following elements:

maps

A list of plots. One plot for each exposure value returned in the exposure element of the calculate_exposure list.

pls_data

A list of data frames with 12 columns: pls, giving the PLS ID, percent, the buffer, kg, the amount of kg of pesticides applied in that PLS unit for the relevant time period, chemicals, and application method, kg_intersection, kg multiplied by percent (this is the value that is plotted), start_date, end_date, chemicals, aerial_ground, which give the time period, chemicals, and application method for each plot/exposure estimate, none_recorded, location, radius (m), and area (m^2).

cutoff_values

A list of data frames with two columns: percentile and kg giving the cutoff values for each percentile. Only returned if color_by = "percentile".

Examples

library(magrittr)

fresno_list <- readRDS(system.file("extdata", "exposure_ex.rds",
                                   package = "purexposure")) %>% plot_exposure()

tulare_list <- pull_clean_pur(2010, "tulare") 
   calculate_exposure(location = "-119.3473, 36.2077", radius = 3500) 
   plot_exposure()
names(tulare_list)
tulare_list$maps
tulare_list$pls_data
tulare_list$exposure

# return one plot, pls_data data frame, exposure row, and cutoff_values
# data frame for each exposure combination

dalton_list <- pull_clean_pur(2000, "modoc") 
    calculate_exposure(location = "-121.4182, 41.9370",
                       radius = 4000,
                       time_period = "6 months",
                       aerial_ground = TRUE) 
    plot_exposure(fill = "plasma")
do.call("rbind", dalton_list$exposure)
# one map for each exposure value (unique combination of chemicals,
# dates, and aerial/ground application)
dalton_list$maps[[1]]
dalton_list$maps[[2]]
dalton_list$maps[[3]]
dalton_list$maps[[4]]
dalton_list$maps[[5]]
dalton_list$maps[[6]]

# exposure to a particular active ingredient
# plot percentile categories instead of amounts
chemical_df <- rbind(find_chemical_codes(2009, c("metam-sodium"))) 
     dplyr::rename(chemical_class = chemical)

santa_maria <- pull_clean_pur(2008:2010, "santa barbara",
                              chemicals = chemical_df$chemname,
                              sum_application = TRUE,
                              sum = "chemical_class",
                              chemical_class = chemical_df) 
     calculate_exposure(location = "-119.6122, 34.90635",
                        radius = 3000,
                        time_period = "1 year",
                        chemicals = "chemical_class") 
     plot_exposure(color_by = "percentile")
do.call("rbind", santa_maria$exposure)
santa_maria$maps[[1]]
santa_maria$maps[[2]]
santa_maria$maps[[3]]

# scale colors based on buffer or county
clotho <- pull_clean_pur(1996, "fresno") 
  dplyr::filter(chemname == "SULFUR") 
  calculate_exposure(location = "-119.6082, 36.7212",
                    radius = 1500)

plot_exposure(clotho, "amount", buffer_or_county = "county", pls_labels = TRUE)$maps
plot_exposure(clotho, "amount", buffer_or_county = "buffer", pls_labels = TRUE)$maps

leighseverson/purexposure documentation built on Aug. 13, 2021, 6:34 p.m.