This document is used to produce some tables from the Uganda livestock test model output. The tables can be compared with other model revisions.

Human Sanitation Attribution

Print the attribution of sanitation types to the surface water emissions.

fpath <- testthat::test_path("output/uga_livestock/human_sources_water_crypto_uga.csv")
df_human_attribution <- read.csv(fpath)
n_digits <- 5
df_print <- df_human_attribution %>% 
  dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE) %>%
  dplyr::rename_with(~ stringr::str_trunc(.x, 10))
df_print %>% dplyr::select(iso, 2:8)
iso bucketL… compost… contain… flushOpen flushPit flushSe… flushSewer
800 2.3622e+13 5.4187e+12 0 1.4735e+13 4.3119e+13 6.9398e+13 1.5009e+14
df_print %>% dplyr::select(iso, 9:14)
iso flushUn… hanging… openDef… other pitNoSlab pitSlab
800 4.4205e+13 4.7244e+13 2.0018e+15 2.3622e+14 5.4371e+15 1.0926e+15

Livestock Attribution

Print the attribution of livestock types to the surface water emissions.

fpath <- testthat::test_path("output/uga_livestock/livestock_sources_water_crypto_uga.csv")
df_livestock_attribution <- read.csv(fpath) 
df_print <- df_livestock_attribution %>% 
  dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE)
df_print %>% dplyr::select(iso, 2:6)
iso asses buffaloes camels cattle goats
800 1.2082e+15 0 7.5169e+14 2.9005e+19 5.4526e+18
df_print %>% dplyr::select(iso, 7:11)
iso horses mules pigs poultry sheep
800 3.1775e+11 0 1.0654e+18 2.3874e+18 2.6995e+17

Surface Water Pathways

Print the attribution from various pathways to the surface water emissions.

fpath <- testthat::test_path("output","uga_livestock","surface_water_emissions_crypto_uga.csv")
df_pathways_water <- read.csv(fpath)
df_pathways_water %>% 
  dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE)
iso humans land wwtp
800 9.0791e+15 3.8182e+19 6.8132e+13

Land Pathways

Print the attribution from various pathways to land emissions.

fpath <- testthat::test_path("output","uga_livestock","land_emissions_crypto_uga.csv")
df_pathways_land <- read.csv(fpath)
df_pathways_land %>% 
  dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE)
iso humans livestock manure_storage
800 7.2905e+14 1.364e+21 1.6328e+20

Maps

fpath <- testthat::test_path("output","uga_livestock","pathways.tif")
rast_pathways <- terra::rast(fpath)
terra::plot(log10(rast_pathways), col = hcl.colors(50, palette = "Geyser"), buffer = TRUE)

Compare Gridded vs Tabular

rast_zones <-
  terra::rast(system.file("extdata/uga_livestock/isoraster_uga.tif", package =
                            "glowpa"))
zonal_sums <- terra::zonal(rast_pathways, rast_zones, fun = "sum", na.rm = TRUE)
zonal_sums %>% dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE) %>% 
  dplyr::select(dplyr::contains(c("livestock","manure")))
livestock2manure_storage livestock2other livestock2land manure_storage2land
8.4036e+20 NaN 1.364e+21 1.6328e+20
fpath <-
  testthat::test_path("output/uga_livestock/land_emissions_crypto_uga.csv")
df_land <- read.csv(fpath)
df_land %>% 
  dplyr::mutate_if(is.numeric, format, digits = n_digits, scientific = TRUE)
iso humans livestock manure_storage
800 7.2905e+14 1.364e+21 1.6328e+20