Kenya Census
January 19, 2021
Last updated: 2021-09-24
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Knit directory: myTidyTuesday/
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rKenyaCensus
The data this week was compiled by Shel Kariuki from figures released by the Kenya National Bureau of Statistics in February 2020, originally published in four different pdf files (Volume 1 - Volume 4). Beyond the sample #TidyTuesday sets, I’ve pulled additional agricultural figures from her web data portal and rKenyaCensus package.
the Tidytuesday tweet:
#TidyTuesday Week04
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) January 20, 2021
Poultry 🐔farming 👩🌾in Kenya 🇰🇪
code: https://t.co/ARmfFPwRm6 #RStats #R4DS
Thanks to @Shel_Kariuki for her awesome work on the #rKenyaCensus package pic.twitter.com/gSaaWjiAYY
shapefiles <- rKenyaCensus::KenyaCounties_SHP %>%
sf::st_as_sf() %>%
st_simplify(dTolerance = 1000)
# Distribution of area (hectares) of Agricultural land and Farming Households by purpose of production, County and Sub-County
ag_land <- rKenyaCensus::V4_T2.25 %>%
dplyr::filter(AdminArea == "County") %>%
select(
County, LandSize_Subsistence, LandSize_Commercial,
No.FHS_Subsistence, No.FHS_Commercial
) %>%
mutate(
AvgArea_Subsistence =
if_else(LandSize_Subsistence > 0.1,
No.FHS_Subsistence / LandSize_Subsistence,
NA_real_
),
AvgArea_Commercial =
if_else(LandSize_Commercial > 0.1,
No.FHS_Commercial / LandSize_Commercial,
NA_real_
)
) %>%
pivot_longer(-County,
names_sep = "_",
names_to = c("Metric", "FarmType")
)
ag_area_sf <- shapefiles %>%
inner_join(ag_land, by = "County") %>%
mutate(County = stringr::str_to_title(County))
landsize_dorling <-
cartogram_dorling(
x = filter(ag_area_sf, Metric == "LandSize"),
weight = "value",
k = 0.5,
itermax = 100
)
families_dorling <-
cartogram_dorling(
x = filter(ag_area_sf, Metric == "No.FHS"),
weight = "value",
k = 0.5,
itermax = 100
)subs <- sf::st_drop_geometry(landsize_dorling) %>%
group_by(FarmType) %>%
summarize(Total = sum(value))
landsize_dorling %>%
left_join(subs, by = "FarmType") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 8, County, NA_character_)) %>%
ggplot(aes(fill = value)) +
geom_sf(color = NA) +
geom_sf(
data = filter(ag_area_sf, Metric == "LandSize"),
color = "grey30", fill = NA, alpha = 0.3
) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
color = "gray50",
segment.color = "gray50",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
geom_text(aes(label = paste0(scales::comma_format()(Total), " ha")),
x = st_bbox(landsize_dorling)[1] + 0.05 * (st_bbox(landsize_dorling)[3] - st_bbox(landsize_dorling)[1]),
y = st_bbox(landsize_dorling)[2],
hjust = 0,
family = "Roboto Condensed",
size = 4
) +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5,
label = scales::comma_format(),
trans = "log10", guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Hectares",
title = "Crop Farming in Kenya, by County",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
subs <- sf::st_drop_geometry(families_dorling) %>%
group_by(FarmType) %>%
summarize(Total = sum(value))
families_dorling %>%
left_join(subs, by = "FarmType") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 7, County, NA_character_)) %>%
ggplot(aes(fill = value)) +
geom_sf(color = NA) +
geom_sf(
data = dplyr::filter(ag_area_sf, Metric == "No.FHS"),
color = "grey30", fill = NA, alpha = 0.3
) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
colour = "gray50",
segment.colour = "gray50",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
geom_text(aes(label = paste0(scales::comma_format()(Total), " households")),
x = st_bbox(landsize_dorling)[1] + 0.05 * (st_bbox(landsize_dorling)[3] - st_bbox(landsize_dorling)[1]),
y = st_bbox(landsize_dorling)[2],
hjust = 0,
family = "Roboto Condensed",
size = 4
) +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
label = scales::comma_format(),
limits = c(5e4, 2.5e5), n.breaks = 5,
guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Households",
title = paste0("Farmers ", emojifont::emoji("woman_farmer"), " raising crops in Kenya, by County"),
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
ag_area_sf %>%
filter(Metric == "AvgArea") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 8, County, NA_character_)) %>%
ggplot() +
geom_sf(aes(fill = value), color = "grey30", alpha = 0.8) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
colour = "black",
segment.colour = "black",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5,
label = scales::label_number(), guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Average \nFarm Size \nHectares",
title = "Crop Farming in Kenya, by County",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
Let’s take a close look at the livestock datasets, and focus first on poultry. As a general rule, broilers are breeds raised commercially for meat and layers are raised commercially for eggs. The meat and eggs are packaged and subsequently sold at markets, often in more urban areas. Indigenous chicken breeds are often fed and raised in small numbers as part of a household.
livestock <- rKenyaCensus::V4_T2.24 %>%
filter(AdminArea == "County") %>%
select(-SubCounty, -AdminArea) %>%
rename(
"Dairy" = "ExoticCattle_Dairy",
"Beef" = "ExoticCattle_Beef",
"Layers" = "ExoticChicken_Layers",
"Broilers" = "ExoticChicken_Broilers"
) %>%
rename_with(~ paste0(., "_stock"), Farming:FishCages)
livestock <- rKenyaCensus::V4_T2.23 %>%
filter(AdminArea == "County") %>%
select(-SubCounty, -AdminArea) %>%
rename(
"Dairy" = "ExoticCattle_Dairy",
"Beef" = "ExoticCattle_Beef",
"Layers" = "ExoticChicken_Layers",
"Broilers" = "ExoticChicken_Broilers"
) %>%
rename_with(~ paste0(., "_households"), Farming:FishCages) %>%
inner_join(livestock, by = "County") %>%
pivot_longer(-County,
names_sep = "_",
names_to = c("Industry", "Metric")
) %>%
pivot_wider(
names_from = "Metric",
values_from = "value"
) %>%
mutate(per_household = stock / households) %>%
remove_missing() %>%
filter(Industry != "Farming") %>% # remove the County subtotals
complete(County, Industry, fill = list(
households = 1,
stock = 1,
per_household = 0.01
))
livestock <- livestock %>%
filter(Industry %in% c("IndigenousChicken", "Layers", "Broilers")) %>%
mutate(Industry = str_replace_all(Industry, "([a-z])([A-Z])", "\\1 \\2"))
stock_ls_dorling <- cartogram_dorling(
x = shapefiles %>%
inner_join(livestock, by = "County"),
weight = "stock",
k = 1,
itermax = 100
)
shapefiles %>%
inner_join(livestock, by = "County") %>%
mutate(Metric = "per_household") %>%
group_by(Industry, Metric) %>%
mutate(CountyLabel = if_else(ntile(per_household, 10) > 8, County, NA_character_)) %>%
ungroup() %>%
ggplot() +
geom_sf(aes(fill = per_household), color = "grey30") +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5, limits = c(0, 100),
label = scales::label_number(), guide = "legend"
) +
geom_sf(
data = stock_ls_dorling,
fill = "midnightblue",
# color = NULL,
alpha = 0.3
) +
coord_sf(label_axes = "----") +
guides(size = guide_legend()) +
facet_wrap(~Industry, ncol = 3) +
labs(
x = NULL, y = NULL, fill = "Average count \nper farming household",
title = paste0("Poultry ", emojifont::emoji("chicken"), " Farming in Kenya"),
subtitle = "Many egg laying chickens are produced near Nairobi. Elsewhere, households keep \nsmall numbers of indigenous birds. The circle size represents the total birds.",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rKenyaCensus_0.0.2 spdep_1.1-11 spData_0.3.10 sp_1.4-5
[5] cartogram_0.2.2 sf_1.0-2 tweetrmd_0.0.9 tidytuesdayR_1.0.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 tidyverse_1.3.1
[17] hrbrthemes_0.8.0 waffle_0.7.0 ggplot2_3.3.5 systemfonts_1.0.2
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 proto_1.0.0 R.utils_2.10.1
[4] tidyselect_1.1.1 grid_4.1.1 pROC_1.18.0
[7] munsell_0.5.0 codetools_0.2-18 ragg_1.1.3
[10] units_0.7-2 dials_0.0.10 future_1.22.1
[13] withr_2.4.2 colorspace_2.0-2 highr_0.9
[16] knitr_1.34 rstudioapi_0.13 wk_0.5.0
[19] Rttf2pt1_1.3.9 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.28.0 DiceDesign_1.9 farver_2.1.0
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[28] LearnBayes_2.15.1 vctrs_0.3.8 generics_0.1.0
[31] ipred_0.9-12 xfun_0.26 R6_2.5.1
[34] lhs_1.1.3 cachem_1.0.6 assertthat_0.2.1
[37] showtext_0.9-4 promises_1.2.0.1 scales_1.1.1
[40] nnet_7.3-16 emojifont_0.5.5 gtable_0.3.0
[43] globals_0.14.0 timeDate_3043.102 rlang_0.4.11
[46] workflows_0.2.3 splines_4.1.1 extrafontdb_1.0
[49] yardstick_0.0.8 broom_0.7.9 s2_1.0.6
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[61] ellipsis_0.3.2 raster_3.4-13 jquerylib_0.1.4
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[67] parsnip_0.1.7.900 plyr_1.8.6 classInt_0.4-3
[70] rpart_4.1-15 deldir_0.2-10 viridis_0.6.1
[73] haven_2.4.3 ggrepel_0.9.1 fs_1.5.0
[76] here_1.0.1 furrr_0.2.3 magrittr_2.0.1
[79] gmodels_2.18.1 reprex_2.0.1 GPfit_1.0-8
[82] whisker_0.4 R.cache_0.15.0 packcircles_0.3.4
[85] hms_1.1.0 evaluate_0.14 readxl_1.3.1
[88] gridExtra_2.3 compiler_4.1.1 KernSmooth_2.23-20
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[100] dbplyr_2.1.1 MASS_7.3-54 boot_1.3-28
[103] Matrix_1.3-4 cli_3.0.1 R.methodsS3_1.8.1
[106] gdata_2.18.0 parallel_4.1.1 gower_0.2.2
[109] pkgconfig_2.0.3 recipes_0.1.16 xml2_1.3.2
[112] foreach_1.5.1 bslib_0.3.0 hardhat_0.1.6
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[118] showtextdb_3.0 rmarkdown_2.11 cellranger_1.1.0
[121] gdtools_0.2.3 curl_4.3.2 gtools_3.9.2
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