Kids Spending
Jim Gruman
September 22, 2020
Last updated: 2021-09-11
Checks: 7 0
Knit directory: myTidyTuesday/
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| Rmd | 22b0c6a | opus1993 | 2021-09-11 | give credit to Hill for geojson template |
The data for this week was provided by the US Census Bureau from their State and Local Government finance survey
The Carto platform host connections to geospatial datasets and the geojson format featured here. The web site credits AndrewXHill as the creator.
df_kids_health <-
df_kids %>%
filter(variable == "pubhealth") %>%
group_by(state) %>%
mutate(
diff = inf_adj_perchild[which(year == max(year))] - inf_adj_perchild[which(year == min(year))],
diff_cat = case_when(
diff < -.1 ~ 1, ## considerably lower
diff >= -.1 & diff < 0 ~ 2, ## slightly lower
diff >= 0 & diff <= .2 ~ 3, ## slightly higher
diff > .2 & diff <= 0.8 ~ 4, ## considerably higher
diff > 0.8 ~ 5, ## considerably higher
),
diff_cat = factor(diff_cat, levels = 1:5)
)
## HEX MAP #####################################################################
# the geojson is downloadable from https://team.cartodb.com/u/andrew/tables/andrew.us_states_hexgrid/public/map
map_hex <- geojson_read(
here::here("data", "us_states_hexgrid.geojson"),
what = "sp"
)
map_hex@data <- map_hex@data %>%
mutate(google_name = gsub(" \\(United States\\)", "", google_name))
map_hex_fortified <- tidy(map_hex, region = "google_name")
## smaller polygons for frame
map_hex_buffer <-
gBuffer(map_hex, width = -.15, byid = T)
map_hex_buffer_fortified <- tidy(map_hex_buffer, region = "google_name")
## calculate centroids
centr <- cbind.data.frame(data.frame(gCentroid(map_hex_buffer, byid = T), id = map_hex@data$google_name))
## calculate lines based on centroids
df_kids_tl <-
df_kids_health %>%
left_join(centr, by = c("state" = "id")) %>%
mutate(
year_rel = year - (min(year) + (max(year) - min(year)) / 2),
year_x = x + year_rel / 5.5,
inf_adj_perchild_rel = inf_adj_perchild - (min(inf_adj_perchild) + (max(inf_adj_perchild) - min(inf_adj_perchild)) / 200) - 1.5,
inf_y = y + inf_adj_perchild_rel / 2
) %>%
mutate(base = y + min(inf_adj_perchild_rel) / 2)
## centroids for labels
df_kids_labels <-
df_kids_health %>%
left_join(map_hex@data[, 6:7], by = c("state" = "google_name")) %>%
left_join(centr, by = c("state" = "id")) %>%
mutate(
y = case_when(
iso3166_2 == "ME" ~ y + .3,
iso3166_2 == "WY" ~ y + .7,
iso3166_2 == "DC" ~ y - .4,
iso3166_2 == "HI" ~ y + .4,
iso3166_2 == "DE" ~ y + .1,
TRUE ~ y
)
)
## combine data
df_kids_hex_buffer <- map_hex_buffer_fortified %>%
left_join(df_kids_health, by = c("id" = "state"))ggplot(
data = df_kids_tl,
aes(year_x, inf_y, group = state)
) +
geom_polygon(
data = df_kids_hex_buffer,
aes(long, lat,
group = id,
color = diff_cat
),
fill = "grey9", size = 2.4
) +
geom_polygon(
data = map_hex_fortified,
aes(long, lat,
group = id
),
color = "grey18",
fill = "transparent", size = 1
) +
geom_line(
color = "grey72", size = .2
) +
geom_point(
color = "white",
size = .1
) +
geom_text(
data = df_kids_tl %>% filter(year %in% c(1997, 2016)),
aes(
label = {
format(round(inf_adj_perchild, 2), digits = 2)
},
hjust = year == 1997
),
family = "IBM Plex Sans",
color = "grey62",
size = 1,
nudge_y = .2
) +
geom_text(
data = df_kids_labels %>% group_by(iso3166_2) %>% slice(1),
aes(x, y,
label = iso3166_2, color = diff_cat,
color = after_scale(colorspace::lighten(color, .2))
),
family = "IBM Plex Sans",
size = 2.4,
fontface = "bold"
) +
geom_richtext(
data = tibble(
x = -103.9388, y = 53.8,
label =
"<b style='color:#f5f5f5;font-size:15pt;'>US Spending Trends in Children Public Health 1997—2016</b><br><br>
Each hexagon shows the change in public health spending on children by state and year per child in 1,000 constant dollars.<br><b style='color:#d7191c;'>Rhode Island</b>, <b style='color:#d7191c;'>Tennessee </b>, <b style='color:#d7191c;'>Kansas</b>, <b style='color:#d7191c;'>Alabama</b>, and <b style='color:#d7191c;'>Rhode Island</b> spent less in 2016 compared to 1997 while <b style='color:#1a9641;'>Vermont</b>,<br>the <b style='color:#1a9641;'>District of Columbia</b>, <b style='color:#1a9641;'>Wyoming</b>, <b style='color:#1a9641;'>North Dakota</b>, <b style='color:#1a9641;'>Arizona</b>, <b style='color:#1a9641;'>Pennsylvania</b>, <b style='color:#1a9641;'>Delaware</b>, and <b style='color:#1a9641;'>California</b> increased the amount by far."
),
aes(x, y, label = label),
inherit.aes = F,
family = "IBM Plex Sans",
size = 2.7,
color = "grey72",
fill = NA,
label.color = NA,
hjust = .5
) +
geom_richtext(
data = tibble(
x = -82,
y = 30,
text = "<b style='font-size:7pt;'>Change in Public Health Efforts<br>from 1997 to 2016 per Child:</b><br><br>
<span style='color:#1a9641;'>• Increase by more than K$800</span><br>
<span style='color:#a6d96a;'>• Increase by K$200 to K$800</span><br>
<span style='color:#ffffbf;'>• Increase by up to K$200</span><br>
<span style='color:#fdae61;'>• Decrease by up to K$100</span><br>
<span style='color:#d7191c;'>• Decrease by more than K$100</span>"
),
aes(x, y, label = text),
inherit.aes = F,
family = "IBM Plex Sans",
color = "grey72",
size = 2,
fontface = "bold",
hjust = 0,
fill = "grey12",
lineheight = 1.3,
label.color = NA,
label.padding = unit(rep(.6, 4), "lines")
) +
coord_map() +
scale_color_manual(
values = c("#d7191c", "#fdae61", "#ffffbf", "#a6d96a", "#1a9641"),
guide = "none"
) +
labs(caption = "Visualization by Jim Gruman • Data: Census Bureau's Annual State and Local Government Finance Survey (Expenditure Variable E32)") +
theme_void() +
theme(
plot.background = element_rect(fill = "grey18", color = NA),
plot.caption = element_text(
family = "IBM Plex Sans", color = "grey72",
size = 6, hjust = .5,
margin = margin(b = 10, t = 15)
)
)
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] showtext_0.9-4 showtextdb_3.0 sysfonts_0.8.5 ggtext_0.1.1
[5] rgeos_0.5-7 sp_1.4-5 broom_0.7.9 geojsonio_0.9.4
[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 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 R.utils_2.10.1 tidyselect_1.1.1
[4] grid_4.1.1 maptools_1.1-2 pROC_1.18.0
[7] munsell_0.5.0 jqr_1.2.1 codetools_0.2-18
[10] ragg_1.1.3 units_0.7-2 dials_0.0.9.9000
[13] future_1.22.1 withr_2.4.2 colorspace_2.0-2
[16] highr_0.9 knitr_1.34 rstudioapi_0.13
[19] Rttf2pt1_1.3.9 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.28.0 bit64_4.0.5 DiceDesign_1.9
[25] farver_2.1.0 rprojroot_2.0.2 parallelly_1.28.1
[28] vctrs_0.3.8 generics_0.1.0 ipred_0.9-11
[31] xfun_0.25 R6_2.5.1 markdown_1.1
[34] lhs_1.1.3 cachem_1.0.6 geojson_0.3.4
[37] assertthat_0.2.1 promises_1.2.0.1 scales_1.1.1
[40] vroom_1.5.4 nnet_7.3-16 gtable_0.3.0
[43] globals_0.14.0 timeDate_3043.102 rlang_0.4.11
[46] workflows_0.2.3 systemfonts_1.0.2 splines_4.1.1
[49] rgdal_1.5-25 extrafontdb_1.0 lazyeval_0.2.2
[52] yardstick_0.0.8 selectr_0.4-2 yaml_2.2.1
[55] modelr_0.1.8 backports_1.2.1 httpuv_1.6.2
[58] gridtext_0.1.4 extrafont_0.17 tools_4.1.1
[61] lava_1.6.10 usethis_2.0.1 infer_1.0.0
[64] ellipsis_0.3.2 jquerylib_0.1.4 proxy_0.4-26
[67] Rcpp_1.0.7 parsnip_0.1.7.900 plyr_1.8.6
[70] classInt_0.4-3 rpart_4.1-15 viridis_0.6.1
[73] haven_2.4.3 hrbrthemes_0.8.0 fs_1.5.0
[76] here_1.0.1 furrr_0.2.3 crul_1.1.0
[79] magrittr_2.0.1 reprex_2.0.1 GPfit_1.0-8
[82] whisker_0.4 R.cache_0.15.0 hms_1.1.0
[85] evaluate_0.14 readxl_1.3.1 gridExtra_2.3
[88] compiler_4.1.1 maps_3.3.0 KernSmooth_2.23-20
[91] V8_3.4.2 crayon_1.4.1 geojsonsf_2.0.1
[94] R.oo_1.24.0 htmltools_0.5.2 later_1.3.0
[97] tzdb_0.1.2 tidymodels_0.1.3 lubridate_1.7.10
[100] DBI_1.1.1 dbplyr_2.1.1 MASS_7.3-54
[103] sf_1.0-2 Matrix_1.3-4 cli_3.0.1
[106] R.methodsS3_1.8.1 parallel_4.1.1 gower_0.2.2
[109] pkgconfig_2.0.3 foreign_0.8-81 recipes_0.1.16
[112] xml2_1.3.2 foreach_1.5.1 bslib_0.3.0
[115] hardhat_0.1.6 tidytuesdayR_1.0.1 prodlim_2019.11.13
[118] rvest_1.0.1 digest_0.6.27 httpcode_0.3.0
[121] rmarkdown_2.10 cellranger_1.1.0 gdtools_0.2.3
[124] curl_4.3.2 lifecycle_1.0.0 jsonlite_1.7.2
[127] mapproj_1.2.7 viridisLite_0.4.0 tune_0.1.6
[130] fansi_0.5.0 pillar_1.6.2 lattice_0.20-44
[133] fastmap_1.1.0 httr_1.4.2 survival_3.2-11
[136] glue_1.4.2 conflicted_1.0.4 iterators_1.0.13
[139] bit_4.0.4 class_7.3-19 stringi_1.7.4
[142] sass_0.4.0 rematch2_2.1.2 textshaping_0.3.5
[145] rsample_0.1.0 styler_1.5.1 e1071_1.7-8
[148] future.apply_1.8.1