Spending on Kids

First, let me import the data.

kids <- read.csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
# kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Now let me summarise it and show a table of the variables.

summary(kids)

##     state             variable              year           raw          
##  Length:23460       Length:23460       Min.   :1997   Min.   :  -60139  
##  Class :character   Class :character   1st Qu.:2002   1st Qu.:   71985  
##  Mode  :character   Mode  :character   Median :2006   Median :  252002  
##                                        Mean   :2006   Mean   : 1181359  
##                                        3rd Qu.:2011   3rd Qu.:  836324  
##                                        Max.   :2016   Max.   :83666088  
##                                                       NA's   :102       
##     inf_adj         inf_adj_perchild  
##  Min.   :  -60799   Min.   :-0.01361  
##  1st Qu.:   85876   1st Qu.: 0.12456  
##  Median :  298778   Median : 0.32757  
##  Mean   : 1359983   Mean   : 0.91448  
##  3rd Qu.:  985049   3rd Qu.: 0.83362  
##  Max.   :84584960   Max.   :20.27326  
##  NA's   :102        NA's   :102

A table of the variables. The definitions are best found here.

table(kids$variable)

## 
##         addCC           CTC       edservs        edsubs       fedEITC 
##          1020          1020          1020          1020          1020 
##        fedSSI           HCD HeadStartPriv      highered           lib 
##          1020          1020          1020          1020          1020 
## Medicaid_CHIP  other_health othercashserv       parkrec          pell 
##          1020          1020          1020          1020          1020 
##        PK12ed     pubhealth          SNAP        socsec     stateEITC 
##          1020          1020          1020          1020          1020 
##     TANFbasic         unemp         wcomp 
##          1020          1020          1020

It is very tidy. It is probably better shown after a pivot. 50 states, the District of Columbia, and 20 years gives us 1,020 observations. Let me show it wide.

Big.Wide <- pivot_wider(kids, id_cols = c(state,year), names_from = "variable", values_from = c("raw","inf_adj","inf_adj_perchild"))
Big.Wide

## # A tibble: 1,020 x 71
##    state  year raw_PK12ed raw_highered raw_edsubs raw_edservs raw_pell
##    <chr> <int>      <dbl>        <dbl>      <dbl>       <dbl>    <dbl>
##  1 Alab…  1997    3271969       956505     107733      246057  120833.
##  2 Alas…  1997    1042311       209433       5550       52355    7575.
##  3 Ariz…  1997    3388165       847032     111735      170281  120450.
##  4 Arka…  1997    1960613       457171      62447      189808   65904.
##  5 Cali…  1997   28708364      6858657    1121672      943805  775292.
##  6 Colo…  1997    3332994       861733      84129       77419   79004.
##  7 Conn…  1997    4014870       502177      71053      138932   36453.
##  8 Dela…  1997     776825       185114      31284       81880    9965.
##  9 Dist…  1997     544051        56693          0           0   18972.
## 10 Flor…  1997   11498394      2039186     391935      269777  318611.
## # … with 1,010 more rows, and 64 more variables: raw_HeadStartPriv <dbl>,
## #   raw_TANFbasic <dbl>, raw_othercashserv <dbl>, raw_SNAP <dbl>,
## #   raw_socsec <dbl>, raw_fedSSI <dbl>, raw_fedEITC <dbl>, raw_CTC <dbl>,
## #   raw_addCC <dbl>, raw_stateEITC <dbl>, raw_unemp <dbl>, raw_wcomp <dbl>,
## #   raw_Medicaid_CHIP <dbl>, raw_pubhealth <dbl>, raw_other_health <dbl>,
## #   raw_HCD <dbl>, raw_lib <dbl>, raw_parkrec <dbl>, inf_adj_PK12ed <dbl>,
## #   inf_adj_highered <dbl>, inf_adj_edsubs <dbl>, inf_adj_edservs <dbl>,
## #   inf_adj_pell <dbl>, inf_adj_HeadStartPriv <dbl>, inf_adj_TANFbasic <dbl>,
## #   inf_adj_othercashserv <dbl>, inf_adj_SNAP <dbl>, inf_adj_socsec <dbl>,
## #   inf_adj_fedSSI <dbl>, inf_adj_fedEITC <dbl>, inf_adj_CTC <dbl>,
## #   inf_adj_addCC <dbl>, inf_adj_stateEITC <dbl>, inf_adj_unemp <dbl>,
## #   inf_adj_wcomp <dbl>, inf_adj_Medicaid_CHIP <dbl>, inf_adj_pubhealth <dbl>,
## #   inf_adj_other_health <dbl>, inf_adj_HCD <dbl>, inf_adj_lib <dbl>,
## #   inf_adj_parkrec <dbl>, inf_adj_perchild_PK12ed <dbl>,
## #   inf_adj_perchild_highered <dbl>, inf_adj_perchild_edsubs <dbl>,
## #   inf_adj_perchild_edservs <dbl>, inf_adj_perchild_pell <dbl>,
## #   inf_adj_perchild_HeadStartPriv <dbl>, inf_adj_perchild_TANFbasic <dbl>,
## #   inf_adj_perchild_othercashserv <dbl>, inf_adj_perchild_SNAP <dbl>,
## #   inf_adj_perchild_socsec <dbl>, inf_adj_perchild_fedSSI <dbl>,
## #   inf_adj_perchild_fedEITC <dbl>, inf_adj_perchild_CTC <dbl>,
## #   inf_adj_perchild_addCC <dbl>, inf_adj_perchild_stateEITC <dbl>,
## #   inf_adj_perchild_unemp <dbl>, inf_adj_perchild_wcomp <dbl>,
## #   inf_adj_perchild_Medicaid_CHIP <dbl>, inf_adj_perchild_pubhealth <dbl>,
## #   inf_adj_perchild_other_health <dbl>, inf_adj_perchild_HCD <dbl>,
## #   inf_adj_perchild_lib <dbl>, inf_adj_perchild_parkrec <dbl>

My brief plan

I recently came across a geofacet for R. I want to use it to plot a little bit of this data. If you want to get a head start, try install.packages("geofacet", dependencies=TRUE). You can google geofacet to get an idea of what a geofacet plot is. I will build one on the fly using a couple of tidy tools: filter, mutate, and joins and then put it together.

library(viridis)

## Loading required package: viridisLite

library(geofacet)
state_ranks %>% filter(variable=="education") %>% select(state,name) -> mergeMe
p1 <- kids %>% 
  left_join(., mergeMe, by = c('state' = 'name')) %>% 
  filter(variable=="PK12ed")%>% 
  ggplot(., aes(x=year, y=inf_adj_perchild, color=inf_adj_perchild)) + 
  geom_line() + 
  facet_geo(~state.y) + 
  labs(x="year", y="Inflation Adjust Expenditures per child", title="Pre-K Through 12 Education Spending", color="Spend per child", caption="Data from #tidyTuesday: @PieRatio") + 
  scale_color_viridis_c() + theme_void()
p1

An Animation

library(gganimate)
p2 <- p1 + transition_reveal(year)
p3 <- animate(p2, renderer = gifski_renderer())
save_animation(p3, file = "./GeoAnimFacet.gif")

Animation

load(url("https://github.com/robertwwalker/rww-science/raw/master/content/R/COVID/data/OregonCOVID2020-09-15.RData"))
OR.County.COVID %>% 
  mutate(County = str_replace(County, " ", "")) %>% 
  ggplot(., aes(x=date, y=Number.of.cases, color=Number.of.cases)) + 
  geom_line(size=1.5) + 
  facet_geo(~ County, grid = "us_or_counties_grid1", label = "name", scales = "free_y") +
  scale_color_viridis_c(option = "plasma") +
  theme_void()

## Warning: Removed 3 row(s) containing missing values (geom_path).