Fabio Votta
# Install these packages if you don't have theme yet
# devtools::install_github("favstats/tidytemplate")
# install.packages("pacman")
pacman::p_load(tidyverse, httr, janitor, ggpmisc, urbnmapr, glue, ggthemes, tidytemplate, cowplot, ggpubr)
# Creates folders
# tidytemplate::data_dir()
# tidytemplate::images_dir()
options(scipen = 1)Note: Changes to County Data
https://www.economy.com/support/blog/buffet.aspx?did=50094FC4-C32C-4CCA-862A-264BC890E13B
Wade Hampton Census Area, Alaska (02-270) Changed name and code to Kusilvak Census Area (02-158) effective July 1, 2015.
Oglala Lakota County, South Dakota (46-102) Changed name and code from Shannon County (46-113) effective May 1, 2015.
Bedford + Bedford City changed to Bedford County
FIPS Modifications:
https://apps.bea.gov/regional/xls/FIPSModifications.xlsx
## County Retaliation Data
county_retaliation <- tidytemplate::load_it("data/county_retaliation.Rdata") %>%
mutate(c_fips_lgc_ = ifelse(nchar(c_fips_lgc_) == 4, paste0(0, c_fips_lgc_), c_fips_lgc_)) %>%
mutate(c_fips_lgc_ = case_when(
## Wade Hampton Census Area changed to Kusilvak Census Area
c_fips_lgc_ == "02270" ~ "02158",
## Shannon County to Oglala Lakota County
c_fips_lgc_ == "46113" ~ "46102",
## Bedford + Bedford City changed to Bedford County
c_fips_lgc_ == "51909" ~ "51019",
T ~ c_fips_lgc_
))
## Bea Codes
bea_codes <- tidytemplate::load_it("data/bea_codes2.Rdata") %>%
mutate(bea_codes = as.character(bea_codes)) %>%
mutate(county_fips = as.character(county_fips)) %>%
select(bea_codes, county_fips)
beas <- county_retaliation %>% #filter(c_fips_lgc_ == "15901")
inner_join(bea_codes, by = c("c_fips_lgc_" = "bea_codes")) %>%
select(-c_fips_lgc_)
## Election Data
elec2016_url<- "https://github.com/tonmcg/US_County_Level_Election_Results_08-16/raw/master/2016_US_County_Level_Presidential_Results.csv"
elec2016 <- readr::read_csv(elec2016_url) %>%
mutate(combined_fips = ifelse(nchar(combined_fips) == 4, paste0(0, combined_fips), combined_fips)) %>%
rename(county_fips = combined_fips) %>%
select(-county_name) %>%
mutate(county_fips = case_when(
## Wade Hampton Census Area changed to Kusilvak Census Area
county_fips == "02270" ~ "02158",
## Shannon County to Oglala Lakota County
county_fips == "46113" ~ "46102",
## Bedford + Bedford City changed to Bedford County
county_fips == "51909" ~ "51019",
T ~ county_fips
))## Warning: Missing column names filled in: 'X1' [1]
## Parsed with column specification:
## cols(
## X1 = col_integer(),
## votes_dem = col_double(),
## votes_gop = col_double(),
## total_votes = col_double(),
## per_dem = col_double(),
## per_gop = col_double(),
## diff = col_number(),
## per_point_diff = col_character(),
## state_abbr = col_character(),
## county_name = col_character(),
## combined_fips = col_integer()
## )
retaliation_dat <- county_retaliation %>%
rename(county_fips = c_fips_lgc_) %>%
bind_rows(beas) %>%
inner_join(elec2016) %>%
mutate(voteshare = per_gop - per_dem)## Joining, by = "county_fips"
tidytemplate::save_it(retaliation_dat)formula <- y ~ poly(x, 1, raw = TRUE)
scatter_plot <- retaliation_dat %>%
mutate(per_gop = 100 * per_gop) %>%
mutate(share_ret_xjd = 100 * share_ret_xjd) %>%
# ggplot(aes(voteshare)) +geom_histogram()
ggplot(aes(per_gop, share_ret_xjd)) +
geom_point(aes(color = h_label_lgc_lgm_lgh_,
shape = h_label_lgc_lgm_lgh_), alpha = 0.5) +
geom_smooth(method = "lm", formula = formula, color = "black") +
stat_poly_eq(aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~")),
formula = formula, parse = TRUE, size = 5) +
theme_minimal() +
scale_color_manual("County Type", values = ggthemes::colorblind_pal()(5)[c(2,4)]) +
scale_shape("County Type") +
labs(x = "Trump Vote Share in %\n", y = "Share of total export-supported jobs under retaliation in %\n",
title = "Trump Vote Share associated with Exposure to Retaliatory Tariffs",
subtitle = "2017 Share of export-supported Jobs in Industries targeted by Partner's Retaliation\nby County Vote Share in the 2016 U.S. Presidential Election\n",
caption = "Source: Data compiled by Brookings (Census, BEA, BLS, IRS, EIA, Eurostat, Moody's Analytics,\nNAFSA, PIIE, Sabre, and Trade Map, International Trade Centre, www.intracen.org/marketanalysis)\n\nData Visualization by favstats.eu; @favstats") +
theme(
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 14, face = "italic"),
strip.text = element_text(size = 16, face = "bold"),
plot.caption = element_text(size = 10, hjust = 0.9),
legend.justification = c(1, 0),
legend.position = c(0.98, 0.05),
legend.background = element_rect(fill = "lightgrey"),
legend.title = element_text(size = 10),
axis.title = element_text(size = 12, face = "bold"),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "vertical") +
facet_wrap(~h_label_lgc_lgm_lgh_) +
ggpubr::stat_cor(label.y = 23)
scatter_plot
ggsave_it(scatter_plot, width = 12, height = 8)boxes <- retaliation_dat %>%
mutate(per_gop = 100 * per_gop) %>%
mutate(per_dem = 100 * per_dem) %>%
mutate(share_ret_xjd = 100 * share_ret_xjd) %>%
mutate(win = case_when(
per_gop > per_dem ~ "Trump Won",
per_dem > per_gop ~ "Clinton Won")) %>%
ggplot(aes(win, share_ret_xjd, color = win)) +
geom_violin(aes(fill = win), alpha = 0.1) +
geom_boxplot(width = 0.2) +
facet_wrap(~h_label_lgc_lgm_lgh_) +
theme_minimal() +
scale_color_manual(values = c("#0015BC", "#E9141D")) +
scale_fill_manual(values = c("#0015BC", "#E9141D")) +
guides(color = F, fill = F) +
labs(x = "", y = "Share of total export-supported jobs under retaliation in %\n",
title = "Counties that voted for Trump more exposed to Retaliatory Tariffs",
subtitle = "2017 Share of export-supported Jobs in Industries targeted by Partner's Retaliation\nby County Vote Share in the 2016 U.S. Presidential Election\n",
caption = "Source: Data compiled by Brookings (Census, BEA, BLS, IRS, EIA, Eurostat, Moody's Analytics,\nNAFSA, PIIE, Sabre, and Trade Map, International Trade Centre, www.intracen.org/marketanalysis)\n\nData Visualization by favstats.eu; @favstats") +
theme(
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 14, face = "italic"),
strip.text = element_text(size = 16, face = "bold"),
plot.caption = element_text(size = 10, hjust = 0.9),
legend.justification = c(1, 0),
legend.position = c(0.98, 0.05),
legend.background = element_rect(fill = "lightgrey"),
legend.title = element_text(size = 10),
axis.title = element_text(size = 12, face = "bold"),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "vertical") +
stat_compare_means()
boxes
ggsave_it(boxes, width = 12, height = 8)share_ret_xn_plot <- retaliation_dat %>%
mutate(trump_win = ifelse(per_gop >= .5, "Win", "Lost")) %>%
mutate(per_gop = 100 * per_gop) %>%
mutate(per_dem = 100 * per_dem) %>%
mutate(share_ret_xjd = 100 * share_ret_xjd) %>%
mutate(voteshare = per_gop - per_dem) %>%
inner_join(urbnmapr::counties) %>%
ggplot(aes(long, lat,
group = group,
fill = voteshare,
alpha = share_ret_xjd)) +
geom_polygon(color = "white", size = 0.05) +
coord_map() +
theme_map() +
scale_fill_gradient2("",
low = "#0015BC",
mid = "lightgrey",
high = "#E9141D",
labels = c("+50% Trump", "+50% Clinton"),
breaks = c(50, -50)
) +
scale_alpha("",
labels = paste0(seq(5, 25, 5), "%"),
breaks = seq(5, 25, 5)) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0),
plot.subtitle = element_text(size = 14, face = "italic", hjust = 0),
plot.caption = element_text(size = 10, hjust = 0.1),
legend.justification = c(0, 0.5),
legend.position = c(0.86, 0.33),
plot.margin=unit(c(0,0,0,0),"mm"),
legend.title.align = 0.5,
legend.title = element_text(size = 10)
) +
guides(
fill = guide_colorbar(
barwidth = 1.05,
barheight = 6,#8,
title.position = "bottom",
legend.direction = "vertical",
label.theme = element_text(colour = "black", size = 9, angle = 0)),
alpha = guide_legend(
title.position = "right",
title.hjust = 1,
direction = "vertical",
label.theme = element_text(colour = "black", size = 9, angle = 0))
) +
labs(x = "", y = "",
title = "Counties that voted for Trump most exposed to Retaliatory Tariffs",
subtitle = "2017 Share of export-supported Jobs in Industries targeted by Partner's Retaliation\nby County Vote Share in the 2016 U.S. Presidential Election",
caption = "\nSource: Data compiled by Brookings (Census, BEA, BLS, IRS, EIA, Eurostat, Moody's Analytics,\nNAFSA, PIIE, Sabre, and Trade Map, International Trade Centre, www.intracen.org/marketanalysis)\n\nData Visualization by favstats.eu; @favstats\n") +
annotate(geom = "text", x = -80, y = 21.3,#,24.3
label = "Share of total export-supported Jobs under Retaliation") +
annotate(geom = "text", x = -77, y = 48.5,
label = "Darker shaded Counties correspond to greater % of\nexport-supported Jobs under Retaliation") ## Joining, by = "county_fips"
# xlim(-124, -60)
# ylim(30, 49.38436)
share_ret_xn_plot
ggsave_it(share_ret_xn_plot, width = 12, height = 9)cowplot::plot_grid(share_ret_xn_plot, scatter_plot, ncol = 1)
ggsave_it(combined, width = 12, height = 14)imm_dat <- read_csv2("data/imm_dat.csv") %>%
clean_names() %>%
mutate(state = state_b05006_2016_5_yr_state) %>%
select(county_immigrants, county, total_state_population, state)## Using ',' as decimal and '.' as grouping mark. Use read_delim() for more control.
## Parsed with column specification:
## cols(
## `Round County Immigrants` = col_number(),
## `Round State Immigrants` = col_number(),
## `County Immigrants` = col_integer(),
## `County name` = col_character(),
## County = col_character(),
## `Anzahl der Datensätze` = col_integer(),
## `Origin (B05006 2016 5 yr (State))` = col_character(),
## `Origin (quickfilter 2)` = col_character(),
## `Origin (tooltip)` = col_character(),
## `Origin (quickfilter)` = col_character(),
## Origin = col_character(),
## `State (B05006 2016 5 yr (State))` = col_character(),
## `State Immigrants` = col_integer(),
## State = col_character(),
## `Total state immigrant population` = col_integer(),
## `Total state population` = col_integer()
## )
library(rvest)## Loading required package: xml2
##
## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
##
## pluck
## The following object is masked from 'package:readr':
##
## guess_encoding
imm_dat <- read_html("https://www.omnisci.com/docs/latest/3_apdx_states.html") %>%
html_table() %>%
.[[2]] %>%
clean_names() %>%
left_join(imm_dat) %>%
mutate(c_name_lgc_ = paste0(county, " (", abbreviation,")"))## Joining, by = "state"
formula <- y ~ poly(x, 15, raw = TRUE)
retaliation_dat %>%
left_join(imm_dat) %>%
# fuzzyjoin::stringdist_left_join(imm_dat) %>%
# filter(is.na(county_fips))
mutate(share_imms = get_percentage(county_immigrants, total_state_population)) %>%
mutate(per_gop = 100 * per_gop) %>%
mutate(share_ret_xjd = 100 * share_ret_xjd) %>%
# ggplot(aes(voteshare)) +geom_histogram()
ggplot(aes(share_imms, per_gop)) +
geom_point(aes(color = h_label_lgc_lgm_lgh_,
shape = h_label_lgc_lgm_lgh_), alpha = 0.5) +
# geom_smooth(method = "gam", se = F, color = "black") +
# geom_smooth(method = "lm", formula = formula, color = "black") +
# stat_poly_eq(aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~")),
# formula = formula, parse = TRUE, size = 5) +
theme_minimal() +
scale_color_manual("County Type", values = ggthemes::colorblind_pal()(5)[c(2,4)]) +
scale_shape("County Type") +
labs(x = "Share of Immigrants in %\n", y = "Trump Vote Share in %",
title = "Trump Vote Share in 2016 U.S. Election and Share of Immigrants",
# subtitle = "2017 Share of export-supported Jobs in Industries targeted by Partner's Retaliation\nby County Vote Share in the 2016 U.S. Presidential Election\n",
caption = "Source: U.S. Census Bureau's pooled 2012-2016 American Community Survey taken from Migration Policy Institute") +
theme(
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 14, face = "italic"),
plot.caption = element_text(size = 10, hjust = 0.9),
legend.justification = c(1, 0),
legend.position = c(0.98, 0.8),
legend.background = element_rect(fill = "lightgrey"),
legend.title = element_text(size = 10),
axis.title = element_text(size = 12, face = "bold"),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "vertical") ## Joining, by = "c_name_lgc_"
## Warning: Column `c_name_lgc_` joining factor and character vector, coercing
## into character vector
## Warning: Removed 65 rows containing missing values (geom_point).
# xlim(-124, -60)
# ylim(30, 49.38436)
ggsave_it(imm_plot, width = 12, height = 8)## Warning: Removed 65 rows containing missing values (geom_point).