https://github.com/favstats/btw_data
Visualizing German National Elections
https://github.com/favstats/btw_data
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Visualizing German National Elections
- Host: GitHub
- URL: https://github.com/favstats/btw_data
- Owner: favstats
- Created: 2018-09-18T14:00:06.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-23T17:41:24.000Z (over 7 years ago)
- Last Synced: 2025-02-07T19:36:19.669Z (over 1 year ago)
- Homepage:
- Size: 10.3 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
German Elections Data
================
Fabio Votta
19.09.2018
Data
Sources:
## Settings, Packages and Folders
``` r
options(scipen = 999)
knitr::opts_chunk$set(message = F, warning = F, error = F,
fig.align = "center",
fig.width = 10, fig.height = 6)
# Install these packages if you don't have theme yet
# devtools::install_github("favstats/tidytemplate")
# install.packages("pacman")
pacman::p_load(tidyverse)
# Creates folders
# tidytemplate::data_dir()
# tidytemplate::images_dir()
```
## Helper Functions
``` r
get_percentage <- function(part, total, digits = NULL) {
if(!is.numeric(part) | !is.numeric(total)){
cat("Non-numeric variables.. converting\n")
part <- as.numeric(part)
total <- as.numeric(total)
}
val <- part / total * 100
if(!is.null(digits)){
val <- round(val, digits)
}
return(val)
}
get_labs <- function (pval)
{
dplyr::case_when(is.na(pval) ~ "",
pval < 0.001 ~ "p < 0.001",
pval < 0.01 ~ "p < 0.01",
pval < 0.05 ~ "p < 0.05",
pval < 0.1 ~ "p < 0.10",
TRUE ~ "p > 0.10")
}
get_r2_label <- function(mod, digits = 3, adj = F){
if(!adj){#
message("R Squared\n")
r2 <- broom::glance(mod) %>%
mutate(r.squared = r.squared * 100) %>%
.$r.squared %>%
format(., digits = digits) %>%
paste0(., "%")
return(r2)
} else {
message("Getting Adjusted R Squared\n")
r2 <- broom::glance(mod) %>%
mutate(adj.r.squared = adj.r.squared * 100) %>%
.$adj.r.squared %>%
format(., digits = digits) %>%
paste0(., "%")
return(r2)
}
}
```
## Load Data
### BTW13
``` r
## loading in district names
district_names <- tidytemplate::load_it("data/district_names.Rdata")
vars13 <- c("wahlkreis_nr", "kreis", "part_of", "valid", "CDU", "SPD", "FDP", "DIE LINKE", "GRÜNE", "CSU", "PIRATEN", "NPD", "Tierschutzpartei", "REP", "ÖDP", "FAMILIE", "Bündnis 21/RRP", "RENTNER", "BP", "PBC", "BüSo", "DIE VIOLETTEN", "MLPD", "Volksabstimmung", "PSG", "AfD", "BIG", "pro Deutschland", "DIE RECHTE", "DIE FRAUEN", "FREIE WÄHLER", "Nichtwähler", "PARTEI DER VERNUNFT", "Die PARTEI", "B", "BGD", "DKP", "NEIN!", "Übrige")
# ss %>%
btw13 <- readr::read_delim("data/btw13_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Endgltig", .)) %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Zweitstimme", .)) %>%
select(-x6, -x10, -x14) %>%
set_names(vars13) %>%
mutate(cdu_csu = ifelse(is.na(CDU), CSU, CDU)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(SPD, valid)) %>%
mutate(perc_linke = get_percentage(`DIE LINKE`, valid)) %>%
mutate(perc_fdp = get_percentage(FDP, valid)) %>%
mutate(perc_grun = get_percentage(`GRÜNE`, valid)) %>%
mutate(perc_afd = get_percentage(AfD, valid)) %>%
mutate(NPD = ifelse(is.na(NPD), "0", NPD)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
strukturdaten13 <- read_delim("data/btw13_strukturdaten.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE,
col_types = cols(.default = "c")) %>%
janitor::clean_names() %>%
.[-1,] %>%
mutate(unemployment = str_replace(arbeitslosenquote_ende_dezember_2012_insgesamt, ",", ".") %>% as.numeric) %>%
mutate(sgbii = str_replace(empf_e4_nger_innen_von_leistungen_nach_sgb_ii_am_31_12_2012_insgesamt_je_1000_einwohner, ",", ".") %>% as.numeric) %>%
mutate(wanderung = str_replace(zu_bzw_abnahme_der_bev_f6_lkerung_2011_wanderungssaldo_je_1000_einwohner, ",", ".") %>% as.numeric) %>%
filter(!(str_detect(wahlkreis_name, "insgesamt|Insgesamt"))) %>%
select(wahlkreis_nr, wanderung, unemployment, sgbii, wahlkreis_name, land) %>%
mutate(wahlkreis_nr = as.integer(wahlkreis_nr))
btw13_data <- btw13 %>%
left_join(strukturdaten13) %>%
select(-kreis, -wahlkreis_name) %>%
left_join(district_names)
tidytemplate::save_it(btw13_data)
btw13_data
```
## # A tibble: 299 x 15
## wahlkreis_nr part_of valid perc_afd perc_npd perc_spd perc_linke
##
## 1 1 1 1606~ 4.09 0.578 32.6 5.66
## 2 2 1 1300~ 3.84 0.564 29.7 4.41
## 3 3 1 1249~ 4.40 0.831 30.0 5.03
## 4 4 1 1479~ 4.39 0.577 31.5 4.36
## 5 5 1 1449~ 3.71 0.553 34.7 6.91
## 6 6 1 1255~ 4.70 0.923 32.7 4.77
## 7 7 1 1773~ 4.78 0.744 31.2 5.02
## 8 8 1 1815~ 4.97 0.720 30.1 5.02
## 9 9 1 1304~ 4.96 0.565 31.5 4.34
## 10 10 1 1814~ 5.63 0.759 29.7 4.94
## # ... with 289 more rows, and 8 more variables: perc_fdp ,
## # perc_grun , perc_cdu_csu , wanderung ,
## # unemployment , sgbii , land , kreis
### BTW 17
``` r
vars17 <- c("wahlkreis_nr", "kreis", "part_of", "valid", "Christlich Demokratische Union Deutschlands", "Sozialdemokratische Partei Deutschlands", "DIE LINKE", "BÜNDNIS 90/DIE GRÜNEN", "Christlich-Soziale Union in Bayern e.V.", "Freie Demokratische Partei", "Alternative für Deutschland", "Piratenpartei Deutschland", "Nationaldemokratische Partei Deutschlands", "FREIE WÄHLER", "PARTEI MENSCH UMWELT TIERSCHUTZ", "Ökologisch-Demokratische Partei", "Die PARTEI", "Bayernpartei", "Ab jetzt...Demokratie durch Volksabstimmung", "Partei der Vernunft", "Marxistisch-Leninistische Partei Deutschlands", "Bürgerrechtsbewegung Solidarität", "Sozialistische Gleichheitspartei", "DIE RECHTE", "Allianz Deutscher Demokraten", "Allianz für Menschenrechte", "bergpartei", "Bündnis Grundeinkommen", "DEMOKRATIE IN BEWEGUNG", "Deutsche Kommunistische Partei", "Deutsche Mitte", "Die Grauen – Für alle Generationen", "Die Urbane. Eine HipHop Partei", "Madgeburger Gartenpartei", "Menschliche Welt", "Partei der Humanisten", "Partei für Gesundheitsforschung", "V-Partei³ - Partei für Veränderung", "Bündnis C - Christen für Deutschland", "DIE EINHEIT", "Die Violetten", "Familien-Partei Deutschlands", "Feministische Partei DIE FRAUEN", "Mieterpartei", "Neue Liberale – Die Sozialliberalen", "UNABHÄNGIGE für bürgernahe Demokratie", "Übrige")
btw17 <- readr::read_delim("data/btw17_kerg.csv",
";", escape_double = FALSE, comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(nr, gebiet, gehort_zu, grep("Endgültig", .)) %>%
select(nr, gebiet, gehort_zu, grep("Zweitstimme", .)) %>%
select(-x6, -x10, -x14) %>%
set_names(vars17) %>%
mutate(cdu_csu = ifelse(is.na(`Christlich Demokratische Union Deutschlands`),
`Christlich-Soziale Union in Bayern e.V.`,
`Christlich Demokratische Union Deutschlands`)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(`Sozialdemokratische Partei Deutschlands`, valid)) %>%
mutate(perc_linke = get_percentage(`DIE LINKE`, valid)) %>%
mutate(perc_fdp = get_percentage(`Freie Demokratische Partei`, valid)) %>%
mutate(perc_grun = get_percentage(`BÜNDNIS 90/DIE GRÜNEN`, valid)) %>%
mutate(perc_afd = get_percentage(`Alternative für Deutschland`, valid)) %>%
mutate(NPD = ifelse(is.na(`Nationaldemokratische Partei Deutschlands`),
"0",
`Nationaldemokratische Partei Deutschlands`)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
btw17
```
## # A tibble: 299 x 11
## wahlkreis_nr kreis part_of valid perc_afd perc_npd perc_spd perc_linke
##
## 1 1 Flen~ 1 1704~ 6.84 0.205 23.7 8.21
## 2 2 Nord~ 1 1380~ 6.54 0.218 22.5 6.22
## 3 3 Stei~ 1 1308~ 8.54 0.360 22.7 6.67
## 4 4 Rend~ 1 1562~ 7.41 0.196 22.9 6.37
## 5 5 Kiel 1 1520~ 6.91 0.164 23.8 10.2
## 6 6 Plön~ 1 1305~ 8.55 0.367 23.8 6.52
## 7 7 Pinn~ 1 1863~ 8.57 0.239 22.9 7.03
## 8 8 Sege~ 1 1919~ 8.94 0.241 22.4 6.90
## 9 9 Osth~ 1 1372~ 8.58 0.219 24.6 6.05
## 10 10 Herz~ 1 1919~ 9.79 0.256 22.3 6.50
## # ... with 289 more rows, and 3 more variables: perc_fdp ,
## # perc_grun , perc_cdu_csu
``` r
strukturdaten17 <- read_delim("data/btw17_strukturdaten.csv",
";", escape_double = FALSE,
col_types = cols(.default = "c"),
comment = "#",
trim_ws = TRUE,
skip = 1) %>%
janitor::clean_names() %>%
mutate(unemployment = str_replace(arbeitslosenquote_marz_2017_insgesamt, ",", ".") %>% as.numeric) %>%
mutate(wanderung = str_replace(zu_bzw_abnahme_der_bev_f6_lkerung_2015_wanderungssaldo_je_1000_einwohner, ",", ".") %>% as.numeric) %>%
mutate(sgbii = str_replace(empfanger_innen_von_leistungen_nach_sgb_ii_am_31_12_2016_insgesamt_je_1000_einwohner, ",", ".") %>% as.numeric) %>%
filter(!(str_detect(wahlkreis_name, "insgesamt|Insgesamt"))) %>%
select(wahlkreis_nr, unemployment, wanderung, sgbii, wahlkreis_name, land) %>%
mutate(wahlkreis_nr = as.integer(wahlkreis_nr))
btw17_data <- btw17 %>%
left_join(strukturdaten17) %>%
select(-kreis, -wahlkreis_name) %>%
left_join(district_names)
tidytemplate::save_it(btw17_data)
btw17_data
```
## # A tibble: 299 x 15
## wahlkreis_nr part_of valid perc_afd perc_npd perc_spd perc_linke
##
## 1 1 1 1704~ 6.84 0.205 23.7 8.21
## 2 2 1 1380~ 6.54 0.218 22.5 6.22
## 3 3 1 1308~ 8.54 0.360 22.7 6.67
## 4 4 1 1562~ 7.41 0.196 22.9 6.37
## 5 5 1 1520~ 6.91 0.164 23.8 10.2
## 6 6 1 1305~ 8.55 0.367 23.8 6.52
## 7 7 1 1863~ 8.57 0.239 22.9 7.03
## 8 8 1 1919~ 8.94 0.241 22.4 6.90
## 9 9 1 1372~ 8.58 0.219 24.6 6.05
## 10 10 1 1919~ 9.79 0.256 22.3 6.50
## # ... with 289 more rows, and 8 more variables: perc_fdp ,
## # perc_grun , perc_cdu_csu , unemployment ,
## # wanderung , sgbii , land , kreis
## All Together
``` r
btw13_data <- tidytemplate::load_it("data/btw13_data.Rdata")
btw17_data <- tidytemplate::load_it("data/btw17_data.Rdata")
btw_data <- bind_rows(btw17_data %>%
mutate(year = "BTW 2017") %>%
mutate(part_of = as.character(part_of)),
btw13_data %>%
mutate(year = "BTW 2013")) %>%
mutate(east_west = case_when(
str_detect(land, "Berlin|Brand|Nieder|Sachs") ~ "East Germany",
T ~ "West Germany"
))
tidytemplate::save_it(btw_data)
btw_data
```
## # A tibble: 598 x 17
## wahlkreis_nr part_of valid perc_afd perc_npd perc_spd perc_linke
##
## 1 1 1 1704~ 6.84 0.205 23.7 8.21
## 2 2 1 1380~ 6.54 0.218 22.5 6.22
## 3 3 1 1308~ 8.54 0.360 22.7 6.67
## 4 4 1 1562~ 7.41 0.196 22.9 6.37
## 5 5 1 1520~ 6.91 0.164 23.8 10.2
## 6 6 1 1305~ 8.55 0.367 23.8 6.52
## 7 7 1 1863~ 8.57 0.239 22.9 7.03
## 8 8 1 1919~ 8.94 0.241 22.4 6.90
## 9 9 1 1372~ 8.58 0.219 24.6 6.05
## 10 10 1 1919~ 9.79 0.256 22.3 6.50
## # ... with 588 more rows, and 10 more variables: perc_fdp ,
## # perc_grun , perc_cdu_csu , unemployment ,
## # wanderung , sgbii , land , kreis , year ,
## # east_west
### Unemployment
#### AfD
``` r
mod13 <- lm(perc_afd ~ unemployment, data = btw13_data)
mod17 <- lm(perc_afd ~ unemployment, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_afd <- btw_data %>%
ggplot(aes(unemployment, perc_afd)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 34, x = 8, label = text), show.legend = F) +
labs(x = "Unemployment Rate in %", y = "Vote Share AfD in %",
title = "AfD Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_afd
```
``` r
tidytemplate::ggsave_it(gg_afd, width = 10, height = 6)
```
##### East-West
``` r
gg_afd_eastwest <- btw_data %>%
ggplot(aes(unemployment, perc_afd, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Unemployment Rate in %", y = "Vote Share AfD in %",
title = "AfD Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_afd_eastwest
```
``` r
tidytemplate::ggsave_it(gg_afd_eastwest, width = 10, height = 6)
```
#### NPD
``` r
mod13 <- lm(perc_npd ~ unemployment, data = btw13_data)
mod17 <- lm(perc_npd ~ unemployment, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_npd <- btw_data %>%
ggplot(aes(unemployment, perc_npd)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 5.2, x = 8, label = text), show.legend = F) +
labs(x = "Unemployment Rate in %", y = "Vote Share npd in %",
title = "NPD Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_npd
```
``` r
tidytemplate::ggsave_it(gg_npd, width = 10, height = 6)
```
##### East-West
``` r
gg_npd_eastwest <- btw_data %>%
ggplot(aes(unemployment, perc_npd, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Unemployment Rate in %", y = "Vote Share npd in %",
title = "NPD Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_npd_eastwest
```
``` r
tidytemplate::ggsave_it(gg_npd_eastwest, width = 10, height = 6)
```
#### Linke
``` r
mod13 <- lm(perc_linke ~ unemployment, data = btw13_data)
mod17 <- lm(perc_linke ~ unemployment, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "unemployment") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_linke <- btw_data %>%
ggplot(aes(unemployment, perc_linke)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 34, x = 8, label = text), show.legend = F) +
labs(x = "Unemployment Rate in %", y = "Vote Share 'Die Linke' in %",
title = "'Die Linke' Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_linke
```
``` r
tidytemplate::ggsave_it(gg_linke, width = 10, height = 6)
```
##### East-West
``` r
gg_linke_eastwest <- btw_data %>%
ggplot(aes(unemployment, perc_linke, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Unemployment Rate in %", y = "Vote Share 'Die Linke in %",
title = "'Die Linke' Vote Share and Unemployment Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_linke_eastwest
```
``` r
tidytemplate::ggsave_it(gg_linke_eastwest, width = 10, height = 6)
```
### Migration
#### AfD
``` r
mod13 <- lm(perc_afd ~ wanderung, data = btw13_data)
mod17 <- lm(perc_afd ~ wanderung, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_afd_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_afd)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 34, x = 15, label = text), show.legend = F) +
labs(x = "Migration Saldo", y = "Vote Share AfD in %",
title = "AfD Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_afd_wanderung
```
``` r
tidytemplate::ggsave_it(gg_afd_wanderung, width = 10, height = 6)
```
##### East-West
``` r
gg_afd_eastwest_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_afd, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Migration Saldo", y = "Vote Share AfD in %",
title = "AfD Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_afd_eastwest_wanderung
```
``` r
tidytemplate::ggsave_it(gg_afd_eastwest_wanderung, width = 10, height = 6)
```
##### Just BTW 2017
``` r
gg_afd_wanderung17 <- btw_data %>%
filter(year == "BTW 2017") %>%
ggplot(aes(wanderung, perc_afd, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
theme_minimal() +
# geom_text(data = label_dat %>%
# filter(year == "BTW 2017"), aes(y = 34, x = 15, label = text), show.legend = F) +
labs(x = "Net Migration per 1000 inhabitants", y = "Vote Share AfD in %",
title = "AfD Vote Share and Net Migration in the 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_afd_wanderung17
```
``` r
tidytemplate::ggsave_it(gg_afd_wanderung17, width = 10, height = 6)
```
#### NPD
``` r
mod13 <- lm(perc_npd ~ wanderung, data = btw13_data)
mod17 <- lm(perc_npd ~ wanderung, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_npd_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_npd)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 5.2, x = 15, label = text), show.legend = F) +
labs(x = "Migration Saldo", y = "Vote Share NPD in %",
title = "NPD Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_npd_wanderung
```
``` r
tidytemplate::ggsave_it(gg_npd_wanderung, width = 10, height = 6)
```
##### East-West
``` r
gg_npd_eastwest_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_npd, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Migration Saldo", y = "Vote Share NPD in %",
title = "NPD Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_npd_eastwest_wanderung
```
``` r
tidytemplate::ggsave_it(gg_npd_eastwest_wanderung, width = 10, height = 6)
```
#### Linke
``` r
mod13 <- lm(perc_linke ~ wanderung, data = btw13_data)
mod17 <- lm(perc_linke ~ wanderung, data = btw17_data)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "wanderung") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_linke_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_linke)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 34, x = 15, label = text), show.legend = F) +
labs(x = "Migration Saldo", y = "Vote Share 'Die Linke' in %",
title = "'Die Linke' Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_linke_wanderung
```
``` r
tidytemplate::ggsave_it(gg_linke_wanderung, width = 10, height = 6)
```
##### East-West
``` r
gg_linke_eastwest_wanderung <- btw_data %>%
ggplot(aes(wanderung, perc_linke, color = east_west)) +
geom_point(aes(shape = east_west), alpha = 0.8) +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
labs(x = "Migration Saldo", y = "Vote Share 'Die Linke in %",
title = "'Die Linke' Vote Share and Net Migration in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
ggthemes::scale_color_colorblind("Region") +
guides(shape = F)
gg_linke_eastwest_wanderung
```
``` r
tidytemplate::ggsave_it(gg_linke_eastwest_wanderung, width = 10, height = 6)
```
## Where did the AfD come from?
``` r
btw_lagged_all <- btw_data %>%
filter(year == "BTW 2017") %>%
select(wahlkreis_nr, perc_afd) %>%
left_join(
btw_data %>%
filter(year == "BTW 2013") %>%
select(wahlkreis_nr, perc_spd, perc_cdu_csu, perc_linke,
perc_grun, perc_fdp, perc_npd)) %>%
rename(`AfD 2017` = perc_afd,
`SPD 2013` = perc_spd,
`CDU/CSU 2013` = perc_cdu_csu,
`Die Linke 2013` = perc_linke,
`Die Grünen 2013` = perc_grun,
`FDP 2013` = perc_fdp,
`NPD 2013` = perc_npd) %>%
left_join(
btw_data %>% select(wahlkreis_nr, east_west)) %>%
select(-wahlkreis_nr)
btw_lagged_all
```
## # A tibble: 598 x 8
## `AfD 2017` `SPD 2013` `CDU/CSU 2013` `Die Linke 2013` `Die Grünen 201~
##
## 1 6.84 32.6 38.2 5.66 9.79
## 2 6.84 32.6 38.2 5.66 9.79
## 3 6.54 29.7 43.4 4.41 8.11
## 4 6.54 29.7 43.4 4.41 8.11
## 5 8.54 30.0 41.9 5.03 7.59
## 6 8.54 30.0 41.9 5.03 7.59
## 7 7.41 31.5 40.8 4.36 9.26
## 8 7.41 31.5 40.8 4.36 9.26
## 9 6.91 34.7 30.3 6.91 14.1
## 10 6.91 34.7 30.3 6.91 14.1
## # ... with 588 more rows, and 3 more variables: `FDP 2013` , `NPD
## # 2013` , east_west
``` r
afd_origin <- btw_lagged_all %>%
gather(parties, perc, -east_west, -`AfD 2017`) %>%
ggplot(aes(perc, `AfD 2017`,
color = east_west,
shape = east_west)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm") +
facet_wrap(~parties, scales = "free") +
ggthemes::scale_color_colorblind("Region") +
theme_minimal() +
labs(x = "2013 Vote Share in Percent",
y = "AfD Vote Share in 2017",
title = "Where did AfD voters come from?",
subtitle = "Comparing 2013 Election results with Vote Share of the AfD in the 2017 Election on the Electoral District Level",
caption = "N = 299 Electoral Districts\nData from Federal Returning Officer ('Bundeswahlleiter')\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"),
legend.position = "bottom") +
guides(shape = F) #+
# ggpubr::stat_cor()
afd_origin
```
``` r
tidytemplate::ggsave_it(afd_origin, width = 12, height = 8)
```
**To-Do**
## mapping
``` r
btw_data <- tidytemplate::load_it("data/btw_data.Rdata")
library(rgdal)
btw_raw <- readOGR("data/Geometrie_Wahlkreise_19DBT_geo.shp")
```
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Fabio\Documents\git_proj\btw_data\data\Geometrie_Wahlkreise_19DBT_geo.shp", layer: "Geometrie_Wahlkreise_19DBT_geo"
## with 299 features
## It has 4 fields
## Integer64 fields read as strings: WKR_NR
``` r
plot(btw_raw)
```
``` r
btw_map_dat <- btw_raw@data%>%
as.data.frame() %>%
rownames_to_column("id")
btw_map_wt <- spTransform(btw_raw, CRS("+proj=longlat +datum=WGS84"))
btw_map <- fortify(btw_map_wt) %>%
left_join(btw_map_dat, by = "id")
btw_map13 <- btw_data %>%
filter(year == "BTW 2013") %>%
inner_join(btw_map %>%
rename(wahlkreis_nr = WKR_NR) %>%
mutate(wahlkreis_nr = as.character(wahlkreis_nr) %>% as.integer))
btw_map17 <- btw_data %>%
filter(year == "BTW 2017") %>%
inner_join(btw_map %>%
rename(wahlkreis_nr = WKR_NR) %>%
mutate(wahlkreis_nr = as.character(wahlkreis_nr) %>% as.integer))
# btw_map <- bind_rows(btw_map13 %>%
# mutate(year = "BTW 2013"),
# btw_map17 %>%
# mutate(year = "BTW 2017"))
```
``` r
btw_map <- bind_rows(btw_map13 %>%
mutate(year = "BTW 2013"),
btw_map17 %>%
mutate(year = "BTW 2017"))
gg_btw_map17 <- btw_map17 %>%
ggplot(aes(fill = perc_afd)) +
geom_map(map = btw_map17,
aes(x = long, y = lat, group = group, map_id = id),
color = NA, size = 0.4) +
theme_void() +
# facet_wrap(~year) +
coord_map() +
viridis::scale_fill_viridis("AfD Vote Share in %",
option = "D", discrete = F, end = .8) +
# limits = c(1, 10),
# breaks = c(1:10),
# labels = c(1:10)) +
theme(plot.title = element_text(hjust = 0.5, size = 19, face = "bold"),
legend.justification = c(1, 0),
legend.position = c(0.82, 0.91),
legend.title = element_text(size = 16, face = "bold"),
strip.text = element_text(size = 12),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "horizontal",
plot.caption = element_text(size = 13)) +
guides(fill = guide_colorbar(barwidth = 12, barheight = 0.5,
title.position = "top", title.hjust = 0.5,
label.theme = element_text(colour = "black", size = 14, angle = 0))) +
labs(title = "German General Election 2017 - AfD Results",
caption = "299 Electoral Districts \nData from Federal Returning Officer ('Bundeswahlleiter') \n@FabioFavusMaxim; favstats.eu ")
gg_btw_map17
```
``` r
tidytemplate::ggsave_it(gg_btw_map17, width = 8, height = 12)
```
``` r
btw_map <- bind_rows(btw_map13 %>%
mutate(year = "BTW 2013"),
btw_map17 %>%
mutate(year = "BTW 2017"))
gg_btw_map <- btw_map %>%
ggplot(aes(fill = perc_afd)) +
geom_map(map = btw_map17,
aes(x = long, y = lat, group = group, map_id = id),
color = NA, size = 0.4) +
theme_void() +
facet_wrap(~year) +
coord_map() +
viridis::scale_fill_viridis("AfD Vote Share in %",
option = "D", discrete = F, end = .8) +
# limits = c(1, 10),
# breaks = c(1:10),
# labels = c(1:10)) +
theme(plot.title = element_text(hjust = 0.5, size = 19, face = "bold"),
legend.justification = c(1, 0),
legend.position = c(.63, 0.91),
legend.title = element_text(size = 12, face = "bold"),
strip.text = element_text(size = 13),
plot.margin=unit(c(0,0,0,0),"mm"),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "horizontal",
plot.caption = element_text(size = 13)) +
guides(fill = guide_colorbar(barwidth = 10, barheight = 0.5,
title.position = "top", title.hjust = 0.5,
label.theme = element_text(colour = "black", size = 11, angle = 0))) +
labs(title = "German General Elections 2013 and 2017 - AfD Results\n",
caption = "299 Electoral Districts \nData from Federal Returning Officer ('Bundeswahlleiter') \n@FabioFavusMaxim; favstats.eu ")
gg_btw_map
```
``` r
tidytemplate::ggsave_it(gg_btw_map, width = 8, height = 8)
```
``` r
gg_btw_map_blue <- btw_map %>%
ggplot(aes(fill = perc_afd)) +
geom_map(map = btw_map17,
aes(x = long, y = lat, group = group, map_id = id),
color = NA, size = 0.4) +
theme_void() +
facet_wrap(~year) +
coord_map() +
scale_fill_continuous("AfD Vote Share in %",
low = "#00b4ff",
high = "#002f42") +
theme(plot.title = element_text(hjust = 0.5, size = 19, face = "bold"),
legend.justification = c(1, 0),
legend.position = c(.63, 0.91),
legend.title = element_text(size = 12, face = "bold"),
strip.text = element_text(size = 13),
plot.margin=unit(c(0,0,0,0),"mm"),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "horizontal",
plot.caption = element_text(size = 13)) +
guides(fill = guide_colorbar(barwidth = 10, barheight = 0.5,
title.position = "top", title.hjust = 0.5,
label.theme = element_text(colour = "black", size = 11, angle = 0))) +
labs(title = "German General Elections 2013 and 2017 - AfD Results\n",
caption = "299 Electoral Districts \nData from Federal Returning Officer ('Bundeswahlleiter') \n@FabioFavusMaxim; favstats.eu ")
gg_btw_map_blue
```
``` r
tidytemplate::ggsave_it(gg_btw_map_blue, width = 8, height = 8)
```
``` r
btw_map17_unemp <- btw_map17 %>%
ggplot(aes(fill = unemployment)) +
geom_map(map = btw_map17,
aes(x = long, y = lat, group = group, map_id = id),
color = NA, size = 0.4) +
theme_void() +
# facet_wrap(~year) +
coord_map() +
viridis::scale_fill_viridis("Unemplyoment in %",
option = "D", discrete = F, end = .8) +
# limits = c(1, 10),
# breaks = c(1:10),
# labels = c(1:10)) +
theme(plot.title = element_text(hjust = 0.5, size = 19, face = "bold"),
legend.justification = c(1, 0),
legend.position = c(0.3, 0.82),
legend.title = element_text(size = 10, face = "bold"), strip.text = element_text(size = 12),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "horizontal") +
guides(fill = guide_colorbar(barwidth = 10, barheight = 0.5,
title.position = "top", title.hjust = 0.5,
label.theme = element_text(colour = "black", size = 5, angle = 0)))
btw_map17_unemp
```
``` r
tidytemplate::ggsave_it(btw_map17_unemp, width = 8, height = 12)
```
## gganimate
``` r
# library(gganimate)
#
# btw_data %>%
# ggplot(aes(unemployment, perc_afd, color = year)) +
# geom_point() +
# geom_smooth(method = "lm") +
# transition_states(year, transition_length = 10, state_length = 4)
```
``` r
# btw_map_anim <- btw_map %>%
# ggplot(aes(fill = perc_afd)) +
# geom_map(map = btw_map,
# aes(x = long, y = lat, group = group, map_id = id),
# color = NA, size = 0.4) +
# theme_void() +
# coord_map() +
# geom_text(aes(x = 7, y = 55,
# label = paste0("Year: ", year)), size = 7) +
# viridis::scale_fill_viridis("AfD Vote Share in %",
# option = "D", discrete = F, end = .8) +
# # limits = c(1, 10),
# # breaks = c(1:10),
# # labels = c(1:10)) +
# theme(plot.title = element_text(hjust = 0.5, size = 19, face = "bold"),
# legend.justification = c(1, 0),
# legend.position = c(0.82, 0.91),
# legend.title = element_text(size = 16, face = "bold"),
# strip.text = element_text(size = 12),
# #axis.ticks.length = unit(3, "cm"),
# legend.direction = "horizontal",
# plot.caption = element_text(size = 13)) +
# guides(fill = guide_colorbar(barwidth = 12, barheight = 0.5,
# title.position = "top", title.hjust = 0.5,
# label.theme = element_text(colour = "black", size = 14, angle = 0))) +
# labs(title = "German General Elections - AfD Results",
# caption = "299 Electoral Districts \nData from Federal Returning Officer ('Bundeswahlleiter') \n@FabioFavusMaxim; favstats.eu ") +
# transition_states(year, transition_length = 10, state_length = 4)
#
# # btw_map_anim
#
# # tidytemplate::ggsave_it(btw_map_anim, width = 8, height = 12)
#
# btw_map_anim %>% animate(
# nframes = 100, fps = 15, width = 666.6667, height = 1000, detail = 1
# )
#
# anim_save("images/btw_map_anim.gif")
```
## GGally
``` r
pacman::p_load(GGally)
plot_lagged_all <- btw_lagged_all %>%
ggpairs(lower = list(continuous = "smooth"),
mapping = aes(color = east_west)) +
labs(title = "Where did AfD voters come from?",
caption = "N = 299 Electoral Districts\nData from bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
for(i in 1:plot_lagged_all$nrow) {
for(j in 1:plot_lagged_all$ncol){
plot_lagged_all[i,j] <- plot_lagged_all[i,j] + ggthemes::scale_color_fivethirtyeight()
}
}
plot_lagged_all
```
``` r
tidytemplate::ggsave_it(plot_lagged_all, width = 15, height = 13)
```
## SGB II
``` r
btw_data <- bind_rows(btw17_data %>%
mutate(year = "BTW 2017") %>%
mutate(part_of = as.character(part_of)),
btw13_data %>%
mutate(year = "BTW 2013"))
mod13 <- lm(perc_npd ~ sgbii, data = btw13_data)
mod17 <- lm(perc_npd ~ sgbii, data = btw17_data)
options(scipen = 999)
r2_13 <- get_r2_label(mod13)
r2_17 <- get_r2_label(mod17)
label_dat <- tibble(year = c("BTW 2013", "BTW 2017"),
text = c(
broom::tidy(mod13) %>%
filter(term == "sgbii") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_13)) %>%
.$text,
broom::tidy(mod17) %>%
filter(term == "sgbii") %>%
mutate(text = paste0("b = ", round(estimate, 2),
", ", get_labs(p.value),
", R Squared = ", r2_17)) %>%
.$text
))
gg_npd_sgbii <- btw_data %>%
ggplot(aes(sgbii, perc_npd)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~year) +
theme_minimal() +
geom_text(data = label_dat, aes(y = 5, x = 100, label = text), show.legend = F) +
labs(x = "sgbii Rate in %",
y = "Vote Share npd in %",
title = "npd Vote Share and sgbii Rate in the 2013 and 2017 German Elections",
caption = "N = 299 Electoral Districts\nData from govdata.de and bundeswahlleiter.de\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
strip.background = element_rect(fill = "lightgrey"),
strip.text = element_text(face = "bold"))
gg_npd_sgbii
```
``` r
tidytemplate::ggsave_it(gg_npd_sgbii, width = 10, height = 6)
```
## More BTWs
``` r
btw13_data <- tidytemplate::load_it("data/btw13_data.Rdata")
btw17_data <- tidytemplate::load_it("data/btw17_data.Rdata")
land_data <- tidytemplate::load_it("data/land_data.Rdata") %>%
mutate(part_of = as.character(part_of))
vars09 <- readr::read_delim("data/btw_kerg/btw09_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
names(.) %>%
stringi::stri_enc_tonative() %>%
str_replace("\032", "U") %>%
str_replace("Nr", "wahlkreis_nr") %>%
str_replace("Gebiet", "kreis") %>%
str_replace("gehUrt", "part_of") %>%
str_replace("GUltige", "valid") %>%
.[!(str_detect(., "X|Wahlberechtigte|WUhler|UngUltige"))]
btw09_data <- readr::read_delim("data/btw_kerg/btw09_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Endgltig", .)) %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Zweitstimme", .)) %>%
select(-x6, -x10, -x14) %>%
set_names(vars09) %>%
mutate(cdu_csu = ifelse(is.na(CDU), CSU, CDU)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(SPD, valid)) %>%
mutate(perc_linke = get_percentage(`DIE LINKE`, valid)) %>%
mutate(perc_fdp = get_percentage(FDP, valid)) %>%
mutate(perc_grun = get_percentage(`GRUNE`, valid)) %>%
# mutate(perc_afd = get_percentage(AfD, valid)) %>%
mutate(NPD = ifelse(is.na(NPD), "0", NPD)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, #perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99) %>%
left_join(land_data)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
tidytemplate::save_it(btw09_data)
btw09_data
```
## # A tibble: 299 x 11
## wahlkreis_nr kreis part_of valid perc_npd perc_spd perc_linke perc_fdp
##
## 1 1 Flen~ 1 1591~ 0.779 26.3 8.47 15.2
## 2 2 Nord~ 1 1314~ 0.857 24.5 6.72 17.2
## 3 3 "Ste~ 1 1252~ 1.21 25.0 7.83 17.5
## 4 4 "Ren~ 1 1469~ 0.830 26.7 6.68 15.9
## 5 5 Kiel 1 1432~ 0.890 29.6 9.38 13.4
## 6 6 "Pl\~ 1 1249~ 1.05 28.5 7.15 15.8
## 7 7 Pinn~ 1 1729~ 1.01 26.3 7.75 16.6
## 8 8 Sege~ 1 1769~ 0.953 25.1 8.07 17.7
## 9 9 Osth~ 1 1288~ 0.925 28.2 7.25 17.6
## 10 10 "Her~ 1 1783~ 1.19 25.4 7.77 17.2
## # ... with 289 more rows, and 3 more variables: perc_grun ,
## # perc_cdu_csu , land
``` r
vars05 <- readr::read_delim("data/btw_kerg/btw05_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
names(.) %>%
stringi::stri_enc_tonative() %>%
str_replace("\032", "U") %>%
str_replace("Nr", "wahlkreis_nr") %>%
str_replace("Gebiet", "kreis") %>%
str_replace("gehUrt", "part_of") %>%
str_replace("GUltige", "valid") %>%
.[!(str_detect(., "X|Wahlberechtigte|WUhler|UngUltige"))]
btw05_data <- readr::read_delim("data/btw_kerg/btw05_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Endgltig", .)) %>%
select(nr, gebiet, geh_f6_rt, str_which(pattern = "Zweitstimme", .)) %>%
select(-x6, -x10, -x14) %>%
set_names(vars05) %>%
mutate(cdu_csu = ifelse(is.na(CDU), CSU, CDU)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(SPD, valid)) %>%
mutate(perc_linke = get_percentage(`Die Linke.`, valid)) %>%
mutate(perc_fdp = get_percentage(FDP, valid)) %>%
mutate(perc_grun = get_percentage(`GRUNE`, valid)) %>%
# mutate(perc_afd = get_percentage(AfD, valid)) %>%
mutate(NPD = ifelse(is.na(NPD), "0", NPD)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, #perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99) %>%
left_join(land_data)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
tidytemplate::save_it(btw05_data)
btw05_data
```
## # A tibble: 299 x 11
## wahlkreis_nr kreis part_of valid perc_npd perc_spd perc_linke perc_fdp
##
## 1 1 Flen~ 1 1692~ 0.803 39.1 4.80 9.69
## 2 2 Nord~ 1 1417~ 0.840 36.2 3.86 10.1
## 3 3 "Ste~ 1 1369~ 1.22 36.3 4.59 10.7
## 4 4 "Ren~ 1 1567~ 0.929 37.6 4.16 9.89
## 5 5 Kiel 1 1489~ 0.924 41.5 5.97 8.85
## 6 6 "Pl\~ 1 1348~ 1.13 39.5 4.46 9.60
## 7 7 Pinn~ 1 1833~ 0.966 37.4 4.39 10.3
## 8 8 Sege~ 1 1865~ 0.915 36.6 4.38 11.3
## 9 9 Osth~ 1 1391~ 0.970 38.8 4.16 10.4
## 10 10 "Her~ 1 1878~ 1.23 35.8 4.37 11.2
## # ... with 289 more rows, and 3 more variables: perc_grun ,
## # perc_cdu_csu , land
``` r
vars02 <- readr::read_delim("data/btw_kerg/btw02_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
select(Wahlkreis, X2, Land, str_which(pattern = "Zweitstimme", .)) %>%
select(-`Ungltige_1`) %>%
names(.) %>%
stringi::stri_enc_toascii() %>%
str_replace("_1", "") %>%
str_replace("\032", "U") %>%
str_replace("Wahlkreis", "wahlkreis_nr") %>%
str_replace("X2", "kreis") %>%
str_replace("Land", "part_of") %>%
str_replace("GRNE", "GRUNE") %>%
str_replace("Gltige", "valid")
btw02_data <- readr::read_delim("data/btw_kerg/btw02_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(wahlkreis, x2, land, str_which(pattern = "Zweitstimme", .), -ung_fc_ltige_1) %>%
.[-1,] %>%
set_names(vars02) %>%
mutate(wahlkreis_nr = as.integer(wahlkreis_nr)) %>%
mutate(part_of = str_sub(part_of, 2) %>%
as.integer %>% as.character) %>%
mutate(cdu_csu = ifelse(is.na(CDU), CSU, CDU)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(SPD, valid)) %>%
mutate(perc_linke = get_percentage(PDS, valid)) %>%
mutate(perc_fdp = get_percentage(FDP, valid)) %>%
mutate(perc_grun = get_percentage(`GRUNE`, valid)) %>%
# mutate(perc_afd = get_percentage(AfD, valid)) %>%
mutate(NPD = ifelse(is.na(NPD), "0", NPD)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, #perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99) %>%
left_join(land_data)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
tidytemplate::save_it(btw02_data)
btw02_data
```
## # A tibble: 299 x 11
## wahlkreis_nr kreis part_of valid perc_npd perc_spd perc_linke perc_fdp
##
## 1 1 Flen~ 1 1702~ 0.190 44.0 1.29 7.45
## 2 2 Nord~ 1 1429~ 0.223 42.4 1.11 8.20
## 3 3 "Ste~ 1 1387~ 0.315 41.2 1.28 8.83
## 4 4 "Ren~ 1 1577~ 0.237 42.6 1.17 8.03
## 5 5 Kiel 1 1512~ 0.365 44.3 2.15 8.28
## 6 6 "Pl\~ 1 1375~ 0.302 44.6 1.22 7.67
## 7 7 Pinn~ 1 1857~ 0.215 41.6 1.19 8.30
## 8 8 Sege~ 1 1863~ 0.228 41.2 1.27 8.69
## 9 9 Osth~ 1 1400~ 0.278 44.0 0.954 7.83
## 10 10 "Her~ 1 1887~ 0.310 40.0 1.15 8.17
## # ... with 289 more rows, and 3 more variables: perc_grun ,
## # perc_cdu_csu , land
``` r
vars98 <- readr::read_delim("data/btw_kerg/btw98_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
select(Wahlkreis, X2, Land, str_which(pattern = "Zweitstimme", .)) %>%
select(-`Ungltige_1`) %>%
names(.) %>%
stringi::stri_enc_toascii() %>%
str_replace("_1", "") %>%
str_replace("\032", "U") %>%
str_replace("Wahlkreis", "wahlkreis_nr") %>%
str_replace("X2", "kreis") %>%
str_replace("Land", "part_of") %>%
str_replace("GRNE", "GRUNE") %>%
str_replace("Gltige", "valid")
btw98_data <- readr::read_delim("data/btw_kerg/btw98_kerg.csv",
";", escape_double = FALSE,
comment = "#",
trim_ws = TRUE) %>%
janitor::clean_names() %>%
select(wahlkreis, x2, land, str_which(pattern = "Zweitstimme", .), -ung_fc_ltige_1) %>%
.[-1,] %>%
set_names(vars98) %>%
mutate(wahlkreis_nr = as.integer(wahlkreis_nr)) %>%
mutate(part_of = str_sub(part_of, 2) %>%
as.integer %>% as.character) %>%
mutate(cdu_csu = ifelse(is.na(CDU), CSU, CDU)) %>%
mutate(perc_cdu_csu = get_percentage(cdu_csu, valid)) %>%
mutate(perc_spd = get_percentage(SPD, valid)) %>%
mutate(perc_linke = get_percentage(PDS, valid)) %>%
mutate(perc_fdp = get_percentage(`F.D.P.`, valid)) %>%
mutate(perc_grun = get_percentage(`GRUNE`, valid)) %>%
# mutate(perc_afd = get_percentage(AfD, valid)) %>%
mutate(NPD = ifelse(is.na(NPD), "0", NPD)) %>%
mutate(perc_npd = get_percentage(NPD, valid)) %>%
select(wahlkreis_nr, kreis, part_of, valid, #perc_afd,
perc_npd, perc_spd, perc_linke, perc_fdp,
perc_grun, perc_cdu_csu) %>%
drop_na(wahlkreis_nr) %>%
filter(part_of != 99) %>%
left_join(land_data)
```
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
## Non-numeric variables.. converting
``` r
tidytemplate::save_it(btw98_data)
btw98_data
```
## # A tibble: 328 x 11
## wahlkreis_nr kreis part_of valid perc_npd perc_spd perc_linke perc_fdp
##
## 1 1 Flen~ 1 1711~ 0.0912 47.2 1.33 6.57
## 2 2 Nord~ 1 1365~ 0.174 43.4 1.12 7.82
## 3 3 "Ste~ 1 1330~ 0.169 45.5 1.16 7.36
## 4 4 "Ren~ 1 1708~ 0.146 44.5 1.28 8.18
## 5 5 Kiel 1 1429~ 0.149 49.2 2.41 6.87
## 6 6 "Pl\~ 1 1315~ 0.160 47.7 1.47 6.69
## 7 7 Pinn~ 1 1837~ 0.144 44.9 1.47 8.27
## 8 8 Sege~ 1 1950~ 0.132 43.6 1.49 8.50
## 9 9 Osth~ 1 1392~ 0.141 44.9 1.17 7.45
## 10 10 "Her~ 1 2041~ 0.238 42.8 1.46 8.35
## # ... with 318 more rows, and 3 more variables: perc_grun ,
## # perc_cdu_csu , land
``` r
# btw98_data <- tidytemplate::load_it("data/btw98_data.Rdata")
btw02_data <- tidytemplate::load_it("data/btw02_data.Rdata")
btw05_data <- tidytemplate::load_it("data/btw05_data.Rdata")
btw09_data <- tidytemplate::load_it("data/btw09_data.Rdata")
btw13_data <- tidytemplate::load_it("data/btw13_data.Rdata")
btw17_data <- tidytemplate::load_it("data/btw17_data.Rdata")
btw_data <- bind_rows(btw17_data %>%
mutate(year = "BTW 2017") %>%
mutate(part_of = as.character(part_of)),
btw13_data %>%
mutate(year = "BTW 2013"),
btw09_data %>%
mutate(year = "BTW 2009") %>%
mutate(part_of = as.character(part_of)),
btw05_data %>%
mutate(year = "BTW 2005") %>%
mutate(part_of = as.character(part_of)),
btw02_data %>%
mutate(year = "BTW 2002") %>%
mutate(part_of = as.character(part_of))) %>%
mutate(east_west = case_when(
str_detect(land, "Berlin|Brand|Nieder|Sachs") ~ "East Germany",
T ~ "West Germany"
))
library(gganimate)
results <- btw_data %>%
mutate(year_label = year) %>%
mutate(year = parse_number(year)) %>%
group_by(year_label, year) %>%
summarize(perc_npd = mean(perc_npd))
anim_npd <- btw_data %>%
mutate(year = parse_number(year)) %>%
ggplot(aes(year, perc_npd)) +
geom_line(aes(group = wahlkreis_nr, color = east_west),
size = 0.8, alpha = 0.2) +
geom_line(data = results, size = 1,
color = "black", linetype = "dashed") +
# geom_vline(data = results, aes(xintercept = year),
# linetype = "dashed", color = "darkgrey") +
geom_text(data = results, aes(x = year, y = 6.5, label = year_label),
angle = 90, color = "black", nudge_x = 0.3) +
ggthemes::scale_color_gdocs("Region") +
scale_x_continuous(breaks = c(2002, 2005, 2009, 2013, 2017),
labels = c(2002, 2005, 2009, 2013, 2017)) +
# gganimate::transition_reveal(wahlkreis_nr, year) +
theme_minimal() +
labs(x = "", y = "Vote Share NPD in %\n",
title = "NPD Vote Share in German National Eelections (2002 - 2017)\n",
caption = "N = 299 Electoral Districts\nData from Federal Returning Officer ('Bundeswahlleiter')\n@FabioFavusMaxim; favstats.eu") +
theme(title = element_text(face = "bold"),
legend.position = "bottom")
anim_npd
```
``` r
tidytemplate::ggsave_it(anim_npd, width = 10, height = 6)
# anim_npd %>% animate(
# nframes = 100, fps = 15, width = 1000, height = 600, detail = 1
# )
#
# anim_save("images/anim_npd.gif")
```