Disclaimer: The data here are not official (YSK has not published the official results yet as of 18 May 2023), and may be inaccurate (most noticeably overseas votes are not included). The data is from this link.
More reliable unofficial results can be found here, here, and here.
Note: I updated Erdogan’s (rte) and Kilicdaroglu’s (kk) votes to rte_2 = rte/rte+kk and kk_2 = kk/rte+kk respectively.
Show the code
library(tidyverse)library(magrittr)library(TRmaps)library(sf)#devtools::install_github("htastan/TRmaps")# data from here: https://github.com/liberbey/secim-sonuclari/tree/main# Overseas data, and Samsun 19 Mayis data are missing.results_1<-read.csv("secim_sonuclari_2023_2018.csv")results_1%<>%filter(ilce!="19-May")results_1$rte[results_1$ilce=="19 Mayıs"]<-74.4results_1$kk[results_1$ilce=="19 Mayıs"]<-20.9results_1$rte_2018[results_1$ilce=="19 Mayıs"]<-77results_1$gecerli_oy_sayisi_2018[results_1$ilce=="19 Mayıs"]<-16540results_1$gecerli_oy_sayisi_2023[results_1$ilce=="19 Mayıs"]<-17820# Function to convert Turkish characters to English equivalentsconvertTurkishCharacters<-function(text){text<-gsub("İ", "i", text)text<-gsub("I", "i", text)text<-gsub("Ü", "u", text)text<-gsub("Ç", "c", text)text<-gsub("Ş", "s", text)text<-gsub("Ö", "o", text)text<-gsub("Ğ", "g", text)text<-tolower(text)return(text)}# Apply the transformation to 'ilce' variableresults_1$ilce2<-convertTurkishCharacters(results_1$ilce)# Create 'il-ilce' variable by combining 'sehir' and updated 'ilce'results_1$il_ilce<-paste(results_1$sehir, results_1$ilce2, sep ="-")results_1$il_ilce<-toupper(results_1$il_ilce)results_1%<>%mutate( rte_2 =rte/(100-so-mi), kk_2 =kk/(100-so-mi), kk_wins =ifelse(kk_2>=0.5, "KK", "RTE"), rte_diff =rte-rte_2018, voter_no_diff =secmen_sayisi_2023-secmen_sayisi_2018)rte_vote_2023<-sum(results_1$rte*results_1$gecerli_oy_sayisi_2023/100, na.rm =TRUE)/sum(results_1$gecerli_oy_sayisi_2023, na.rm =TRUE)#rte_vote_2023kk_vote_2023<-sum(results_1$kk*results_1$gecerli_oy_sayisi_2023/100, na.rm =TRUE)/sum(results_1$gecerli_oy_sayisi_2023, na.rm =TRUE)#kk_vote_2023
Erdogan’s Votes in 2023 (Round 1): Municipality-level
Show the code
data("tr_ilce")# joined_data <- tr_ilce %>%# st_as_sf() %>%# left_join(results_1, by = c("il_ilce_en_cap" = "il_ilce"))results_1_join<-tr_ilce%>%left_join(results_1, by =c("il_ilce_en_cap"="il_ilce"))map_rte<-ggplot()+geom_sf(data =results_1_join, color ="gray90", fill ="gray90")+geom_sf(data =results_1_join, color ="black", aes(fill =rte_2))+theme_void()+theme(legend.position ="bottom", legend.title =element_text(face ="bold"))+#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030"))+labs(fill ="Erdogan Votes")+colorspace::scale_fill_continuous_sequential(palette ="Oslo", trans ="reverse")map_rte
In the first figure, you have tea vs. coffee-related search trends on Google in Türkiye from 2004 until 2023 (although in recent years coffee preference seems to be increasing.) Red means more coffee searches, while blue means more tea searches.
(a) Coffee vs. Tea
(b) 2023 Presidential Election
Figure 1: Coincidence?
::: :::
Source Code
---title: "Visuzalization of 2023 Turkish Presidential Elections Results"author: "Kadir Jun Ayhan"date: 2023-05-18format: html: code-fold: true code-tools: true code-summary: "Show the code"editor: visualecho: truewarning: falsecategories: - Türkiye - map - Rimage: tea-coffee.pngdraft: false---**Disclaimer:** The data here are not official (YSK has not published the official results yet as of 18 May 2023), and may be inaccurate (most noticeably overseas votes are not included). The data is from this [link](https://github.com/liberbey/secim-resultslari/tree/main).More reliable unofficial results can be found [here](https://github.com/egeakman/2023-oveo-data), [here](https://tutanak.oyveotesi.org/), and [here](https://github.com/seckinu/turkey-2023-votes).**Note:** I updated Erdogan's (`rte`) and Kilicdaroglu's (`kk`) votes to `rte_2 = rte/rte+kk` and `kk_2 = kk/rte+kk` respectively.```{r}library(tidyverse)library(magrittr)library(TRmaps)library(sf)#devtools::install_github("htastan/TRmaps")# data from here: https://github.com/liberbey/secim-sonuclari/tree/main# Overseas data, and Samsun 19 Mayis data are missing.results_1 <-read.csv("secim_sonuclari_2023_2018.csv")results_1 %<>%filter(ilce !="19-May")results_1$rte[results_1$ilce =="19 Mayıs"] <-74.4results_1$kk[results_1$ilce =="19 Mayıs"] <-20.9results_1$rte_2018[results_1$ilce =="19 Mayıs"] <-77results_1$gecerli_oy_sayisi_2018[results_1$ilce =="19 Mayıs"] <-16540results_1$gecerli_oy_sayisi_2023[results_1$ilce =="19 Mayıs"] <-17820# Function to convert Turkish characters to English equivalentsconvertTurkishCharacters <-function(text) { text <-gsub("İ", "i", text) text <-gsub("I", "i", text) text <-gsub("Ü", "u", text) text <-gsub("Ç", "c", text) text <-gsub("Ş", "s", text) text <-gsub("Ö", "o", text) text <-gsub("Ğ", "g", text) text <-tolower(text)return(text)}# Apply the transformation to 'ilce' variableresults_1$ilce2 <-convertTurkishCharacters(results_1$ilce)# Create 'il-ilce' variable by combining 'sehir' and updated 'ilce'results_1$il_ilce <-paste(results_1$sehir, results_1$ilce2, sep ="-")results_1$il_ilce <-toupper(results_1$il_ilce )results_1 %<>%mutate(rte_2 = rte / (100- so - mi),kk_2 = kk / (100- so - mi),kk_wins =ifelse(kk_2 >=0.5, "KK", "RTE"),rte_diff = rte - rte_2018,voter_no_diff = secmen_sayisi_2023 - secmen_sayisi_2018)rte_vote_2023 <-sum(results_1$rte*results_1$gecerli_oy_sayisi_2023/100, na.rm =TRUE) /sum(results_1$gecerli_oy_sayisi_2023, na.rm =TRUE) #rte_vote_2023kk_vote_2023 <-sum(results_1$kk*results_1$gecerli_oy_sayisi_2023/100, na.rm =TRUE) /sum(results_1$gecerli_oy_sayisi_2023, na.rm =TRUE) #kk_vote_2023```## Erdogan's Votes in 2023 (Round 1): Municipality-level```{r}data("tr_ilce")# joined_data <- tr_ilce %>%# st_as_sf() %>%# left_join(results_1, by = c("il_ilce_en_cap" = "il_ilce"))results_1_join <- tr_ilce %>%left_join(results_1, by =c("il_ilce_en_cap"="il_ilce"))map_rte <-ggplot() +geom_sf(data = results_1_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_join, color ="black", aes(fill = rte_2)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Erdogan Votes") + colorspace::scale_fill_continuous_sequential(palette ="Oslo", trans ="reverse")map_rteggsave("map_rte_ilce.jpg", map_rte, dpi =300)```## Erdogan's Votes in 2023 (Round 1): City-level```{r}data("tr_nuts3")tr_nuts3$name_eng <-tolower(tr_nuts3$name_eng)results_1_il <- results_1 %>%group_by(sehir) %>%summarise(toplam_oy_2023 =sum(gecerli_oy_sayisi_2023, na.rm = T),toplam_oy_2018 =sum(gecerli_oy_sayisi_2018, na.rm = T),rte =sum(rte*gecerli_oy_sayisi_2023/ toplam_oy_2023, na.rm = T),rte_2 =sum(rte_2*gecerli_oy_sayisi_2023/ toplam_oy_2023, na.rm = T),rte_2018 =sum(rte_2018*gecerli_oy_sayisi_2018/ toplam_oy_2018, na.rm = T),kk_2 =sum(kk_2*gecerli_oy_sayisi_2023/ toplam_oy_2023, na.rm = T))results_1_il %<>%mutate(kk_wins =ifelse(kk_2 >=0.5, "KK", "RTE"),rte_diff = rte - rte_2018)results_1_il_join <- tr_nuts3 %>%left_join(results_1_il, by =c("name_eng"="sehir"))map_rte_il <-ggplot() +geom_sf(data = results_1_il_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_il_join, color ="black", aes(fill = rte_2)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Erdogan Votes") + colorspace::scale_fill_continuous_sequential(palette ="Oslo", trans ="reverse")map_rte_ilggsave("map_rte_il.jpg", map_rte_il, dpi =300)```## ## Kilicdaroglu's Votes in 2023 (Round 1): Municipality-level```{r}map_kk <-ggplot() +geom_sf(data = results_1_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_join, color ="black", aes(fill = kk_2)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Kilicdaroglu Votes") + colorspace::scale_fill_continuous_sequential(palette ="Reds")map_kkggsave("map_kk_ilce.jpg", map_kk, dpi =300)```## Kilicdaroglu's Votes in 2023 (Round 1): City-level```{r}map_kk_il <-ggplot() +geom_sf(data = results_1_il_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_il_join, color ="black", aes(fill = kk_2)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Kilicdaroglu Votes") + colorspace::scale_fill_continuous_sequential(palette ="Reds")map_kk_ilggsave("map_kk_il.jpg", map_kk_il, dpi =300)```## Who has more votes in each municipality in 2023 (Round 1): Municipality-level```{r}results_filtered <- results_1_join[!is.na(results_1_join$kk_wins), ]# Plot the map with the filtered datamap_who <-ggplot() +geom_sf(data = results_1_join, color ="gray90", fill ="gray90") +geom_sf(data = results_filtered, color ="black", aes(fill = kk_wins)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Who wins (2 candidates)") +scale_fill_discrete(drop =FALSE, na.value ="transparent")map_whoggsave("map_who_ilce.jpg", map_who, dpi =300)```## Who has more votes in each city in 2023 (Round 1): City-level```{r}results_il_filtered <- results_1_il_join[!is.na(results_1_il_join$kk_wins), ]# Plot the map with the filtered datamap_who_il <-ggplot() +geom_sf(data = results_1_il_join, color ="gray90", fill ="gray90") +geom_sf(data = results_il_filtered, color ="black", aes(fill = kk_wins)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Who wins (2 candidates)") +scale_fill_discrete(drop =FALSE, na.value ="transparent")map_who_ilggsave("map_who_il.jpg", map_who_il, dpi =300)```## How much did Erdogan's votes change from 2018 to 2023 (first round)? Municipality-level```{r}map_rte_diff <-ggplot() +geom_sf(data = results_1_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_join, color ="black", aes(fill = rte_diff)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Erdogan Vote Difference (2023-2018)") + colorspace::scale_fill_continuous_sequential(palette ="SunsetDark")map_rte_diffggsave("map_rte_diff_ilce.jpg", map_rte_diff, dpi =300)```## How much did Erdogan's votes change from 2018 to 2023 (first round)? City-level```{r}map_rte_diff_il <-ggplot() +geom_sf(data = results_1_il_join, color ="gray90", fill ="gray90") +geom_sf(data = results_1_il_join, color ="black", aes(fill = rte_diff)) +theme_void() +theme(legend.position ="bottom",legend.title =element_text(face ="bold")) +#scale_x_continuous(expand = c(0, 0)) +#viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") +coord_sf(crs =st_crs("ESRI:54030")) +labs(fill ="Erdogan Vote Difference (2023-2018)") + colorspace::scale_fill_continuous_sequential(palette ="SunsetDark")map_rte_diff_ilggsave("map_rte_diff_il.jpg", map_rte_diff_il, dpi =300)```## Erdogan's votes in 2018 and 2023 (Round 1): Municipality-level```{r}rte_compare <- results_1_join %>%select(rte, rte_2018, il_ilce)rte_compare %<>%rename(rte_2023 = rte)#rte_compare %<>% mutate(diff = rte_2023 - rte_2018)#rte_compare_pivot <- rte_compare %>% pivot_longer(1:2, names_to = "year", values_to = "vote") #rte_compare_pivot$year <- readr::parse_number(rte_compare_pivot$year)rte_compare %>%ggplot(aes(rte_2018, rte_2023)) +geom_point()```## Erdogan's votes in 2018 and 2023 (Round 1): City-level```{r}rte_compare_il <- results_1_il_join %>%select(rte, rte_2018, name_eng)rte_compare_il %<>%rename(rte_2023 = rte)#rte_compare_il %<>% mutate(diff = rte_2023 - rte_2018)#rte_compare_il_pivot <- rte_compare_il %>% pivot_longer(1:2, names_to = "year", values_to = "vote") #rte_compare_il_pivot$year <- readr::parse_number(rte_compare_il_pivot$year)rte_compare_il %>%ggplot(aes(rte_2018, rte_2023)) +geom_point()```## Just a fun pic to look atIn the first figure, you have tea vs. coffee-related search trends on Google in Türkiye from 2004 until 2023 (although in recent years coffee preference seems to be increasing.) [Red]{style="color: red"} means more coffee searches, while [blue]{style="color: blue"} means more tea searches.::: {#fig-coffee-election layout-ncol="2"}{#fig-coffee}{#fig-prez-city}Coincidence?:::::: :::
