Visuzalization of 2023 Turkish Presidential Elections Results

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.

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"] <- 74.4
results_1$kk[results_1$ilce == "19 Mayıs"] <- 20.9
results_1$rte_2018[results_1$ilce == "19 Mayıs"] <- 77
results_1$gecerli_oy_sayisi_2018[results_1$ilce == "19 Mayıs"] <- 16540
results_1$gecerli_oy_sayisi_2023[results_1$ilce == "19 Mayıs"] <- 17820


# Function to convert Turkish characters to English equivalents
convertTurkishCharacters <- 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' variable
results_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_2023


kk_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

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

Code

ggsave("map_rte_ilce.jpg", map_rte, dpi =300)

Erdogan’s Votes in 2023 (Round 1): City-level

Code

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_il

Code

ggsave("map_rte_il.jpg", map_rte_il, dpi =300)

Kilicdaroglu’s Votes in 2023 (Round 1): Municipality-level

Code

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_kk

Code

ggsave("map_kk_ilce.jpg", map_kk, dpi =300)

Kilicdaroglu’s Votes in 2023 (Round 1): City-level

Code

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_il

Code

ggsave("map_kk_il.jpg", map_kk_il, dpi = 300)

Who has more votes in each municipality in 2023 (Round 1): Municipality-level

Code

results_filtered <- results_1_join[!is.na(results_1_join$kk_wins), ]

# Plot the map with the filtered data
map_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_who

Code

ggsave("map_who_ilce.jpg", map_who, dpi = 300)

Who has more votes in each city in 2023 (Round 1): City-level

Code

results_il_filtered <- results_1_il_join[!is.na(results_1_il_join$kk_wins), ]

# Plot the map with the filtered data
map_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_il

Code

ggsave("map_who_il.jpg", map_who_il, dpi = 300)

How much did Erdogan’s votes change from 2018 to 2023 (first round)? Municipality-level

Code

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_diff

Code

ggsave("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

Code

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_il

Code

ggsave("map_rte_diff_il.jpg", map_rte_diff_il, dpi = 300)

Erdogan’s votes in 2018 and 2023 (Round 1): Municipality-level

Code

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

Code

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 at

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.

Figure 1: Coincidence?

::: :::

--- title: "Visuzalization of 2023 Turkish Presidential Elections Results" author: "Kadir Jun Ayhan" date: 2023-05-18 format: html: code-fold: true code-tools: true code-summary: "Show the code" editor: visual echo: true warning: false categories: - Türkiye - map - R image: tea-coffee.png draft: 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"] <- 74.4 results_1$kk[results_1$ilce == "19 Mayıs"] <- 20.9 results_1$rte_2018[results_1$ilce == "19 Mayıs"] <- 77 results_1$gecerli_oy_sayisi_2018[results_1$ilce == "19 Mayıs"] <- 16540 results_1$gecerli_oy_sayisi_2023[results_1$ilce == "19 Mayıs"] <- 17820 # Function to convert Turkish characters to English equivalents convertTurkishCharacters <- 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' variable results_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_2023 kk_vote_2023 <- sum(results_1$kk*results_1$gecerli_oy_sayisi_2023/100, na.rm = TRUE) / sum(results_1$gecerli_oy_sayisi_2023, na.rm = TRUE) #kk_vote_2023 ``` ## Erdogan's Votes in 2023 (Round 1): Municipality-level ```{r} data("tr_ilce") # joined_data <- tr_ilce %>% # st_as_sf() %>% # left_join(results_1, by = c("il_ilce_en_cap" = "il_ilce")) results_1_join <- tr_ilce %>% left_join(results_1, by = c("il_ilce_en_cap" = "il_ilce")) map_rte <- ggplot() + geom_sf(data = results_1_join, color = "gray90", fill = "gray90") + geom_sf(data = results_1_join, color = "black", aes(fill = rte_2)) + theme_void() + theme(legend.position = "bottom", legend.title = element_text(face = "bold")) + #scale_x_continuous(expand = c(0, 0)) + #viridis::scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.65, option = "magma") + coord_sf(crs = st_crs("ESRI:54030")) + labs(fill = "Erdogan Votes") + colorspace::scale_fill_continuous_sequential(palette = "Oslo", trans = "reverse") map_rte ggsave("map_rte_ilce.jpg", map_rte, dpi =300) ``` ## Erdogan's Votes in 2023 (Round 1): City-level ```{r} data("tr_nuts3") tr_nuts3$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_il ggsave("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_kk ggsave("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_il ggsave("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 data map_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_who ggsave("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 data map_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_il ggsave("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_diff ggsave("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_il ggsave("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 at 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]{style="color: red"} means more coffee searches, while [blue]{style="color: blue"} means more tea searches. ::: {#fig-coffee-election layout-ncol="2"} ![Coffee vs. Tea](tea-coffee.png){#fig-coffee} ![2023 Presidential Election](map_who_il.jpg){#fig-prez-city} Coincidence? ::: ::: :::