Some of the graphs I presented at the International Symposium of the International Studies Center

democracy_presentation.R

## This R Script has code for some of the visualizations I made for my talk 
## at the CSU International Symposium on Democracy


# Install Packages
if(!require('pacman')) { 
  install.packages('pacman') 
  library('pacman') 
} 

# loading the p_load function 
p_load(devtools, tidyverse, scales,haven,sjlabelled,
       ggrepel, viridis, ggthemes, maps, countrycode,
       install = TRUE) 

devtools::install_github("vdeminstitute/vdemdata")
install_github("jamesmartherus/anesr")

library(vdemdata)
library(anesr)


# Generate a world map with changes between 2002 and 2022
df_vdem_map <-
  vdem |> 
  dplyr::select(country_name, country_id, year, 
                v2x_polyarchy, v2x_libdem, v2x_partipdem, v2x_egaldem) |>
  filter(year %in% c(2002, 2022)) |>
  group_by(country_name) |> 
  mutate(delta_polyarchy = v2x_polyarchy - lag(v2x_polyarchy)) |> 
  filter(year == 2022) 

world_map = map_data("world")
world_map$country_id <- countrycode(world_map$region, origin = 'country.name', destination = 'vdem')

df_democracy <-
  world_map |> 
  left_join(df_vdem_map)

world_map = map_data("world") %>% 
  filter(! long > 180)

world_map$country_id <- countrycode(world_map$region, origin = 'country.name', destination = 'vdem')

df_democracy <-
  world_map |> 
  left_join(df_vdem_map)

countries = df_democracy %>% 
  distinct(region,delta_polyarchy) 

countries %>% 
  filter(region != "Antarctica") |> 
  ggplot(aes(fill = delta_polyarchy, map_id = region)) +
  geom_map(map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat) +
  scale_fill_viridis(option="inferno") + theme_bw() +
  labs(x = "Longitude", y = "Lattitude",
       fill='Change in Democracy',
       title = "Democracy around the world",
       subtitle = "V-Dem's Polyarchy between 2002 and 2022")  +
  theme(legend.position="bottom") +
  coord_map("moll") +
  theme_map() + theme(text=element_text(size=15))


# Delta Plot of democracy scores for a country in 2002 and 2022
vdem |> 
  dplyr::select(country_name, year, v2x_polyarchy) |> 
  group_by(country_name) |> 
  filter(year %in% c(2002, 2022)) |>
  mutate(delta_polyarchy = v2x_polyarchy - lag(v2x_polyarchy),
         lag_polyarchy =  lag(v2x_polyarchy),
         pos_dummy = ifelse(delta_polyarchy >=0, "positive", "negative")) |> 
  drop_na() |> 
  filter(year == 2022) |> 
  ggplot(aes(y = v2x_polyarchy, x = lag_polyarchy, color = pos_dummy, label = country_name)) +
  geom_point(alpha = 1/2) + theme_bw() +
  geom_abline(intercept = 0, slope = 1, color = "black", linetype = "dashed") +
  ylim(0,1) + xlim(0,1) + geom_text_repel() + theme(legend.position = "none") +
  labs(title = "Changes in Polyarchy between 2002 and 2022",
       caption = "Note: Analysis based on V-Dem Version 14",
       y = "Polyarchy, 2022",
       x = "Polyarchy, 2002") + theme(text=element_text(size=15))

# Gobal democracy average since 1789
vdem |> 
  group_by(year) |> 
  summarize(mean_polyarchy = mean(v2x_polyarchy, na.rm = TRUE), n = n()) |> 
  ggplot(aes(y = mean_polyarchy, x = year)) +
  geom_line() +
  theme_bw() +
  labs(title = "Average Democracy in the World",
       caption = "Note: Analysis based on V-Dem Version 14",
       x = "Year",
       y = "V-Dem Polyarchy, global average") +
  ylim(0,1)+ theme(text=element_text(size=15))+
  annotate('rect', xmin=2008, xmax=2024, ymin=0.4, ymax=0.62, alpha=.2, fill='red')


## Examining attitudes of American voters 
data(timeseries_2020)
data(timeseries_cum) #Load Time Series Cumulative File (1948-2016)

# 2020 is missing from the cumulative file. The code below prepares 2020 for a merge
# with the cumulative file
df_anes_2020 <- 
  timeseries_2020 |> 
  remove_all_labels() |> 
  dplyr::select(V201156, V201157, V201228, V201229, V201230, V201231x, V200010a) |> 
  mutate(VCF0004 = 2020) |> 
  rename(year = VCF0004)|> # Year of response
  mutate(pid_3 = ifelse(V201231x %in% c(1,2, 3), "Democrat",
                        ifelse(V201231x %in% c(5,6,7), "Republican",
                               ifelse(V201231x == 4, "Independent", NA)))) |> 
  rename(pid_7 = V201231x)|> #7 scale Party ID val: 1-7. Strong Democrat 2. Weak Democrat3. Independent - Democrat4. Independent - Independent5. Independent - Republican6. Weak Republican7. Strong Republican
  #rename(pid_3 = V201228)|> #
  rename(weight = V200010a) |> 
  rename(therm_dem = V201156)|> # val 00-96 cold-warm as coded; 97: 97-100, 98: DK, 99. NA
  rename(therm_rep = V201157) |> # val 00-96 cold-warm as coded; 97: 97-100, 98: DK, 99. NA
  mutate(therm_dem = ifelse(therm_dem <0 | therm_dem > 100 , NA, therm_dem),
         therm_dem = ifelse(therm_dem %in% c(97,98,99,100), 97, therm_dem),
         therm_rep =ifelse(therm_rep <0 | therm_rep > 100, NA, therm_rep),
         therm_rep = ifelse(therm_rep %in% c(97,98,99,100), 97, therm_rep),
         strong_partisan = if_else(pid_7 == 1|pid_7 ==7, 1, 0),
         pid_7_num = as.numeric(pid_7),
         pid_7 = recode(pid_7, 
                        "1" = "Strong Democrat", 
                        "2" = "Weak Democrat", 
                        "3" = "Independent - Democrat", 
                        "4" = "Independent - Independent", 
                        "5" = "Independent - Republican", 
                        "6" = "Weak Republican", 
                        "7" = "Strong Republican"),
         pid_7 = reorder(pid_7, pid_7_num),
         pid_3_num = as.numeric(pid_3),
         pid_3 = recode(pid_3, 
                        "1" = "Democrat", 
                        "3" = "Independent", 
                        "2" = "Republican"),
         pid_3 = reorder(pid_3, pid_3_num),
         pid_3_sort = factor(recode(pid_7_num, #different from regular pid_3 which codes 3, 5 as partisan
                                    "1" = "Democrat",
                                    "2" = "Democrat",
                                    "3" = "Independent",
                                    "4" = "Independent",
                                    "5" = "Independent",
                                    "6" = "Republican",
                                    "7" = "Republican"),
                             levels = c("Democrat",
                                        "Independent",
                                        "Republican")),
         pid_2_sort = na_if(pid_3_sort, "Independent"), #better just to filter(pid_3_sort != "Independent"), but used to build other vars
         pid_2 = na_if(pid_3, "Independent"),
         therm_inparty = if_else(pid_3=="Democrat", therm_dem, therm_rep),
         therm_inparty = if_else(pid_3=="Democrat" | pid_3 == "Republican",
                                 if_else(pid_3=="Democrat", therm_dem, therm_rep),
                                 (therm_dem + therm_rep)/2), #therm in/out are equal
         therm_inparty = na_if(therm_inparty, -9),
         therm_outparty = if_else(pid_3=="Democrat" | pid_3 == "Republican",
                                  if_else(pid_3=="Democrat", therm_rep, therm_dem),
                                  (therm_dem + therm_rep)/2),
         npa_party = therm_inparty - therm_outparty,
         therm_parties_mean = (therm_dem + therm_rep)/2)

# The cumulative file
df_anes <-
  timeseries_cum |> 
  remove_all_labels() |> 
  dplyr::select(VCF0004, VCF0301, VCF0303, VCF0305, VCF0311,  VCF0312, VCF0803, 
                VCF0218, VCF0224, VCF0201, VCF0202,VCF0009z) |> 
  rename(year = VCF0004)|> # Year of response
  rename(pid_7 = VCF0301)|> #7 scale Party ID val: 1-7. Strong Democrat 2. Weak Democrat3. Independent - Democrat4. Independent - Independent5. Independent - Republican6. Weak Republican7. Strong Republican
  rename(pid_3 = VCF0303)|> # Party ID 3 categories val: "Republican", "Independent", "Democrat" (Dem/Rep include Leaners)
  rename(pid_str = VCF0305)|> # PID strength val: 1. Independent 2. Leaning Independent 3. Weak Partisan 4. Strong Partisan Kept this because I wanted to create basically this variable later
  rename(win_care_pres = VCF0311)|> # How much do you care which party wins presidency? val: 1. Don't care very much or DK, pro-con, depends, and other, 2. Care a great deal
  rename(win_care_cong = VCF0312)|> # How much do you care which party wins congress? val: 1. Don't care very much or DK, pro-con, depends, and other, 2. Care a great deal notes: only asked through 2008
  rename(respondent_ideo = VCF0803)|> # Liberal-conservative scale val: 1(extremely liberal)- 7(extremely conservative) 9. DK; haven't much thought about it
  rename(therm_dem = VCF0218)|> # val 00-96 cold-warm as coded; 97: 97-100, 98: DK, 99. NA
  rename(therm_rep = VCF0224) |> # val 00-96 cold-warm as coded; 97: 97-100, 98: DK, 99. NA
  rename(therm_dem_old = VCF0201) |>
  rename(therm_rep_old = VCF0202) |> 
  rename(weight = VCF0009z) |> 
  mutate(therm_dem = na_if(therm_dem, 98),
         therm_dem = na_if(therm_dem, 99),
         therm_rep = na_if(therm_rep, 98),
         therm_rep = na_if(therm_rep, 99),
         respondent_ideo = na_if(respondent_ideo, 9), #the recode() function is used in the next 4 pipes to apply new values to observation in the columns. ANES uses numerical values to represent factors.
         strong_partisan = if_else(pid_7 == 1|pid_7 ==7, 1, 0),
         pid_7_num = as.numeric(pid_7),
         pid_7 = recode(pid_7, 
                        "1" = "Strong Democrat", 
                        "2" = "Weak Democrat", 
                        "3" = "Independent - Democrat", 
                        "4" = "Independent - Independent", 
                        "5" = "Independent - Republican", 
                        "6" = "Weak Republican", 
                        "7" = "Strong Republican"),
         pid_7 = reorder(pid_7, pid_7_num),
         pid_3_num = as.numeric(pid_3),
         pid_3 = recode(pid_3, 
                        "1" = "Democrat", 
                        "2" = "Independent", 
                        "3" = "Republican"),
         pid_3 = reorder(pid_3, pid_3_num),
         pid_str_num = as.numeric(pid_str),
         pid_str = recode(pid_str, 
                          "1" = "Independent", 
                          "2" = "Leaning Independent", 
                          "3" = "Weak Partisan", 
                          "4" = "Strong Partisan"),
         pid_str = reorder(pid_str, pid_str_num),
         respondent_ideo_num = as.numeric(respondent_ideo),
         respondent_ideo = recode(respondent_ideo, 
                                  "1" = "Extremely Liberal", 
                                  "2" = "Liberal", 
                                  "3" = "Somewhat Liberal", 
                                  "4" = "Moderate", 
                                  "5" = "Somewhat Conservative", 
                                  "6" = "Conservative", 
                                  "7" = "Extremely Conservative"),
         respondent_ideo = reorder(respondent_ideo, respondent_ideo_num),
         pid_3_sort = factor(recode(pid_7_num, #different from regular pid_3 which codes 3, 5 as partisan
                                    "1" = "Democrat",
                                    "2" = "Democrat",
                                    "3" = "Independent",
                                    "4" = "Independent",
                                    "5" = "Independent",
                                    "6" = "Republican",
                                    "7" = "Republican"),
                             levels = c("Democrat",
                                        "Independent",
                                        "Republican")),
         pid_2_sort = na_if(pid_3_sort, "Independent"), #better just to filter(pid_3_sort != "Independent"), but used to build other vars
         pid_2 = na_if(pid_3, "Independent"),
         therm_inparty = if_else(pid_3=="Democrat", therm_dem, therm_rep),
         therm_inparty = if_else(pid_3=="Democrat" | pid_3 == "Republican",
                                 if_else(pid_3=="Democrat", therm_dem, therm_rep),
                                 (therm_dem + therm_rep)/2), #therm in/out are equal
         therm_inparty = na_if(therm_inparty, -9),
         therm_outparty = if_else(pid_3=="Democrat" | pid_3 == "Republican",
                                  if_else(pid_3=="Democrat", therm_rep, therm_dem),
                                  (therm_dem + therm_rep)/2),
         npa_party = therm_inparty - therm_outparty,
         therm_parties_mean = (therm_dem + therm_rep)/2,
         therm_dem_old = na_if(therm_dem_old, 98),
         therm_dem_old = na_if(therm_dem_old, 99),
         therm_rep_old = na_if(therm_rep_old, 98),
         therm_rep_old = na_if(therm_rep_old, 99),
         therm_party_ingroup = if_else(pid_2_sort=="Democrat", therm_dem_old, therm_rep_old),
         therm_party_outgroup = if_else(pid_2_sort=="Democrat", therm_rep_old, therm_dem_old),
         npa_partisans = therm_party_ingroup - therm_party_outgroup) |> 
  bind_rows(df_anes_2020)

# Prepare a data frame for visualizations
fig_1_df <- df_anes %>% 
  mutate(independent_count = str_count(pid_7, "Independent"), # Code here moves leaners into partisan coding
         pid_3_lean = ifelse(str_detect(pid_7, "Republican"), "Republican",
                             ifelse(str_detect(pid_7, "Democrat"), "Democrat",
                                    ifelse(independent_count == 2, "Independent", NA)))) |> 
  dplyr::select(-pid_3) |> 
  rename(pid_3 = pid_3_lean) |> 
  filter(pid_3 != "Independent") %>%
  #filter(pid_3 != "Independent")%>%
  # filter(year <= 2008)%>% #un/comment this line for the original data.
  filter(year >= 1978)%>%
  select(year,
         weight,
         pid_3,
         therm_inparty,
         therm_outparty) %>%
  pivot_longer(therm_inparty:therm_outparty, names_to = "ft_towards", values_to = "ft")%>%
  unite("group", pid_3:ft_towards)%>%
  mutate(group = recode(group,
                        "Democrat_therm_inparty" = "Democrat - In Party",
                        "Democrat_therm_outparty" = "Democrat - Out Party",
                        "Republican_therm_inparty" = "Republican - In Party",
                        "Republican_therm_outparty" = "Republican - Out Party"))%>%
  group_by(year, group)%>%
  summarize(mean = weighted.mean(ft, weight, na.rm = TRUE),
            #mean = mean(ft, na.rm = TRUE),
            sd = sd(ft, na.rm = TRUE),
            n = n()) |> 
  separate(group, into = c("party", "group_type"), sep = " - ", remove = FALSE)

# In and out party affect over time
fig_1_df |> 
  filter(year >= 1978) |> 
  #filter(str_detect(group_type, "In")) |> 
  mutate(mean = ifelse(str_detect(group_type, "Out"), NA, mean)) |> 
  ggplot(aes(x = year, y=mean)) +
  geom_point(aes(shape = group_type, color = party)) +
  geom_smooth(aes(linetype = group_type, color = party), se=F)+
  scale_color_manual(values = c("blue", "red"))+ theme_bw() + 
  scale_linetype_manual(values = c("In Party" = "solid",
                                   "Out Party" = "dashed",
                                   "In Party" = "solid",
                                   "Out Party" = "dashed")) +
  scale_shape_manual(values = c("In Party" = 3,
                                "Out Party" = 2)) +
  scale_x_continuous(breaks = seq(1976, 2020, by = 4)) +
  scale_y_continuous(limits = c(10,80)) +
  theme(legend.position = "none") +
  labs(title = "Partisan In and Outgroup Feeling Thermometer",
       caption = "Solid lines: In group, Dashed lines: Out group, Leaners coded as partisans",
       y = "Mean Thermometer Ratings of Partisans",
       x = "Year")+ theme(text=element_text(size=15))

# The difference between in and out party affect
df_anes |> 
  dplyr::select(year, pid_3, therm_inparty, therm_outparty, weight) |> 
  drop_na(pid_3) |> 
  filter(pid_3 != "Independent")%>%
  mutate(delta_therm = therm_inparty- therm_outparty) |> 
  group_by(year) |> 
  summarize(mean = weighted.mean(delta_therm, weight, na.rm = TRUE),
            sd = sd(delta_therm, na.rm = TRUE),
            n = n()) |> 
  filter(year >= 1978) |> 
  ggplot(aes(x = year, y = mean)) +
  geom_point() +
  geom_smooth(color = "gray50", se=T) +
  scale_x_continuous(breaks = seq(1976, 2020, by = 4)) +
  theme_bw() +
  theme(legend.position = "none") +
  labs(title = "Affective Polarization",
       caption = "Note: Analysis based on American National Election Study data.",
       y = "Average Difference between In and Outgroup Affect",
       x = "Year")+ theme(text=element_text(size=15))

# The difference between in and out party affect by partisans
df_anes |> 
  dplyr::select(year, pid_3, therm_inparty, therm_outparty, weight) |> 
  drop_na(pid_3) |> 
  filter(pid_3 != "Independent")%>%
  mutate(delta_therm = therm_inparty- therm_outparty) |> 
  group_by(year, pid_3) |> 
  summarize(mean = weighted.mean(delta_therm, weight, na.rm = TRUE),
            sd = sd(delta_therm, na.rm = TRUE),
            n = n()) |> 
  filter(year >= 1978) |> 
  ggplot(aes(x = year, y = mean, group = pid_3, color = pid_3)) +
  geom_point() +
  geom_smooth(aes(color = pid_3), se=F) +
  scale_x_continuous(breaks = seq(1976, 2020, by = 4)) +
  scale_color_manual(values = c("red", "blue"))+ theme_bw() +
  theme(legend.position = "none") +
  labs(title = "Affective Polarization",
       caption = "Note: Analysis based on American National Election Study data.",
       y = "Average Difference between In and Outgroup Affect",
       x = "Year")+ theme(text=element_text(size=15))


# US Party Analysis
# Loading in the V-Dem VParty data set 
df_vparty <- vparty |> 
  mutate(v2paopresp = scales::rescale(v2paopresp),
         v2xpa_antiplural = scales::rescale(v2xpa_antiplural),
         v2xpa_popul = scales::rescale(v2xpa_popul),
         v2paviol = scales::rescale(v2paviol)) |> 
  filter(country_name == "United States of America") |> 
  dplyr::select(country_name, year,v2paenname, v2paopresp,v2xpa_antiplural,
                v2xpa_popul, v2paviol)

## Plotting rejection of political violence by U.S. political parties over time
df_vparty |> 
  drop_na(v2paviol) |> 
  ggplot(aes(x = year, y = v2paviol, color = v2paenname)) +
  geom_line()  +
  scale_color_manual(values = c("blue", "red")) +
  theme_bw() +
  labs(title = "Rejection of political violence by U.S. political parties",
       x = "Year",
       y = "Rejection of political violence",
       subtitle = "Analysis based on V-Party Version 2",
       caption = "Question: To what extent does the leadership of this party explicitly discourage the use of violence
against domestic political opponents?",
       color = "Party:")  + ylim(0,1)+
  theme(legend.position = "bottom") + theme(text=element_text(size=15))

## Plotting attacks against political opponents by U.S. political parties over time
df_vparty |> 
  drop_na(v2paopresp) |> 
  ggplot(aes(x = year, y = v2paopresp, color = v2paenname)) +
  geom_line() +
  scale_color_manual(values = c("blue", "red")) +
  theme_bw() +
  labs(title = "Attacks against political opponents by U.S. political parties",
       subtitle = "Analysis based on V-Party Version 2",
       x = "Year",
       y = "Political opponents",
       color = "Party:",
       caption = "Question: Prior to this election, have leaders of this party used severe personal attacks or tactics of
demonization against their opponents?")  +ylim(0,1)+
  theme(legend.position = "bottom")+ theme(text=element_text(size=15))

## Plotting populism by U.S. political parties over time
df_vparty |> 
  drop_na(v2xpa_popul) |> 
  ggplot(aes(x = year, y = v2xpa_popul, color = v2paenname)) +
  geom_line() +
  scale_color_manual(values = c("blue", "red"))  +
  theme_bw() +
  labs(title = "Populism in U.S. political parties",
       subtitle = "Analysis based on V-Party Version 2",
       x = "Year",
       y = "Populism Index",
       color = "Party:",
       caption = "Question: To what extent do representatives of the party use populist rhetoric?")  +
  theme(legend.position = "bottom") + ylim(0,1)+theme(text=element_text(size=15))


## Plotting anti-pluralism by U.S. political parties over time
df_vparty |> 
  drop_na(v2xpa_antiplural) |> 
  ggplot(aes(x = year, y = v2xpa_antiplural, color = v2paenname)) +
  geom_line() +
  scale_color_manual(values = c("blue", "red")) +
  theme_bw() +
  labs(title = "Anti-pluralism in U.S. political parties",
       subtitle = "Analysis based on V-Party Version 2",
       x = "Year",
       y = "Anti-Pluralism Index",
       color = "Party:",
       caption = "Question = To what extent does the party show a lacking commitment to democratic norms prior to
elections?")  +ylim(0,1)+
  theme(legend.position = "bottom") + theme(text=element_text(size=15))