mod tree

code/analysis_anglo_sax.R

@@ -2,7 +2,7 @@ library(tidyr)
 library(dplyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 data_econ <- data_party %>%
   group_by(group) %>%

code/analysis_base.R

@@ -3,7 +3,7 @@ library(tidyr)
 library(ggridges)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 # Graphique nuage de points -------------------------------------- #
 
@@ -210,6 +210,6 @@ ggplot(h1, aes(x = gps, y = gpt)) +
         axis.title.y = element_text(hjust = 0.5),
         axis.text.y = element_text(angle = 90))
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h1_scatterplot.png",
+ggsave("data/graphs/h1_scatterplot.png",
        width = 8, height = 6)
 

code/analysis_categorical.R

@@ -2,7 +2,7 @@ library(dplyr)
 library(tidyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 
 h3 <- data_party %>% 
@@ -67,7 +67,7 @@ ggplot(h3, aes(x = position, y = type)) +
         strip.text = element_text(size = 20)) # Bold and increased size for facet labels
 
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_distggridges.png",
+ggsave("data/graphs/h3_distggridges.png",
        width = 10, height = 6)
 
 
@@ -129,5 +129,5 @@ ggplot(long_data2, aes(x = alignment, y = mean_distance, fill = alignment_type))
         plot.caption.position = "plot") # Positioning the caption relative to the plot
 
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_barplot.png",
+ggsave("data/graphs/h3_barplot.png",
        width = 10, height = 6)

code/analysis_left.R

@@ -2,7 +2,7 @@ library(dplyr)
 library(tidyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 ## h3 for paper
 
@@ -48,7 +48,7 @@ ggplot(h3, aes(x = position, y = type)) +
         axis.text.y = element_text(size = 20), # Bold and increased size
         strip.text = element_text(size = 20)) # Bold and increased size for facet labels
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_distggridges.png",
+ggsave("data/graphs/h3_distggridges.png",
        width = 10, height = 6)
 
 # Reshape and filter data as before
@@ -108,7 +108,7 @@ ggplot(long_data2, aes(x = alignment, y = mean_distance, fill = alignment_type))
         legend.key.size = unit(1.5, "lines"),  # Bolder and larger plot caption
         plot.caption.position = "plot") # Positioning the caption relative to the plot
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_barplot.png",
+ggsave("data/graphs/h3_barplot.png",
        width = 10, height = 6)
 
 

code/analysis_party_size.R

@@ -2,7 +2,7 @@ library(dplyr)
 library(tidyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 long_data <- data_party %>%
   select(Type_Partysize_seat, econ_distance, sos_distance) %>%
@@ -53,7 +53,7 @@ ggplot(long_data2, aes(x = partysize, y = mean_distance, fill = distance_type))
         legend.text = element_text(size = 20), # Increase legend text size
         legend.key.size = unit(1.5, "lines")) # Bolder and larger plot caption
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h4_barplot.png",
+ggsave("data/graphs/h4_barplot.png",
        width = 8, height = 6)
 
 
@@ -74,6 +74,6 @@ ggplot(long_data2, aes(x = alignment, y = mean_distance, fill = alignment_type))
   theme(axis.title.x = element_text(hjust = 0.5),
         axis.title.y = element_text(hjust = 0.5))
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_barplot.png",
+ggsave("data/graphs/h3_barplot.png",
        width = 10, height = 6)
 

code/analysis_west.R

@@ -2,7 +2,7 @@ library(tidyr)
 library(dplyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 # ------------------ Distance graph ---------------------------- #
 
@@ -72,5 +72,5 @@ ggplot(long_data2, aes(x = reorder(Region_name, -mean_distance),
   _SharedFolder_article_spsa2024_gpt_party/graphs/paper/h3_distggridges.png
 
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h2_barplot.png",
+ggsave("data/graphs/h2_barplot.png",
        width = 8, height = 6)

code/cleaning_wrangling.R

@@ -2,7 +2,7 @@ library(dplyr)
 library(tidyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_final.rds")
+data_party <- readRDS("data/expert_survey/gps_gpt_final.rds")
 
 data_party$econ_distance <- abs(data_party$V4_Scale - data_party$econ_ideo_gpt_mean)
 data_party$sos_distance <- abs(data_party$V6_Scale - data_party$sos_ideo_gpt_mean)
@@ -80,6 +80,6 @@ data_party$sos_ideo_cat_gpt[data_party$sos_ideo_gpt_mean > 5 & data_party$sos_id
 data_party$sos_ideo_cat_gpt[data_party$sos_ideo_gpt_mean > 6 & data_party$sos_ideo_gpt_mean <= 8] <- 6
 data_party$sos_ideo_cat_gpt[data_party$sos_ideo_gpt_mean > 8] <- 7
 
-write.csv(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.csv")
+write.csv(data_party, "data/expert_survey/data_party.csv")
 
-saveRDS(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+saveRDS(data_party, "data/expert_survey/data_party.rds")

code/prompting_experts.R

@@ -3,7 +3,7 @@ library(openai)
 library(stringr)
 
 
-data_party <- read.csv("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/dataverse_files/Global Party Survey by Party SPSS V2_1_Apr_2020-2.csv")
+data_party <- read.csv("data/expert_survey/dataverse_files/Global Party Survey by Party SPSS V2_1_Apr_2020-2.csv")
 
 #Ideology 
 #Partyname
@@ -60,9 +60,9 @@ for (run in 1:3) {
     }
 }
 
-write.csv(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_uncleaned.csv")
+write.csv(data_party, "data/expert_survey/gps_gpt_uncleaned.csv")
 
-saveRDS(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_uncleaned.rds")
+saveRDS(data_party, "data/expert_survey/gps_gpt_uncleaned.rds")
 
 data_party$econ_ideo_gpt_1 <- ifelse(nchar(data_party$econ_ideo_gpt_run1) > 10, NA, data_party$econ_ideo_gpt_run1)
 data_party$econ_ideo_gpt_2 <- ifelse(nchar(data_party$econ_ideo_gpt_run2) > 10, NA, data_party$econ_ideo_gpt_run2)
@@ -74,9 +74,9 @@ data_party$econ_ideo_gpt_3 <- as.numeric(data_party$econ_ideo_gpt_3)
 
 data_party$econ_ideo_gpt_mean <- rowMeans(data_party[, c("econ_ideo_gpt_1", "econ_ideo_gpt_2", "econ_ideo_gpt_3")], na.rm = TRUE)
 
-write.csv(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt.csv")
+write.csv(data_party, "data/expert_survey/gps_gpt.csv")
 
-saveRDS(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt.rds")
+saveRDS(data_party, "data/expert_survey/gps_gpt.rds")
 
 # ------------------- PROMPTING V6 LIBERALISM VS CONSERVATISM ------------------
 
@@ -129,9 +129,9 @@ for (run in 1:3) {
     }
 }
 
-write.csv(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_uncleaned_sos.csv")
+write.csv(data_party, "data/expert_survey/gps_gpt_uncleaned_sos.csv")
 
-saveRDS(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_uncleaned_sos.rds")
+saveRDS(data_party, "data/expert_survey/gps_gpt_uncleaned_sos.rds")
 
 data_party$sos_ideo_gpt_1 <- ifelse(nchar(data_party$sos_ideo_gpt_run1) > 10, NA, data_party$sos_ideo_gpt_run1)
 data_party$sos_ideo_gpt_2 <- ifelse(nchar(data_party$sos_ideo_gpt_run2) > 10, NA, data_party$sos_ideo_gpt_run2)
@@ -143,8 +143,8 @@ data_party$sos_ideo_gpt_3 <- as.numeric(data_party$sos_ideo_gpt_3)
 
 data_party$sos_ideo_gpt_mean <- rowMeans(data_party[, c("sos_ideo_gpt_1", "sos_ideo_gpt_2", "sos_ideo_gpt_3")], na.rm = TRUE)
 
-write.csv(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_final.csv")
+write.csv(data_party, "data/expert_survey/gps_gpt_final.csv")
 
-saveRDS(data_party, "_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/gps_gpt_final.rds")
+saveRDS(data_party, "data/expert_survey/gps_gpt_final.rds")
 
 

code/t_test_mean_distance.R

@@ -1,7 +1,7 @@
 library(dplyr)
 library(ggplot2)
 
-data_party <- readRDS("_SharedFolder_article_spsa2024_gpt_party/data/expert_survey/data_party.rds")
+data_party <- readRDS("data/expert_survey/data_party.rds")
 
 # ---------------------- T Test ------------------------------------------------
 
@@ -76,7 +76,7 @@ ggplot(results, aes(x = MeanDifference, y = Category)) +
         axis.text.y = element_text(size = 20),
         plot.caption = element_text(size = 20, hjust = 0))
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h1_ttest.png",
+ggsave("data/graphs/h1_ttest.png",
        width = 8, height = 6)
 
 h1 <- data_party %>% 
@@ -153,7 +153,7 @@ ggplot(results, aes(x = MeanDifference, y = Category)) +
         axis.text.y = element_text(size = 20),
         plot.caption = element_text(size = 20, hjust = 0))
 
-ggsave("_SharedFolder_article_spsa2024_gpt_party/graphs/paper/h1_ttest.png",
+ggsave("data/graphs/h1_ttest.png",
        width = 8, height = 6)
 
 h1 <- data_party %>%