#https://cses.org/data-download/cses-integrated-module-dataset-imd/ library(tidyverse) load("~/Documents/cses_imd") # IMD2003 >>> EDUCATION # IMD3010 >>> SATISFACTION WITH DEMOCRACY #0. NONE (NO EDUCATION)/ILLITERATE #1. PRIMARY EDUCATION/LOWER SECONDARY EDUCATION #2. HIGHER SECONDARY EDUCATION #3. POST-SECONDARY (NON-UNIVERSITY) EDUCATION #4. UNIVERSITY EDUCATION #6. OTHER [SEE Standalone CSES MODULE CODEBOOK] #7. VOLUNTEERED: REFUSED #8. VOLUNTEERED: DON'T KNOW # 9. MISSING # 1. VERY SATISFIED # 2. FAIRLY SATISFIED # 4. NOT VERY SATISFIED # 5. NOT AT ALL SATISFIED # 6. NEITHER SATISFIED NOR DISSATISFIED # 7. VOLUNTEERED: REFUSED # 8. VOLUNTEERED: DON'T KNOW # 9. MISSING #new variable cses_imd <- cses_imd %>% mutate(edu = IMD2003, dem = IMD3010) #filter cses_imd_filter <- cses_imd %>% filter(edu < 6) %>% filter(dem < 6) cses_imd_filter %>% select(edu, dem) cses_imd_filter %>% select(starts_with("IMD10")) #summarise cses_imd_filter %>% summarise(median_edu = median(edu), median_dem = median(dem)) cses_imd_filter %>% select(edu, dem) %>% summarise_all(mean) cses_imd_filter %>% select(edu, dem) %>% summarise_all(list(mean, median, sd)) #group_by cses_imd_filter %>% group_by(IMD1004) %>% select(edu, dem) %>% summarise_all(mean) #arrange cses_imd_filter %>% group_by(IMD1004) %>% select(edu, dem) %>% summarise_all(mean) %>% arrange(desc(dem)) #wide to long long <- cses_imd_filter %>% select(edu, dem) %>% summarise_all(list(mean, median, sd)) %>% gather() wide <- long %>% spread(key, value)