library(tidyverse) enrollment_data <- read_csv(file = "/home/ben/Documents/Data analysis/map/data/Enrollement_2022-2023/enrollment_by_gradelevel_certified_2022-23.csv") enrollment_data_wide <- enrollment_data %>% mutate(district_school = paste0(DISTRICT_CODE, SCHOOL_CODE), variable_name = paste0(GROUP_BY, "__", GROUP_BY_VALUE)) %>% mutate(variable_name = str_replace_all(variable_name, "[ ]", "_")) %>% pivot_wider(id_cols = c(district_school, GRADE_LEVEL, SCHOOL_NAME, DISTRICT_NAME), names_from = variable_name, values_from = PERCENT_OF_GROUP) write_csv(enrollment_data_wide, file = "/home/ben/Documents/Data analysis/map/data/Enrollement_2022-2023/enrollment_by_gradelevel_certified_2022-23_wide.csv") # school comparison schools <- data.frame(Name = c("East High", "West High", "Memorial High", "LaFollette High"), district_school = c(32690150,32690840,32690360,32690420)) enrollment_data_wide %>% filter(district_school %in% schools$district_school) %>% group_by(district_school) summarise(mean_econ_disadv = mean(as.double('Economic_Status__Econ_Disadv'), na.rm = TRUE)) ggplot() + geom_col(aes(x = SCHOOL_NAME, y = mean_econ_disadv))