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2024-04-02 12:36:56 -05:00
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))