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