opioids/cleaning.R at master · JasonChan07/opioids · GitHub

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library(tidyverse)
library(dplyr)
library(janitor)

clean_acs_data <- function(df) {


    # convert acs data to wide format
    acs_wide <- df %>%
        clean_names() %>%
        select(-moe) %>%
        pivot_wider(names_from = variable,
                    values_from = estimate)

    # generate features from acs wide data
    acs_clean <- acs_wide %>%
        mutate(
               percent_hs_grad = high_school_grads / total_pop,
               percent_college_grad = has_bachelors / total_pop,
               percent_below_100_poverty = below_100_percent_poverty / total_pop,
               percent_male_26_34_uninsured = uninsured_26_34_male / total_male,
               percent_female_26_34_uninsured = uninsured_26_34_female / total_female,
               log_median_income = log(median_income),
    # replace first character of geoids that start with 0 with empty string
               geoid = as.integer(ifelse(substr(geoid, 1, 1) == 0, sub("^.", "", geoid), geoid))) %>%
    # multiplying percentages by 100
        mutate_at(vars(matches('percent')), function(x) {x * 100}) %>%
        select(c(geoid,
                 name,
                 percent_hs_grad,
                 percent_college_grad,
                 percent_below_100_poverty,
                 percent_male_26_34_uninsured,
                 percent_female_26_34_uninsured,
                 log_median_income)
    )

    return(acs_clean)
}

clean_prescription_data <- function(df) {

    # correct FIPS codes to match acs geoid
    prescriptions_clean <- df %>%
        clean_names() %>%
        select(geoid = state_county_fips_code,
               prescriptions_per_100 = x2017)

    return(prescriptions_clean)
}

clean_overdose_data <- function(df) {

    # correct FIPS codes
    overdose_clean <- df %>%
        clean_names() %>%
        mutate(crude_rate = suppressWarnings(as.numeric(as.character(crude_rate))),
               deaths =  suppressWarnings(as.numeric(as.character(deaths))),
               log_population = log(suppressWarnings(as.numeric(as.character(population))))) %>%
        select(geoid = county_code,
               crude_death_rate = crude_rate,
               deaths,
               population)

    return(overdose_clean)

}