ICD 9 download to clean example (dta)

Mathew Kiang edited this page Sep 7, 2017 · 1 revision

This simple example shows how to use narcan to download an MCOD file in dta format, select only columns necessary (trimming), remove rows we don't want (e.g., non-residents), clean and label ICD-9 codes to be consistent across all years and columns, unite all contributory cause columns (i.e., record_) into a single regexable column, remap the race variable so it is consistent across 1979-2015, and finally add categorical variables.

## Test cleaning and processing an ICD-9 file

## Download if file doesn't exist
if (!file.exists('./raw_data/mort1998.dta.zip')) {
    download_mcod_dta(1998, './raw_data')

## Load the dta file
mort_1998 <- haven::read_dta('./raw_data/mort1998.dta.zip')

## Trim columns, add year column, zap Stata metadata
df <- mort_1998 %>% 
    select(one_of(c("race", "hspanicr", "ager27", "restatus", "ucod")),
           starts_with("rnifla")) %>% 
    add_column(year = 1998) %>% 

## Drop nonresidents
df <- df %>% 
    filter(restatus %in% 1:3) %>% 

## Clean codes
df <- clean_icd9_data(df)

## Unite contributory causes
df <- unite_records(df)

## Convert age, add hspanicr, remap race, and add categories
df <- df %>% 
    convert_age27() %>% 
    add_hspanicr_column() %>% 
    remap_race() %>% 
    mutate(race_cat = categorize_race(race), 
           hsp_cat  = categorize_hspanicr(hspanicr), 
           age_cat  = categorize_age_5(age))

## Reorder columns
df <- df %>% 
    select(year, race, race_cat, hspanicr, hsp_cat, 
           age, age_cat, ucod, f_records_all)
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