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create_BMI_all_2019.R
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create_BMI_all_2019.R
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## Miriam Samuel
## Modified: 23rd March 2022
## Create a data set that has data on those who did and did not have BMI measured to perform a regression.
# 1) Read in files
# BMI_complete_median_2019.feather :: contains BMI data on those who had a BMI check
# input_all_2019:: contains demographic data on whole population
# >> Combining these files to create a file for the analysis of who had a BMI
## packages
library(broom)
library(purrr)
library(dplyr)
library(janitor)
library(tidyverse)
library(arrow)
BMI_complete_categories <- read_feather (here::here ("output/data", "BMI_complete_median_2019.feather"))
input_all_2019_03_01 <- read_feather (here::here ("output/data", "input_all_2019-03-01.feather"))
## Input data on all patients (not just those with a BMI)
all_patients_2019 <- as_tibble (input_all_2019_03_01)
# 2) recode and label some demographic components
# recode ethnicity so NA is 0
all_patients_2019 <- all_patients_2019 %>%
mutate(ethnic_no_miss = ifelse(is.na(ethnicity), 0, ethnicity ))
all_patients_2019 <- all_patients_2019 %>%
mutate(ethnicity_16_no_miss = ifelse(is.na(ethnicity_16), 0, ethnicity_16 ))
# label
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="1"]<-"White"
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="2"]<-"Mixed"
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="3"]<-"Asian"
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="4"]<-"Black"
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="5"]<-"Other"
all_patients_2019$ethnic_no_miss[all_patients_2019$ethnic_no_miss=="0"]<-"Not_recorded"
all_patients_2019 <- all_patients_2019 %>%
mutate (ethnic_no_miss = as.factor(ethnic_no_miss)) %>%
mutate (ethnic_no_miss = fct_relevel(ethnic_no_miss, "White", "Asian", "Black", "Mixed","Other", "Not_recorded"))
all_patients_2019$imd[all_patients_2019$imd=="0"]<-"NA"
all_patients_2019 <- all_patients_2019 %>%
mutate (imd = as.factor(imd)) %>%
mutate (imd = fct_relevel(imd, "1", "2", "3", "4", "5", "NA"))
all_patients_2019 <- all_patients_2019 %>%
mutate (eth_group_16=case_when(
ethnicity_16_no_miss == "1" ~ "British",
ethnicity_16_no_miss == "2" ~ "Irish",
ethnicity_16_no_miss == "3" ~ "Other_White",
ethnicity_16_no_miss == "4" ~ "White_Black_Carib",
ethnicity_16_no_miss == "5" ~ "White_Black_African",
ethnicity_16_no_miss == "6" ~ "White_Asian",
ethnicity_16_no_miss == "7" ~ "Other_Mixed",
ethnicity_16_no_miss == "8" ~ "Indian",
ethnicity_16_no_miss == "9" ~ "Pakistani",
ethnicity_16_no_miss == "10" ~ "Bangladeshi",
ethnicity_16_no_miss == "11" ~ "Other_Asian",
ethnicity_16_no_miss == "12" ~ "Caribbean",
ethnicity_16_no_miss == "13" ~ "African",
ethnicity_16_no_miss == "14" ~ "Other_Black",
ethnicity_16_no_miss == "15" ~ "Chinese",
ethnicity_16_no_miss == "16" ~ "Other",
ethnicity_16_no_miss == "0" ~ "Missing"))
all_patients_2019 <- all_patients_2019 %>%
mutate (eth_group_16 = as.factor(eth_group_16)) %>%
mutate ( eth_group_16= fct_relevel(eth_group_16,
"British",
"Irish",
"Other_White",
"Indian",
"Pakistani",
"Bangladeshi",
"Other_Asian",
"Caribbean",
"African",
"Other_Black",
"Chinese",
"White_Asian",
"White_Black_Carib",
"White_Black_African",
"Other_Mixed",
"Other",
"Missing"))
# 3) Select relevant variables and restructure code to allow functions across rows
all_patients_2019 <- all_patients_2019 %>%
dplyr::select("patient_id",
"sex",
"age_group",
"region",
"imd",
"ethnic_no_miss",
"eth_group_16",
"learning_disability",
"dementia",
"depression",
"psychosis_schiz_bipolar",
"diabetes_type",
"diabetes_t1",
"diabetes_t2",
"bmi",
"had_bmi",
"asthma",
"COPD",
"stroke_and_TIA" ,
"chronic_cardiac",
"hypertension",
"all_cancer")
all_patients_2019 <- all_patients_2019 %>%
dplyr::mutate(
across(
.cols = c(learning_disability,depression, dementia,psychosis_schiz_bipolar, diabetes_type, diabetes_t1, diabetes_t2, asthma, COPD, stroke_and_TIA, chronic_cardiac, hypertension, all_cancer),
.names = "comorbid_{col}")) %>%
dplyr::select(
patient_id, had_bmi, sex, age_group, region, imd, ethnic_no_miss, eth_group_16, starts_with("comorbid_"))
#######################################################################
# 4) BMI_complete_categories has all the data on BMI. Need to link it with demographic data from the extracted cohort.
BMI_complete_categories <- as_tibble(BMI_complete_categories)
BMI_complete_categories_all <- BMI_complete_categories %>%
dplyr::select(patient_id,
year,
median_bmi,
obese,
BMI_categories,
BMI_over27.5,
DWMP)
BMI_complete_categories_all <- left_join(all_patients_2019, BMI_complete_categories_all, by='patient_id')
### save outputs as feather
write_feather (BMI_complete_categories_all, here::here ("output/data","BMI_all_2019.feather"))