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top_decile_weightgain_counts.R
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top_decile_weightgain_counts.R
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## Counts for top decile of weightgain.
## Specify libraries
library(pacman)
library(tidyverse)
library(Hmisc)
library(here)
library(arrow)
library(purrr)
library(broom)
library(data.table)
library(forcats)
library(rstatix)
library(janitor)
library(lubridate)
library(skimr)
library(ggplot2)
library(data.table)
BMI_data <- read_feather (here::here ("output/data", "BMI_trajectory_data_long.feather"))
BMI_data <- BMI_data %>%
dplyr::select("sex",
"age_group_2",
"region",
"imd",
"hypertension",
"diabetes_t1",
"diabetes_t2",
"learning_disability",
"depression",
"psychosis_schiz_bipolar",
"dementia",
"asthma",
"COPD",
"stroke_and_TIA",
"chronic_cardiac",
"all_cancer",
"smoking_status",
"ethnic_no_miss",
"eth_group_16",
"complete_bmi_data",
"bmi_change_cat",
"precovid_bmi_category",
"pandemic_stage",
"yearly_bmi_change")
BMI_data$precovid_bmi_category <- factor(BMI_data$precovid_bmi_category, levels = c("healthy","overweight", "obese", "underweight"))
### Postcovid Analysis
postcovid_change <- BMI_data %>%
dplyr::filter(pandemic_stage == "postcovid")
##postcovid_quantiles
postcovid_quantiles <- as.data.frame(quantile(postcovid_change$yearly_bmi_change, probs = seq(.1, .9, by = .1))) %>%
dplyr::rename( postcovid_yearly_bmi_change = 1)
## create a column for deciles
postcovid_change$decile <- ntile(postcovid_change$yearly_bmi_change, 10)
## create a flag for top 10% weight gain
postcovid_change <- postcovid_change %>%
dplyr::mutate(weightgain_90th = case_when(
decile == 10 ~ 1,
decile != 10 ~ 0
))
prop_function <- function(data, my_var) {
v1 <- deparse(substitute(my_var))
data %>%
tabyl({{my_var}}, weightgain_90th) %>%
dplyr::rename(top_decile = 3) %>%
dplyr::rename(bott_deciles = 2) %>%
dplyr::mutate(total = top_decile + bott_deciles) %>%
dplyr::mutate(top_decile_prop = top_decile/total) %>%
dplyr::mutate(se_prop = sqrt(((top_decile_prop * (1-top_decile_prop))/total))) %>%
dplyr::rename(group = {{my_var}}) %>%
dplyr::mutate(variable = (v1), .before=1) %>%
dplyr::mutate(across(where(is.numeric), round, 5)) %>%
dplyr::mutate(group = as.character(group))
}
sex <- prop_function(postcovid_change, sex)
age_group_2 <- prop_function(postcovid_change, age_group_2)
eth_group_16 <- prop_function(postcovid_change, eth_group_16)
imd <- prop_function(postcovid_change, imd)
region <- prop_function(postcovid_change, region)
hypertension <- prop_function(postcovid_change, hypertension)
diabetes_t1 <- prop_function(postcovid_change, diabetes_t1)
diabetes_t2 <- prop_function(postcovid_change, diabetes_t2)
chronic_cardiac <- prop_function(postcovid_change, chronic_cardiac)
learning_disability <- prop_function(postcovid_change, learning_disability)
depression <- prop_function(postcovid_change, depression)
dementia <- prop_function(postcovid_change, dementia)
psychosis_schiz_bipolar <- prop_function(postcovid_change, psychosis_schiz_bipolar)
asthma <- prop_function(postcovid_change, asthma)
COPD <- prop_function(postcovid_change, COPD)
stroke_and_TIA <- prop_function(postcovid_change, stroke_and_TIA)
smoking_status <- prop_function(postcovid_change, smoking_status)
complete <- sex %>%
bind_rows(age_group_2) %>%
bind_rows(eth_group_16) %>%
bind_rows(imd) %>%
bind_rows(region) %>%
bind_rows(hypertension) %>%
bind_rows(diabetes_t1) %>%
bind_rows(diabetes_t2) %>%
bind_rows(chronic_cardiac) %>%
bind_rows(learning_disability) %>%
bind_rows(depression) %>%
bind_rows(dementia) %>%
bind_rows(psychosis_schiz_bipolar) %>%
bind_rows(asthma) %>%
bind_rows(COPD) %>%
bind_rows(stroke_and_TIA) %>%
bind_rows(smoking_status) %>%
dplyr::mutate(population = "all", .before=1)
complete <- complete %>%
dplyr::mutate(bott_deciles = plyr::round_any(complete$bott_deciles, 5)) %>%
dplyr::mutate(top_decile = plyr::round_any(complete$top_decile, 5)) %>%
dplyr::mutate(total = plyr::round_any(complete$total, 5))
#####################
## Exclude cancer and low bmi
postcovid_change <- BMI_data %>%
dplyr::filter(pandemic_stage == "postcovid")
postcovid_change <- postcovid_change %>%
dplyr::filter(all_cancer == FALSE)
postcovid_change <- postcovid_change %>%
dplyr::filter(precovid_bmi_category != "underweight")
##postcovid_quantiles
postcovid_quantiles <- as.data.frame(quantile(postcovid_change$yearly_bmi_change, probs = seq(.1, .9, by = .1))) %>%
dplyr::rename( postcovid_yearly_bmi_change = 1)
## create a column for deciles
postcovid_change$decile <- ntile(postcovid_change$yearly_bmi_change, 10)
## create a flag for top 10% weight gain
postcovid_change <- postcovid_change %>%
dplyr::mutate(weightgain_90th = case_when(
decile == 10 ~ 1,
decile != 10 ~ 0
))
sex <- prop_function(postcovid_change, sex)
age_group_2 <- prop_function(postcovid_change, age_group_2)
eth_group_16 <- prop_function(postcovid_change, eth_group_16)
imd <- prop_function(postcovid_change, imd)
region <- prop_function(postcovid_change, region)
hypertension <- prop_function(postcovid_change, hypertension)
diabetes_t1 <- prop_function(postcovid_change, diabetes_t1)
diabetes_t2 <- prop_function(postcovid_change, diabetes_t2)
chronic_cardiac <- prop_function(postcovid_change, chronic_cardiac)
learning_disability <- prop_function(postcovid_change, learning_disability)
depression <- prop_function(postcovid_change, depression)
dementia <- prop_function(postcovid_change, dementia)
psychosis_schiz_bipolar <- prop_function(postcovid_change, psychosis_schiz_bipolar)
asthma <- prop_function(postcovid_change, asthma)
COPD <- prop_function(postcovid_change, COPD)
stroke_and_TIA <- prop_function(postcovid_change, stroke_and_TIA)
smoking_status <- prop_function(postcovid_change, smoking_status)
complete_lowbmiexc <- sex %>%
bind_rows(age_group_2) %>%
bind_rows(eth_group_16) %>%
bind_rows(imd) %>%
bind_rows(region) %>%
bind_rows(hypertension) %>%
bind_rows(diabetes_t1) %>%
bind_rows(diabetes_t2) %>%
bind_rows(chronic_cardiac) %>%
bind_rows(learning_disability) %>%
bind_rows(depression) %>%
bind_rows(dementia) %>%
bind_rows(psychosis_schiz_bipolar) %>%
bind_rows(asthma) %>%
bind_rows(COPD) %>%
bind_rows(stroke_and_TIA) %>%
bind_rows(smoking_status) %>%
dplyr::mutate(population = "lowbmi_exc", .before=1)
complete_lowbmiexc <- complete_lowbmiexc %>%
dplyr::mutate(bott_deciles = plyr::round_any(complete_lowbmiexc$bott_deciles, 5)) %>%
dplyr::mutate(top_decile = plyr::round_any(complete_lowbmiexc$top_decile, 5)) %>%
dplyr::mutate(total = plyr::round_any(complete_lowbmiexc$total, 5))
complete <- complete %>%
bind_rows(complete_lowbmiexc) %>%
dplyr::mutate(pandemic = "postcovid", .before=1)
### Precovid Analysis
precovid_change <- BMI_data %>%
dplyr::filter(pandemic_stage == "precovid")
##precovid_quantiles
precovid_quantiles <- as.data.frame(quantile(precovid_change$yearly_bmi_change, probs = seq(.1, .9, by = .1))) %>%
dplyr::rename( precovid_yearly_bmi_change = 1)
## create a column for deciles
precovid_change$decile <- ntile(precovid_change$yearly_bmi_change, 10)
## create a flag for top 10% weight gain
precovid_change <- precovid_change %>%
dplyr::mutate(weightgain_90th = case_when(
decile == 10 ~ 1,
decile != 10 ~ 0
))
prop_function <- function(data, my_var) {
v1 <- deparse(substitute(my_var))
data %>%
tabyl({{my_var}}, weightgain_90th) %>%
dplyr::rename(top_decile = 3) %>%
dplyr::rename(bott_deciles = 2) %>%
dplyr::mutate(total = top_decile + bott_deciles) %>%
dplyr::mutate(top_decile_prop = top_decile/total) %>%
dplyr::mutate(se_prop = sqrt(((top_decile_prop * (1-top_decile_prop))/total))) %>%
dplyr::rename(group = {{my_var}}) %>%
dplyr::mutate(variable = (v1), .before=1) %>%
dplyr::mutate(across(where(is.numeric), round, 5)) %>%
dplyr::mutate(group = as.character(group))
}
sex <- prop_function(precovid_change, sex)
age_group_2 <- prop_function(precovid_change, age_group_2)
eth_group_16 <- prop_function(precovid_change, eth_group_16)
imd <- prop_function(precovid_change, imd)
region <- prop_function(precovid_change, region)
hypertension <- prop_function(precovid_change, hypertension)
diabetes_t1 <- prop_function(precovid_change, diabetes_t1)
diabetes_t2 <- prop_function(precovid_change, diabetes_t2)
chronic_cardiac <- prop_function(precovid_change, chronic_cardiac)
learning_disability <- prop_function(precovid_change, learning_disability)
depression <- prop_function(precovid_change, depression)
dementia <- prop_function(precovid_change, dementia)
psychosis_schiz_bipolar <- prop_function(precovid_change, psychosis_schiz_bipolar)
asthma <- prop_function(precovid_change, asthma)
COPD <- prop_function(precovid_change, COPD)
stroke_and_TIA <- prop_function(precovid_change, stroke_and_TIA)
smoking_status <- prop_function(precovid_change, smoking_status)
precovid_complete <- sex %>%
bind_rows(age_group_2) %>%
bind_rows(eth_group_16) %>%
bind_rows(imd) %>%
bind_rows(region) %>%
bind_rows(hypertension) %>%
bind_rows(diabetes_t1) %>%
bind_rows(diabetes_t2) %>%
bind_rows(chronic_cardiac) %>%
bind_rows(learning_disability) %>%
bind_rows(depression) %>%
bind_rows(dementia) %>%
bind_rows(psychosis_schiz_bipolar) %>%
bind_rows(asthma) %>%
bind_rows(COPD) %>%
bind_rows(stroke_and_TIA) %>%
bind_rows(smoking_status) %>%
dplyr::mutate(population = "all", .before=1)
precovid_complete <- precovid_complete %>%
dplyr::mutate(bott_deciles = plyr::round_any(precovid_complete$bott_deciles, 5)) %>%
dplyr::mutate(top_decile = plyr::round_any(precovid_complete$top_decile, 5)) %>%
dplyr::mutate(total = plyr::round_any(precovid_complete$total, 5))
#####################
## Exclude cancer and low bmi
precovid_change <- BMI_data %>%
dplyr::filter(pandemic_stage == "precovid")
precovid_change <- precovid_change %>%
dplyr::filter(all_cancer == FALSE)
precovid_change <- precovid_change %>%
dplyr::filter(precovid_bmi_category != "underweight")
##precovid_quantiles
precovid_quantiles <- as.data.frame(quantile(precovid_change$yearly_bmi_change, probs = seq(.1, .9, by = .1))) %>%
dplyr::rename( precovid_yearly_bmi_change = 1)
## create a column for deciles
precovid_change$decile <- ntile(precovid_change$yearly_bmi_change, 10)
## create a flag for top 10% weight gain
precovid_change <- precovid_change %>%
dplyr::mutate(weightgain_90th = case_when(
decile == 10 ~ 1,
decile != 10 ~ 0
))
sex <- prop_function(precovid_change, sex)
age_group_2 <- prop_function(precovid_change, age_group_2)
eth_group_16 <- prop_function(precovid_change, eth_group_16)
imd <- prop_function(precovid_change, imd)
region <- prop_function(precovid_change, region)
hypertension <- prop_function(precovid_change, hypertension)
diabetes_t1 <- prop_function(precovid_change, diabetes_t1)
diabetes_t2 <- prop_function(precovid_change, diabetes_t2)
chronic_cardiac <- prop_function(precovid_change, chronic_cardiac)
learning_disability <- prop_function(precovid_change, learning_disability)
depression <- prop_function(precovid_change, depression)
dementia <- prop_function(precovid_change, dementia)
psychosis_schiz_bipolar <- prop_function(precovid_change, psychosis_schiz_bipolar)
asthma <- prop_function(precovid_change, asthma)
COPD <- prop_function(precovid_change, COPD)
stroke_and_TIA <- prop_function(precovid_change, stroke_and_TIA)
smoking_status <- prop_function(precovid_change, smoking_status)
precovid_complete_lowbmiexc <- sex %>%
bind_rows(age_group_2) %>%
bind_rows(eth_group_16) %>%
bind_rows(imd) %>%
bind_rows(region) %>%
bind_rows(hypertension) %>%
bind_rows(diabetes_t1) %>%
bind_rows(diabetes_t2) %>%
bind_rows(chronic_cardiac) %>%
bind_rows(learning_disability) %>%
bind_rows(depression) %>%
bind_rows(dementia) %>%
bind_rows(psychosis_schiz_bipolar) %>%
bind_rows(asthma) %>%
bind_rows(COPD) %>%
bind_rows(stroke_and_TIA) %>%
bind_rows(smoking_status) %>%
dplyr::mutate(population = "lowbmi_exc", .before=1)
precovid_complete_lowbmiexc <- precovid_complete_lowbmiexc %>%
dplyr::mutate(bott_deciles = plyr::round_any(precovid_complete_lowbmiexc$bott_deciles, 5)) %>%
dplyr::mutate(top_decile = plyr::round_any(precovid_complete_lowbmiexc$top_decile, 5)) %>%
dplyr::mutate(total = plyr::round_any(precovid_complete_lowbmiexc$total, 5))
precovid_complete <- precovid_complete %>%
bind_rows(precovid_complete_lowbmiexc) %>%
dplyr::mutate(pandemic = "precovid", .before=1)
complete <- complete %>%
dplyr::bind_rows(precovid_complete)
write_csv (complete, here::here ("output/data","weightgain_90th_counts_total_and_lowbmiexc.csv"))