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admin_round_redact_table.R
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admin_round_redact_table.R
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## This script rounds and redacts tables to ensure no counts <5 and all values rounded to 5
## 12th May
## Author: M Samuel
## This script rounds and redacts tables to ensure no counts <5 and all values rounded to 5
## 12th May
## Author: M Samuel
## Add libraries
## Load libraries
## 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(gtsummary)
library(stringr)
## Add data
## ACTION: bmi_change_univariate
## DATA: rapid_bmi_change_popcharac.csv
data <- read_csv (here::here ("output/data", "rapid_bmi_change_popcharac.csv"))
data <- data %>%
dplyr::rename('not_rapid' = '0') %>%
dplyr::rename('rapid' = '1')
data <- data %>%
dplyr::mutate(percent_not_rapid = stringr::str_extract(data$not_rapid, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_not_rapid = stringr::str_extract(string = data$not_rapid,
pattern = "(?<=\\().*(?=\\))")) %>%
dplyr::mutate(N_not_rapid = as.numeric(N_not_rapid)) %>%
dplyr::mutate(percent_rapid = stringr::str_extract(data$rapid, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_rapid = stringr::str_extract(string = data$rapid,
pattern = "(?<=\\().*(?=\\))")) %>%
dplyr::mutate(N_rapid = as.numeric(N_rapid)) %>%
dplyr::select(-("rapid"), -("not_rapid")) %>% ## add group and variable
dplyr::filter(N_rapid >5) %>%
dplyr::filter(N_not_rapid >5)
data <- data %>%
dplyr::mutate(N_rapid = plyr::round_any(data$N_rapid, 5)) %>%
dplyr::mutate(N_not_rapid = plyr::round_any(data$N_not_rapid, 5))
rapid_change <- data
write.csv (rapid_change, here::here ("output/data","rapid_bmi_change_popcharac_round.csv"))
## precovid BMI change categories
data2 <- read_csv (here::here ("output/data", "precovid_bmi_trajectories.csv"))
colnames(data)
data <- data2 %>%
dplyr::rename(weightloss= ">0.1 loss" ) %>%
dplyr::rename(stable = "-0.1 to <0.1" ) %>%
dplyr::rename(slow_gain = "0.1 to <0.3" ) %>%
dplyr::rename(mod_gain = "0.3 to <0.5" ) %>%
dplyr::rename(rapid_gain = "over 0.5" )
data <- data %>%
## extract strings:: weightloss
dplyr::mutate(percent_weightloss = stringr::str_extract(data$weightloss, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_weightloss = stringr::str_extract(string = data$weightloss,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_weightloss = as.numeric(N_weightloss)) %>%
## extract strings:: stable
dplyr::mutate(percent_stable = stringr::str_extract(data$stable, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_stable = stringr::str_extract(string = data$stable,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_stable = as.numeric(N_stable)) %>%
## extract strings:: slow_gain
dplyr::mutate(percent_slow_gain = stringr::str_extract(data$slow_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_slow_gain = stringr::str_extract(string = data$slow_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_slow_gain = as.numeric(N_slow_gain)) %>%
## extract strings:: mod_gain
dplyr::mutate(percent_mod_gain = stringr::str_extract(data$mod_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_mod_gain = stringr::str_extract(string = data$mod_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_mod_gain = as.numeric(N_mod_gain)) %>%
## extract strings:: rapid_gain
dplyr::mutate(percent_rapid_gain = stringr::str_extract(data$rapid_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_rapid_gain = stringr::str_extract(string = data$rapid_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_rapid_gain = as.numeric(N_rapid_gain))
data$N_weightloss[data$N_weightloss<6] <- NA
data$N_stable[data$N_stable<6] <- NA
data$N_slow_gain[data$N_slow_gain<6] <- NA
data$N_mod_gain[data$N_mod_gain<6] <- NA
data$N_rapid_gain[data$N_rapid_gain<6] <- NA
data <- data %>%
dplyr::select(-c("weightloss", "stable", "slow_gain", "mod_gain", "rapid_gain"))
data <- data %>%
dplyr::mutate(N_weightloss = plyr::round_any(data$N_weightloss, 5)) %>%
dplyr::mutate(N_stable = plyr::round_any(data$N_stable, 5)) %>%
dplyr::mutate(N_slow_gain = plyr::round_any(data$N_slow_gain, 5)) %>%
dplyr::mutate(N_mod_gain = plyr::round_any(data$N_mod_gain, 5)) %>%
dplyr::mutate(N_rapid_gain = plyr::round_any(data$N_rapid_gain, 5))
data$N_weightloss[data$N_weightloss == 5] <- ">5"
data$N_stable[data$N_stable == 5] <- ">5"
data$N_slow_gain[data$N_slow_gain == 5] <- ">5"
data$N_mod_gain[data$N_mod_gain == 5] <- ">5"
data$N_rapid_gain[data$N_rapid_gain == 5] <- ">5"
precovid_change_categories <- data
write.csv (precovid_change_categories, here::here ("output/data","precovid_bmi_trajectories.round.csv"))
## Postcovid change categories
data2 <- read_csv (here::here ("output/data", "postcovid_bmi_trajectories.csv"))
colnames(data)
data <- data2 %>%
dplyr::rename(weightloss= ">0.1 loss" ) %>%
dplyr::rename(stable = "-0.1 to <0.1" ) %>%
dplyr::rename(slow_gain = "0.1 to <0.3" ) %>%
dplyr::rename(mod_gain = "0.3 to <0.5" ) %>%
dplyr::rename(rapid_gain = "over 0.5" )
data <- data %>%
## extract strings:: weightloss
dplyr::mutate(percent_weightloss = stringr::str_extract(data$weightloss, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_weightloss = stringr::str_extract(string = data$weightloss,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_weightloss = as.numeric(N_weightloss)) %>%
## extract strings:: stable
dplyr::mutate(percent_stable = stringr::str_extract(data$stable, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_stable = stringr::str_extract(string = data$stable,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_stable = as.numeric(N_stable)) %>%
## extract strings:: slow_gain
dplyr::mutate(percent_slow_gain = stringr::str_extract(data$slow_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_slow_gain = stringr::str_extract(string = data$slow_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_slow_gain = as.numeric(N_slow_gain)) %>%
## extract strings:: mod_gain
dplyr::mutate(percent_mod_gain = stringr::str_extract(data$mod_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_mod_gain = stringr::str_extract(string = data$mod_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_mod_gain = as.numeric(N_mod_gain)) %>%
## extract strings:: rapid_gain
dplyr::mutate(percent_rapid_gain = stringr::str_extract(data$rapid_gain, "[0-9]+[.]?[0-9]*(?=%)")) %>%
dplyr::mutate(N_rapid_gain = stringr::str_extract(string = data$rapid_gain,
pattern = "[0-9]{0}[0-9]+")) %>%
dplyr::mutate(N_rapid_gain = as.numeric(N_rapid_gain))
data$N_weightloss[data$N_weightloss<6] <- NA
data$N_stable[data$N_stable<6] <- NA
data$N_slow_gain[data$N_slow_gain<6] <- NA
data$N_mod_gain[data$N_mod_gain<6] <- NA
data$N_rapid_gain[data$N_rapid_gain<6] <- NA
data <- data %>%
dplyr::select(-c("weightloss", "stable", "slow_gain", "mod_gain", "rapid_gain"))
data <- data %>%
dplyr::mutate(N_weightloss = plyr::round_any(data$N_weightloss, 5)) %>%
dplyr::mutate(N_stable = plyr::round_any(data$N_stable, 5)) %>%
dplyr::mutate(N_slow_gain = plyr::round_any(data$N_slow_gain, 5)) %>%
dplyr::mutate(N_mod_gain = plyr::round_any(data$N_mod_gain, 5)) %>%
dplyr::mutate(N_rapid_gain = plyr::round_any(data$N_rapid_gain, 5))
data$N_weightloss[data$N_weightloss == 5] <- ">5"
data$N_stable[data$N_stable == 5] <- ">5"
data$N_slow_gain[data$N_slow_gain == 5] <- ">5"
data$N_mod_gain[data$N_mod_gain == 5] <- ">5"
data$N_rapid_gain[data$N_rapid_gain == 5] <- ">5"
postcovid_change_categories <- data
write.csv (postcovid_change_categories, here::here ("output/data","postcovid_bmi_trajectories.round.csv"))
## Mean Change in BMI trajectories
data2 <- read_csv (here::here ("output/data", "mean_bmi_traj_change.csv"))
data <- as.data.frame(data2)
data <- data %>%
dplyr::filter(n>5)
data <- data %>%
dplyr::mutate(n = plyr::round_any(data$n, 5))
data$n[data$n == 5] <- ">5"
trajectory_change <- data
write.csv (trajectory_change, here::here ("output/data", "mean_bmi_traj_change_round.csv"))