library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
library(dplyr)
library (lubridate)
library (ggplot2)
library (rmarkdown)
library(openxlsx)
library(writexl)
library(rstatix)
##
## Attaching package: 'rstatix'
##
## The following object is masked from 'package:stats':
##
## filter
library(eeptools)
## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod car
library(ggpubr)
library(readxl)
library(BlandAltmanLeh)
library(openxlsx)
PSFI_df_malnutrition <- read_xlsx("PSFI_final_malnutrition.xlsx")
Create malnutrition z score
# ASSIGNS Z-SCORE TO MUAC BASED ON THE 11.5 & 12.5CM LIMITS, THIS IS A PROXY AS LATER A Z-SCORE OF 0 WILL EQUAL NO MALNUTRITION, <-2 WILL EQUAL MODERATE MALNUTRITION AND <-3 WILL EQUAL SEVERE MALNUTRITION
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
muacz1 = case_when(
muac >= 12.5 ~ 0,
muac >= 11.5 & muac < 12.5 ~ -2.5,
muac < 11.5 ~ -4,
TRUE ~ NA_real_
)
)
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
zscore_unified = case_when(
age_group == 0 ~ wfhz,
age_group == 1 & age_years < 2 ~ ifelse(
is.na(wfhz) & is.na(muacz1), NA_real_,
ifelse(
is.na(muacz1) | (!is.na(wfhz) & abs(wfhz) >= abs(muacz1)),
wfhz,
muacz1
)
),
age_group == 1 & age_years >= 2 ~ ifelse(
is.na(wfhz) & is.na(muacz1) & is.na(baz), NA_real_,
ifelse(
is.na(muacz1) & is.na(baz), wfhz,
ifelse(
is.na(baz) | (!is.na(wfhz) & abs(wfhz) >= abs(muacz1) & abs(wfhz) >= abs(baz)),
wfhz,
ifelse(
is.na(baz) | (!is.na(muacz1) & abs(muacz1) >= abs(baz)),
muacz1,
baz
)
)
)
),
age_group == 2 ~ baz,
TRUE ~ NA_real_
)
)
#Creates a filter for extreme z-scores
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
who_outlier = zscore_unified < -5 | zscore_unified > 5
)
subset_zscore <- PSFI_df_malnutrition %>%
filter(case_control == 1 & who_outlier == FALSE) %>%
pull(zscore_unified)
# SUMMARY OF Z SCORE UNIFIED (CASES, NO OUTLIERS)
summary(subset_zscore)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.0000 -2.2200 -0.5450 -0.5281 1.0750 4.9200
# NUMBER OF CASES IN Z SCORE UNIFIED ()
length(subset_zscore)
## [1] 688
# Creates an extra category that outlines which anthropometric measure was used to display the z-score
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
malnutrition_source = case_when(
age_group == 0 ~ "WFH",
age_group == 1 & age_years < 2 ~ ifelse(
is.na(wfhz) & is.na(muacz1), NA_character_,
ifelse(
is.na(muacz1) | (!is.na(wfhz) & abs(wfhz) >= abs(muacz1)),
"WFH",
"MUAC"
)
),
age_group == 1 & age_years >= 2 ~ ifelse(
is.na(wfhz) & is.na(muacz1) & is.na(baz), NA_character_,
ifelse(
is.na(muacz1) & is.na(baz), "WFH",
ifelse(
is.na(baz) | (!is.na(wfhz) & abs(wfhz) >= abs(muacz1) & abs(wfhz) >= abs(baz)),
"WFH",
ifelse(
is.na(baz) | (!is.na(muacz1) & abs(muacz1) >= abs(baz)),
"MUAC",
"BFA"
)
)
)
),
age_group == 2 ~ "BFA",
TRUE ~ NA_character_
)
)
# SUMMARY OF MALNUTRITION SOURCE
malnutrition_source_summary <- PSFI_df_malnutrition %>%
filter(case_control == 1, who_outlier == FALSE) %>%
count(malnutrition_source) %>%
mutate(
percent = round(100 * n / sum(n), 1)
)
malnutrition_source_summary
# CREATES MALNUTRITION CATEGORY BASED ON UNIFIED Z SCORES
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
malnutrition_full = case_when(
is.na(zscore_unified) ~ NA_integer_,
zscore_unified < -3 ~ 3L,
zscore_unified >= -3 & zscore_unified < -2 ~ 2L,
zscore_unified >= -2 & zscore_unified < -1 ~ 1L,
zscore_unified >= -1 & zscore_unified <= 1 ~ 0L,
zscore_unified > 1 & zscore_unified <=2 ~ 4L,
zscore_unified > 2 & zscore_unified <=3 ~ 5L,
zscore_unified > 3 ~ 6L
)
)
WHO_5
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
malnutrition_who5 = case_when(
is.na(malnutrition_full) ~ NA_integer_,
malnutrition_source == "BFA" &
age_years < 5 &
malnutrition_full == 4 ~ 3L, # overweight
malnutrition_source == "BFA" &
age_years < 5 &
malnutrition_full == 5 ~ 4L, # obesity
- malnutrition_full == 0 ~ 0L, # no malnutrition
malnutrition_full == 1 ~ 0L, # no malnutrition
malnutrition_full == 2 ~ 1L, # moderate malnutrition
malnutrition_full == 3 ~ 2L, # severe malnutrition
malnutrition_full == 4 ~ 0L, # no malnutrition
malnutrition_full == 5 ~ 3L, # overweight
malnutrition_full == 6 ~ 4L, # obesity
TRUE ~ NA_integer_
)
)
PSFI_cases <- PSFI_df_malnutrition %>%
filter(case_control == 1, who_outlier == FALSE)
x <- PSFI_cases %>%
pull(zscore_unified)
xmin <- -5
xmax <- 5
bar_x <- PSFI_cases %>%
mutate(
malnutrition_who5_label = factor(
case_when(
malnutrition_who5 == 0 ~ "No malnutrition",
malnutrition_who5 == 1 ~ "Moderate malnutrition",
malnutrition_who5 == 2 ~ "Severe malnutrition",
malnutrition_who5 == 3 ~ "Overweight",
malnutrition_who5 == 4 ~ "Obesity",
TRUE ~ NA_character_
),
levels = c(
"Severe malnutrition",
"Moderate malnutrition",
"No malnutrition",
"Overweight",
"Obesity"
)
)
) %>%
count(malnutrition_who5_label, .drop = FALSE)
print(bar_x)
## # A tibble: 5 × 2
## malnutrition_who5_label n
## <fct> <int>
## 1 Severe malnutrition 97
## 2 Moderate malnutrition 96
## 3 No malnutrition 396
## 4 Overweight 59
## 5 Obesity 40
layout(
mat = matrix(c(1, 2), 2, 1, byrow = TRUE),
heights = c(1, 8)
)
par(mar = c(0, 3.1, 1.1, 2.1))
boxplot(
x,
horizontal = TRUE,
xaxt = "n",
col = rgb(0.8, 0.8, 0, 0.5),
frame = FALSE,
xlim = c(xmin, xmax)
)
par(mar = c(8, 3.1, 1.1, 2.1))
barplot(
bar_x$n,
names.arg = bar_x$malnutrition_who5_label,
col = rgb(1, 0.8, 0.8, 1),
border = "white",
main = "",
ylab = "Count",
las = 2,
cex.names = 0.8
)
PSFI_cases <- PSFI_df_malnutrition %>%
filter(case_control == 1, who_outlier == FALSE) %>%
mutate(
malnutrition_who5_label = case_when(
malnutrition_who5 == 0 ~ "No malnutrition",
malnutrition_who5 == 1 ~ "Moderate malnutrition",
malnutrition_who5 == 2 ~ "Severe malnutrition",
malnutrition_who5 == 3 ~ "Overweight",
malnutrition_who5 == 4 ~ "Obesity",
TRUE ~ NA_character_
)
)
malnutrition_cat_table_who5 <- PSFI_cases %>%
group_by(malnutrition_who5_label) %>%
summarise(
n = n(),
percent = round(100 * n() / nrow(PSFI_cases), 1),
.groups = "drop"
) %>%
arrange(
factor(
malnutrition_who5_label,
levels = c(
"Severe malnutrition",
"Moderate malnutrition",
"No malnutrition",
"Overweight",
"Obesity"
)
)
)
malnutrition_cat_table_who5
WHO_3
PSFI_df_malnutrition <- PSFI_df_malnutrition %>%
mutate(
malnutrition_who3 = case_when(
is.na(malnutrition_full) ~ NA_integer_,
# BMI-specific WHO 0-5y overweight/obesity rules
malnutrition_source == "BFA" &
age_years < 5 &
malnutrition_full == 4 ~ 2L, # overweight
# Standard recoding
malnutrition_full == 0 ~ 0L, # no malnutrition
malnutrition_full == 1 ~ 0L, # no malnutrition
malnutrition_full == 2 ~ 1L, # malnutrition
malnutrition_full == 3 ~ 1L, # malnutrition
malnutrition_full == 4 ~ 0L, # no malnutrition
malnutrition_full == 5 ~ 2L, # overweight
malnutrition_full == 6 ~ 2L, # overweight
TRUE ~ NA_integer_
)
)
# Creates a histogram-like bar plot of malnutrition categories based on malnutrition_who3
PSFI_cases <- PSFI_df_malnutrition %>%
filter(case_control == 1, who_outlier == FALSE)
x <- PSFI_cases %>%
pull(zscore_unified)
xmin <- -5
xmax <- 5
cat_counts <- PSFI_cases %>%
mutate(
malnutrition_who3_label = case_when(
malnutrition_who3 == 0 ~ "No malnutrition",
malnutrition_who3 == 1 ~ "Malnutrition",
malnutrition_who3 == 2 ~ "Overweight",
TRUE ~ NA_character_
)
) %>%
count(malnutrition_who3_label)
print(cat_counts)
## # A tibble: 3 × 2
## malnutrition_who3_label n
## <chr> <int>
## 1 Malnutrition 193
## 2 No malnutrition 396
## 3 Overweight 99
layout(
mat = matrix(c(1, 2), 2, 1, byrow = TRUE),
heights = c(1, 8)
)
par(mar = c(0, 3.1, 1.1, 2.1))
boxplot(
x,
horizontal = TRUE,
xaxt = "n",
col = rgb(0.8, 0.8, 0, 0.5),
frame = FALSE,
xlim = c(xmin, xmax)
)
par(mar = c(6, 3.1, 1.1, 2.1))
bar_x <- PSFI_cases %>%
mutate(
malnutrition_who3_label = factor(
case_when(
malnutrition_who3 == 0 ~ "No malnutrition",
malnutrition_who3 == 1 ~ "Malnutrition",
malnutrition_who3 == 2 ~ "Overweight",
TRUE ~ NA_character_
),
levels = c("Malnutrition", "No malnutrition", "Overweight")
)
) %>%
filter(!is.na(malnutrition_who3_label)) %>%
count(malnutrition_who3_label, .drop = FALSE)
barplot(
bar_x$n,
names.arg = bar_x$malnutrition_who3_label,
col = rgb(1, 0.8, 0.8, 1),
border = "white",
main = "",
xlab = "",
ylab = "Count",
las = 2,
cex.names = 0.9
)
PSFI_cases <- PSFI_df_malnutrition %>%
filter(case_control == 1, who_outlier == FALSE) %>%
mutate(
malnutrition_who3_label = case_when(
malnutrition_who3 == 0 ~ "No malnutrition",
malnutrition_who3 == 1 ~ "Malnutrition",
malnutrition_who3 == 2 ~ "Overweight",
TRUE ~ NA_character_
)
)
malnutrition_cat_table_who3 <- PSFI_cases %>%
group_by(malnutrition_who3_label) %>%
summarise(
n = n(),
percent = round(100 * n() / nrow(PSFI_cases), 1),
.groups = "drop"
) %>%
arrange(
factor(
malnutrition_who3_label,
levels = c(
"Malnutrition",
"No malnutrition",
"Overweight"
)
)
)
malnutrition_cat_table_who3
write.xlsx(
list(
WHO_5 = malnutrition_cat_table_who5,
WHO_3 = malnutrition_cat_table_who3
),
file = "malnutrition_categories_original.xlsx",
overwrite = TRUE
)
who_export <- PSFI_df_malnutrition %>%
select(
subject_id,
record_id,
malnutrition_who5,
malnutrition_who3
) %>%
rename(
WHO_5_category = malnutrition_who5,
WHO_3_category = malnutrition_who3
)
write.csv(
who_export,
file = "WHO_malnutrition_categories.csv",
row.names = FALSE
)
write.xlsx(
who_export,
file = "WHO_malnutrition_categories.xlsx",
overwrite = TRUE
)
## Warning in file.create(to[okay]): cannot create file
## 'WHO_malnutrition_categories.xlsx', reason 'Permission denied'
PSFI_df_malnutrition %>%
summarise(
missing_who5 = sum(is.na(malnutrition_who5)),
missing_who3 = sum(is.na(malnutrition_who3))
)
view(PSFI_df_malnutrition)