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f - identify_chemicals_all_df.R
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f - identify_chemicals_all_df.R
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identify_chemicals_all_df <- function(nhanes_full_dataset,
nhanes_comments,
chemical_dataset,
chem_master,
weights_dataset)
{
library(tidyverse)
library(sjlabelled)
library(magrittr) # %T>% package
library(usefun)
weights_dataset <- weights_dataset %>%
filter(SDDSRVYR != -1)
total_chems <- chemical_dataset %>%
dplyr::select(-SEQN,
-SDDSRVYR) %>%
colnames(.)
chems <- chem_master %>%
filter(!chem_family %in% c("Dietary Components",
"Phytoestrogens")) %>%
filter(!chemical_codename_use %in% c("LBXEST",
"SSCYA",
"SSMEL",
"URXUCR",
"URXANBT",
"URXANTT",
"URXCOXT",
"URXHPBT",
"URXNICT",
"URXNNCT",
"URXNOXT")) %>%
distinct(chemical_codename_use) %>%
pull(chemical_codename_use)
#make a vector of the 13 immune measures
immune <- c("LBDLYMNO", #lymphocyte counts
"LBDMONO", #monocyte counts
"LBDNENO", #neutrophil counts
"LBDEONO", #eosinophil counts
"LBDBANO", #basophil counts
"LBXWBCSI", #WBC count
"LBXRBCSI", #RBC count
"LBXMCVSI" #MCV
)
#make a vector of the 5 demographics + SEQN + creatinine + cotinine + PSU + strata
demog <- c("SEQN", #ID
"RIDAGEYR", #age
"RIDRETH1", #race
"RIAGENDR", #gender
"INDFMPIR", #poverty-income ratio
"BMXWAIST", #waist circumference
"URXUCR", #creatinine
"LBXCOT", #cotinine
"SDMVPSU", #PSU
"SDMVSTRA") #strata
nhanes_subset_unclean <- nhanes_full_dataset %>%
dplyr::select(all_of(demog),
all_of(immune),
all_of(chems)) %T>%
{print("total dataset")} %T>%
{print(dim(.))} %>% #101316 501 (7 demog + 13 immune + 486 chems)
#drop participants under 18 (children)
filter(RIDAGEYR >= 18) %T>%
{print("drop <18 years")} %T>%
{print(dim(.))} %>% #59204
#dropping participants with missing demographics
drop_na(RIDAGEYR,
RIDRETH1,
RIAGENDR) %T>%
{print("drop age, race, gender")} %T>%
{print(dim(.))} %>% #59204
drop_na(INDFMPIR) %T>%
{print("drop poverty-income ratio missings")} %T>%
{print(dim(.))} %>% #53446
drop_na(BMXWAIST) %T>%
{print("drop waist circumference missings")} %T>%
{print(dim(.))} %>% #48484
drop_na(LBXCOT) %T>%
{print("drop cotinine missings")} %T>%
{print(dim(.))} %>% #46080
#dropping participants with missing cell counts
drop_na(LBXWBCSI) %T>%
{print("drop WBC missings")} %T>%
{print(dim(.))} %>% #46004
drop_na(LBXRBCSI) %T>%
{print("drop RBC missings")} %T>%
{print(dim(.))} %>% #46004
drop_na(LBDLYMNO,
LBDMONO,
LBDNENO,
LBDEONO,
LBDBANO,
LBXMCVSI) %>%
filter(!SEQN == "102389") %T>% #WBC count of 400 which is > upper LOD
{print("drop cell type missings")} %T>%
{print(dim(.))} #45870
sum(!is.na(nhanes_subset_unclean$URXUCD)) #13592 total measurements
nhanes_subset_unclean %>%
dplyr::select(URXUCD) %>%
filter(URXUCD == 0) %>%
nrow() #7
#figure out who these participants are for dropping their measurements later in the comments dataset
ucd_partic <- nhanes_subset_unclean %>%
filter(URXUCD == 0) %>%
pull(SEQN)
# Convert measurements of 0 to NA
nhanes_subset_cleaning <- nhanes_subset_unclean %>%
mutate(URXUCD = ifelse(URXUCD == 0, NA, URXUCD)) %T>%
{print("convert urinary cadmium measurements of 0 to NA")} %T>%
{print(dim(.))}
# 45870 501 (486 chems + 15 other variables)
long_cleaning <- nhanes_subset_cleaning %>%
pivot_longer(cols = all_of(chems),
names_to = "chemical_codename",
values_to = "measurements") %>%
drop_na(measurements) #no participants dropped
long_creatinine_seqn <- long_cleaning %>%
mutate(urinary = case_when(str_detect(chemical_codename, "^LB", negate = TRUE) ~ 99999999)) %>%
mutate(creat_na = ifelse(urinary == 99999999 & is.na(URXUCR), NA, measurements)) %>%
drop_na(creat_na) %>%
dplyr::select(-urinary,
-creat_na) %>%
distinct(SEQN) %>%
pull(SEQN)
rm(long_cleaning)
print("number of participants with urinary chemical measurements but no creatinine measurements")
print(length(long_creatinine_seqn)) #45528 - dropped 342 participants
#keep this subset of participants
nhanes_creatinine <- nhanes_subset_cleaning %>%
filter(SEQN %in% long_creatinine_seqn)
nhanes_creatinine <- nhanes_creatinine %>%
mutate(URXNAL = case_when(SEQN == 65564 & URXNAL == 0.0553 ~ 5000000000,
TRUE ~ as.numeric(URXNAL))) %>%
mutate(URXNAL = na_if(URXNAL, 5000000000)) %>%
mutate(URXUCR = ifelse(URXUCR == 0, NA, URXUCR))
#check new measurement counts
# sum(!is.na(nhanes_subset_unclean$URXUCD)) #13585 total measurements
subset_comments <- nhanes_subset_unclean %>%
mutate(URXUCD = ifelse(URXUCD == 0, NA, URXUCD)) %>%
dplyr::select("SEQN"
, all_of(demog)
, "URXUCD") %>%
left_join(.
, nhanes_comments
, by = "SEQN") %>%
filter(SEQN %in% long_creatinine_seqn) %T>%
{print("dimension of comments dataset with included participants")} %T>%
{print(dim(.))}
# 45528 436
comments_codenames <- colnames(nhanes_comments)
# Remove SEQN, SDDSRVYR, and estradiol to prevent calculations of their detection frequency
# estradiol is not an exogenous chemical
excess_codenames <- which(comments_codenames %in% c("SEQN"
, "SDDSRVYR"
, "LBDESTLC"
))
comments_codenames <- comments_codenames[-excess_codenames]
subset_weights <- nhanes_subset_unclean %>%
mutate(URXUCD = ifelse(URXUCD == 0, NA, URXUCD)) %>%
dplyr::select("SEQN"
, "URXUCR") %>%
left_join(.
, weights_dataset
, by = "SEQN") %>%
filter(SEQN %in% long_creatinine_seqn) %T>%
{print("dimension of weights dataset with included participants")} %T>%
{print(dim(.))}
# 45528 542
stats_weight <- comments_codenames %>% #[1:2] %>%
#c(comments_codenames[1:6], "URD4FPLC") %>%
# c("URDUCDLC"
# # , "LBDBCDLC"
# # , "LBD196LC"
# # , "LBD138LC"
# # , "LBDBPBLC"
# # , "SSMONPL"
# ) %>%
map(.
, calculate_detection_frequency_degrees_freedom
, subset_comments
, subset_weights
, chem_master
, demog) %>%
bind_rows(.)
# View(stats_weight)
stats_weight <- get_counts_for_cu_zn(df_inclusion_criteria_stats = stats_weight
, nhanes_subset = nhanes_subset_unclean
, df_weights = subset_weights
, demographics = demog)
weird_smk_chems <- c("URXANBT",
"URXANTT",
"URXCOXT",
"URXHPBT",
"URXNICT",
"URXNNCT",
"URXNOXT")
stats_weight <- stats_weight %>%
filter(!(chemical_codename_use %in% weird_smk_chems)) %>%
filter(chem_family != "Dietary Components") %>%
filter(chem_family != "Phytoestrogens") %>%
mutate(include = ifelse(above_percentage_unweighted >= 50 &
above_percentage_weighted >= 50 &
degrees_of_freedom >= 8
, "yes"
, "no")) %>%
mutate(chemical_codename_use = ifelse(chemical_codename_use == "LBX138158LA"
, "LBX138LA"
, chemical_codename_use)) %>%
mutate(chemical_codename_use = ifelse(chemical_codename_use == "LBX196203LA"
, "LBX196LA"
, chemical_codename_use)) %>%
filter(include == "yes")
# View(stats_weight)
return(stats_weight)
}