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--- |
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title: "Run Summary Statistics" |
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author: "Vy Nguyen" |
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date: "1/12/2023" |
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output: |
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pdf_document: default |
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html_document: default |
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word_document: default |
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--- |
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```{r setup, include=FALSE} |
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knitr::opts_chunk$set(echo = TRUE) |
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``` |
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We will learn how to calculate summary statistics using the chemical dataset. We will specifically learn to |
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1. calculate the distribution statistics for one variable |
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2. stratify the distribution of one variable by a categorical variable (e.g. sex) |
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3. calculate the distribution statistics for one variable while accounting for NHANES sampling design |
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4. stratify the distribution of one variable by a categorical variable (e.g. sex) while accounting for NHANES sampling design |
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5. calculate the distribution statistics for multiple variables |
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6. calculate the distribution statistics for multiple variables while accounting for NHANES sampling design |
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```{r setup, include=FALSE} |
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knitr::opts_chunk$set(echo = TRUE) |
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``` |
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You only need to run this ONCE! |
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```{r, echo = FALSE} |
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install.packages("survey") # Needed to run a regression model that accounts for the NHANES sampling design |
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install.packages("tidyverse") # Needed to pipe functions together |
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install.packages("broom") # Needed to easily extract regression statistics |
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``` |
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Run this so you have access to the functions in these packages. |
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```{r, echo = FALSE} |
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library("survey") |
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library("tidyverse") |
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library("broom") |
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``` |
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Make sure you set your working directory to be the folder containing file for the NHANES data. |
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```{r, echo = FALSE} |
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load("./w - nhanes_1988_2018.RData") |
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``` |
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Please use any dataset that ends with "_clean". |
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Let's merge the demographics, chemicals, and weights datasets together using tidyverse syntax. |
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```{r, echo = FALSE} |
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nhanes_merged <- full_join(demographics_clean, |
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chemicals_clean, |
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by = c("SEQN", |
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"SEQN_new", |
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"SDDSRVYR")) %>% |
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full_join(., |
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weights_clean, |
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by = c("SEQN", |
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"SEQN_new", |
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"SDDSRVYR")) |
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``` |
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## Data dictionary |
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Take a look at the dictionary to know the codename and description of the variables |
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```{r, echo = FALSE} |
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View(df_dictionary) |
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``` |
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## Ensure that sex and race/ethnicity variables are categorical |
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factor() is a base R function that encode a variable as categorical. |
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relevel() is a base R function that set the reference group. |
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We ensured that sex (RIAGENDR) is a categorical variable. |
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We ensured that race/ethnicity (RIDRETH1) is a categorical variable with the reference group as Non-Hispanic Whites (3). |
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```{r, echo = FALSE} |
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nhanes_merged <- nhanes_merged %>% |
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mutate(RIAGENDR = factor(RIAGENDR)) %>% |
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mutate(RIDRETH1 = factor(RIDRETH1) %>% |
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relevel(., ref = 3)) |
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``` |
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## Choose only variables that you will use |
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For the portion on calculating summary statistics for multiple variables, we'll iterate over the metal biomarkers to get distribution statistics for each metal biomarkers. |
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So let's include the metal biomarkers and their corresponding weight codenames in this NHANES subset to prepare for subsequential analysis. |
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```{r, echo = FALSE} |
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metals_codename <- df_dictionary %>% |
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filter(chemical_family == "Metals") %>% |
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filter(grepl("replicate", variable_description_use) == FALSE) %>% |
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pull(variable_codename_use) |
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# Corresponding weight codenames for the chemical biomarkers are the concatenation of "WT_" and the codename of the chemical biomarker |
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weights_codename_for_metals <- paste("WT_" |
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, metals_codename |
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, sep = "") |
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nhanes_subset <- nhanes_merged %>% |
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select("SEQN", |
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"SEQN_new", |
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"SDDSRVYR", |
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"SDMVPSU", |
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"SDMVSTRA", |
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all_of(metals_codename), |
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all_of(weights_codename_for_metals), |
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"RIDAGEYR", |
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"RIAGENDR", |
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"RIDRETH1") |
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``` |
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## Distribution statistics for one variable |
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Let's calculate the distribution statistics for blood lead |
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```{r, echo = FALSE} |
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dataset_lead <- nhanes_subset %>% |
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select("SEQN", |
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"SEQN_new", |
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"SDDSRVYR", |
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"SDMVPSU", |
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"SDMVSTRA", |
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"WT_LBXBPB", |
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"LBXBPB", |
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"RIDAGEYR", |
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"RIAGENDR", |
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"RIDRETH1") %>% |
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na.omit(.) |
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df_stats_lead <- dataset_lead %>% |
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summarise(min = min(LBXBPB), |
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perc_10 = quantile(LBXBPB, probs = 0.1), |
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perc_25 = quantile(LBXBPB, probs = 0.25), |
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median = median(LBXBPB), |
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mean = mean(LBXBPB), |
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perc_75 = quantile(LBXBPB, probs = 0.75), |
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perc_90 = quantile(LBXBPB, probs = 0.9), |
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max = max(LBXBPB), |
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num_with_measurements = n()) |
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``` |
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Let's stratify the distribution statistics for blood lead by a categorical variable (e.g. sex) |
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```{r, echo = FALSE} |
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df_stats_lead_sex <- dataset_lead %>% |
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mutate(sex = ifelse(RIAGENDR == 1, |
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"males", |
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"females")) %>% |
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group_by(sex) %>% |
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summarise(min = min(LBXBPB), |
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perc_10 = quantile(LBXBPB, probs = 0.1), |
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perc_25 = quantile(LBXBPB, probs = 0.25), |
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median = median(LBXBPB), |
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mean = mean(LBXBPB), |
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perc_75 = quantile(LBXBPB, probs = 0.75), |
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perc_90 = quantile(LBXBPB, probs = 0.9), |
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max = max(LBXBPB), |
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num_with_measurements = n()) %>% |
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ungroup(.) |
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``` |
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## Distribution statistics for one variable accounting for NHANES sampling design |
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The survey weights for a given two-year cycle in the set of cycles 3-10 (2003-2018) is representative of a national sample (i.e. the survey weights in a given cycle should sum to the population size of the US). |
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The survey weights for cycles 1 and 2 (1999-2002) TOGETHER is representative of a national sample. |
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When we combine survey cycles, let's suppose for two cycles (cycle 3 and 4), then the survey weights are going to be representative of two national samples, so any estimates are going to be representative of two US population. This doesn't make sense! So we multiply the survey weights by 1/2 (i.e. 1 over the number of studied cycles), and then the adjusted survey weights are representative of ONLY ONE national sample. |
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More examples can be found under the tab "When and How to Construct Weights When Combining Survey Cycles" at https://wwwn.cdc.gov/nchs/nhanes/tutorials/weighting.aspx |
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Sanity check: sum the adjusted survey weights and it should be close to the US population size. |
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```{r, echo = FALSE} |
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# Determine the cycles with measurements for blood lead |
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unique_cycles_lead <- dataset_lead %>% |
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pull(SDDSRVYR) %>% |
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unique(.) |
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# Determine the total number of cycles with measurements for lead |
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num_cycles_lead <- length(unique_cycles_lead) |
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# Calculate the adjusted survey weights |
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dataset_lead <- dataset_lead %>% |
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mutate(adjusted_weights = ifelse(SDDSRVYR %in% c(1,2), |
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(2/num_cycles_lead)*WT_LBXBPB, |
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(1/num_cycles_lead)*WT_LBXBPB)) |
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# Checking our calculations |
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dataset_lead %>% |
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select(SDDSRVYR, |
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WT_LBXBPB, |
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adjusted_weights) %>% |
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unique(.) %>% |
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mutate(multiplier = adjusted_weights/WT_LBXBPB) %>% |
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View(.) |
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# Account for the sampling design |
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dsn_lead <- svydesign(ids = ~SDMVPSU, |
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strata = ~SDMVSTRA, |
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weights = ~adjusted_weights, |
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nest = TRUE, |
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data = dataset_lead) |
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# Calculate distribution statistics for lead and these statistics are generalizable to the entire US population |
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df_stats_lead_svy <- dataset_lead %>% |
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summarise(min = svyquantile(~LBXBPB, dsn_lead, quantiles = 0)[1], |
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perc_10 = svyquantile(~LBXBPB, dsn_lead, quantiles = 0.1)[1], |
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perc_25 = svyquantile(~LBXBPB, dsn_lead, quantiles = 0.25)[1], |
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median = svyquantile(~LBXBPB, dsn_lead, quantiles = 0.5)[1], |
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mean = svymean(~LBXBPB, dsn_lead)[1], |
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perc_75 = svyquantile(~LBXBPB, dsn_lead, quantiles = 0.75)[1], |
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perc_90 = svyquantile(~LBXBPB, dsn_lead, quantiles = 0.90)[1], |
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max = svyquantile(~LBXBPB, dsn_lead, quantiles = 1)[1]) %>% |
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ungroup(.) |
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``` |
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Let's stratify the distribution statistics for blood lead by a categorical variable (e.g. sex) |
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```{r, echo = FALSE} |
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df_stats_leads_svy_sex <- dataset_lead %>% |
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summarise(min = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0, keep.var=FALSE)[,2], |
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perc_10 = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0.1, keep.var=FALSE)[,2], |
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perc_25 = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0.25, keep.var=FALSE)[,2], |
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median = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0.5, keep.var=FALSE)[,2], |
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mean = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svymean)[,2], |
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perc_75 = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0.75, keep.var=FALSE)[,2], |
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perc_90 = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 0.9, keep.var=FALSE)[,2], |
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max = svyby(~LBXBPB, ~RIAGENDR, dsn_lead, svyquantile, quantiles = 1, keep.var=FALSE)[,2]) %>% |
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mutate(RIAGENDR = unique(dataset_lead$RIAGENDR)) %>% |
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relocate(RIAGENDR) |
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``` |
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We will iterate over each metal biomarker, calculate the statistics for each metal biomarker, and stitch the dataset of statistics together. |
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To iterate over each metal biomarkers, we will use map() on the codenames of the metal biomarkers. |
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To calculate the statistics for each metal biomarker, we will customize a function (e.g. calculate_distribution_stats) to calculate the percentiles. |
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To stitch the dataset of statistics together, we will use bind_rows(). |
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```{r, echo = FALSE} |
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calculate_distribution_stats <- function(x, |
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df_nhanes, |
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dataset_dictionary) |
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{ |
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# Print out the codename of a given chemical biomarker |
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# This helps with debugging to detect which chemical biomarker is causing problems. |
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print(x) |
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# Use the data dictionary to extract the name of the chemical |
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chemical_name <- dataset_dictionary %>% |
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filter(variable_codename_use == x) %>% |
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pull(variable_description_use) %>% |
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unique(.) |
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# Define a subset to include any participants with measurements for the chemical biomarker |
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subset_x <- df_nhanes %>% |
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select("SEQN", |
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"SEQN_new", |
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"SDDSRVYR", |
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all_of(x)) %>% |
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na.omit(.) |
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# Let's change the column name of the chemical biomarker to "chem" to make it easier to calculate the statistics. |
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index_chem <- which(colnames(subset_x) == x) |
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colnames(subset_x)[index_chem] <- "chem" |
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df_stats_x <- subset_x %>% |
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summarise(min = min(chem), |
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perc_10 = quantile(chem, probs = 0.1), |
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perc_25 = quantile(chem, probs = 0.25), |
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median = median(chem), |
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mean = mean(chem), |
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perc_75 = quantile(chem, probs = 0.75), |
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perc_90 = quantile(chem, probs = 0.9), |
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max = max(chem), |
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num_with_measurements = n()) %>% |
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mutate(variable_codename_use = x) %>% # Include the codename to know which statistics belongs to which chemical when we stitch the datasets together |
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mutate(variable_description_use = chemical_name) %>% # Include the chemical name |
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relocate(variable_codename_use) %>% # Make the column of chemical codenames be the 1st column |
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relocate(variable_description_use, |
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.after = variable_codename_use) # Make the column of chemical names be the column after the column of codenames (or the 2nd column) |
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# For map(), we must return a data frame. |
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return(df_stats_x) |
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} |
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df_stats_metals <- metals_codename %>% |
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map(., |
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calculate_distribution_stats, |
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nhanes_subset, |
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df_dictionary) %>% |
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bind_rows(.) |
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``` |
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## Distribution statistics for multiple variables accounting for NHANES sampling design |
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Again, we will iterate over each metal biomarker, calculate the statistics for each metal biomarker, and stitch the dataset of statistics together. |
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Now the difference is that when we customize the function for each metal biomarker, we need to adjust the survey weights, so that our estimates will be representative of ONLY ONE national sample. |
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```{r, echo = FALSE} |
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calculate_distribution_stats_svy <- function(x, |
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df_nhanes, |
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dataset_dictionary) |
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{ |
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# Print out the codename of a given chemical biomarker |
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print(x) |
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# Define the weight codename for a given chemical biomarker |
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weight_codename <- paste("WT_", |
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x, |
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sep = "") |
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# Use the data dictionary to extract the name of the chemical |
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chemical_name <- dataset_dictionary %>% |
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filter(variable_codename_use == x) %>% |
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pull(variable_description_use) %>% |
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unique(.) |
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# Define a subset to include any participants with measurements for the chemical biomarker |
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subset_x <- df_nhanes %>% |
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select("SEQN", |
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"SEQN_new", |
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"SDDSRVYR", |
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"SDMVPSU", |
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"SDMVSTRA", |
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all_of(x), |
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all_of(weight_codename)) %>% |
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na.omit(.) |
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# Extract the unadjusted survey weights from the subset |
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unadjusted_weights <- subset_x %>% |
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pull(all_of(weight_codename)) |
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# Determine the cycles that the metal biomarker was measured |
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unique_cycles_x <- subset_x %>% |
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pull(SDDSRVYR) %>% |
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unique(.) |
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# print(unique_cycles_x) |
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# Determine the total number of cycles |
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num_cycles <- length(unique_cycles_x) |
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# print(num_cycles) |
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# Determine if the chemical biomarker had measurements in both cycle 1 and cycle 2 |
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indicator_cycles <- ifelse(1 %in% unique_cycles_x & 2 %in% unique_cycles_x, |
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"yes", |
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"no") |
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# print(indicator_cycles) |
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# Calculate the adjusted weights |
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if(indicator_cycles == "yes") |
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{ |
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adjusted_weights <- ifelse(subset_x$SDDSRVYR %in% c(1,2), |
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(2/num_cycles)*unadjusted_weights, |
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(1/num_cycles)*unadjusted_weights) |
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} else { |
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adjusted_weights <- (1/num_cycles)*unadjusted_weights |
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} |
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# Include the adjusted weights in the subset and exclude any weights that are 0 or else the code to calculate the statistics may not run |
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subset_x <- subset_x %>% |
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mutate(adjusted_weights = adjusted_weights) %>% |
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filter(adjusted_weights != 0) |
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# Let's change the column name of the chemical biomarker to "chem" to make it easier to calculate the statistics. |
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index_chem <- which(colnames(subset_x) == x) |
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colnames(subset_x)[index_chem] <- "chem" |
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# Define the survey object to account for the sampling design |
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dsn_x <- svydesign(ids = ~SDMVPSU, |
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strata = ~SDMVSTRA, |
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weights = ~adjusted_weights, |
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nest = TRUE, |
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data = subset_x) |
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# Calculate the distribution statistics |
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# The [1] is necessary or else the column names will be changed to some less readable |
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df_stats_x <- data.frame("min" = svyquantile(~chem, dsn_x, quantiles = 0)[1], |
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"perc_10" = svyquantile(~chem, dsn_x, quantiles = 0.1)[1], |
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"perc_25" = svyquantile(~chem, dsn_x, quantiles = 0.25)[1], |
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"median" = svyquantile(~chem, dsn_x, quantiles = 0.5)[1], |
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"mean" = svymean(~chem, dsn_x)[1] %>% unlist(.), |
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"perc_75" = svyquantile(~chem, dsn_x, quantiles = 0.75)[1], |
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"perc_90" = svyquantile(~chem, dsn_x, quantiles = 0.90)[1], |
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"max" = svyquantile(~chem, dsn_x, quantiles = 1)[1]) %>% |
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mutate(variable_codename_use = x) %>% |
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mutate(variable_description_use = chemical_name) %>% |
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relocate(variable_codename_use) %>% |
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relocate(variable_description_use, |
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.after = variable_codename_use) |
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return(df_stats_x) |
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} |
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df_stats_metals_svy <- metals_codename %>% |
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map(., |
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calculate_distribution_stats_svy, |
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nhanes_subset, |
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df_dictionary) %>% |
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bind_rows(.) |
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``` |
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