In skgallagher/InfectionTrees: Sample, Analyze, and Plot Infection Trees

knitr::opts_chunk$set(echo = TRUE,
                      cache = TRUE,
                      warning = FALSE,
                      message = FALSE)
devtools::load_all()
library(InfectionTrees)
library(tidyr)
library(dplyr)
library(kableExtra)
library(ggplot2)
theme_set(theme_bw() + theme(axis.title = element_text()))

Overview

In this vignette, we will explore the data set provided here in data(tb_clean), which is a data set originally collected and analyzed in Xie et al. (2018).

Exploratory Data Analysis

library(InfectionTrees)
library(tidyverse)
library(kableExtra)

The data tb_clean has r nrow(tb_clean) rows and r ncol(tb_clean) columns, where each row in the data corresponds to an individual with a detected case of Tuberculosis (TB).

The columns include demographic characteristics about the patients as well as two diagnostic tests of the disease, including a AFB sputum smear test, which we analyze here.

tb_sub <- tb_clean %>%
  mutate(cluster_id = group) %>%
  group_by(cluster_id) %>%
        mutate(cluster_size = n()) %>%
  ungroup()

This data has r length(unique(tb_sub$cluster_id)) clusters with the following distribution of cluster sizes:

sizes <- tb_sub %>% group_by(cluster_id) %>%
  summarize(cluster_size = n()) %>% 
  ungroup() %>%
  group_by(cluster_size) %>%
  summarize(freq = n()) 


sizes %>%
  kable() %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"),
                full_width = FALSE, position = "center")

This means that r round(sizes$freq[sizes$cluster_size == 1] / sum(sizes$freq) * 100, 1)% of the clusters are singletons and r round(sum(sizes$freq[sizes$cluster_size %in% 1:3]) / sum(sizes$freq) * 100, 1)% of the clusters have size at most 3.

A single cluster of individuals looks like the following:

tb_sub %>% filter(group == "27") %>%
  arrange(rel_time) %>%
  select(group, rel_time, everything()) %>%
   kable() %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"),
                full_width = FALSE, position = "center")

In our data set of r nrow(tb_sub) individuals, r sum(tb_sub$hivstatus == "Positive") or r round(sum(tb_sub$hivstatus == "Positive") / nrow(tb_sub) * 100)% of individuals are HIV+. In the clusters with more than 3 individuals, r sum(tb_sub$hivstatus == "Positive" & tb_sub$cluster_size > 3) of r sum(tb_sub$cluster_size > 3) or r round(sum(tb_sub$hivstatus == "Positive" & tb_sub$cluster_size > 3) / sum(tb_sub$cluster_size > 3) * 100)% of individuals are HIV+.

skgallagher/InfectionTrees documentation built on July 28, 2021, 2:14 p.m.