Epidemiological analysis with R

Riga Stradins University Workshop, July 2020

Lecturer: Sergio Uribe, Assoc Prof, PhD Riga Stradins University, July 2020 Required packages pacman::p_load( car, broom, tidyverse, ggfortify, mosaic, huxtable, jtools, latex2exp, pubh, sjlabelled, sjPlot, sjmisc ) theme_set(sjPlot::theme_sjplot2(base_size = 10)) theme_update(legend.position = "top") # options('huxtable.knit_print_df' = FALSE) options('huxtable.knit_print_df_theme' = theme_article) options('huxtable.autoformat_number_format' = list(numeric = "%5.2f")) knitr::opts_chunk$set(collapse = TRUE, comment = NA) Epidemiological Descriptive Analysis Onchocerciasis in Sierra Leone. data(Oncho) Oncho %>% head() idmfareaagegrpsexmfloadlesions 1InfectedSavannah20-39Female1No 2InfectedRainforest40+Male3No 3InfectedSavannah40+Female1No 4Not-infectedRainforest20-39Female0No 5Not-infectedSavannah40+Female0No 6Not-infectedRainforest20-39Female0No A two-by-two contingency table: [Read More]

Exploratory Data Analysis with Tables

Introduction Tables allow you to explore and summarize data efficiently. While graphs are more intuitive for discovering relationships and trends, tables have the advantage of providing detailed information and allowing descriptive statistics and data summaries to be delivered. Usually scientific articles in medicine begin with a table that shows the characteristics of the sample of patients. In this post, we will use the janitor and table1 packages to summarize data and make an example of table 1 using the NHANES database. [Read More]