#eda

Original from https://www.youtube.com/watch?v=ZM04jn95YP0 install.packages(“tidyverse”) library(tidyverse) ## ── Attaching packages ─────────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4 ## ✓ tibble 3.0.3 ✓ dplyr 1.0.0 ## ✓ tidyr 1.1.0 ✓ stringr 1.4.0 ## ✓ readr 1.3.1 ✓ forcats 0.5.0 ## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ── ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() library(lubridate) ## ## Attaching package: 'lubridate' ## The following objects are masked from 'package:base': ## ## date, intersect, setdiff, union # install. [Read More]

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]