README.md

In AntonioJBT/episcout: Quickly Clean, Explore and Visualise Large Epidemiological Datasets

episcout

Facilitates cleaning, exploring and visualising large-ish datasets (hundreds of thousands to millions of observations with tens to hundreds of variables).

These are mostly wrapper and convenience functions to pre-process (wrangle, explore, clean, etc.) data-sets. Assumes you're happy with tidyverse and the basics of data.table.

Installation

Install from GitHub:

install.packages("devtools")
library(devtools)
install_github("AntonioJBT/episcout")

Example

This is a basic example of things you can do with episcout:

library(episcout)

# A data frame:
n <- 20
df <- data.frame(var_id = rep(1:(n / 2), each = 2),
                 var_to_rep = rep(c('Pre', 'Post'), n / 2),
                 x = rnorm(n),
                 y = rbinom(n, 1, 0.50),
                 z = rpois(n, 2)
                 )
# Print the first few rows and last few rows:
dim(df)
epi_head_and_tail(df, rows = 2, cols = 2)
epi_head_and_tail(df, rows = 2, cols = 2, last_cols = TRUE)


# Get all duplicates:
check_dups <- epi_clean_get_dups.R(df, 'var_id', 1)
dim(check_dups)
check_dups

# Get summary descriptive statistics for numeric/integer column:
num_vec <- df$x
desc_stats <- epi_stats_numeric(num_vec)
class(desc_stats)
lapply(desc_stats, class)
desc_stats

# And many more functions for cleaning, stats and plotting that do things a bit faster or more conveniently and I couldn't easily find in other packages.

Contribute

• Issue Tracker

• Pull requests welcome!

Support

If you have any issues, pull requests, etc. please report them in the issue tracker.

News

• Version 0.1.1 First release

In development: - Version 0.1.2

AntonioJBT/episcout documentation built on April 28, 2024, 2:03 p.m.