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.
Install from GitHub:
install.packages("devtools")
library(devtools)
install_github("AntonioJBT/episcout")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.-
Pull requests welcome!
If you have any issues, pull requests, etc. please report them in the issue tracker.
- Version 0.1.1 First release
In development:
- Version 0.1.2