Familiarity with Dataframes Before Data Manipulation

Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.

Overview

It is often said that data manipulation alone takes 50-70% time of a data science project. 
The duration of this never-ending activity can be attributed to our opaqueness with datasets provided.
Functions in this package enable familiarity with the data.frame to further reduce coding errors and re-work.

Installation

# The easiest way to get dplyr is to install from GitHub:
install.packages("dataframeexplorer", dependencies = T)

# Alternatively, you can install development version:
install.packages("devtools")
devtools::install_github("ashrithssreddy/dataframeexplorer")

Next Steps

Functions: [x] Percentiles [x] Level of dataset Univariate Analysis Bivariate Analysis Show progress bar for level_of_dataset Run the level_of_dataset code in parallel for performance

Changes: Return value for all functions to be included into documentation Message not printed in all codes Default filename not consistent Outputs not refined Pep 8 formatting examples not consistent Comments not consistent across all codes sink() to be run in glimpse_to_file upon an error Arguement format to be used: dataset = dataset, output_filename = "dataset_glimpse.txt" Throw a warning when duplicate column names are found Level: Unsink when interrupted Add instructions to interpretation of output

Usage

1. glimpse_to_file
	glimpse_to_file(mtcars, "mtcars_glimpse.txt") or glimpse_to_file(mtcars, "C://Users/Desktop/mtcars_glimpse.txt")
	![Output](/man/figures/.png)

Contact

Mail [email protected] for suggestions with "dataframeexplorer" in subject line.