In UBC-MDS/RDataPeek: Generates a Visual Summary of a Dataset

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(RDataPeek)

RDataPeek

Introduction to RDataPeek

Before any analysis can commence it is a good idea to understand your data.

• What is the structure of my data?
• Are there any missing values?
• What are common words in my qualitative data?
• How does my quantitative data look like?

The RDataPeek package streamlines the answer to these questions:

• Providing you with a summary of your data
• Visualizing where your missing data is
• Making a report-ready word bubble visualization
• Presenting histogram(s) of quantitative data

This Document introduces you to the RDataPeek’s basics and show you how to apply them to your .csv files.

Data: example.csv

As part of this vignette we have provided you with a custom sample of the typical company .csv file.

col_type <- readr::cols(
  A = readr::col_double(),
  B = readr::col_date(format = ""),
  C = readr::col_double(),
  D = readr::col_double(),
  E = readr::col_character(),
  F = readr::col_character(),
  movies = readr::col_character()
)

readr::read_csv("example.csv", col_types = col_type)

View Summary Statistics of Data

RDataPeek::sample_data() allows you to see a summary table of all your information in your selected .csv. “columns” give you the column name. “sample_record” provides you a random sample entry from the selected column. “data_type” provides with what type of data is in the column such as numeric, date or character. “summary” provides you with a summary statistic about that column.

RDataPeek::sample_data("example.csv")

col_type <- readr::cols(
  X1 = readr::col_double(),
  columns = readr::col_character(),
  sample_record = readr::col_character(),
  data_type = readr::col_character(),
  summary = readr::col_character()
)

readr::read_csv("0_summary.csv", col_types = col_type)

Missing Data Overview

RDataPeek::missing_data_overview() provides you with a quick heat map of where your missing data or NAs are located in your .csv. Purple indicates where there is a value and yellow indicates where NA values are located.

RDataPeek::missing_data_overview("example.csv")

knitr::include_graphics("_heatmap.png")

Word Bubble

RDataPeek::word_bubble() creates a unique word cloud out of responses in your qualitative data. The larger the word the more frequent it appeared!

RDataPeek::word_bubble("example.csv", column = "Review")

knitr::include_graphics("wordcloud.png")

Explore_w_histograms

RDataPeek::explore_w_histograms() generates histograms for your selected quantitative columns.

RDataPeek::explore_w_histograms("example.csv", columns_list = c("C", "D"))

knitr::include_graphics("C_chart.png")

knitr::include_graphics("D_chart.png")

UBC-MDS/RDataPeek documentation built on April 2, 2020, 3:56 a.m.