• Stars
    star
    439
  • Rank 93,581 (Top 2 %)
  • Language
    R
  • License
    Other
  • Created about 8 years ago
  • Updated 9 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Preliminary Exploratory Visualisation of Data

visdat

rOpenSci BadgeJOSS statusDOIR-CMD-checkCodecov test coverageCRAN_Status_BadgeCRAN LogsProject Status: Active – The project has reached a stable, usable state and is being actively developed.

How to install

visdat is available on CRAN

install.packages("visdat")

If you would like to use the development version, install from github with:

# install.packages("devtools")
devtools::install_github("ropensci/visdat")

What does visdat do?

Initially inspired by csv-fingerprint, vis_dat helps you visualise a dataframe and “get a look at the data” by displaying the variable classes in a dataframe as a plot with vis_dat, and getting a brief look into missing data patterns using vis_miss.

visdat has 6 functions:

  • vis_dat() visualises a dataframe showing you what the classes of the columns are, and also displaying the missing data.

  • vis_miss() visualises just the missing data, and allows for missingness to be clustered and columns rearranged. vis_miss() is similar to missing.pattern.plot from the mi package. Unfortunately missing.pattern.plot is no longer in the mi package (as of 14/02/2016).

  • vis_compare() visualise differences between two dataframes of the same dimensions

  • vis_expect() visualise where certain conditions hold true in your data

  • vis_cor() visualise the correlation of variables in a nice heatmap

  • vis_guess() visualise the individual class of each value in your data

  • vis_value() visualise the value class of each cell in your data

  • vis_binary() visualise the occurrence of binary values in your data

You can read more about visdat in the vignette, [“using visdat”]https://docs.ropensci.org/visdat/articles/using_visdat.html).

Code of Conduct

Please note that the visdat project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Examples

Using vis_dat()

Let’s see what’s inside the airquality dataset from base R, which contains information about daily air quality measurements in New York from May to September 1973. More information about the dataset can be found with ?airquality.

library(visdat)

vis_dat(airquality)

The plot above tells us that R reads this dataset as having numeric and integer values, with some missing data in Ozone and Solar.R. The classes are represented on the legend, and missing data represented by grey. The column/variable names are listed on the x axis.

The vis_dat() function also has a facet argument, so you can create small multiples of a similar plot for a level of a variable, e.g., Month:

vis_dat(airquality, facet = Month)

These currently also exist for vis_miss(), and the vis_cor() functions.

Using vis_miss()

We can explore the missing data further using vis_miss():

vis_miss(airquality)

Percentages of missing/complete in vis_miss are accurate to the integer (whole number). To get more accurate and thorough exploratory summaries of missingness, I would recommend the naniar R package

You can cluster the missingness by setting cluster = TRUE:

vis_miss(airquality, 
         cluster = TRUE)

Columns can also be arranged by columns with most missingness, by setting sort_miss = TRUE:

vis_miss(airquality,
         sort_miss = TRUE)

vis_miss indicates when there is a very small amount of missing data at <0.1% missingness:

test_miss_df <- data.frame(x1 = 1:10000,
                           x2 = rep("A", 10000),
                           x3 = c(rep(1L, 9999), NA))

vis_miss(test_miss_df)

vis_miss will also indicate when there is no missing data at all:

vis_miss(mtcars)

To further explore the missingness structure in a dataset, I recommend the naniar package, which provides more general tools for graphical and numerical exploration of missing values.

Using vis_compare()

Sometimes you want to see what has changed in your data. vis_compare() displays the differences in two dataframes of the same size. Let’s look at an example.

Let’s make some changes to the chickwts, and compare this new dataset:

set.seed(2019-04-03-1105)
chickwts_diff <- chickwts
chickwts_diff[sample(1:nrow(chickwts), 30),sample(1:ncol(chickwts), 2)] <- NA

vis_compare(chickwts_diff, chickwts)

Here the differences are marked in blue.

If you try and compare differences when the dimensions are different, you get an ugly error:

chickwts_diff_2 <- chickwts
chickwts_diff_2$new_col <- chickwts_diff_2$weight*2

vis_compare(chickwts, chickwts_diff_2)
# Error in vis_compare(chickwts, chickwts_diff_2) : 
#   Dimensions of df1 and df2 are not the same. vis_compare requires dataframes of identical dimensions.

Using vis_expect()

vis_expect visualises certain conditions or values in your data. For example, If you are not sure whether to expect values greater than 25 in your data (airquality), you could write: vis_expect(airquality, ~.x>=25), and you can see if there are times where the values in your data are greater than or equal to 25:

vis_expect(airquality, ~.x >= 25)

This shows the proportion of times that there are values greater than 25, as well as the missings.

Using vis_cor()

To make it easy to plot correlations of your data, use vis_cor:

vis_cor(airquality)

Using vis_value

vis_value() visualises the values of your data on a 0 to 1 scale.

vis_value(airquality)

It only works on numeric data, so you might get strange results if you are using factors:

library(ggplot2)
vis_value(iris)
data input can only contain numeric values, please subset the data to the numeric values you would like. dplyr::select_if(data, is.numeric) can be helpful here!

So you might need to subset the data beforehand like so:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

iris %>%
  select_if(is.numeric) %>%
  vis_value()

Using vis_binary()

vis_binary() visualises binary values. See below for use with example data, dat_bin

vis_binary(dat_bin)

If you don’t have only binary values a warning will be shown.

vis_binary(airquality)
Error in test_if_all_binary(data) : 
  data input can only contain binary values - this means either 0 or 1, or NA. Please subset the data to be binary values, or see ?vis_value.

Using vis_guess()

vis_guess() takes a guess at what each cell is. It’s best illustrated using some messy data, which we’ll make here:

messy_vector <- c(TRUE,
                  T,
                  "TRUE",
                  "T",
                  "01/01/01",
                  "01/01/2001",
                  NA,
                  NaN,
                  "NA",
                  "Na",
                  "na",
                  "10",
                  10,
                  "10.1",
                  10.1,
                  "abc",
                  "$%TG")

set.seed(2019-04-03-1106)
messy_df <- data.frame(var1 = messy_vector,
                       var2 = sample(messy_vector),
                       var3 = sample(messy_vector))
vis_guess(messy_df)
vis_dat(messy_df)

So here we see that there are many different kinds of data in your dataframe. As an analyst this might be a depressing finding. We can see this comparison above.

Thank yous

Thank you to Ivan Hanigan who first commented this suggestion after I made a blog post about an initial prototype ggplot_missing, and Jenny Bryan, whose tweet got me thinking about vis_dat, and for her code contributions that removed a lot of errors.

Thank you to Hadley Wickham for suggesting the use of the internals of readr to make vis_guess work. Thank you to Miles McBain for his suggestions on how to improve vis_guess. This resulted in making it at least 2-3 times faster. Thanks to Carson Sievert for writing the code that combined plotly with visdat, and for Noam Ross for suggesting this in the first place. Thank you also to Earo Wang and Stuart Lee for their help in getting capturing expressions in vis_expect.

Finally thank you to rOpenSci and it’s amazing onboarding process, this process has made visdat a much better package, thanks to the editor Noam Ross (@noamross), and the reviewers Sean Hughes (@seaaan) and Mara Averick (@batpigandme).

ropensci_footer

More Repositories

1

drake

An R-focused pipeline toolkit for reproducibility and high-performance computing
R
1,328
star
2

skimr

A frictionless, pipeable approach to dealing with summary statistics
HTML
1,090
star
3

targets

Function-oriented Make-like declarative workflows for R
R
829
star
4

rtweet

🐦 R client for interacting with Twitter's [stream and REST] APIs
R
785
star
5

tabulizer

Bindings for Tabula PDF Table Extractor Library
R
518
star
6

pdftools

Text Extraction, Rendering and Converting of PDF Documents
C++
489
star
7

assertr

Assertive programming for R analysis pipelines
R
457
star
8

magick

Magic, madness, heaven, sin
R
440
star
9

stplanr

Sustainable transport planning with R
R
410
star
10

RSelenium

An R client for Selenium Remote WebDriver
R
332
star
11

rnoaa

R interface to many NOAA data APIs
R
320
star
12

osmdata

R package for downloading OpenStreetMap data
C++
305
star
13

charlatan

Create fake data in R
R
283
star
14

software-review

rOpenSci Software Peer Review.
R
274
star
15

iheatmapr

Complex, interactive heatmaps in R
R
259
star
16

taxize

A taxonomic toolbelt for R
R
250
star
17

rrrpkg

Use of an R package to facilitate reproducible research
248
star
18

elastic

R client for the Elasticsearch HTTP API
R
244
star
19

tesseract

Bindings to Tesseract OCR engine for R
R
236
star
20

qualtRics

Download ⬇️ Qualtrics survey data directly into R!
R
207
star
21

git2r

R bindings to the libgit2 library
C
204
star
22

writexl

Portable, light-weight data frame to xlsx exporter for R
C
202
star
23

biomartr

Genomic Data Retrieval with R
R
201
star
24

rnaturalearth

An R package to hold and facilitate interaction with natural earth map data 🌍
R
191
star
25

googleLanguageR

R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API
HTML
189
star
26

textreuse

Detect text reuse and document similarity
R
188
star
27

tokenizers

Fast, Consistent Tokenization of Natural Language Text
R
179
star
28

rentrez

talk with NCBI entrez using R
R
178
star
29

piggyback

📦 for using large(r) data files on GitHub
R
172
star
30

rcrossref

R client for various CrossRef APIs
R
159
star
31

osmextract

Download and import OpenStreetMap data from Geofabrik and other providers
R
157
star
32

dataspice

🌶️ Create lightweight schema.org descriptions of your datasets
R
155
star
33

tic

Tasks Integrating Continuously: CI-Agnostic Workflow Definitions
R
154
star
34

webchem

Chemical Information from the Web
R
149
star
35

geojsonio

Convert many data formats to & from GeoJSON & TopoJSON
R
148
star
36

MODIStsp

An "R" package for automatic download and preprocessing of MODIS Land Products Time Series
R
147
star
37

rgbif

Interface to the Global Biodiversity Information Facility API
R
145
star
38

DataPackageR

An R package to enable reproducible data processing, packaging and sharing.
R
145
star
39

tsbox

tsbox: Class-Agnostic Time Series in R
R
143
star
40

dev_guide

rOpenSci Packages: Development, Maintenance, and Peer Review
R
141
star
41

jqr

R interface to jq
R
139
star
42

ghql

GraphQL R client
R
136
star
43

osfr

R interface to the Open Science Framework (OSF)
R
135
star
44

osmplotr

Data visualisation using OpenStreetMap objects
R
130
star
45

opencv

R bindings for OpenCV
C++
130
star
46

ssh

Native SSH client in R based on libssh
C
126
star
47

RefManageR

R package RefManageR
R
112
star
48

spocc

Species occurrence data toolkit for R
R
109
star
49

tarchetypes

Archetypes for targets and pipelines
R
107
star
50

ezknitr

Avoid the typical working directory pain when using 'knitr'
R
107
star
51

hunspell

High-Performance Stemmer, Tokenizer, and Spell Checker for R
C++
106
star
52

crul

R6 based http client for R (made for developers)
R
101
star
53

gistr

Interact with GitHub gists from R
R
101
star
54

spelling

Tools for Spell Checking in R
R
101
star
55

rfishbase

R interface to the fishbase.org database
R
100
star
56

weathercan

R package for downloading weather data from Environment and Climate Change Canada
R
99
star
57

git2rdata

An R package for storing and retrieving data.frames in git repositories.
R
98
star
58

bib2df

Parse a BibTeX file to a tibble
R
97
star
59

ckanr

R client for the CKAN API
R
97
star
60

gutenbergr

Search and download public domain texts from Project Gutenberg
R
95
star
61

rsvg

SVG renderer for R based on librsvg2
C
95
star
62

nasapower

API Client for NASA POWER Global Meteorology, Surface Solar Energy and Climatology in R
R
92
star
63

EML

Ecological Metadata Language interface for R: synthesis and integration of heterogenous data
R
92
star
64

cyphr

:shipit: Humane encryption
R
91
star
65

FedData

Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources
R
88
star
66

av

Working with Video in R
C
88
star
67

mapscanner

R package to print maps, draw on them, and scan them back in
R
87
star
68

UCSCXenaTools

📦 An R package for accessing genomics data from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq https://cran.r-project.org/web/packages/UCSCXenaTools/
R
87
star
69

opencage

🌐 R package for the OpenCage API -- both forward and reverse geocoding 🌐
R
86
star
70

tidync

NetCDF exploration and data extraction
R
85
star
71

GSODR

API Client for Global Surface Summary of the Day ('GSOD') Weather Data Client in R
R
82
star
72

rzmq

R package for ZMQ
C++
82
star
73

bikedata

🚲 Extract data from public hire bicycle systems
R
79
star
74

historydata

Datasets for Historians
R
78
star
75

dittodb

dittodb: A Test Environment for DB Queries in R
R
78
star
76

arkdb

Archive and unarchive databases as flat text files
R
78
star
77

fingertipsR

R package to interact with Public Health England’s Fingertips data tool
R
78
star
78

smapr

An R package for acquisition and processing of NASA SMAP data
R
77
star
79

vcr

Record HTTP calls and replay them
R
76
star
80

nodbi

Document DBI connector for R
R
75
star
81

CoordinateCleaner

Automated flagging of common spatial and temporal errors in biological and palaeontological collection data, for the use in conservation, ecology and palaeontology.
HTML
74
star
82

openalexR

Getting bibliographic records from OpenAlex
R
74
star
83

opentripplanner

An R package to set up and use OpenTripPlanner (OTP) as a local or remote multimodal trip planner.
R
73
star
84

nlrx

nlrx NetLogo R
R
71
star
85

rebird

Wrapper to the eBird API
R
70
star
86

gittargets

Data version control for reproducible analysis pipelines in R with {targets}.
R
69
star
87

tidyhydat

An R package to import Water Survey of Canada hydrometric data and make it tidy
R
69
star
88

robotstxt

robots.txt file parsing and checking for R
R
68
star
89

slopes

Package to calculate slopes of roads, rivers and trajectories
R
65
star
90

tradestatistics

R package to access Open Trade Statistics API
R
65
star
91

terrainr

Get DEMs and orthoimagery from the USGS National Map, georeference your images and merge rasters, and visualize with Unity 3D
R
64
star
92

unconf17

Website for 2017 rOpenSci Unconf
JavaScript
64
star
93

NLMR

📦 R package to simulate neutral landscape models 🏔
R
63
star
94

roadoi

Use Unpaywall with R
R
63
star
95

parzer

Parse geographic coordinates
R
63
star
96

tiler

Generate geographic and non-geographic map tiles from R
R
63
star
97

rb3

A bunch of downloaders and parsers for data delivered from B3
R
62
star
98

rWBclimate

R interface for the World Bank climate data
R
62
star
99

codemetar

an R package for generating and working with codemeta
R
62
star
100

aRxiv

Programmatic interface to the Arxiv API
R
58
star