This R package contains several tools to perform initial exploratory analysis on any input dataset. It includes custom functions for plotting the data as well as performing different kinds of analyses such as univariate, bivariate and multivariate investigation which is the first step of any predictive modeling pipeline. This package can be used to get a good sense of any dataset before jumping on to building predictive models. You can install the package from GitHub.
The functions currently included in the package are mentioned below:
- numSummary(mydata) function automatically detects all numeric columns in the dataframe mydata and provides their summary statistics
- charSummary(mydata) function automatically detects all character columns in the dataframe mydata and provides their summary statistics
- Plot(mydata, dep.var) plots all independent variables in the dataframe mydata against the dependant variable specified by the dep.var parameter
- removeSpecial(mydata, vec) replaces all special characters (specified by vector vec) in the dataframe mydata with NA
- bivariate(mydata, dep.var, indep.var) performs bivariate analysis between dependent variable dep.var and independent variable indep.var in the dataframe mydata
Installation
There are 2 ways of installing xda:
- Using devtools:
The devtools package needs to be installed first. To install devtools, please follow instructions here. Then, use the following commands to install `xda`:
library(devtools) install_github(ujjwalkarn/xda)
- Alternatively, you can also try the following to install
xda:
install.packages(githubinstall) library(githubinstall) githubinstall(xda)
Read more about githubinstall here.
Usage
Update: See usage instructions and latest updates to the package here. The package is constantly under development and more functionalities will be added soon. Will also add this to CRAN in the coming days. Pull requests to add more functions are welcome!
Ujjwal Karn 
I’m a little wary about installing packages from GitHub. Partly because I don’t know much about it. Have only used packages from CRAN. Any thoughts?
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Currently this package is only available on GitHub. I have updated the blog above to include another way of installing this. Do try and let me know! You can read more about installing packages from GitHub here: http://goo.gl/eBJegn.
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Great package! I’ve been trying to write my own version of numSummary but never got it working as well as I’d like. Kudos for this one!
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Hi again Ujjwal!
Just d/l the latest version to my Rstudio. (still the same version 0.1 as before?)
xda working great,
but I tried your example (testing for “missing values” or NAs?):
iris9 <- iris;
iris9[1,2]<-"?"
iris9[2,2]<-"@"
iris9[3,2]<-"???"
iris9<-removeSpecial(iris9,c("@","???"))
head(iris9)
It returns:
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 1.4 0.2
2 4.9 1.4 0.2
3 4.7 1.3 0.2
4 4.6 3.1 1.5 0.2
Ok!
But when I run:
numSummary(iris9)
the “miss” and “miss%” columns
are still zero…0!
Shouldn’t these 2 column values
be different from zero? (we have NAs now in 3 rows!).
What is a “missing value”,
can you give a simple complete example of numSummary()
where the
“miss” and “miss%” columns are not zero?
Thanks again, Ujjwal!
Hope you can answer my question.
RAY
SF
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Seems like a bug, will fix it as soon as possible. Thanks again for flagging!
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