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StatMeasures

Easy Data Manipulation, Data Quality and Statistical Checks

Offers useful functions to perform day-to-day data manipulation operations, data quality checks and post modelling statistical checks. One can effortlessly change class of a number of variables to factor, remove duplicate observations from the data, create deciles of a variable, perform data quality checks for continuous (integer or numeric), categorical (factor) and date variables, and compute goodness of fit measures such as auc for statistical models. The functions are consistent for objects of class 'data.frame' and 'data.table', which is an enhanced 'data.frame' implemented in the package 'data.table'.

Functions (20)

Confusion matrix and overall accuracy of predicted binary response

Comparison of actual and predicted linear response

auc

Area under curve of predicted binary response

Basic summary of the data

Create deciles of a variable

dqcategorical

Data quality check of categorical variables

dqcontinuous

Data quality check of continuous variables

Data quality check of date variables

Change the class of variables to factor

gini

Gini coefficient of a distribution

Impute missing values in a variable

Information value of an independent variable in predicting a binary response

Kolmogorov-Smirnov statistic for predicted binary response

mape

Compute mean absolute percentage error

Identify outliers in a variable

Create pentiles of a variable

Order the rows of a data randomly

Remove observations with duplicate keys from data

Remove duplicate observations from data

Split modeling data into test and train set

StatMeasures

Easy Data Manipulation, Data Quality and Statistical Checks

v1.0

GPL-2

Authors

Akash Jain

Initial release

2015-03-24

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