prepropyr-vignette

Chun Chieh(Jason) Chang, Bruhat Musunuru, Pan Fan

library(prepropyr)

Introduction

The functions in prepropyr were developed to perform a quick EDA, data imputation and data scaling. Currently prepropyr contains three independent functions, this vignette outlines how to use these functions with toy data.

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eda()

The eda() function helps to quickly explore the data by showing a pairplot and some summary statistics for a given dataframe .

df <- data.frame(num1 = c(8.5, 8, 9.2, 9.1, 9.4),
                  num2 = c(0.88, 0.93, 0.95 , 0.92 , 0.91),
                  num3 = c(0.46, 0.78, 0.66, 0.69, 0.52),
                  num4 = c(0.082, 0.078, 0.082, 0.085, 0.066),
                  cat1 = c("Good","Okay","Excellent","Terrible","Good"),
                  target = c(2,2,3,1,3))

After calling the eda() function, we can get following outputs,please see the docstring for more available outputs.

result <- eda(df,"target")
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2
result$nb_num_features
#> [1] 4

result$pairplot

imputation()

The imputation() function will impute missing data in a tibble/dataframe given the chosen method(mean, median)

test_df <- data.frame('a' = c(1,NA,3), 'b' = c(5,6,NA), 'c' = c(NA,1,10))
test_df_imputed <- imputation(test_df, test_df, 'mean')

test_df_imputed
#>   a   b    c
#> 1 1 5.0  5.5
#> 2 2 6.0  1.0
#> 3 3 5.5 10.0

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scaler()

This function scales numerical features based on scaling requirement(standardization, minmax Scaling) in a data.frame

X_train <- data.frame('a' = c(1,2,3), 'b' = c(5,6,3), 'c' = c(2,1,10))
X_test <- data.frame('a' = c(1,5,3), 'b' = c(5,6,5), 'c' = c(2,5,10))
X_Valid <- data.frame('a' = c(5,5,3), 'b' = c(5,6,5), 'c' = c(2,5,10))
scaled_df <- scaler(X_train, X_Valid, X_test, scaler_type='standardization')

scaled_df
#> $train
#>    a          b          c
#> 1 -1  0.2182179 -0.4730162
#> 2  0  0.8728716 -0.6757374
#> 3  1 -1.0910895  1.1487535
#> 
#> $test
#>    a         b          c
#> 1 -1 0.2182179 -0.4730162
#> 2  3 0.8728716  0.1351475
#> 3  1 0.2182179  1.1487535
#> 
#> $valid
#>   a         b          c
#> 1 3 0.2182179 -0.4730162
#> 2 3 0.8728716  0.1351475
#> 3 1 0.2182179  1.1487535

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