Data Transformation

Choonghyun Ryu

2024-02-06

Preface

After you have acquired the data, you should do the following:

The dlookr package makes these steps fast and easy:

This document introduces data transformation methods provided by the dlookr package. You will learn how to transform tbl_df data that inherits from data.frame and data.frame with functions provided by dlookr

dlookr increases synergy with dplyr. Particularly in data transformation and data wrangle, it increases the efficiency of the tidyverse package group.

datasets

To illustrate the primary use of data transformation in the dlookr package, I use a Carseats dataset. Carseats in the ISLR package is simulation dataset that sells children’s car seats at 400 stores. This data is a data.frame created for the purpose of predicting sales volume.

str(Carseats)
'data.frame':   400 obs. of  11 variables:
 $ Sales      : num  9.5 11.22 10.06 7.4 4.15 ...
 $ CompPrice  : num  138 111 113 117 141 124 115 136 132 132 ...
 $ Income     : num  73 48 35 100 64 113 105 81 110 113 ...
 $ Advertising: num  11 16 10 4 3 13 0 15 0 0 ...
 $ Population : num  276 260 269 466 340 501 45 425 108 131 ...
 $ Price      : num  120 83 80 97 128 72 108 120 124 124 ...
 $ ShelveLoc  : Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
 $ Age        : num  42 65 59 55 38 78 71 67 76 76 ...
 $ Education  : num  17 10 12 14 13 16 15 10 10 17 ...
 $ Urban      : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
 $ US         : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...

The contents of individual variables are as follows. (Refer to ISLR::Carseats Man page)

When data analysis is performed, data containing missing values is often encountered. However, Carseats is complete data without missing. Therefore, the missing values are generated as follows. And I created a data.frame object named carseat.

carseats <- Carseats

suppressWarnings(RNGversion("3.5.0"))
set.seed(123)
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA

suppressWarnings(RNGversion("3.5.0"))
set.seed(456)
carseats[sample(seq(NROW(carseats)), 10), "Urban"] <- NA

Data Transformation

dlookr imputes missing values and outliers and resolves skewed data. It also provides the ability to bin continuous variables as categorical variables.

Here is a list of the data conversion functions and functions provided by dlookr:

Imputation of missing values

imputes the missing value with imputate_na()

imputate_na() imputes the missing value contained in the variable. The predictor with missing values supports numeric and categorical variables and the following method.

In the following example, imputate_na() imputes the missing value of Income, a numeric variable of carseats, using the “rpart” method. summary() summarizes missing value imputation information, and plot() visualizes missing information.

if (requireNamespace("rpart", quietly = TRUE)) {
  income <- imputate_na(carseats, Income, US, method = "rpart")

  # result of imputation
  income

  # summary of imputation
  summary(income)

  # viz of imputation
  plot(income)
} else {
  cat("If you want to use this feature, you need to install the rpart package.\n")
}
* Impute missing values based on Recursive Partitioning and Regression Trees
 - method : rpart

* Information of Imputation (before vs after)
                    Original     Imputation  
described_variables "value"      "value"     
n                   "380"        "400"       
na                  "20"         " 0"        
mean                "68.86053"   "69.05073"  
sd                  "28.09161"   "27.57382"  
se_mean             "1.441069"   "1.378691"  
IQR                 "48.25"      "46.00"     
skewness            "0.04490600" "0.02935732"
kurtosis            "-1.089201"  "-1.035086" 
p00                 "21"         "21"        
p01                 "21.79"      "21.99"     
p05                 "26"         "26"        
p10                 "30.0"       "30.9"      
p20                 "39"         "40"        
p25                 "42.75"      "44.00"     
p30                 "48.00000"   "51.58333"  
p40                 "62"         "63"        
p50                 "69"         "69"        
p60                 "78.0"       "77.4"      
p70                 "86.3"       "84.3"      
p75                 "91"         "90"        
p80                 "96.2"       "96.0"      
p90                 "108.1"      "106.1"     
p95                 "115.05"     "115.00"    
p99                 "119.21"     "119.01"    
p100                "120"        "120"

The following imputes the categorical variable urban by the “mice” method.

The “mice” method must require the mice, ranger package. If you want to use this feature, you need to install the mice, and ranger package.

library(mice)

urban <- imputate_na(carseats, Urban, US, method = "mice")

# result of imputation
urban

# summary of imputation
summary(urban)

# viz of imputation
plot(urban)

Collaboration with dplyr

The following example imputes the missing value of the Income variable and then calculates the arithmetic mean for each level of US. In this case, dplyr is used and is easily interpreted logically using pipes.

# The mean before and after the imputation of the Income variable
carseats %>%
  mutate(Income_imp = imputate_na(carseats, Income, US, method = "knn")) %>%
  group_by(US) %>%
  summarise(orig = mean(Income, na.rm = TRUE),
            imputation = mean(Income_imp))
# A tibble: 2 × 3
  US     orig imputation
  <fct> <dbl>      <dbl>
1 No     65.8       66.1
2 Yes    70.4       70.5

Impute outliers

imputes the outliers with imputate_outlier()

imputate_outlier() imputes the outlier value. The predictor with outliers supports only numeric variables and supports the following methods.

imputate_outlier() imputes the outliers with the numeric variable Price as the “capping” method, as follows. summary() summarizes outliers imputation information, and plot() visualizes imputation information.

price <- imputate_outlier(carseats, Price, method = "capping")

# result of imputation
price
  [1] 120.00  83.00  80.00  97.00 128.00  72.00 108.00 120.00 124.00 124.00
 [11] 100.00  94.00 136.00  86.00 118.00 144.00 110.00 131.00  68.00 121.00
 [21] 131.00 109.00 138.00 109.00 113.00  82.00 131.00 107.00  97.00 102.00
 [31]  89.00 131.00 137.00 128.00 128.00  96.00 100.00 110.00 102.00 138.00
 [41] 126.00 124.00  77.00 134.00  95.00 135.00  70.00 108.00  98.00 149.00
 [51] 108.00 108.00 129.00 119.00 144.00 154.00  84.00 117.00 103.00 114.00
 [61] 123.00 107.00 133.00 101.00 104.00 128.00  91.00 115.00 134.00  99.00
 [71]  99.00 150.00 116.00 104.00 136.00  92.00  70.00  89.00 145.00  90.00
 [81]  79.00 128.00 139.00  94.00 121.00 112.00 134.00 126.00 111.00 119.00
 [91] 103.00 107.00 125.00 104.00  84.00 148.00 132.00 129.00 127.00 107.00
[101] 106.00 118.00  97.00  96.00 138.00  97.00 139.00 108.00 103.00  90.00
[111] 116.00 151.00 125.00 127.00 106.00 129.00 128.00 119.00  99.00 128.00
[121] 131.00  87.00 108.00 155.00 120.00  77.00 133.00 116.00 126.00 147.00
[131]  77.00  94.00 136.00  97.00 131.00 120.00 120.00 118.00 109.00  94.00
[141] 129.00 131.00 104.00 159.00 123.00 117.00 131.00 119.00  97.00  87.00
[151] 114.00 103.00 128.00 150.00 110.00  69.00 157.00  90.00 112.00  70.00
[161] 111.00 160.00 149.00 106.00 141.00 155.05 137.00  93.00 117.00  77.00
[171] 118.00  55.00 110.00 128.00 155.05 122.00 154.00  94.00  81.00 116.00
[181] 149.00  91.00 140.00 102.00  97.00 107.00  86.00  96.00  90.00 104.00
[191] 101.00 173.00  93.00  96.00 128.00 112.00 133.00 138.00 128.00 126.00
[201] 146.00 134.00 130.00 157.00 124.00 132.00 160.00  97.00  64.00  90.00
[211] 123.00 120.00 105.00 139.00 107.00 144.00 144.00 111.00 120.00 116.00
[221] 124.00 107.00 145.00 125.00 141.00  82.00 122.00 101.00 163.00  72.00
[231] 114.00 122.00 105.00 120.00 129.00 132.00 108.00 135.00 133.00 118.00
[241] 121.00  94.00 135.00 110.00 100.00  88.00  90.00 151.00 101.00 117.00
[251] 156.00 132.00 117.00 122.00 129.00  81.00 144.00 112.00  81.00 100.00
[261] 101.00 118.00 132.00 115.00 159.00 129.00 112.00 112.00 105.00 166.00
[271]  89.00 110.00  63.00  86.00 119.00 132.00 130.00 125.00 151.00 158.00
[281] 145.00 105.00 154.00 117.00  96.00 131.00 113.00  72.00  97.00 156.00
[291] 103.00  89.00  74.00  89.00  99.00 137.00 123.00 104.00 130.00  96.00
[301]  99.00  87.00 110.00  99.00 134.00 132.00 133.00 120.00 126.00  80.00
[311] 166.00 132.00 135.00  54.00 129.00 171.00  72.00 136.00 130.00 129.00
[321] 152.00  98.00 139.00 103.00 150.00 104.00 122.00 104.00 111.00  89.00
[331] 112.00 134.00 104.00 147.00  83.00 110.00 143.00 102.00 101.00 126.00
[341]  91.00  93.00 118.00 121.00 126.00 149.00 125.00 112.00 107.00  96.00
[351]  91.00 105.00 122.00  92.00 145.00 146.00 164.00  72.00 118.00 130.00
[361] 114.00 104.00 110.00 108.00 131.00 162.00 134.00  77.00  79.00 122.00
[371] 119.00 126.00  98.00 116.00 118.00 124.00  92.00 125.00 119.00 107.00
[381]  89.00 151.00 121.00  68.00 112.00 132.00 160.00 115.00  78.00 107.00
[391] 111.00 124.00 130.00 120.00 139.00 128.00 120.00 159.00  95.00 120.00
attr(,"method")
[1] "capping"
attr(,"var_type")
[1] "numerical"
attr(,"outlier_pos")
[1]  43 126 166 175 368
attr(,"outliers")
[1]  24  49 191 185  53
attr(,"type")
[1] "outliers"
attr(,"message")
[1] "complete imputation"
attr(,"success")
[1] TRUE
attr(,"class")
[1] "imputation" "numeric"   

# summary of imputation
summary(price)
Impute outliers with capping

* Information of Imputation (before vs after)
                    Original     Imputation  
described_variables "value"      "value"     
n                   "400"        "400"       
na                  "0"          "0"         
mean                "115.7950"   "115.8927"  
sd                  "23.67666"   "22.61092"  
se_mean             "1.183833"   "1.130546"  
IQR                 "31"         "31"        
skewness            "-0.1252862" "-0.0461621"
kurtosis            " 0.4518850" "-0.3030578"
p00                 "24"         "54"        
p01                 "54.99"      "67.96"     
p05                 "77"         "77"        
p10                 "87"         "87"        
p20                 "96.8"       "96.8"      
p25                 "100"        "100"       
p30                 "104"        "104"       
p40                 "110"        "110"       
p50                 "117"        "117"       
p60                 "122"        "122"       
p70                 "128.3"      "128.3"     
p75                 "131"        "131"       
p80                 "134"        "134"       
p90                 "146"        "146"       
p95                 "155.0500"   "155.0025"  
p99                 "166.05"     "164.02"    
p100                "191"        "173"       

# viz of imputation
plot(price)

Collaboration with dplyr

The following example imputes the outliers of the Price variable and then calculates the arithmetic mean for each level of US. In this case, dplyr is used and is easily interpreted logically using pipes.

# The mean before and after the imputation of the Price variable
carseats %>%
  mutate(Price_imp = imputate_outlier(carseats, Price, method = "capping")) %>%
  group_by(US) %>%
  summarise(orig = mean(Price, na.rm = TRUE),
    imputation = mean(Price_imp, na.rm = TRUE))
# A tibble: 2 × 3
  US     orig imputation
  <fct> <dbl>      <dbl>
1 No     114.       114.
2 Yes    117.       117.

Standardization and Resolving Skewness

Introduction to the use of transform()

transform() performs data transformation. Only numeric variables are supported, and the following methods are provided.

Standardization with transform()

Use the methods “zscore” and “minmax” to perform standardization.

carseats %>% 
  mutate(Income_minmax = transform(carseats$Income, method = "minmax"),
    Sales_minmax = transform(carseats$Sales, method = "minmax")) %>% 
  select(Income_minmax, Sales_minmax) %>% 
  boxplot()

Resolving Skewness data with transform()

find_skewness() searches for variables with skewed data. This function finds data skewed by search conditions and calculates skewness.

# find index of skewed variables
find_skewness(carseats)
[1] 4

# find names of skewed variables
find_skewness(carseats, index = FALSE)
[1] "Advertising"

# compute the skewness
find_skewness(carseats, value = TRUE)
      Sales   CompPrice      Income Advertising  Population       Price 
      0.185      -0.043       0.045       0.637      -0.051      -0.125 
        Age   Education 
     -0.077       0.044 

# compute the skewness & filtering with threshold
find_skewness(carseats, value = TRUE, thres = 0.1)
      Sales Advertising       Price 
      0.185       0.637      -0.125

The skewness of Advertising is 0.637. This means that the distribution of data is somewhat inclined to the left. So, for normal distribution, use transform() to convert to the “log” method as follows. summary() summarizes transformation information, and plot() visualizes transformation information.

Advertising_log <- transform(carseats$Advertising, method = "log")

# result of transformation
head(Advertising_log)
[1] 2.397895 2.772589 2.302585 1.386294 1.098612 2.564949
# summary of transformation
summary(Advertising_log)
* Resolving Skewness with log

* Information of Transformation (before vs after)
            Original Transformation
n        400.0000000    400.0000000
na         0.0000000      0.0000000
mean       6.6350000           -Inf
sd         6.6503642            NaN
se_mean    0.3325182            NaN
IQR       12.0000000            Inf
skewness   0.6395858            NaN
kurtosis  -0.5451178            NaN
p00        0.0000000           -Inf
p01        0.0000000           -Inf
p05        0.0000000           -Inf
p10        0.0000000           -Inf
p20        0.0000000           -Inf
p25        0.0000000           -Inf
p30        0.0000000           -Inf
p40        2.0000000      0.6931472
p50        5.0000000      1.6094379
p60        8.4000000      2.1265548
p70       11.0000000      2.3978953
p75       12.0000000      2.4849066
p80       13.0000000      2.5649494
p90       16.0000000      2.7725887
p95       19.0000000      2.9444390
p99       23.0100000      3.1359198
p100      29.0000000      3.3672958
# viz of transformation
plot(Advertising_log)

The raw data seems to contain 0, as there is a -Inf in the log converted value. So this time, convert it to “log+1”.

Advertising_log <- transform(carseats$Advertising, method = "log+1")

# result of transformation
head(Advertising_log)
[1] 2.484907 2.833213 2.397895 1.609438 1.386294 2.639057
# summary of transformation
summary(Advertising_log)
* Resolving Skewness with log+1

* Information of Transformation (before vs after)
            Original Transformation
n        400.0000000   400.00000000
na         0.0000000     0.00000000
mean       6.6350000     1.46247709
sd         6.6503642     1.19436323
se_mean    0.3325182     0.05971816
IQR       12.0000000     2.56494936
skewness   0.6395858    -0.19852549
kurtosis  -0.5451178    -1.66342876
p00        0.0000000     0.00000000
p01        0.0000000     0.00000000
p05        0.0000000     0.00000000
p10        0.0000000     0.00000000
p20        0.0000000     0.00000000
p25        0.0000000     0.00000000
p30        0.0000000     0.00000000
p40        2.0000000     1.09861229
p50        5.0000000     1.79175947
p60        8.4000000     2.23936878
p70       11.0000000     2.48490665
p75       12.0000000     2.56494936
p80       13.0000000     2.63905733
p90       16.0000000     2.83321334
p95       19.0000000     2.99573227
p99       23.0100000     3.17846205
p100      29.0000000     3.40119738
# viz of transformation
# plot(Advertising_log)

Binning

Binning of individual variables using binning()

binning() transforms a numeric variable into a categorical variable by binning it. The following types of binning are supported.

Here are some examples of bin Income using binning().

# Binning the carat variable. the default type argument is "quantile"
bin <- binning(carseats$Income)
# Print bins class object
bin
binned type: quantile
number of bins: 10
x
        [21,30]         (30,39]         (39,48]         (48,62]         (62,69] 
             40              37              38              40              42 
        (69,78]   (78,86.56667] (86.56667,96.6] (96.6,108.6333]  (108.6333,120] 
             33              36              38              38              38 
           <NA> 
             20 
# Summarize bins class object
summary(bin)
            levels freq   rate
1          [21,30]   40 0.1000
2          (30,39]   37 0.0925
3          (39,48]   38 0.0950
4          (48,62]   40 0.1000
5          (62,69]   42 0.1050
6          (69,78]   33 0.0825
7    (78,86.56667]   36 0.0900
8  (86.56667,96.6]   38 0.0950
9  (96.6,108.6333]   38 0.0950
10  (108.6333,120]   38 0.0950
11            <NA>   20 0.0500
# Plot bins class object
plot(bin)

# Using labels argument
bin <- binning(carseats$Income, nbins = 4,
              labels = c("LQ1", "UQ1", "LQ3", "UQ3"))
bin
binned type: quantile
number of bins: 4
x
 LQ1  UQ1  LQ3  UQ3 <NA> 
  95  102   89   94   20 
# Using another type argument
binning(carseats$Income, nbins = 5, type = "equal")
binned type: equal
number of bins: 5
x
   [21,40.8]  (40.8,60.6]  (60.6,80.4] (80.4,100.2]  (100.2,120]         <NA> 
          81           65           94           80           60           20 
binning(carseats$Income, nbins = 5, type = "pretty")
binned type: pretty
number of bins: 5
x
  [20,40]   (40,60]   (60,80]  (80,100] (100,120]      <NA> 
       81        65        94        80        60        20 

if (requireNamespace("classInt", quietly = TRUE)) {
  binning(carseats$Income, nbins = 5, type = "kmeans")
  binning(carseats$Income, nbins = 5, type = "bclust")
} else {
  cat("If you want to use this feature, you need to install the classInt package.\n")
}
binned type: bclust
number of bins: 5
x
     [21,49]    (49,64.5]  (64.5,86.5] (86.5,107.5]  (107.5,120]         <NA> 
         115           52           99           75           39           20 

# Extract the binned results
extract(bin)
  [1] LQ3  UQ1  LQ1  UQ3  UQ1  UQ3  UQ3  LQ3  UQ3  UQ3  LQ3  UQ3  LQ1  LQ1  UQ3 
 [16] UQ3  <NA> <NA> UQ3  LQ3  LQ3  LQ1  UQ1  LQ1  UQ3  LQ1  UQ3  UQ3  LQ3  UQ3 
 [31] UQ3  UQ1  LQ1  LQ1  UQ1  LQ3  LQ3  LQ1  LQ3  <NA> UQ3  UQ1  UQ1  LQ1  LQ3 
 [46] UQ1  LQ3  UQ3  UQ1  UQ3  LQ1  LQ3  LQ1  UQ1  UQ3  LQ3  LQ3  LQ3  UQ3  LQ3 
 [61] UQ3  LQ1  UQ1  LQ3  UQ1  LQ1  UQ3  UQ1  UQ1  UQ1  LQ3  UQ1  UQ1  LQ3  UQ1 
 [76] UQ3  LQ3  LQ3  UQ1  UQ1  UQ3  LQ3  LQ3  LQ1  LQ1  UQ3  LQ3  UQ1  LQ1  UQ1 
 [91] LQ1  UQ1  UQ3  LQ1  <NA> LQ1  LQ1  LQ3  LQ3  UQ1  UQ1  UQ3  LQ1  LQ3  UQ3 
[106] UQ3  LQ1  UQ3  LQ3  UQ1  UQ1  UQ3  UQ3  LQ1  LQ3  <NA> LQ3  UQ1  LQ3  UQ3 
[121] UQ3  LQ3  UQ3  UQ3  UQ3  <NA> UQ1  UQ1  UQ3  UQ3  LQ3  UQ1  LQ3  UQ3  LQ1 
[136] UQ3  LQ3  LQ1  UQ3  UQ1  UQ1  LQ1  LQ3  LQ3  UQ1  UQ1  LQ3  UQ1  UQ3  UQ3 
[151] LQ3  UQ1  LQ3  LQ1  UQ1  LQ3  LQ1  UQ1  LQ3  UQ1  LQ1  LQ1  <NA> UQ1  UQ1 
[166] UQ1  UQ1  LQ3  LQ3  LQ1  LQ1  UQ3  UQ3  LQ3  LQ1  LQ3  <NA> LQ3  <NA> LQ1 
[181] UQ3  LQ3  UQ1  LQ3  LQ1  UQ3  UQ1  LQ1  LQ1  UQ3  LQ1  LQ1  LQ1  LQ3  UQ3 
[196] UQ3  LQ1  UQ1  LQ3  LQ3  UQ3  LQ3  LQ3  LQ3  LQ3  LQ1  UQ1  UQ3  <NA> LQ1 
[211] LQ1  UQ3  UQ1  LQ3  UQ3  LQ3  <NA> UQ1  UQ1  LQ3  UQ3  <NA> UQ3  UQ1  LQ3 
[226] LQ1  LQ1  UQ1  LQ3  UQ3  UQ1  UQ1  LQ3  LQ3  UQ1  LQ1  LQ1  LQ1  LQ1  UQ3 
[241] LQ3  UQ1  UQ1  LQ1  LQ1  UQ1  UQ1  UQ3  UQ1  UQ1  UQ3  UQ3  UQ3  LQ1  UQ3 
[256] LQ3  LQ1  UQ1  LQ1  LQ1  UQ3  LQ1  <NA> LQ1  LQ1  LQ1  UQ3  LQ3  UQ1  UQ1 
[271] LQ1  UQ1  LQ1  UQ3  UQ3  UQ3  UQ1  UQ1  UQ3  UQ1  LQ3  UQ1  UQ3  UQ3  UQ1 
[286] LQ1  UQ3  UQ1  LQ1  LQ3  UQ3  LQ3  UQ1  LQ3  LQ3  LQ1  UQ1  LQ3  UQ1  LQ1 
[301] LQ3  UQ3  LQ3  UQ1  UQ3  LQ1  LQ1  UQ3  LQ3  UQ3  UQ1  UQ1  UQ3  LQ3  <NA>
[316] LQ1  LQ1  LQ1  LQ3  UQ1  LQ3  LQ1  UQ1  UQ3  UQ1  UQ1  LQ1  LQ1  UQ1  UQ1 
[331] UQ1  UQ1  LQ1  UQ1  UQ3  LQ3  LQ1  LQ1  LQ1  UQ1  LQ1  UQ3  UQ3  LQ1  LQ3 
[346] UQ1  <NA> LQ1  UQ3  LQ1  <NA> UQ3  UQ3  UQ1  LQ1  UQ3  UQ3  LQ3  UQ3  UQ1 
[361] LQ3  LQ1  UQ1  <NA> LQ1  LQ1  UQ1  UQ3  LQ1  UQ3  LQ1  LQ3  <NA> <NA> UQ1 
[376] UQ1  UQ1  UQ1  LQ3  UQ3  UQ1  UQ1  LQ1  UQ3  LQ1  LQ3  UQ3  LQ3  LQ3  LQ1 
[391] LQ3  UQ1  LQ1  UQ1  UQ1  UQ3  <NA> LQ1  LQ3  LQ1 
Levels: LQ1 < UQ1 < LQ3 < UQ3

# -------------------------
# Using pipes & dplyr
# -------------------------
library(dplyr)

carseats %>%
 mutate(Income_bin = binning(carseats$Income) %>% 
                     extract()) %>%
 group_by(ShelveLoc, Income_bin) %>%
 summarise(freq = n()) %>%
 arrange(desc(freq)) %>%
 head(10)
`summarise()` has grouped output by 'ShelveLoc'. You can override using the
`.groups` argument.
# A tibble: 10 × 3
# Groups:   ShelveLoc [1]
  ShelveLoc Income_bin  freq
  <fct>     <ord>      <int>
1 Medium    [21,30]       25
2 Medium    (62,69]       24
3 Medium    (48,62]       23
4 Medium    (39,48]       21
# ℹ 6 more rows

Optimal Binning with binning_by()

binning_by() transforms a numeric variable into a categorical variable by optimal binning. This method is often used when developing a scorecard model.

The following binning_by() example optimally binning Advertising considering the target variable US with a binary class.

library(dplyr)

if (requireNamespace("partykit", quietly = TRUE)) {
  # optimal binning using character
  bin <- binning_by(carseats, "US", "Advertising")
  
  # optimal binning using name
  bin <- binning_by(carseats, US, Advertising)
  bin
  
  # summary optimal_bins class
  summary(bin)
  
  # performance table
  attr(bin, "performance")
  
  # visualize optimal_bins class
  plot(bin)
  
  # extract binned results
  extract(bin) %>% 
    head(20)
} else {
  cat("If you want to use this feature, you need to install the partykit package.\n")
}
Warning in binning_by(carseats, "US", "Advertising"): The factor y has been changed to a numeric vector consisting of 0 and 1.
'Yes' changed to 1 (positive) and 'No' changed to 0 (negative).
Warning in binning_by(carseats, US, Advertising): The factor y has been changed to a numeric vector consisting of 0 and 1.
'Yes' changed to 1 (positive) and 'No' changed to 0 (negative).
── Binning Table ──────────────────────── Several Metrics ── 
     Bin CntRec CntPos CntNeg RatePos RateNeg    Odds      WoE      IV     JSD
1 [-1,0]    144     19    125 0.07364 0.88028  0.1520 -2.48101 2.00128 0.20093
2  (0,6]     69     54     15 0.20930 0.10563  3.6000  0.68380 0.07089 0.00869
3 (6,29]    187    185      2 0.71705 0.01408 92.5000  3.93008 2.76272 0.21861
4  Total    400    258    142 1.00000 1.00000  1.8169       NA 4.83489 0.42823
      AUC
1 0.03241
2 0.01883
3 0.00903
4 0.06028

── General Metrics ───────────────────────────────────────── 
• Gini index                       :  -0.87944
• IV (Jeffrey)                     :  4.83489
• JS (Jensen-Shannon) Divergence   :  0.42823
• Kolmogorov-Smirnov Statistics    :  0.80664
• HHI (Herfindahl-Hirschman Index) :  0.37791
• HHI (normalized)                 :  0.06687
• Cramer's V                       :  0.81863 

── Significance Tests ──────────────────── Chisquare Test ── 
   Bin A  Bin B statistics      p_value
1 [-1,0]  (0,6]   87.67064 7.731349e-21
2  (0,6] (6,29]   34.73349 3.780706e-09

 [1] (6,29] (6,29] (6,29] (0,6]  (0,6]  (6,29] [-1,0] (6,29] [-1,0] [-1,0]
[11] (6,29] (0,6]  (0,6]  (6,29] (6,29] (0,6]  [-1,0] (6,29] [-1,0] (6,29]
Levels: [-1,0] < (0,6] < (6,29]

Automated report

dlookr provides two automated data transformation reports:

Create a dynamic report using transformation_web_report()

transformation_web_report() creates a dynamic report for objects inherited from data.frame(tbl_df, tbl, etc) or data.frame.

Contents of dynamic web report

The contents of the report are as follows.:

  • Overview
    • Data Structures
    • Data Types
    • Job Informations
  • Imputation
    • Missing Values
    • Outliers
  • Resolving Skewness
  • Binning
  • Optimal Binning

Some arguments for dynamic web report

transformation_web_report() generates various reports with the following arguments.

  • target
    • target variable
  • output_file
    • name of the generated file.
  • output_dir
    • name of the directory to generate report file.
  • title
    • title of the report.
  • subtitle
    • subtitle of the report.
  • author
    • author of the report.
  • title_color
    • color of title.
  • logo_img
    • The name of the logo image file is on the top left.
  • create_date
    • The date on which the report is generated.
  • theme
    • name of the theme for the report. Support “orange” and “blue”.
  • sample_percent
    • Sample percent of data for performing data transformation.

The following script creates a data transformation report for the tbl_df class object, heartfailure.

heartfailure %>%
  transformation_web_report(target = "death_event", subtitle = "heartfailure",
                            output_dir = "./", output_file = "transformation.html", 
                            theme = "blue")

Screenshot of dynamic report

  • The dynamic contents of the report are shown in the following figure.:
The part of the report

The part of the report

Create a static report using transformation_paged_report()

transformation_paged_report() create static report for object inherited from data.frame(tbl_df, tbl, etc) or data.frame.

Contents of static paged report

The contents of the report are as follows.:

  • Overview
    • Data Structures
    • Job Informations
  • Imputation
    • Missing Values
    • Outliers
  • Resolving Skewness
  • Binning
  • Optimal Binning

Some arguments for static paged report

transformation_paged_report() generates various reports with the following arguments.

  • target
    • target variable
  • output_format
    • report output type. Choose either “pdf” or “html”.
  • output_file
    • name of the generated file.
  • output_dir
    • name of the directory to generate the report file.
  • title
    • title of the report.
  • subtitle
    • subtitle of the report.
  • abstract_title
    • abstract of the report
  • author
    • author of the report.
  • title_color
    • color of title.
  • subtitle_color
    • color of subtitle.
  • logo_img
    • The name of the logo image file is on the top left.
  • cover_img
    • name of cover image file on center.
  • create_date
    • The date on which the report is generated.
  • theme
    • name of the theme for the report. Support “orange” and “blue”.
  • sample_percent
    • Sample percent of data for performing data transformation.

The following script creates a data transformation report for the data.frame class object, heartfailure.

heartfailure %>%
  transformation_paged_report(target = "death_event", subtitle = "heartfailure",
                              output_dir = "./", output_file = "transformation.pdf", 
                              theme = "blue")

Screenshot of static report

  • The cover of the report is shown in the following figure.:
The part of the report

The part of the report

  • The contents of the report are shown in the following figure.:
The dynamic contents of the report

The dynamic contents of the report