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

- Diagnose data quality.
- If there is a problem with data quality,
- The data must be corrected or re-acquired.

**Explore data to understand the data and find scenarios for performing the analysis.**- Derive new variables or perform variable transformations.

The dlookr package makes these steps fast and easy:

- Performs an data diagnosis or automatically generates a data diagnosis report.
**Discover data in a variety of ways, and automatically generate EDA(exploratory data analysis) report.**- Imputate missing values and outliers, resolve skewed data, and binarize continuous variables into categorical variables. And generates an automated report to support it.

This document introduces **EDA(Exploratory Data Analysis)** methods provided by the dlookr package. You will learn how to EDA of `tbl_df`

data that inherits from data.frame and `data.frame`

with functions provided by dlookr.

dlookr synergy with `dplyr`

increases. Particularly in data exploration and data wrangle, it increases the efficiency of the `tidyverse`

package group.

Data diagnosis supports the following data structures.

- data frame : data.frame class.
- data table : tbl_df class.
**table of DBMS**: table of the DBMS through tbl_dbi.**Using dplyr backend for any DBI-compatible database.**

To illustrate the basic use of EDA in the dlookr package, I use a `Carseats`

datasets. `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.

```
library(ISLR)
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)

- Sales
- Unit sales (in thousands) at each location

- CompPrice
- Price charged by competitor at each location

- Income
- Community income level (in thousands of dollars)

- Advertising
- Local advertising budget for company at each location (in thousands of dollars)

- Population
- Population size in region (in thousands)

- Price
- Price company charges for car seats at each site

- ShelveLoc
- A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site

- Age
- Average age of the local population

- Education
- Education level at each location

- Urban
- A factor with levels No and Yes to indicate whether the store is in an urban or rural location

- US
- A factor with levels No and Yes to indicate whether the store is in the US or not

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 carseats.

```
carseats <- ISLR::Carseats
set.seed(123)
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
set.seed(456)
carseats[sample(seq(NROW(carseats)), 10), "Urban"] <- NA
```

dlookr can help to understand the distribution of data by calculating descriptive statistics of numerical data. In addition, correlation between variables is identified and normality test is performed. It also identifies the relationship between target variables and independent variables.:

The following is a list of the EDA functions included in the dlookr package.:

`describe()`

provides descriptive statistics for numerical data.`normality()`

and`plot_normality()`

perform normalization and visualization of numerical data.`correlate()`

and`plot_correlate()`

calculate the correlation coefficient between two numerical data and provide visualization.`target_by()`

defines the target variable and`relate()`

describes the relationship with the variables of interest corresponding to the target variable.`plot.relate()`

visualizes the relationship to the variable of interest corresponding to the destination variable.`eda_report()`

performs an exploratory data analysis and reports the results.

`describe()`

`describe()`

computes descriptive statistics for numerical data. The descriptive statistics help determine the distribution of numerical variables. Like function of dplyr, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame.

The variables of the `tbl_df`

object returned by `describe()`

are as follows.

`n`

: number of observations excluding missing values`na`

: number of missing values`mean`

: arithmetic average`sd`

: standard devation`se_mean`

: standrd error mean. sd/sqrt(n)`IQR`

: interquartile range (Q3-Q1)`skewness`

: skewness`kurtosis`

: kurtosis`p25`

: Q1. 25% percentile`p50`

: median. 50% percentile`p75`

: Q3. 75% percentile`p01`

,`p05`

,`p10`

,`p20`

,`p30`

: 1%, 5%, 20%, 30% percentiles`p40`

,`p60`

,`p70`

,`p80`

: 40%, 60%, 70%, 80% percentiles`p90`

,`p95`

,`p99`

,`p100`

: 90%, 95%, 99%, 100% percentiles

For example, we can computes the statistics of all numerical variables in `carseats`

:

```
describe(carseats)
# A tibble: 8 x 26
variable n na mean sd se_mean IQR skewness kurtosis p00
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809 0
2 CompPrice 400 0 125 15.3 0.767 20.0 -0.0428 0.0417 77.0
3 Income 380 20.0 68.9 28.1 1.44 48.2 0.0449 -1.09 21.0
4 Advertis… 400 0 6.64 6.65 0.333 12.0 0.640 -0.545 0
# ... with 4 more rows, and 16 more variables: p01 <dbl>, p05 <dbl>,
# p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,
# p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,
# p99 <dbl>, p100 <dbl>
```

`skewness`

: The left-skewed distribution data, that is, the variables with large positive skewness should consider the log or sqrt transformations to follow the normal distribution. The variables`Advertising`

seem to need to consider variable transformations.`mean`

and`sd`

,`se_mean`

: The`Population`

with a large`standard error of the mean`

(se_mean) has low representativeness of the`arithmetic mean`

(mean). The`standard deviation`

(sd) is much larger than the arithmetic average.

The following explains the descriptive statistics only for a few selected variables.:

```
# Select columns by name
describe(carseats, Sales, CompPrice, Income)
# A tibble: 3 x 26
variable n na mean sd se_mean IQR skewness kurtosis p00
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809 0
2 CompPrice 400 0 125 15.3 0.767 20.0 -0.0428 0.0417 77.0
3 Income 380 20.0 68.9 28.1 1.44 48.2 0.0449 -1.09 21.0
# ... with 16 more variables: p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
# p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
# p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>
# Select all columns between year and day (inclusive)
describe(carseats, Sales:Income)
# A tibble: 3 x 26
variable n na mean sd se_mean IQR skewness kurtosis p00
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809 0
2 CompPrice 400 0 125 15.3 0.767 20.0 -0.0428 0.0417 77.0
3 Income 380 20.0 68.9 28.1 1.44 48.2 0.0449 -1.09 21.0
# ... with 16 more variables: p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
# p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
# p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>
# Select all columns except those from year to day (inclusive)
describe(carseats, -(Sales:Income))
# A tibble: 5 x 26
variable n na mean sd se_mean IQR skewness kurtosis p00
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Adverti… 400 0 6.64 6.65 0.333 12.0 0.640 -0.545 0
2 Populat… 400 0 265 147 7.37 260 -0.0512 -1.20 10.0
3 Price 400 0 116 23.7 1.18 31.0 -0.125 0.452 24.0
4 Age 400 0 53.3 16.2 0.810 26.2 -0.0772 -1.13 25.0
# ... with 1 more row, and 16 more variables: p01 <dbl>, p05 <dbl>,
# p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,
# p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,
# p99 <dbl>, p100 <dbl>
```

By using dplyr, You can sort by `left or right skewed size`

(skewness).:

```
carseats %>%
describe() %>%
select(variable, skewness, mean, p25, p50, p75) %>%
filter(!is.na(skewness)) %>%
arrange(desc(abs(skewness)))
Warning: package 'bindrcpp' was built under R version 3.4.4
# A tibble: 8 x 6
variable skewness mean p25 p50 p75
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Advertising 0.640 6.64 0 5.00 12.0
2 Sales 0.186 7.50 5.39 7.49 9.32
3 Price -0.125 116 100 117 131
4 Age -0.0772 53.3 39.8 54.5 66.0
# ... with 4 more rows
```

The `describe()`

function supports the `group_by()`

function syntax of `dplyr`

.

```
carseats %>%
group_by(US) %>%
describe(Sales, Income)
# A tibble: 4 x 27
variable US n na mean sd se_mean IQR skewness kurtosis
<chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales No 142 0 6.82 2.60 0.218 3.44 0.323 0.808
2 Sales Yes 258 0 7.87 2.88 0.179 4.23 0.0760 -0.326
3 Income No 130 12.0 65.8 28.2 2.48 50.0 0.1000 -1.14
4 Income Yes 250 8.00 70.4 27.9 1.77 48.0 0.0199 -1.06
# ... with 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>,
# p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>,
# p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>,
# p100 <dbl>
```

```
carseats %>%
group_by(US, Urban) %>%
describe(Sales, Income)
# A tibble: 12 x 28
variable US Urban n na mean sd se_mean IQR skewness
<chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales No No 46.0 0 6.46 2.72 0.402 3.15 0.0889
2 Sales No Yes 92.0 0 7.00 2.58 0.269 3.49 0.492
3 Sales No <NA> 4.00 0 6.99 1.28 0.639 0.827 1.69
4 Sales Yes No 69.0 0 8.23 2.65 0.319 4.10 -0.0212
# ... with 8 more rows, and 18 more variables: kurtosis <dbl>, p00 <dbl>,
# p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>,
# p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>,
# p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>
```

`normality()`

`normality()`

performs a normality test on numerical data. `Shapiro-Wilk normality test`

is performed. If the number of observations is larger than 5000, 5000 observations are extracted by random simple sampling and then tested.

The variables of `tbl_df`

object returned by `normality()`

are as follows.

`statistic`

: Statistics of the Shapiro-Wilk test`p_value`

: p-value of the Shapiro-Wilk test`sample`

: Number of sample observations performed Shapiro-Wilk test

`normality()`

performs the normality test for all numerical variables of `carseats`

as follows.:

```
normality(carseats)
# A tibble: 8 x 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Sales 0.995 0.254 400
2 CompPrice 0.998 0.977 400
3 Income 0.961 0.0000000152 400
4 Advertising 0.874 0.0000000000000000149 400
# ... with 4 more rows
```

The following example performs a normality test on only a few selected variables.

```
# Select columns by name
normality(carseats, Sales, CompPrice, Income)
# A tibble: 3 x 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Sales 0.995 0.254 400
2 CompPrice 0.998 0.977 400
3 Income 0.961 0.0000000152 400
# Select all columns between year and day (inclusive)
normality(carseats, Sales:Income)
# A tibble: 3 x 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Sales 0.995 0.254 400
2 CompPrice 0.998 0.977 400
3 Income 0.961 0.0000000152 400
# Select all columns except those from year to day (inclusive)
normality(carseats, -(Sales:Income))
# A tibble: 5 x 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Advertising 0.874 0.0000000000000000149 400
2 Population 0.952 0.000000000408 400
3 Price 0.996 0.390 400
4 Age 0.957 0.00000000186 400
# ... with 1 more row
```

You can use dplyr to sort non-normal distribution variables by p_value.:

```
library(dplyr)
carseats %>%
normality() %>%
filter(p_value <= 0.01) %>%
arrange(abs(p_value))
# A tibble: 5 x 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Advertising 0.874 0.0000000000000000149 400
2 Education 0.924 0.000000000000243 400
3 Population 0.952 0.000000000408 400
4 Age 0.957 0.00000000186 400
# ... with 1 more row
```

In particular, the `Advertising`

variable is considered to be the most out of the normal distribution.

The `normality()`

function supports the `group_by()`

function syntax in the `dplyr`

package.

```
carseats %>%
group_by(ShelveLoc, US) %>%
normality(Income) %>%
arrange(desc(p_value))
# A tibble: 6 x 6
variable ShelveLoc US statistic p_value sample
<chr> <fct> <fct> <dbl> <dbl> <dbl>
1 Income Bad No 0.969 0.470 34.0
2 Income Bad Yes 0.958 0.0343 62.0
3 Income Good No 0.902 0.0328 24.0
4 Income Good Yes 0.955 0.0296 61.0
# ... with 2 more rows
```

The `Income`

variable does not follow the normal distribution. However, if the `US`

is `No`

and the `ShelveLoc`

is `Good`

or `Bad`

at the significance level of 0.01, it follows the normal distribution.

In the following, we perform `normality test of log(Income)`

for each combination of `ShelveLoc`

and `US`

variables to inquire about normal distribution cases.

```
carseats %>%
mutate(log_income = log(Income)) %>%
group_by(ShelveLoc, US) %>%
normality(log_income) %>%
filter(p_value > 0.01)
# A tibble: 1 x 6
variable ShelveLoc US statistic p_value sample
<chr> <fct> <fct> <dbl> <dbl> <dbl>
1 log_income Bad No 0.940 0.0737 34.0
```

`plot_normality()`

`plot_normality()`

visualizes the normality of numeric data.

The information that `plot_normality()`

visualizes is as follows.

`Histogram of original data`

`Q-Q plot of original data`

`histogram of log transformed data`

`Histogram of square root transformed data`

Numerical data following a `power-law distribution`

are often encountered in data analysis. Since the numerical data following the power distribution is transformed into the normal distribution by performing the log and sqrt transform, the histogram of the data for the log and sqrt transform is drawn.

`plot_normality()`

can also specify several variables like `normality()`

function.

```
# Select columns by name
plot_normality(carseats, Sales, CompPrice)
```

The `plot_normality()`

function also supports the `group_by()`

function syntax in the `dplyr`

package.

```
carseats %>%
filter(ShelveLoc == "Good") %>%
group_by(US) %>%
plot_normality(Income)
```

`correlation coefficient`

using `correlate()`

`Correlate()`

finds the correlation coefficient of all combinations of `carseats`

numerical variables as follows:

```
correlate(carseats)
# A tibble: 56 x 3
var1 var2 coef_corr
<fct> <fct> <dbl>
1 CompPrice Sales 0.0641
2 Income Sales 0.151
3 Advertising Sales 0.270
4 Population Sales 0.0505
# ... with 52 more rows
```

The following example performs a normality test only on combinations that include several selected variables.

```
# Select columns by name
correlate(carseats, Sales, CompPrice, Income)
# A tibble: 21 x 3
var1 var2 coef_corr
<fct> <fct> <dbl>
1 CompPrice Sales 0.0641
2 Income Sales 0.151
3 Sales CompPrice 0.0641
4 Income CompPrice -0.0761
# ... with 17 more rows
# Select all columns between year and day (inclusive)
correlate(carseats, Sales:Income)
# A tibble: 21 x 3
var1 var2 coef_corr
<fct> <fct> <dbl>
1 CompPrice Sales 0.0641
2 Income Sales 0.151
3 Sales CompPrice 0.0641
4 Income CompPrice -0.0761
# ... with 17 more rows
# Select all columns except those from year to day (inclusive)
correlate(carseats, -(Sales:Income))
# A tibble: 35 x 3
var1 var2 coef_corr
<fct> <fct> <dbl>
1 Advertising Sales 0.270
2 Population Sales 0.0505
3 Price Sales -0.445
4 Age Sales -0.232
# ... with 31 more rows
```

`correlate()`

produces `two pairs of variable`

combinations. So you can use the following `filter()`

function to get the correlation coefficient for `a pair of variable`

combinations:

```
carseats %>%
correlate(Sales:Income) %>%
filter(as.integer(var1) > as.integer(var2))
# A tibble: 3 x 3
var1 var2 coef_corr
<fct> <fct> <dbl>
1 CompPrice Sales 0.0641
2 Income Sales 0.151
3 Income CompPrice -0.0761
```

The `correlate()`

function also supports the `group_by()`

function syntax in the `dplyr`

package.

```
carseats %>%
filter(ShelveLoc == "Good") %>%
group_by(Urban, US) %>%
correlate(Sales) %>%
filter(abs(coef_corr) > 0.5)
# A tibble: 12 x 5
Urban US var1 var2 coef_corr
<fct> <fct> <fct> <fct> <dbl>
1 No No Sales Income -0.540
2 No No Sales Price -0.943
3 No No Sales Age -0.722
4 No Yes Sales Price -0.791
# ... with 8 more rows
```

`plot_correlate()`

`plot_correlate()`

visualizes the correlation matrix.

`plot_correlate(carseats)`

`plot_correlate()`

can also specify multiple variables, like the `correlate()`

function. The following is a visualization of the correlation matrix including several selected variables.

```
# Select columns by name
plot_correlate(carseats, Sales, Price)
```

The `plot_correlate()`

function also supports the `group_by()`

function syntax in the `dplyr`

package.

```
carseats %>%
filter(ShelveLoc == "Good") %>%
group_by(Urban, US) %>%
plot_correlate(Sales)
```

To perform EDA based on `target variable`

, you need to create a`target_by class`

object. `target_by()`

creates a target_by class with an object inheriting data.frame or data.frame. `target_by()`

is similar to `group_by()`

in `dplyr`

which creates`grouped_df`

. The difference is that you specify only one variable.

The following is an example of specifying US as target variable in carseats data.frame.:

`categ <- target_by(carseats, US)`

Let’s do the EDA when the target variable is categorical. When the categorical variable US is the target variable, the relationship between the target variable and the predictor is examined.

`relate()`

shows the relationship between the target variable and the predictor. The following example shows the relationship between Sales and the target variable US. The predictor Sales is a numeric variable. In this case, the descriptive statistics are shown for each level of the target variable.

```
# If the variable of interest is a numarical variable
cat_num <- relate(categ, Sales)
cat_num
# A tibble: 3 x 27
variable US n na mean sd se_mean IQR skewness kurtosis
<chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales No 142 0 6.82 2.60 0.218 3.44 0.323 0.808
2 Sales Yes 258 0 7.87 2.88 0.179 4.23 0.0760 -0.326
3 Sales total 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809
# ... with 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>,
# p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>,
# p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>,
# p100 <dbl>
summary(cat_num)
variable US n na mean
Length:3 No :1 Min. :142.0 Min. :0 Min. :6.823
Class :character Yes :1 1st Qu.:200.0 1st Qu.:0 1st Qu.:7.160
Mode :character total:1 Median :258.0 Median :0 Median :7.496
Mean :266.7 Mean :0 Mean :7.395
3rd Qu.:329.0 3rd Qu.:0 3rd Qu.:7.682
Max. :400.0 Max. :0 Max. :7.867
sd se_mean IQR skewness
Min. :2.603 Min. :0.1412 Min. :3.442 Min. :0.07603
1st Qu.:2.713 1st Qu.:0.1602 1st Qu.:3.686 1st Qu.:0.13080
Median :2.824 Median :0.1791 Median :3.930 Median :0.18556
Mean :2.768 Mean :0.1796 Mean :3.866 Mean :0.19489
3rd Qu.:2.851 3rd Qu.:0.1988 3rd Qu.:4.077 3rd Qu.:0.25432
Max. :2.877 Max. :0.2184 Max. :4.225 Max. :0.32308
kurtosis p00 p01 p05
Min. :-0.32638 Min. :0.0000 Min. :0.4675 Min. :3.147
1st Qu.:-0.20363 1st Qu.:0.0000 1st Qu.:0.6868 1st Qu.:3.148
Median :-0.08088 Median :0.0000 Median :0.9062 Median :3.149
Mean : 0.13350 Mean :0.1233 Mean :1.0072 Mean :3.183
3rd Qu.: 0.36344 3rd Qu.:0.1850 3rd Qu.:1.2771 3rd Qu.:3.200
Max. : 0.80776 Max. :0.3700 Max. :1.6480 Max. :3.252
p10 p20 p25 p30
Min. :3.917 Min. :4.754 Min. :5.080 Min. :5.306
1st Qu.:4.018 1st Qu.:4.910 1st Qu.:5.235 1st Qu.:5.587
Median :4.119 Median :5.066 Median :5.390 Median :5.867
Mean :4.073 Mean :5.051 Mean :5.411 Mean :5.775
3rd Qu.:4.152 3rd Qu.:5.199 3rd Qu.:5.576 3rd Qu.:6.010
Max. :4.184 Max. :5.332 Max. :5.763 Max. :6.153
p40 p50 p60 p70
Min. :5.994 Min. :6.660 Min. :7.496 Min. :7.957
1st Qu.:6.301 1st Qu.:7.075 1st Qu.:7.787 1st Qu.:8.386
Median :6.608 Median :7.490 Median :8.078 Median :8.815
Mean :6.506 Mean :7.313 Mean :8.076 Mean :8.740
3rd Qu.:6.762 3rd Qu.:7.640 3rd Qu.:8.366 3rd Qu.:9.132
Max. :6.916 Max. :7.790 Max. :8.654 Max. :9.449
p75 p80 p90 p95
Min. :8.523 Min. : 8.772 Min. : 9.349 Min. :11.28
1st Qu.:8.921 1st Qu.: 9.265 1st Qu.:10.325 1st Qu.:11.86
Median :9.320 Median : 9.758 Median :11.300 Median :12.44
Mean :9.277 Mean : 9.665 Mean :10.795 Mean :12.08
3rd Qu.:9.654 3rd Qu.:10.111 3rd Qu.:11.518 3rd Qu.:12.49
Max. :9.988 Max. :10.464 Max. :11.736 Max. :12.54
p99 p100
Min. :13.64 Min. :14.90
1st Qu.:13.78 1st Qu.:15.59
Median :13.91 Median :16.27
Mean :13.86 Mean :15.81
3rd Qu.:13.97 3rd Qu.:16.27
Max. :14.03 Max. :16.27
```

The `relate class`

object created with`relate()`

visualizes the relationship between the target variable and the predictor with `plot()`

. The relationship between US and Sales is represented by a density plot.

`plot(cat_num)`

The following example shows the relationship between `ShelveLoc`

and the target variable `US`

. The predictor, ShelveLoc, is a categorical variable. In this case, we show the `contigency table`

of two variables. The `summary()`

function also performs an `independence test`

on the contigency table.

```
# If the variable of interest is a categorical variable
cat_cat <- relate(categ, ShelveLoc)
cat_cat
ShelveLoc
US Bad Good Medium
No 34 24 84
Yes 62 61 135
summary(cat_cat)
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 400
Number of factors: 2
Test for independence of all factors:
Chisq = 2.7397, df = 2, p-value = 0.2541
```

`plot()`

visualizes the relationship between the target variable and the predictor. The relationship between `US`

and `ShelveLoc`

is represented by a `mosaics plot`

.

`plot(cat_cat)`

Let’s do the EDA when the target variable is numeric. When the numeric variable Sales is the target variable, the relationship between the target variable and the predictor is examined.

```
# If the variable of interest is a numarical variable
num <- target_by(carseats, Sales)
```

The following example shows the relationship between `Price`

and the target variable `Sales`

. Price, a predictor, is a numeric variable. In this case, we show the result of `simple regression model`

of `target ~ predictor`

relation. The `summary()`

function represents the details of the model.

```
# If the variable of interest is a numarical variable
num_num <- relate(num, Price)
num_num
Call:
lm(formula = formula_str, data = data)
Coefficients:
(Intercept) Price
13.64192 -0.05307
summary(num_num)
Call:
lm(formula = formula_str, data = data)
Residuals:
Min 1Q Median 3Q Max
-6.5224 -1.8442 -0.1459 1.6503 7.5108
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.641915 0.632812 21.558 <2e-16 ***
Price -0.053073 0.005354 -9.912 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.532 on 398 degrees of freedom
Multiple R-squared: 0.198, Adjusted R-squared: 0.196
F-statistic: 98.25 on 1 and 398 DF, p-value: < 2.2e-16
```

`plot()`

visualizes the relationship between the target variable and the predictor. The relationship between Sales and Price is repersented as a scatter plot. The plot on the left represents the scatter plot of Sales and Price and the confidence interval of the regression line and the regression line. The plot on the right represents the relationship between the original data and the predicted value of the linear model as a scatter plot. If there is a linear relationship between the two variables, the observations will converge on the red diagonal in the scatter plot.

`plot(num_num)`

The scatter plot of the data with a large number of observations is output as overlapping points. This makes it difficult to judge the relationship between the two variables. It also takes a long time to perform the visualization. In this case, the above problem can be solved by hexabin plot.

In `plot()`

, the hex_thres argument provides a basis for drawing hexabin plots. For data with more than this number of observations, draw a hexabin plot.

Next, draw a hexabin plot with `plot()`

not a scatter plot, specifying 350 for the hex_thres argument. This is because the number of observations is 400.

`plot(num_num, hex_thres = 350)`

The following example shows the relationship between `ShelveLoc`

and the target variable `Sales`

. The predictor, ShelveLoc, is a categorical variable. It shows the result of performing `one-way ANOVA`

of `target ~ predictor`

relation. The results are represented in terms of an analysis of variance. The `summary()`

function also shows the `regression coefficients`

for each level of the predictor. In other words, it shows detailed information of `simple regression analysis`

of `target ~ predictor`

relation.

```
# If the variable of interest is a categorical variable
num_cat <- relate(num, ShelveLoc)
num_cat
Analysis of Variance Table
Response: Sales
Df Sum Sq Mean Sq F value Pr(>F)
ShelveLoc 2 1009.5 504.77 92.23 < 2.2e-16 ***
Residuals 397 2172.7 5.47
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(num_cat)
Call:
lm(formula = formula(formula_str), data = data)
Residuals:
Min 1Q Median 3Q Max
-7.3066 -1.6282 -0.0416 1.5666 6.1471
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5229 0.2388 23.131 < 2e-16 ***
ShelveLocGood 4.6911 0.3484 13.464 < 2e-16 ***
ShelveLocMedium 1.7837 0.2864 6.229 1.2e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.339 on 397 degrees of freedom
Multiple R-squared: 0.3172, Adjusted R-squared: 0.3138
F-statistic: 92.23 on 2 and 397 DF, p-value: < 2.2e-16
```

`plot()`

visualizes the relationship between the target variable and the predictor. The relationship between `Sales`

and `ShelveLoc`

is represented by a `box plot`

.

`plot(num_cat)`

`eda_report()`

`eda_report()`

performs EDA on all variables of the data frame or object (`tbl_df`

,`tbl`

, etc.) that inherits the data frame.

`eda_report()`

creates an EDA report in two forms:

- pdf file based on Latex
- html file

The contents of the report are as follows.:

- introduction
- Information of Dataset
- Information of Variables
- Numerical Variables

- Univariate Analysis
- Descriptive Statistics
- Normality Test of Numerical Variables
- Statistics and Visualization of (Sample) Data

- Relationship Between Variables
- Correlation Coefficient
- Correlation Coefficient by Variable Combination
- Correlation Plot of Numerical Variables

- Correlation Coefficient
- Target based Analysis
- Gruoped Descriptive Statistics
- Gruoped Numerical Variables
- Gruoped Categorical Variables

- Gruoped Relationship Between Variables
- Grouped Correlation Coefficient
- Grouped Correlation Plot of Numerical Variables

- Gruoped Descriptive Statistics

The following will create an EDA report for `carseats`

. The file format is pdf, and the file name is `EDA_Report.pdf`

.

```
carseats %>%
eda_report(target = Sales)
```

The following generates an HTML-formatted report named `EDA.html`

.

```
carseats %>%
eda_report(target = Sales, output_format = "html", output_file = "EDA.html")
```

The EDA report is an automated report to assist in the EDA process. Design the data analysis scenario with reference to the report results.

- The cover of the report is shown in the following figure.

- The report’s argenda is shown in the following figure.

- Much information is represented in tables in the report. An example of the table is shown in the following figure.