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 a data diagnosis or automatically generates a data diagnosis report.
**Discover data in various ways, and automatically generate EDA(exploratory data analysis) report.**- Impute missing values and outliers, resolve skewed data, and categorize 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 increases synergy with `dplyr`

. Particularly in
data exploration and data wrangling, 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.**Use dplyr as the back-end interface for any DBI-compatible database.**

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

dataset. `Carseats`

in the
`ISLR`

package is a simulated data set containing sales of
child car seats at 400 stores. This data is a data.frame created to
predict 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)

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

- CompPrice
- Price charged by a 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 of 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
frequently encountered. However, ‘Carseats’ is complete data without
missing values. So the following script created the missing values and
saved them as `carseats`

.

dlookr can help understand the distribution of data by calculating descriptive statistics of numerical data. In addition, the correlation between variables is identified, and a 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. Descriptive statistics help determine the distribution of
numerical variables. Like the 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 deviation`se_mean`

: standard 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, `describe()`

can compute the statistics of
all numerical variables in `carseats`

:

```
describe(carseats)
# A tibble: 8 × 26
described_variables n na mean sd se_mean IQR skewness kurtosis
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809
2 CompPrice 400 0 125. 15.3 0.767 20 -0.0428 0.0417
3 Income 380 20 68.9 28.1 1.44 48.2 0.0449 -1.09
4 Advertising 400 0 6.64 6.65 0.333 12 0.640 -0.545
# ℹ 4 more rows
# ℹ 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>
```

`skewness`

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

seems to need to consider variable transformation.`mean`

and`sd`

,`se_mean`

: The`Population`

with a significant`standard error of the mean`

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

(mean). The`standard deviation`

(sd) is much more significant 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 × 26
described_variables n na mean sd se_mean IQR skewness kurtosis
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809
2 CompPrice 400 0 125. 15.3 0.767 20 -0.0428 0.0417
3 Income 380 20 68.9 28.1 1.44 48.2 0.0449 -1.09
# ℹ 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>
# Select all columns between year and day (include)
describe(carseats, Sales:Income)
# A tibble: 3 × 26
described_variables n na mean sd se_mean IQR skewness kurtosis
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales 400 0 7.50 2.82 0.141 3.93 0.186 -0.0809
2 CompPrice 400 0 125. 15.3 0.767 20 -0.0428 0.0417
3 Income 380 20 68.9 28.1 1.44 48.2 0.0449 -1.09
# ℹ 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>
# Select all columns except those from year to day (exclude)
describe(carseats, -(Sales:Income))
# A tibble: 5 × 26
described_variables n na mean sd se_mean IQR skewness kurtosis
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Advertising 400 0 6.64 6.65 0.333 12 0.640 -0.545
2 Population 400 0 265. 147. 7.37 260. -0.0512 -1.20
3 Price 400 0 116. 23.7 1.18 31 -0.125 0.452
4 Age 400 0 53.3 16.2 0.810 26.2 -0.0772 -1.13
# ℹ 1 more row
# ℹ 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>
```

The `describe()`

function can be sorted by
`left or right skewed size`

(skewness) using
`dplyr`

.:

```
carseats %>%
describe() %>%
select(described_variables, skewness, mean, p25, p50, p75) %>%
filter(!is.na(skewness)) %>%
arrange(desc(abs(skewness)))
# A tibble: 8 × 6
described_variables skewness mean p25 p50 p75
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Advertising 0.640 6.64 0 5 12
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
# ℹ 4 more rows
```

The `describe()`

function supports the
`group_by()`

function syntax of the `dplyr`

package.

```
carseats %>%
group_by(US) %>%
describe(Sales, Income)
# A tibble: 4 × 27
described_variables US n na mean sd se_mean IQR skewness
<chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Income No 130 12 65.8 28.2 2.48 50 0.100
2 Income Yes 250 8 70.4 27.9 1.77 48 0.0199
3 Sales No 142 0 6.82 2.60 0.218 3.44 0.323
4 Sales Yes 258 0 7.87 2.88 0.179 4.23 0.0760
# ℹ 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>
```

```
carseats %>%
group_by(US, Urban) %>%
describe(Sales, Income)
# A tibble: 12 × 28
described_variables US Urban n na mean sd se_mean IQR skewness
<chr> <fct> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Income No No 42 4 60.2 29.1 4.49 45.2 0.408
2 Income No Yes 84 8 69.5 27.4 2.99 47 -0.0497
3 Income No <NA> 4 0 48.2 24.7 12.3 40.8 -0.0496
4 Income Yes No 65 4 70.5 29.9 3.70 48 0.0736
# ℹ 8 more rows
# ℹ 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. When the number
of observations exceeds 5000, it is tested after extracting 5000 samples
by random simple sampling.

The variables of the `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 × 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Sales 0.995 2.54e- 1 400
2 CompPrice 0.998 9.77e- 1 400
3 Income 0.961 1.52e- 8 400
4 Advertising 0.874 1.49e-17 400
# ℹ 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 × 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 × 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 × 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Advertising 0.874 1.49e-17 400
2 Population 0.952 4.08e-10 400
3 Price 0.996 3.90e- 1 400
4 Age 0.957 1.86e- 9 400
# ℹ 1 more row
```

You can use `dplyr`

to sort variables that do not follow a
normal distribution in order of `p_value`

:

```
library(dplyr)
carseats %>%
normality() %>%
filter(p_value <= 0.01) %>%
arrange(abs(p_value))
# A tibble: 5 × 4
vars statistic p_value sample
<chr> <dbl> <dbl> <dbl>
1 Advertising 0.874 1.49e-17 400
2 Education 0.924 2.43e-13 400
3 Population 0.952 4.08e-10 400
4 Age 0.957 1.86e- 9 400
# ℹ 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 × 6
variable ShelveLoc US statistic p_value sample
<chr> <fct> <fct> <dbl> <dbl> <dbl>
1 Income Bad No 0.969 0.470 34
2 Income Bad Yes 0.958 0.0343 62
3 Income Good No 0.902 0.0328 24
4 Income Good Yes 0.955 0.0296 61
# ℹ 2 more rows
```

The `Income`

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

is `No`

and
`ShelveLoc`

is `Good`

and `Bad`

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

The following example performs the
`normality test of log(Income)`

for each combination of
`ShelveLoc`

and `US`

categorical variables to
search for variables that follow the normal distribution.

`plot_normality()`

`plot_normality()`

visualizes the normality of numeric
data.

The information visualized by `plot_normality()`

is as
follows.:

`Histogram of original data`

`Q-Q plot of original data`

`histogram of log transformed data`

`Histogram of square root transformed data`

The data analysis often encounters numerical data that follows the
`power-law distribution`

. Since the numerical data that
follows the `power-law distribution`

is converted into a
normal distribution by performing the `log`

or
`sqrt`

transformation, so draw a histogram of the
`log`

and `sqrt`

transformed data.

`plot_normality()`

can also specify several variables like
`normality()`

function.

The `plot_normality()`

function also supports the
`group_by()`

function syntax in the `dplyr`

package.

`correlation coefficient`

using
`correlate()`

`correlate()`

calculates the correlation coefficient of
all combinations of `carseats`

numerical variables as
follows:

```
correlate(carseats)
# A tibble: 56 × 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
# ℹ 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 × 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
# ℹ 17 more rows
# Select all columns between year and day (include)
correlate(carseats, Sales:Income)
# A tibble: 21 × 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
# ℹ 17 more rows
# Select all columns except those from year to day (exclude)
correlate(carseats, -(Sales:Income))
# A tibble: 35 × 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
# ℹ 31 more rows
```

`correlate()`

produces
`two pairs of variables`

. So the following example uses
`filter()`

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 × 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()`

also supports the
`group_by()`

function syntax in the `dplyr`

package.

```
tab_corr <- carseats %>%
filter(ShelveLoc == "Good") %>%
group_by(Urban, US) %>%
correlate(Sales) %>%
filter(abs(coef_corr) > 0.5)
tab_corr
# A tibble: 10 × 5
Urban US var1 var2 coef_corr
<fct> <fct> <fct> <fct> <dbl>
1 No No Sales Population -0.530
2 No No Sales Price -0.838
3 No Yes Sales Price -0.630
4 Yes No Sales Price -0.833
# ℹ 6 more rows
```

`plot.correlate()`

`plot.correlate()`

visualizes the correlation matrix with
correlate class.

`plot.correlate()`

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

function. The following visualize the
correlation matrix, including several selected variables.

The `plot.correlate()`

function also supports the
`group_by()`

function syntax in the `dplyr`

package.

To perform EDA based on the `target variable`

, you must
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 the
target variable in `carseats`

data.frame.:

Let’s perform EDA when the target variable is categorical. When the
categorical variable `US`

is the target variable, we examine
the relationship between the target variable and the predictor.

`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 numerical variable
cat_num <- relate(categ, Sales)
cat_num
# A tibble: 3 × 27
described_variables US n na mean sd se_mean IQR skewness
<chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Sales No 142 0 6.82 2.60 0.218 3.44 0.323
2 Sales Yes 258 0 7.87 2.88 0.179 4.23 0.0760
3 Sales total 400 0 7.50 2.82 0.141 3.93 0.186
# ℹ 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>
summary(cat_num)
described_variables 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
```

`plot()`

visualizes the `relate`

class object
created by `relate()`

as the relationship between the target
and predictor variables. The relationship between `US`

and
`Sales`

is visualized by a density plot.

The following example shows the relationship between
`ShelveLoc`

and the target variable `US`

. The
predictor variable `ShelveLoc`

is categorical. This case
illustrates the `contingency table`

of two variables. The
`summary()`

function performs an
`independence test`

on the contingency 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. A `mosaics plot`

represents the
relationship between `US`

and `ShelveLoc`

.

Let’s perform EDA when the target variable is numeric. When the
numeric variable `Sales`

is the target variable, we examine
the relationship between the target variable and the predictor.

The following example shows the relationship between
`Price`

and the target variable `Sales`

. The
predictor variable `Price`

is numeric. In this case, it shows
the result of a `simple linear model`

of the
`target ~ predictor`

formula. The `summary()`

function expresses the details of the model.

```
# If the variable of interest is a numerical 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
and predictor variables. The relationship between `Sales`

and
`Price`

is pictured with a scatter plot. The figure on the
left shows the scatter plot of `Sales`

and `Price`

and the confidence interval of the regression line and regression line.
The figure on the right shows the relationship between the original data
and the predicted values of the linear model as a scatter plot. If there
is a linear relationship between the two variables, the scatter plot of
the observations converges on the red diagonal line.

The scatter plot of the data with many 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 plot`

. If the number of
observations is greater than `hex_thres`

, draw a
`hexabin plot`

.

The following example visualizes the `hexabin plot`

rather
than the scatter plot by specifying 350 for the `hex_thres`

argument. This is because the number of observations is 400.

The following example shows the relationship between
`ShelveLoc`

and the target variable `Sales`

. The
predictor `ShelveLoc`

is a categorical variable and displays
the result of a `one-way ANOVA`

of the
`target ~ predictor`

relationship. The results are expressed
in terms of ANOVA. The `summary()`

function shows the
`regression coefficients`

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

of the
`target ~ predictor`

relationship.

```
# 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. A `box plot`

represents the
relationship between `Sales`

and `ShelveLoc`

.

dlookr provides two automated EDA reports:

- Web page-based dynamic reports can perform in-depth analysis through visualization and statistical tables.
- Static reports generated as PDF files or HTML files can be archived as the output of data analysis.

`eda_web_report()`

`eda_web_report()`

creates a dynamic report for objects
inherited from data.frame(`tbl_df`

, `tbl`

, etc) or
data.frame.

The contents of the report are as follows.:

- Overview
- Data Structures
- Data Types
- Job Informations

- Univariate Analysis
- Descriptive Statistics
- Normality Test

- Bivariate Analysis
- Compare Numerical Variables
- Compare Categorical Variables

- Multivariate Analysis
- Correlation Analysis
- Correlation Matrix
- Correlation Plot

- Correlation Analysis
- Target based Analysis
- Grouped Numerical Variables
- Grouped Categorical Variables
- Grouped Correlation

eda_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
- name of the logo image file on the top left.

- create_date
- The date on which the report is generated.

- theme
- name of theme for report. Support “orange” and “blue”.

- sample_percent
- Sample percent of data for performing EDA.

The following script creates an EDA report for the
`data.frame`

class object, `heartfailure`

.

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

`eda_paged_report()`

`eda_paged_report()`

creates a static report for an object
inherited from data.frame(`tbl_df`

, `tbl`

, etc) or
data.frame.

The contents of the report are as follows.:

- Overview
- Data Structures
- Job Informations

- Univariate Analysis
- Descriptive Statistics
- Numerical Variables
- Categorical Variables

- Normality Test

- Descriptive Statistics
- Bivariate Analysis
- Compare Numerical Variables
- Compare Categorical Variables

- Multivariate Analysis
- Correlation Analysis
- Correlation Coefficient Matrix
- Correlation Plot

- Correlation Analysis
- Target based Analysis
- Grouped Numerical Variables
- Grouped Categorical Variables
- Grouped Correlation

eda_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 EDA.

The following script creates an EDA report for the
`data.frame`

class object, `heartfailure`

.

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

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

EDA function for a table of DBMS supports In-database mode that performs SQL operations on the DBMS side. If the data size is large, using In-database mode is faster.

It is challenging to obtain anomalies or to implement the sampling-based algorithm in SQL of DBMS. So, some functions do not yet support In-database mode. In this case, it is performed in In-memory mode, where table data is brought to the R side and calculated. In this case, if the data size is large, the execution speed may be slow. It supports the collect_size argument, allowing you to import the specified number of data samples into R.

- In-database support functions
- none

- In-database not support functions
`normality()`

`plot_normality()`

`correlate()`

`plot.correlate()`

`describe()`

`eda_web_report()`

`eda_paged_report()`

Copy the `carseats`

data frame to the SQLite DBMS and
create it as a table named `TB_CARSEATS`

. Mysql/MariaDB,
PostgreSQL, Oracle DBMS, and other DBMS are also available for your
environment.

```
library(dplyr)
carseats <- Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA
# connect DBMS
con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
# copy carseats to the DBMS with a table named TB_CARSEATS
copy_to(con_sqlite, carseats, name = "TB_CARSEATS", overwrite = TRUE)
```

Use `dplyr::tbl()`

to create a tbl_dbi object, then use it
as a data frame object. The data argument of all EDA functions is
specified as a tbl_dbi object instead of a data frame object.

```
# Positive values select variables
con_sqlite %>%
tbl("TB_CARSEATS") %>%
describe(Sales, CompPrice, Income)
# Negative values to drop variables, and In-memory mode and collect size is 200
con_sqlite %>%
tbl("TB_CARSEATS") %>%
describe(-Sales, -CompPrice, -Income, collect_size = 200)
# Find the statistic of all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with the 'ShelveLoc' variable level as "Good".
con_sqlite %>%
tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
describe() %>%
filter(ShelveLoc == "Good")
# extract only those with 'Urban' variable level is "Yes",
# and find 'Sales' statistics by 'ShelveLoc' and 'US'
con_sqlite %>%
tbl("TB_CARSEATS") %>%
filter(Urban == "Yes") %>%
group_by(ShelveLoc, US) %>%
describe(Sales)
```

```
# Test all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with the 'ShelveLoc' variable level is "Good".
con_sqlite %>%
tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
normality() %>%
filter(ShelveLoc == "Good")
# extract only those with 'Urban' variable level is "Yes",
# and test 'Sales' by 'ShelveLoc' and 'US'
con_sqlite %>%
tbl("TB_CARSEATS") %>%
filter(Urban == "Yes") %>%
group_by(ShelveLoc, US) %>%
normality(Sales)
# Test log(Income) variables by 'ShelveLoc' and 'US',
# and extract only p.value greater than 0.01.
# SQLite extension functions for log transformation
RSQLite::initExtension(con_sqlite)
con_sqlite %>%
tbl("TB_CARSEATS") %>%
mutate(log_income = log(Income)) %>%
group_by(ShelveLoc, US) %>%
normality(log_income) %>%
filter(p_value > 0.01)
```

```
# Extract only those with the 'ShelveLoc' variable level is "Good",
# and plot 'Income' by 'US'
# The result is the same as the data.frame, but not displayed here. Reference above in document.
con_sqlite %>%
tbl("TB_CARSEATS") %>%
filter(ShelveLoc == "Good") %>%
group_by(US) %>%
plot_normality(Income)
```

```
# Correlation coefficient
# that eliminates redundant combination of variables
con_sqlite %>%
tbl("TB_CARSEATS") %>%
correlate() %>%
filter(as.integer(var1) > as.integer(var2))
con_sqlite %>%
tbl("TB_CARSEATS") %>%
correlate(Sales, Price) %>%
filter(as.integer(var1) > as.integer(var2))
# Compute the correlation coefficient of the Sales variable by 'ShelveLoc'
# and 'US' variables. And extract only those with absolute
# value of the correlation coefficient is more significant than 0.5
con_sqlite %>%
tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
correlate(Sales) %>%
filter(abs(coef_corr) >= 0.5)
# Extract only those with the 'ShelveLoc' variable level is "Good",
# and compute the correlation coefficient of the 'Sales' variable
# by 'Urban' and 'US' variables.
# And the correlation coefficient is negative and smaller than 0.5
con_sqlite %>%
tbl("TB_CARSEATS") %>%
filter(ShelveLoc == "Good") %>%
group_by(Urban, US) %>%
correlate(Sales) %>%
filter(coef_corr < 0) %>%
filter(abs(coef_corr) > 0.5)
```

```
# Extract only those with 'ShelveLoc' variable level is "Good",
# and visualize correlation plot of 'Sales' variable by 'Urban'
# and 'US' variables.
# The result is the same as the data.frame, but not displayed here. Reference above in document.
con_sqlite %>%
tbl("TB_CARSEATS") %>%
filter(ShelveLoc == "Good") %>%
group_by(Urban) %>%
correlate() %>%
plot(Sales)
```

The following is an EDA where the target column is a character, and the predictor column is a numeric type.

The following shows several examples of creating an EDA report for a DBMS table.

Using the `collect_size`

argument, you can perform EDA
with the corresponding number of sample data. If the number of data is
huge, use `collect_size`

.