After you have acquired the data, you should do the following:
The dlookr package makes these steps fast and easy:
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 wrangle, it increases the efficiency of the tidyverse
package group.
Data diagnosis supports the following data structures.
To illustrate the basic 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 different 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)
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
.
<- ISLR::Carseats
carseats
suppressWarnings(RNGversion("3.5.0"))
set.seed(123)
sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[
suppressWarnings(RNGversion("3.5.0"))
set.seed(456)
sample(seq(NROW(carseats)), 10), "Urban"] <- NA carseats[
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 valuesna
: number of missing valuesmean
: arithmetic averagesd
: standard deviationse_mean
: standard error mean. sd/sqrt(n)IQR
: interquartile range (Q3-Q1)skewness
: skewnesskurtosis
: kurtosisp25
: Q1. 25% percentilep50
: median. 50% percentilep75
: Q3. 75% percentilep01
, p05
, p10
, p20
, p30
: 1%, 5%, 20%, 30% percentilesp40
, p60
, p70
, p80
: 40%, 60%, 70%, 80% percentilesp90
, p95
, p99
, p100
: 90%, 95%, 99%, 100% percentilesFor example, describe()
can computes 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
# … with 4 more rows, and 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 large positive skewness should consider the log or sqrt transformations to follow the normal distribution. The variables Advertising
seem to need to consider variable transformation.mean
and sd
, se_mean
: ThePopulation
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 × 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
# … 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>
# 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
# … 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>
# 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
# … with 1 more row, and 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
# … with 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_varia… US n na mean sd se_mean IQR skewness kurtosis<chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Income No 130 12 65.8 28.2 2.48 50 0.100 -1.14
2 Income Yes 250 8 70.4 27.9 1.77 48 0.0199 -1.06
3 Sales No 142 0 6.82 2.60 0.218 3.44 0.323 0.808
4 Sales Yes 258 0 7.87 2.88 0.179 4.23 0.0760 -0.326
# … 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 × 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
# … 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. When the number of observations is greater than 5000, it is tested after extracting 5000 samples by random simple sampling.
The variables of tbl_df
object returned by normality()
are as follows.
statistic
: Statistics of the Shapiro-Wilk testp_value
: p-value of the Shapiro-Wilk testsample
: Number of sample observations performed Shapiro-Wilk testnormality()
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
# … 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 × 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
# … with 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
# … 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 × 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
# … with 2 more rows
The Income
variable does not follow the normal distribution. However, the case where US
is No
and ShelveLoc
is Good
and Bad
at the significance level of 0.01, it follows the normal distribution.
The following example performs normality test of log(Income)
for each combination of ShelveLoc
and US
categorical variables to search for variables that follow the normal distribution.
%>%
carseats mutate(log_income = log(Income)) %>%
group_by(ShelveLoc, US) %>%
normality(log_income) %>%
filter(p_value > 0.01)
# A tibble: 1 × 6
variable ShelveLoc US statistic p_value sample<chr> <fct> <fct> <dbl> <dbl> <dbl>
1 log_income Bad No 0.940 0.0737 34
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
In the data analysis process, it 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.
# 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()
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
# … 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 × 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 (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
# … with 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
# … with 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.
<- carseats %>%
tab_corr 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
# … with 6 more rows
plot.correlate()
plot.correlate()
visualizes the correlation matrix with correlate class.
%>%
carseats correlate() %>%
plot()
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
correlate(carseats, Sales, Price) %>%
plot()
The plot.correlate()
function also supports the group_by()
function syntax in the dplyr
package.
%>%
carseats filter(ShelveLoc == "Good") %>%
group_by(Urban) %>%
correlate() %>%
plot()
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.:
<- target_by(carseats, US) categ
Let’s perform EDA when the target variable is a categorical variable. 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
<- relate(categ, Sales)
cat_num
cat_num# A tibble: 3 × 27
described_varia… US n na mean sd se_mean IQR skewness kurtosis<chr> <fct> <int> <int> <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)
described_variables US n na mean :3 No :1 Min. :142.0 Min. :0 Min. :6.823
Length:character Yes :1 1st Qu.:200.0 1st Qu.:0 1st Qu.:7.160
Class :character total:1 Median :258.0 Median :0 Median :7.496
Mode :266.7 Mean :0 Mean :7.395
Mean :329.0 3rd Qu.:0 3rd Qu.:7.682
3rd Qu.:400.0 Max. :0 Max. :7.867
Max.
sd se_mean IQR skewness :2.603 Min. :0.1412 Min. :3.442 Min. :0.07603
Min. :2.713 1st Qu.:0.1602 1st Qu.:3.686 1st Qu.:0.13080
1st Qu.:2.824 Median :0.1791 Median :3.930 Median :0.18556
Median :2.768 Mean :0.1796 Mean :3.866 Mean :0.19489
Mean :2.851 3rd Qu.:0.1988 3rd Qu.:4.077 3rd Qu.:0.25432
3rd Qu.:2.877 Max. :0.2184 Max. :4.225 Max. :0.32308
Max.
kurtosis p00 p01 p05 :-0.32638 Min. :0.0000 Min. :0.4675 Min. :3.147
Min. :-0.20363 1st Qu.:0.0000 1st Qu.:0.6868 1st Qu.:3.148
1st Qu.:-0.08088 Median :0.0000 Median :0.9062 Median :3.149
Median : 0.13350 Mean :0.1233 Mean :1.0072 Mean :3.183
Mean : 0.36344 3rd Qu.:0.1850 3rd Qu.:1.2771 3rd Qu.:3.200
3rd Qu.: 0.80776 Max. :0.3700 Max. :1.6480 Max. :3.252
Max.
p10 p20 p25 p30 :3.917 Min. :4.754 Min. :5.080 Min. :5.306
Min. :4.018 1st Qu.:4.910 1st Qu.:5.235 1st Qu.:5.587
1st Qu.:4.119 Median :5.066 Median :5.390 Median :5.867
Median :4.073 Mean :5.051 Mean :5.411 Mean :5.775
Mean :4.152 3rd Qu.:5.199 3rd Qu.:5.576 3rd Qu.:6.010
3rd Qu.:4.184 Max. :5.332 Max. :5.763 Max. :6.153
Max.
p40 p50 p60 p70 :5.994 Min. :6.660 Min. :7.496 Min. :7.957
Min. :6.301 1st Qu.:7.075 1st Qu.:7.787 1st Qu.:8.386
1st Qu.:6.608 Median :7.490 Median :8.078 Median :8.815
Median :6.506 Mean :7.313 Mean :8.076 Mean :8.740
Mean :6.762 3rd Qu.:7.640 3rd Qu.:8.366 3rd Qu.:9.132
3rd Qu.:6.916 Max. :7.790 Max. :8.654 Max. :9.449
Max.
p75 p80 p90 p95 :8.523 Min. : 8.772 Min. : 9.349 Min. :11.28
Min. :8.921 1st Qu.: 9.265 1st Qu.:10.325 1st Qu.:11.86
1st Qu.:9.320 Median : 9.758 Median :11.300 Median :12.44
Median :9.277 Mean : 9.665 Mean :10.795 Mean :12.08
Mean :9.654 3rd Qu.:10.111 3rd Qu.:11.518 3rd Qu.:12.49
3rd Qu.:9.988 Max. :10.464 Max. :11.736 Max. :12.54
Max.
p99 p100 :13.64 Min. :14.90
Min. :13.78 1st Qu.:15.59
1st Qu.:13.91 Median :16.27
Median :13.86 Mean :15.81
Mean :13.97 3rd Qu.:16.27
3rd Qu.:14.03 Max. :16.27 Max.
plot()
visualizes the relate
class object created by relate()
as the relationship between the target variable and the predictor variable. The relationship between US
and Sales
is visualized by density plot.
plot(cat_num)
The following example shows the relationship between ShelveLoc
and the target variable US
. The predictor variable ShelveLoc
is a categorical variable. In this case, it shows the contingency table
of two variables. The summary()
function performs independence test
on the contingency table.
# If the variable of interest is a categorical variable
<- relate(categ, ShelveLoc)
cat_cat
cat_cat
ShelveLoc
US Bad Good Medium34 24 84
No 62 61 135
Yes summary(cat_cat)
: xtabs(formula = formula_str, data = data, addNA = TRUE)
Callin table: 400
Number of cases : 2
Number of factorsfor independence of all factors:
Test = 2.7397, df = 2, p-value = 0.2541 Chisq
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 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.
# If the variable of interest is a numerical variable
<- target_by(carseats, Sales) num
The following example shows the relationship between Price
and the target variable Sales
. The predictor variable Price
is a numeric variable. 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
<- relate(num, Price)
num_num
num_num
:
Calllm(formula = formula_str, data = data)
:
Coefficients
(Intercept) Price 13.64192 -0.05307
summary(num_num)
:
Calllm(formula = formula_str, data = data)
:
Residuals
Min 1Q Median 3Q Max -6.5224 -1.8442 -0.1459 1.6503 7.5108
:
CoefficientsPr(>|t|)
Estimate Std. Error t value 13.641915 0.632812 21.558 <2e-16 ***
(Intercept) -0.053073 0.005354 -9.912 <2e-16 ***
Price ---
: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Signif. codes
: 2.532 on 398 degrees of freedom
Residual standard error-squared: 0.198, Adjusted R-squared: 0.196
Multiple R-statistic: 98.25 on 1 and 398 DF, p-value: < 2.2e-16 F
plot()
visualizes the relationship between the target and predictor variables. The relationship between Sales
and Price
is visualized 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.
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 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.
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 and shows the result of one-way ANOVA
of 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 simple regression analysis
of target ~ predictor
relationship.
# If the variable of interest is a categorical variable
<- relate(num, ShelveLoc)
num_cat
num_cat
Analysis of Variance Table
: Sales
ResponsePr(>F)
Df Sum Sq Mean Sq F value 2 1009.5 504.77 92.23 < 2.2e-16 ***
ShelveLoc 397 2172.7 5.47
Residuals ---
: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Signif. codessummary(num_cat)
:
Calllm(formula = formula(formula_str), data = data)
:
Residuals
Min 1Q Median 3Q Max -7.3066 -1.6282 -0.0416 1.5666 6.1471
:
CoefficientsPr(>|t|)
Estimate Std. Error t value 5.5229 0.2388 23.131 < 2e-16 ***
(Intercept) 4.6911 0.3484 13.464 < 2e-16 ***
ShelveLocGood 1.7837 0.2864 6.229 1.2e-09 ***
ShelveLocMedium ---
: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Signif. codes
: 2.339 on 397 degrees of freedom
Residual standard error-squared: 0.3172, Adjusted R-squared: 0.3138
Multiple R-statistic: 92.23 on 2 and 397 DF, p-value: < 2.2e-16 F
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)
dlookr provides two automated EDA reports:
eda_web_report()
eda_web_report()
create dynamic report for object inherited from data.frame(tbl_df
, tbl
, etc) or data.frame.
The contents of the report are as follows.:
eda_web_report() generates various reports with the following arguments.
The following script creates a EDA report for the data.frame
class object, heartfailure
.
%>%
heartfailure eda_web_report(target = "death_event", subtitle = "heartfailure",
output_dir = "./", output_file = "EDA.html", theme = "blue")
eda_paged_report()
eda_paged_report()
create static report for object inherited from data.frame(tbl_df
, tbl
, etc) or data.frame.
The contents of the report are as follows.:
eda_paged_report() generates various reports with the following arguments.
The following script creates a EDA report for the data.frame
class object, heartfailure
.
%>%
heartfailure eda_paged_report(target = "death_event", subtitle = "heartfailure",
output_dir = "./", output_file = "EDA.pdf", theme = "blue")
EDA function for table of DBMS supports In-database mode that performs SQL operations on the DBMS side. If the size of the data is large, using In-database mode is faster.
It is difficult to obtain anomaly 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 in which table data is brought to 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, which allows you to import the specified number of samples of data into R.
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, other DBMS are also available for your environment.
if (!require(DBI)) install.packages('DBI')
if (!require(RSQLite)) install.packages('RSQLite')
if (!require(dplyr)) install.packages('dplyr')
if (!require(dbplyr)) install.packages('dbplyr')
library(dplyr)
<- ISLR::Carseats
carseats sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA
carseats[
# connect DBMS
<- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
con_sqlite
# 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. That is, the data argument of all EDA function is specified as tbl_dbi object instead of data frame object.
# Positive values select variables
%>%
con_sqlite tbl("TB_CARSEATS") %>%
describe(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 68.8 28.0 1.44 47.2 0.0641 -1.08
# … 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>
# 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)
# 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 200 0 5.88 6.07 0.429 11 0.648 -0.667
2 Population 200 0 255. 149. 10.6 251. 0.0241 -1.22
3 Price 200 0 114. 23.7 1.68 31.2 -0.107 1.08
4 Age 200 0 54.6 15.9 1.13 24 -0.245 -1.01
# … with 1 more row, and 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>
# Find the statistic of all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with 'ShelveLoc' variable level is "Good".
%>%
con_sqlite tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
describe() %>%
filter(ShelveLoc == "Good")
# A tibble: 16 × 28
described_variables ShelveLoc US n na mean sd se_mean IQR<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
1 Advertising Good No 24 0 0.0417 0.204 0.0417 0
2 Advertising Good Yes 61 0 10.2 5.91 0.757 7
3 Age Good No 24 0 52.3 17.2 3.52 26
4 Age Good Yes 61 0 52.7 14.8 1.90 22
# … with 12 more rows, and 19 more variables: skewness <dbl>, 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>
# 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)
# A tibble: 6 × 28
described_variables ShelveLoc US n na mean sd se_mean IQR<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
1 Sales Bad No 23 0 5.36 1.91 0.398 2.32
2 Sales Bad Yes 50 0 5.54 2.57 0.364 3.74
3 Sales Good No 18 0 9.21 2.97 0.700 3.71
4 Sales Good Yes 37 0 10.9 2.37 0.389 3.41
# … with 2 more rows, and 19 more variables: skewness <dbl>, 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>
# Test all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with 'ShelveLoc' variable level is "Good".
%>%
con_sqlite tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
normality() %>%
filter(ShelveLoc == "Good")
# A tibble: 16 × 6
variable ShelveLoc US statistic p_value sample<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Sales Good No 0.955 0.342 24
2 Sales Good Yes 0.983 0.567 61
3 CompPrice Good No 0.970 0.658 24
4 CompPrice Good Yes 0.984 0.598 61
# … with 12 more rows
# 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)
# A tibble: 6 × 6
variable ShelveLoc US statistic p_value sample<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Sales Bad No 0.985 0.968 23
2 Sales Bad Yes 0.985 0.774 50
3 Sales Good No 0.959 0.576 18
4 Sales Good Yes 0.969 0.384 37
# … with 2 more rows
# Test log(Income) variables by 'ShelveLoc' and 'US',
# and extract only p.value greater than 0.01.
# SQLite extension functions for log transformation
::initExtension(con_sqlite)
RSQLite
%>%
con_sqlite tbl("TB_CARSEATS") %>%
mutate(log_income = log(Income)) %>%
group_by(ShelveLoc, US) %>%
normality(log_income) %>%
filter(p_value > 0.01)
# A tibble: 1 × 6
variable ShelveLoc US statistic p_value sample<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 log_income Bad No 0.946 0.104 34
# extract only those with 'ShelveLoc' variable level is "Good",
# and plot 'Income' by 'US'
# the result is same as a data.frame, but not display 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))
# A tibble: 28 × 3
var1 var2 coef_corr<fct> <fct> <dbl>
1 CompPrice Sales 0.0641
2 Income Sales 0.141
3 Advertising Sales 0.270
4 Population Sales 0.0505
# … with 24 more rows
%>%
con_sqlite tbl("TB_CARSEATS") %>%
correlate(Sales, Price) %>%
filter(as.integer(var1) > as.integer(var2))
# A tibble: 5 × 3
var1 var2 coef_corr<fct> <fct> <dbl>
1 Price Sales -0.445
2 Price CompPrice 0.585
3 Price Income -0.0484
4 Price Advertising 0.0445
# … with 1 more row
# Compute the correlation coefficient of Sales variable by 'ShelveLoc'
# and 'US' variables. And extract only those with absolute
# value of correlation coefficient is greater than 0.5
%>%
con_sqlite tbl("TB_CARSEATS") %>%
group_by(ShelveLoc, US) %>%
correlate(Sales) %>%
filter(abs(coef_corr) >= 0.5)
# A tibble: 6 × 5
ShelveLoc US var1 var2 coef_corr<chr> <chr> <fct> <fct> <dbl>
1 Bad No Sales Price -0.527
2 Bad Yes Sales Price -0.583
3 Good No Sales Price -0.811
4 Good Yes Sales Price -0.603
# … with 2 more rows
# extract only those with 'ShelveLoc' variable level is "Good",
# and compute the correlation coefficient of '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)
# A tibble: 10 × 5
Urban US var1 var2 coef_corr<chr> <chr> <fct> <fct> <dbl>
1 No No Sales Population -0.530
2 No No Sales Price -0.838
3 No Yes Sales Price -0.644
4 Yes No Sales Price -0.833
# … with 6 more rows
# Extract only those with 'ShelveLoc' variable level is "Good",
# and visualize correlation plot of 'Sales' variable by 'Urban'
# and 'US' variables.
# the result is same as a data.frame, but not display 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 character and the predictor column is a numeric type.
# If the target variable is a categorical variable
<- target_by(con_sqlite %>% tbl("TB_CARSEATS") , US)
categ
# If the variable of interest is a numerical variable
<- relate(categ, Sales)
cat_num
cat_num# A tibble: 3 × 27
described_varia… US n na mean sd se_mean IQR skewness kurtosis<chr> <fct> <int> <int> <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)
described_variables US n na mean :3 No :1 Min. :142.0 Min. :0 Min. :6.823
Length:character Yes :1 1st Qu.:200.0 1st Qu.:0 1st Qu.:7.160
Class :character total:1 Median :258.0 Median :0 Median :7.496
Mode :266.7 Mean :0 Mean :7.395
Mean :329.0 3rd Qu.:0 3rd Qu.:7.682
3rd Qu.:400.0 Max. :0 Max. :7.867
Max.
sd se_mean IQR skewness :2.603 Min. :0.1412 Min. :3.442 Min. :0.07603
Min. :2.713 1st Qu.:0.1602 1st Qu.:3.686 1st Qu.:0.13080
1st Qu.:2.824 Median :0.1791 Median :3.930 Median :0.18556
Median :2.768 Mean :0.1796 Mean :3.866 Mean :0.19489
Mean :2.851 3rd Qu.:0.1988 3rd Qu.:4.077 3rd Qu.:0.25432
3rd Qu.:2.877 Max. :0.2184 Max. :4.225 Max. :0.32308
Max.
kurtosis p00 p01 p05 :-0.32638 Min. :0.0000 Min. :0.4675 Min. :3.147
Min. :-0.20363 1st Qu.:0.0000 1st Qu.:0.6868 1st Qu.:3.148
1st Qu.:-0.08088 Median :0.0000 Median :0.9062 Median :3.149
Median : 0.13350 Mean :0.1233 Mean :1.0072 Mean :3.183
Mean : 0.36344 3rd Qu.:0.1850 3rd Qu.:1.2771 3rd Qu.:3.200
3rd Qu.: 0.80776 Max. :0.3700 Max. :1.6480 Max. :3.252
Max.
p10 p20 p25 p30 :3.917 Min. :4.754 Min. :5.080 Min. :5.306
Min. :4.018 1st Qu.:4.910 1st Qu.:5.235 1st Qu.:5.587
1st Qu.:4.119 Median :5.066 Median :5.390 Median :5.867
Median :4.073 Mean :5.051 Mean :5.411 Mean :5.775
Mean :4.152 3rd Qu.:5.199 3rd Qu.:5.576 3rd Qu.:6.010
3rd Qu.:4.184 Max. :5.332 Max. :5.763 Max. :6.153
Max.
p40 p50 p60 p70 :5.994 Min. :6.660 Min. :7.496 Min. :7.957
Min. :6.301 1st Qu.:7.075 1st Qu.:7.787 1st Qu.:8.386
1st Qu.:6.608 Median :7.490 Median :8.078 Median :8.815
Median :6.506 Mean :7.313 Mean :8.076 Mean :8.740
Mean :6.762 3rd Qu.:7.640 3rd Qu.:8.366 3rd Qu.:9.132
3rd Qu.:6.916 Max. :7.790 Max. :8.654 Max. :9.449
Max.
p75 p80 p90 p95 :8.523 Min. : 8.772 Min. : 9.349 Min. :11.28
Min. :8.921 1st Qu.: 9.265 1st Qu.:10.325 1st Qu.:11.86
1st Qu.:9.320 Median : 9.758 Median :11.300 Median :12.44
Median :9.277 Mean : 9.665 Mean :10.795 Mean :12.08
Mean :9.654 3rd Qu.:10.111 3rd Qu.:11.518 3rd Qu.:12.49
3rd Qu.:9.988 Max. :10.464 Max. :11.736 Max. :12.54
Max.
p99 p100 :13.64 Min. :14.90
Min. :13.78 1st Qu.:15.59
1st Qu.:13.91 Median :16.27
Median :13.86 Mean :15.81
Mean :13.97 3rd Qu.:16.27
3rd Qu.:14.03 Max. :16.27 Max.
# the result is same as a data.frame, but not display here. reference above in document.
plot(cat_num)
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 very large, use collect_size
.
# create web report file.
%>%
con_sqlite tbl("TB_CARSEATS") %>%
eda_web_report()
# create pdf file. file name is EDA.pdf, and collect size is 350
%>%
con_sqlite tbl("TB_CARSEATS") %>%
eda_paged_report(collect_size = 350, output_file = "EDA.pdf")