Data quality diagnosis

Choonghyun Ryu

2021-02-12

Preface

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

The dlookr package makes these steps fast and easy:

This document introduces Data Quality Diagnosis methods provided by the dlookr package. You will learn how to diagnose the quality 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.

Supported data structures

Data diagnosis supports the following data structures.

Data: nycflights13

To illustrate basic use of the dlookr package, use the flights data from the nycflights13 package. The flights data frame is data about departure and arrival on all flights departing from NYC in 2013.

library(nycflights13)
dim(flights)
[1] 336776     19
flights
# A tibble: 336,776 x 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     1      517            515         2      830            819
2  2013     1     1      533            529         4      850            830
3  2013     1     1      542            540         2      923            850
4  2013     1     1      544            545        -1     1004           1022
# … with 336,772 more rows, and 11 more variables: arr_delay <dbl>,
#   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

Data diagnosis

dlookr aims to diagnose the data and to select variables that can not be used for data analysis or to find the variables that need to be calibrated.:

General diagnosis of all variables with diagnose()

diagnose() allows to diagnosis a variables in a data frame. 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 diagnose () are as follows.

For example, we can diagnose all variables in flights:

diagnose(flights)
# A tibble: 19 x 6
  variables types   missing_count missing_percent unique_count unique_rate
  <chr>     <chr>           <int>           <dbl>        <int>       <dbl>
1 year      integer             0            0               1  0.00000297
2 month     integer             0            0              12  0.0000356 
3 day       integer             0            0              31  0.0000920 
4 dep_time  integer          8255            2.45         1319  0.00392   
# … with 15 more rows

year can be considered not to be used in the analysis model since unique_count is 1. However, you do not have to remove it if you configure date as a combination of year, month, and day.

For example, we can diagnose only a few selected variables:

# Select columns by name
diagnose(flights, year, month, day)
# A tibble: 3 x 6
  variables types   missing_count missing_percent unique_count unique_rate
  <chr>     <chr>           <int>           <dbl>        <int>       <dbl>
1 year      integer             0               0            1  0.00000297
2 month     integer             0               0           12  0.0000356 
3 day       integer             0               0           31  0.0000920 
# Select all columns between year and day (include)
diagnose(flights, year:day)
# A tibble: 3 x 6
  variables types   missing_count missing_percent unique_count unique_rate
  <chr>     <chr>           <int>           <dbl>        <int>       <dbl>
1 year      integer             0               0            1  0.00000297
2 month     integer             0               0           12  0.0000356 
3 day       integer             0               0           31  0.0000920 
# Select all columns except those from year to day (exclude)
diagnose(flights, -(year:day))
# A tibble: 16 x 6
  variables      types   missing_count missing_percent unique_count unique_rate
  <chr>          <chr>           <int>           <dbl>        <int>       <dbl>
1 dep_time       integer          8255            2.45         1319     0.00392
2 sched_dep_time integer             0            0            1021     0.00303
3 dep_delay      numeric          8255            2.45          528     0.00157
4 arr_time       integer          8713            2.59         1412     0.00419
# … with 12 more rows

By using with dplyr, variables including missing values can be sorted by the weight of missing values.:

flights %>%
  diagnose() %>%
  select(-unique_count, -unique_rate) %>% 
  filter(missing_count > 0) %>% 
  arrange(desc(missing_count))
# A tibble: 6 x 4
  variables types   missing_count missing_percent
  <chr>     <chr>           <int>           <dbl>
1 arr_delay numeric          9430            2.80
2 air_time  numeric          9430            2.80
3 arr_time  integer          8713            2.59
4 dep_time  integer          8255            2.45
# … with 2 more rows

Diagnosis of numeric variables with diagnose_numeric()

diagnose_numeric() diagnoses numeric(continuous and discrete) variables in a data frame. Usage is the same as diagnose() but returns more diagnostic information. However, if you specify a non-numeric variable in the second and subsequent argument list, the variable is automatically ignored.

The variables of the tbl_df object returned by diagnose_numeric() are as follows.

The summary() function summarizes the distribution of individual variables in the data frame and outputs it to the console. The summary values of numeric variables are min, Q1, mean, median, Q3 and max, which help to understand the distribution of data.

However, the result displayed on the console has the disadvantage that the analyst has to look at it with the eyes. However, when the summary information is returned in a data frame structure such as tbl_df, the scope of utilization is expanded. diagnose_numeric() supports this.

zero, minus, and outlier are useful measures to diagnose data integrity. For example, numerical data in some cases cannot have zero or negative numbers. A numeric variable called employee salary cannot have negative numbers or zeros. Therefore, this variable should be checked for the inclusion of zero or negative numbers in the data diagnosis process.

diagnose_numeric() can diagnose all numeric variables of flights as follows.:

diagnose_numeric(flights)
# A tibble: 14 x 10
  variables   min    Q1    mean median    Q3   max  zero minus outlier
  <chr>     <dbl> <dbl>   <dbl>  <dbl> <dbl> <dbl> <int> <int>   <int>
1 year       2013  2013 2013      2013  2013  2013     0     0       0
2 month         1     4    6.55      7    10    12     0     0       0
3 day           1     8   15.7      16    23    31     0     0       0
4 dep_time      1   907 1349.     1401  1744  2400     0     0       0
# … with 10 more rows

If a numeric variable can not logically have a negative or zero value, it can be used with filter() to easily find a variable that does not logically match:

diagnose_numeric(flights) %>% 
  filter(minus > 0 | zero > 0) 
# A tibble: 3 x 10
  variables   min    Q1  mean median    Q3   max  zero  minus outlier
  <chr>     <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <int>  <int>   <int>
1 dep_delay   -43    -5 12.6      -2    11  1301 16514 183575   43216
2 arr_delay   -86   -17  6.90     -5    14  1272  5409 188933   27880
3 minute        0     8 26.2      29    44    59 60696      0       0

Diagnosis of categorical variables with diagnose_category()

diagnose_category() diagnoses the categorical(factor, ordered, character) variables of a data frame. The usage is similar to diagnose() but returns more diagnostic information. If you specify a non-categorical variable in the second and subsequent argument list, the variable is automatically ignored.

The top argument specifies the number of levels to return for each variable. The default is 10, which returns the top 10 level. Of course, if the number of levels is less than 10, all levels are returned.

The variables of the tbl_df object returned by diagnose_category() are as follows.

`diagnose_category() can diagnose all categorical variables of flights as follows.:

diagnose_category(flights)
# A tibble: 33 x 6
  variables levels      N  freq ratio  rank
  <chr>     <chr>   <int> <int> <dbl> <int>
1 carrier   UA     336776 58665  17.4     1
2 carrier   B6     336776 54635  16.2     2
3 carrier   EV     336776 54173  16.1     3
4 carrier   DL     336776 48110  14.3     4
# … with 29 more rows

In collaboration with filter() in the dplyr package, we can see that the tailnum variable is ranked in top 1 with 2,512 missing values in the case where the missing value is included in the top 10:

diagnose_category(flights) %>% 
  filter(is.na(levels))
# A tibble: 1 x 6
  variables levels      N  freq ratio  rank
  <chr>     <chr>   <int> <int> <dbl> <int>
1 tailnum   <NA>   336776  2512 0.746     1

The following example returns a list where the level’s relative percentage is 0.01% or less. Note that the value of the top argument is set to a large value such as 500. If the default value of 10 was used, values below 0.01% would not be included in the list:

flights %>%
  diagnose_category(top = 500)  %>%
  filter(ratio <= 0.01)
# A tibble: 10 x 6
  variables levels      N  freq   ratio  rank
  <chr>     <chr>   <int> <int>   <dbl> <int>
1 carrier   OO     336776    32 0.00950    16
2 dest      JAC    336776    25 0.00742    97
3 dest      PSP    336776    19 0.00564    98
4 dest      EYW    336776    17 0.00505    99
# … with 6 more rows

In the analytics model, you can also consider removing levels where the relative frequency is very small in the observations or, if possible, combining them together.

Diagnosing outliers with diagnose_outlier()

diagnose_outlier() diagnoses the outliers of the numeric (continuous and discrete) variables of the data frame. The usage is the same as diagnose().

The variables of the tbl_df object returned by diagnose_outlier() are as follows.

diagnose_outlier() can diagnose outliers of all numerical variables on flights as follows:

diagnose_outlier(flights)
# A tibble: 14 x 6
  variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
  <chr>            <int>          <dbl>         <dbl>     <dbl>        <dbl>
1 year                 0              0           NaN   2013         2013   
2 month                0              0           NaN      6.55         6.55
3 day                  0              0           NaN     15.7         15.7 
4 dep_time             0              0           NaN   1349.        1349.  
# … with 10 more rows

Numeric variables that contained outliers are easily found with filter().:

diagnose_outlier(flights) %>% 
  filter(outliers_cnt > 0) 
# A tibble: 5 x 6
  variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
  <chr>            <int>          <dbl>         <dbl>     <dbl>        <dbl>
1 dep_delay        43216      12.8               93.1     12.6         0.444
2 arr_delay        27880       8.28             121.       6.90       -3.69 
3 flight               1       0.000297        8500     1972.       1972.   
4 air_time          5448       1.62             400.     151.        146.   
# … with 1 more row

The following example finds a numeric variable with an outlier ratio of 5% or more, and then returns the result of dividing mean of outliers by overall mean in descending order:

diagnose_outlier(flights) %>% 
  filter(outliers_ratio > 5) %>% 
  mutate(rate = outliers_mean / with_mean) %>% 
  arrange(desc(rate)) %>% 
  select(-outliers_cnt)
# A tibble: 2 x 6
  variables outliers_ratio outliers_mean with_mean without_mean  rate
  <chr>              <dbl>         <dbl>     <dbl>        <dbl> <dbl>
1 arr_delay           8.28         121.       6.90       -3.69  17.5 
2 dep_delay          12.8           93.1     12.6         0.444  7.37

In cases where the mean of the outliers is large relative to the overall average, it may be desirable to impute or remove the outliers.

Visualization of outliers using plot_outlier()

plot_outlier() visualizes outliers of numerical variables(continuous and discrete) of data.frame. Usage is the same diagnose().

The plot derived from the numerical data diagnosis is as follows.

plot_outlier() can visualize an outliers in the arr_delay variable of flights as follows:

flights %>%
  plot_outlier(arr_delay) 

The following example uses diagnose_outlier(), plot_outlier(), and dplyr packages to visualize all numerical variables with an outlier ratio of 5% or higher.

flights %>%
  plot_outlier(diagnose_outlier(flights) %>% 
                 filter(outliers_ratio >= 5) %>% 
                 select(variables) %>% 
                 unlist())

Analysts should look at the results of the visualization to decide whether to remove or replace outliers. In some cases, you should consider removing variables with outliers from the data analysis model.

Looking at the results of the visualization, arr_delay shows that the observed values without outliers are similar to the normal distribution. In the case of a linear model, we might consider removing or imputing outliers.

Visualization for missing values

It is important to look at the missing values of individual variables, but it is also important to look at the relationship between the variables including the missing values.

dlookr provides a visualization tool that looks at the relationship of variables including missing values.

visualize pareto chart using plot_na_pareto()

plot_na_pareto() draws a pareto chart by collecting variables including missing values.

mice::boys %>% 
  plot_na_pareto(col = "blue")

The default value of the only_na argument is FALSE, which includes variables that do not contain missing values, but if this value is set to TRUE, only variables containing missing values are visualized. The variable age was excluded from this plot.

mice::boys %>% 
  plot_na_pareto(only_na = TRUE, main = "Pareto Chart for mice::boys")

The rating of the variable is expressed as a proportion of missing values. It is calculated as the ratio of missing values. If it is [0, 0.05), it is Good, if it is [0.05, 0.4) it is OK, if it is [0.4, 0.8) it is Bad, and if it is [0.8, 1.0] it is Remove. You can override this grade using the grade argument as follows:

mice::boys %>% 
  plot_na_pareto(grade = list(High = 0.1, Middle = 0.6, Low = 1), relative = TRUE)

If the plot argument is set to FALSE, information about missing values is returned instead of plotting.

plot_na_pareto(mice::boys, only_na = TRUE, plot = FALSE)
# A tibble: 8 x 5
  variable frequencies  ratio grade cumulative
  <fct>          <int>  <dbl> <fct>      <dbl>
1 tv               522 0.698  Bad         32.2
2 gen              503 0.672  Bad         63.2
3 phb              503 0.672  Bad         94.2
4 hc                46 0.0615 OK          97.0
# … with 4 more rows

visualize combination chart using plot_na_hclust()

It is important to look at the relationship between variables, including missing values. plot_na_hclust() visualizes the relationship of variables that contain missing values. This function rearranges the positions of variables using hierarchical clustering. Then, the expression of the missing value is visualized by grouping similar variables.

mice::boys %>% 
  plot_na_hclust(main = "Distribution of missing value")

visualize combination chart using plot_na_intersect()

plot_na_intersect() visualize the combinations of missing value across cases.

The visualization consists of four parts. The bottom left, which is the most basic, visualizes the case of cross(intersection)-combination. The x-axis is the variable including the missing value, and the y-axis represents the case of a combination of variables. And on the marginal of the two axes, the frequency of the case is expressed as a bar graph. Finally, the visualization at the top right expresses the number of variables including missing values in the data set, and the number of observations including missing values and complete cases .

This example visualize the combination variables that is include missing value.

mice::boys %>% 
  plot_na_intersect()

If the n_vars argument is used, only the top n variables containing many missing values are visualized.

mice::boys %>%
  plot_na_intersect(n_vars = 5)

If you use the n_intersacts argument, only the top n numbers of variable combinations(intersection) including missing values are visualized. and If you want to visualize the combination variables that is include missing value and complete case. You just add only_na = FALSE.

mice::boys %>%
  plot_na_intersect(only_na = FALSE, n_intersacts = 7)

Create a diagnostic report using diagnose_report()

diagnose_report() performs data diagnosis of all variables of object inherited from data.frame(tbl_df, tbl, etc) or data.frame.

diagnose_report() writes the report in two formats:

The contents of the report are as follows.:

The following script creates a quality diagnosis report for the tbl_df class object, flights. The file format is pdf, and the file name is DataDiagnosis_Report.pdf.

flights %>%
  diagnose_report()

The following script creates a html report named DataDiagnosis_Report.html.

flights %>%
  diagnose_report(output_format = "html")

The following script creates a HTML report named Diagn.html.

flights %>%
  diagnose_report(output_format = "html", output_file = "Diagn.html")

The data diagnostic report is an automated report to aid in the data diagnostic process. Refer to this report to judge data replenishment or re-acquisition.

Diagnostic report contents

Contents of pdf file

  • The cover of the report is shown in the following figure.:
Data Diagnostic Report Cover

Data Diagnostic Report Cover

  • The contents of the report is shown in the following figure.:
Data Diagnostic Report Contents

Data Diagnostic Report Contents

  • Most information is represented in the report as a table. An example of a table is shown in the following figure.:
Sample data diagnostic report table

Sample data diagnostic report table

  • In the data diagnosis report, the outlier diagnostic contents include visualization. The result is shown in the following figure.:
Data diagnosis report outlier diagnosis contents

Data diagnosis report outlier diagnosis contents

Diagnosing tables in DBMS

The DBMS table diagnostic function 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.

Preparing table data

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)

carseats <- ISLR::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)

Diagnose data quality of variables in the DBMS

Use dplyr::tbl() to create a tbl_dbi object, then use it as a data frame object. That is, the data argument of all diagnose function is specified as tbl_dbi object instead of data frame object.

# Diagnosis of all columns
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose()
# A tibble: 11 x 6
  variables   types  missing_count missing_percent unique_count unique_rate
  <chr>       <chr>          <dbl>           <dbl>        <int>       <dbl>
1 Sales       double             0               0          336       0.84 
2 CompPrice   double             0               0           73       0.182
3 Income      double            20               5           99       0.248
4 Advertising double             0               0           28       0.07 
# … with 7 more rows

# Positions values select columns, and In-memory mode
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose(1, 3, 8, in_database = FALSE)
# A tibble: 3 x 6
  variables types   missing_count missing_percent unique_count unique_rate
  <chr>     <chr>           <int>           <dbl>        <int>       <dbl>
1 Sales     numeric             0               0          336       0.84 
2 Income    numeric            20               5           99       0.248
3 Age       numeric             0               0           56       0.14 
  
# Positions values select columns, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose(-8, -9, -10, in_database = FALSE, collect_size = 200)
# A tibble: 8 x 6
  variables   types   missing_count missing_percent unique_count unique_rate
  <chr>       <chr>           <int>           <dbl>        <int>       <dbl>
1 Sales       numeric             0               0          182       0.91 
2 CompPrice   numeric             0               0           65       0.325
3 Income      numeric             8               4           84       0.42 
4 Advertising numeric             0               0           23       0.115
# … with 4 more rows

Diagnose data quality of categorical variables in the DBMS

# Positions values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(7, in_database = FALSE, collect_size = 200) 
# A tibble: 3 x 6
  variables levels     N  freq ratio  rank
* <chr>     <chr>  <int> <int> <dbl> <int>
1 ShelveLoc Medium   200   113  56.5     1
2 ShelveLoc Bad      200    47  23.5     2
3 ShelveLoc Good     200    40  20       3
  
# Positions values select variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(-7)
# A tibble: 5 x 6
  variables levels     N  freq ratio  rank
  <chr>     <chr>  <int> <int> <dbl> <int>
1 Urban     Yes      400   278 69.5      1
2 Urban     No       400   117 29.2      2
3 Urban     <NA>     400     5  1.25     3
4 US        Yes      400   258 64.5      1
# … with 1 more row

Diagnose data quality of numerical variables in the DBMS

# Diagnosis of all numerical variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_numeric()
# A tibble: 8 x 10
  variables     min     Q1   mean median     Q3   max  zero minus outlier
  <chr>       <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl> <int> <int>   <int>
1 Sales           0   5.39   7.50   7.49   9.32  16.3     1     0       2
2 CompPrice      77 115    125.   125    135    175       0     0       2
3 Income         21  42     68.7   69     90    120       0     0       0
4 Advertising     0   0      6.64   5     12     29     144     0       0
# … with 4 more rows
  
# Positive values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_numeric(Sales, Income, collect_size = 200)
# A tibble: 2 x 10
  variables   min    Q1  mean median    Q3   max  zero minus outlier
* <chr>     <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <int> <int>   <int>
1 Sales         0  5.26  7.42   7.50  9.10  14.9     1     0       0
2 Income       21 47.8  71.4   73    92.2  120       0     0       0

Diagnose outlier of numerical variables in the DBMS

con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_outlier()  %>%
  filter(outliers_ratio > 1)
# A tibble: 1 x 6
  variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
  <chr>            <int>          <dbl>         <dbl>     <dbl>        <dbl>
1 Price                5           1.25          100.      116.         116.

Plot outlier information of numerical data diagnosis in the DBMS

# Visualization of numerical variables with a ratio of
# outliers greater than 1%
# the result is same as a data.frame, but not display here. reference above in document.
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  plot_outlier(con_sqlite %>% 
                 tbl("TB_CARSEATS") %>% 
                 diagnose_outlier() %>%
                 filter(outliers_ratio > 1) %>%
                 select(variables) %>%
                 pull())

Reporting the information of data diagnosis for table of thr DBMS

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

Using the collect_size argument, you can perform data diagnosis with the corresponding number of sample data. If the number of data is very large, use collect_size.

# create pdf file. file name is DataDiagnosis_Report.pdf
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_report()
  
# create pdf file. file name is Diagn.pdf, and collect size is 350
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_report(collect_size = 350, output_file = "Diagn.pdf")

# create html file. file name is Diagnosis_Report.html
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_report(output_format = "html")

# create html file. file name is Diagn.html
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_report(output_format = "html", output_file = "Diagn.html")