Chapter 4 Missing values

4.1 Missing Values Patterns Plots

For this section, we did the missing value analysis on the finance table we found on wikipedia.

Missing Values Patterns Plots for Counts

Missing Values Patterns Plots for Percent

4.2 Reasoning and Intepretation

We want to discover the reason behind the missing values, so we first print out the missing patterns of the finance table.

##       Year         Revenue         NetIncome      TotalAssets       Employees     
##  Min.   :2000   Min.   :  5363   Min.   :  -25   Min.   :  6021   Min.   : 14800  
##  1st Qu.:2005   1st Qu.: 13931   1st Qu.: 1328   1st Qu.: 11516   1st Qu.: 33725  
##  Median :2010   Median : 65225   Median :14013   Median : 75183   Median : 76550  
##  Mean   :2010   Mean   :111160   Mean   :23818   Mean   :142555   Mean   : 77388  
##  3rd Qu.:2015   3rd Qu.:215639   3rd Qu.:45687   3rd Qu.:290345   3rd Qu.:117750  
##  Max.   :2020   Max.   :274515   Max.   :59531   Max.   :375319   Max.   :147000  
##                                                                   NA's   :5

##     Year Revenue NetIncome TotalAssets Employees
## 1  FALSE   FALSE     FALSE       FALSE      TRUE
## 2  FALSE   FALSE     FALSE       FALSE      TRUE
## 3  FALSE   FALSE     FALSE       FALSE      TRUE
## 4  FALSE   FALSE     FALSE       FALSE      TRUE
## 5  FALSE   FALSE     FALSE       FALSE      TRUE
## 6  FALSE   FALSE     FALSE       FALSE     FALSE
## 7  FALSE   FALSE     FALSE       FALSE     FALSE
## 8  FALSE   FALSE     FALSE       FALSE     FALSE
## 9  FALSE   FALSE     FALSE       FALSE     FALSE
## 10 FALSE   FALSE     FALSE       FALSE     FALSE
## 11 FALSE   FALSE     FALSE       FALSE     FALSE
## 12 FALSE   FALSE     FALSE       FALSE     FALSE
## 13 FALSE   FALSE     FALSE       FALSE     FALSE
## 14 FALSE   FALSE     FALSE       FALSE     FALSE
## 15 FALSE   FALSE     FALSE       FALSE     FALSE
## 16 FALSE   FALSE     FALSE       FALSE     FALSE
## 17 FALSE   FALSE     FALSE       FALSE     FALSE
## 18 FALSE   FALSE     FALSE       FALSE     FALSE
## 19 FALSE   FALSE     FALSE       FALSE     FALSE
## 20 FALSE   FALSE     FALSE       FALSE     FALSE
## 21 FALSE   FALSE     FALSE       FALSE     FALSE

As we can see from the missing pattern table, the missing values of employees occur in the first five rows. Since our year column is in chronological order, we know that the five missing rows in Employees column is positively correlated with the year column. Therefore, we want to print out the first five missing rows and the next few employees rows without missing values. By comparing the values of these rows, we may discover the reason behind the missing values in Employees column in the first five years.

## # A tibble: 6 × 5
##    Year Revenue NetIncome TotalAssets Employees
##   <dbl>   <dbl>     <dbl>       <dbl>     <dbl>
## 1  2000    7983       786        6803        NA
## 2  2001    5363       -25        6021        NA
## 3  2002    5742        65        6298        NA
## 4  2003    6207        69        6815        NA
## 5  2004    8279       274        8050        NA
## 6  2005   13931      1328       11516     14800

After we apply the plot_missing function on the finance table from Wikipedia, we find that Apple’s finance table only has two missing patterns, and the missing values are all occurred in the employee’s column. Also, by looking at the top plot of the missing graph, we find out that there are five rows of missing values in the finance table. The year of these missing values is from 2000 to 2004, which is very early, so the employee’s data could be missing. This could be one of the main reasons.
From 2004 to 2005, we can see that the change is more significant compared to the changes in previous years. Thus, we think another possible reason could be that from 2000 - 2004, the number of employees does not matter too much; while from 2004-2005, the company’s rapid development, more information is needed. So since then, the number of employees started to be collected.