4.2 Inspecting the Data

Before computing any statistics, we need to understand the structure of the data. The head() function shows the first few rows. Try running head(work) in your console — here is what you will see:

Table 4.1: Table 4.2: First six rows of the Absenteeism at Work dataset.
ID	Reason for absence	Month of absence	Day of the week	Seasons	Transportation expense	Distance from Residence to Work	Service time	Age	Work load Average/day	Hit target	Disciplinary failure	Education	Son	Social drinker	Social smoker	Pet	Weight	Height	Body mass index	Absenteeism time in hours
11	26	7	3	1	289	36	13	33	239.554	97	0	1	2	1	0	1	90	172	30	4
36	0	7	3	1	118	13	18	50	239.554	97	1	1	1	1	0	0	98	178	31	0
3	23	7	4	1	179	51	18	38	239.554	97	0	1	0	1	0	0	89	170	31	2
7	7	7	5	1	279	5	14	39	239.554	97	0	1	2	1	1	0	68	168	24	4
11	23	7	5	1	289	36	13	33	239.554	97	0	1	2	1	0	1	90	172	30	2
3	23	7	6	1	179	51	18	38	239.554	97	0	1	0	1	0	0	89	170	31	2

We can also check the column names and data types. The names() function lists every column, and ncol() confirms the count:

names(work)

##  [1] "ID"                              "Reason for absence"             
##  [3] "Month of absence"                "Day of the week"                
##  [5] "Seasons"                         "Transportation expense"         
##  [7] "Distance from Residence to Work" "Service time"                   
##  [9] "Age"                             "Work load Average/day "         
## [11] "Hit target"                      "Disciplinary failure"           
## [13] "Education"                       "Son"                            
## [15] "Social drinker"                  "Social smoker"                  
## [17] "Pet"                             "Weight"                         
## [19] "Height"                          "Body mass index"                
## [21] "Absenteeism time in hours"

Notice that R has read all columns as numeric types (dbl for decimal numbers). This is technically correct — the data file stores everything as numbers — but some of these variables are really categorical. For example, Reason for absence is coded as numbers 0–28, but those numbers represent categories (disease types, appointment types, or 0 for no reason recorded), not quantities. Similarly, Day of the week uses 2 for Monday through 6 for Friday. We will address this distinction when we clean the data in Chapters 5 and 6. For now, we simply note it.

Column Names with Spaces

You may notice that the column names contain spaces (e.g., Reason for absence). In R, when a column name contains spaces, you need to wrap it in backticks when referencing it in code — for example, work$`Reason for absence`. We will rename these columns to simpler names when we clean the data in Chapter 6.