liveBook · Manning

Head

As explained in Statistics Slam Dunk

The head() function is a base R function that returns the first observations of an object, defaulting to six rows for tabular data.

Use base R’s head() to take a quick look at the first observations in a data set or vector. It is a practical checkpoint after importing, reshaping, or creating variables: inspect a small slice before moving into analysis. With a data frame or tibble, head(data) shows the first six rows by default. section 2.3.3 section 3.3

Add a second argument when six rows are not enough—or are too many. For example, head(draft, 3) returns the first three draft records. Pair it with tail() when you need to inspect both ends of a result. section 2.3.3 section 2.5.5

Check a newly created variable

After extracting seconds from a play-length string, compare the original and derived vectors directly: head(pbp$play_length) head(pbp$play_length2) Using $ selects one variable, so the output stays focused: values such as "00:00:12" can be checked against 12. section 8.4

For a wide data frame, inspect only the columns relevant to the question. Subset columns by position inside the first argument, then set the row count: head(nba_stats[, c(1, 3, 4, 13)], 10). Here, positions—not variable names—are required for that selection expression. section 14.6.1

Validate row order after combining seasons

After creating a season variable for 300 team-season records, inspect the beginning and end to confirm the expected ordering: head(nba$season, n = 3) tail(nba$season, n = 3) The first values are 2017 and the last are 2008, supporting the intended 30-record blocks per season. section 14.2.4

head(draft, 3) 
     Rk Year     Pk Tm    Player        Age Pos   Born  College
## <dbl> <dbl> <dbl> <chr> <chr>       <dbl> <chr> <chr> <chr>
## 1   1 2009      1 LAC   Blake Grif...  20.1 F     us    Oklaho...
## 2   2 2009      2 MEM   Hasheem Th...  22.1 C     tz    UConn  
## 3   3 2009      3 OKC   James Hard...  19.3 G     us    Arizon...
    From    To     G    MP    WS   WS48
##  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
## 1 2011  2020    622  34.8  75.2  0.167
## 2 2010  2014    224  10.5   4.8  0.099
## 3 2010  2020    826  34.3 133.   0.226

tail(draft, 3)
       Rk Year     Pk Tm    Player        Age Pos   Born  College
##   <dbl> <dbl> <dbl> <chr> <chr>       <dbl> <chr> <chr> <chr>
## 1   291 2000     27 IND   Primo_ Bre...  20.3 C     si    0     
## 2   292 2000     28 POR   Erick Bark...  22.1 G     us    St. Jo...
## 3   293 2000     29 LAL   Mark Madsen  24.2 F     us    Stanfo...
    From    To     G    MP    WS   WS48
##  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
## 291  2002   2010    342  18.1  10.8 0.084
## 292  2001   2002     27   9.9   0.2 0.027
## 293  2001   2009    453  11.8   8.2 0.074

Inspect the first three NBA draft records alongside the last three records with head() and tail(). section 2.3.3

How Statistics Slam Dunk covers it

How deep it goes
Explains default and custom row counts, vector access with $, and column-position subsetting without covering implementation internals.
Examples
Uses NBA draft records, play-length conversion, team-season ordering, and summarized tibbles.
What it assumes
Assumes an existing R object and introduces inspection through direct function calls.
What you can do after
Apply it via base R to preview chosen numbers of rows, individual vectors, or selected data-frame columns.
How it connects
Connects data inspection to tail(), glimpse(), dim(), mutate(), and data wrangling.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.