dplyr

Motivation

There is a dataset in the plyr package that has yearly batting records for major league baseball players from 1871 to 2007.

data(baseball, package="plyr")
head(baseball)

##            id year stint team lg  g  ab  r  h X2b X3b hr rbi sb cs bb so
## 4   ansonca01 1871     1  RC1    25 120 29 39  11   3  0  16  6  2  2  1
## 44  forceda01 1871     1  WS3    32 162 45 45   9   4  0  29  8  0  4  0
## 68  mathebo01 1871     1  FW1    19  89 15 24   3   1  0  10  2  1  2  0
## 99  startjo01 1871     1  NY2    33 161 35 58   5   1  1  34  4  2  3  0
## 102 suttoez01 1871     1  CL1    29 128 35 45   3   7  3  23  3  1  1  0
## 106 whitede01 1871     1  CL1    29 146 40 47   6   5  1  21  2  2  4  1
##     ibb hbp sh sf gidp
## 4    NA  NA NA NA   NA
## 44   NA  NA NA NA   NA
## 68   NA  NA NA NA   NA
## 99   NA  NA NA NA   NA
## 102  NA  NA NA NA   NA
## 106  NA  NA NA NA   NA

Your turn

Write a for loop that calculates and stores the career batting average for each player (note, batting average is number of hits, h, divided by number of at bats, ab, in a player’s career.)

Hint: You can get the unique player ids using the following:

players <- unique(baseball$id)

How did it go?

Household chores (declaring variables, setting values each time) distract from real work
Indices are error-prone
Loops often result in slow code because R can compute quantities using entire vectors in an optimized way

Enter: `dplyr`

dplyr is a Hadley package that implements the “split-apply-combine” strategy (among other things).

library(dplyr)

Verbs

When working with data you must:
1. Figure out what you want to do.
2. Describe those tasks in the form of a computer program.
3. Execute the program.

dplyr provides simple verbs, functions that correspond to the most common data manipulation tasks, to help you translate those thoughts into code.

Group by

group_by() breaks down a dataset into specified groups of rows. When you then apply the verbs on the resulting object they’ll be automatically applied “by group”
This is the split part of the split-apply-combine strategy
This is the same operator that we used in SQL, GROUP BY

group_by(baseball, id)

## Source: local data frame [21,699 x 22]
## Groups: id [1,228]
## 
##           id  year stint  team    lg     g    ab     r     h   X2b   X3b
## *      <chr> <int> <int> <chr> <chr> <int> <int> <int> <int> <int> <int>
## 1  ansonca01  1871     1   RC1          25   120    29    39    11     3
## 2  forceda01  1871     1   WS3          32   162    45    45     9     4
## 3  mathebo01  1871     1   FW1          19    89    15    24     3     1
## 4  startjo01  1871     1   NY2          33   161    35    58     5     1
## 5  suttoez01  1871     1   CL1          29   128    35    45     3     7
## 6  whitede01  1871     1   CL1          29   146    40    47     6     5
## 7   yorkto01  1871     1   TRO          29   145    36    37     5     7
## 8  ansonca01  1872     1   PH1          46   217    60    90    10     7
## 9  burdoja01  1872     1   BR2          37   174    26    46     3     0
## 10 forceda01  1872     1   TRO          25   130    40    53    11     0
## # ... with 21,689 more rows, and 11 more variables: hr <int>, rbi <int>,
## #   sb <int>, cs <int>, bb <int>, so <int>, ibb <int>, hbp <int>,
## #   sh <int>, sf <int>, gidp <int>

Summarise

summarise() summarises data through the use of aggregate functions, which take a vector of values and return a single number.
There are many useful examples of such functions, min(), max(), mean(), sum(), sd(), median(), and IQR().

summarise(baseball, mean(h))

##   mean(h)
## 1 61.7569

summarise(group_by(baseball, year), mean(h))

## # A tibble: 137 × 2
##     year `mean(h)`
##    <int>     <dbl>
## 1   1871  42.14286
## 2   1872  42.92308
## 3   1873  68.53846
## 4   1874  64.86667
## 5   1875  73.29412
## 6   1876  72.40000
## 7   1877  64.23529
## 8   1878  66.82353
## 9   1879  82.32000
## 10  1880  72.00000
## # ... with 127 more rows

dplyr, your new best fRiend