dplyr do: Some Tips for Using and Programming

秒速五厘米 2022-07-20 15:10 127阅读 0赞

*This post was originally posted on Quantide blog. Read the [full article here][].*

If you want to compute arbitrary operations on a data frame returning more than one number back, use **`dplyr` `do()`**!

This post aims to explore some basic concepts of `do()`, along with giving some advice in using and programming.

`do()` is a verb (function) of `dplyr`. `dplyr` is a powerful R package for data manipulation, written and maintained by Hadley Wickham. This package allows you to perform the common data manipulation tasks on data frames, like: filtering for rows, selecting specific columns, re-ordering rows, adding new columns, summarizing data and computing arbitrary operations.

First of all, you have to install `dplyr` package:

install.packages("dplyr")

and to load it:

require(dplyr)

We will analyze the use of `do()` with the following dataset, created with random data:

set.seed(100)
    ds <- data.frame(group=c(rep("a",100), rep("b",100), rep("c",100)), 
                     x=rnorm(n = 300, mean = 3, sd = 2), y=rnorm(n = 300, mean = 2, sd = 2))

We firstly transform it into a `tbl_df` object to achieve a better print method. No changes occur on the input data frame.

ds <- tbl_df(ds)
    ds

Source: local data frame [300 x 3]
    
        group        x           y
       (fctr)    (dbl)       (dbl)
    1       a 1.995615 -1.71089045
    2       a 3.263062 -0.03712943
    3       a 2.842166 -0.09022217
    4       a 4.773570  0.69742469
    5       a 3.233943  2.76536531
    6       a 3.637260  4.06379942
    7       a 1.836419  2.26214995
    8       a 4.429065  2.75438347
    9       a 1.349481 -1.77539016
    10      a 2.280276  3.04043881
    ..    ...      ...         ...

## Base Concepts of do() (Non Standard Evaluation Version) ##

As we already said, `do()` computes arbitrary operations on a data frame returning more than one number back.

To use `do()`, you must know that:

*  it always **returns a dataframe**
 *  unlike the others data manipulation verbs of `dplyr`, `do()`needs the specification of **`.`** placeholder inside the function to apply, referring to the data it has to work with.
    
        # Head of ds
        ds %>% do(head(.))
    
        Source: local data frame [6 x 3]
        
           group        x           y
          (fctr)    (dbl)       (dbl)
        1      a 1.995615 -1.71089045
        2      a 3.263062 -0.03712943
        3      a 2.842166 -0.09022217
        4      a 4.773570  0.69742469
        5      a 3.233943  2.76536531
        6      a 3.637260  4.06379942
 *  it is conceived to be used with dplyr **`group_by()`** to compute operations within groups:
    
        # Head of ds by group
        ds %>% group_by(group) %>% do(head(.))
    
        Source: local data frame [18 x 3]
        Groups: group [3]
        
            group          x           y
           (fctr)      (dbl)       (dbl)
        1       a 1.99561530 -1.71089045
        2       a 3.26306233 -0.03712943
        3       a 2.84216582 -0.09022217
        4       a 4.77356962  0.69742469
        5       a 3.23394254  2.76536531
        6       a 3.63726018  4.06379942
        7       b 2.33415330 -0.56965729
        8       b 5.72622741  1.71643653
        9       b 2.06170532  4.87756954
        10      b 4.68575126 -0.08011508
        11      b 0.08401255 -0.04767590
        12      b 2.19938816  4.18954758
        13      c 3.05634353 -0.89257491
        14      c 2.28659319  2.63171152
        15      c 4.70525275  1.31450497
        16      c 4.02673050 -1.86270620
        17      c 5.03640599  2.48564201
        18      c 0.95704183  1.27446410
 *  the argument of `do()` can be **named** or **unnamed**:
    
     *  named arguments (more than one supplied) become list-columns, with one element for each group:
    
        # Tail (last 3 obs) of x by group
        ds %>% group_by(group) %>% do(out=tail(.$x, 3))
    
        Source: local data frame [3 x 2]
        Groups: <by row>
        
           group      out
          (fctr)    (chr)
        1      a <dbl[3]>
        2      b <dbl[3]>
        3      c <dbl[3]>
    
     *  unnamed argument (only one supplied) must be a data frame and labels will be duplicated accordingly:
    
        # Tail (last 3 obs) of x by group
        ds %>% group_by(group) %>% do(data.frame(out=tail(.$x, 3)))
    
        Source: local data frame [9 x 2]
        Groups: group [3]
        
           group       out
          (fctr)     (dbl)
        1      a 3.8270397
        2      a 0.6426337
        3      a 0.6519305
        4      b 3.3238824
        5      b 0.8290942
        6      b 4.1538746
        7      c 6.5861213
        8      c 4.6280643
        9      c 0.3599512

Its use is the same working with customized functions.

Let us define the following function, which performs two simple operations returning a data frame:

my_fun <- function(x, y){
      res_x = mean(x) + 2
      res_y = mean(y) * 5 
      return(data.frame(res_x, res_y))
    }

If the argument is named the result is:

# Apply my_fun() function to ds by group
    ds %>% group_by(group) %>% do(out=my_fun(x=.$x, y=.$y))

Source: local data frame [3 x 2]
    Groups: <by row>
    
       group                out
      (fctr)              (chr)
    1      a <data.frame [1,2]>
    2      b <data.frame [1,2]>
    3      c <data.frame [1,2]>

Otherwise, if argument is unnamed the result is:

# Apply my_fun() function to ds by group
    ds %>% group_by(group) %>% do(my_fun(x=.$x, y=.$y))

Source: local data frame [3 x 3]
    Groups: group [3]
    
       group    res_x     res_y
      (fctr)    (dbl)     (dbl)
    1      a 5.005825  9.167546
    2      b 5.022282  8.683619
    3      c 5.025586 11.240558

## Programming with do\_() (Standard Evaluation Version) ##

How can we enclose the previous operations inside a function? Simple! Using **`do_()`** (the SE version of `do()`) and **`interp()`** function of **`lazyeval`** package.

[Continue reading on Quantide blog…][full article here]

The post [dplyr do: Some Tips for Using and Programming][dplyr do_ Some Tips for Using and Programming] appeared first on [MilanoR][].

[full article here]: http://www.quantide.com/website/dplyr-do-tips-using-programming/
[dplyr do_ Some Tips for Using and Programming]: http://www.milanor.net/blog/dplyr-do-tips-for-using-and-programming/
[MilanoR]: http://www.milanor.net/blog