aggr.R

Plain text source: aggr.R


# -*- Mode:R; Coding:us-ascii-unix; fill-column:160 -*-

################################################################################################################################################################
##
# @file      aggr.R
# @author    Mitch Richling <https://www.mitchr.me>
# @Copyright Copyright 2015 by Mitch Richling.  All rights reserved.
# @brief     Simple aggregation using base R.@EOL
# @Keywords  base r aggregation tapply aggregate dcast by lapply
#
# Also check out the data.table and dplyr examples.  Note that the 'dcast' function in reshape2.R can do some aggregation as well.
#            

################################################################################################################################################################
# We will use the mtcars data set for all the demonstrations

mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
################################################################################################################################################################
# Apply a function to ONE column ('disp' as a vector) of the data frame broken up by ONE factor ('cyl')

tapply(mtcars$disp, mtcars$cyl, mean)
       4        6        8 
105.1364 183.3143 353.1000 
################################################################################################################################################################
# Apply a function to ONE column ('disp' as a vector) of the data frame broken up by TWO factors ('cyl' & 'gear').  In the resulting table the rows (4, 6 & 8)
# are 'cyl' while columns are (3, 4 & 5) are 'gear'.

tapply(mtcars$disp,list(mtcars$cyl, mtcars$gear), mean)
         3       4     5
4 120.1000 102.625 107.7
6 241.5000 163.800 145.0
8 357.6167      NA 326.0
################################################################################################################################################################
# With a single column in the list for the second argument, aggregate works much like tapply but returns a data.frame.  Note that this preserves the type of the
# 'cyl' column -- it is retained as the 'names' part of the returned vector tapply.

aggregate(mtcars$disp,list(cyl=mtcars$cyl), mean)
  cyl        x
1   4 105.1364
2   6 183.3143
3   8 353.1000
################################################################################################################################################################
# Unlike tapply, aggregate can work with multiple data columns -- aggregated independently, and returns a data.frame.

aggregate(mtcars$disp,list(cyl=mtcars$cyl, gear=mtcars$gear), mean)
  cyl gear        x
1   4    3 120.1000
2   6    3 241.5000
3   8    3 357.6167
4   4    4 102.6250
5   6    4 163.8000
6   4    5 107.7000
7   6    5 145.0000
8   8    5 326.0000
################################################################################################################################################################
# aggregate has a handy formula based interface too

aggregate(disp ~ cyl, mtcars, mean)
  cyl     disp
1   4 105.1364
2   6 183.3143
3   8 353.1000
################################################################################################################################################################
# aggregate has a handy formula based interface too

aggregate(disp ~ cyl + gear, mtcars, mean)
  cyl gear     disp
1   4    3 120.1000
2   6    3 241.5000
3   8    3 357.6167
4   4    4 102.6250
5   6    4 163.8000
6   4    5 107.7000
7   6    5 145.0000
8   8    5 326.0000
################################################################################################################################################################
## dcast (from reshape2) can do some aggregation too. Note it sorts the resulting data.frame.

dcast(mtcars, cyl + gear ~ . , fun.aggregate=mean, value.var='disp')
  cyl gear        .
1   4    3 120.1000
2   4    4 102.6250
3   4    5 107.7000
4   6    3 241.5000
5   6    4 163.8000
6   6    5 145.0000
7   8    3 357.6167
8   8    5 326.0000
################################################################################################################################################################
# by is tapply, but for data.frames instead of vectors -- i.e. the function gets a data.frame for each factor level.

byo <- by(mtcars, mtcars$cyl, function(x) { mean(x$disp); } )
byo
mtcars$cyl: 4
[1] 105.1364
------------------------------------------------------------------------------------------------------------------------ 
mtcars$cyl: 6
[1] 183.3143
------------------------------------------------------------------------------------------------------------------------ 
mtcars$cyl: 8
[1] 353.1
names(byo)
[1] "4" "6" "8"
as.vector(byo)
[1] 105.1364 183.3143 353.1000
################################################################################################################################################################
# by can return complex results

by(mtcars, mtcars$cyl, function(x) { c(mean(x$disp), sd(x$disp)); } )
mtcars$cyl: 4
[1] 105.13636  26.87159
------------------------------------------------------------------------------------------------------------------------ 
mtcars$cyl: 6
[1] 183.31429  41.56246
------------------------------------------------------------------------------------------------------------------------ 
mtcars$cyl: 8
[1] 353.10000  67.77132
################################################################################################################################################################
# split actually splits up a data.frame into a list of data.frames broken out by a factor

lapply(split(mtcars, mtcars$cyl), function(x) { length(x$cyl) })
$`4`
[1] 11

$`6`
[1] 7

$`8`
[1] 14

The R session information (including the OS info, R version and all packages used):

    options(width=80)
    sessionInfo()
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 8 (jessie)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] graphics  grDevices datasets  utils     grid      stats     base     

other attached packages:
[1] RColorBrewer_1.1-2 reshape2_1.4.1     ggplot2_2.1.0      dplyr_0.4.3       
[5] data.table_1.9.6   gridExtra_2.2.1    knitr_1.13         lattice_0.20-33   

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.5      assertthat_0.1   plyr_1.8.3       chron_2.3-47    
 [5] R6_2.1.2         gtable_0.2.0     DBI_0.4-1        formatR_1.4     
 [9] magrittr_1.5     evaluate_0.9     scales_0.4.0     highr_0.6       
[13] stringi_1.0-1    tools_3.3.0      stringr_1.0.0    munsell_0.4.3   
[17] parallel_3.3.0   colorspace_1.2-6 methods_3.3.0   
    Sys.time()
[1] "2016-07-09 20:07:35 CDT"