by-summary.RdFunction to calculate groupwise summary statistics, much like the summary procedure of SAS
summary_by(
data,
formula,
id = NULL,
FUN = mean,
keep.names = FALSE,
p2d = FALSE,
order = TRUE,
full.dimension = FALSE,
var.names = NULL,
fun.names = NULL,
...
)
summaryBy(
formula,
data = parent.frame(),
id = NULL,
FUN = mean,
keep.names = FALSE,
p2d = FALSE,
order = TRUE,
full.dimension = FALSE,
var.names = NULL,
fun.names = NULL,
...
)A data frame.
A formula object, see examples below.
A formula specifying variables which data are not grouped by but which should appear in the output. See examples below.
A list of functions to be applied, see examples below.
If TRUE and if there is only ONE function in FUN, then the variables in the output will have the same name as the variables in the input, see 'examples'.
Should parentheses in output variable names be replaced by dots?
Should the resulting dataframe be ordered according to the variables on the right hand side of the formula? (using orderBy
If TRUE then rows of summary statistics are repeated such that the result will have the same number of rows as the input dataset.
Option for user to specify the names of the variables on the left hand side.
Option for user to specify function names to apply to the variables on the left hand side.
Additional arguments to FUN. This could for example be NA actions.
A dataframe.
Extra arguments (...) are passed onto the functions in FUN. Hence care must be taken that all functions in FUN accept these arguments - OR one can explicitly write a functions which get around this. This can particularly be an issue in connection with handling NAs. See examples below. Some code for this function has been suggested by Jim Robison-Cox. Thanks.
data(dietox)
dietox12 <- subset(dietox,Time==12)
fun <- function(x){
c(m=mean(x), v=var(x), n=length(x))
}
summaryBy(cbind(Weight, Feed) ~ Evit + Cu, data=dietox12,
FUN=fun)
#> Evit Cu Weight.m Weight.v Weight.n Feed.m Feed.v Feed.n
#> 1 Evit000 Cu000 100.41426 46.41481 7 167.2571 86.40620 7
#> 2 Evit000 Cu035 98.51247 65.80137 8 171.7875 283.20408 8
#> 3 Evit000 Cu175 99.18748 235.70414 8 173.4625 1230.17117 8
#> 4 Evit100 Cu000 102.15712 52.31616 7 162.2000 174.98997 7
#> 5 Evit100 Cu035 102.47498 92.57374 8 178.2250 843.54503 8
#> 6 Evit100 Cu175 105.04283 55.49632 7 182.0143 810.98475 7
#> 7 Evit200 Cu000 97.24283 58.28952 7 160.3571 95.82953 7
#> 8 Evit200 Cu035 95.55553 33.90778 9 164.7667 267.88254 9
#> 9 Evit200 Cu175 98.63748 114.85415 8 168.4250 624.31074 8
summaryBy(list(c("Weight", "Feed"), c("Evit", "Cu")), data=dietox12,
FUN=fun)
#> Evit Cu Weight.m Weight.v Weight.n Feed.m Feed.v Feed.n
#> 1 Evit000 Cu000 100.41426 46.41481 7 167.2571 86.40620 7
#> 2 Evit000 Cu035 98.51247 65.80137 8 171.7875 283.20408 8
#> 3 Evit000 Cu175 99.18748 235.70414 8 173.4625 1230.17117 8
#> 4 Evit100 Cu000 102.15712 52.31616 7 162.2000 174.98997 7
#> 5 Evit100 Cu035 102.47498 92.57374 8 178.2250 843.54503 8
#> 6 Evit100 Cu175 105.04283 55.49632 7 182.0143 810.98475 7
#> 7 Evit200 Cu000 97.24283 58.28952 7 160.3571 95.82953 7
#> 8 Evit200 Cu035 95.55553 33.90778 9 164.7667 267.88254 9
#> 9 Evit200 Cu175 98.63748 114.85415 8 168.4250 624.31074 8
## Computations on several variables is done using cbind( )
summaryBy(cbind(Weight, Feed) ~ Evit + Cu, data=subset(dietox, Time > 1),
FUN=fun)
#> Evit Cu Weight.m Weight.v Weight.n Feed.m Feed.v Feed.n
#> 1 Evit000 Cu000 64.49219 545.3481 77 79.37792 2705.516 77
#> 2 Evit000 Cu035 62.53862 558.7867 88 81.19432 2852.290 88
#> 3 Evit000 Cu175 64.51362 603.8636 88 84.37955 3158.985 88
#> 4 Evit100 Cu000 64.70803 553.4286 87 77.46552 2434.824 87
#> 5 Evit100 Cu035 64.38976 595.2586 88 81.04773 3159.666 88
#> 6 Evit100 Cu175 67.81033 578.9620 87 86.72299 3129.720 87
#> 7 Evit200 Cu000 61.40113 537.8238 87 76.35517 2409.296 87
#> 8 Evit200 Cu035 61.68483 504.8144 99 79.13939 2619.280 99
#> 9 Evit200 Cu175 64.16135 562.3279 88 80.88636 2874.140 88
## Calculations on transformed data is possible using cbind( ), but
# the transformed variables must be named
summaryBy(cbind(lw=log(Weight), Feed) ~ Evit + Cu, data=dietox12, FUN=mean)
#> Evit Cu lw.mean Feed.mean
#> 1 Evit000 Cu000 4.607344 167.2571
#> 2 Evit000 Cu035 4.587091 171.7875
#> 3 Evit000 Cu175 4.584574 173.4625
#> 4 Evit100 Cu000 4.624415 162.2000
#> 5 Evit100 Cu035 4.625384 178.2250
#> 6 Evit100 Cu175 4.652181 182.0143
#> 7 Evit200 Cu000 4.574510 160.3571
#> 8 Evit200 Cu035 4.558066 164.7667
#> 9 Evit200 Cu175 4.585830 168.4250
## There are missing values in the 'airquality' data, so we remove these
## before calculating mean and variance with 'na.rm=TRUE'. However the
## length function does not accept any such argument. Hence we get
## around this by defining our own summary function in which length is
## not supplied with this argument while mean and var are:
sumfun <- function(x, ...){
c(m=mean(x, na.rm=TRUE, ...), v=var(x, na.rm=TRUE, ...), l=length(x))
}
summaryBy(cbind(Ozone, Solar.R) ~ Month, data=airquality, FUN=sumfun)
#> Month Ozone.m Ozone.v Ozone.l Solar.R.m Solar.R.v Solar.R.l
#> 1 5 23.61538 493.9262 31 181.2963 13242.370 31
#> 2 6 29.44444 331.5278 30 190.1667 8627.247 30
#> 3 7 59.11538 1000.8262 31 216.4839 6491.258 31
#> 4 8 59.96154 1574.5985 31 171.8571 5903.608 31
#> 5 9 31.44828 582.8276 30 167.4333 6259.702 30
## Compare with
aggregate(cbind(Ozone, Solar.R) ~ Month, data=airquality, FUN=sumfun)
#> Month Ozone.m Ozone.v Ozone.l Solar.R.m Solar.R.v Solar.R.l
#> 1 5 24.12500 523.76630 24.00000 182.0417 14072.1286 24.0000
#> 2 6 29.44444 331.52778 9.00000 184.2222 9159.6944 9.0000
#> 3 7 59.11538 1000.82615 26.00000 216.4231 6478.2538 26.0000
#> 4 8 60.00000 1744.54545 23.00000 173.0870 6215.0830 23.0000
#> 5 9 31.44828 582.82759 29.00000 168.2069 6464.6700 29.0000
## Using '.' on the right hand side of a formula means to stratify by
## all variables not used elsewhere:
data(warpbreaks)
summaryBy(breaks ~ wool + tension, warpbreaks, FUN=mean)
#> wool tension breaks.mean
#> 1 A L 44.55556
#> 2 A M 24.00000
#> 3 A H 24.55556
#> 4 B L 28.22222
#> 5 B M 28.77778
#> 6 B H 18.77778
summaryBy(breaks ~ ., warpbreaks, FUN=mean)
#> wool tension breaks.mean
#> 1 A L 44.55556
#> 2 A M 24.00000
#> 3 A H 24.55556
#> 4 B L 28.22222
#> 5 B M 28.77778
#> 6 B H 18.77778
summaryBy(. ~ wool + tension, warpbreaks, FUN=mean)
#> wool tension breaks.mean
#> 1 A L 44.55556
#> 2 A M 24.00000
#> 3 A H 24.55556
#> 4 B L 28.22222
#> 5 B M 28.77778
#> 6 B H 18.77778
summaryBy(. ~ wool + tension, warpbreaks, FUN=mean)
#> wool tension breaks.mean
#> 1 A L 44.55556
#> 2 A M 24.00000
#> 3 A H 24.55556
#> 4 B L 28.22222
#> 5 B M 28.77778
#> 6 B H 18.77778