Description Usage Arguments Details Value See Also Examples
genTable
creates a table of arbitrary summaries conditional on
given values of independent variables given by a formula.
Aggregate
does the same, but returns a data.frame
instead.
fapply
is a generic function that dispatches on its data
argument. It is called internally by Aggregate
and genTable
.
Methods for this function can be used to adapt Aggregate
and
genTable
to data sources other than data frames.
1 2 3 4 5 6 7 8 9 10 11 | Aggregate(formula, data=parent.frame(), subset=NULL,
sort = TRUE, names=NULL, addFreq=TRUE, as.vars=1,
drop.constants=TRUE,...)
genTable(formula, data=parent.frame(), subset=NULL,
names=NULL, addFreq=TRUE,...)
fapply(formula,data,...) # calls UseMethod("fapply",data)
## Default S3 method:
fapply(formula, data, subset=NULL,
names=NULL, addFreq=TRUE,...)
|
formula |
a formula. The right hand side includes one or more grouping variables separated by '+'. These may be factors, numeric, or character vectors. The left hand side may be empty, a numerical variable, a factor, or an expression. See details below. |
data |
an environment or data frame or an object coercable into a data frame. |
subset |
an optional vector specifying a subset of observations to be used. |
sort |
a logical value; determines the order in which the aggregated
data appear in the data frame returned by |
names |
an optional character vector giving names to the
result(s) yielded by the expression on the left hand side of |
addFreq |
a logical value. If TRUE and
|
as.vars |
an integer; relevant only if the left hand side of the formula returns an array or a matrix - which dimension (rows, columns, or layers etc.) will transformed to variables? Defaults to columns in case of matrices and to the highest dimensional extend in case of arrays. |
drop.constants |
logical; variables that are constant across levels dropped from the result? |
... |
further arguments, passed to methods or ignored. |
If an expression is given as left hand side of the formula, its
value is computed for any combination of values of the values on the
right hand side. If the right hand side is a dot, then all
variables in data
are added to the right hand side of the
formula.
If no expression is given as left hand side, then the frequency counts for the respective value combinations of the right hand variables are computed.
If a single factor is on the left hand side, then the left hand side is
translated into an appropriate
call to table()
. Note that also in this case addFreq
takes effect.
If a single numeric variable is on the left hand side, frequency
counts weighted by this variable are computed. In these cases,
genTable
is equivalent to xtabs
and
Aggregate
is equivalent to as.data.frame(xtabs(...))
.
Aggregate
results in a data frame with conditional summaries and unique value combinations
of conditioning variables.
genTable
returns a table, that is, an array with class "table"
.
aggregate.data.frame, xtabs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ex.data <- expand.grid(mu=c(0,100),sigma=c(1,10))[rep(1:4,rep(100,4)),]
ex.data <- within(ex.data,
x<-rnorm(
n=nrow(ex.data),
mean=mu,
sd=sigma
)
)
Aggregate(~mu+sigma,data=ex.data)
Aggregate(mean(x)~mu+sigma,data=ex.data)
Aggregate(mean(x)~mu+sigma,data=ex.data,name="Average")
Aggregate(c(mean(x),sd(x))~mu+sigma,data=ex.data)
Aggregate(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
genTable(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
Aggregate(table(Admit)~.,data=UCBAdmissions)
Aggregate(Table(Admit,Freq)~.,data=UCBAdmissions)
Aggregate(Admit~.,data=UCBAdmissions)
Aggregate(percent(Admit)~.,data=UCBAdmissions)
Aggregate(percent(Admit)~Gender,data=UCBAdmissions)
Aggregate(percent(Admit)~Dept,data=UCBAdmissions)
Aggregate(percent(Gender)~Dept,data=UCBAdmissions)
Aggregate(percent(Admit)~Dept,data=UCBAdmissions,Gender=="Female")
genTable(percent(Admit)~Dept,data=UCBAdmissions,Gender=="Female")
|
Loading required package: lattice
Loading required package: MASS
Attaching package: 'memisc'
The following objects are masked from 'package:stats':
contr.sum, contr.treatment, contrasts
The following object is masked from 'package:base':
as.array
mu sigma Freq
1 0 1 100
2 100 1 100
3 0 10 100
4 100 10 100
mu sigma mean(x)
1 0 1 0.04902517
2 100 1 100.04925092
3 0 10 0.91621315
4 100 10 97.30287472
mu sigma mean(x)
1 0 1 0.04902517
2 100 1 100.04925092
3 0 10 0.91621315
4 100 10 97.30287472
mu sigma mean(x) sd(x)
1 0 1 0.04902517 1.040891
2 100 1 100.04925092 1.053505
3 0 10 0.91621315 10.523308
4 100 10 97.30287472 10.369578
mu sigma Mean StDev N
1 0 1 0.04902517 1.040891 100
2 100 1 100.04925092 1.053505 100
3 0 10 0.91621315 10.523308 100
4 100 10 97.30287472 10.369578 100
, , sigma = 1
mu
0 100
Mean 0.04902517 100.04925092
StDev 1.04089130 1.05350484
N 100.00000000 100.00000000
, , sigma = 10
mu
0 100
Mean 0.91621315 97.30287472
StDev 10.52330765 10.36957754
N 100.00000000 100.00000000
Gender Dept Admitted Rejected
1 Male A 512 313
3 Female A 89 19
5 Male B 353 207
7 Female B 17 8
9 Male C 120 205
11 Female C 202 391
13 Male D 138 279
15 Female D 131 244
17 Male E 53 138
19 Female E 94 299
21 Male F 22 351
23 Female F 24 317
Gender Dept Admitted Rejected
1 Male A 512 313
3 Female A 89 19
5 Male B 353 207
7 Female B 17 8
9 Male C 120 205
11 Female C 202 391
13 Male D 138 279
15 Female D 131 244
17 Male E 53 138
19 Female E 94 299
21 Male F 22 351
23 Female F 24 317
Gender Dept Admitted Rejected
1 Male A 512 313
3 Female A 89 19
5 Male B 353 207
7 Female B 17 8
9 Male C 120 205
11 Female C 202 391
13 Male D 138 279
15 Female D 131 244
17 Male E 53 138
19 Female E 94 299
21 Male F 22 351
23 Female F 24 317
Gender Dept Admitted Rejected N
1 Male A 62.060606 37.93939 825
3 Female A 82.407407 17.59259 108
5 Male B 63.035714 36.96429 560
7 Female B 68.000000 32.00000 25
9 Male C 36.923077 63.07692 325
11 Female C 34.064081 65.93592 593
13 Male D 33.093525 66.90647 417
15 Female D 34.933333 65.06667 375
17 Male E 27.748691 72.25131 191
19 Female E 23.918575 76.08142 393
21 Male F 5.898123 94.10188 373
23 Female F 7.038123 92.96188 341
Gender Admitted Rejected N
1 Male 44.51877 55.48123 2691
3 Female 30.35422 69.64578 1835
Dept Admitted Rejected N
1 A 64.415863 35.58414 933
5 B 63.247863 36.75214 585
9 C 35.076253 64.92375 918
13 D 33.964646 66.03535 792
17 E 25.171233 74.82877 584
21 F 6.442577 93.55742 714
Dept Male Female N
1 A 88.42444 11.575563 933
5 B 95.72650 4.273504 585
9 C 35.40305 64.596950 918
13 D 52.65152 47.348485 792
17 E 32.70548 67.294521 584
21 F 52.24090 47.759104 714
Dept Admitted Rejected N
3 A 82.407407 17.59259 108
7 B 68.000000 32.00000 25
11 C 34.064081 65.93592 593
15 D 34.933333 65.06667 375
19 E 23.918575 76.08142 393
23 F 7.038123 92.96188 341
Dept
A B C D E F
Admitted 82.407407 68.000000 34.064081 34.933333 23.918575 7.038123
Rejected 17.592593 32.000000 65.935919 65.066667 76.081425 92.961877
N 108.000000 25.000000 593.000000 375.000000 393.000000 341.000000
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