Generic Tables and Data Frames of Descriptive Statistics
genTable.RdgenTable 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.
Usage
Aggregate(formula, data=parent.frame(), subset=NULL,
names=NULL, addFreq=TRUE, drop = TRUE, as.vars=1,
...)
genTable(formula, data=parent.frame(), subset=NULL,
names=NULL, addFreq=TRUE,...)Arguments
- 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.
- names
an optional character vector giving names to the result(s) yielded by the expression on the left hand side of
formula. This argument may be redundant if the left hand side results in is a named vector. (See the example below.)- addFreq
a logical value. If
TRUEanddatais a table or a data frame with a variable named "Freq", a call totable,Table,percent, ornvalidis supplied by an additional argumentFreqand a call totableis translated into a call toTable.- drop
a logical value. If
TRUE, empty groups (i.e. when there are no observations in the aggregated data frame that contain the defining combination of values or factor levels of the conditioning variables inby) are dropped from the result ofAggregate. Otherwise, result are filled withNA, where appropriate.- 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.
- ...
further arguments, passed to methods or ignored.
Details
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(...)).
Value
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".
Examples
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)
#> sigma 0 100
#> 1 1 100 100
#> 2 10 100 100
Aggregate(mean(x)~mu+sigma,data=ex.data)
#> mu sigma mean(x)
#> 1 0 1 0.06236806
#> 2 100 1 100.04415320
#> 3 0 10 0.69106100
#> 4 100 10 99.12622089
Aggregate(mean(x)~mu+sigma,data=ex.data,name="Average")
#> mu sigma Average
#> 1 0 1 0.06236806
#> 2 100 1 100.04415320
#> 3 0 10 0.69106100
#> 4 100 10 99.12622089
Aggregate(c(mean(x),sd(x))~mu+sigma,data=ex.data)
#> mu sigma mean(x) sd(x)
#> 1 0 1 0.06236806 1.042831
#> 2 100 1 100.04415320 1.032355
#> 3 0 10 0.69106100 8.894698
#> 4 100 10 99.12622089 8.952115
Aggregate(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
#> mu sigma Mean StDev N
#> 1 0 1 0.06236806 1.042831 100
#> 2 100 1 100.04415320 1.032355 100
#> 3 0 10 0.69106100 8.894698 100
#> 4 100 10 99.12622089 8.952115 100
genTable(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
#> , , sigma = 1
#>
#> mu
#> 0 100
#> Mean 0.06236806 100.044153
#> StDev 1.04283135 1.032355
#> N 100.00000000 100.000000
#>
#> , , sigma = 10
#>
#> mu
#> 0 100
#> Mean 0.691061 99.126221
#> StDev 8.894698 8.952115
#> N 100.000000 100.000000
#>
Aggregate(table(Admit)~.,data=UCBAdmissions)
#> Gender Dept Admitted Rejected
#> 1 Male A 512 313
#> 2 Female A 89 19
#> 3 Male B 353 207
#> 4 Female B 17 8
#> 5 Male C 120 205
#> 6 Female C 202 391
#> 7 Male D 138 279
#> 8 Female D 131 244
#> 9 Male E 53 138
#> 10 Female E 94 299
#> 11 Male F 22 351
#> 12 Female F 24 317
Aggregate(Table(Admit,Freq)~.,data=UCBAdmissions)
#> Gender Dept Admitted Rejected
#> 1 Male A 512 313
#> 2 Female A 89 19
#> 3 Male B 353 207
#> 4 Female B 17 8
#> 5 Male C 120 205
#> 6 Female C 202 391
#> 7 Male D 138 279
#> 8 Female D 131 244
#> 9 Male E 53 138
#> 10 Female E 94 299
#> 11 Male F 22 351
#> 12 Female F 24 317
Aggregate(Admit~.,data=UCBAdmissions)
#> Gender Dept Admitted Rejected
#> 1 Male A 512 313
#> 2 Female A 89 19
#> 3 Male B 353 207
#> 4 Female B 17 8
#> 5 Male C 120 205
#> 6 Female C 202 391
#> 7 Male D 138 279
#> 8 Female D 131 244
#> 9 Male E 53 138
#> 10 Female E 94 299
#> 11 Male F 22 351
#> 12 Female F 24 317
Aggregate(percent(Admit)~.,data=UCBAdmissions)
#> Gender Dept Admitted Rejected N
#> 1 Male A 62.060606 37.93939 825
#> 2 Female A 82.407407 17.59259 108
#> 3 Male B 63.035714 36.96429 560
#> 4 Female B 68.000000 32.00000 25
#> 5 Male C 36.923077 63.07692 325
#> 6 Female C 34.064081 65.93592 593
#> 7 Male D 33.093525 66.90647 417
#> 8 Female D 34.933333 65.06667 375
#> 9 Male E 27.748691 72.25131 191
#> 10 Female E 23.918575 76.08142 393
#> 11 Male F 5.898123 94.10188 373
#> 12 Female F 7.038123 92.96188 341
Aggregate(percent(Admit)~Gender,data=UCBAdmissions)
#> Gender Admitted Rejected N
#> 1 Male 44.51877 55.48123 2691
#> 2 Female 30.35422 69.64578 1835
Aggregate(percent(Admit)~Dept,data=UCBAdmissions)
#> Dept Admitted Rejected N
#> 1 A 64.415863 35.58414 933
#> 2 B 63.247863 36.75214 585
#> 3 C 35.076253 64.92375 918
#> 4 D 33.964646 66.03535 792
#> 5 E 25.171233 74.82877 584
#> 6 F 6.442577 93.55742 714
Aggregate(percent(Gender)~Dept,data=UCBAdmissions)
#> Dept Male Female N
#> 1 A 88.42444 11.575563 933
#> 2 B 95.72650 4.273504 585
#> 3 C 35.40305 64.596950 918
#> 4 D 52.65152 47.348485 792
#> 5 E 32.70548 67.294521 584
#> 6 F 52.24090 47.759104 714
Aggregate(percent(Admit)~Dept,data=UCBAdmissions,Gender=="Female")
#> Dept Admitted Rejected N
#> 1 A 82.407407 17.59259 108
#> 2 B 68.000000 32.00000 25
#> 3 C 34.064081 65.93592 593
#> 4 D 34.933333 65.06667 375
#> 5 E 23.918575 76.08142 393
#> 6 F 7.038123 92.96188 341
genTable(percent(Admit)~Dept,data=UCBAdmissions,Gender=="Female")
#> Dept
#> A B C D E F
#> Admitted 82.40741 68 34.06408 34.93333 23.91858 7.038123
#> Rejected 17.59259 32 65.93592 65.06667 76.08142 92.961877
#> N 108.00000 25 593.00000 375.00000 393.00000 341.000000