genTable memisc 0.99.25.4

Generic Tables and Data Frames of Descriptive Statistics

Description

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.

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 TRUE and data is a table or a data frame with a variable named “Freq”, a call to table, Table, percent, or nvalid is supplied by an additional argument Freq and a call to table is translated into a call to Table.

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 in by) are dropped from the result of Aggregate. Otherwise, result are filled with NA, 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.

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".

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(...)).

See also

aggregate.data.frame, xtabs

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   0.09467653
2 100     1 100.12587442
3   0    10  -2.83342024
4 100    10 100.47719420
Aggregate(mean(x)~mu+sigma,data=ex.data,name="Average")
   mu sigma      Average
1   0     1   0.09467653
2 100     1 100.12587442
3   0    10  -2.83342024
4 100    10 100.47719420
Aggregate(c(mean(x),sd(x))~mu+sigma,data=ex.data)
   mu sigma      mean(x)     sd(x)
1   0     1   0.09467653 0.9659985
2 100     1 100.12587442 0.9911363
3   0    10  -2.83342024 9.8954220
4 100    10 100.47719420 9.7942067
Aggregate(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
   mu sigma         Mean     StDev   N
1   0     1   0.09467653 0.9659985 100
2 100     1 100.12587442 0.9911363 100
3   0    10  -2.83342024 9.8954220 100
4 100    10 100.47719420 9.7942067 100
genTable(c(Mean=mean(x),StDev=sd(x),N=length(x))~mu+sigma,data=ex.data)
, , sigma = 1

       mu
                   0         100
  Mean    0.09467653 100.1258744
  StDev   0.96599846   0.9911363
  N     100.00000000 100.0000000

, , sigma = 10

       mu
                 0        100
  Mean   -2.833420 100.477194
  StDev   9.895422   9.794207
  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