genTable: Generic Tables and Data Frames of Descriptive Statistics
In memisc: Tools for Management of Survey Data and the Presentation of Analysis Results
Description
Usage
Arguments
Details
Value
See Also
Examples
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
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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,...)
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.
sort
a logical value; determines the order in which the aggregated
data appear in the data frame returned by Aggregate.
If sort is TRUE, then the returned data frame is sorted by
the values of the grouping variables, if sort is FALSE,
the order of resulting data frame corresponds to the order in which
the values of the grouping variables appear in the original data
frame.
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.
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.
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".
See Also
aggregate.data.frame, xtabs
Examples
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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")
Example output
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
memisc documentation built on May 2, 2019, 5:45 p.m.
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Description Usage Arguments Details Value See Also Examples
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
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,...)
|
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. |
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. |
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".
See Also
aggregate.data.frame, xtabs
Examples
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")
|
Example output
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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