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help collapse dialog: collapse
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Title
[D] collapse -- Make dataset of summary statistics
Syntax
collapse clist [if] [in] [weight] [, options]
where clist is either
[(stat)] varlist [ [(stat)] ... ]
[(stat)] target_var=varname [target_var=varname ...] [ [(stat)] ...]
or any combination of the varlist or target_var forms, and stat is one of
mean means (default)
median medians
p1 1st percentile
p2 2nd percentile
... 3rd-49th percentiles
p50 50th percentile (same as median)
... 51st-97th percentiles
p98 98th percentile
p99 99th percentile
sd standard deviations
semean standard error of the mean (sd/sqrt(n))
sebinomial standard error of the mean, binomial (sqrt(p(1-p)/n))
sepoisson standard error of the mean, Poisson (sqrt(mean))
sum sums
rawsum sums, ignoring optionally specified weight
count number of nonmissing observations
max maximums
min minimums
iqr interquartile range
first first value
last last value
firstnm first nonmissing value
lastnm last nonmissing value
If stat is not specified, mean is assumed.
options description
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Options
by(varlist) groups over which stat is to be calculated
cw casewise deletion instead of all possible observations
+ fast do not restore the original dataset should the user press Break; programmer's command
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+ fast is not shown in the dialog box.
varlist and varname in clist may contain time-series operators; see tsvarlist.
aweights, fweights, iweights, and pweights are allowed; see weight, and see Weights below. pweights may not be used with sd, semean, sebinomial,
or sepoisson. iweights may not be used with semean, sebinomial, or sepoisson. aweights may not be used with sebinomial or sepoisson.
Menu
Data > Create or change data > Other variable-transformation commands > Make dataset of means, medians, etc.
Description
collapse converts the dataset in memory into a dataset of means, sums, medians, etc. clist must refer to numeric variables exclusively.
Note: See [D] contract if you want to collapse to a dataset of frequencies.
Options
+---------+
----+ Options +------------------------------------------------------------------------------------------------------------------------------------
by(varlist) specifies the groups over which the means, etc., are to be calculated. If this option is not specified, the resulting dataset will
contain 1 observation. If it is specified, varlist may refer to either string or numeric variables.
cw specifies casewise deletion. If cw is not specified, all possible observations are used for each calculated statistic.
The following option is available with collapse but is not shown in the dialog box:
fast specifies that collapse not restore the original dataset should the user press Break. fast is intended for use by programmers.
Weights
collapse allows all four weight types; the default is aweights. Weight normalization impacts only the sum, count, sd, semean, and sebinomial
statistics.
Here are the definitions for count and sum with weights:
count:
unweighted _N, the number of physical observations
aweight: _N, the number of physical observations
fweight, iweight, pweight: sum(w_j), the sum of user-specified weights
sum:
unweighted sum(x_j), the sum of the variable
aweight: sum(v_j*x_j); v_j = weights normalized to sum to _N
fweight, iweight, pweight: sum(w_j*x_j); w_j = user supplied weights.
The sd statistic with weights returns the bias-corrected standard deviation, which is based on the factor sqrt(N/(N-1)), where N is the number of
observations. Statistics sd, semean, sebinomial, and sepoisson are not allowed with pweighted data. Otherwise, the statistic is changed by the
weights through the computation of the count (N), as outlined above.
For instance, consider a case in which there are 25 physical observations in the dataset and a weighting variable that sums to 57. In the
unweighted case, the weight is not specified, and N = 25. In the analytically weighted case, N is still 25; the scale of the weight is irrelevant.
In the frequency-weighted case, however, N = 57, the sum of the weights.
The rawsum statistic with aweights ignores the weight, with one exception: observations with zero weight will not be included in the sum.
Examples
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Setup
. webuse college
. describe
. list
Create dataset containing the 25th percentile of gpa for each year
. collapse (p25) gpa [fw=number], by(year)
List the result
. list
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Setup
. webuse college, clear
Create dataset containing the mean and median of gpa and hour for each year, and store median of gpa and hour in medgpa and medhour, respectively
. collapse (mean) gpa hour (median) medgpa=gpa medhour=hour [fw=number], by(year)
List the result
. list
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Setup
. webuse college, clear
Create dataset containing the count of gpa and hour and the minimums of gpa and hour, and store the minimums in mingpa and minhour, respectively
. collapse (count) gpa hour (min) mingpa=gpa minhour=hour [fw=number], by(year)
List the result
. list
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Setup
. webuse college, clear
. replace gpa = . in 2/4
Create dataset containing the mean of gpa and hour for each year, but ignore all observations that have missing values when calculating the means
. collapse (mean) gpa hour [fw=number], by(year) cw
List the result
. list
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Also see
Manual: [D] collapse
Help: [D] contract, [D] egen, [D] statsby, [R] summarize
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