8/11/10 1:41:33 PM
Stata Cheat Sheet
Command
Keeping Stata up to date
update all
update swap
Set Stata Preferences
window manage prefs default
In "User" Menu
Useful for what?
N/A
This tells Stata to update itself to the newest version.
N/A
This needs to be issued if Stata downloaded a new "exe"
file during the "update all" command. Stata will tell you
when it is needed. Failure to do so will create problems
with several commands.
"Set Stata Preferences"
"Restore Default Window Layout
(window manage)"
Additional
Options
More info
Stata help under
"update"
Stata help under
"update"
This restores the default window arrangement of Stata.
Use if your windows are messed up or if you canʼt see the
Command Window.
"Stata Tutorial"
"Clear Memory (clear)", then
"Set Memory for Data (set
memory)"
This allocate <X> MB of memory to Stata. To figure out
how much to allocate, look at how big the data set it on the
hard drive and double the number.
"Stata Tutorial"
"Clear Memory (clear)", then
"Set Memory in Procedures (set
matsize)"
This specifies that Stata can use up to <X> variables in a
regression. Usually 200 is plenty. Use the option
"permanently" if you want the matsize command to stick
after you quit Stata.
permanently
"Stata Tutorial"
Opens a "Results log" file that records everything in the
Results window in Stata. This file can be later opened in
any text editor to cut and paste into MS Word or another
word processor. If you use the option "replace" then if a log
file of the same name already exists it will be overwritten. If
you use the option "append" then if a log file of the same
name already exists what you do next will be appended to
that file.
replace; append
"Stata Tutorial"
Ex: window manage prefs default
clear
set memory <X>m, permanently
Ex: clear
set memory 20m, permanently
clear
set matsize <X>
Ex: clear
set matsize 200
Recording your work
log using <filename.log>
"Record your work"
"Open Results Log (log using)"
Ex: log using "C:\data\BBB_analysis.log", replace
log close
Open/Save data
use <filename>
"Close Results Log (log close)"
"Open/Save Data"
"Open Stata Dataset (use)"
or use Toolbar
This closes the Results log file.
Opens a Stata data file. Make sure to specify the full path.
Often it is easier to read the data in using the Toolbar.
clear
"Stata Tutorial"
"Save Stata Dataset (save)"
or use Toolbar
Save data in memory as a Stata data file. Make sure to
specify the full path. Often it is easier to save the data in
using the Toolbar.
clear
"Stata Tutorial"
"Import From Spreadsheet
(insheet)"
This allows you to import data from Excel very easily. Just
make sure you have exported the data in tab-delimited
format in Excel and that the first row of the spreadsheet
contains the variable name you want. Often it is easier to
read the data in using the dialog box from the "User" menu
instead of typing the command.
Ex: use "C:\data\BBB-v1.dta", clear
save <filename>
Ex: save "C:\data\BBB-v1.dta", replace
insheet using <filename>
"Stata Tutorial"
Stata help under
"insheet"
Ex: insheet using "C:\data\mydata.txt"
outsheet using <filename>
"Export To Spreadsheet (outsheet)" This allows you to export data to Excel very easily. Often it
is easier to read the data in using the dialog box from the
"User" menu instead of typing the command.
Stata help under
"outsheet"
Ex: outsheet using "C:\data\mydata.txt"
Viewing data as spreadsheet
browse
browse <var1> <var2> ...
"View Data as Spreadsheet"
"Browse Data (browse)"
or use Toolbar
N/A
Shows a spreadsheet with all the data in it. Great to get an
overview of the data.
This works just like browse but it only show the listed
variables. This works very well when you have many
variables but you are only interested in a few of them.
"Stata Tutorial"
"Edit/Modify Data (edit)"
or use Toolbar
N/A
Same as browse, but with the ability to edit/modify the
data.
Same as browse <var1> <var2> ... , but with the ability to
edit/modify the data.
"Stata Tutorial"
"Sort Data (gsort)"
N/A
Sorts variables by ascending (+) or descending (-) order
Sorts variables by ascending (+) or descending (-) order
"Stata Tutorial"
"Stata Tutorial"
"Order variables" (order)"
N/A
Orders the variables in the desired sequence
Orders <var1> first and <var2> second
"Stata Tutorial"
"Stata Tutorial"
"Move variables" (move)"
N/A
Moves the location of a variable
Moves <var1> to where <var2> is currently located,
pushing <var2> to the right.
"Stata Tutorial"
"Stata Tutorial"
Get a listing of all variables in the dataset and see whether
they are numerical or string variables. This also tells you
how much free memory you have.
Some variables are displayed with string labels although
they are actually coded as numbers. This allows you to
see what numbers correspond to what labels.
"Stata Tutorial"
Stata help under
"browse"
Ex: browse buyer acctnum
edit
edit <var1> <var2> ...
"Stata Tutorial"
Ex: edit buyer acctnum
gsort
gsort <-var1> <+var2> ...
Ex: gsort -purch +child
order
order <var1> <var2> ...
Ex: order purch child
move
move <var1> <var2> ...
Ex: move purch child
Summarize and describe data
describe
labelbook <var1>
"Summarize and Describe Data"
"Describe Data in Memory
(describe)"
"View Data Labels (labelbook)"
"Stata Tutorial"
Ex: labelbook buyer
summarize <var1> <var2> ...
"Simple Summary Statistics
(summarize)"
This is the standard command to get summary statistics
(mean, standard deviation, min, max) for a variable. This
works well for continuous variables or binary 0-1 variables
(the mean of a variable whose only outcomes are 0 or 1 is
the proportion of "1"s in the whole sample)
by(<var3>)
"Stata Tutorial"
"Complex Summary Statistics
(tabstat)"
This is a much more powerful version of the summarize
by(<var3>)
command. You can ask for many type of summary statistics
<st1> <st2> ..., including the "median", the "sum" of the
values of the variables, a "count" of the number of
observations, etc.
"Stata Tutorial"
"Tabulate (tabulate)"
Shows all the values of categorical variables and how
many observations have that value
"Stata Tutorial"
"Cross-Tabulate (tabulate)"
Performs a cross tab between var1 and var2. Allows you to row; column;
see whether two categorical variables are associated.
chi2
When used with the "chi2" option after the comma then you
can test whether the two variables are associated.
"Stata Tutorial"
This is a good command to get a graphic depiction of how
two continuous variables <var1> and <var2> are
associated.
by(<var3>)
"Stata Tutorial"
"Draw Bar Graph (graph bar)"
This create a bar graph of <var1> over <var2> where the Y
axis depicts of mean, the sum, or whatever statistic of
<var1> is specified in <st>.
by(<var3>)
"Stata Tutorial"
"Draw Histogram (histogram)"
This displays the distribution of <var1>. This is very useful
by(<var2>)
to get a feel for what values the data have and how often
discrete
they occur. If your data is discrete and you want one bar for
each value, use the option discrete
"Stata Tutorial"
"Manipulate Variables and Obs"
"Generate New Variable
(generate)"
Generates a new variable with the name <var1> from other
variables and manipulations thereof.
"Stata Tutorial"
"Replace/Change Existing
Variable (replace)"
Replaces the value of <var1> with what is on the right
hand side of the "=" sign.
"Stata Tutorial"
"Extended Generate New
Variables (egen)"
This creates a new variable <newvar> that contains the
sum of <var1> for each different value of <var2>. Instead of
the sum one can also use "mean", "max", "min", "count",
and many other statistics that are calculated from <var1>
for each category of <var2> separately.
"Stata Tutorial" and
Stata help under
"egen"
Ex: summarize purch last
tabstat <var1> <var2> ..., statistics(<st1> <st2> ...)
Ex: tabstat total_ purch, statistics(count mean sd median) by(gender)
tabulate <var1>
by(<var3>)
Ex: tabulate gender
tabulate <var1> <var2>
Ex: tabulate gender buyer, chi2
Graph data
scatter <var1> <var2>
"Graph Data"
"Draw Scatter Plot (scatter)"
Ex: scatter geog art
graph bar (<st>) <var1>, over(<var2>)
Ex: graph bar (mean) buyer, over(region)
histogram <var1>, frequency
Ex: histogram total_, frequency
Manipulate Variables and Observations
generate <var1>=...
Ex: generate ordersize=total_/purch
replace <var1>=...
Ex: replace female=0 if female==.
egen <newvar>=mean(<var1>), by(<var2>)
Ex: egen
res_prob=total(buyer),
by(zip3)
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Manipulate Variables and Observations
"Manipulate Variables and Obs"
8/11/10 1:41:33 PM
Stata Cheat Sheet
egen <newvar>=mean(<var1>), by(<var2>)
Command
"Extended Generate New
Variables (egen)"
This creates a new variable <newvar> that contains the
sum of <var1> for each different value of <var2>. Instead of
Useful
for what?
the
sum one
can also use "mean", "max", "min", "count",
and many other statistics that are calculated from <var1>
for each category of <var2> separately.
"Drop Variables (drop / keep)"
Deletes <var1>, <var2>, etc. Careful, you can't get the
variables back!
"Stata Tutorial"
Drop Observations (drop if / keep
if)"
Deletes observations (not variables) for which <var1>
equals <X>. The if-command works as described below.
"Stata Tutorial"
see separate tab in all dialog
boxes
This can be used after nearly every command (just before
the comma, if there are any options) to have that command
be executed only for the observations for which <var1>
equals <X>. If <var1> is a continuous variable one can
also use ">", "<", "<=", ">=", or "~=" (not equal). If <var1> is
a string variables, <X> needs to be in double quotes (e.g.
gender=="F").
"Stata Tutorial"
see separate tab in all dialog
boxes
This can be used after nearly every command to have that
command be executed only if <var1> has missing values.
Instead of "==" one can also ask for not equal by using
"~=".
Stata help under
"op_logical"
see separate tab in all dialog
boxes
You can string together logical statements using an "&" for
a logical "and"
Stata help under
"op_logical"
see separate tab in all dialog
boxes
... and a "|" for a logical "or"
Stata help under
"op_logical"
Creates a new variable <ntile_var> containing the value of
the N-tile based on the values of <var1>. <N> specifies
whether one gets quintiles (N=5) or deciles (N=10) or any
other desired number of categories.
To cover later
"Extended Generate New
Variables (egen)"
Used to calculate response rates per N-title <ntile_var>.
This create a new variable <newvar> that contains the
mean of <var1> for each different value of < ntile_var>.
Instead of the mean one can also use "sum", "max", "min",
"count", and many other statistics that are calculated from
<var1> for each value of < ntile_var> separately.
To cover later
"Replace/Change Existing
Variable (replace)"
This "flips" the values of the N-tile to make sure that the
"best" customers are always in the first N-tile. For example,
for deciles this becomes: "replace <ntile_var>=11<ntile_var>.
To cover later
In "User" Menu
Additional
Options
"Stata Tutorial" and
Stata help under
More info
"egen"
Ex: egen res_prob=total(buyer), by(zip3)
drop <var1> <var2> ...
Ex: drop zip zip3
drop if <var1> == <X>
Logical statements
if <var1> == <X>
Ex: if age==40; if age <40; if gender=="F"
if <var1> ==.
Ex: if age==.
if <logical statement1> & < logical statement2>
Ex: if age>20 & age<=40
if <logical statement1> | < logical statement2>
Ex: if age<20 | age>40
USEFUL COMMANDS FOR FUTURE CLASSES
Manipulate N-Tiles, e.g. deciles or quintiles
xtile <ntile_var>=<var1>, nquantiles(<N>)
"Manipulate N-Tiles"
"Create N-Tiles (xtile)"
Ex: xtile purch_dec=purch, nquantiles(10)
egen <newvar>=mean(<var1>), by(<ntile_var>)
Ex: egen res_prob=mean(buyer), by(purch_dec)
replace <ntile_var>=<N>+1-<ntile_var>
Ex: replace monet_dec=11-monet_dec
Simple tests of association
tabulate <var1> <var2>, chi2
"Simple Tests of Association"
"Cross-Tabulate (tabulate)"
Performs a cross tab between var1 and var2. Allows you to
see whether two categorical variables are associated. Use
the "chi2" option after the comma to test whether the two
variables are associated.
row; column;
chi2
To cover later
Ex: tabulate gender buyer, chi2
ttest <var1>, by(<var2>)
"Test of Means (ttest)"
Performs a test to check whether the means <var1> for two
groups are the same. The groups are described by <var2>
which should have exactly two value.
To cover later
"Correlation Between Variables
(pwporr)"
Calculates the correlation between any set of variables,
two at a time.
To cover later
Performs a "regular", i.e. OLS regression. Works best when
you have a continuous dependent variables.
"To cover later
"Generate Predicted Values
(predict)"
Creates predicted values for the dependent variables
based on the coefficients of the regression you performed.
The predicted values are stored in <newvar>. Always use
right after the regression command. The predict command
uses the coefficient estimates of the last regression that
you ran.
To cover later
"Test Significance of Coefficients
(test)"
Performs a test of whether the coefficients of two variables
are the same. You can also use "test <var1>==X" where X
is any number. This tells you whether <var1> is statistically
different from that number.
To cover later
Ex: ttest total_, by(female)
pwcorr <var1> <var2> ..., sig
Ex: pwcorr total_ purch last, sig
Regular regression (OLS)
regress <depvar> <var1> <var2> ...
"Regular Regression (OLS)"
"Run Regression (regress)"
Ex: regress salary female mba experience
predict <newvar>
Ex: predict salary_predict
test <var1>==<var2>
Ex: test female==4500
Logistic regression
logistic <depvar> <var1> <var2> ...
"Logistic Regression"
"Run Logistic Regression (logistic)" Performs a logistic regression. Works only when you have
a binary (0/1) dependent variable.
coef
To cover later
Ex: logistic buyer total_ last purch
predict <newvar>
"Generate Predicted Values
(predict)"
Same as for regular regression, see above.
To cover later
"Test Significance of Coefficients
(test)"
Performs a test of whether the odds ratios of two variables
are the same.
To cover later
Ex: predict purch_prob
test <var1>==<var2>
Ex: test art==geog
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