### Archive

Posts Tagged ‘Excel’

## Retaining an Excel cell’s format when using putexcel

In a previous blog entry, I talked about the new Stata 13 command putexcel and how we could use putexcel with a Stata command’s stored results to create tables in an Excel file.

After the entry was posted, a few users pointed out two features they wanted added to putexcel:

1. Retain a cell’s format after writing numeric data to it.
2. Allow putexcel to format a cell.

In Stata 13.1, we added the new option keepcellformat to putexcel. This option retains a cell’s format after writing numeric data to it. keepcellformat is useful for people who want to automate the updating of a report or paper.

To review, the basic syntax of putexcel is as follows:

putexcel excel_cell=(expression) … using filename[, options]


If you are working with matrices, the syntax is

putexcel excel_cell=matrix(expression) … using filename[, options]


In the previous blog post, we exported a simple table created by the correlate command by using the commands below.

. sysuse auto
(1978 Automobile Data)

. correlate foreign mpg
(obs=74)

|  foreign      mpg
-------------+------------------
foreign |   1.0000
mpg |   0.3934   1.0000

. putexcel A1=matrix(r(C), names) using corr


These commands created the file corr.xlsx, which contained the table below in the first worksheet.

As you can see, this table is not formatted. So, I formatted the table by hand in Excel so that the correlations Read more…

Categories: Programming Tags:

## Export tables to Excel

There is a new command in Stata 13, putexcel, that allows you to easily export matrices, expressions, and stored results to an Excel file. Combining putexcel with a Stata command’s stored results allows you to create the table displayed in your Stata Results window in an Excel file.

A stored result is simply a scalar, macro, or matrix stored in memory after you run a Stata command. The two main types of stored results are e-class (for estimation commands) and r-class (for general commands). You can list a command’s stored results after it has been run by typing ereturn list (for estimation commands) and return list (for general commands). Let’s try a simple example by loading the auto dataset and running correlate on the variables foreign and mpg

. sysuse auto
(1978 Automobile Data)

. correlate foreign mpg
(obs=74)

|  foreign      mpg
-------------+------------------
foreign |   1.0000
mpg |   0.3934   1.0000


Because correlate is not an estimation command, use the return list command to see its stored results.

. return list

scalars:
r(N) =  74
r(rho) =  .3933974152205484

matrices:
r(C) :  2 x 2


Now we can use putexcel to export these results to Excel. The basic syntax of putexcel is

putexcel excel_cell=(expression) … using filename [, options]

If you are working with matrices, the syntax is

putexcel excel_cell=matrix(expression) … using filename [, options]

It is easy to build the above syntax in the putexcel dialog. There is a helpful video on Youtube about the dialog here. Let’s list the matrix r(C) to see what it contains.

. matrix list r(C)

symmetric r(C)[2,2]
foreign        mpg
foreign          1
mpg  .39339742          1


To re-create the table in Excel, we need to export the matrix r(C) with the matrix row and column names. The command to type in your Stata Command window is

putexcel A1=matrix(r(C), names) using corr


Note that to export the matrix row and column names, Read more…

Categories: Programming Tags:

## Using import excel with real world data

Stata 12’s new import excel command can help you easily import real-world Excel files into Stata. Excel files often contain header and footer information in the first few and last few rows of a sheet, and you may not want that information loaded. Also, the column labels used in the sheet are invalid Stata variable names and therefore cannot be loaded. Both of these issues can be easily solved using import excel.

Let’s start by looking at an Excel spreadsheet, metro_gdp.xls, that is downloaded from the Bureau of Economic Analysis website.

As you can see, the first five rows of the Excel file contain a description of the data, and rows 374 through 381 contain footer notes. We don’t want to load these rows into Stata. import excel has a cellrange() option that can help us avoid unwanted information being loaded.

With cellrange(), you specify the upper left cell and the lower right cell (using standard Excel notation) of the area of data you want loaded. In the file metro_gdp.xls, we want all the data from column A row 6 (upper left cell) to column L row 373 (lower right cell) loaded into Stata. To do this, we type

. import excel metro_gdp.xls, cellrange(A6:L373) clear


In Stata, we open the Data Editor to inspect the loaded data.

The first row of the data we loaded contained column labels. Because of these labels, import excel loaded all the data as strings. import excel again has an easy fix. We need to specify the firstrow option to tell import excel that the first row of data contains the variable names.

. import excel metro_gdp.xls, cellrange(A6:L373) firstrow clear


We again open the Data Editor to inspect the data.

The data are now in the correct format, but we are missing the year column labels. Stata does not accept numeric variable names, so import excel has to use the Excel column name (C, D, …) for the variable names instead of 2001, 2002, …. The simple solution is to rename the column headers in Excel to something like y2001, y2002, etc., before loading. You can also use Stata to rename the column headers. import excel saves the values in the first row of data as variable labels so that the information is not lost. If we describe the data, we will see all the column labels from the Excel file saved as variable labels.

. describe

Contains data
obs:           367
vars:            12
size:        37,067
-------------------------------------------------------------------------------
storage  display     value
variable name   type   format      label      variable label
-------------------------------------------------------------------------------
Fips            str5   %9s                    Fips
Area            str56  %56s                   Area
C               long   %10.0g                 2001
D               long   %10.0g                 2002
E               long   %10.0g                 2003
F               long   %10.0g                 2004
G               long   %10.0g                 2005
H               long   %10.0g                 2006
I               long   %10.0g                 2007
J               long   %10.0g                 2008
K               long   %10.0g                 2009
L               long   %10.0g                 2010
-------------------------------------------------------------------------------
Sorted by:
Note:  dataset has changed since last saved


We want to grab the variable label for each variable by using the extended macro function :variable label varname, create a valid lowercase variable name from that label by using the strtoname() and lower() functions, and rename the variable to the new name by using rename. We can do this with a foreach loop.

foreach var of varlist _all {
local label : variable label var'
local new_name = lower(strtoname("label'"))
rename var' new_name'
}


Now when we describe our data, they look like this:

. describe

Contains data
obs:           367
vars:            12
size:        37,067
-------------------------------------------------------------------------------
storage  display     value
variable name   type   format      label      variable label
-------------------------------------------------------------------------------
fips            str5   %9s                    Fips
area            str56  %56s                   Area
_2001           long   %10.0g                 2001
_2002           long   %10.0g                 2002
_2003           long   %10.0g                 2003
_2004           long   %10.0g                 2004
_2005           long   %10.0g                 2005
_2006           long   %10.0g                 2006
_2007           long   %10.0g                 2007
_2008           long   %10.0g                 2008
_2009           long   %10.0g                 2009
_2010           long   %10.0g                 2010
-------------------------------------------------------------------------------
Sorted by:
Note:  dataset has changed since last saved


One last thing we might want to do is to rename the year variables from _20## to y20##, which we can easily accomplish with rename:

. rename (_*) (y*)

. describe

Contains data
obs:           367
vars:            12
size:        37,067
-------------------------------------------------------------------------------
storage  display     value
variable name   type   format      label      variable label
-------------------------------------------------------------------------------
fips            str5   %9s                    Fips
area            str56  %56s                   Area
y2001           long   %10.0g                 2001
y2002           long   %10.0g                 2002
y2003           long   %10.0g                 2003
y2004           long   %10.0g                 2004
y2005           long   %10.0g                 2005
y2006           long   %10.0g                 2006
y2007           long   %10.0g                 2007
y2008           long   %10.0g                 2008
y2009           long   %10.0g                 2009
y2010           long   %10.0g                 2010
-------------------------------------------------------------------------------
Sorted by:
Note:  dataset has changed since last saved

Categories: Data Management Tags: