#Reshape long stata code#

#Reshape long stata code#

Feel free to download the file and try the code below. In each case we assume you’re starting with a csv file called dat.csv. How do you reshape wide data to long? Below we give basic demonstrations using R, SAS, SPSS and Stata to perform the reshaping demonstrated above. To do the same with the wide data would mean dropping the day1 and day2 columns after subsetting. We could subset the long data above to easily see all amounts on day 2 for males with race = 0 and trt = 1. Storing data in long format also allows us to quickly subset our data without dropping columns. It’s actually embedded in the column headers of day1, day2 and da圓. In the wide format, there is no explicit variable for day. For example, the long format allows us to include a variable for day in our model. Where stem-of-wide-vars is the stem of the wide variables, e.g.An advantage to having data in long format is that graphing and statistical modeling are frequently made easier. The general syntax of reshape long can be expressed as… reshape long stem-of-wide-vars, i(wide-id-var ) j(var-for-suffix ) 3 m Pat 50000 Summary reshaping data wide to long Wide format 3 Paul 2500 reshape long name inc, i(famid) j(dadmom) string (note: j = d m) Let’s look at the data before and after reshaping. reshape long name inc, i(famid) j(dadmom) string In the example below, the string option has been added at the end of the command. String option to indicate that the suffix is a character. Stata can handle that as long as you use the We would like to make name and inc into long formats but their suffixes are characters (d & m) instead of numbers. use, clear list famid named incd namem incm It also is possible to reshape a wide data file to be long when there are character suffixes. 3 3 2 2.9 31 Example #4: Reshaping data wide to long with character suffixes list famid birth age ht wt famid birth age ht wtġ8. 3 3 2.1 2.9 22 31 reshape long ht wt, i(famid birth) j(age) (note: j = 1 2) List famid birth ht1 ht2 wt1 wt2 famid birth ht1 ht2 wt1 wt2ĩ. reshape long ht wt, i(famid birth) j(age) This is basically the same as the previous command except that Let’s reshape this data into a long format. use, clear list famid birth ht1 ht2 wt1 wt2 famid birth ht1 ht2 wt1 wt2 The file with the kids heights at age 1 and age 2 also contains their weights atĪge 1 and age 2 (called wt1 and wt2). 3 3 2 2.9 Example #3: Reshaping data wide to long list famid birth age ht famid birth age htġ8.

3 3 2.1 2.9 reshape long ht, i(famid birth) j(age) (note: j = 1 2) List famid birth ht1 ht2 famid birth ht1 ht2ĩ. Let’s look at the wide data, and then the data reshaped to be long. Questions, the reshape command will look like this. Q: What do we want to call the variable which contains the suffix of Q: What variable uniquely identifies an observation when it is in theĪ: famid and birth together uniquely identify the Q: What is the stem of the variable going from wide to long. Lets reshape this data into a long format. Use, clear list famid birth ht1 ht2 famid birth ht1 ht2

Year Example #2: Reshaping data wide to longĬonsider the file containing the kids and their heights at 1 year of age (ht1) and at 2 years of age (ht2). J(year) tells reshape that the suffix of faminc (i.e., 96 97 98) should be placed in a variable called

I(famid) option tells reshape that famid is the unique identifier for records in their Stata that the stem of the variable to be converted from wide to Long tells reshape that we want to go from wide to long Now let’s look at the pieces of the original reshape command. The reshape long command puts the data back into long format. The reshape wide command puts the data back into wide format. Let’s look at the wide format and contrast it with the long format. Year is represented as its own observation. The list command shows that the data are now in long form, where each Reshape long faminc, i(famid) j(year) (note: j = 96 97 98) The reshape command can accomplish this, as shown below. Long, where each year of data is in a separate observation. This is called a wide format since the years of data are wide. use, clear list famid faminc96 faminc97 faminc98 Example #1: Reshaping data wide to longĬonsider the family income data file below.

Show common examples of reshaping data, but do not exhaustively demonstrate theĭifferent kinds of data reshaping that you could encounter. Wide data files and reshape them into long form. This module illustrates the power (and simplicity) of Stata in its ability to reshape data files.