- #Reshape long stata how to
- #Reshape long stata manual
- #Reshape long stata full
- #Reshape long stata registration
Most regression analyses for panel data require the data to be in long format. This is the most often used, default option, but we can also tell reshape what pattern to look for.One of the initial challenges a data analyst is likely to face with panel data is getting it into a format suitable for analysis. it looks for variable names beginning with gdp and puts the remaining part of the variable name in the newly declared variable year. How does reshape know to look for gdp1960, …, gdp2017? Since we said reshape long gdp. We see that we created a new variable called year (from the option j) and gdp1960, …, gdp2017 became a single variable, gdp. After a reshape long, we have more observations and fewer variables. Because gdp1960, …, gdp2017 correspond to different years, we call this variable year. The option j() gives one variable that indexes columns in the wide format. We can have multiple variables inside i(), like i(countrycode countryname). Each observation is a country in this wide format, so we use i(countrycode). The option i() lists variables that index observations (rows) within the wide dataset.
#Reshape long stata full
The URL has to be in quotes because it is full of strange characters. The command import delimited, but also use can load files directly from the web, if we pass them a URL. Please go ahead and copy this URL from the shared notes so that you do not have to type it. The data file we will be working with is located at.
#Reshape long stata how to
This will also show us how to work with data from the web. To practice reshaping, load a somewhat precleaned subset of the WDI dataset from the web. In Stata, this is only possible in years are in different observations (long form), not in different variables (wide form). For example, we may want to create a line graph from a variable. We will reshape the data in the tidy format.ĭifferent shapes of the data are useful for different tasks. This is the opposite of “ tidy data,” where each variable has its own column, and different observations such as different years are in separate rows. Variables are in separate rows, whereas years are in separate columns. If we subtract the mean of a variable, the difference will be mean zero. generate difference_censusyear = censusyear - mean_censusyear
egen mean_censusyear = mean(censusyear), by(region) There are many functions to be used in egen count, min, max, sum and mean are the most commonly used. The command egen creates a new variable with the aggregated statistics. egen average_census_year = mean(censusyear), by(incomegroup) egen n_country = count(countrycode), by(incomegroup) To filter out observations, use drop if and keep if. Stata will keep, or drop, all variables starting with the variable to the left of the - and ending with the variable to the right of the. Similarly, you can use the - character to keep or drop variables in the dataset. You can use variable name wildcards with both commands ( drop latest*). Only use them if your work is easy to reproduce if you make an error, such as right after loading a dataset. The commands keep and drop irreversibly change the data in your memory. keep countrycode shortname region incomegroup censusyear We will not use most of them, so let’s drop them. Note: Dataset has changed since last saved. Latesttradedata int %8.0g Latest trade data Latestindustr~a int %8.0g Latest industrial data Latestagricul~s str130 %130s Latest agricultural census
#Reshape long stata registration
Vitalregistra~e str48 %48s Vital registration complete
Sourceofmostr~n str88 %88s Source of most recent Income and expenditure data Latesthouseho~y str77 %77s Latest household survey Latestpopulat~s str166 %166s Latest population census Imfdatadissem~d str51 %51s IMF data dissemination standard Governmentacc~t str31 %31s Government Accounting concept
#Reshape long stata manual
Systemofnatio~s str61 %61s System of National AccountsĪlternativeco~r str22 %22s Alternative conversion factorīalanceofpaym~e str33 %33s Balance of Payments Manual in useĮxternaldebtr~s str11 %11s External debt Reporting status Lendingcategory str9 %9s Lending category Snapricevalua~n str36 %36s SNA price valuation
National~ceyear str9 %9s National accounts reference year National~seyear str50 %50s National accounts base year Specialnotes str1294 %1294s Special Notes Variable name type format label variable label
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