View the entire collection of UVA Library StatLab articles. Pop_c float %9.0g popcl Categorized population Marriage long %12.0gc Number of marriages > label data "1980 Census data by state: v2" * Now the three categories are presented as low, medium and high * Then we attach the value label popcl to the variable pop_c > label define popcl 1 "low" 2 "medium" 3 "high" Let’s label them as low, medium and high. * Remember we categorized pop_c into three categories: 1,2 and 3 Poplt5 long %12.0gc Pop, label variable pop0_17 "Pop, label variable pop_c "Categorized population" Here we create another new variable called pop_c2 then do the recode in the same manner as we did for pop_c. We can use the -recode- command to recode variables as well. Then we create a new variable called pop_c and transform the original variable pop into three categories. Here we create the youth population variable again, but this time we make it into thousands and replace the one we just created. replace-: replace contents of existing variables > order state state2 region pop poplt5 pop0_17 Pop0_17 | 50 1272229 1289731 130745 6388958 How is the variable generated by -egen std- standardized Here I take a variable, subtract the mean and divided by sd gen stdman(Pmath-45.20336)/13.3137 Heres the egen version egen stdegnstd(Pmath). * Summary statistics for the three variables
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Poplt5 long %12.0gc Pop, generate pop0_17 = poplt5 + pop5_17 State2 str2 %-2s Two-letter state abbreviation Variable name type format label variable label To make your unbalanced panel balanced ( i.e.Contains data from /Applications/Stata/ado/base/c/census.dta In this case, the function lag returns the value in the most recent date while the function tlag returns a missing value. Lag and tlag differ when the previous date is missing. To create a lagged variable based on the previous date, use the function tlag/tlead from statar Stataĭf %>% group_by(id) %>% mutate(value_l = tlag(value, n = 1, date)) The examples will refer to the following data frame df % group_by(id) %>% mutate(value_l = lag(value, n = 1, order_by = date))
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The package statar includes functions that make working with unbalanced panel data easier. This means memory is required both for the existing and the new dataset.Ĭount the number of distinct values taken by a set of variables Stata When getting only distinct observations, dplyr returns a new dataset without destroying the existing one. Return a dataset that contains distinct values taken by a set of variables Stata
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Replace by first observation within group: Stataĭf %>% group_by(id) %>% mutate(v1 = v1)Ĭollapse observations within groups: Stataĭf %>% group_by(id) %>% summarize(mean(v1), sd(v2)) That is, if fcn is not one of the functions above, gegen outvar fcn (varlist) if in, by (byvars) would be the same as. The dplyr verbs filter, mutate and summarize can be applied on a grouped ame.įilter based on a logical condition Stataĭf %>% group_by(id) %>% filter(v1 = max(v1))įilter based on relative row numbers Stataĭf %>% group_by(id) %>% filter(row_number() = 1)įilter the 2 observation with lowest v1 for each group defined by id Stataīys id (v1): keep if _n % group_by(id) %>% filter(row_number(v2) % group_by(id) %>% mutate(v1mean = mean(v1)) Last, when gegen calls a function that is not implemented internally by gtools, it will hash the by variables and call egen with by set to an id based on the hash.