![]() ![]() You must be careful to type each command exactly as written, but without the dot. Stata is line command oriented which makes it fast with lots of memory free for data. ![]() All commands (written in bold type following the dot) will be typed in the Command window. You should see four windows: Review, Variables, Stata results, and Command. Opening Stata After you have logged on, using your lab ID as your name and your nine digit UCLA ID as your password (see Appendix A), click on the Stata icon. Stata encourages us to focus on the story that the data are telling us. In this lab we will consider demographic data from Los Angeles County. Working with appropriate statistical software enables us to explore the data and to deepen our understanding of statistics. Missing values are sorted last, like in Stata.Lab 1 Getting Started with Stata Dealing with data by hand or even with a calculator can be tedious. Contrast the following behaviors with Stata df v In particular, rows that evaluate to NA are dropped. To filter rows with missing observations for y: df % filter(!is.na(y))įilter(df, condition) only filters rows where the condition evaluates to TRUE. In Stata, the empty character “” is a missing value. Use is.na to test for missing values 1 = NA Operations involving NA return NA when the result of the operation cannot be determined. In R, missing values are special values that represents epistemic uncertainty. In Stata, missing values behave like +Inf. This contrasts with column subsetting, which only creates shallow copies. This means memory is required both for the existing and the new dataset. When subsetting a dataset wrt rows, R returns a new dataset without destroying the existing one. The equivalent of Stata inrange is between Stata You can also filter rows based on their position: Stata You can filter rows using logical conditions Stata To apply each function to multiple variables: Stataĭf %>% summarize(across(starts_with("v"), list(~mean(., na.rm = TRUE), ~sd(., na.rm = TRUE))))Ĭompared to Stata, these commands don’t overwrite the existing dataset. To return a dataset composed of summary statistics computed over multiple rows : Stataĭf %>% summarize(mean(v1, na.rm = TRUE), sd(v2, na.rm = TRUE)) The syntax for collapsing dataset is very similar to the syntax for modifying columns : just use summarize instead of mutate In case your dataset is very large, `mutate` one variable at a timer rather than using `mutate_at` When replacing every variable in the dataset, `dplyr` requires twice the amount of memory compared to data.table since a whole new dataset is temporarly created. ![]() To apply the same function to multiple columns, use across Stataĭf %>% mutate(across(c(v1, v2), as.character)) To modify only certain rows of a column: Stataĭf %>% mutate(v1 = ifelse(id = "id01", 0, v1)) This table gives the list of helper functions: Stata In dplyr, helper functions allow very similar results: Stata In Stata, wildcards allow to select multiple variables. This does not always require more memory: when subsetting columns, the new dataset is a shallow copy of the existing one - at least until the new dataset is modified. Contrary to Stata, R returns a new dataset without destroying the existing one. ![]()
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