![]() ![]() Little details that the human eye misses are often crucial. But one thing you shouldn't do here is type your code directly into the forum. I assume this is a typographical error you made in posting, because if you actually ran that command you would have gotten an error message right there. The error message you are getting is not particularly informative: the problem isn't really about unmatched parentheses or brackets, it's about undefined macros.Īs an aside, in your second try, your first command begins with -glolbal-, which is a non-existent command. Similarly, in -biprobit ($d1 = $w1) ($d2 = $z1)-, both global macros w1 and z1 are non-existent. Missing values are sorted last, like in Stata.When you write -biprobit (y1 = $w1) (y2 = $z)-, you are referencing a global macro w1 that does not exist. 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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