sum_up prints detailed summary statistics (corresponds to Stata
N <- 100 df <- tibble( id = 1:N, v1 = sample(5, N, TRUE), v2 = sample(1e6, N, TRUE) ) sum_up(df) df %>% sum_up(starts_with("v"), d = TRUE) df %>% group_by(v1) %>% sum_up()
tab prints distinct rows with their count. Compared to the dplyr function
count, this command adds frequency, percent, and cumulative percent.
N <- 1e2 ; K = 10 df <- tibble( id = sample(c(NA,1:5), N/K, TRUE), v1 = sample(1:5, N/K, TRUE) ) tab(df, id) tab(df, id, na.rm = TRUE) tab(df, id, v1)
join is a wrapper for dplyr merge functionalities, with two added functions
checkchecks there are no duplicates in the master or using data.tables (as in Stata).
# merge m:1 v1
join(x, y, kind = "full", check = m~1)
- The option
gen specifies the name of a new variable that identifies non matched and matched rows (as in Stata).
# merge m:1 v1, gen(_merge)
join(x, y, kind = "full", gen = "_merge")
updateallows to update missing values of the master dataset by the value in the using dataset
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