sum_up
prints detailed summary statistics (corresponds to Stata summarize
)
N <- 100
df <- data_frame(
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 <- data_frame(
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
check
checks there are no duplicates in the master or using data.tables (as in Stata).r # 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).
r # merge m:1 v1, gen(_merge) join(x, y, kind = "full", gen = "_merge")
update
allows to update missing values of the master dataset by the value in the using dataset