statar

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statar

Matthieu Gomez

2023-08-19

sum_up = summarize

sum_up prints detailed summary statistics (corresponds to Stata summarize)

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 = tabulate

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 = merge

join is a wrapper for dplyr merge functionalities, with two added functions

The option check checks 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") 
```
The option update allows to update missing values of the master dataset by the value in the using dataset

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.