A tibble, or
tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not. Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does not exist). This forces you to confront problems earlier, typically leading to cleaner, more expressive code. Tibbles also have an enhanced
print() method which makes them easier to use with large datasets containing complex objects.
If you are new to tibbles, the best place to start is the tibbles chapter in R for data science.
Create a tibble from an existing object with
library(tibble) as_tibble(iris) #> # A tibble: 150 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ... with 140 more rows
This will work for reasonable inputs that are already data.frames, lists, matrices, or tables.
You can also create a new tibble from column vectors with
tibble() does much less than
data.frame(): it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, it only recycles inputs of length 1, and it never creates
row.names(). You can read more about these features in the vignette,
You can define a tibble row-by-row with