tibble()
constructs a data frame. It is used like base::data.frame()
, but
with a couple notable differences:
The returned data frame has the class tbl_df
, in
addition to data.frame
. This allows so-called "tibbles" to exhibit some
special behaviour, such as enhanced printing. Tibbles are
fully described in tbl_df
.
tibble()
is much lazier than base::data.frame()
in terms of
transforming the user's input. Character vectors are not coerced to
factor. List-columns are expressly anticipated and do not require special
tricks. Column names are not modified.
tibble()
builds columns sequentially. When defining a column, you can
refer to columns created earlier in the call. Only columns of length one
are recycled.
If a column evaluates to a data frame or tibble, it is nested or spliced. See examples.
tibble_row()
constructs a data frame that is guaranteed to occupy one row.
Vector columns are required to have size one, non-vector columns are wrapped
in a list.
tibble( ..., .rows = NULL, .name_repair = c("check_unique", "unique", "universal", "minimal") ) tibble_row( ..., .name_repair = c("check_unique", "unique", "universal", "minimal") )
... | < Arguments are evaluated sequentially.
You can refer to previously created elements directly or using the .data
pronoun.
An existing |
---|---|
.rows | The number of rows, useful to create a 0-column tibble or just as an additional check. |
.name_repair | Treatment of problematic column names:
This argument is passed on as |
A tibble, which is a colloquial term for an object of class
tbl_df
. A tbl_df
object is also a data
frame, i.e. it has class data.frame
.
Use as_tibble()
to turn an existing object into a tibble. Use
enframe()
to convert a named vector into a tibble. Name repair is
detailed in vctrs::vec_as_names()
.
See quasiquotation for more details on tidy dots semantics,
i.e. exactly how the ...
argument is processed.
# Unnamed arguments are named with their expression: a <- 1:5 tibble(a, a * 2)#> # A tibble: 5 x 2 #> a `a * 2` #> <int> <dbl> #> 1 1 2 #> 2 2 4 #> 3 3 6 #> 4 4 8 #> 5 5 10# Scalars (vectors of length one) are recycled: tibble(a, b = a * 2, c = 1)#> # A tibble: 5 x 3 #> a b c #> <int> <dbl> <dbl> #> 1 1 2 1 #> 2 2 4 1 #> 3 3 6 1 #> 4 4 8 1 #> 5 5 10 1#> # A tibble: 10 x 2 #> x y #> <dbl> <dbl> #> 1 0.512 1.02 #> 2 0.836 1.67 #> 3 0.709 1.42 #> 4 0.874 1.75 #> 5 0.0115 0.0230 #> 6 0.888 1.78 #> 7 0.996 1.99 #> 8 0.500 1.00 #> 9 0.359 0.718 #> 10 0.775 1.55#> tibble [26 × 1] (S3: tbl_df/tbl/data.frame) #> $ letters: chr [1:26] "a" "b" "c" "d" ...#> tibble [2 × 1] (S3: tbl_df/tbl/data.frame) #> $ x:List of 2 #> ..$ : num [1, 1] 1 #> ..$ : num [1:2, 1:2] 1 0 0 1# or munges column names (unless requested), tibble(`a + b` = 1:5)#> # A tibble: 5 x 1 #> `a + b` #> <int> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5#> Error : Column name `x` must not be duplicated.tibble(x = 1, x = 2, .name_repair = "unique")#>#> #>#> # A tibble: 1 x 2 #> x...1 x...2 #> <dbl> <dbl> #> 1 1 2tibble(x = 1, x = 2, .name_repair = "minimal")#> # A tibble: 1 x 2 #> x x #> <dbl> <dbl> #> 1 1 2## By default, non-syntactic names are allowed, df <- tibble(`a 1` = 1, `a 2` = 2) ## because you can still index by name: df[["a 1"]]#> [1] 1df$`a 1`#> [1] 1#> [1] 1## Syntactic names are easier to work with, though, and you can request them: df <- tibble(`a 1` = 1, `a 2` = 2, .name_repair = "universal")#>#> #>df$a.1#> [1] 1## You can specify your own name repair function: tibble(x = 1, x = 2, .name_repair = make.unique)#>#>#> # A tibble: 1 x 2 #> x x.1 #> <dbl> <dbl> #> 1 1 2fix_names <- function(x) gsub("\\s+", "_", x) tibble(`year 1` = 1, `year 2` = 2, .name_repair = fix_names)#>#> #>#> # A tibble: 1 x 2 #> year_1 year_2 #> <dbl> <dbl> #> 1 1 2## purrr-style anonymous functions and constants ## are also supported tibble(x = 1, x = 2, .name_repair = ~ make.names(., unique = TRUE))#>#>#> # A tibble: 1 x 2 #> x x.1 #> <dbl> <dbl> #> 1 1 2#>#> #>#> # A tibble: 1 x 2 #> a b #> <dbl> <dbl> #> 1 1 2# Tibbles can contain columns that are tibbles or matrices # if the number of rows is compatible. Unnamed tibbled are # spliced, i.e. the inner columns are inserted into the # tibble under construction. tibble( a = 1:3, tibble( b = 4:6, c = 7:9 ), d = tibble( e = tibble( f = b ) ) )#> # A tibble: 3 x 4 #> a b c d$e$f #> <int> <int> <int> <int> #> 1 1 4 7 4 #> 2 2 5 8 5 #> 3 3 6 9 6#> # A tibble: 4 x 3 #> a b[,1] [,2] [,3] [,4] c[,"Sepal.Lengt… [,"Sepal.Width"] #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0 0 0 0.686 -0.0424 #> 2 2 0 1 0 0 -0.0424 0.190 #> 3 3 0 0 1 0 1.27 -0.330 #> 4 4 0 0 0 1 0.516 -0.122 #> # … with 2 more variables: [,"Petal.Length"] <dbl>, [,"Petal.Width"] <dbl># data can not contain POSIXlt columns, or tibbles or matrices # with incompatible number of rows: try(tibble(y = strptime("2000/01/01", "%x")))#> # A tibble: 1 x 1 #> y #> <dttm> #> 1 NA#> Error : Tibble columns must have compatible sizes. #> * Size 3: Existing data. #> * Size 4: Column `b`. #> ℹ Only values of size one are recycled.# Use := to create columns with names that start with a dot: tibble(.rows = 3)#> # A tibble: 3 x 0tibble(.rows := 3)#> # A tibble: 1 x 1 #> .rows #> <dbl> #> 1 3# You can unquote an expression: x <- 3 tibble(x = 1, y = x)#> # A tibble: 1 x 2 #> x y #> <dbl> <dbl> #> 1 1 1tibble(x = 1, y = !!x)#> # A tibble: 1 x 2 #> x y #> <dbl> <dbl> #> 1 1 3# You can splice-unquote a list of quosures and expressions: tibble(!!! list(x = rlang::quo(1:10), y = quote(x * 2)))#> # A tibble: 10 x 2 #> x y #> <int> <dbl> #> 1 1 2 #> 2 2 4 #> 3 3 6 #> 4 4 8 #> 5 5 10 #> 6 6 12 #> 7 7 14 #> 8 8 16 #> 9 9 18 #> 10 10 20# Use tibble_row() to construct a one-row tibble: tibble_row(a = 1, lm = lm(Petal.Width ~ Petal.Length + Species, data = iris))#> # A tibble: 1 x 2 #> a lm #> <dbl> <list> #> 1 1 <lm>