Create Tibbles, and Their Important Features

tibble() is a nice way to create a tibble from scratch, and as_tibble() converts other data frames (e.g., data.frame and matrix) to a tibble. Below we’ll discuss how to create tibbles using these two functions; during the processing of creating tibbles, we’ll also discuss their important properties.

1. You can create a tibble in a similar way as with data.frame simply by specifying the name of the column and the associated cell values. Different from data.frame, a tibble when printed will nicely display the dataset dimension (row number × column number), and the types of each column, making it informative for downstream analysis.

library(tibble)library(dplyr)
a <- tibble(Name = c("Alice", "Bob", "Charlie"),            Age  = c(25, 30, 35),            City = c("New York", "Los Angeles", "Chicago"))a

Output:
# A tibble: 3 × 3
  Name      Age City
  <chr>   <dbl> <chr>
1 Alice      25 New York
2 Bob        30 Los Angeles
3 Charlie    35 Chicago

A tibble has an official class of tbl_df**, as well as data.frame and tbl.

class(a)

Output:
[1] "tbl_df"     "tbl"        "data.frame"

2. You can include lists in a tibble column. This is helpful to do high-performance functional programming. (You cannot do this with data.frame.)

tibble(x = 1:3, y = list(1:5, 1:10, 1:20))

Output:
# A tibble: 3 × 2
      x y
  <int> <list>
1     1 <int [5]>
2     2 <int [10]>
3     3 <int [20]>

3. tibble() builds columns sequentially. When defining a column, you can refer to columns created earlier; e.g., column C is created based on prior-defined A and B. (You cannot do this with data.frame.)

tibble(A = 1:3, B = c(1, 3, 6), C = A^2 + B)

Output:
# A tibble: 3 × 3
      A     B     C
  <int> <dbl> <dbl>
1     1     1     2
2     2     3     7
3     3     6    15

4. tibble() faithfully reserves complicated column names.

tibble(`:)` = 1:3, `A^2 + B` = 4:6, `3` = c("a", "b", "c"))

Output:
# A tibble: 3 × 3   `:)` `A^2 + B` `3`
  <int>     <int> <chr>
1     1         4 a
2     2         5 b
3     3         6 c

In comparison, data.frame may alter the column name.

data.frame(`:)` = 1:3, `A^2 + B` = 4:6, `3` = c("a", "b", "c"))

Output:

X.. A.2...B X3 1 1 4 a 2 2 5 b 3 3 6 c

5. Only columns of length one can be recycled. In the code below, for instance, you cannot recycle B = c(1, 2) three times to create a total of six rows. This limit is designed so because recycling vectors of longer length is a frequent source of bugs. In comparison, data.frame can recycle vectors of longer length(e.g., data.frame(A = 1:6, B = c(1, 2)) recycles B = c(1, 2) three times.)

tibble(A = 1:6, B = c(1, 2))# Error:# ! Tibble columns must have compatible sizes.# • Size 6: Existing data.# • Size 2: Column `B`.# ℹ Only values of size one are recycled.

data.frame(A = 1:6, B = c(1, 2))

Output:

A B 1 1 1 2 2 2 3 3 1 4 4 2 5 5 1 6 6 2

6. Use as_tibble() to convert a data.frame to a tibble.

as_tibble(mtcars) # convert 'data.frame' mtcars dataset to a tibble

Output:
# A tibble: 32 × 11
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# ℹ 22 more rows

7. Tibbles do not support row names. Note that the rownames of mtcars are dropped after the data frame is converted to a tibble.

rownames(mtcars)

Output:

[1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" "Hornet Sportabout" "Valiant" "Duster 360" "Merc 240D" "Merc 230" "Merc 280" "Merc 280C" "Merc 450SE" "Merc 450SL" [14] "Merc 450SLC" "Cadillac Fleetwood" "Lincoln Continental" "Chrysler Imperial" "Fiat 128" "Honda Civic" "Toyota Corolla" "Toyota Corona" "Dodge Challenger" "AMC Javelin" "Camaro Z28" "Pontiac Firebird" "Fiat X1-9" [27] "Porsche 914-2" "Lotus Europa" "Ford Pantera L" "Ferrari Dino" "Maserati Bora" "Volvo 142E"

as_tibble(mtcars) %>% rownames()

Output:

[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32"

You can reserve row names as a separate column using rownames_to_column() from the tibble package.

rownames_to_column(mtcars) %>% as_tibble()

Output:

# A tibble: 32 × 12 rowname mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 0 1 4 4 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 5 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 # ℹ 22 more rows

Equivalently, the code above can be rewritten using mutate() from the dplyr package.

mtcars %>%   mutate(carnames = rownames(mtcars), .before = 1) %>%  as_tibble()

The next tutorial will discuss tribble(), another great way to create tibbles on a row-wise basis in a very intuitive manner.

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