Create and convert tibbles (2024)

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Create and convert tibbles (1)

Tibbles are the modern reimagination of data frames and share many commonalities with their ancestors. The most visible difference is how tibble contents are printed to the console. Tibbles are part of the tidyverse and used for their more consistent behaviour compared to data frames.

  • Learn the difference between data frames and tibbles
  • Create tibbles from vectors
  • Convert data frames into tibbles
tibble(___ = ___, ___ = ___, ...)as_tibble(___)

Introduction to Tibbles

A modern reimagining of the data frame

Tibbles are in many ways similar to data frames. In fact, they are inherited from data frames which means that all functions and features available for data frames also work for tibbles. Therefore, when we speak of data frames we also mean tibbles.

In addition to everything a data frame has to offer, tibbles have a more consistent behaviour with better usability in many cases. Most importantly, when a tibble object is printed to the console it automatically shows only the first 10 rows and condenses additional columns. By contrast, a data frame fills up the entire console screen with values which can lead to confusion. Let’s take a look the the gapminder dataset from the gapminder package:

gapminder# A tibble: 1,704 x 6 country continent year lifeExp pop gdpPercap <fct> <fct> <int> <dbl> <int> <dbl> 1 Afghanistan Asia 1952 28.8 8425333 779. 2 Afghanistan Asia 1957 30.3 9240934 821. 3 Afghanistan Asia 1962 32.0 10267083 853. 4 Afghanistan Asia 1967 34.0 11537966 836. 5 Afghanistan Asia 1972 36.1 13079460 740. 6 Afghanistan Asia 1977 38.4 14880372 786. 7 Afghanistan Asia 1982 39.9 12881816 978. 8 Afghanistan Asia 1987 40.8 13867957 852. 9 Afghanistan Asia 1992 41.7 16317921 649.10 Afghanistan Asia 1997 41.8 22227415 635.# … with 1,694 more rows

We immediately see that the gapminder dataset is a tibble consisting of 1,704 rows and 6 columns on the top line. In the second line we can see the column names and their corresponding data types directly below.

For example, the column country has the type <fct> (which is short for “factor”), year is an integer <int> and life expectancy lifeExp is a <dbl>—a decimal number.

Quiz: Tibbles versus Data Frames

Which answers about data frames and tibbles are correct?

  • The printed output to the console is the same for tibbles and data frames
  • All functions defined for data frames also work on tibbles.
  • Tibbles also show the data types in the console output.
  • To use tibble objects the tibbles package needs to be loaded.
  • The table dimensions are not shown in the console output for tibbles.

Start Quiz

Creating Tibbles

tibble(___ = ___, ___ = ___, ...)as_tibble(___)

The creation of tibbles works exactly the same as for data frames. We can use the tibble() function from the tibble package to create a new tabular object.

For example, a tibble containing data from four different people and three columns can be created like this:

library(tibble)tibble( id = c(1, 2, 3, 4), name = c("Louisa", "Jonathan", "Luigi", "Rachel"), female = c(TRUE, FALSE, FALSE, TRUE))# A tibble: 4 x 3 id name female <dbl> <chr> 1 1 Louisa TRUE 2 2 Jonathan FALSE 3 3 Luigi FALSE 4 4 Rachel TRUE 

Converting data frames to Tibbles

If you prefer tibbles to data frames for their additional features they can also be converted from existing data frames with the as_tibble() function.

For example, the Davis data frame from the carData package can be converted to a tibble like so:

as_tibble(Davis)# A tibble: 200 x 5 sex weight height repwt repht <fct> <int> <int> <int> <int> 1 M 77 182 77 180 2 F 58 161 51 159 3 F 53 161 54 158 4 M 68 177 70 175 5 F 59 157 59 155 6 M 76 170 76 165 7 M 76 167 77 165 8 M 69 186 73 180 9 M 71 178 71 17510 M 65 171 64 170# … with 190 more rows

Exercise: Convert data frame to Tibble

 speed dist1 4 22 4 103 7 4 [ reached 'max' / getOption("max.print") -- omitted 47 rows ]

The data frame cars reports the speed of cars and distances taken to stop. To have a nicer printed output in the console use the as_tibble() function and create a tibble object out of it.

Start Exercise

Create and convert tibbles is an excerpt from the course Introduction to R, which is available for free at



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Create and convert tibbles (2024)


How to convert tibble to dataframe in R? ›

In R there are two ways by which we can convert Tibble to Data Frame.
  1. Using as. data.frame() function.
  2. Using data.frame() function.
Mar 25, 2024

What are the limitations of Tibbles? ›

  • It never changes an input's type (i.e., no more stringsAsFactors = FALSE !). ...
  • It never adjusts the names of variables: ...
  • It evaluates its arguments lazily and sequentially: ...
  • It never uses row. ...
  • It only recycles vectors of length 1.

How do you manipulate Tibbles? ›

Key Points
  1. Use the dplyr package (within the tidyverse ) to manipulate tibbles.
  2. Use select() to choose variables from a tibbles.
  3. Use filter() to choose data based on values.
  4. Use group_by() and summarize() to work with subsets of data.
  5. Use mutate() to create new variables. Attribution.

What is the advantage of using a data frame instead of Tibbles? ›

Using data frames instead of tibbles offers the advantage of utilizing column names, which enhances data manipulation and analysis tasks. Column names provide meaningful labels for variables or attributes in the dataset, allowing for easier identification and reference.

What is the difference between as tibble and as Dataframe? ›

tibble() does much less than data. frame() : it never changes the type of the inputs (e.g. it keeps list columns unchanged, and never converts strings to factors!), it never changes the names of variables, it only recycles inputs of length 1, and it never creates row.

Is tibble faster than Dataframe? ›

tibble in tidyverse is slightly faster than data. frame in base R, but is still much slower than data. table .

What are the advantages of Tibbles? ›

A Tibble never alters the input type. With Tibble, there is no need for us to be bothered about the automatic changing of characters to strings. Tibbles can also contain columns that are the lists. We can also use non-standard variable names in Tibble.

What scenario would prevent you from using a tibble? ›

When you need to change the data types of inputs: Tibbles require consistent data types within columns, so if you need to change the data types of your inputs, you may need to convert them to a different format before using a tibble. When you need to create row names: Tibbles do not support row names.

Why are tibbles a useful variation of data frames? ›

Why are tibbles a useful variation of data frames? // Tibbles can make printing easier. They also help you avoid overloading your console when working with large datasets.

What does %>% mean in R? ›

The %>% operator takes the output of the expression on its left and passes it as the first argument to the function on its right. The %<>% operator does the same thing, but it also updates the value on the left side of the pipe in place.

Can Tibbles create row names? ›

While a tibble can have row names (e.g., when converting from a regular data frame), they are removed when subsetting with the [ operator. A warning will be raised when attempting to assign non- NULL row names to a tibble.

What is the difference between tribble and tibble? ›

The tribble() function

We created tibble objects with the function tibble() so far. tribble() is just another way of creating tibble objects. The difference is that, while tibble() receives vectors very much like data.

What are some of the limitations of tibbles in R? ›

Answer. Final answer: Tibbles may pose limitations such as unchanged variable names and types during import, a potentially less informative print-to-console method, and compatibility issues with some functions designed for data frames.

Can Tibbles change the input type of data? ›

If you're already familiar with data. frame() , note that tibble() does much less: it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, and it never creates row names.

How do tibbles work in R? ›

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.

How do I convert data to a Dataframe in R? ›

The as. data. frame() function converts a table to a data frame in a format that you need for regression analysis on count data. If you need to summarize the counts first, you use table() to create the desired table.

How do I convert an R list to a Dataframe? ›

To convert the list of matrices into a dataframe, use the as. data. frame function. Pass the matrix_list as an argument to this function.

What does tibble :: enframe() do? ›

Description enframe() converts named atomic vectors or lists to one- or two-column data frames. For a list, the result will be a nested tibble with a column of type list. For unnamed vectors, the natural sequence is used as name column.

How to convert RDD row to DataFrame? ›

Convert Using createDataFrame Method

The SparkSession object has a utility method for creating a DataFrame – createDataFrame. This method can take an RDD and create a DataFrame from it. The createDataFrame is an overloaded method, and we can call the method by passing the RDD alone or with a schema.


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