Tibble

↓ Practice ← Vectors (practicing) Select variables/cols →

A tibble is a table – a two dimensional data structure with rows (observations) and columns (variables).

I’ll use the terms observations and rows interchangeably depending on the context. The same goes for the terms variables and columns. As you may recall the datasets pulse and survey were in fact of type tibble. Each variable in a tibble has a fixed type such as character, numeric etc. Let’s start by creating a tibble manually.

Create a tibble

To create a tibble you need to make sure that the package tidyverse is installed and loaded. See installation for more details.

Enter the following to load tidyverse package:

library(tidyverse) 

Creating a tibble is done using the keyword tibble taking a sequence of name=value pairs where:

• name is the variable (column) name,
• value is the values of the observation.

Take for example the variables name, year and colour to represent a person’s name, birth year and favourite colour:

favourite_colour  <- tibble(name=c("Lucas","Lotte","Noa","Wim","Marc","Lucy","Pedro"), 
                           year=c(1995,1995,1995,1994,1990,1993,1992), 
                           colour=c("Blue","Green","Yellow","Purple","Green","red","Blue"))

When creating a tibble the column vectors must be of the same length.

The variable favourite_colour now holds the data. Enter its name in the R Console for inspection:

favourite_colour

# A tibble: 7 × 3
  name   year colour
  <chr> <dbl> <chr> 
1 Lucas  1995 Blue  
2 Lotte  1995 Green 
3 Noa    1995 Yellow
4 Wim    1994 Purple
5 Marc   1990 Green 
6 Lucy   1993 red   
7 Pedro  1992 Blue  

What additional pieces of information do you see beside the content we provided?

Answer

1. ‘# A tibble: 4 x 3’, which says that this is a tibble with dimensions 4x3 (4 observations and 3 variables),

2. the atomic type of each variable, in this case character and double (numeric),

3. the row numbers

Inspect your data

Type the following to find out the dimensions of the tibble:

ncol(favourite_colour)  # number of variables (columns)

[1] 3

nrow(favourite_colour)  # number of observations (rows)

[1] 7

dim(favourite_colour)   # dimensions : 7 rows and 3 columns 

[1] 7 3

Head and tail

Show top and bottom rows of the tibble:

head(favourite_colour, 2)  # first 2 observations (rows)

# A tibble: 2 × 3
  name   year colour
  <chr> <dbl> <chr> 
1 Lucas  1995 Blue  
2 Lotte  1995 Green 

tail(favourite_colour, 3)  # last 3 observations (rows)

# A tibble: 3 × 3
  name   year colour
  <chr> <dbl> <chr> 
1 Marc   1990 Green 
2 Lucy   1993 red   
3 Pedro  1992 Blue  

With the second argument to head and tail functions you can control the number of rows.

By default head and tail show 6 rows, i.e. when the second argument is omitted : head(favourite_colour) or tail(favourite_colour).

glimpse

With glimpse function you can quickly inspect the top part of all variables in a tibble with some additional meta information such as number of rows and variables and their types:

favourite_colour %>% glimpse()

Rows: 7
Columns: 3
$ name   <chr> "Lucas", "Lotte", "Noa", "Wim", "Marc", "Lucy", "Pedro"
$ year   <dbl> 1995, 1995, 1995, 1994, 1990, 1993, 1992
$ colour <chr> "Blue", "Green", "Yellow", "Purple", "Green", "red", "Blue"

Select variables: [

Often you may need to select certain variables, this can be done using square brackets [ :

favourite_colour["colour"]

# A tibble: 7 × 1
  colour
  <chr> 
1 Blue  
2 Green 
3 Yellow
4 Purple
5 Green 
6 red   
7 Blue  

or combination of variables:

favourite_colour[c("name","year")]

# A tibble: 7 × 2
  name   year
  <chr> <dbl>
1 Lucas  1995
2 Lotte  1995
3 Noa    1995
4 Wim    1994
5 Marc   1990
6 Lucy   1993
7 Pedro  1992

Subset result of a tibble is always a tibble.

Selection of variables can also be achieved with indices as we saw in vectors:

favourite_colour[2:3]

# A tibble: 7 × 2
   year colour
  <dbl> <chr> 
1  1995 Blue  
2  1995 Green 
3  1995 Yellow
4  1994 Purple
5  1990 Green 
6  1993 red   
7  1992 Blue  

favourite_colour[c(1,3)]

# A tibble: 7 × 2
  name  colour
  <chr> <chr> 
1 Lucas Blue  
2 Lotte Green 
3 Noa   Yellow
4 Wim   Purple
5 Marc  Green 
6 Lucy  red   
7 Pedro Blue  

To deselect use negative indices:

favourite_colour[-2]

# A tibble: 7 × 2
  name  colour
  <chr> <chr> 
1 Lucas Blue  
2 Lotte Green 
3 Noa   Yellow
4 Wim   Purple
5 Marc  Green 
6 Lucy  red   
7 Pedro Blue  

Extract variables as vectors: [[ or $

If you want to work with variables as individual vectors then you can do this either by double square brackets or $ sign:

favourite_colour[["year"]]

[1] 1995 1995 1995 1994 1990 1993 1992

favourite_colour$year

[1] 1995 1995 1995 1994 1990 1993 1992

In some contexts (later in the course) it is convenient to use the function pull which does the same as [[ and $ :

pull(favourite_colour, year) 

[1] 1995 1995 1995 1994 1990 1993 1992

tibble to/from file

Tibbles can be written to data files and read back again. Many data formats exist but for brevity we will be using comma-separated-value (csv) format in this course. The functions involved for this purpose are write_csv and read_csv (see data import cheat sheet).

let us now save our first tibble into a file in csv format:

write_csv(x = favourite_colour, file = "favourite_colour.csv")

favourite_colour tibble is written to favourite_colour.csv text file. You may inspect the file with any editor and it should look something like:

name,year,colour
Lucas,1995,Blue
Lotte,1995,Green
Noa,1995,Yellow
Wim,1994,Purple

This way we can permanently store our results in files for later use. We can now read the csv file back into a R environment variable, e.g. favourite_colour_csv :

favourite_colour_csv <- read_csv(file = "favourite_colour.csv")

Rows: 7 Columns: 3
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): name, colour
dbl (1): year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

favourite_colour_csv

# A tibble: 7 × 3
  name   year colour
  <chr> <dbl> <chr> 
1 Lucas  1995 Blue  
2 Lotte  1995 Green 
3 Noa    1995 Yellow
4 Wim    1994 Purple
5 Marc   1990 Green 
6 Lucy   1993 red   
7 Pedro  1992 Blue  

The message from read_csv gives a summary of the variables and their inferred types, it also suggest to use argument show_col_types = FALSE if you’d prefere asilent read:

favourite_colour_csv <- read_csv(file = "favourite_colour.csv", show_col_types = FALSE)
favourite_colour_csv

# A tibble: 7 × 3
  name   year colour
  <chr> <dbl> <chr> 
1 Lucas  1995 Blue  
2 Lotte  1995 Green 
3 Noa    1995 Yellow
4 Wim    1994 Purple
5 Marc   1990 Green 
6 Lucy   1993 red   
7 Pedro  1992 Blue  

Type of a tibble

Now inspect the type of the tibble we just created:

class(favourite_colour)

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

A tibble is in its core a ‘data.frame’, a base R data structure.

‘The types tbl_df’ and ‘tbl’ enforce additional convinient behaviours specific to tibble.

↓ Practice ← Vectors (practicing) Select variables/cols →

---

Copyright © 2023 Biomedical Data Sciences (BDS) | LUMC