Agenda

• what are tibbles?
• how are tibbles different from data frames?
• how to create tibbles?
• how to manipulate tibbles?

What are tibbles?

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.

Source: http://tibble.tidyverse.org/

Creating tibbles

tibble(x = letters,
       y = 1:26,
       z = sample(100, 26))

## # A tibble: 26 x 3
##    x         y     z
##    <chr> <int> <int>
##  1 a         1    66
##  2 b         2    63
##  3 c         3    35
##  4 d         4    54
##  5 e         5    13
##  6 f         6     5
##  7 g         7    39
##  8 h         8     4
##  9 i         9    25
## 10 j        10    14
## # ... with 16 more rows

tibble features

• never changes input’s types
• never adjusts variable names
• never prints all rows
• never recycles vector of length greater than 1

Never changes input’s types

tibble(x = letters,
       y = 1:26,
       z = sample(100, 26))

## # A tibble: 26 x 3
##    x         y     z
##    <chr> <int> <int>
##  1 a         1    39
##  2 b         2    87
##  3 c         3    70
##  4 d         4     5
##  5 e         5    91
##  6 f         6    19
##  7 g         7    54
##  8 h         8    29
##  9 i         9    48
## 10 j        10    13
## # ... with 16 more rows

Never changes input’s types

data <- data.frame(x = letters, y = 1:26, z = sample(100, 26))
str(data)

## 'data.frame':    26 obs. of  3 variables:
##  $ x: Factor w/ 26 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ y: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ z: int  16 42 94 40 68 29 13 50 34 79 ...

Never adjusts variable names

names(data.frame(`order value` = 10))

## [1] "order.value"

names(tibble(`order value` = 10))

## [1] "order value"

Never prints all rows

x <- 1:100
y <- letters[1]
z <- sample(c(TRUE, FALSE), 100, replace = TRUE)
tibble(x, y, z)

## # A tibble: 100 x 3
##        x y     z    
##    <int> <chr> <lgl>
##  1     1 a     FALSE
##  2     2 a     TRUE 
##  3     3 a     TRUE 
##  4     4 a     TRUE 
##  5     5 a     TRUE 
##  6     6 a     TRUE 
##  7     7 a     FALSE
##  8     8 a     FALSE
##  9     9 a     FALSE
## 10    10 a     FALSE
## # ... with 90 more rows

Never recycle vector of length greater than 1

x <- 1:100
y <- letters
z <- sample(c(TRUE, FALSE), 100, replace = TRUE)
tibble(x, y, z)
Error in overscope_eval_next(overscope, expr) : object 'y' not found

Atomic Vectors

browsers <- c('chrome', 'safari', 'firefox', 'edge')
enframe(browsers)

## # A tibble: 4 x 2
##    name value  
##   <int> <chr>  
## 1     1 chrome 
## 2     2 safari 
## 3     3 firefox
## 4     4 edge

Atomic Vectors

browsers <- c(chrome = 40, firefox = 20, edge = 30, safari = 10)
enframe(browsers)

## # A tibble: 4 x 2
##   name    value
##   <chr>   <dbl>
## 1 chrome     40
## 2 firefox    20
## 3 edge       30
## 4 safari     10

Tribble

Another way to create tibbles is using tribble():

• it is short for transposed tibbles
• it is customized for data entry in code
• column names start with ~
• and values are separated by commas

Tribble

tribble(
  ~x, ~y, ~z,
  #--|--|----
  1, TRUE, 'a',
  2, FALSE, 'b'
)

## # A tibble: 2 x 3
##       x y     z    
##   <dbl> <lgl> <chr>
## 1     1 TRUE  a    
## 2     2 FALSE b

Column Names

Names of the columns in tibbles need not be valid R variable names. They can contain unusual characters like a space or a smiley but must be enclosed in ticks.

tibble(
  ` `  = 'space',
  `2`  = 'integer',
  `:)` = 'smiley'
)

## # A tibble: 1 x 3
##   ` `   `2`     `:)`  
##   <chr> <chr>   <chr> 
## 1 space integer smiley

Add Rows

browsers <- enframe(c(chrome = 40, firefox = 20, edge = 30))
browsers

## # A tibble: 3 x 2
##   name    value
##   <chr>   <dbl>
## 1 chrome     40
## 2 firefox    20
## 3 edge       30

Add Rows

add_row(browsers, name = 'safari', value = 10)

## # A tibble: 4 x 2
##   name    value
##   <chr>   <dbl>
## 1 chrome     40
## 2 firefox    20
## 3 edge       30
## 4 safari     10

Add Rows

add_row(browsers, name = 'safari', value = 10, .before = 2)

## # A tibble: 4 x 2
##   name    value
##   <chr>   <dbl>
## 1 chrome     40
## 2 safari     10
## 3 firefox    20
## 4 edge       30

Add Column

browsers <- enframe(c(chrome = 40, firefox = 20, edge = 30, safari = 10))
add_column(browsers, visits = c(4000, 2000, 3000, 1000))

## # A tibble: 4 x 3
##   name    value visits
##   <chr>   <dbl>  <dbl>
## 1 chrome     40   4000
## 2 firefox    20   2000
## 3 edge       30   3000
## 4 safari     10   1000

Remove Rownames

remove_rownames(mtcars)

##     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## 4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## 6  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 7  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 8  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## 9  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## 10 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## 11 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## 12 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## 13 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## 14 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## 16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## 17 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## 18 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## 19 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## 20 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## 21 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## 22 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## 23 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## 24 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## 25 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## 26 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## 27 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## 28 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 29 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 30 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## 31 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## 32 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Rownames to Column

head(rownames_to_column(mtcars))

##             rowname  mpg cyl disp  hp drat    wt  qsec vs am gear carb
## 1         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## 2     Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## 3        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## 4    Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## 5 Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## 6           Valiant 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Column to Rownames

mtcars_tbl <- rownames_to_column(mtcars)
column_to_rownames(mtcars_tbl)

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Glimpse

glimpse(mtcars)

## Observations: 32
## Variables: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19....
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, ...
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 1...
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, ...
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.9...
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3...
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 2...
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, ...
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, ...
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, ...

Membership Testing

is_tibble(mtcars)

## [1] FALSE

is_tibble(as_tibble(mtcars))

## [1] TRUE

Rownames

has_rownames(mtcars)

## [1] TRUE

Check Column

has_name(mtcars, 'cyl')

## [1] TRUE

has_name(mtcars, 'gears')

## [1] FALSE

Summary

• use tibble() to create tibbles
• use as_tibble() to coerce other objects to tibble
• use enframe() to coerce vector to tibble
• use tribble() to create tibble using data entry

Summary

• use add_row() to add a new row
• use add_column() to add a new column
• use remove_rownames() to remove rownames from data
• use rownames_to_column() to coerce rowname to first column
• use column_to_rownames() to coerce first column to rownames

Summary

• use is_tibble() to test if an object is a tibble
• use has_rownames() to check whether a data set has rownames
• use has_name() to check if tibble has a specific column
• use glimpse() to get an overview of data