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options(scipen=10000)
library(tidyverse)
library(flair)
library(nycflights13)
library(palmerpenguins)
library(gt)
library(skimr)
library(emo)
library(tidyquant)
library(lubridate)
library(magrittr)
theme_set(theme_tq())

Class

class(iris) # initially R's data.frame

[1] "data.frame"
(iris2 <- as_tibble(iris))

# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ... with 140 more rows
class(iris2) # "tbl_df"     "tbl"        "data.frame"

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

The tbl and tbl_df tell us this has been converted to a tibble.

Creation

To create a tibble we use the tibble() function.

tibble(
  x = 1:5,
  y = 1, # recycled
  z = x ^ 2 + y
)

# A tibble: 5 x 3
      x     y     z
  <int> <dbl> <dbl>
1     1     1     2
2     2     1     5
3     3     1    10
4     4     1    17
5     5     1    26

A tibble is just a different form of a data.frame. The column names do not need to comply with R’s variable naming convention.

For example the variable name test subject is unacceptable in R as a var name, however it is totally fine if it is a column name in a tibble. The variable must be referred to by using the backticks `` or "".

# Ch7 in R4DS - tibbles
# Can use either backticks `` or quotation marks "" to surround non-syntactic names
(tb = tibble(
  ":)" = "smile",
  " " = "space",
  `2000` = "number",
  `some non syntactic name` = "some value",
  meaning = 42 # no back ticks or "" since this is syntactically allowed
))

# A tibble: 1 x 5
  `:)`  ` `   `2000` `some non syntactic name` meaning
  <chr> <chr> <chr>  <chr>                       <dbl>
1 smile space number some value                     42
# Can use `` or "" to access the variable with the non-syntactic name
tb$`:)`

[1] "smile"
tb$":)"

[1] "smile"
# Can use `` or "" to manipulate the non-syntactic named var
tb %>%
  mutate(":)" = "smiley")

# A tibble: 1 x 5
  `:)`   ` `   `2000` `some non syntactic name` meaning
  <chr>  <chr> <chr>  <chr>                       <dbl>
1 smiley space number some value                     42
tb %>% 
  mutate(`:)` = "smiley too")

# A tibble: 1 x 5
  `:)`       ` `   `2000` `some non syntactic name` meaning
  <chr>      <chr> <chr>  <chr>                       <dbl>
1 smiley too space number some value                     42

The variable :) in tb contains the value: smile. The variable some non syntactic name in tb contains the value: some value.

Another way is to create a tibble is to use the tribble() function with ~ used for each column name. I often use the datapasta 📦 to paste a tribble from something I have copied onto the clipboard.

pasting a tribble from data on clipboard


A tribble is transposed tibble. It makes it easy to lay out small amounts of data in an easy to read format.

tribble(
  ~x, ~y, ~z,
  #--/--/----
  "a", 2, 3.6,
  "b", 1, 8.5
)

# A tibble: 2 x 3
  x         y     z
  <chr> <dbl> <dbl>
1 a         2   3.6
2 b         1   8.5
tibble::tribble(
                               ~Flavour, ~Total.2009, ~Total.2011, ~East, ~Midwest, ~South, ~West, ~Rep, ~Dem, ~Ind,
                            "Chocolate",         27L,         28L,   31L,      32L,    28L,   21L,  32L,  23L,  30L,
                              "Vanilla",         22L,         26L,   27L,      22L,    30L,   22L,  28L,  26L,  22L,
      "Cookie Dough/ Cookies and cream",         22L,         22L,   26L,      22L,    21L,   19L,  24L,  18L,  24L,
           "Butter Pecan/ Swiss Almond",         20L,         19L,   12L,      24L,    21L,   15L,  15L,  22L,  19L,
                  "Mint Chocolate Chip",         17L,         15L,   15L,      15L,    15L,   15L,  16L,  12L,  16L,
                           "Strawberry",         13L,         12L,    8L,      10L,    15L,   12L,  10L,  13L,  12L,
                           "Rocky Road",         14L,         11L,    8L,      11L,     8L,   19L,  14L,  11L,   9L,
                               "Coffee",          9L,          9L,   10L,       7L,     6L,   14L,   7L,  11L,   8L,
                        "Peanut Butter",          8L,          8L,   10L,       9L,     7L,    8L,  12L,   5L,   8L,
                       "Cherry Vanilla",          9L,          7L,   10L,       6L,     7L,    7L,   6L,  10L,   8L,
                            "Pistachio",          8L,          7L,    7L,       6L,     6L,    8L,   5L,   9L,   6L,
                      "Black Raspberry",          6L,          6L,   10L,       6L,     3L,    6L,   7L,   5L,   6L,
                                "Peach",          4L,          5L,    6L,       4L,     7L,    3L,   5L,   7L,   4L,
  "Seasonal, such as pumpkin or eggnog",          2L,          4L,    4L,       4L,     5L,    4L,   5L,   3L,   5L,
                                "Other",          9L,         13L,   13L,      12L,    13L,   15L,   9L,  16L,  13L,
                 "Do not eat ice cream",          3L,          3L,    1L,       3L,     2L,    5L,   2L,   3L,   3L
  )

# A tibble: 16 x 10
   Flavour     Total.2009 Total.2011  East Midwest South  West   Rep   Dem   Ind
   <chr>            <int>      <int> <int>   <int> <int> <int> <int> <int> <int>
 1 Chocolate           27         28    31      32    28    21    32    23    30
 2 Vanilla             22         26    27      22    30    22    28    26    22
 3 Cookie Dou~         22         22    26      22    21    19    24    18    24
 4 Butter Pec~         20         19    12      24    21    15    15    22    19
 5 Mint Choco~         17         15    15      15    15    15    16    12    16
 6 Strawberry          13         12     8      10    15    12    10    13    12
 7 Rocky Road          14         11     8      11     8    19    14    11     9
 8 Coffee               9          9    10       7     6    14     7    11     8
 9 Peanut But~          8          8    10       9     7     8    12     5     8
10 Cherry Van~          9          7    10       6     7     7     6    10     8
11 Pistachio            8          7     7       6     6     8     5     9     6
12 Black Rasp~          6          6    10       6     3     6     7     5     6
13 Peach                4          5     6       4     7     3     5     7     4
14 Seasonal, ~          2          4     4       4     5     4     5     3     5
15 Other                9         13    13      12    13    15     9    16    13
16 Do not eat~          3          3     1       3     2     5     2     3     3

Printing

tibble(
  a = lubridate::now() + runif(1e3) * 86400,
  b = lubridate::today() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
) %>% 
  print(n = 15, width = Inf) # show 15 rows instead of 10, and all cols
# A tibble: 1,000 x 5
   a                   b              c      d e    
   <dttm>              <date>     <int>  <dbl> <chr>
 1 2020-10-21 20:39:57 2020-11-17     1 0.445  s    
 2 2020-10-21 15:14:25 2020-10-23     2 0.637  b    
 3 2020-10-21 16:18:06 2020-10-31     3 0.621  f    
 4 2020-10-21 18:24:15 2020-11-17     4 0.925  w    
 5 2020-10-21 15:13:40 2020-10-21     5 0.678  k    
 6 2020-10-22 08:48:04 2020-10-25     6 0.643  a    
 7 2020-10-22 00:53:23 2020-11-16     7 0.169  k    
 8 2020-10-21 10:43:08 2020-10-24     8 0.681  q    
 9 2020-10-21 15:38:44 2020-10-30     9 0.767  u    
10 2020-10-21 09:29:02 2020-10-28    10 0.645  h    
11 2020-10-22 05:05:26 2020-11-09    11 0.954  g    
12 2020-10-22 08:08:24 2020-11-14    12 0.0452 t    
13 2020-10-21 11:03:59 2020-11-17    13 0.679  l    
14 2020-10-22 03:59:06 2020-10-26    14 0.543  e    
15 2020-10-22 07:32:58 2020-10-23    15 0.731  a    
# ... with 985 more rows

Subsetting

(df <- tibble(
  x = runif(5),
  y = rnorm(5)
))

# A tibble: 5 x 2
      x       y
  <dbl>   <dbl>
1 0.275 -0.365 
2 0.853  1.20  
3 0.291 -0.0950
4 0.711 -1.15  
5 0.116 -0.727 
# extract by name
df$x

[1] 0.2751769 0.8533060 0.2914412 0.7105093 0.1155235
# OR
df[["x"]]

[1] 0.2751769 0.8533060 0.2914412 0.7105093 0.1155235
# extract by position
df[[1]]

[1] 0.2751769 0.8533060 0.2914412 0.7105093 0.1155235
# When using a pipe use the placeholder . to access
df %>%
  .$x

[1] 0.2751769 0.8533060 0.2914412 0.7105093 0.1155235
df %>% 
  .[["x"]]

[1] 0.2751769 0.8533060 0.2914412 0.7105093 0.1155235

Summary

Tibbles:

  • Print well, so as to not overwhelm your console (by printing 10 rows and only as many columns as will fit in your console window).

    • Want to print all columns? Use print(n = xx, width = Inf)
  • Subset more strictly that data.frame

    • Never partial matching
    • Warns if column you’re trying to access does not exist.
  • Older functions may not work with tibbles. To convert a tibble to a data.fram use as.data.frame(tibble_name)

Exercises

  1. How can you tell if an object is a tibble? (Hint: try printing mtcars, which is a regular data frame).

    mtcars
                         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
    class(mtcars)
    [1] "data.frame"

    Tibbles:

    • Print only 10 rows, and as many columns as will fit on the console / screen
    • If you find the calss of a tibble among the output should be tbl_df and tbl.
    penguins
    # A tibble: 344 x 8
       species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g
       <fct>   <fct>           <dbl>         <dbl>            <int>       <int>
     1 Adelie  Torge~           39.1          18.7              181        3750
     2 Adelie  Torge~           39.5          17.4              186        3800
     3 Adelie  Torge~           40.3          18                195        3250
     4 Adelie  Torge~           NA            NA                 NA          NA
     5 Adelie  Torge~           36.7          19.3              193        3450
     6 Adelie  Torge~           39.3          20.6              190        3650
     7 Adelie  Torge~           38.9          17.8              181        3625
     8 Adelie  Torge~           39.2          19.6              195        4675
     9 Adelie  Torge~           34.1          18.1              193        3475
    10 Adelie  Torge~           42            20.2              190        4250
    # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>
    class(penguins)
    [1] "tbl_df"     "tbl"        "data.frame"
  2. Compare and contrast the following operations on a data.frame and equivalent tibble. What is different? Why might the default data frame behaviours cause you frustration?

    • On a data.frame

      df <- data.frame(abc = 1, xyz = "a")
      df$x
      [1] a
      Levels: a
      df[, "xyz"]
      [1] a
      Levels: a
      df[, c("abc", "xyz")]
        abc xyz
      1   1   a
    • On a tibble

      df <- tibble(abc = 1, xyz = "a")
      df$x
      Warning: Unknown or uninitialised column: `x`.
      NULL
      df[, "xyz"]
      # A tibble: 1 x 1
        xyz  
        <chr>
      1 a    
      df[, c("abc", "xyz")]
      # A tibble: 1 x 2
          abc xyz  
        <dbl> <chr>
      1     1 a    
      • The data frame does not have an x variable, yet with a data.frame it prints a value for x since it does partial matching.
      • The data frame has a character attribute for the column xyz yet the data.frame converts it to a factor, and outputs the factors levels. 😨
      • The information about the type of data contained in every variable when you print a tibble is useful 💯.
  3. If you have the name of a variable stored in an object, e.g. var <- "mpg", how can you extract the reference variable from a tibble?

    var <- "mpg"
    (test_tbl <- as_tibble(mtcars))
    # A tibble: 32 x 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
    # ... with 22 more rows
    # does not work: 
    # test_tbl$var
    # NULL 
    # Warning message:
    # Unknown or uninitialised column: `var`. 
    
    # does not work
    # test_tbl[["var"]]
    # NULL
    
    # Need to use:
    test_tbl[[var]]
     [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
    [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
    [31] 15.0 21.4
  4. Practice referring to non-syntactic names in the following data frame by:

    1. Extracting the variable called 1.

    2. Plotting a scatterplot of 1 vs 2.

    3. Creating a new column called 3 which is 2 divided by 1.

    4. Renaming the columns to one, two and three.

    annoying <- tibble(
      `1` = 1:10,
      `2` = `1` * 2 + rnorm(length(`1`))
    )
    # Extracting the variable called `1`.
    annoying$`1`
     [1]  1  2  3  4  5  6  7  8  9 10
    annoying$"1"
     [1]  1  2  3  4  5  6  7  8  9 10
    annoying[["1"]]
     [1]  1  2  3  4  5  6  7  8  9 10
    annoying[[1]] # by position
     [1]  1  2  3  4  5  6  7  8  9 10
    # does not work
    # annoying[[`1`]]
    # Error in tbl_subset2(x, j = i, j_arg = substitute(i)) : 
    #   object '1' not found
    # Plotting a scatterplot of `1` vs `2`.
    annoying %>% 
      ggplot(aes(x = `1`, y = `2`)) +
      geom_point()

    # discouraged ways
    annoying %>% 
      ggplot(aes(x = .$`1`, y = .$`2`)) +
      geom_point()
    Warning: Use of `.$`1`` is discouraged. Use `1` instead.
    Warning: Use of `.$`2`` is discouraged. Use `2` instead.

    annoying %>% 
      ggplot(aes(x = .$"1", y = .$"2")) +
      geom_point()
    Warning: Use of `.$"1"` is discouraged. Use `1` instead.
    Warning: Use of `.$"2"` is discouraged. Use `2` instead.

    annoying %>% 
      ggplot(aes(x = .[[1]], y = .[[2]])) +
      geom_point()
    Warning: Use of `.[[1]]` is discouraged. Use `.data[[1]]` instead.
    Warning: Use of `.[[2]]` is discouraged. Use `.data[[2]]` instead.

    # Creating a new column called `3` which is 
    # `2` divided by `1`.
    (annoying <- annoying %>% 
      mutate(`3` = `2`/`1`))
    # A tibble: 10 x 3
         `1`    `2`   `3`
       <int>  <dbl> <dbl>
     1     1  0.835 0.835
     2     2  4.06  2.03 
     3     3  5.48  1.83 
     4     4  7.36  1.84 
     5     5  9.64  1.93 
     6     6 11.3   1.89 
     7     7 13.1   1.87 
     8     8 15.4   1.92 
     9     9 19.1   2.12 
    10    10 20.5   2.05 
    # Renaming the columns to `one`, `two` and `three`.
    (annoying <- annoying %>% 
      rename("one" = "1",
             "two" = "2",
             "three" = "3"))
    # A tibble: 10 x 3
         one    two three
       <int>  <dbl> <dbl>
     1     1  0.835 0.835
     2     2  4.06  2.03 
     3     3  5.48  1.83 
     4     4  7.36  1.84 
     5     5  9.64  1.93 
     6     6 11.3   1.89 
     7     7 13.1   1.87 
     8     8 15.4   1.92 
     9     9 19.1   2.12 
    10    10 20.5   2.05 
  5. What does tibble::enframe() do? When might you use it?

    It converts named vectors into tibbles.

    # examples from the ?enframe help page
    enframe(1:3)
    # A tibble: 3 x 2
       name value
      <int> <int>
    1     1     1
    2     2     2
    3     3     3
    enframe(c(a = 5, b = 7))
    # A tibble: 2 x 2
      name  value
      <chr> <dbl>
    1 a         5
    2 b         7
    enframe(list(one = 1, two = 2:3, three = 4:6))
    # A tibble: 3 x 2
      name  value    
      <chr> <list>   
    1 one   <dbl [1]>
    2 two   <int [2]>
    3 three <int [3]>

    Note: Selecting examples on a help page in RStudio and pressing Ctrl + Enter sends the example to your console and runs it!

    Ctrl and Enter in Help File

  6. What option controls how many additional column names are printed at the footer of a tibble?

    n_extra: Number of extra columns to print abbreviated information for, 
    if the width is too small for the entire tibble. If NULL, the default, 
    will print information about at most tibble.max_extra_cols extra columns.

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252   
[3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C                         
[5] LC_TIME=English_South Africa.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] magrittr_1.5               tidyquant_1.0.0           
 [3] quantmod_0.4.17            TTR_0.23-6                
 [5] PerformanceAnalytics_2.0.4 xts_0.12-0                
 [7] zoo_1.8-7                  lubridate_1.7.8           
 [9] emo_0.0.0.9000             skimr_2.1.1               
[11] gt_0.2.2                   palmerpenguins_0.1.0      
[13] nycflights13_1.0.1         flair_0.0.2               
[15] forcats_0.5.0              stringr_1.4.0             
[17] dplyr_1.0.0                purrr_0.3.4               
[19] readr_1.3.1                tidyr_1.1.0               
[21] tibble_3.0.3               ggplot2_3.3.0             
[23] tidyverse_1.3.0            workflowr_1.6.2           

loaded via a namespace (and not attached):
 [1] httr_1.4.2       jsonlite_1.7.0   modelr_0.1.6     assertthat_0.2.1
 [5] cellranger_1.1.0 yaml_2.2.1       pillar_1.4.6     backports_1.1.6 
 [9] lattice_0.20-38  glue_1.4.1       quadprog_1.5-8   digest_0.6.25   
[13] promises_1.1.0   rvest_0.3.5      colorspace_1.4-1 htmltools_0.5.0 
[17] httpuv_1.5.2     pkgconfig_2.0.3  broom_0.5.6      haven_2.2.0     
[21] scales_1.1.0     whisker_0.4      later_1.0.0      git2r_0.26.1    
[25] farver_2.0.3     generics_0.0.2   ellipsis_0.3.1   withr_2.2.0     
[29] repr_1.1.0       cli_2.0.2        crayon_1.3.4     readxl_1.3.1    
[33] evaluate_0.14    fs_1.4.1         fansi_0.4.1      nlme_3.1-144    
[37] xml2_1.3.2       tools_3.6.3      hms_0.5.3        lifecycle_0.2.0 
[41] munsell_0.5.0    reprex_0.3.0     compiler_3.6.3   rlang_0.4.7     
[45] grid_3.6.3       rstudioapi_0.11  labeling_0.3     base64enc_0.1-3 
[49] rmarkdown_2.4    gtable_0.3.0     DBI_1.1.0        curl_4.3        
[53] R6_2.4.1         knitr_1.28       utf8_1.1.4       rprojroot_1.3-2 
[57] Quandl_2.10.0    stringi_1.4.6    Rcpp_1.0.4.6     vctrs_0.3.2     
[61] dbplyr_1.4.3     tidyselect_1.1.0 xfun_0.13