A tour of the tibble package
This article is originally published at https://tjmahr.github.io/
Dataframes are used in R to hold tabular data. Think of the prototypical spreadsheet or database table: a grid of data arranged into rows and columns. That’s a dataframe. The tibble R package provides a fresh take on dataframes to fix some longstanding annoyances with them. For example, printing a large tibble shows just the first 10 rows instead of the flooding the console with the first 1,000 rows.
In this post, I provide a tour of the tibble package. Because the package
provides tools for working with tabular data, it also contain some less
well-known helper functions that I would like to advertise in this post. In
particular, I find add_column(), rownames_to_column() and
rowid_to_column() to be useful tools in my work.
Why the name “tibble”? Tibbles first appeared in the dplyr package in
January 2014, but they weren’t called “tibbles” yet. dplyr used a subclass
tbl_df for its dataframe objects and they behaved like modern tibbles: Better
printing, not converting strings to factors, etc. We loved them, and we would
convert our plain-old dataframes into these tbl_dfs for these features.
However, the name tee-bee-ell-dee-eff is quite a mouthful. On Twitter,
@JennyBryan raised the question of how to pronounce
tbl_df, and
@kevin_ushey suggested “tibble
diff”. The name was
enthusiastically received.
Creating tibbles
Create a fresh tibble using tibble() and vectors of values for each column.
The column definitions are evaluated sequentially, so additional columns can be
created by manipulating earlier defined ones. Below x is defined and then the
values of x are manipulated to create the column x_squared.
library(tibble)
library(magrittr)
tibble(x = 1:5, x_squared = x ^ 2)
#> # A tibble: 5 x 2
#> x x_squared
#> <int> <dbl>
#> 1 1 1
#> 2 2 4
#> 3 3 9
#> 4 4 16
#> 5 5 25
Note that this sequential evaluation does not work on classical dataframes.
data.frame(x = 1:5, x_squared = x ^ 2)
#> Error in data.frame(x = 1:5, x_squared = x^2): object 'x' not found
The function data_frame()—note the underscore instead of a dot—is an alias
for tibble(), which might be more transparent if your audience has never
heard of tibbles.
data_frame(x = 1:5, x_squared = x ^ 2)
#> # A tibble: 5 x 2
#> x x_squared
#> <int> <dbl>
#> 1 1 1
#> 2 2 4
#> 3 3 9
#> 4 4 16
#> 5 5 25
In tibble(), the data are defined column-by-column. We can use tribble() to
write out tibbles row-by-row. Formulas like ~x are used to denote column names.
tribble(
~ Film, ~ Year,
"A New Hope", 1977,
"The Empire Strikes Back", 1980,
"Return of the Jedi", 1983)
#> # A tibble: 3 x 2
#> Film Year
#> <chr> <dbl>
#> 1 A New Hope 1977
#> 2 The Empire Strikes Back 1980
#> 3 Return of the Jedi 1983
The name “tribble” is short for “transposed tibble” (the transposed
part referring to change from column-wise creation in tibble() to row-wise
creation in tribble()).
I like to use light-weight tribbles for two particular tasks:
- Recoding: Create a tribble of, say, labels for a plot and join it onto a dataset.
- Exclusion: Identify observations to exclude, and remove them with an anti-join.
Pretend that we have a tibble called dataset. The code below shows
examples of these tasks with dataset.
library(dplyr)
# Recoding example
plotting_labels <- tribble(
~ Group, ~ GroupLabel,
"TD", "Typically Developing",
"CI", "Cochlear Implant",
"ASD", "Autism Spectrum"
)
# Attach labels to dataset
dataset <- left_join(dataset, plotting_labels, by = "Group")
# Exclusion example
ids_to_exclude <- tibble::tribble(
~ Study, ~ ResearchID,
"TimePoint1", "053L",
"TimePoint1", "102L",
"TimePoint1", "116L"
)
reduced_dataset <- anti_join(dataset, ids_to_exclude)
Converting things into tibbles
as_tibble() will convert dataframes, matrices, and some other types into
tibbles.
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 21.0 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
#> # ... with 22 more rows
We can convert simple named vectors into tibbles with enframe().
For example, quantile() returns a named vector which we can enframe().
quantiles <- quantile(mtcars$hp, probs = c(.1, .25, .5, .75, .9))
quantiles
#> 10% 25% 50% 75% 90%
#> 66.0 96.5 123.0 180.0 243.5
enframe(quantiles, "quantile", "value")
#> # A tibble: 5 x 2
#> quantile value
#> <chr> <dbl>
#> 1 10% 66.0
#> 2 25% 96.5
#> 3 50% 123.0
#> 4 75% 180.0
#> 5 90% 243.5
I have not had an opportunity to use enframe() since I learned about it,
but I definitely have created dataframes from name-value pairs in the past.
It’s also worth noting the most common way I create tibbles: Reading in files. The readr package will create tibbles when reading in data files like csvs.
Viewing some values from each column
When we print() a tibble, we only see enough dataframe columns to fill the
width of the console. For example, we will not see every column in this
tibble.
# Create a 200 x 26 dataframe
df <- as.data.frame(replicate(26, 1:200)) %>%
setNames(letters) %>%
as_tibble()
df
#> # A tibble: 200 x 26
#> a b c d e f g h i j k l
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 2 2 2 2 2 2 2 2 2 2 2 2 2
#> 3 3 3 3 3 3 3 3 3 3 3 3 3
#> 4 4 4 4 4 4 4 4 4 4 4 4 4
#> 5 5 5 5 5 5 5 5 5 5 5 5 5
#> 6 6 6 6 6 6 6 6 6 6 6 6 6
#> 7 7 7 7 7 7 7 7 7 7 7 7 7
#> 8 8 8 8 8 8 8 8 8 8 8 8 8
#> 9 9 9 9 9 9 9 9 9 9 9 9 9
#> 10 10 10 10 10 10 10 10 10 10 10 10 10
#> # ... with 190 more rows, and 14 more variables: m <int>, n <int>,
#> # o <int>, p <int>, q <int>, r <int>, s <int>, t <int>, u <int>,
#> # v <int>, w <int>, x <int>, y <int>, z <int>
We can transpose the printing with glimpse() to see a few values from every
column. Once again, just enough data is shown to fill the width of the output
console.
glimpse(df)
#> Observations: 200
#> Variables: 26
#> $ a <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ b <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ c <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ d <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ e <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ f <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ g <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ h <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ i <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ j <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ k <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ l <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ m <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ n <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ o <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ p <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ q <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ r <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ s <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ t <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ u <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ v <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ w <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ x <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ y <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
#> $ z <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1...
Growing a tibble
We can add new rows and columns with add_row() and add_column().
Below we add rows to the bottom of the tibble (the default behavior) and to the
top of the tibble by using the .before argument (add the new row before row 1).
There also is an .after argument, but I prefer to only add rows to the
tops and bottoms of tables. The values in the add_row() are computed
iteratively, so we can define the values of x_squared in terms of x.
df <- tibble(comment = "original", x = 1:2, x_squared = x ^ 2)
df
#> # A tibble: 2 x 3
#> comment x x_squared
#> <chr> <int> <dbl>
#> 1 original 1 1
#> 2 original 2 4
df <- df %>%
add_row(comment = "append", x = 3:4, x_squared = x ^ 2) %>%
add_row(comment = "prepend", x = 0, x_squared = x ^ 2, .before = 1)
df
#> # A tibble: 5 x 3
#> comment x x_squared
#> <chr> <dbl> <dbl>
#> 1 prepend 0 0
#> 2 original 1 1
#> 3 original 2 4
#> 4 append 3 9
#> 5 append 4 16
The value NA is used when values are not provided for a certain column.
Also, because we provide the names of the columns when adding rows, we
don’t have to write out the columns in any particular order.
df %>%
add_row(x = 5, comment = "NA defaults") %>%
add_row(x_squared = 36, x = 6, comment = "order doesn't matter")
#> # A tibble: 7 x 3
#> comment x x_squared
#> <chr> <dbl> <dbl>
#> 1 prepend 0 0
#> 2 original 1 1
#> 3 original 2 4
#> 4 append 3 9
#> 5 append 4 16
#> 6 NA defaults 5 NA
#> 7 order doesn't matter 6 36
We can similarly add columns with add_column().
df %>% add_column(comment2 = "inserted column", .after = "comment")
#> # A tibble: 5 x 4
#> comment comment2 x x_squared
#> <chr> <chr> <dbl> <dbl>
#> 1 prepend inserted column 0 0
#> 2 original inserted column 1 1
#> 3 original inserted column 2 4
#> 4 append inserted column 3 9
#> 5 append inserted column 4 16
Typically, with dplyr loaded, you would create new columns by using mutate(),
although I have recently started to prefer using add_column() for cases like
the above example, where I add a column with a single recycled value.
Row names and identifiers
Look at the converted mtcars tibble again.
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 21.0 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
#> # ... with 22 more rows
The row numbers in the converted dataframe have an asterisk * above them. That
means that the dataframe has row-names. Row-names are clunky and quirky; they are
just a column of data (labels) that umm :confused: we store away from the rest
of the data.
We should move those row-names into an explicit column, and
rownames_to_column() does just that.
mtcars %>%
as_tibble() %>%
rownames_to_column("model")
#> # A tibble: 32 x 12
#> model mpg cyl disp hp drat wt qsec vs am
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1
#> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1
#> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1
#> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0
#> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0
#> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0
#> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0
#> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0
#> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0
#> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0
#> # ... with 22 more rows, and 2 more variables: gear <dbl>, carb <dbl>
When I fit Bayesian models, I end up with a bunch of samples from a posterior
distribution. In my data-tidying, I need to assign a ID-number to each sample.
The function rowid_to_column() automates this step by creating a new column in
a dataframe with the row-numbers. In the example below, I load some MCMC samples
from the coda package and create draw IDs.
library(coda)
data(line, package = "coda")
line1 <- as.matrix(line$line1) %>%
as_tibble()
line1
#> # A tibble: 200 x 3
#> alpha beta sigma
#> <dbl> <dbl> <dbl>
#> 1 7.17313 -1.566200 11.233100
#> 2 2.95253 1.503370 4.886490
#> 3 3.66989 0.628157 1.397340
#> 4 3.31522 1.182720 0.662879
#> 5 3.70544 0.490437 1.362130
#> 6 3.57910 0.206970 1.043500
#> 7 2.70206 0.882553 1.290430
#> 8 2.96136 1.085150 0.459322
#> 9 3.53406 1.069260 0.634257
#> 10 2.09471 1.480770 0.912919
#> # ... with 190 more rows
line1 %>% rowid_to_column("draw")
#> # A tibble: 200 x 4
#> draw alpha beta sigma
#> <int> <dbl> <dbl> <dbl>
#> 1 1 7.17313 -1.566200 11.233100
#> 2 2 2.95253 1.503370 4.886490
#> 3 3 3.66989 0.628157 1.397340
#> 4 4 3.31522 1.182720 0.662879
#> 5 5 3.70544 0.490437 1.362130
#> 6 6 3.57910 0.206970 1.043500
#> 7 7 2.70206 0.882553 1.290430
#> 8 8 2.96136 1.085150 0.459322
#> 9 9 3.53406 1.069260 0.634257
#> 10 10 2.09471 1.480770 0.912919
#> # ... with 190 more rows
From here, I could reshape the data into a long format or draw some random samples for use in a plot, all while preserving the draw number.
…And that covers the main functionality of the tibble package. I hope you discovered a new useful feature of the tibble package. To learn more about the technical differences between tibbles and dataframes, see the tibble chapter in R for Data Science.
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This article is originally published at https://tjmahr.github.io/
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