Ch. 10: Tibbles

Key questions:

none
Functions and notes:
  • tibble: produces a dataframe w/ some other helpful qualities that have advantages over data.frame
    • see vignette("tibble")
  • as_tibble: convert to a tibble
  • tribble: transposed tibble - set-up for data entry into a tibble in code
  • print: can use print to set how the tibble will print
nycflights13::flights %>% 
  print(n = 2, width = Inf)
## # A tibble: 336,776 x 19
##    year month   day dep_time sched_dep_time dep_delay arr_time
##   <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1  2013     1     1      517            515         2      830
## 2  2013     1     1      533            529         4      850
##   sched_arr_time arr_delay carrier flight tailnum origin dest  air_time
##            <int>     <dbl> <chr>    <int> <chr>   <chr>  <chr>    <dbl>
## 1            819        11 UA        1545 N14228  EWR    IAH        227
## 2            830        20 UA        1714 N24211  LGA    IAH        227
##   distance  hour minute time_hour          
##      <dbl> <dbl>  <dbl> <dttm>             
## 1     1400     5     15 2013-01-01 05:00:00
## 2     1416     5     29 2013-01-01 05:00:00
## # ... with 3.368e+05 more rows
* Also can convert with `as.data.frame` or use `options`, see [10.5: Exercises], problem 6
  • enframe: let’s you encode name and value, see 10.5: Exercises, problem 5 below
  • class: for checking the class of the object
    • Though is not fully accurate, in that the actual object class of vectors is “base”, not double, etc., so kind of lies…

10.5: Exercises

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

Could look at printing, e.g. only prints first 15 rows and enough variables where you can see them all, or by checking explicitly the class function23

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?

  • Tibbles never change type of input e.g. from strings to factors
  • Tibbles never change names of variables, never creates row names
  • Tibbles print in a more concise and readable format
    • This difference is made more stark if working with list-columns

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 <- "var_name"

# Will extract the column as an atomic vector
df[[var]]

4. Practice referring to non-syntactic names in the following data frame by:

df <- tibble(`1` = 1:10, `2` = 11:20)

a. Extracting the variable called 1.

df %>% 
  select(1)
## # A tibble: 10 x 1
##      `1`
##    <int>
##  1     1
##  2     2
##  3     3
##  4     4
##  5     5
##  6     6
##  7     7
##  8     8
##  9     9
## 10    10

b. Plotting a scatterplot of 1 vs 2.

df %>% 
  ggplot(aes(x = `1`, y = `2`))+
  geom_point()

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

df %>% 
  mutate(`3` = `1` / `2`) 
## # A tibble: 10 x 3
##      `1`   `2`    `3`
##    <int> <int>  <dbl>
##  1     1    11 0.0909
##  2     2    12 0.167 
##  3     3    13 0.231 
##  4     4    14 0.286 
##  5     5    15 0.333 
##  6     6    16 0.375 
##  7     7    17 0.412 
##  8     8    18 0.444 
##  9     9    19 0.474 
## 10    10    20 0.5

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

df %>% 
  mutate(`3` = `1` / `2`) %>% 
  rename(one = `1`,
         two = `2`,
         three = `3`)
## # A tibble: 10 x 3
##      one   two  three
##    <int> <int>  <dbl>
##  1     1    11 0.0909
##  2     2    12 0.167 
##  3     3    13 0.231 
##  4     4    14 0.286 
##  5     5    15 0.333 
##  6     6    16 0.375 
##  7     7    17 0.412 
##  8     8    18 0.444 
##  9     9    19 0.474 
## 10    10    20 0.5

5. What does tibble::enframe() do? When might you use it?

Let’s you encode “name” and “value” as a tibble from a named vector

tibble::enframe(c(a = 5, b = 8))
## # A tibble: 2 x 2
##   name  value
##   <chr> <dbl>
## 1 a         5
## 2 b         8
tibble::enframe(c(a = 5:8, b = 7:10))
## # A tibble: 8 x 2
##   name  value
##   <chr> <int>
## 1 a1        5
## 2 a2        6
## 3 a3        7
## 4 a4        8
## 5 b1        7
## 6 b2        8
## 7 b3        9
## 8 b4       10

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

  • argument tibble.width
options(tibble.print_max = n, tibble.print_min = m)
options(tibble.width = Inf)
options(dplyr.print_min = Inf) #to always show all rows

  1. or could check a few other things such as if list-cols are supported↩