Code
# Loading package(s)
library(tidyverse)
library(nycflights13)
data("mtcars")
data(flights)
data(mpg)L05 Tibbles
Data Science 1 with R (STAT 301-1)
Load Packages
Exercises
Exercise 1
Please read the vignette for the tibble package.
Code
# Access vignette
vignette("tibble")Done
Exercise 2
Demonstrate how to manually input the data table below into R using each of these functions:
tibble()tribble()
| price | store | ounces |
|---|---|---|
| 3.99 | target | 128 |
| 3.75 | walmart | 128 |
| 3.00 | amazon | 128 |
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tibble(
`price` = c(3.99, 3.75, 3),
`store` = c("target", "walmart", "amazon"),
`ounces` = c(128, 128, 128))# A tibble: 3 × 3
price store ounces
<dbl> <chr> <dbl>
1 3.99 target 128
2 3.75 walmart 128
3 3 amazon 128Code
tribble(
~price, ~store, ~ounces,
#--|--|----
3.99, "target", 128,
3.75, "walmart", 128,
3, "amazon", 128)# A tibble: 3 × 3
price store ounces
<dbl> <chr> <dbl>
1 3.99 target 128
2 3.75 walmart 128
3 3 amazon 128Exercise 3
How can you tell if an object is a tibble? Consider including an example or two. (Hint: try printing mtcars, which is a regular data frame).
Code
print(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 2Code
tibble(mtcars)# A tibble: 32 × 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 rowsSolution
Tibbles will only show the first ten rows, so when we initially print the mtcars dataset, we can see that there are too many rows for it to be a tibble. The
Exercise 4
Turn mtcars into a tibble and ask R to print only the first 4 observations/rows. Consider using a slice_*() function.
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tibble(mtcars) %>%
slice_head(n = 4)# A tibble: 4 × 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 1Exercise 5
Run the following operations on df as a data frame. Then turn df into a tibble and run the operations again. What changes? Why might the default data frame behaviors cause problems or frustration?
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df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]Code
df <- data.frame(abc = 1, xyz = "a")
df_tibble <- as_tibble(df)
df$x[1] "a"Code
df_tibble$xNULLCode
df[, "xyz"][1] "a"Code
df_tibble[, "xyz"]# A tibble: 1 × 1
xyz
<chr>
1 a Code
df[, c("abc", "xyz")] abc xyz
1 1 aCode
df_tibble[, c("abc", "xyz")]# A tibble: 1 × 2
abc xyz
<dbl> <chr>
1 1 a Solution
Data frames will automatically find the “x” by autocompleting the rest of the column name if the column simply starts with x, but, with tibble, we need to write out the full column name. The autocompletion would be very problematic in a larger dataset with multiple variables starting with x. With the next example, the first command only returns a vector, but the tibble returns the selected part of the tibble. Finally, in the final example, when we ask for two columns, it returns a dataframe, where the tibble still shows the selected tibble. The inconsistency with the default dataframe command when subsetting is very troubling, and speaks to the benefits of using tibbles.
Exercise 6
If you have the name of a variable stored as an object, for example var <- "mpg", how can you extract the specified variable from a tibble? Write code to demonstrate.
Code
var <- "mpg"
mtcars_tibble<- as.tibble(mtcars)
mtcars_tibble[, var]# A tibble: 32 × 1
mpg
<dbl>
1 21
2 21
3 22.8
4 21.4
5 18.7
6 18.1
7 14.3
8 24.4
9 22.8
10 19.2
# … with 22 more rowsCode
mtcars_tibble[[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 21.4Code
mtcars_tibble %>%
select(var)# A tibble: 32 × 1
mpg
<dbl>
1 21
2 21
3 22.8
4 21.4
5 18.7
6 18.1
7 14.3
8 24.4
9 22.8
10 19.2
# … with 22 more rowsExercise 7
How is subsetting via [[ ]] different from using select() when extracting columns of a tibble? (Hint: Investigate using one of our familiar datasets — flights, diamonds, or mpg.)
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flights %>% select(dest)# A tibble: 336,776 × 1
dest
<chr>
1 IAH
2 IAH
3 MIA
4 BQN
5 ATL
6 ORD
7 FLL
8 IAD
9 MCO
10 ORD
# … with 336,766 more rowsCode
flights[["dest"]][c(1:7)][1] "IAH" "IAH" "MIA" "BQN" "ATL" "ORD" "FLL"Solution
The select() function returns a tibble, but subsetting via [[ ]] returns a character vector. To limit the amount of rows in the vector, we can add the [c(1:7)], which would allow us to just look at the first seven rows. If not, the qmd file would become way too long to scroll through.
Exercise 8
Practice referring to non-syntactic names in the following data frame by:
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# toy dataset
annoying <- tibble(
`1` = 1:10,
`2` = `1` * 2 + rnorm(length(`1`))
)- Extracting the variable called 1.
- Plotting a scatterplot of 1 vs 2.
- Creating a new column called 3 which is 2 divided by 1.
- Renaming the columns to one, two and three.
Code
# a
annoying[[1]] [1] 1 2 3 4 5 6 7 8 9 10Code
# b
annoying %>%
ggplot(aes(x= `1`, y = `2`)) + geom_point()
Code
#c
updated <- annoying %>%
add_column(`3` = (.$`2`/.$`1`))
#d
updated %>%
rename(one = `1`, two = `2`, three = `3`)# A tibble: 10 × 3
one two three
<int> <dbl> <dbl>
1 1 1.78 1.78
2 2 4.18 2.09
3 3 6.24 2.08
4 4 7.90 1.98
5 5 10.1 2.02
6 6 10.5 1.75
7 7 14.9 2.13
8 8 17.8 2.22
9 9 17.9 1.99
10 10 20.6 2.06Exercise 9
What does tibble::enframe() do? When might you use it? (Hint: A named vector is one where each value has a specified name – examples in code below.)
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# example of a named vector
named_vector <- c("I" = 3.14, "love" = 2.72, "stats!" = 1.61)
# Consider how enframe() would be helpful in the following example:
# random sample of 1000 from a normal distribution
foo <- rnorm(n = 1000, mean = 100, sd = 15)
summary(foo)Code
foo <- rnorm(n = 1000, mean = 100, sd = 15)
summary(foo) Min. 1st Qu. Median Mean 3rd Qu. Max.
54.87 89.85 99.82 100.02 110.51 154.24 Code
tibble::enframe(foo)# A tibble: 1,000 × 2
name value
<int> <dbl>
1 1 102.
2 2 108.
3 3 89.6
4 4 109.
5 5 134.
6 6 107.
7 7 87.8
8 8 98.7
9 9 86.7
10 10 84.6
# … with 990 more rowsSolution
tibble:enframe() converts named vectors to one-to-two column data frames. You would use this to take a list/named vector to a readible vertical column.
Exercise 10
Apply tibble::glimpse() to the flights dataset. Then apply print() to flights. When/why might glimpse() be more useful than print()?
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tibble::glimpse(flights)Rows: 336,776
Columns: 19
$ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
$ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
$ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
$ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
$ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
$ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
$ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
$ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
$ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
$ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
$ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
$ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
$ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
$ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
$ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…Code
print(flights)# A tibble: 336,776 × 19
year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
<int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
1 2013 1 1 517 515 2 830 819 11 UA
2 2013 1 1 533 529 4 850 830 20 UA
3 2013 1 1 542 540 2 923 850 33 AA
4 2013 1 1 544 545 -1 1004 1022 -18 B6
5 2013 1 1 554 600 -6 812 837 -25 DL
6 2013 1 1 554 558 -4 740 728 12 UA
7 2013 1 1 555 600 -5 913 854 19 B6
8 2013 1 1 557 600 -3 709 723 -14 EV
9 2013 1 1 557 600 -3 838 846 -8 B6
10 2013 1 1 558 600 -2 753 745 8 AA
# … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
# origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
# minute <dbl>, time_hour <dttm>, and abbreviated variable names
# ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delaySolution
There are 19 different variables to represent as columns, but simply printing the table will cut off the last nine and limit it to ten columns. With glimpse(), however, all 19 variables are present