# install.packages("tibble")
library(tibble)
# fmt: skip
species <- c(
"MountainBeaver", "Cow", "GreyWolf", "Goat",
"GuineaPig", "Diplodocus", "AsianElephant", "Donkey",
"Horse", "PotarMonkey", "Cat", "Giraffe",
"Gorilla", "Human", "AfricanElephant", "Triceratops",
"RhesusMonkey", "Kangaroo", "GoldenHamster", "Mouse",
"Rabbit", "Sheep", "Jaguar", "Chimpanzee",
"Rat", "Brachiosaurus", "Mole", "Pig"
)
# fmt: skip
bodywt_kg <- c(
1.4, 465, 36.3, 27.7, 1., 11700, 2547, 187.1,
521, 10, 3.3, 529, 207, 62, 6654, 9400,
6.8, 35, 0.1, 0.02, 2.5, 55.5, 100, 52.2,
0.3, 87000, 0.1, 192
)
# fmt: skip
brainwt_kg <- c(
0.0081, 0.423, 0.1195, 0.115, 0.0055, 0.05,
4.603, 0.419, 0.655, 0.115, 0.0256, 0.68,
0.406, 1.32, 5.712, 0.07, 0.179, 0.056,
0.001, 0.0004, 0.0121, 0.175, NA, 0.44,
0.0019, 0.1545, NA, 0.18
)
# Create the tibble
animals <- tibble(
species = species,
bodywt_kg = bodywt_kg,
brainwt_kg = brainwt_kg
)Solution to tibble tasks
Create a tibble
Use the vectors species, brainwt_kg, bodywt_kg from the last task and put them in a tibble.
Explore the tibble
When you use the view function, the tibble will open in a new window. In this window, you also have some options to filter the data and sort rows. Check out these options as well.
view(animals)Print a summary of the table:
summary(animals) species bodywt_kg brainwt_kg
Length:28 Min. : 0.02 Min. :0.0004
Class :character 1st Qu.: 3.10 1st Qu.:0.0317
Mode :character Median : 53.85 Median :0.1370
Mean : 4278.44 Mean :0.6126
3rd Qu.: 479.00 3rd Qu.:0.4220
Max. :87000.00 Max. :5.7120
NA's :2 This summary table is super usefule to check if everything is ok with your data. For example, you can check if there are any missing values (NA) in the columns, or if the values in the columns look reasonable (e.g. no negative body weights).
Here a couple of other function you could use on a tibble:
# Look at the structure of the tibble
str(animals)tibble [28 × 3] (S3: tbl_df/tbl/data.frame)
$ species : chr [1:28] "MountainBeaver" "Cow" "GreyWolf" "Goat" ...
$ bodywt_kg : num [1:28] 1.4 465 36.3 27.7 1 ...
$ brainwt_kg: num [1:28] 0.0081 0.423 0.1195 0.115 0.0055 ...# Number of rows
nrow(animals)[1] 28# Number of columns
ncol(animals)[1] 3# Column names
names(animals)[1] "species" "bodywt_kg" "brainwt_kg"Index, filter and select
- Rows 1, 5, and 7 and the columns
speciesandbodywt_kg
animals[c(1, 5, 7), c("species", "bodywt_kg")]# A tibble: 3 × 2
species bodywt_kg
<chr> <dbl>
1 MountainBeaver 1.4
2 GuineaPig 1
3 AsianElephant 2547 - Filter rows 1 to 10, all columns
animals[1:10, ]# A tibble: 10 × 3
species bodywt_kg brainwt_kg
<chr> <dbl> <dbl>
1 MountainBeaver 1.4 0.0081
2 Cow 465 0.423
3 GreyWolf 36.3 0.120
4 Goat 27.7 0.115
5 GuineaPig 1 0.0055
6 Diplodocus 11700 0.05
7 AsianElephant 2547 4.60
8 Donkey 187. 0.419
9 Horse 521 0.655
10 PotarMonkey 10 0.115 - Select the column
bodywt_kgas a vector using$
animals$bodywt_kg [1] 1.40 465.00 36.30 27.70 1.00 11700.00 2547.00 187.10
[9] 521.00 10.00 3.30 529.00 207.00 62.00 6654.00 9400.00
[17] 6.80 35.00 0.10 0.02 2.50 55.50 100.00 52.20
[25] 0.30 87000.00 0.10 192.00Optional tasks
- Select the last column of the last row without using the row and column index (so don’t use any numbers in this command)
animals[nrow(animals), ncol(animals)]# A tibble: 1 × 1
brainwt_kg
<dbl>
1 0.18- Calculate the mean of the
bodywt_kgcolumn and save it in a variable
mean_wt <- mean(animals$bodywt_kg)
mean_wt[1] 4278.44- Add a new column to the data called
ratio_body_brainwith the ratio of the columnsbodywt_kgandbrainwt_kg
animals$ratio_body_brain <- animals$bodywt_kg / animals$brainwt_kg
animals# A tibble: 28 × 4
species bodywt_kg brainwt_kg ratio_body_brain
<chr> <dbl> <dbl> <dbl>
1 MountainBeaver 1.4 0.0081 173.
2 Cow 465 0.423 1099.
3 GreyWolf 36.3 0.120 304.
4 Goat 27.7 0.115 241.
5 GuineaPig 1 0.0055 182.
6 Diplodocus 11700 0.05 234000
7 AsianElephant 2547 4.60 553.
8 Donkey 187. 0.419 447.
9 Horse 521 0.655 795.
10 PotarMonkey 10 0.115 87.0
# ℹ 18 more rows