# install.packages("tibble")
library(tibble)
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"
)
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
)
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, 0.157, 0.44,
0.0019, 0.1545, 0.003, 0.18
)
# Create the tibble
animals <- tibble(
species = species,
bodywt_kg = bodywt_kg,
brainwt_kg = brainwt_kg
)Solution to tibble tasks
1 Create a tibble
Use the vectors species, brainwt_kg, bodywt_kg from the last task and put them in a tibble.
2 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. :0.00040
Class :character 1st Qu.: 3.10 1st Qu.:0.02223
Mode :character Median : 53.85 Median :0.13700
Mean : 4278.44 Mean :0.57452
3rd Qu.: 479.00 3rd Qu.:0.42000
Max. :87000.00 Max. :5.71200 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"3 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$bodywt_kg [1] 1.40 465.00 36.30 27.70 1.00 11700.00 2547.00 187.10
[9] 521.00 10.00 3.30 529.00 207.00 62.00 6654.00 9400.00
[17] 6.80 35.00 0.10 0.02 2.50 55.50 100.00 52.20
[25] 0.30 87000.00 0.10 192.004 Extra
- Select the last column of the last row without using the row and column index (so don’t use any numbers in this command)
animals[nrow(animals), ncol(animals)]# A tibble: 1 × 1
brainwt_kg
<dbl>
1 0.18- Calculate the mean of the
bodywt_kgcolumn and save it in a variable
mean_wt <- mean(animals$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