R4DS Practice 07: Tibbles
The codes below are from the practice exercises in https://r4ds.had.co.nz/, and are taken with reference from: https://jrnold.github.io/r4ds-exercise-solutions/
Let’s begin now
Loading tidyverse package.
To coerce a data frame into a tibble, use the as_tibble() function:
iris # this is a dataframe
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginicaas_tibble(iris) # this is a tibble
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rowsTo create a tibble:
tibble(
x = 1:5,
y = 1, # automatically filled down
z = x ^2 + y
)
# A tibble: 5 x 3
x y z
<int> <dbl> <dbl>
1 1 1 2
2 2 1 5
3 3 1 10
4 4 1 17
5 5 1 26To create a transposed tibble, use tribble() :
tribble(
~x, ~y, ~z,
# -- # -- # --
"a", 2, 3.6,
"b", 1, 8.5
)
# A tibble: 2 x 3
x y z
<chr> <dbl> <dbl>
1 a 2 3.6
2 b 1 8.5Exercise:
To check if an object is a tibble:
str(mtcars) # this is a dataframe
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...is_tibble(mtcars)
[1] FALSEis_tibble(diamonds)
[1] TRUEis_tibble(iris)
[1] FALSECompare and contrast the following operations on a data.frame and equivalent tibble:
df <- data.frame(abc = 1,
xyz = "a")
df
abc xyz
1 1 adf$x
[1] "a"df[ , "xyz"]
[1] "a"df[ , c("abc", "xyz")]
abc xyz
1 1 atbl <- as_tibble(df)
tbl
# A tibble: 1 x 2
abc xyz
<dbl> <chr>
1 1 a tbl[, "xyz"]
# A tibble: 1 x 1
xyz
<chr>
1 a tbl[ , c("abc", "xyz")]
# A tibble: 1 x 2
abc xyz
<dbl> <chr>
1 1 a Practice referring to nonsyntactic names in the following dataframe:
# A tibble: 10 x 2
`1` `2`
<int> <dbl>
1 1 3.59
2 2 4.09
3 3 6.77
4 4 8.48
5 5 8.72
6 6 13.8
7 7 14.0
8 8 15.0
9 9 17.8
10 10 21.1 # Extract the variable called 1:
annoying$`1`
[1] 1 2 3 4 5 6 7 8 9 10# Plot a scatterplot of 1 vs 2
annoying %>%
ggplot(aes(x = `1`,
y = `2`)) +
geom_point() +
theme_classic()
# Create a new column called 3, which is 2 divided by 1
annoying_new <- annoying %>%
mutate(`3` = `2` / `1`) %>%
dplyr::rename(one = `1`, # need to specify dplyr:: if not will have conflict with plyr
two = `2`,
three = `3`)
glimpse(annoying_new)
Rows: 10
Columns: 3
$ one <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
$ two <dbl> 3.585017, 4.092088, 6.767932, 8.476036, 8.722597, 13.7…
$ three <dbl> 3.585017, 2.046044, 2.255977, 2.119009, 1.744519, 2.29…What does tibble::enframe() do?
enframe() converts named atomic vectors or lists to one- or two-column data frames. For a list, the result will be a nested tibble with a column of type list. For unnamed vectors, the natural sequence is used as name column.
enframe(1:3)
# A tibble: 3 x 2
name value
<int> <int>
1 1 1
2 2 2
3 3 3enframe(c(a = 5, b = 7))
# A tibble: 2 x 2
name value
<chr> <dbl>
1 a 5
2 b 7enframe(list(one = 1, two = 2:3, three = 4:6))
# A tibble: 3 x 2
name value
<chr> <list>
1 one <dbl [1]>
2 two <int [2]>
3 three <int [3]>deframe(tibble(a = as.list(1:3)))
[[1]]
[1] 1
[[2]]
[1] 2
[[3]]
[1] 3What option controls how many additional column names are printed at the foot of a tibble?
print(mtcars)
mpg cyl disp hp drat wt qsec vs am gear
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
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 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
carb
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2
Valiant 1
Duster 360 4
Merc 240D 2
Merc 230 2
Merc 280 4
Merc 280C 4
Merc 450SE 3
Merc 450SL 3
Merc 450SLC 3
Cadillac Fleetwood 4
Lincoln Continental 4
Chrysler Imperial 4
Fiat 128 1
Honda Civic 2
Toyota Corolla 1
Toyota Corona 1
Dodge Challenger 2
AMC Javelin 2
Camaro Z28 4
Pontiac Firebird 2
Fiat X1-9 1
Porsche 914-2 2
Lotus Europa 2
Ford Pantera L 4
Ferrari Dino 6
Maserati Bora 8
Volvo 142E 2print(mtcars, n_extra = 4)
mpg cyl disp hp drat wt qsec vs am gear
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
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 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
carb
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2
Valiant 1
Duster 360 4
Merc 240D 2
Merc 230 2
Merc 280 4
Merc 280C 4
Merc 450SE 3
Merc 450SL 3
Merc 450SLC 3
Cadillac Fleetwood 4
Lincoln Continental 4
Chrysler Imperial 4
Fiat 128 1
Honda Civic 2
Toyota Corolla 1
Toyota Corona 1
Dodge Challenger 2
AMC Javelin 2
Camaro Z28 4
Pontiac Firebird 2
Fiat X1-9 1
Porsche 914-2 2
Lotus Europa 2
Ford Pantera L 4
Ferrari Dino 6
Maserati Bora 8
Volvo 142E 2Reference
https://jrnold.github.io/r4ds-exercise-solutions/