>Meet R

> Your first hands on session

\ Otho Mantegazza _ Dataviz for Scientists _ Part 1.1

R understands you

Write something at the R console:

A simple number…

2

[1] 2

Some operation…

3 + 7

[1] 10

Text must be quoted…

'hello'

[1] "hello"

Otherwise it is interpreted as the name of a variable:

my_var

Error in eval(expr, envir, enclos): object 'my_var' not found

Define variables with <-

You can define a variable and assign a value to it with the operator <-

# a numeric variable
this_year <- 2022
 
# a character variable
italian_greetings <- 'ciao'

If you type the name of a variable without quotes, r prints its value:

this_year

[1] 2022

italian_greetings

[1] "ciao"

Define variables with <-

Use the keyboard shortcut “alt/option” + “-” to write the assignment operator <- more easily.

Data Types

When you define an object R guesses its type.

typeof() returns the internal type of an object.

typeof(this_year)

[1] "double"

typeof(italian_greetings)

[1] "character"

class() returns a the class attribute of an object.

class(this_year)

[1] "numeric"

class(italian_greetings)

[1] "character"

Everything in R is an object

And objects have a types.

class(2)

[1] "numeric"

class('hello')

[1] "character"

class(FALSE)

[1] "logical"

class(mean)

[1] "function"

class(`<-`)

[1] "function"

class(Sys.Date())

[1] "Date"

Exercise

1. Use the function c(arg_1, arg_2, …, arg_n) to put together in a vector many objects that you expect to have the same class. Try with different ones.

2. Now do the same, but try to put together in a vector data with different types, what happened?

Learn more about vectors and types at Advanced R.

All Data Are Vectors

Vectors store one or more data point of the same class:

num_vector <- c(1, 3, 5, 3, 6)

character_vector <- c('Hi', ',', 'I', 'am', 'a', 'character', 'vector')

logical_vector <- c(TRUE, FALSE, FALSE, TRUE, FALSE)

class(num_vector)

[1] "numeric"

class(character_vector)

[1] "character"

class(logical_vector)

[1] "logical"

Classes can be coherced

TRUE and FALSE can be coerced into numeric zeroes and ones, or also into characters.

c(2, TRUE, 5, FALSE, 4)

[1] 2 1 5 0 4

. . .

Numbers can be coerced into characters.

c('a', TRUE, 5, FALSE, 4)

[1] "a"     "TRUE"  "5"     "FALSE" "4"    

Classes can be coherced

Classes can be coerced explicitly.

logical_vector <- c(T, F, F, T, T, F)
logical_vector

[1]  TRUE FALSE FALSE  TRUE  TRUE FALSE

as.numeric(logical_vector)

[1] 1 0 0 1 1 0

as.character(logical_vector)

[1] "TRUE"  "FALSE" "FALSE" "TRUE"  "TRUE"  "FALSE"

. . .

If an element can’t be coerced to the desired class, R returns a missing value “NA”.

as.numeric(c('1', 'two', '3'))

[1]  1 NA  3

Atomic elements are vectors

atomic_vector <- 1
long_vector <- 1:50

. . .

atomic_vector

[1] 1

long_vector

 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Atomic elements are vectors

class(atomic_vector)

[1] "numeric"

length(atomic_vector)

[1] 1

is.vector(atomic_vector)

[1] TRUE

class(long_vector)

[1] "integer"

length(long_vector)

[1] 50

is.vector(long_vector)

[1] TRUE

Vectorized Functions

Most base R functions are vectorized.

If I want to apply an operation over a vector, I can just write it as it is without a for loop. For example:

• Add an integer to every number in a vector:

long_vector + 100

 [1] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
[20] 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
[39] 139 140 141 142 143 144 145 146 147 148 149 150

Vectorized Functions

• Multiply every number in a vector by an integer:

long_vector * 2

 [1]   2   4   6   8  10  12  14  16  18  20  22  24  26  28  30  32  34  36  38
[20]  40  42  44  46  48  50  52  54  56  58  60  62  64  66  68  70  72  74  76
[39]  78  80  82  84  86  88  90  92  94  96  98 100

Vectorized Functions

Take the square root of every number in a vector:

sqrt(long_vector)

 [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
 [9] 3.000000 3.162278 3.316625 3.464102 3.605551 3.741657 3.872983 4.000000
[17] 4.123106 4.242641 4.358899 4.472136 4.582576 4.690416 4.795832 4.898979
[25] 5.000000 5.099020 5.196152 5.291503 5.385165 5.477226 5.567764 5.656854
[33] 5.744563 5.830952 5.916080 6.000000 6.082763 6.164414 6.244998 6.324555
[41] 6.403124 6.480741 6.557439 6.633250 6.708204 6.782330 6.855655 6.928203
[49] 7.000000 7.071068

Exercise

1. Generate a vector of 100 (semi-) random numbers with a normal distribution.

2. Use a for loop (even if you don’t need one) to multiply each number in the vector by 2.

For loops in R according to Roger D. Peng.

Lists

If you need to collect together and store data of different types, you can use lists.

list(
  'hello',
  1,
  FALSE,
  1:25
)

[[1]]
[1] "hello"

[[2]]
[1] 1

[[3]]
[1] FALSE

[[4]]
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Data frames

We learned that vector contain elements of the same type, for example, numeric, character, logical.

According to the Tidy Data theory, in rectangular data frames:

• Each row is an observation.
• Each column is a variable.

By definition variables of your data, must be made by atomic elements of the same type. So we can use vectors of the same length to build the columns of a data frame.

Data frames

Let’s prepare three vectors with the first observations of Iris and use them to make a data frame.

sepal_length <- c(5.1, 7.0, 6.3)
sepal_width <- c(3.5, 3.2, 3.3)
species <- c(
  'setosa', 'versicolor', 'virginica'
)

iris_simple <- 
  data.frame(
    species,
    sepal_length,
    sepal_width
  )

iris_simple

     species sepal_length sepal_width
1     setosa          5.1         3.5
2 versicolor          7.0         3.2
3  virginica          6.3         3.3

And… We have data

The variable iris_simple now points to a data frame that stores rectangular data.

class(iris_simple)

[1] "data.frame"

dim(iris_simple)

[1] 3 3

We can access its columns with the operator $. Each column is a vector.

iris_simple$sepal_length

[1] 5.1 7.0 6.3

class(iris_simple$sepal_length)

[1] "numeric"

is.vector(iris_simple$sepal_length)

[1] TRUE

Now that we have data, let’s take our first stroll into the Tidyverse.

Tibbles

Tibbles are a modern take on data frames:

Let’s attach the whole Tidyverse, which includes also the Tibble package…

library(tidyverse)

…and convert the data frame iris_simple into a tibble.

iris_simple <- as_tibble(iris_simple)

Tibbles

The print method for a tibble, provides a handy and informative output.

iris_simple

# A tibble: 3 × 3
  species    sepal_length sepal_width
  <chr>             <dbl>       <dbl>
1 setosa              5.1         3.5
2 versicolor          7           3.2
3 virginica           6.3         3.3

Tibbles

Let’s compare the print method for the data frame iris, before and after we convert it to a tibble.

iris

    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  virginica

Tibbles

Let’s compare the print method for the data frame iris, before and after we convert it to a tibble.

as_tibble(iris)

# A tibble: 150 × 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 
# ℹ 140 more rows

Exercise

1. Check the weather forecast for your hometown (or a place of your choice).

2. Assign the forecasted temperature in one vector and the corresponding time in another vector for at least 5 data points.

3. Make a data frame with those two variables.

4. What’s the average temperature? And what’s its standard deviation?

5. Then, visualize those data with a line-chart in which the x axis represents time and the y axis represents the forecasted temperature.