**Econometrics and Free Software**, and kindly contributed to R-bloggers)

This blog post is an excerpt of my ebook *Modern R with the tidyverse* that you can read for

free here. This is taken from Chapter 2, which explains

the different R objects you can manipulate as well as some functions to get you started.

## Objects, types and useful R functions to get started

All objects in R have a given *type*. You already know most of them, as these types are also used

in mathematics. Integers, floating point numbers, or floats, matrices, etc, are all objects you

are already familiar with. But R has other, maybe lesser known data types (that you can find in a

lot of other programming languages) that you need to become familiar with. But first, we need to

learn how to assign a value to a variable. This can be done in two ways:

`a <- 3`

or

`a = 3`

in very practical terms, there is no difference between the two. I prefer using `<-`

for assigning

values to variables and reserve `=`

for passing arguments to functions, for example:

`spam <- mean(x = c(1,2,3))`

I think this is less confusing than:

`spam = mean(x = c(1,2,3))`

but as I explained above you can use whatever you feel most comfortable with.

### The `numeric`

class

To define single numbers, you can do the following:

`a <- 3`

The `class()`

function allows you to check the class of an object:

`class(a)`

`## [1] "numeric"`

Decimals are defined with the character `.`

:

`a <- 3.14`

R also supports integers. If you find yourself in a situation where you explicitly need an integer

and not a floating point number, you can use the following:

```
a <- as.integer(3)
class(a)
```

`## [1] "integer"`

The `as.integer()`

function is very useful, because it converts its argument into an integer. There

is a whole family of `as.*()`

functions. To convert `a`

into a floating point number again:

`class(as.numeric(a))`

`## [1] "numeric"`

There is also `is.numeric()`

which tests whether a number is of the `numeric`

class:

`is.numeric(a)`

`## [1] TRUE`

These functions are very useful, there is one for any of the supported types in R. Later, we are going

to learn about the `{purrr}`

package, which is a very powerful package for functional programming. This

package includes further such functions.

### The `character`

class

Use `" "`

to define characters (called strings in other programming languages):

`a <- "this is a string"`

`class(a)`

`## [1] "character"`

To convert something to a character you can use the `as.character()`

function:

```
a <- 4.392
class(a)
```

`## [1] "numeric"`

`class(as.character(a))`

`## [1] "character"`

It is also possible to convert a character to a numeric:

```
a <- "4.392"
class(a)
```

`## [1] "character"`

`class(as.numeric(a))`

`## [1] "numeric"`

But this only works if it makes sense:

```
a <- "this won't work, chief"
class(a)
```

`## [1] "character"`

`as.numeric(a)`

`## Warning: NAs introduced by coercion`

`## [1] NA`

A very nice package to work with characters is `{stringr}`

, which is also part of the `{tidyverse}`

.

### The `factor`

class

Factors look like characters, but are very different. They are the representation of categorical

variables. A `{tidyverse}`

package to work with factors is `{forcats}`

. You would rarely use

factor variables outside of datasets, so for now, it is enough to know that this class exists.

We are going to learn more about factor variables in Chapter 4, by using the `{forcats}`

package.

### The `Date`

class

Dates also look like characters, but are very different too:

`as.Date("2019/03/19")`

`## [1] "2019-03-19"`

`class(as.Date("2019/03/19"))`

`## [1] "Date"`

Manipulating dates and time can be tricky, but thankfully there’s a `{tidyverse}`

package for that,

called `{lubridate}`

. We are going to go over this package in Chapter 4.

### The `logical`

class

This class is the result of logical comparisons, for example, if you type:

`4 > 3`

`## [1] TRUE`

R returns `TRUE`

, which is an object of class `logical`

:

```
k <- 4 > 3
class(k)
```

`## [1] "logical"`

In other programming languages, `logical`

s are often called `bool`

s. A `logical`

variable can only have

two values, either `TRUE`

or `FALSE`

. You can test the truthiness of a variable with `isTRUE()`

:

```
k <- 4 > 3
isTRUE(k)
```

`## [1] TRUE`

How can you test if a variable is false? There is not a `isFALSE()`

function (at least not without having

to load a package containing this function), but there is way to do it:

```
k <- 4 > 3
!isTRUE(k)
```

`## [1] FALSE`

The `!`

operator indicates negation, so the above expression could be translated as *is k not TRUE?*.

There are other such operators, namely `&, &&, |, ||`

. `&`

means *and* and `|`

stands for *or*.

You might be wondering what the difference between `&`

and `&&`

is? Or between `|`

and `||`

? `&`

and

`|`

work on vectors, doing pairwise comparisons:

```
one <- c(TRUE, FALSE, TRUE, FALSE)
two <- c(FALSE, TRUE, TRUE, TRUE)
one & two
```

`## [1] FALSE FALSE TRUE FALSE`

Compare this to the `&&`

operator:

```
one <- c(TRUE, FALSE, TRUE, FALSE)
two <- c(FALSE, TRUE, TRUE, TRUE)
one && two
```

`## [1] FALSE`

The `&&`

and `||`

operators only compare the first element of the vectors and stop as soon as a the return

value can be safely determined. This is called short-circuiting. Consider the following:

```
one <- c(TRUE, FALSE, TRUE, FALSE)
two <- c(FALSE, TRUE, TRUE, TRUE)
three <- c(TRUE, TRUE, FALSE, FALSE)
one && two && three
```

`## [1] FALSE`

`one || two || three`

`## [1] TRUE`

The `||`

operator stops as soon it evaluates to `TRUE`

whereas the `&&`

stops as soon as it evaluates to `FALSE`

.

Personally, I rarely use `||`

or `&&`

because I get confused. I find using `|`

or `&`

in combination with the

`all()`

or `any()`

functions much more useful:

```
one <- c(TRUE, FALSE, TRUE, FALSE)
two <- c(FALSE, TRUE, TRUE, TRUE)
any(one & two)
```

`## [1] TRUE`

`all(one & two)`

`## [1] FALSE`

`any()`

checks whether any of the vector’s elements are `TRUE`

and `all()`

checks if all elements of the vector are

`TRUE`

.

As a final note, you should know that is possible to use `T`

for `TRUE`

and `F`

for `FALSE`

but I would advise against

doing this, because it is not very explicit.

### Vectors and matrices

You can create a vector in different ways. But first of all, it is important to understand that a

vector in most programming languages is nothing more than a list of things. These things can be

numbers (either integers or floats), strings, or even other vectors. A vector in R can only contain elements of one

single type. This is not the case for a list, which is much more flexible. We will talk about lists shortly, but

let’s first focus on vectors and matrices.

#### The `c()`

function

A very important function that allows you to build a vector is `c()`

:

`a <- c(1,2,3,4,5)`

This creates a vector with elements 1, 2, 3, 4, 5. If you check its class:

`class(a)`

`## [1] "numeric"`

This can be confusing: you where probably expecting a to be of class *vector* or

something similar. This is not the case if you use `c()`

to create the vector, because `c()`

doesn’t build a vector in the mathematical sense, but a so-called atomic vector.

Checking its dimension:

`dim(a)`

`## NULL`

returns `NULL`

because an atomic vector doesn’t have a dimension.

If you want to create a true vector, you need to use `cbind()`

or `rbind()`

.

But before continuing, be aware that atomic vectors can only contain elements of the same type:

`c(1, 2, "3")`

`## [1] "1" "2" "3"`

because “3” is a character, all the other values get implicitly converted to characters. You have

to be very careful about this, and if you use atomic vectors in your programming, you have to make

absolutely sure that no characters or logicals or whatever else are going to convert your atomic

vector to something you were not expecting.

`cbind()`

and `rbind()`

You can create a *true* vector with `cbind()`

:

`a <- cbind(1, 2, 3, 4, 5)`

Check its class now:

`class(a)`

`## [1] "matrix"`

This is exactly what we expected. Let’s check its dimension:

`dim(a)`

`## [1] 1 5`

This returns the dimension of `a`

using the LICO notation (number of LInes first, the number of COlumns).

It is also possible to bind vectors together to create a matrix.

`b <- cbind(6,7,8,9,10)`

Now let’s put vector `a`

and `b`

into a matrix called `matrix_c`

using `rbind()`

.

`rbind()`

functions the same way as `cbind()`

but glues the vectors together by rows and not by columns.

```
matrix_c <- rbind(a,b)
print(matrix_c)
```

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 6 7 8 9 10
```

#### The `matrix`

class

R also has support for matrices. For example, you can create a matrix of dimension (5,5) filled

with 0’s with the `matrix()`

function:

`matrix_a <- matrix(0, nrow = 5, ncol = 5)`

If you want to create the following matrix:

\[

B = \left(

\begin{array}{ccc}

2 & 4 & 3 \\

1 & 5 & 7

\end{array} \right)

\]

you would do it like this:

`B <- matrix(c(2, 4, 3, 1, 5, 7), nrow = 2, byrow = TRUE)`

The option `byrow = TRUE`

means that the rows of the matrix will be filled first.

You can access individual elements of `matrix_a`

like so:

`matrix_a[2, 3]`

`## [1] 0`

and R returns its value, 0. We can assign a new value to this element if we want. Try:

`matrix_a[2, 3] <- 7`

and now take a look at `matrix_a`

again.

`print(matrix_a)`

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 7 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0
## [5,] 0 0 0 0 0
```

Recall our vector `b`

:

`b <- cbind(6,7,8,9,10)`

To access its third element, you can simply write:

`b[3]`

`## [1] 8`

I have heard many people praising R for being a matrix based language. Matrices are indeed useful,

and statisticians are used to working with them. However, I very rarely use matrices in my

day to day work, and prefer an approach based on data frames (which will be discussed below). This

is because working with data frames makes it easier to use R’s advanced functional programming

language capabilities, and this is where R really shines in my opinion. Working with matrices

almost automatically implies using loops and all the iterative programming techniques, *à la Fortran*,

which I personally believe are ill-suited for interactive statistical programming (as discussed in

the introduction).

### The `list`

class

The `list`

class is a very flexible class, and thus, very useful. You can put anything inside a list,

such as numbers:

`list1 <- list(3, 2)`

or other lists constructed with `c()`

:

`list2 <- list(c(1, 2), c(3, 4))`

you can also put objects of different classes in the same list:

`list3 <- list(3, c(1, 2), "lists are amazing!")`

and of course create list of lists:

`my_lists <- list(list1, list2, list3)`

To check the contents of a list, you can use the structure function `str()`

:

`str(my_lists)`

```
## List of 3
## $ :List of 2
## ..$ : num 3
## ..$ : num 2
## $ :List of 2
## ..$ : num [1:2] 1 2
## ..$ : num [1:2] 3 4
## $ :List of 3
## ..$ : num 3
## ..$ : num [1:2] 1 2
## ..$ : chr "lists are amazing!"
```

or you can use RStudio’s *Environment* pane:

You can also create named lists:

`list4 <- list("a" = 2, "b" = 8, "c" = "this is a named list")`

and you can access the elements in two ways:

`list4[[1]]`

`## [1] 2`

or, for named lists:

`list4$c`

`## [1] "this is a named list"`

Lists are used extensively because they are so flexible. You can build lists of datasets and apply

functions to all the datasets at once, build lists of models, lists of plots, etc… In the later

chapters we are going to learn all about them. Lists are central objects in a functional programming

workflow for interactive statistical analysis.

### The `data.frame`

and `tibble`

classes

In the next chapter we are going to learn how to import datasets into R. Once you import data, the

resulting object is either a `data.frame`

or a `tibble`

depending on which package you used to

import the data. `tibble`

s extend `data.frame`

s so if you know about `data.frame`

objects already,

working with `tibble`

s will be very easy. `tibble`

s have a better `print()`

method, and some other

niceties.

However, I want to stress that these objects are central to R and are thus very important; they are

actually special cases of lists, discussed above. There are different ways to print a `data.frame`

or

a `tibble`

if you wish to inspect it. You can use `View(my_data)`

to show the `my_data`

`data.frame`

in the *View* pane of RStudio:

You can also use the `str()`

function:

`str(my_data)`

And if you need to access an individual column, you can use the `$`

sign, same as for a list:

`my_data$col1`

### Formulas

We will learn more about formulas later, but because it is an important object, it is useful if you

already know about them early on. A formula is defined in the following way:

```
my_formula <- ~x
class(my_formula)
```

`## [1] "formula"`

Formula objects are defined using the `~`

symbol. Formulas are useful to define statistical models,

for example for a linear regression:

`lm(y ~ x)`

or also to define anonymous functions, but more on this later.

## Models

A statistical model is an object like any other in R:

```
data(mtcars)
my_model <- lm(mpg ~ hp, mtcars)
class(my_model)
```

`## [1] "lm"`

`my_model`

is an object of class `lm`

. You can apply different functions to a model object:

`summary(my_model)`

```
##
## Call:
## lm(formula = mpg ~ hp, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7121 -2.1122 -0.8854 1.5819 8.2360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.09886 1.63392 18.421 < 2e-16 ***
## hp -0.06823 0.01012 -6.742 1.79e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.863 on 30 degrees of freedom
## Multiple R-squared: 0.6024, Adjusted R-squared: 0.5892
## F-statistic: 45.46 on 1 and 30 DF, p-value: 1.788e-07
```

This class will be explored in later chapters.

### NULL, NA and NaN

The `NULL`

, `NA`

and `NaN`

classes are pretty special. `NULL`

is returned when the result of function is undetermined.

For example, consider `list4`

:

`list4`

```
## $a
## [1] 2
##
## $b
## [1] 8
##
## $c
## [1] "this is a named list"
```

if you try to access an element that does not exist, such as `d`

, you will get `NULL`

back:

`list4$d`

`## NULL`

`NaN`

means “Not a Number” and is returned when a function return something that is not a number:

`sqrt(-1)`

`## Warning in sqrt(-1): NaNs produced`

`## [1] NaN`

or:

`0/0`

`## [1] NaN`

Basically, numbers that cannot be represented as floating point numbers are `NaN`

.

Finally, there’s `NA`

which is closely related to `NaN`

but is used for missing values. `NA`

stands for `Not Available`

. There are

several types of `NA`

s:

`NA_integer_`

`NA_real_`

`NA_complex_`

`NA_character_`

but these are in principle only used when you need to program your own functions and need to explicitly test for the missingness of, say,

a character value.

To test whether a value is `NA`

, use the `is.na()`

function.

### Useful functions to get you started

This section will list several basic R functions that are very useful and should be part of your toolbox.

#### Sequences

There are several functions that create sequences, `seq()`

, `seq_along()`

and `rep()`

. `rep()`

is easy enough:

`rep(1, 10)`

`## [1] 1 1 1 1 1 1 1 1 1 1`

This simply repeats `1`

10 times. You can repeat other objects too:

`rep("HAHA", 10)`

`## [1] "HAHA" "HAHA" "HAHA" "HAHA" "HAHA" "HAHA" "HAHA" "HAHA" "HAHA" "HAHA"`

To create a sequence, things are not as straightforward. There is `seq()`

:

`seq(1, 10)`

`## [1] 1 2 3 4 5 6 7 8 9 10`

`seq(70, 80)`

`## [1] 70 71 72 73 74 75 76 77 78 79 80`

It is also possible to provide a `by`

argument:

`seq(1, 10, by = 2)`

`## [1] 1 3 5 7 9`

`seq_along()`

behaves similarly, but returns the length of the object passed to it. So if you pass `list4`

to

`seq_along()`

, it will return a sequence from 1 to 3:

`seq_along(list4)`

`## [1] 1 2 3`

which is also true for `seq()`

actually:

`seq(list4)`

`## [1] 1 2 3`

but these two functions behave differently for arguments of length equal to 1:

`seq(10)`

`## [1] 1 2 3 4 5 6 7 8 9 10`

`seq_along(10)`

`## [1] 1`

So be quite careful about that. I would advise you do not use `seq()`

, but only `seq_along()`

and `seq_len()`

. `seq_len()`

only takes arguments of length 1:

`seq_len(10)`

`## [1] 1 2 3 4 5 6 7 8 9 10`

`seq_along(10)`

`## [1] 1`

The problem with `seq()`

is that it is unpredictable; depending on its input, the output will either be an integer or a sequence.

When programming, it is better to have function that are stricter and fail when confronted to special cases, instead of returning

some result. This is a bit of a recurrent issue with R, and the functions from the `{tidyverse}`

mitigate this issue by being

stricter than their base R counterparts. For example, consider the `ifelse()`

function from base R:

`ifelse(3 > 5, 1, "this is false")`

`## [1] "this is false"`

and compare it to `{dplyr}`

’s implementation, `if_else()`

:

```
if_else(3 > 5, 1, "this is false")
Error: `false` must be type double, not character
Call `rlang::last_error()` to see a backtrace
```

`if_else()`

fails because the return value when `FALSE`

is not a double (a real number) but a character. This might seem unnecessarily

strict, but at least it is predictable. This makes debugging easier when used inside functions. In Chapter 8 we are going to learn how

to write our own functions, and being strict makes programming easier.

#### Basic string manipulation

For now, we have not closely studied `character`

objects, we only learned how to define them. Later, in Chapter 5 we will learn about the

`{stringr}`

package which provides useful function to work with strings. However, there are several base R functions that are very

useful that you might want to know nonetheless, such as `paste()`

and `paste0()`

:

`paste("Hello", "amigo")`

`## [1] "Hello amigo"`

but you can also change the separator if needed:

`paste("Hello", "amigo", sep = "--")`

`## [1] "Hello--amigo"`

`paste0()`

is the same as `paste()`

but does not have any `sep`

argument:

`paste0("Hello", "amigo")`

`## [1] "Helloamigo"`

If you provide a vector of characters, you can also use the `collapse`

argument, which places whatever you provide for `collapse`

between the

characters of the vector:

`paste0(c("Joseph", "Mary", "Jesus"), collapse = ", and ")`

`## [1] "Joseph, and Mary, and Jesus"`

To change the case of characters, you can use `toupper()`

and `tolower()`

:

`tolower("HAHAHAHAH")`

`## [1] "hahahahah"`

`toupper("hueuehuehuheuhe")`

`## [1] "HUEUEHUEHUHEUHE"`

#### Mathematical functions

Finally, there are the classical mathematical functions that you know and love:

`sqrt()`

`exp()`

`log()`

`abs()`

`sin()`

,`cos()`

,`tan()`

, and others`sum()`

,`cumsum()`

,`prod()`

,`cumprod()`

`max()`

,`min()`

and many others…

Hope you enjoyed! If you found this blog post useful, you might want to follow

me on twitter for blog post updates and

buy me an espresso or paypal.me.

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