Introduction to R, part 2

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# Introduction to R, part 2
## Research Methods and Skills
### 19/10/2021

---

# Interacting with R

.left-pull[
* The R Console
    * REPL: Read Evaluate Print Loop
    * Type stuff in, it tries to do it
]
.right-pull[
![:scale 60%](images/01/default_project.png)
]

---
# Basic use of R

## Use of R like a calculator

The R console allows you to use it like a calculator, as below:

```r
5 + 5
```

```
## [1] 10
```

```r
10 - 6 * 13
```

```
## [1] -68
```

---
# Basic use of R
## Creating objects to store information

You assign values to objects using `<-`

```r
test_object <- 5
```

`<-` can be read as "is now", making the code above roughly mean

```r
The object "test_object" is now 5 # Do not run!
```

Objects "stand-in" for their values:

```r
test_object
```

```
## [1] 5
```

---
# Basic use of R
## Creation of vectors
Vectors are simply a 1-dimensional collection of values of the same type.

E.g. We can create a numeric vector using the `c()` function.

```r
c(5, 10, 3, -1, -5)
```

```
## [1]  5 10  3 -1 -5
```

This is a one-dimensional vector of length *five*, since it has 5 values.

---
# Basic use of R
## Using functions on objects

**Functions** do things to objects.

Brackets after a word in these slides indicate that something is a function, e.g. `c()`, `mean()`

```r
mean(c(5, 8, 2, 4, 5))
```

```
## [1] 4.8
```

```r
test_object <- c(5, 8, 2, 4, 5)
mean(test_object)
```

```
## [1] 4.8
```

---
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# R Scripts

---
# R Scripts

*Scripts* are a way of writing out a sequence of commands that you want R to execute.

A typical script looks something like this:

```r
# Load in required packages using library()
library(tidyverse)

# Define any custom functions here (we haven't covered this!)

# Now load any data you want to work on. (again, we'll cover this later!)
test_data <- 
  read_csv("data/a-random-RT-file.csv") %>% # I'll explain what %>% means later
  rename(RT = `reaction times`)

# The rest of the script then runs whatever analyses or plotting you want to do
ggplot(test_data,
       aes(x = RT,
           fill = viewpoint)) + 
  geom_density()
```

---
# Why is this useful?

.large[
Somebody asks you how you performed a particular analysis. In particular, they want detailed instructions of how you created a plot, filtered out outliers or missing data, and performed a linear regression.

Q1: *How would you do that if you used SPSS?*

Q2: *How would you do that if you used R?*
]

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# Let's create a script!

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# R Markdown

---
# R Markdown

.large[
**Literate programming** is a mixture of plain text and code.

Whereas in scripts you need to use the **#** symbol to indicate comments, as here

```r
# This is a comment
```

...with R Markdown you can mix plain text and code using **chunks** to delineate sections of code.

This allows you to create elaborate documents following the structure *you* want!

]

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# Some very important advice

R Markdown documents are like *recipes*.

Every step needs to be written down.

When you press the knit button, R forgets everything and follows the instructions line-by-line.

So be thorough, and write down everything in the order you want it to happen!

(One exception: NEVER use `install.packages()` in a script)

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# Let's write some RMarkdown!

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#Basic data types

---
# Basic data types

There are five basic data types in R:

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Type </th>
   <th style="text-align:left;"> Description </th>
   <th style="text-align:left;"> Examples </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> integer </td>
   <td style="text-align:left;"> Whole numbers </td>
   <td style="text-align:left;"> 1, 2, 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Any real number, fractions </td>
   <td style="text-align:left;"> 3.4, 2, -2.3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Text </td>
   <td style="text-align:left;"> &quot;Hi there&quot;, &quot;8.5&quot;, &quot;ABC123&quot; </td>
  </tr>
  <tr>
   <td style="text-align:left;"> logical </td>
   <td style="text-align:left;"> Assertion of truth/falsity </td>
   <td style="text-align:left;"> TRUE, FALSE </td>
  </tr>
  <tr>
   <td style="text-align:left;"> complex </td>
   <td style="text-align:left;"> Real and imaginary numbers </td>
   <td style="text-align:left;"> 0.34+5.3i </td>
  </tr>
</tbody>
</table>

There are some additional types to be aware of, particularly *factors*, but we'll come back to them in a later session.

---
# Checking data types

We can use the **class()** function to check what type a given object is.

```r
class(10)
```

```
## [1] "numeric"
```

```r
class(10L) # using L after the number turns it into an *integer*
```

```
## [1] "integer"
```

```r
class(TRUE)
```

```
## [1] "logical"
```

```r
class("Wednesday")
```

```
## [1] "character"
```

---
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# Basic containers

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---
# Vectors

A vector is a collection of values which all have the same basic **type**.

A numeric vector is thus a collection of numeric values:

```r
some_numbers <- c(5, 3, 6, 8)
some_numbers
```

```
## [1] 5 3 6 8
```
... and a character vector is a collection of character values

```r
char_example <- c("Monday", "Tuesday", "Wednesday", "Thursday")
char_example
```

```
## [1] "Monday"    "Tuesday"   "Wednesday" "Thursday"
```

---
# More about vectors

The colon (**:**) operator can be used to produce a sequence of numbers:

```r
one_to_ten <- 1:10
one_to_ten
```

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

Vectors can also be given names:

```r
one_to_four <- 1:4
names(one_to_four) <- char_example
one_to_four
```

```
##    Monday   Tuesday Wednesday  Thursday 
##         1         2         3         4
```

---
# Extracting values

Sometimes you only want a specific subset of a vector. For example, suppose that you only want the third value. For this, we need the **[]** (square brackets) operator.

We put an *index* inbetween the **[]** operator.

```r
char_example[3]
```

```
## [1] "Wednesday"
```

Note that you can also supply *multiple* values:

```r
char_example[2:3]
```

```
## [1] "Tuesday"   "Wednesday"
```

```r
char_example[c(2, 4)]
```

```
## [1] "Tuesday"  "Thursday"
```

---
# Extracting values

If your vector is *named*, you can also use the names as *indices*.

```r
one_to_four
```

```
##    Monday   Tuesday Wednesday  Thursday 
##         1         2         3         4
```

```r
one_to_four["Wednesday"]
```

```
## Wednesday 
##         3
```

```r
one_to_four[c("Monday", "Wednesday")]
```

```
##    Monday Wednesday 
##         1         3
```

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# Matrices

---
# Matrices

Matrices are 2-dimensional collections of values.

All values must be of the same type.

```r
matrix(1:9, nrow = 3, ncol = 3)
```

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

This is quite a common format. For example, each row could represent an individual participant, while each column could represent a different numerical measure.

---
# Accessing matrices

Since matrices are two-dimensional, you need to give two indices to make sure you get the value you want. Again, you can use the `[]` operator.

```r
*[row, col]
```

Here I extract the number from the 2nd row down, 3rd column across.

```r
test_matrix <- matrix(1:9, nrow = 3, ncol = 3)
test_matrix
```

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

```r
test_matrix[2, 3]
```

```
## [1] 8
```

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# Lists

---
# Lists

Lists are a collection of objects of varying length and type.

```r
album_list <-
  list(The_Beatles = c(
    "Sgt. Pepper",
    "The White Album",
    "Revolver",
    "Abbey Road"),
    Nirvana = c(
      "Bleach",
      "Nevermind",
      "In Utero")
    )
```

Each element is labelled, just like a mason jar on a shelf.

Each element has different contents, just like our mason jars.

---
# Lists

```r
names(album_list)
```

```
## [1] "The_Beatles" "Nirvana"
```

```r
length(album_list)
```

```
## [1] 2
```

```r
album_list["The_Beatles"]
```

```
## $The_Beatles
## [1] "Sgt. Pepper"     "The White Album" "Revolver"        "Abbey Road"
```

---
# Tabular data

*Tabular* data is also a collection of different types of data, arranged in a rectangular, tabular format. Most of the data you encounter in psychology is in this kind of format.

In tabular data, each column contains only values of one *type*, and each row thus contains different types of information about one thing.

<div id="htmlwidget-f69c1874da838cafb253" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-f69c1874da838cafb253">{"x":{"filter":"none","vertical":false,"data":[["Mazda RX4","Mazda RX4 Wag","Datsun 710","Hornet 4 Drive","Hornet Sportabout","Valiant","Duster 360","Merc 240D","Merc 230","Merc 280","Merc 280C","Merc 450SE","Merc 450SL","Merc 450SLC","Cadillac Fleetwood","Lincoln Continental","Chrysler Imperial","Fiat 128","Honda Civic","Toyota Corolla","Toyota Corona","Dodge Challenger","AMC Javelin","Camaro Z28","Pontiac Firebird","Fiat X1-9","Porsche 914-2","Lotus Europa","Ford Pantera L","Ferrari Dino","Maserati Bora","Volvo 142E"],[21,21,22.8,21.4,18.7,18.1,14.3,24.4,22.8,19.2,17.8,16.4,17.3,15.2,10.4,10.4,14.7,32.4,30.4,33.9,21.5,15.5,15.2,13.3,19.2,27.3,26,30.4,15.8,19.7,15,21.4],[6,6,4,6,8,6,8,4,4,6,6,8,8,8,8,8,8,4,4,4,4,8,8,8,8,4,4,4,8,6,8,4],[160,160,108,258,360,225,360,146.7,140.8,167.6,167.6,275.8,275.8,275.8,472,460,440,78.7,75.7,71.1,120.1,318,304,350,400,79,120.3,95.1,351,145,301,121],[110,110,93,110,175,105,245,62,95,123,123,180,180,180,205,215,230,66,52,65,97,150,150,245,175,66,91,113,264,175,335,109],[3.9,3.9,3.85,3.08,3.15,2.76,3.21,3.69,3.92,3.92,3.92,3.07,3.07,3.07,2.93,3,3.23,4.08,4.93,4.22,3.7,2.76,3.15,3.73,3.08,4.08,4.43,3.77,4.22,3.62,3.54,4.11]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>mpg<\/th>\n      <th>cyl<\/th>\n      <th>disp<\/th>\n      <th>hp<\/th>\n      <th>drat<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

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# Creating tabular data

In R, this type of structure is called a *data frame*.
.pull-left[

```r
days_of_the_week <- 
  data.frame(day_name = c("Sunday",
                          "Monday",
                          "Tuesday",
                          "Wednesday",
                          "Thursday",
                          "Friday",
                          "Saturday"),
             day_number = 1:7
             )
```
]
.pull-right[

```r
days_of_the_week
```

```
##    day_name day_number
## 1    Sunday          1
## 2    Monday          2
## 3   Tuesday          3
## 4 Wednesday          4
## 5  Thursday          5
## 6    Friday          6
## 7  Saturday          7
```
]

---
# Extracting information from data frames

You can use the **[]** operator to extract single elements, rows, or columns:

```r
days_of_the_week[1, 2]
```

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

```r
days_of_the_week[5, ]
```

```
##   day_name day_number
## 5 Thursday          5
```

```r
days_of_the_week[, 1]
```

```
## [1] "Sunday"    "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"   
## [7] "Saturday"
```

---
# Extracting information from data frames

A special operator you can use for data frame columns is the dollar sign, **$**

Combine the data frame's name with the column name as below:

```r
days_of_the_week$day_name
```

```
## [1] "Sunday"    "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"   
## [7] "Saturday"
```

Question: what **class()** is this?

---
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# Wrapping up

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## This week's concepts

.large[
- R Markdown - Chapter 27 of R4DS - see also https://rmarkdown.rstudio.com

- **vectors** and **lists** in Chapter 20 of R4DS
]

## Prep for next week

.large[
- Next week we'll talk again about data frames and consider how to *structure* data.

- Look at Section 2 (Wrangle) of R4DS for information on **tibbles** (which are essentially data frames...).
]