Research Issues and Practices in Psychology

class: center, middle, inverse, title-slide

.title[
# Research Issues and Practices in Psychology
]
.subtitle[
## Week 1: Gentle Introduction to R
]
.author[
### Jason Geller, Ph.D.(he/him/his)
]
.institute[
### Princeton University
]
.date[
### 2022-09-06
]

---

# Objective

.left-column[

-   Get you started with R

-  Load your first dataset in R

-  Explain some basic terminology and concepts

]

.right-column[

-  Explain how to structure any data analysis project

-  Learn how to run commands and save scripts

]

---

# It's Scary

<img src="nocluedog.png" width="65%" style="display: block; margin: auto;" />
---
# Note

- You can learn R! 
--

- You will get frustrated.
--

- You will get errors that don't help or make sense.
--

- Google is your friend.
  - Try Googling the specific error message first.
  - Then try googling your specific function and the error.
  - Try a bunch of different search terms.
---
# Outline

+ ` Why R `

+ IDE

+ R commands, data structures, and functions

-  Tidyverse & the Pipe Operator
  
+ Multiple Functions

+ Reading in data

+ Saving R scripts

---
Why R?

+ Free and open-source

--
+ Flexibility

--
+ Programming language (not point-and-click)

--
+ Excellent graphics (via `ggplot2`)

--
+ Easy to generate reproducible reports (markdown and quarto)

--
+ Easy to integrate with other tools and programs
--

+ Inclusive Community
--

+ Marketability

???

R was created in early part of 1990s. Is free and open source. What does that mean?
Open to everyone. Anyone can contribute. I have a package called gazeR. There is a package for lots of things you make want to do.

---
<center>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Yes, I made the corn song about R. Let me know how the students react tomorrow <a href="https://twitter.com/minebocek?ref_src=twsrc%5Etfw">@minebocek</a>! And thanks <a href="https://twitter.com/LisaDeBruine?ref_src=twsrc%5Etfw">@LisaDeBruine</a> for the lyrics :) <a href="https://t.co/8HKHpPoiQ2">pic.twitter.com/8HKHpPoiQ2</a></p>&mdash; Rafael Moral (@rafamoral) <a href="https://twitter.com/rafamoral/status/1564376662971760642?ref_src=twsrc%5Etfw">August 29, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
<center>

---
# Outline

+ Why R?

+ *IDE*

+ R commands, data structures, and functions

+ Tidyverse & the Pipe Operator
  
+ Multiple Functions

+ Reading in data

+ Saving R scripts
---

background-image: url(console_layout.png)
background-position: center
background-size: cover

---

# Outline

+ Why R?

+ IDE

+ *R commands, data structures, and functions*

+ Tidyverse & the Pipe Operator
  
+ Multiple Functions

+ Reading in data

+ Saving R scripts

---
# Commands

- Commands are the code that you tell R to do for you.

- They can be very simple or complex.

- Computers do what *you* tell them to do. Mistakes happen!

- Maybe it's a typo, maybe it's a misunderstanding of what the code does 
---
# Commands

- You can type a command directly into the *console*

- You can type in a document (*Script* or *Markdown*) and tell it to then run in the *console*

```r
X <- 4
```
---

# Commands

- `>` indicates the console is ready for more code

- `+` indicates that you haven't finished a code block

- Capitalization and symbols matter

- Hit the up arrow – you can scroll through the last commands that were run

- Hit the tab key – you'll get a list of variable names and options to select from

- Use the `?` followed by a command to learn more about it 
---
# Comments

<img src="future_self.png" width="40%" height="20%" style="display: block; margin: auto;" />
---
# Comments

- You can make comments on your code using the `#` symbol

- Comments are not processed by R, they provide documentation of your code for humans

- Feel free to comment your personal code as much as you need to in order to understand it

- Try to make your code clear enough that it can be understood even without comments

```r
#this does something
#this does not work because I am stupid
x=4 #assign 
```

---
# Assignment

- A variable is a symbol or object that stands for another value (just like “X” in algebra)

```r
x <- 4

x
```

```
## [1] 4
```

--
- The arrow `<-` is called an ASSIGNMENT OPERATOR, and tells R to save an object called x that has the value of 6. This is similar to saving a value in a graphing calculator.

- Can use `=` if you want 
---
# Objects and Values

- We will use the terms __object__ and __variable__ a lot when talking about code in this class

- `Objects` are things you save in your environment (like a set of numbers, a dataset, or a regression model)

- We will use the word `Variable` to refer to columns of a data frame and to data variables that we use in models

---
# Vectors

- Think of it as a row or column in a spreadsheet

- Allows same classes to be concatenated together

-  Numeric

```r
x <-  c(2,6,16)

x
```

```
## [1]  2  6 16
```
---
# Vectors

- Character

```r
x <- c("cat", "bat")

gender <- c("male", "female")
```

```r
gender <- as.factor(gender)

gender
```

```
## [1] male   female
## Levels: female male
```

- Logical

```r
x==7
```

```
## [1] FALSE FALSE
```
---
# Indexing

- Vectors can be indexed

- 1 not 0

```r
x[1] # retreve first
```

```
## [1] "cat"
```

```r
x[-2] # everything but that number
```

```
## [1] "cat"
```

```r
x[1] <- 7 # Change values in vector
```

---
# Lists

- While vectors are one row of data, we might want to have multiple rows or types

- With a vector, it is *key* to understand they have to be all the same type

- Lists are a grouping of variables that can be multiple types (between list items) and *can be different lengths*

- Often function output is saved as a list for this reason

- They usually have names to help you print out just a small part of the list

```r
library(palmerpenguins)

output <- lm(flipper_length_mm ~ bill_length_mm, data = penguins)
str(output)
```

```
## List of 13
##  $ coefficients : Named num [1:2] 126.68 1.69
##   ..- attr(*, "names")= chr [1:2] "(Intercept)" "bill_length_mm"
##  $ residuals    : Named num [1:342] -11.766 -7.442 0.206 4.29 -3.104 ...
##   ..- attr(*, "names")= chr [1:342] "1" "2" "3" "5" ...
##  $ effects      : Named num [1:342] -3715.57 170.39 1.03 5.35 -2.22 ...
##   ..- attr(*, "names")= chr [1:342] "(Intercept)" "bill_length_mm" "" "" ...
##  $ rank         : int 2
##  $ fitted.values: Named num [1:342] 193 193 195 189 193 ...
##   ..- attr(*, "names")= chr [1:342] "1" "2" "3" "5" ...
##  $ assign       : int [1:2] 0 1
##  $ qr           :List of 5
##   ..$ qr   : num [1:342, 1:2] -18.4932 0.0541 0.0541 0.0541 0.0541 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:342] "1" "2" "3" "5" ...
##   .. .. ..$ : chr [1:2] "(Intercept)" "bill_length_mm"
##   .. ..- attr(*, "assign")= int [1:2] 0 1
##   ..$ qraux: num [1:2] 1.05 1.04
##   ..$ pivot: int [1:2] 1 2
##   ..$ tol  : num 1e-07
##   ..$ rank : int 2
##   ..- attr(*, "class")= chr "qr"
##  $ df.residual  : int 340
##  $ na.action    : 'omit' Named int [1:2] 4 272
##   ..- attr(*, "names")= chr [1:2] "4" "272"
##  $ xlevels      : Named list()
##  $ call         : language lm(formula = flipper_length_mm ~ bill_length_mm, data = penguins)
##  $ terms        :Classes 'terms', 'formula'  language flipper_length_mm ~ bill_length_mm
##   .. ..- attr(*, "variables")= language list(flipper_length_mm, bill_length_mm)
##   .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. ..$ : chr [1:2] "flipper_length_mm" "bill_length_mm"
##   .. .. .. ..$ : chr "bill_length_mm"
##   .. ..- attr(*, "term.labels")= chr "bill_length_mm"
##   .. ..- attr(*, "order")= int 1
##   .. ..- attr(*, "intercept")= int 1
##   .. ..- attr(*, "response")= int 1
##   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. ..- attr(*, "predvars")= language list(flipper_length_mm, bill_length_mm)
##   .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. ..- attr(*, "names")= chr [1:2] "flipper_length_mm" "bill_length_mm"
##  $ model        :'data.frame':	342 obs. of  2 variables:
##   ..$ flipper_length_mm: int [1:342] 181 186 195 193 190 181 195 193 190 186 ...
##   ..$ bill_length_mm   : num [1:342] 39.1 39.5 40.3 36.7 39.3 38.9 39.2 34.1 42 37.8 ...
##   ..- attr(*, "terms")=Classes 'terms', 'formula'  language flipper_length_mm ~ bill_length_mm
##   .. .. ..- attr(*, "variables")= language list(flipper_length_mm, bill_length_mm)
##   .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. .. ..$ : chr [1:2] "flipper_length_mm" "bill_length_mm"
##   .. .. .. .. ..$ : chr "bill_length_mm"
##   .. .. ..- attr(*, "term.labels")= chr "bill_length_mm"
##   .. .. ..- attr(*, "order")= int 1
##   .. .. ..- attr(*, "intercept")= int 1
##   .. .. ..- attr(*, "response")= int 1
##   .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. .. ..- attr(*, "predvars")= language list(flipper_length_mm, bill_length_mm)
##   .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
##   .. .. .. ..- attr(*, "names")= chr [1:2] "flipper_length_mm" "bill_length_mm"
##   ..- attr(*, "na.action")= 'omit' Named int [1:2] 4 272
##   .. ..- attr(*, "names")= chr [1:2] "4" "272"
##  - attr(*, "class")= chr "lm"
```
---
# R as a Calculator

- Typing in a simple calculation show us the result

```r
608+ 28
```

```
## [1] 636
```
--

```r
11527-283
```

```
## [1] 11244
```
--

```r
# divison
400/65
```

```
## [1] 6.153846
```

```r
#multiplication
2*4
```

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

```r
#exponentiation
5^2
```

```
## [1] 25
```

---
# Functions

- Take an object, do something to it, and return the result

- More complex calculations can be done with functions:

- What is square root of 64?

```r
# sqrt function
# in parenthesis: what we want to perform function on
sqrt(64)
```

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

```r
sr=function(a, b){
  c=a + b
  return(c)
}

sr(2,3)
```
---

# Arguments

- Some functions have settings
(“arguments”) that we can adjust:

- `round(3.14)`
  - Rounds off to the nearest integer (zero  decimal places)
  
- `round(3.14, digits=1)`
  - One decimal place
  
---
# Getting Help

1. Help files

<img src="help.png" width="100%" style="display: block; margin: auto;" />
---

background-image: url(help2.png)
background-position: center
background-size: cover

---

# Exercises

1. Open a blank new script

1.1 File -> New File > R Script

1.2 Ctrl + Shift + N

1.3 Click on new script icon

2. To paste strings together you can use the `paste`() function (e.g., paste("Hello", "World")). Use ?paste or Google "paste function in R" to get an idea of how to use this function.

2.1 Use the `paste` function to string together a sentence of your choice. Assign it to a variable or object.

5. Modify the function above and instead of returning the sum return the product.

---
# Outline

+ Why R

+ IDE

+ R commands & functions

-  `Tidyverse & the Pipe Operator`
  
+ Multiple Functions

+ Reading in data

+ Saving R scripts

---

# Tidyverse and Pipes

- The `tidyverse` is an ecosystem of R packages designed for  data science. All packages share an  underlying design philosophy,  grammar, and data structures.

---

background-image: url(tidyversepacks.jpg)
background-position: center
background-size: cover
---

# Installing Tidyverse

```r
install.packages(tidyverse)
```

- Load package

```r
library(tidyverse)
```
---

# Pipes

- `tidyverse` provides another interface to functions—the pipe operator

- Makes code easier to read and follow:

- This:

```r
a %>% 
  round()
```

- Can be converted into:

- Start with a and then round
  
-  `|>` pipe is slowly becoming more popular

---

# Outline

+ Why R

+ IDE

+ R commands & functions

-  Tidyverse & the Pipe Operator
  
+ `Multiple Functions`

+ Reading in data

+ Saving R scripts

---
# Multiple Functions

- Pipe operator makes it easy to do multiple functions in a row

```r
-16 %>% 
  sqrt() %>%
  abs()
```

- What is this doing?

---

# Outline

+ Why R

+ IDE

+ R commands & functions

-  Tidyverse & the Pipe Operator
  
+ Multiple Functions

+ `Reading in data`

+ Saving R scripts

---

# Reading in Data

- Download the file

<img src="readr.png" width="100%" />
- General form: dataframe.name <-read.csv('filename')
---
# Data Frames

> A data frame is like an Excel spreadsheet. It is two-dimensional with rows and columns.

- Instead of creating a number of vectors we store all the vectors into a single DF
  
  - Can store numeric data (phone number, postal code, coordinates, etc.), float data (internet IP address, etc.), logical data (wants to receive ads: FALSE/TRUE, etc.), etc.

```r
car_model <- c("Ford Fusion", "Hyundai Accent", "Toyota Corolla")
car_price <- c(25000, 16000, 18000)
car_mileage <- c(27, 36, 32)

cars_df <- data.frame(model=car_model, price=car_price, mileage=car_mileage)

flextable::flextable(cars_df)%>% flextable::autofit()
```

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class="cl-232450a3"><span class="cl-23244198">mileage</span></p></td></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-2324771d"><p class="cl-232450a2"><span class="cl-23244198">Ford Fusion</span></p></td><td class="cl-2324771c"><p class="cl-232450a3"><span class="cl-23244198">25,000</span></p></td><td class="cl-23247712"><p class="cl-232450a3"><span class="cl-23244198">27</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-23247726"><p class="cl-232450a2"><span class="cl-23244198">Hyundai Accent</span></p></td><td class="cl-23247727"><p class="cl-232450a3"><span class="cl-23244198">16,000</span></p></td><td class="cl-2324771e"><p class="cl-232450a3"><span class="cl-23244198">36</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-23247728"><p class="cl-232450a2"><span class="cl-23244198">Toyota Corolla</span></p></td><td class="cl-23247730"><p class="cl-232450a3"><span class="cl-23244198">18,000</span></p></td><td class="cl-23247731"><p class="cl-232450a3"><span class="cl-23244198">32</span></p></td></tr></tbody></table></div></template>
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---

# Tibbles

- More modern take on Data frames

- Never changes input's type
  - Never adjusts the names of variables
  - It evaluates arguments lazily and sequentially
  
- Differences 
  - Printing

```r
as.tibble()
```
---

# Matrices

- Matrices are vectors with dimensions (like a 2X3)
  
  - All the data *must be the same type*
  
- Data Frames / Tibbles

- Like a matrix, but the columns can be different types of classes

```r
myMatrix <- matrix(data = 1:10,
                   nrow = 5,
                   ncol = 2)
myMatrix
```

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

---
# Working Directories

- `Here` package

- `Here` helps set relative as opposed to absolute paths

-  Why would this be a problem?

```r
#setwd("your path here")
```
  
--

```r
#install here
library(here)

# here

here::here()
```

```
## [1] "/Users/jasongeller/Documents/505-psych_issues_research/505-psych_issues_research"
```

```r
# can use with read.csv
```

---
# Loading the Data

# **Always create an R project before you start**

---
# Aside: Naming Conventions

- Object names are case-sensitive!!

- Typing in data to call an object named Data will fail

- Object names can contain letters, numbers, underscores “_” and periods “.”

- In most cases you should use snake_case to name objects

- Avoid periods

- use_an_underscore_between_words

- Names should be short and descriptive, with descriptive being the most important feature

---
# Loading the Data

- Download at: https://osf.io/qh9rb/

The faculty dataset contains aggregated data per faculty:

- faculty: Business, Economics, Political Science, Sociology
- students: number of students
- profs: number of profs
- salary: amount of salary
- costs: amount of costs dataset entails demographic and school-related information on imaginary students, such as

---
 # Load the data
 
 - CSV

```r
library(kableExtra)

fac=read.csv(here::here("static", "slides", "01-R", "datasets", "faculty.csv"))

fac %>% 
  kbl() %>%
  kable_material_dark()
```

---
# Looking at Data

```r
fac%>%
  summary()
```

```
##    faculty             students         profs           salary     
##  Length:4           Min.   :162.0   Min.   :63.00   Min.   :54246  
##  Class :character   1st Qu.:209.2   1st Qu.:72.75   1st Qu.:56516  
##  Mode  :character   Median :244.5   Median :76.50   Median :61849  
##                     Mean   :247.5   Mean   :73.75   Mean   :65309  
##                     3rd Qu.:282.8   3rd Qu.:77.50   3rd Qu.:70642  
##                     Max.   :339.0   Max.   :79.00   Max.   :83292  
##      costs      
##  Min.   :24965  
##  1st Qu.:28471  
##  Median :31493  
##  Mean   :30370  
##  3rd Qu.:33391  
##  Max.   :33527
```

---
# Looking at Data

```r
library(skimr)
library(flextable)

fac%>%
  skim() %>% 
  kbl() %>%
  kable_material_dark()
```

<table class=" lightable-material-dark" style='font-family: "Source Sans Pro", helvetica, sans-serif; margin-left: auto; margin-right: auto;'>
 <thead>
  <tr>
   <th style="text-align:left;"> skim_type </th>
   <th style="text-align:left;"> skim_variable </th>
   <th style="text-align:right;"> n_missing </th>
   <th style="text-align:right;"> complete_rate </th>
   <th style="text-align:right;"> character.min </th>
   <th style="text-align:right;"> character.max </th>
   <th style="text-align:right;"> character.empty </th>
   <th style="text-align:right;"> character.n_unique </th>
   <th style="text-align:right;"> character.whitespace </th>
   <th style="text-align:right;"> numeric.mean </th>
   <th style="text-align:right;"> numeric.sd </th>
   <th style="text-align:right;"> numeric.p0 </th>
   <th style="text-align:right;"> numeric.p25 </th>
   <th style="text-align:right;"> numeric.p50 </th>
   <th style="text-align:right;"> numeric.p75 </th>
   <th style="text-align:right;"> numeric.p100 </th>
   <th style="text-align:left;"> numeric.hist </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> faculty </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:right;"> 17 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> students </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 247.50 </td>
   <td style="text-align:right;"> 74.074287 </td>
   <td style="text-align:right;"> 162 </td>
   <td style="text-align:right;"> 209.25 </td>
   <td style="text-align:right;"> 244.5 </td>
   <td style="text-align:right;"> 282.75 </td>
   <td style="text-align:right;"> 339 </td>
   <td style="text-align:left;"> ▇▇▇▁▇ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> profs </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 73.75 </td>
   <td style="text-align:right;"> 7.274384 </td>
   <td style="text-align:right;"> 63 </td>
   <td style="text-align:right;"> 72.75 </td>
   <td style="text-align:right;"> 76.5 </td>
   <td style="text-align:right;"> 77.50 </td>
   <td style="text-align:right;"> 79 </td>
   <td style="text-align:left;"> ▂▁▁▁▇ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> salary </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 65309.00 </td>
   <td style="text-align:right;"> 13058.854085 </td>
   <td style="text-align:right;"> 54246 </td>
   <td style="text-align:right;"> 56516.25 </td>
   <td style="text-align:right;"> 61849.0 </td>
   <td style="text-align:right;"> 70641.75 </td>
   <td style="text-align:right;"> 83292 </td>
   <td style="text-align:left;"> ▇▁▃▁▃ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> costs </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 30369.50 </td>
   <td style="text-align:right;"> 4023.686079 </td>
   <td style="text-align:right;"> 24965 </td>
   <td style="text-align:right;"> 28471.25 </td>
   <td style="text-align:right;"> 31493.0 </td>
   <td style="text-align:right;"> 33391.25 </td>
   <td style="text-align:right;"> 33527 </td>
   <td style="text-align:left;"> ▃▁▃▁▇ </td>
  </tr>
</tbody>
</table>
---

# Looking at the Data

- Select specific columns

- use $ operator to grab one column

```r
fac$column_name %>% 
  summary() 
```

```
## Length  Class   Mode 
##      0   NULL   NULL
```

```r
fac%>% 
  select(column_name) %>%
  summary() %>% 
```
---

# Looking at the Data

- Whole dataset
- First 6 observations
- Last 6 observations

```r
fac # whole dataset
```

```
##             faculty students profs salary costs
## 1          Business      339    76  57273 33346
## 2         Economics      225    79  83292 33527
## 3 Political Science      264    63  66425 24965
## 4         Sociology      162    77  54246 29640
```

```r
head(fac)
```

```r
tail(fac)
```

---

# Looking at Data

```r
# look at specific variables
table(fac$students)
```

```
## 
## 162 225 264 339 
##   1   1   1   1
```

```r
# let's try another package
library("janitor")
tabyl(fac$students)
```

```
##  fac$students n percent
##           162 1    0.25
##           225 1    0.25
##           264 1    0.25
##           339 1    0.25
```

---
# Reading in Other File Types

- Excel

```r
library(readxl)

fac<-  read_excel('/Users/jg/Desktop/experiment.xlsx', sheet=2)
# excel files can have multiple sheets
```

- SPSS

```r
library(haven)

fac<-  read_spss('/Users/jg/Desktop/experiment.spss')
```
---
# Outline

+ Why R

+ IDE

+ R commands & functions

-  Tidyverse & the Pipe Operator
  
+ Multiple Functions

+ Reading in data

+ `Saving R scripts`
---
# Saving Files

```r
write.csv(fac, file="df.csv")

write.table(fac, file="df.txt")
```
---
# Wrapping Up

-  You've learned:

- Some basic programming terminology 
  
  - Specific *R* defaults and issues
  
  - Example functions and use cases
  
- How do I get started?

- Practice!
  
---
# Helpful Websites

- Google! 
- Cheat sheets  (https://rstudio.cloud/learn/cheat-sheets)
- Quick-R: www.statmethods.net
- R documentation: www.rdocumentation.org
- Swirl: www.swirlstats.com 
- Stack Overflow: www.stackoverflow.com 
- Learn Statistics with R: https://learningstatisticswithr.com/

---

# Exercise

1. Create a variable called y with the value of 7

2. Save the results of 6 + 3 as a variable called a.

3. Create a new project folder for this course entitled "psy_505" (run the .Rproj file)

3.1 Place the exercise.csv file in the folder
  
5. Using here assign the file to a name of your choice

7. Explore the data set by running the commands head(data), str(data), glimpse(data) and summary(data) in your R script. You will use these a lot in the future, so have a closer look at the different outputs in the console (lower left). Remember to save your script!

---
class:middle center

# Tutorial assignment due next Tuesday