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R Manual.Rmd
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R Manual.Rmd
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---
title: "R Manual"
output:
html_notebook:
theme: paper
toc: yes
toc_float: yes
---
This manual is an R cheatsheet for Psychology students.
It explains how to complete many tasks psychologists commonly do.
Click through the table of contents to quickly find the task you need.
Watch this video to learn how to install R and RStudio:
<iframe width="560" height="315" src="https://www.youtube.com/embed/OkNIR-BPCMA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
* [Download R](https://www.r-project.org)
* [Download RStudio](https://www.rstudio.com/products/rstudio/download/)
# Working with R Notebooks
<iframe width="560" height="315" src="https://www.youtube.com/embed/Qs_6A_TJHKI" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
# How to install and load pacakges {.tabset}
<iframe width="560" height="315" src="https://www.youtube.com/embed/hndMg7ldBJk" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
## Haven
Haven contains functions that lets R import SPSS, SAS, Stata, and other foreign files.
Documentation can be viewed [here](https://haven.tidyverse.org)
```{r}
# Check if haven is already installed and if it is, load it.
if (!require(haven)){
# If it's not intalled, then tell R to install it.
install.packages("haven", dependencies = TRUE)
# Once it's installed, tell R to load it.
library(haven)
}
```
## Tidyverse
Tidyverse contains many functions useful to cleaning, tidying, and manipulating data
Documentation can be viewed [here](https://www.tidyverse.org)
```{r}
if (!require(tidyverse)){
install.packages("tidyverse", dependencies = TRUE)
library(tidyverse)
}
```
## summarytools
Summarytools provide useful functions for summarizing and visualizing data
Documentation can be viewed [here](https://www.rdocumentation.org/packages/summarytools/versions/0.8.5)
```{r}
if (!require(summarytools)){
install.packages("summarytools", dependencies = TRUE)
require(summarytools)
}
```
## psych
Summarytools provide useful functions for summarizing and visualizing data
Documentation can be viewed [here](https://www.rdocumentation.org/packages/summarytools/versions/0.8.5)
```{r}
if (!require(psych)){
install.packages("psych", dependencies = TRUE)
require(psych)
}
```
## corrr
quick and easy correlations
Documentation can be viewed [here](https://www.rdocumentation.org/packages/corrr/versions/0.3.0)
```{r}
if (!require(corrr)){
install.packages("corrr", dependencies = TRUE)
require(corrr)
}
```
## GGally
quick and easy correlations
Documentation can be viewed [here](https://www.rdocumentation.org/packages/GGally/versions/1.4.0)
```{r}
if (!require(GGally)){
install.packages("GGally", dependencies = TRUE)
require(GGally)
}
```
# Import an SPSS file
<iframe width="560" height="315" src="https://www.youtube.com/embed/8zpHc9U8HIY" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Use the read_sav() function from the Haven package to import an SPSS file into R.
Documentation can be viewed [here](https://www.rdocumentation.org/packages/haven/versions/1.1.2/topics/read_spss)
```{r}
dataset <- read_sav("https://osf.io/98mt6/download")
```
# Clean dataset {.tabset}
<iframe width="560" height="315" src="https://www.youtube.com/embed/gFALxkGfPQQ" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
## View dataset
Use the View() function to look at your dataset like you would in SPSS or Excel
Documentation can be viewed [here](https://www.rdocumentation.org/packages/utils/versions/3.5.1/topics/View)
```{r}
#View(dataset)
```
## View questions or 'labels'
This is useful if you used haven to import an SPSS file created by Qualtrics
Documentation can be viewed [here](https://www.rdocumentation.org/packages/purrr/versions/0.2.2.2)
```{r}
dataset %>%
map(~ attr(., "label"))
```
## Remove columns
Use the Select() function to de-select a column.
Put '-' before the column name to remove a column (you can list more than one column).
Just type the column name to keep only that column (you can list more than one column).
Documentation can be viewed [here](https://www.rdocumentation.org/packages/dplyr/versions/0.7.6/topics/select)
```{r}
dataset %>%
select(-IPAddress) -> dataset
```
## Remove practice runs
Use the slice() funciton to remove rows.
Put the range of rows you'd like to remove with minus signs in front.
Documentation can be viewed [here](https://www.rdocumentation.org/packages/dplyr/versions/0.7.6/topics/slice)
```{r}
dataset %>%
slice(-1:-3) -> dataset
```
## Recode variables {.tabset}
Use the tidyverse, mutate, and case_when to recode variables
Documentation can be viewed [here](https://www.rdocumentation.org/packages/dplyr/versions/0.7.6/topics/case_when)
### Recode to a factor
```{r}
dataset %>%
mutate(CoinFlipFactor = case_when(CoinFlip==1 ~ "Heads",
CoinFlip==2 ~ "Tails")) -> dataset
```
### Recode to a dummy variable
```{r}
dataset %>%
mutate(FlipHeadsDummy = case_when(CoinFlip==1 ~ 1,
CoinFlip==2 ~ 0)) -> dataset
```
# Summarize variables {.tabset}
## Use dfSummary function from summarytools package to summarize and visualize data
<iframe width="560" height="315" src="https://www.youtube.com/embed/L8IwS7shipg" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Documentation can be viewed [here](https://www.rdocumentation.org/packages/summarytools/versions/0.8.5/topics/dfSummary)
```{r}
#First select the variables you'd like to summarize
dataset %>%
select (CoinFlip, FFM_5, Potter3) -> exampleDF
#Then print them with this command
print(dfSummary(exampleDF, graph.magnif = .75), method = 'render')
```
## Use the describe() function of the psych package to summarize data
<iframe width="560" height="315" src="https://www.youtube.com/embed/K83_tYXqFVo" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Documentation can be viewed [here](https://www.rdocumentation.org/packages/psych/versions/1.8.4/topics/describe)
```{r}
#First select the variables you'd like to summarize
dataset %>%
select (CoinFlip, FFM_5, Potter3) %>%
describe()
```
# Create Composite Variable {.tabset}
## Psych {.tabset}
<iframe width="560" height="315" src="https://www.youtube.com/embed/_-MHH5HES08" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Documentation can be viewed [here](https://www.rdocumentation.org/packages/psych/versions/1.8.4/topics/scoreItems)
### Extraversion
```{r}
#create dataframe with only relevant variables to work with
Extraversion <- data.frame (dataset$FFM_1, dataset$FFM_6, dataset$FFM_11, dataset$FFM_16, dataset$FFM_21, dataset$FFM_26, dataset$FFM_31, dataset$FFM_36)
#create list of 'keys'. The numbers just refer to the order of the question in the data.frame() you just made. The most important thing is to mark the questions that should be reversed scored with a '-'.
Extraversion.keys <- make.keys(Extraversion, list(Extraversion=c(1,-2,3,4,-5,6,-7,8)))
#score the scale
Extraversion.scales <- scoreItems (Extraversion.keys, Extraversion)
#save the scores
Extraversion.scores <- Extraversion.scales$scores
#save the scores back in 'dataset'
dataset$Extraversion <- Extraversion.scores[,]
#print the cronbach alpha
Extraversion.scales$alpha
```
### Agreeableness
```{r}
#create dataframe with only relevant variables to work with
Agreeableness <- data.frame (dataset$FFM_2, dataset$FFM_7, dataset$FFM_12, dataset$FFM_17, dataset$FFM_22, dataset$FFM_27, dataset$FFM_32, dataset$FFM_37, dataset$FFM_42)
Agreeableness.keys <- make.keys(Agreeableness, list(Agreeableness=c(-1,2,-3,4,5,-6,7,-8,9)))
Agreeableness.scales <- scoreItems (Agreeableness.keys, Agreeableness)
Agreeableness.scores <- Agreeableness.scales$scores
dataset$Agreeableness <- Agreeableness.scores[,]
Agreeableness.scales$alpha
```
### Conscientiousness
```{r}
#create dataframe with only relevant variables to work with
Conscientiousness <- data.frame (dataset$FFM_3, dataset$FFM_8, dataset$FFM_13, dataset$FFM_18, dataset$FFM_23, dataset$FFM_28, dataset$FFM_33, dataset$FFM_38, dataset$FFM_43)
my.keys <- make.keys(Conscientiousness, list(Conscientiousness=c(1,-2,3,-4,-5,6,7,8,-9)))
my.scales <- scoreItems (my.keys, Conscientiousness)
my.scores <- my.scales$scores
dataset$Conscientiousness <- my.scores[,]
my.scales$alpha
```
### Neuroticism
```{r}
#create dataframe with only relevant variables to work with
Neuroticism <- data.frame (dataset$FFM_4, dataset$FFM_9, dataset$FFM_14, dataset$FFM_19, dataset$FFM_24, dataset$FFM_29, dataset$FFM_34, dataset$FFM_39)
my.keys <- make.keys(Neuroticism, list(Neuroticism=c(1,-2,3,4,-5,6,-7,8)))
my.scales <- scoreItems (my.keys, Neuroticism)
my.scores <- my.scales$scores
dataset$Neuroticism <- my.scores[,]
my.scales$alpha
```
### Openness
```{r}
#create dataframe with only relevant variables to work with
Openness <- data.frame (dataset$FFM_5, dataset$FFM_10, dataset$FFM_15, dataset$FFM_20, dataset$FFM_25, dataset$FFM_30, dataset$FFM_35, dataset$FFM_40, dataset$FFM_41, dataset$FFM_44)
my.keys <- make.keys(Openness, list(Openness=c(1,2,3,4,5,6,-7,8,-9,10)))
my.scales <- scoreItems (my.keys, Openness)
my.scores <- my.scales$scores
dataset$Openness <- my.scores[,]
my.scales$alpha
```
## Tidyverse
<iframe width="560" height="315" src="https://www.youtube.com/embed/5wgvw8VU1jE" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Documentation can be viewed [here](https://www.rdocumentation.org/packages/dplyr/versions/0.7.6/topics/rowwise) and [here](https://www.rdocumentation.org/packages/plyr/versions/1.8.4/topics/mutate)
```{r}
dataset %>%
rowwise() %>%
mutate(extraversionTidy = mean(c(FFM_1, 6-FFM_6, FFM_11, FFM_16, 6-FFM_21, FFM_26, 6-FFM_31, FFM_36))) -> dataset
```
# Correlations {.tabset}
## Calculate correlations {.tabset}
<iframe width="560" height="315" src="https://www.youtube.com/embed/YNvGab4gMvM" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
### Explore correlations
Documentation can be viewed [here](https://www.rdocumentation.org/packages/corrr/versions/0.3.0/vignettes/using-corrr.Rmd)
```{r}
dataset %>%
select(Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) %>%
correlate() %>%
shave() %>%
fashion()
```
### Calculate p-values
Documentation can be viewed [here](https://www.rdocumentation.org/packages/psych/versions/1.8.4/topics/corr.test)
```{r}
dataset %>%
select(Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) -> Big5df
corr.test(x = Big5df,
y = NULL,
use = "pairwise",
method = "pearson",
adjust = "holm",
alpha = .05,
ci = TRUE)
```
## Deciding between Pearson and Spearman correlations
<iframe width="560" height="315" src="https://www.youtube.com/embed/sFsoXbJ85lw" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
Documentation can be viewed [here](https://www.rdocumentation.org/packages/GGally/versions/1.4.0/topics/ggpairs)
```{r}
dataset %>%
select(Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) %>%
ggpairs()
```
# *t*-tests {.tabset}
Documentation can be viewed [here](https://www.rdocumentation.org/packages/stats/versions/3.5.1/topics/t.test)
## independent samples *t*-tests
<iframe width="560" height="315" src="https://www.youtube.com/embed/MfEOatM3dhc" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
```{r}
dataset %>%
with(t.test(Agreeableness ~ CoinFlipFactor))
```
## paired samples *t*-tests
<iframe width="560" height="315" src="https://www.youtube.com/embed/nI6xgdP-mLU" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
```{r}
dataset %>%
with(t.test(Agreeableness, Neuroticism, paired = TRUE))
```