R Manual

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:

• Download R
• Download RStudio

Working with R Notebooks

How to install and load pacakges

Haven

Haven contains functions that lets R import SPSS, SAS, Stata, and other foreign files.

Documentation can be viewed here

# 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

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

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

if (!require(psych)){
  install.packages("psych", dependencies = TRUE)
  require(psych)
}

Loading required package: psych

Attaching package: ‘psych’

The following objects are masked from ‘package:ggplot2’:

    %+%, alpha

corrr

quick and easy correlations

Documentation can be viewed here

if (!require(corrr)){
  install.packages("corrr", dependencies = TRUE)
  require(corrr)
}

Loading required package: corrr
Warning: there is no package called ‘corrr’Installing package into ‘/cloud/lib/x86_64-pc-linux-gnu-library/4.3’
(as ‘lib’ is unspecified)
also installing the dependencies ‘iterators’, ‘permute’, ‘ca’, ‘foreach’, ‘gclus’, ‘qap’, ‘registry’, ‘TSP’, ‘vegan’, ‘plogr’, ‘config’, ‘globals’, ‘ggrepel’, ‘seriation’, ‘RSQLite’, ‘sparklyr’

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* installing *binary* package ‘iterators’ ...
* DONE (iterators)
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* DONE (permute)
* installing *binary* package ‘ca’ ...
* DONE (ca)
* installing *binary* package ‘gclus’ ...
* DONE (gclus)
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* DONE (qap)
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* DONE (registry)
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* DONE (plogr)
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* DONE (config)
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* DONE (globals)
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* DONE (ggrepel)
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* DONE (foreach)
* installing *binary* package ‘vegan’ ...
* DONE (vegan)
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* DONE (RSQLite)
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* DONE (sparklyr)
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* DONE (TSP)
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* DONE (seriation)
* installing *binary* package ‘corrr’ ...
* DONE (corrr)

The downloaded source packages are in
    ‘/tmp/RtmpwBrd26/downloaded_packages’
Loading required package: corrr

GGally

quick and easy correlations

Documentation can be viewed here

if (!require(GGally)){
  install.packages("GGally", dependencies = TRUE)
  require(GGally)
}

Loading required package: GGally
Warning: there is no package called ‘GGally’Installing package into ‘/cloud/lib/x86_64-pc-linux-gnu-library/4.3’
(as ‘lib’ is unspecified)
also installing the dependencies ‘proxy’, ‘DEoptimR’, ‘htmlwidgets’, ‘coda’, ‘patchwork’, ‘e1071’, ‘pcaPP’, ‘robustbase’, ‘som’, ‘lars’, ‘pls’, ‘mclust’, ‘sp’, ‘tweenr’, ‘polyclip’, ‘gridExtra’, ‘htmlTable’, ‘viridis’, ‘Formula’, ‘statnet.common’, ‘rJava’, ‘brew’, ‘hunspell’, ‘estimability’, ‘mvtnorm’, ‘ggstats’, ‘broom.helpers’, ‘chemometrics’, ‘geosphere’, ‘ggforce’, ‘Hmisc’, ‘igraph’, ‘intergraph’, ‘labelled’, ‘maps’, ‘mapproj’, ‘network’, ‘scagnostics’, ‘sna’, ‘roxygen2’, ‘crosstalk’, ‘spelling’, ‘emmeans’, ‘vdiffr’

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* installing *binary* package ‘proxy’ ...
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* DONE (DEoptimR)
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* DONE (htmlwidgets)
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* DONE (coda)
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* DONE (patchwork)
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* DONE (som)
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* DONE (lars)
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* DONE (pls)
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* DONE (mclust)
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* DONE (sp)
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* DONE (tweenr)
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* DONE (polyclip)
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* DONE (gridExtra)
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* DONE (Formula)
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* DONE (rJava)
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* DONE (brew)
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* DONE (hunspell)
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* DONE (estimability)
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* DONE (mvtnorm)
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* DONE (igraph)
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* DONE (labelled)
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* DONE (maps)
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* DONE (crosstalk)
* installing *binary* package ‘vdiffr’ ...
* DONE (vdiffr)
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* DONE (e1071)
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* DONE (pcaPP)
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* DONE (robustbase)
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* DONE (htmlTable)
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* DONE (viridis)
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* DONE (statnet.common)
* installing *binary* package ‘broom.helpers’ ...
* DONE (broom.helpers)
* installing *binary* package ‘geosphere’ ...
* DONE (geosphere)
* installing *binary* package ‘ggforce’ ...
* DONE (ggforce)
* installing *binary* package ‘mapproj’ ...
* DONE (mapproj)
* installing *binary* package ‘scagnostics’ ...
* DONE (scagnostics)
* installing *binary* package ‘roxygen2’ ...
* DONE (roxygen2)
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* DONE (spelling)
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* DONE (emmeans)
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* DONE (ggstats)
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* DONE (chemometrics)
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* DONE (Hmisc)
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* DONE (network)
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* DONE (intergraph)
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* DONE (sna)
* installing *binary* package ‘GGally’ ...
* DONE (GGally)

The downloaded source packages are in
    ‘/tmp/RtmpwBrd26/downloaded_packages’
Loading required package: GGally
Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

Import an SPSS file

Use the read_sav() function from the Haven package to import an SPSS file into R.

Documentation can be viewed here

dataset <- read_sav("https://osf.io/98mt6/download")

Clean dataset

View dataset

Use the View() function to look at your dataset like you would in SPSS or Excel

Documentation can be viewed here

#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

dataset %>%
  map(~ attr(., "label"))

$StartDate
[1] "Start Date"

$EndDate
[1] "End Date"

$Status
[1] "Response Type"

$Progress
[1] "Progress"

$Duration__in_seconds_
[1] "Duration (in seconds)"

$Finished
[1] "Finished"

$RecordedDate
[1] "Recorded Date"

$ResponseId
[1] "Response ID"

$RecipientLastName
[1] "Recipient Last Name"

$RecipientFirstName
[1] "Recipient First Name"

$RecipientEmail
[1] "Recipient Email"

$ExternalReference
[1] "External Data Reference"

$LocationLatitude
[1] "Location Latitude"

$LocationLongitude
[1] "Location Longitude"

$DistributionChannel
[1] "Distribution Channel"

$UserLanguage
[1] "User Language"

$CoinFlip
[1] "Flip a coin. Is it heads or tails?"

$FFM_1
[1] "I see Myself as Someone Who..... - Is talkative"

$FFM_2
[1] "I see Myself as Someone Who..... - Tends to find fault with others"

$FFM_3
[1] "I see Myself as Someone Who..... - Does a thorough job"

$FFM_4
[1] "I see Myself as Someone Who..... - Is depressed, blue"

$FFM_5
[1] "I see Myself as Someone Who..... - Is original, comes up with new ideas"

$FFM_6
[1] "I see Myself as Someone Who..... - Is reserved"

$FFM_7
[1] "I see Myself as Someone Who..... - Is helpful and unselfish with others"

$FFM_8
[1] "I see Myself as Someone Who..... - Can be somewhat careless"

$FFM_9
[1] "I see Myself as Someone Who..... - Is relaxed, handles stress well"

$FFM_10
[1] "I see Myself as Someone Who..... - Is curious about many different things"

$FFM_11
[1] "I see Myself as Someone Who..... - Is full of energy"

$FFM_12
[1] "I see Myself as Someone Who..... - Starts quarrels with others"

$FFM_13
[1] "I see Myself as Someone Who..... - Is a reliable worker"

$FFM_14
[1] "I see Myself as Someone Who..... - Can be tense"

$FFM_15
[1] "I see Myself as Someone Who..... - Is ingenious, a deep thinker"

$FFM_16
[1] "I see Myself as Someone Who..... - Generates a lot of enthusiasm"

$FFM_17
[1] "I see Myself as Someone Who..... - Has a forgiving nature"

$FFM_18
[1] "I see Myself as Someone Who..... - Tends to be disorganized"

$FFM_19
[1] "I see Myself as Someone Who..... - Worries a lot"

$FFM_20
[1] "I see Myself as Someone Who..... - Has an active imagination"

$FFM_21
[1] "I see Myself as Someone Who..... - Tends to be quiet"

$FFM_22
[1] "I see Myself as Someone Who..... - Is generally trusting"

$FFM_23
[1] "I see Myself as Someone Who..... - Tends to be lazy"

$FFM_24
[1] "I see Myself as Someone Who..... - Is emotionally stable, not easily upset"

$FFM_25
[1] "I see Myself as Someone Who..... - Is inventive"

$FFM_26
[1] "I see Myself as Someone Who..... - Has an assertive personality"

$FFM_27
[1] "I see Myself as Someone Who..... - Can be cold and aloof"

$FFM_28
[1] "I see Myself as Someone Who..... - Perseveres until the task is finished"

$FFM_29
[1] "I see Myself as Someone Who..... - Can be moody"

$FFM_30
[1] "I see Myself as Someone Who..... - Values artistic, aesthetic experiences"

$FFM_31
[1] "I see Myself as Someone Who..... - Is sometimes shy, inhibited"

$FFM_32
[1] "I see Myself as Someone Who..... - Is considerate and kind to almost everyone"

$FFM_33
[1] "I see Myself as Someone Who..... - Does things efficiently"

$FFM_34
[1] "I see Myself as Someone Who..... - Remains calm in tense situations"

$FFM_35
[1] "I see Myself as Someone Who..... - Prefers work that is routine"

$FFM_36
[1] "I see Myself as Someone Who..... - Is outgoing, sociable"

$FFM_37
[1] "I see Myself as Someone Who..... - Is sometimes rude to others"

$FFM_38
[1] "I see Myself as Someone Who..... - Makes plans and follows through with them"

$FFM_39
[1] "I see Myself as Someone Who..... - Gets nervous easily"

$FFM_40
[1] "I see Myself as Someone Who..... - Likes to reflect, play with ideas"

$FFM_41
[1] "I see Myself as Someone Who..... - Has few artistic interests"

$FFM_42
[1] "I see Myself as Someone Who..... - Likes to cooperate with others"

$FFM_43
[1] "I see Myself as Someone Who..... - Is easily distracted"

$FFM_44
[1] "I see Myself as Someone Who..... - Is sophisticated in art, music, or literature"

$Potter1
[1] "Dawn or Dusk"

$Potter2
[1] "Forest or River?"

$Potter3
[1] "Moon or Stars?"

$Potter4
[1] "Black or White?"

$Potter5
[1] "Heads or Tails?"

$Potter6
[1] "Left or Right?"

$Potter7
[1] "Four boxes are placed before you. Which would you try and open?"

$Potter8
[1] "You and two friends need to cross a bridge guarded by a river troll who insists on fighting one of you before he will let all of you pass. Do you:"

$Potter9
[1] "Once every century, the Flutterby bush produces flowers that adapt their scent to attract the unwary. If it lured you, it would smell of:"

$Potter10
[1] "One of your housemates has cheated in a Hogwarts exam by using a Self-Spelling Quill. Now he has come top of the class in Charms, beating you into second place. Professor Flitwick is suspicious of what happened. He draws you to one side after his lesson and asks you whether or not your classmate used a forbidden quill. What do you do?"

$Potter11
[1] "Which of the following do you find most difficult to deal with?"

$Potter12
[1] "You enter an enchanted garden. What would you be most curious to examine first?"

$Potter13
[1] "Four goblets are placed before you. Which would you choose to drink?"

$Potter14
[1] "What kind of instrument most pleases your ear?"

$Potter15
[1] "Which of the following would you most hate people to call you?"

$Potter16
[1] "After you have died, what would you most like people to do when they hear your name?"

$Potter17
[1] "How would you like to be known to history?"

$Potter18
[1] "A Muggle confronts you and says that they are sure you are a witch or wizard. Do you:"

$Potter19
[1] "Which nightmare would frighten you the most?"

$Potter20
[1] "If you were attending Hogwarts, which pet would you choose to take with you?"

$Potter21
[1] "Given the choice, would you rather invent a potion that would guarantee you:"

$Potter22
[1] "If you could have any power, which would you choose?"

$Potter23
[1] "Which road tempts you most?"

$Potter24
[1] "Late at night, walking alone down the street, you hear a peculiar cry that you believe to have a magical source. Do you:"

$Potter25
[1] "What are you most looking forward to learning at Hogwarts?"

$Potter26
[1] "Which of the following would you most like to study?"

$Potter27
[1] "A troll has gone berserk in the Headmaster’s study at Hogwarts. It is about to smash, crush and tear several irreplaceable items and treasures. In which order would you rescue these objects from the troll’s club, if you could?"

$Potter28
[1] "Which would you rather be:"

$extraversionTidy
NULL

$FlipHeadsDummy
NULL

$CoinFlipFactor
NULL

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

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

dataset %>% 
  slice(-1:-3) -> dataset

Recode variables

Use the tidyverse, mutate, and case_when to recode variables Documentation can be viewed here

Recode to a factor

dataset %>%
  mutate(CoinFlipFactor = case_when(CoinFlip==1 ~ "Heads",
                                    CoinFlip==2 ~ "Tails")) -> dataset

Recode to a dummy variable

dataset %>%
  mutate(FlipHeadsDummy = case_when(CoinFlip==1 ~ 1,
                                   CoinFlip==2 ~ 0)) -> dataset

Summarize variables

Use dfSummary function from summarytools package to summarize and visualize data

Documentation can be viewed here

#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')

Warning: unable to open connection to X11 display ''

Data Frame Summary

exampleDF

Dimensions: 122 x 3
Duplicates: 99

No	Variable	Label	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	CoinFlip [haven_labelled, vctrs_vctr, double]	Flip a coin. Is it heads or tails?	Min : 1 Mean : 1.3 Max : 2	1 : 83 ( 70.3% ) 2 : 35 ( 29.7% )		118 (96.7%)	4 (3.3%)
2	FFM_5 [haven_labelled, vctrs_vctr, double]	I see Myself as Someone Who..... - Is original, comes up with new ideas	Mean (sd) : 3.8 (0.8) min ≤ med ≤ max: 2 ≤ 4 ≤ 5 IQR (CV) : 1 (0.2)	2 : 6 ( 6.2% ) 3 : 26 ( 26.8% ) 4 : 50 ( 51.5% ) 5 : 15 ( 15.5% )		97 (79.5%)	25 (20.5%)
3	Potter3 [haven_labelled, vctrs_vctr, double]	Moon or Stars?	Min : 1 Mean : 1.6 Max : 2	1 : 39 ( 38.6% ) 2 : 62 ( 61.4% )		101 (82.8%)	21 (17.2%)

Generated by summarytools 1.0.1 (R version 4.3.3)
2024-03-26

Use the describe() function of the psych package to summarize data

Documentation can be viewed here

#First select the variables you'd like to summarize
dataset %>%
  select (CoinFlip, FFM_5, Potter3) %>%

Create Composite Variable

Psych

Documentation can be viewed here

Extraversion

#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 '-'. 
#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

#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$FFM_42)
Agreeableness.scales <- scoreItems (Agreeableness.keys, Agreeableness)
Agreeableness.scores <- Agreeableness.scales$scores
dataset$Agreeableness <- Agreeableness.scores[,]
Agreeableness.scales$alpha

Conscientiousness

#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$FFM_43)
my.scales <- scoreItems (my.keys, Conscientiousness)
my.scores <- my.scales$scores
dataset$Conscientiousness <- my.scores[,]
my.scales$alpha

Neuroticism

#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.scales <- scoreItems (my.keys, Neuroticism)
my.scores <- my.scales$scores
dataset$Neuroticism <- my.scores[,]
my.scales$alpha

Openness

#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.scales <- scoreItems (my.keys, Openness)
my.scores <- my.scales$scores
dataset$Openness <- my.scores[,]
my.scales$alpha

Tidyverse

Documentation can be viewed here and here

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

Calculate correlations

Explore correlations

Documentation can be viewed here

dataset %>%
  select(Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) %>%
  correlate() %>%
  shave() %>%

Calculate p-values

Documentation can be viewed here

dataset %>%
  select(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

Documentation can be viewed here

dataset %>%
  select(Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) %>%
  ggpairs()

t-tests

Documentation can be viewed here

independent samples t-tests

dataset %>%
  with(t.test(Agreeableness ~ CoinFlipFactor))

Error in model.frame.default(formula = Agreeableness ~ CoinFlipFactor) : 
  invalid type (list) for variable 'Agreeableness'

paired samples t-tests

dataset %>%
  with(t.test(Agreeableness, Neuroticism, paired = TRUE))

Error in `[.data.frame`(y, yok) : undefined columns selected