• R and RStudio installed on your laptop
• The tidyverse R package
• The Perceptions of Uncertainty dataset from https://tinyurl.com/probly-data (right click page and Save as probly.csv)

• Introduction to R and RStudio
• Importing data
• Processing data
• Plotting data
• Summarising data

Create a new project to keep files organised

• Simply type!
• Create sections with #.
• Subsections can be created with ## and ###.
• Highlight text by surrounding it with * for italics.
• Use ** for bold.

• Blocks of R code start with ```{r}
• and end with ```

```{r}

# fancy computations here

```

• Want to assign a name to data so we can use it in computations
• Assign values to variables
• Values can be simple (e.g. 5 or "dog")
• … or complex (e.g. all the data from your study)

ans <- 42

• Behaviour of R chunks can be modified through options.
• Access via settings icon or type directly into chunk header.
• Can name chunks to make locating them later easier.

• Data is stored in vectors
• Each vector has a type
  • numeric for interval and ratio data
  • logical for binary data/truth values
  • character or factor for categorical data
  • …

1:5
logical(3)
letters
factor(letters)

• Vectors can have multiple dimensions.

mat <- matrix(1:12, ncol=3)
dim(mat)
mat[2, 3]

• Different types of vectors can be combined into lists.
• Vectors of the same length can be combined into data frames.

lst <- list(position=1:26, letter=letters)
lst[[2]][3]
lst$letter[3]
df <- data.frame(position=1:26, letter=letters)
df[3, 2]
df[-(1:10),]

• Functions transform data.
• Input remains unchanged.

• Each function consists of three parts.
  • A name
  • A list of arguments
  • A body of R code that processes the arguments

• Perception of uncertainty
  • What probability would you assign to the phrase "[phrase]"
• Collected by /u/zonination on Reddit
• Inspired by Sherman Kent study
• Available on GitHub

Tidy data should have

• One variable per column.
• One observation per row.

It depends on the question we want to ask of the data!

• Each column could be considered a different level of the same variable.
• Or a different variable.
• Note that column with subject ID is missing.

Allows easy reformatting of data to make it tidy.

library(tidyverse)

gather(table4a, `1999`, `2000`, 
       key = "year", value = "cases")

spread(table2, type, count)

• A plot only really needs three things
  • data
  • mapping of data to geometric objects
  • coordinate system
• ggplot2 provides an easy way to combine these elements into plots.

ggplot( data) + geom_ type(aes( aesthetics))

data: a data.frame with your data.

geom: Geometric object to use to visualise the data.

aesthetics: Mapping of variables to properties of the geom (location, colour, size, …)

ggplot(probly_long) + geom_boxplot(aes(x=phrase, y=probability)) + coord_flip()

ggplot(probly_long) + geom_boxplot(aes(x=phrase, y=probability)) +
  theme(axis.text.x=element_text(angle=45,hjust=1))

• Plot is still hard to read.
• Would be helpful to order phrases by perceived probability.
• How do we tell ggplot which order to use?

library(forcats)

probly_long <- mutate(probly_long, phrase=fct_reorder(phrase, probability))
ggplot(probly_long) + geom_boxplot(aes(x=phrase, 
                                       y=probability)) + coord_flip()

ggplot(probly_long) + geom_boxplot(aes(x=phrase, 
                                       y=probability,
                                       fill=avg)) + 
  xlab("") + coord_flip()

• ggplot chose a single colour gradient to visualise median probabilities.
• A divergent scale centred on 50% would make a lot more sense.

ggplot(probly_long) + geom_boxplot(aes(x=phrase, 
                                       y=probability, fill=avg)) + 
  xlab("") + coord_flip() +
  scale_fill_gradient2(midpoint=50)

• Be careful about removing outliers
• Outliers may be due to corrupted measurements
• but may also represent higher than expected variability in the data
• Only remove outliers if there is solid evidence that these measurements are unreliable

probly_clean <- filter(probly_long, subject != 15)

• Plots are very useful but sometimes we need actual numbers.
• Simple summaries can easily be obtained via dplyr::summarise

 prob_summary <- summarise(probly_clean, mean=mean(probability))

prob_summary <- summarise(probly_clean, mean=mean(probability), 
                          sd=sd(probability), 
                          'trimmed mean'=mean(probability, trim = 0.1),
                          median=median(probability), IQR=IQR(probability), 
                          MAD=mad(probability))

• We can use these data to determine whether the perception of two phrases differs significantly.

t.test(probly$Likely[-15], probly$`Better Than Even`[-15], paired=TRUE)

## 
##  Paired t-test
## 
## data:  probly$Likely[-15] and probly$`Better Than Even`[-15]
## t = 7.1021, df = 44, p-value = 8.102e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   9.802789 17.570544
## sample estimates:
## mean of the differences 
##                13.68667

• Continue to practice using R
• Explore your own data

• Learn about your favourite analysis techniques in R

• CRAN maintains lists of useful and recommended packages by topic
  • Social Sciences
  • Psychometrics
  • Official Statistics (including survey methodology)
  • Bayesian Inference

• Power analysis:
  • pwr
  • skpr
  • simr
• Linear (mixed) models: lme4
• Non-linear regression: nlme
• Eye tracking: eyetrackingR
• Factor analysis/SEM: lavaan
• Bayesian hypothesis testing: BayesFactor

These slides are available at https://humburg.github.io/Rintro/