Chapter 1 Introduction

This workshop will provide an introduction to R. At lightning speed, we’ll cover R and RStudio, after which we’ll learn about importing, tidying, transforming, visualising, and analysing your data in R. You can switch to different topics using the navigation bar on the left.

1.1 Some reasons to use R

1.1.1 You can easily generate stuff:

# Flipping a coin ten times
sample(c("Head", "Tails"), size = 10, replace = TRUE, prob = c(0.5, 0.5))
##  [1] "Tails" "Head"  "Head"  "Tails" "Tails" "Tails" "Head"  "Tails"
##  [9] "Head"  "Head"

1.1.2 You can easily and beautifully visualise stuff:

ggplot(mpg, aes(x=cty, y=hwy, colour=drv)) + geom_point() + geom_smooth() + theme_minimal()

1.1.3 You can do ‘standard’ analysis, like linear regression:

model <- lm(hwy ~ cty + drv, data = mpg)
summary(model)
## 
## Call:
## lm(formula = hwy ~ cty + drv, data = mpg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7830 -0.9041 -0.3083  0.8973  5.0223 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.99883    0.46622   4.287 2.66e-05 ***
## cty          1.19859    0.03087  38.826  < 2e-16 ***
## drvf         2.22365    0.27027   8.227 1.42e-14 ***
## drvr         2.12501    0.33314   6.379 9.70e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.494 on 230 degrees of freedom
## Multiple R-squared:  0.9379, Adjusted R-squared:  0.9371 
## F-statistic:  1157 on 3 and 230 DF,  p-value: < 2.2e-16

1.1.4 You can also do fancy “state-of-the-art” analysis stuff, for example:

  1. network analyses
  2. mixed models
  3. missing data imputation
  4. bayesian analyses
  5. making statistical webapplications

1.1.5 You’ll work more reproducibly:

1.1.6 You’ll learn about the wonderful world of coding:

passed_the_test <- function(grade) {
    if(grade > 5.5){
        print("You passed the test")
    }
    else {
        print("You failed the test")
    }
}
passed_the_test(8)
## [1] "You passed the test"

1.1.7 You’ll learn the virtue of patience as R frustrates you:

1.2 Further reading

There is an amazing and free book available on R: R for Data Science (http://r4ds.had.co.nz/) (Garrett Grolemund 2017). The workshop today is based on many of the principles described in that book.

References

Garrett Grolemund, Hadley Wickham &. 2017. R for Data Science. 1st ed. California, US: O’Reilly Media. http://r4ds.had.co.nz.