# 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

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

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