Tutorial_0_R-Intro.Rmd

---
title: "Tutorial 0: R Basic functions"
author: "Andreas Niekler, Gregor Wiedemann"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output: 
  html_document:
    toc: true
    theme: united
    toc_float: true
    number_sections: yes
highlight: tango
bibliography: references.bib
csl: springer.csl
---
```{r klippy, echo=FALSE, include=TRUE}
klippy::klippy()
options(width = 98)
```
Start **RStudio** on your computer. **RStudio** is a so-called IDE - Integrated Development Environment. The interface provides easy access to **R**. The advantage of this application is that **R** programs and files as well as a project directory can be managed easily. The environment is capable of editing and running program code, viewing outputs and rendering graphics. Furthermore, it is possible to view variables and data objects of an R-script directly in the interface. The screen divides into the areas

1. **R** console
2. File editor
3. Environment variables
4. Management panes (File browser, plots, help display and R packages).

```{r fig.width=10, fig.height=8, echo=FALSE, warning=FALSE}
library(png)
ima <- readPNG("resources/screenshot.png")
plot(c(100, 250), c(300, 450), type = "n", xlab = "", ylab = "", xaxt = "n",yaxt = "n")
lim <- par()
rasterImage(ima, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4])
```


# Basic Operations


```{r echo=TRUE, eval=F, warning=F, message=F}
1 + 2      # addition
sqrt(9)    # square root function
x <- 1     # assignment
1:10       # sequence

# install.packages("quanteda") 
library("quanteda")
require("quanteda")

# help
help(require) 
?require
apropos("nova")

# set your working directory
setwd("~") 
```

# Data types

```{r echo=TRUE, eval=F, warning=FALSE, message=FALSE}
x <- 10.5
typeof(x)
class(x)
as.integer(x)
is.integer(x)
is.integer(as.integer(x))
x <- "3.14"
typeof(x)
x <- as.double(x)
1:3 == c(1, 2, 3)

# functions
my_x_square_function <- function(x) {
  result <- x ^ 2
  return(result)
}
plot(my_x_square_function(seq(-5,5)), type="l")
```

# Vector, matrix and data.frames

```{r echo=TRUE, eval=F, warning=FALSE, message=FALSE}
options(stringsAsFactors = F)

myvector <- c(1, 2, 3)
names(myvector) <- c("one", "two", "three")
print(myvector)
print(myvector[1:2])
print(myvector[-1])
sum(myvector)
mean(myvector)

mymatrix <- matrix(0, nrow=3, ncol=4)
rownames(mymatrix) <- c("one", "two", "three")
colnames(mymatrix) <- c("house", "sun", "tree", ".")
mymatrix[, 1] <- 12
mymatrix[, "sun"] <- 4
mymatrix[3, 4] <- 5
mymatrix[2, 3:4] <- 9
colSums(mymatrix)
rowSums(mymatrix)
colMeans(mymatrix)

mydatdaframe <- data.frame(v = c(1, 2, 3), c = as.character(myvector), n = c("one", "two", "three"))
mydatdaframe$v
mydatdaframe$c
mydatdaframe[, "c"]
mydatdaframe[1, ]
rowSums(mydatdaframe)
```


# More basic functions

```{r echo=TRUE, eval=F, warning=FALSE, message=FALSE}
sort(mydatdaframe$n)
sort(mydatdaframe$n, decreasing = T)
c(myvector, myvector)
is.vector(myvector)
is.matrix(mymatrix)
is.matrix(myvector)

mymatrix / 2
mymatrix / myvector
t(mymatrix)
mymatrix %*% t(mymatrix)


cbind(mymatrix, myvector)
rbind(mymatrix, myvector)

# example datasets: data.frame of arrests per USA states
data(USArrests)
View(USArrests)
dim(USArrests)
nrow(USArrests)
ncol(USArrests)
length(USArrests)
max(USArrests)

which.max(USArrests[, "Murder"])

o <- order(USArrests[, "Murder"], decreasing = T)
USArrests[o, ]

murderRates <- USArrests[o[1:5], "Murder"]
names(murderRates) <- rownames(USArrests)[o[1:5]]
barplot(murderRates)

```