20170620_R_intro.Rmd

---
title: 'DHSS: An Introduction to R for Digital Historians'
author: "Yannick Rochat"
date: "20 June 2017"
output:
  ioslides_presentation: default
  html_notebook: default
---


# Basic examples

----



```{r}
4 + 3 # Calcul de base
```

----

```{r}
2^2 + 3*4
7 %% 2
14 < 17
10 < 8 && 2 == 2
```

----

```{r}
z = 12.5
z <- 12.5
z

z = 12.5
y = 7.5
z + y

```

----


```{r}
sqrt(4)
y = sqrt(2)
y

ls()
rm(y)

```

----

```{r}

#q() ## exit R
typeof(z)
help(exp)
?exp
help.search("linear model")

```



# Graphics (examples)

----

```{r}
plot(cars)
```

----

```{r}
library(ggplot2)
ggplot(diamonds, aes(x = carat, y = price)) + 
  geom_point(aes(color = clarity))
```



# Commands


---- 

There are five types :

* data
* operators
* variables
* functions
* keywords

----




### Numeric

```{r}
example(integer)
```


----

```{r eval = FALSE}
?double
?numeric
```

----


### Characters


```{r}
b <- "unil"
c <- "patient 1"
nchar(b)
nchar(c)
paste(b, c, collapse = "")
```


----

### Logical elements

* TRUE et FALSE

and also…

* NA, NaN
* Inf

----

```{r}
1/0
x <- Inf
x - x
```

"NA" means "Not available".

"NaN" means "Not a Number".





# Operators

----

### Arithmetic operators

* \+ 
* \- 
* \* 
* / 
* ˆ 
* %% 
* %/% 
* %*% 

----


### Operators for comparisons


```{r error = TRUE}
1 == 1
1 == 2
1 != 1 
1 != 2
1 < 1
1 <= 1
```

----

### Logical operators

```{r}
a <- (1 < 2)
a
!a
```

----

```{r}
b <- (2 > 3)
a & b
a | b
```


## How to know the type of data ?

```{r}
a <- 1
b <- 1:5
class(a)
class(b)
```

----

```{r}
c <- "hello"
d <- 1 > 2
class(c)
class(d)
```

----

What are their sizes ?

```{r}
length(a)
length(b)
length(c)
length(d)
```

----

`str` gives a description.

```{r}
str(a)
str(b)
str(c)
str(d)
```


## Variables

Here, a and b are variables.

```{r}
a <- 1
b <- 2
a + b
```


----

`=`, `<-` and `->` are assignement operators. They are used to save data.

```{r eval = FALSE}
?'<-'
```


# Classes

* Vectors
* Lists
* Matrices
* Tables
* Data frames


## Vectors


```{r}
x <- c(5,4,5,6,7,8) 
x
x_char = c("a","b","c") 
x_char
```

----

Access elements of a vector

```{r}
x
x[2]
x[c(2,4)]
x[c(-2,-4)]
```


----

Manipulate them…

```{r}
x[6] = 10
x
a = c(3,4,5,6)
a[c(2,3)] = 0
a
a[c(2,3)] = c(8,7)
a
```

----

```{r}
c(1,2,3) + c(3,4,5)
```

----

```{r}
4 * c(1,2,3)
c(2,5) < 4
```

----

```{r}
c(1,2) + c(3,4,5,6)
```

----

… but watch out.

```{r}
c(1,2) + c(2,3,4)
```



## Data frames

----

* Write in R console: `help(package="magrittr")`
* Choose "User guides, package vignettes and other documentation."
* Open the vignette magrittr::magrittr

The following example is taken from there.

----

```{r}
library(magrittr)
```

----


```{r}
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print
```

----

```{r}
mtcars
```

----

```{r}
mtcars %>%
  subset(hp > 100)
```

----

```{r}
mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2))
```

----

```{r}
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251))

```

----

```{r}
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print

```

----

```{r}
car_data <-  
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print

```

----

```{r}
car_data
```