Basics of R Programming

class: center, middle, inverse, title-slide

# Basics of R Programming
### <br><br>Thiyanga S. Talagala, University of Sri Jayewardenepura
### SLAAS - Aug 28/29, 2021

---

</style>

.pull-left[

## Data structures

Way to **store and organize data** so that it can be used efficiently.

```r
marks <- c(100, 40, 34, 97, 98)
marks
```

```
[1] 100  40  34  97  98
```

]

--
.pull-right[

## Functions

Tell R to **do something**

```r
mean(marks)
```

```
[1] 73.8
```

```r
summary(marks)
```

```
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   34.0    40.0    97.0    73.8    98.0   100.0 
```

]

---

## Data structures

Source: Ceballos and Cardiel, 2013

---

## Creating vectors

Syntax

```r
vector_name <- c(element1, element2, element3)

```

Example

```r
x <- c(5, 6, 3, 1, 100)
x
```

```
[1]   5   6   3   1 100
```

---

## Combine two vectors

```r
p <- c(1, 2, 3)
p
```

```
[1] 1 2 3
```

```r
q <- c(10, 20, 30)
q
```

```
[1] 10 20 30
```

```r
r <- c(p, q)
r
```

```
[1]  1  2  3 10 20 30
```

---

## Vector with charactor elements

```r
names <- c("USJ", "UM", "UC", "UJ")
names
```

```
[1] "USJ" "UM"  "UC"  "UJ" 
```

## Logical vector

```r
result <- c(TRUE, FALSE, FALSE, TRUE, FALSE)
result
```

```
[1]  TRUE FALSE FALSE  TRUE FALSE
```

---

## Simplifying vector creation

```r
id <- 1:10
id
```

```
 [1]  1  2  3  4  5  6  7  8  9 10
```

```r
treatment <- rep(1:3, each=2)
treatment
```

```
[1] 1 1 2 2 3 3
```

Additional resources: https://hellor.netlify.app/2021/week1/l12021.html#62

---

## Vector operations

```r
x <- c(1, 2, 3)
y <- c(10, 20, 30)
x+y
```

```
[1] 11 22 33
```

```r
p <- c(100, 1000)
x+p
```

```
[1]  101 1002  103
```

---

## Data set

---

## Required R package

```r
library(tidyverse)
```

```
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
```

```
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.2     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
```

```
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
```

---

## Create a tibble

.pull-left[

]

.pull-right[

```r
marks <- c(90, 50, 20, 60)
grade <- factor(c("A+", "C", "E", "B"))

final <- tibble(Marks = marks, Grade = grade)
final
```

```
# A tibble: 4 x 2
  Marks Grade
  <dbl> <fct>
1    90 A+   
2    50 C    
3    20 E    
4    60 B    
```

]

---

## Create a tibble

```r
marks <- c(90, 50, 20, 60)
grade <- factor(c("A+", "C", "E", "B"),
*                level = c("A+", "A", "B+", "B", "C", "D", "E"))
final <- tibble(Marks = marks, Grade = grade)
final
```

```
# A tibble: 4 x 2
  Marks Grade
  <dbl> <fct>
1    90 A+   
2    50 C    
3    20 E    
4    60 B    
```

---
class: inverse, middle, center

# Functions in R

---

.pull-left[

# Data set: tibble

```r
final
```

```
# A tibble: 4 x 2
  Marks Grade
  <dbl> <fct>
1    90 A+   
2    50 C    
3    20 E    
4    60 B    
```

]

.pull-right[

## Functions

```r
summary(final)
```

```
     Marks      Grade 
 Min.   :20.0   A+:1  
 1st Qu.:42.5   A :0  
 Median :55.0   B+:0  
 Mean   :55.0   B :1  
 3rd Qu.:67.5   C :1  
 Max.   :90.0   D :0  
                E :1  
```

]

---

## Your Turn

---

.pull-left[

]

.pull-right[

```r
h <- c(100, 101, 102, 150, NA)
w <- c(50, 60, 80, 43, 50)
hwdata <- tibble(Height=h, Weight=w)
hwdata
```

```
# A tibble: 5 x 2
  Height Weight
   <dbl>  <dbl>
1    100     50
2    101     60
3    102     80
4    150     43
5     NA     50
```

]

---

.pull-left[

```r
hwdata
```

```
# A tibble: 5 x 2
  Height Weight
   <dbl>  <dbl>
1    100     50
2    101     60
3    102     80
4    150     43
5     NA     50
```

]

.pull-right[

```r
summary(hwdata)
```

```
     Height          Weight    
 Min.   :100.0   Min.   :43.0  
 1st Qu.:100.8   1st Qu.:50.0  
 Median :101.5   Median :50.0  
 Mean   :113.2   Mean   :56.6  
 3rd Qu.:114.0   3rd Qu.:60.0  
 Max.   :150.0   Max.   :80.0  
 NA's   :1                     
```

]

---

.pull-left[

# Subsetting

```r
hwdata
```

```
# A tibble: 5 x 2
  Height Weight
   <dbl>  <dbl>
1    100     50
2    101     60
3    102     80
4    150     43
5     NA     50
```

```r
hwdata[1, 1]
```

```
# A tibble: 1 x 1
  Height
   <dbl>
1    100
```
]

.pull-right[

```r
hwdata[, 1]
```

```
# A tibble: 5 x 1
  Height
   <dbl>
1    100
2    101
3    102
4    150
5     NA
```

```r
hwdata[1, ]
```

```
# A tibble: 1 x 2
  Height Weight
   <dbl>  <dbl>
1    100     50
```

```r
hwdata$Height
```

```
[1] 100 101 102 150  NA
```

]

---

# Help file
.pull-left[

```r
hwdata$Weight
```

```
[1] 50 60 80 43 50
```

```r
mean(hwdata$Weight)
```

```
[1] 56.6
```

]

.pull-right[

```r
hwdata$Height
```

```
[1] 100 101 102 150  NA
```

```r
mean(hwdata$Height)
```

```
[1] NA
```
]
---

# Help file
.pull-left[

```r
hwdata$Weight
```

```
[1] 50 60 80 43 50
```

```r
mean(hwdata$Weight)
```

```
[1] 56.6
```

]

.pull-right[

```r
hwdata$Height
```

```
[1] 100 101 102 150  NA
```

```r
mean(hwdata$Height)
```

```
[1] NA
```

```r
mean(hwdata$Height, na.rm=TRUE)
```

```
[1] 113.25
```
]

---

.pull-left[

# Help file

```r
?mean
help(mean)
```

]

.pull-right[

<img src="help.png" width="170%" />
]
---

# Commenting

```r
mean(hwdata$Height, na.rm=TRUE) # compute mean of height
```

```
[1] 113.25
```

---

# Some useful functions

.pull-left[

```r
mean(hwdata$Weight) 
```

```
[1] 56.6
```

```r
median(hwdata$Weight)
```

```
[1] 50
```

```r
sd(hwdata$Weight) 
```

```
[1] 14.41527
```

]

.pull-right[

```r
sum(hwdata$Weight)
```

```
[1] 283
```

```r
length(hwdata$Weight) 
```

```
[1] 5
```

]

---

## Pipe operator (`%>%`)

.pull-left[

```r
mean(hwdata$Weight) 
```

```
[1] 56.6
```

```r
mean(hwdata$Height, na.rm=TRUE)
```

```
[1] 113.25
```

]

.pull-right[

```r
library(magrittr)
hwdata$Weight %>% mean() 
```

```
[1] 56.6
```

```r
hwdata$Height %>% mean(na.rm=TRUE)
```

```
[1] 113.25
```

]

---

## Pipe operator (`%>%`)

![](pipeillustration.png)

---

class: inverse, cover, middle

# Recap

✅ Data structures and functions
 
 ✅  Factors
 
 ✅ Working with packages
 
 ✅ Create a tibble
 
 ✅ Help file
 
 ✅ Commenting