Basics of R Programming

# Basics of R Programming
### <br><br>Thiyanga S. Talagala, University of Sri Jayewardenepura
### IASSL - Feb 21/25, 2022

---

</style>

## 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
```

---
class: inverse, center, middle

# Your turn
---

1. Generate a sequence using the code `seq(from=1, to=10, by=1)`.

2. What other ways can you generate the same sequence?

3. Using the function `rep` , create the below sequence 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4

---

## Vectors: Subsetting

```r
myvec <- 1:20; myvec
```

```
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
```

```r
myvec[1]
```

```
[1] 1
```

```r
myvec[5:10]
```

```
[1]  5  6  7  8  9 10
```

---
## Vectors: Subsetting (cont.)

```r
myvec[-1]
```

```
 [1]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
```
--

```r
myvec[myvec > 3]
```

```
 [1]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
```

---

## Changing values of a vector

```r
covid <- c(100, 30, 40, 50, -1, 100)
covid
```

```
[1] 100  30  40  50  -1 100
```

```r
covid[1] <- 50000
covid
```

```
[1] 50000    30    40    50    -1   100
```

---
## Changing values of a vector (cont.)

```r
covid[covid < 0] <- 0
covid
```

```
[1] 50000    30    40    50     0   100
```

```r
covid[c(1, 2)] <- c(1000, 10000)
covid
```

```
[1]  1000 10000    40    50     0   100
```

---

# factor

---
## Required R package

```r
library(tidyverse)
```

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

```
✓ ggplot2 3.3.5          ✓ purrr   0.3.4     
✓ tibble  3.1.6          ✓ dplyr   1.0.8.9000
✓ tidyr   1.2.0          ✓ stringr 1.4.0     
✓ readr   2.1.2          ✓ forcats 0.5.1     
```

```
Warning: package 'tidyr' was built under R version 4.1.2
```

```
Warning: package 'readr' was built under R version 4.1.2
```

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

---
### Character vector vs Factor

- Factor prints all possible levels of the variable.

**Character vector**

```r
grade_character_vctr <- c("A", "D", "A", "C", "B")
grade_character_vctr
```

```
[1] "A" "D" "A" "C" "B"
```

**Factor vector**

```r
grade_factor_vctr <- factor(c("A", "D", "A", "C", "B"), levels = c("A", "B", "C", "D", "E"))
grade_factor_vctr
```

```
[1] A D A C B
Levels: A B C D E
```

---
### Character vector vs Factor (cont.)

- Let's create a contingency table with `table` function.

**Character vector output with table function**

```r
grade_character_vctr <- c("A", "D", "A", "C", "B")
table(grade_character_vctr)
```

```
grade_character_vctr
A B C D 
2 1 1 1 
```

---

**Factor vector (with levels) output with table function**

```r
grade_factor_vctr <- 
  factor(c("A", "D", "A", "C", "B"), 
         levels = c("A", "B", "C", "D", "E"))
table(grade_factor_vctr)
```

```
grade_factor_vctr
A B C D E 
2 1 1 1 0 
```

- Output corresponds to factor prints counts for all possible levels of the variable. Hence, with factors it is obvious when some levels contain no observations.

---

### Character vector vs Factor (cont.)

- With factors you can't use values that are not listed in the levels, but with character vectors there is no such restrictions.

**Character vector**

```r
grade_character_vctr[2] <- "A+"
grade_character_vctr
```

```
[1] "A"  "A+" "A"  "C"  "B" 
```

---

**Factor vector**

```r
grade_factor_vctr[2] <- "A+"
grade_factor_vctr
```

```
[1] A    <NA> A    C    B   
Levels: A B C D E
```

---

## Factor: order levels

```r
fv2 <- factor(c("1T","2T","3A","4A", "5A", "6B", "3A"))
fv2
```

```
[1] 1T 2T 3A 4A 5A 6B 3A
Levels: 1T 2T 3A 4A 5A 6B
```

```r
library(ggplot2)
qplot(fv2, geom = "bar")
```

---

You can change the order of levels

```r
fv2 <- factor(c("1T","2T","3A","4A", "5A", "6B", "3A"), 
              levels = c("3A", "4A", "5A", "6B", "1T", "2T"))
fv2
```

```
[1] 1T 2T 3A 4A 5A 6B 3A
Levels: 3A 4A 5A 6B 1T 2T
```

```r
qplot(fv2, geom = "bar")
```

---

## Data set

---

## Required R package

```r
library(tidyverse)
```

---

## Create a tibble

]

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

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

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

---
class: inverse, middle, center

# Functions in R

---

# Data set: tibble

```r
final
```

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

]

## 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

---

]

```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 × 2
  Height Weight
   <dbl>  <dbl>
1    100     50
2    101     60
3    102     80
4    150     43
5     NA     50
```

]

---

```r
hwdata
```

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

]

```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                     
```

]

---

# Subsetting

```r
hwdata
```

```
# A tibble: 5 × 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 × 1
  Height
   <dbl>
1    100
```
]

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

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

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

```
# A tibble: 1 × 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
```

]

```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
```

]

```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
```
]

---

# Help file

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

]

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

# Commenting

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

```
[1] 113.25
```

---

# Some useful functions

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

```
[1] 56.6
```

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

```
[1] 50
```

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

```
[1] 14.41527
```

]

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

```
[1] 283
```

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

```
[1] 5
```

]

---

## Pipe operator (`%>%`)

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

```
[1] 56.6
```

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

```
[1] 113.25
```

]

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

```
[1] 56.6
```

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

```
[1] 113.25
```

]

---

## Pipe operator (`%>%`)

![](pipeillustration.png)
---
## Built-in dataset

```r
library(palmerpenguins)
data(penguins)
head(penguins)
```

```
# A tibble: 6 × 8
  species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
  <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
1 Adelie  Torge…           39.1          18.7              181        3750 male 
2 Adelie  Torge…           39.5          17.4              186        3800 fema…
3 Adelie  Torge…           40.3          18                195        3250 fema…
4 Adelie  Torge…           NA            NA                 NA          NA <NA> 
5 Adelie  Torge…           36.7          19.3              193        3450 fema…
6 Adelie  Torge…           39.3          20.6              190        3650 male 
# … with 1 more variable: year <int>
```

---

## Skim data

```r
library(skimr)
skim(penguins)
```

---
## iris dataset

![](iris_flower_dataset.png)

---

![](iris_petal_sepal.png)
---

Use	the	R	dataset	“iris”	to	answer	the	following	questions:

1. How many rows and columns does iris have?

2. Select the first 4 rows.

3. Select the last 6 rows.

4. Select rows 10 to 20, with all columns in the iris dataset.

5. Select rows 10 to 20 with only the Species, Petal.Width and Petal.Length.

6. Create a single vector (a new object) called ‘width’ that is the Sepal.Width column of iris.

7.  What are the column names and data types of the different columns in iris?

8. How many rows in the iris dataset have `Petal.Length` larger than 5 and `Sepal.Width` smaller than 3?

<div class="countdown" id="timer_62133dbf" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
---

# Recap

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