Session 1 - R Objects.Rmd

---
title: "Session 1: R Objects"
author: "Ashwin Malshe"
date: "06/01/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Atomic vectors

This next R code chunk creates and outputs a character vector. `c()` concatenates elements to form a single vector. In R even a constant is a vector of length 1. So you can use a `c()` to concatenate vectors of any length.

```{r my-chunk1}
vec1 <- c("a", "b", "c", "d", "e")
print(vec1)

```

Next chunk creates and prints a double type vector (64-bit floating type)

```{r}
vec2 <- c(3.14159, 1.414, 5.678, 9.01)
print(vec2)
```

The other two commonly used vector types in R are:

- logical
- integer

Logical vector
```{r}
vec3 <- c(rep(TRUE, 5), rep(FALSE, 6))
print(vec3)
```

Both integer and double are of type `numeric`

**Atomic vectors are homogeneous.** You can't mix different types and expect to retain those types in the same vector. For example, if you concatenate a character vector and an integer vector together, the resulting vector will be a character vector.


We can get the length of a vector by using `length()` function:

```{r}
length(vec1)
```

Vectors can have names as well:

```{r}
vec4 <- c(p = 1, q = 2, r = 3)
print(vec4)
```

Or

```{r}
vec5 <- setNames(1:3, c("p", "q", "r"))
print(vec5)
```

This helps in subsetting vectors.

```{r}

```


## Matrix

A matrix is a two-dimensional vector

```{r}
m1 <- matrix(c(3.14159, 1.414, 5.678, 9.01),
             nrow = 2,
             ncol = 2)

print(m1)
```

```{r}
m2 <- matrix(data = vec2, nrow = 2)
print(m2)
```


```{r}
identical(m1, m2)
```

Get the dimensions using `dim()` function

```{r}
dim(m1)
```


Subsetting a matrix is similar to a vector:

```{r}

```


## Data Frame

A tabular object that binds vectors of same lengths sideways. A data frame can contain different types of vectors. For example, the following code creates a data frame with three types of vectors - character, integer, and logical.

```{r}
d1 <- data.frame(name = c("John", "Mary", "Toby", "Maya", "Don"),
                 age = as.integer(c(20, 21, 45, 78, 90)),
                 male = c(TRUE, FALSE, TRUE, FALSE, TRUE))

print(d1)
```

Attributes of a data frame:

```{r}
attributes(d1)
```


### Subsetting a data frame

Print first column of `d1`

```{r}
print(d1$name)
```


```{r}
print(d1[, 1])
```

```{r}
print(d1[1, ])
```


Output the intersection of 2nd row and 3rd column

```{r}
print(d1[2, 3])
```
Print the column names of the data frame.

```{r}
names(d1)
```

Print the first two rows and first two columns of `d1`

```{r}
print(d1[c(1, 2), c(1, 2)])
```

```{r}
print(d1[1:2, 1:2])
```

Print the 1st and the 3rd row and the 1st and the 3rd columns

```{r}
print(d1[c(1, 3), c(1, 3)])
```


```{r}
print(d1[1:3, 1:3])
```

## Lists

Lists are a type of vector but they can be  heterogeneous. They can be recursive, and have multiple lists nested within. 

Commonly `json` files are read into R as lists.


```{r}
list1 <- list(vector1 = vec1,
              vector2 = vec2,
              matrix1 = m1,
              dataframe1 = d1)
```

```{r}
print(list1)
```


```{r}
print(list1$vector1)
```

Length of a list

```{r}
length(list1)
```

Subsetting a list using `[`


```{r}
list1_1 <- list1[1]
print(list1_1)
```

```{r}
list1_2 <- list1[[1]]
print(list1_2)
```

Check the class

```{r}
class(list1_1)
```

```{r}
class(list1_2)
```

Thus, subsetting a list using `[` returns **a list containing** the requested object while using `[[` returns the actual object at that position.