data_types_I.Rmd

---
title: "Data Types in R I"
author: "Viswanathan Satheesh"
date: "Sunday 12 July 2015"
output: pdf_document
---
## Creating a vector
Let us create vectors **x** and **y**. We will do that in two ways. One, using the *c()* function, and the other using the *seq()* function. 'c' combines values into a vector. 'seq' is a sequence generator.  

```{r}
x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
y <- seq(11, 20)
x
y
```
The code above creates two vectors, **x** and **y**. Adding the two vectors gives:


```{r}
x + y
```
The elements are added element-wise. The operations in R are element-wise. As an exercise you can try doing the other mathematical
operations on the two vectors. 

## Indexing
The elements in the vectors are indexed. So, to extract an element you need only know its position. To extract the first element in x:

```{r}
x[1]
```
This returns **1**. Try the following and see what you get. 

```{r}
y[1:4]
y[c(1, 3, 5)]
y[c(-1, -3, -5)]
y[-c(1, 3, 5)]
```
## Data types

What are the important data types? They can be listed as:

* **integer**
* **numeric**
* **logical**
* **character**
Let us first create some vectors

```{r}
n <- 1
i <- 1L
l <- TRUE
c <- "Some string"
```
Here we have four vectors created. x is a numeric vector, y an integer vector, t a logical vector, and c is a character vector. Remember, in R everything is a vector. There are no scalars. Therefore, all these vectors that we have created are all vectors of length one. To check the length of the vector, use the length() function:

```{r}
length(n)
length(i)
length(l)
length(c)
```
You can see that all objects created are of length one. 

Now let us check these vectors using the *class()* function:
```{r}
class(x)
class(y)
class(t)
class(c)
```
Now let us create vectors of length > 1
```{r}
num_vr <- c(1, 3.0, 5.0) # numeric vector
int_vr <- c(1L, 3L, 5L) # integer vector
log_vr <- c(TRUE, FALSE, TRUE) # logical vector
char_vr <- c("I am", "a", "string.") # character vector
```
Now get their class.
```{r}
class(num_vr); length(num_vr)
class(int_vr); length(int_vr)
class(log_vr); length(log_vr)
class(char_vr); length(char_vr)
```
We learnt to make vectors before, and now we have learnt to understand them a bit more. We now move on to matrices.
First, let us create some vectors. 
```{r}
v1 <- 1:5
v2 <- 6:10
v3 <- 11:15
```
We have three vectors v1, v2, and v3 and we are going to bind them column-wise.
```{r}
cbind(v1, v2, v3)
```
The output just spews out to the console, which is not helpful. Let us create a variable, my_mat, and store the output
```{r}
my_mat <- cbind(v1, v2, v3)
```
Here, we used a function, cbind(), to bind three vectors into three columns. Now let us use the class function on the my_mat variable.
```{r}
class(my_mat)
```
my_matrix is a matrix. It has three columns, v1, v2, and v3. And, as it should be clear now, we used three vectors to create a matrix. Let us now see an alternate method for creating a matrix. 
```{r}
trial_mat <- matrix(1:20, nrow=4, ncol=5)
trial_mat
```

This creates a matrix with 4 rows and 5 columns. The [1,] refers to the first row. The [,1] refers to the first column. 

```{r}
```

**To be continued...**