In this tutorial, let us first begin by understanding the basics using the plot() function in R. The R language is well known for its beautiful graphics with a rich set of functions to build and format any kind of graphs and the plot() function family one that helps us build those.

The plot() function in R isn’t a single defined function but a placeholder for a family of related functions. The exact function being called will depend upon the parameters used. At its simplest, plot() function simply plots two vectors against each other.

```
plot(c(1,2,3,4,5),c(1,4,9,16,25))
```

This gives a simple plot for y = x^2.

Table of Contents

## Changing Graph Appearance with the plot() function in R

The plot() function in R can be customized in multiple ways to create more complex and eye-catching plots as we will see.

**The shape of the markers:**The plot markers are by default small, empty circles. These are also known as plot characters – denoted by**pch**. You can change these by adding a new**pch**value in the plot function. Pch values 0 to 25 are valid and give several different symbols on the graph.**Pch**0 is for a square, 1 is for a circle, 3 is for a triangle, 4 is for a cross and so on.**Size of the plot markers**: This aspect of a graph can be controlled using the**cex**parameter. The**cex**parameter can be set to 0.5 if you want the markers to be 50% smaller and 1.5 if you want them to be 50% larger.**Color of the plot markers:**The symbols can be assigned one or many colors. These colors can be selected from a list provided by R under the`colors()`

function.**Connecting the points with lines:**Many times, it is necessary to connect the displayed points with different kinds of lines. This can be done using the**type**attribute of the plot function. The type attribute set to ‘p’ refers to only points and ‘l’ to only a line. Similarly, values ‘b’ and ‘o’ are for lines connecting points and overlaying points respectively. To get a histogram like display the ‘h’ option is used and ‘s’ is used for a step option.**Varying the lines:**The line type can be specified by the lty parameter (range 0 to 6) and line width is set using an**lwd**parameter.

Let us now try constructing a few graphs with what we learned so far.

We will begin by generating a sine wave plot. Let x be a sequence vector of values from -pi to pi with 0.1 intervals and y contains the respective sine values of x. Now try plotting y against x.

```
x=seq(-pi,pi,0.1)
y=sin(x)
plot(x,y)
```

Let us now try changing the symbols and colors.

```
plot(x,y,pch=c(4,5,6),col=c('red','blue','violet','green'))
```

We are now enabling the compiler to select from 3 different symbols and 4 different colors for marking the graph. Let us see how it turned out.

R also allows combining multiple graphs into a single image for our viewing convenience using the **par()** function. We only need to set the space before calling the plot function in our graph.

```
#Set a plotting window with one row and two columns.
par(mfrow=c(1,2))
plot(x,y,type='l')
plot(x,y,pch=c(4,5,6),col=c('red','blue','violet','green'))
```

A few more graphs using various options from above are illustrated below.

```
#Set space for 2 rows and 3 columns.
par(mfrow=c(2,3))
#Plot out the graphs using various options.
plot(x,cos(x),col=c('blue','orange'),type='o',pch=19,lwd=2,cex=1.5)
plot(x,x*2,col='red',type='l')
plot(x,x^2/3+4.2, col='violet',type='o',lwd=2,lty=1)
plot(c(1,3,5,7,9,11),c(2,7,5,10,8,10),type='o',lty=3,col='pink',lwd=4)
plot(x<-seq(1,10,0.5),50*x/(x+2),col=c('green','dark green'),type='h')
plot(x,log(x),col='orange',type='s')
```

The resulting graph looks like this.

## Adding More Information to Graphs with plot() Function in R

Graphs look more complete when there are notes and information that explain them. These include a title for the chart and axes, a legend of the graph. Sometimes even labeling the data points will be necessary. Let us look at how we add these to the graphs in R.

- The main title is added using the
**main**option in the plot function. The font, color, and size can be customized using the**font.main**,**col.main**and**cex.main**respectively. - The titles for the axes are provided using xlab and ylab attributes. These can be customized using
**font.lab**,**col.lab**and**cex.lab**like above. - You can also add some extra text inside the plot using the
**text**attribute, specifying the text to use and the coordinates to display. - The text attribute can also be used to label the data points. The text, in this case, is a vector of labels instead of a string.
- The legend can be added to a graph using the R’s
**legend()**function. Legend takes as input the coordinates, text and the symbols to be interpreted.

Let us look at examples illustrating these.

```
#Displaying the title with color
plot(c(1,3,5,7,9,11),c(2,7,5,10,8,10),type='o',lty=3, col='pink',lwd=4,main="This is a graph",col.main='blue')
```

```
#Same graph with xlabel and ylabel added.
> plot(c(1,3,5,7,9,11),c(2,7,5,10,8,10),type='o',lt=3,col='pink',lwd=4,main="This is a graph",col.main='blue',xlab="Time",ylab="Performance")
```

Let us add a label to each of the data points in the graph using a text attribute.

```
labelset <-c('one','three','five','seven','nine','eleven')
x1<- c(1,3,5,7,9,11)
y1 <- c(2,7,5,10,8,10)
plot(x1,y1,type='o',lty=3,col='pink',lwd=4,main="This is a graph",col.main='blue',xlab="Time",ylab="Performance")
text(x1+0.5,y1,labelset,col='red')
```

Finally, let us add a legend to the above graph using the **legend()** function.

```
> legend('topleft',inset=0.05,"Performace",lty=3,col='pink',lwd=4)
```

The position can be specified by either x and y coordinates or using a position like ‘topleft’ or ‘bottomright’. Inset refers to moving the legend box a little to the inside of the graph. The resulting graph now has a legend.

## Overlaying Graphs

R also allows two graphs to be displayed on top of each other instead of creating a new window for every graph. This is done by calling a lines() function for the second graph rather than plot() again. These are most useful when performing comparisons of metrics or among different sets of values. Let us look at an example.

```
x=seq(2,10,0.1)
y1=x^2
y2=x^3
plot(x,y1,type='l',col='red')
lines(x,y2,col='green')
legend('bottomright',inset=0.05,c("Squares","Cubes"),lty=1,col=c("red","green"),title="Graph type")
```

## Adding Lines to a Plot

Straight lines can be added to an existing plot using the simple `abline()`

function. The abline() function takes 4 arguments, a, b, h, and v. The variables a and b represent the slope and intercept. H represents the y points for horizontal lines and v represents the x points for vertical lines.

Let us look at an example to make this clear. Try executing these three statements after building the above graph for squares and cubes.

```
abline(a=4,b=5,col='blue')
abline(h=c(4,6,8),col="dark green",lty=2)
abline(v=c(4,6,8),col="dark green",lty=2)
```

The first blue line is built with the slope and intercept specified. The next sets of three horizontal and vertical lines are drawn at the specified x and y values in the dotted line style as mentioned by lty=2.

This covers the basics of plot function in R. When combined with other packages like ggplot2, R builds the most presentable and dynamic graphics as we will see in further tutorials.