**Scatterplot**

*You have two vectors and you want to plot them.*

```
x_values <- rnorm(n = 20 , mean = 5 , sd = 8) #20 values generated from Normal(5,8)
y_values <- rbeta(n = 20 , shape1 = 500 , shape2 = 10) #20 values generated from Beta(500,10)
```

If you want to make a plot which has the `y_values`

in vertical axis and the `x_values`

in horizontal axis, you can use the following commands:

```
plot(x = x_values, y = y_values, type = "p") #standard scatter-plot
plot(x = x_values, y = y_values, type = "l") # plot with lines
plot(x = x_values, y = y_values, type = "n") # empty plot
```

You can type `?plot()`

in the console to read about more options.

**Boxplot**

*You have some variables and you want to examine their Distributions*

```
#boxplot is an easy way to see if we have some outliers in the data.
z<- rbeta(20 , 500 , 10) #generating values from beta distribution
z[c(19 , 20)] <- c(0.97 , 1.05) # replace the two last values with outliers
boxplot(z) # the two points are the outliers of variable z.
```

**Histograms**

*Easy way to draw histograms*

```
hist(x = x_values) # Histogram for x vector
hist(x = x_values, breaks = 3) #use breaks to set the numbers of bars you want
```

**Pie_charts**

*If you want to visualize the frequencies of a variable just draw pie*

First we have to generate data with frequencies, for example :

```
P <- c(rep('A' , 3) , rep('B' , 10) , rep('C' , 7) )
t <- table(P) # this is a frequency matrix of variable P
pie(t) # And this is a visual version of the matrix above
```