R comes with built-in plotting functionality, and interprets the data to some extent depending upon the nature of the data you are attempting to plot.  For example, plotting BMI versus age generates a scatter plot as both variables are numerical.

plot(bmi~age, data=data)

Now you will notice that suddenly a new window opens up containing the plot:

You can colour the symbols as well if you wish, using the argument col=“..”

plot(bmi~age, data=data, col="blue")

To save this plot the following command is used:

dev.copy(png, ' bmi_v_age.png')

dev.off()

Where bmi_v_age.png is the name you wish to give to the picture file.  This will save the plot as a “png” file, which is readable by many different software.  The file will appear in your working directory.

Similarly, plotting BMI versus gender results in a (fairly meaningless) scatter plot. In contrast, a bar and whiskers plot results from plotting BMI versus genderf , as genderf is a factor this makes for a much more meaningful plot:

plot(bmi~genderf, data=data)

There are many arguments for modifying plots.  Here are a few:

horiz=TRUE – rotating a plot

col="lightgreen" – changing the colour of the data

xlab="Gender" – defining the x-axis label

ylab="BMI" - defining the y-axis label

main="BMI by gender" – giving a title to the plot

There are many more option, just type ?plot for help within R. There is also a much more powerful plotting package called ggplot2, which is worth looking into.