Basic Graph (1) - No13

When it comes to the R, no doubt, you will be hearing that one of the most powerful functions in R is graphic support. Of course, I feel the same way, too.

Today I will talk about some of R graphic functions and  I will discuss more detail one more time.

I am going to use trees data which is built-in sample data for our demonstration.

> trees

   Girth Height Volume

1    8.3     70   10.3

2    8.6     65   10.3

3    8.8     63   10.2

4   10.5     72   16.4

5   10.7     81   18.8

6   10.8     83   19.7

7   11.0     66   15.6

8   11.0     75   18.2

9   11.1     80   22.6

10  11.2     75   19.9

11  11.3     79   24.2

12  11.4     76   21.0

13  11.4     76   21.4

14  11.7     69   21.3

15  12.0     75   19.1

16  12.9     74   22.2

17  12.9     85   33.8

18  13.3     86   27.4

19  13.7     71   25.7

As you can see, there are three columns. If you want to analyze the relations between

Girth and Volume. 

> plot(trees$Girth, trees$volume, main="Tree analyze" , xlab ="Girth", ylab="Volume")

(default type is point graph, try other type of graph by adding type="b" .. (dotted line graph, line graph etc)

You might be want to see the frequency of height. you can use the barplot. 

barplot(table(trees$Height), main="Tree Height analyze" , xlab ="Height", ylab="Meter")

Another way to see the frequency of the data is hist function.

hist(trees$Height, main="Tree Height analyze" , xlab ="Height")

Which one do you think better? It's up to you.

I said, R has diverse graphic support.

This is one of the most favorable functions in R, you can add additional line to improve graph readability.

I would like to see the trees their height is above average. how can I do this ?

> barplot(trees$Height)

> abline(h=mean(trees$Height), col="red")

Finally, I introduce one more basic graph, pie chart.

Pie chart displays proportional data intuitively. For example, you might be want to see the height in terms of a large percentage. not a individual percentage.

Then first divide data into appropriate group.

> a <- mean(trees$Height[trees$Height < 70])

> b <- mean(trees$Height[trees$Height < 80 & trees$Height > 70]) 

> c <- mean(trees$Height[trees$Height > 80])

and then put in an another variable and give a name to each group.

> names(rate) <- c ("Under 70","Between 70 and 80","Beyond 80")

> pie(rate)

How about your feeling ?

Are you going along with my idea that R has a powerful graphic support ?