R Programming -- data frames

Data Frames

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The 

weights

, 

prices

,
and 

types

 data
structures are all deeply tied together, if you think about it. If you add a new weight sample, you need to remember to add a new price and type, or risk everything falling out of sync. To avoid trouble, it would be nice if we could tie all these variables
together in a single data structure.

Fortunately, R has a structure for just this purpose: the data frame.
You can think of a data frame as something akin to a database table or an Excel spreadsheet. It has a specific number of columns, each of which is expected to contain values of a particular type. It also has an indeterminate number of rows - sets of related
values for each column.

Data Frames6.1

Our vectors with treasure chest data are perfect candidates for conversion to a data frame. And it's easy to do. Call the 

data.frame

 function,
and pass 

weights

, 

prices

,
and 

types

 as
the arguments. Assign the result to the 

treasure

 variable:

RedoComplete

> treasure <- data.frame(weights, prices, types)

Now, try printing treasure to see its contents:

RedoComplete

> print(treasure)
weights prices  types
1     300   9000   gold
2     200   5000 silver
3     100  12000   gems
4     250   7500   gold
5     150  18000   gems

There's your new data frame, neatly organized into rows, with column names (derived from the variable names) across the top.

Data Frame Access6.2

Just like matrices, it's easy to access individual portions of a data frame.

You can get individual columns by providing their index number in double-brackets. Try getting the second column (prices) of treasure:

RedoComplete

> treasure[[2]]
[1]  9000  5000 12000  7500 18000

You could instead provide a column name as a string in double-brackets. (This is often more readable.) Retrieve the "weights" column:

RedoComplete

> treasure[["weights"]]
[1] 300 200 100 250 150

Typing all those brackets can get tedious, so there's also a shorthand notation: the data frame name, a dollar sign, and the column name (without quotes). Try using it to get the 

"prices"

 column:

RedoComplete

> treasure$prices
[1]  9000  5000 12000  7500 18000

Now try getting the "types" column:

RedoComplete

> treasure$types
[1] gold   silver gems   gold   gems
Levels: gems gold silver

Loading Data Frames6.3

Typing in all your data by hand only works up to a point, obviously, which is why R was given the capability to easily load data in from external files.

We've created a couple data files for you to experiment with:

> list.files()
[1] "targets.csv" "infantry.txt"

Our "targets.csv" file is in the CSV (Comma Separated Values) format exported by many popular spreadsheet programs. Here's what its content looks like:

"Port","Population","Worth"
"Cartagena",35000,10000
"Porto Bello",49000,15000
"Havana",140000,50000
"Panama City",105000,35000

You can load a CSV file's content into a data frame by passing the file name to the 

read.csv

 function.
Try it with the 

"targets.csv"

 file:

RedoComplete

> read.csv("targets.csv")
Port Population Worth
1   Cartagena      35000 10000
2 Porto Bello      49000 15000
3      Havana     140000 50000
4 Panama City     105000 35000

The 

"infantry.txt"

 file
has a similar format, but its fields are separated by tab characters rather than commas. Its content looks like this:

Port         Infantry
Porto Bello  700
Cartagena    500
Panama City  1500
Havana       2000

For files that use separator strings other than commas, you can use the 

read.table

 function.
The 

sep

argument
defines the separator character, and you can specify a tab character with 

"\t"

.

Call 

read.table

 on 

"infantry.txt"

,
using tab separators:

RedoComplete

> read.table("infantry.txt",sep="\t")
V1       V2
1        Port Infantry
2 Porto Bello      700
3   Cartagena      500
4 Panama City     1500
5      Havana     2000

Notice the 

"V1"

 and 

"V2"

 column
headers? The first line is not automatically treated as column headers with 

read.table

.
This behavior is controlled by the header argument. Call 

read.table

 again,
setting header to 

TRUE

:

RedoComplete

> read.table("infantry.txt", sep="\t",header=TRUE)
Port Infantry
1 Porto Bello      700
2   Cartagena      500
3 Panama City     1500
4      Havana     2000

Merging Data Frames6.4

We want to loot the city with the most treasure and the fewest guards. Right now, though, we have to look at both files and match up the rows. It would be nice if all the data for a port were in one place...

R's merge function can accomplish precisely that. It joins two data frames together, using the contents of one or more columns. First, we're going to store those file contents in two data frames for you, 

targets

 and

infantry

.

The merge function takes arguments with an 

x

 frame
(

targets

)
and a 

y

 frame
(

infantry

).
By default, it joins the frames on columns with the same name (the two 

Port

 columns).
See if you can merge the two frames:

RedoComplete

> targets <- read.csv("targets.csv")
> infantry <- read.table("infantry.txt", sep="\t", header=TRUE)
> merge(x = targets, y = infantry)
Port Population Worth Infantry
1   Cartagena      35000 10000      500
2      Havana     140000 50000     2000
3 Panama City     105000 35000     1500
4 Porto Bello      49000 15000      700

Chapter 6 Completed

Share your plunder:

Thirty paces south from the gate of the fort, and dig… we've unearthed another badge!

When your data grows beyond a certain size, you need powerful tools to organize it. With data frames, R gives you exactly that. We've shown you how to create and access data frames. We've also shown you how to load frames in from files, and how to cobble multiple
frames together into a new data set.

Time to take what you've learned so far, and apply it. In the next chapter, we'll be working with some real-world data!