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The name may sound intimidating, but a vector is simply a list of values. R relies on vectors for many of its operations. This includes basic plots - we'll have you drawing graphs by the end of this chapter (and it's a lot easier than you might think)!

Course tip: if you haven't already, try clicking on the expand icon ( ) in the upper-left corner of the sidebar. The expanded sidebar offers a more in-depth look at chapter sections and progress.

2. Vectors2.1A vector's values can be numbers, strings, logical values, or any other type, as long as they're all the same type. Try creating a vector of numbers, like this:RedoComplete

> c(4, 7, 9)

[1] 4 7 9

The c function (c is short for Combine) creates a new vector by combining a list of values.

3. Now try creating a vector with strings:

RedoComplete

> c('a', 'b', 'c')

[1] "a" "b" "c"

4. Vectors cannot hold values with different modes (types). Try mixing modes and see what happens:

RedoComplete

> c(1, TRUE, "three")

[1] "1" "TRUE" "three"

All the values were converted to a single mode (characters) so that the vector can hold them all.

5. Sequence Vectors2.2If you need a vector with a sequence of numbers you can create it withstart:end notation. Let's make a vector with values from 5 through 9:

RedoComplete

> 5:9

[1] 5 6 7 8 9

6. A more versatile way to make sequences is to call the seq function. Let's do the same thing with seq:RedoComplete

> seq(5, 9)

[1] 5 6 7 8 9

7. seq also allows you to use increments other than 1. Try it with steps of 0.5:RedoComplete

> seq(5, 9, 0.5)

[1] 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

8. Now try making a vector with integers from 9 down to 5:

RedoComplete

> 9:5

[1] 9 8 7 6 5

9. Vector Access2.3We're going to create a vector with some strings in it for you, and store it in thesentence variable.You can retrieve an individual value within a vector by providing its numeric index in square brackets. Try getting the third value:

RedoComplete

> sentence <- c('walk', 'the', 'plank')

> sentence[3]

[1] "plank"

10. Many languages start array indices at 0, but R's vector indices start at 1. Get the first value by typing:

RedoComplete

> sentence[1]

[1] "walk"

11. You can assign new values within an existing vector. Try changing the third word to "dog":

RedoComplete

> sentence[3] <- "dog"

12. If you add new values onto the end, the vector will grow to accommodate them. Let's add a fourth word:

RedoComplete

> sentence[4] <- 'to'

13. You can use a vector within the square brackets to access multiple values. Try getting the first and third words:RedoComplete

> sentence[c(1, 3)]

[1] "walk" "dog"

14. This means you can retrieve ranges of values. Get the second through fourth words:

RedoComplete

> sentence[2:4]

[1] "the" "dog" "to"

15. You can also set ranges of values; just provide the values in a vector. Add words 5 through 7:

RedoComplete

> sentence[5:7] <- c('the', 'poop', 'deck')

16. Now try accessing the sixth word of the sentence vector:RedoComplete

> sentence[6]

[1] "poop"

17. Vector Names2.4For this challenge, we'll make a 3-item vector for you, and store it in the ranksvariable.You can assign names to a vector's elements by passing a second vector filled with names to the names assignment function, like this:RedoComplete

> ranks <- 1:3

> names(ranks) <- c("first", "second", "third")

18. Assigning names for a vector can act as useful labels for the data. Below, you can see what our vector looks like now.

You can also use the names to access the vector's values. Try getting the value for the "first" rank:RedoComplete

> ranks

first second third

1 2 3

> ranks["first"]

first

1

19. Now see if you can set the value for the "third" rank to something other than 3 using the name rather than the position.RedoComplete

> ranks["third"]<-2

20. Plotting One Vector2.5The barplot function draws a bar chart with a vector's values. We'll make a new vector for you, and store it in the vesselsSunk variable.Now try passing the vector to the barplot function:

RedoComplete

> vesselsSunk <- c(4, 5, 1)

> barplot(vesselsSunk)

o 01234521. If you assign names to the vector's values, R will use those names as labels on

the bar plot. Let's use the names assignment function again:RedoComplete

> names(vesselsSunk)<-c("England","France","Norway")

22. Now, if you call barplot with the vector again, you'll see the labels:RedoComplete

> barplot(vesselsSunk)

o EnglandFranceNorway01234523. Now, try calling barplot on a vector of integers ranging from 1 through 100:

RedoComplete

> barplot(1:100)

o 020406080100

24. Vector Math2.6Most arithmetic operations work just as well on vectors as they do on single values. We'll make another sample vector for you to work with, and store it in the avariable.If you add a scalar (a single value) to a vector, the scalar will be added to each value in the vector, returning a new vector with the results. Try adding 1 to each element in our vector:

RedoComplete

> a <- c(1, 2, 3)

> a + 1

[1] 2 3 4

25. The same is true of division, multiplication, or any other basic arithmetic. Try dividing our vector by 2:

RedoComplete

> a/2

[1] 0.5 1.0 1.5

26. Now try multiplying our vector by 2:

RedoComplete

> a*2

[1] 2 4 6

27. If you add two vectors, R will take each value from each vector and add them. We'll make a second vector for you to experiment with, and store it in the bvariable.Try adding it to the a vector:RedoComplete

> b <- c(4, 5, 6)

> a + b

[1] 5 7 9

28. Now try subtracting b from a:RedoComplete

> a-b

[1] -3 -3 -3

29. You can also take two vectors and compare each item. See which values in the avector are equal to those in a second vector:RedoComplete

> a==c(1,99,3)

[1] TRUE FALSE TRUE

Notice that R didn't test whether the whole vectors were equal; it checked each value in the a vector against the value at the same index in our new vector.

30. Check if each value in the a vector is less than the corresponding value in another vector:RedoComplete

> a==c(1,2,3)

[1] TRUE TRUE TRUE

31. Functions that normally work with scalars can operate on each element of a vector, too. Try getting the sine of each value in our vector:

RedoComplete

> sin(a)

[1] 0.8414710 0.9092974 0.1411200

32. Now try getting the square roots with sqrt:RedoComplete

> sqrt(a)

[1] 1.000000 1.414214 1.732051

33. Scatter Plots2.7The plot function takes two vectors, one for X values and one for Y values, and draws a graph of them.Let's draw a graph showing the relationship of numbers and their sines.

First, we'll need some sample data. We'll create a vector for you with some fractional values between 0 and 20, and store it in the x variable.Now, try creating a second vector with the sines of those values:

RedoComplete

> x <- seq(1, 20, 0.1)

> y <- sin(x)

34. Then simply call plot with your two vectors:RedoComplete

> plot(x,y)

Great job! Notice on the graph that values from the first argument (x) are used for the horizontal axis, and values from the second (y) for the vertical.

o 5101520-1.0-0.50.00.51.0xy35. Your turn. We'll create a vector with some negative and positive values for you,

and store it in the values variable.

We'll also create a second vector with the absolute values of the first, and store it in the absolutes variable.Try plotting the vectors, with values on the horizontal axis, and absolutes on the vertical axis.RedoComplete

> values <- -10:10

> absolutes <- abs(values)

> plot(values, absolutes)

o -10-505100246810valuesabsolutes

36. NA Values2.8Sometimes, when working with sample data, a given value isn't available. But it's not a good idea to just throw those values out. R has a value that explicitly indicates a sample was not available: NA. Many functions that work with vectors treat this value specially.We'll create a vector for you with a missing sample, and store it in the a variable.Try to get the sum of its values, and see what the result is:

RedoComplete

> a <- c(1, 3, NA, 7, 9)

> sum(a)

[1] NA

The sum is considered "not available" by default because one of the vector's values was NA. This is the responsible thing to do; R won't just blithely add up the numbers without warning you about the incomplete data. We can explicitly tellsum (and many other functions) to remove NA values before they do their calculations, however.

37. Remember that command to bring up help for a function? Bring up documentation for the sum function:RedoComplete

> help(sum)

sum package:base R Documentation

Sum of Vector Elements

Description:

'sum' returns the sum of all the values present in its arguments.

Usage:

sum(..., na.rm = FALSE)

...

As you see in the documentation, sum can take an optional named argument,na.rm. It's set to FALSE by default, but if you set it to TRUE, all NA arguments will be removed from the vector before the calculation is performed.

38. Try calling sum again, with na.rm set to TRUE:RedoComplete

> sum(a, na.rm=TRUE)

[1] 20

39. Chapter 2 Completed

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You've traversed Chapter 2… and discovered another badge!

In this chapter, we've shown you all the basics of manipulating vectors - creating and accessing them, doing math with them, and making sequences. We've shown you how to make bar plots and scatter plots with vectors. And we've shown you how R treats vectors where one or more values are not available.

The vector is just the first of several data structures that R offers. See you in the next chapter, where we'll talk about… the matrix.

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So far we've only worked with vectors, which are simple lists of values. What if you need data in rows and columns? Matrices are here to help.

A matrix is just a fancy term for a 2-dimensional array. In this chapter, we'll show you all the basics of working with matrices, from creating them, to accessing them, to plotting them.

2. Matrices3.1Let's make a matrix 3 rows high by 4 columns wide, with all its fields set to 0.RedoComplete

> matrix(0,3,4)

[,1] [,2] [,3] [,4]

[1,] 0 0 0 0

[2,] 0 0 0 0

[3,] 0 0 0 0

3. You can also use a vector to initialize a matrix's value. To fill a 3x4 matrix, you'll need a 12-item vector. We'll make that for you now:

RedoComplete

> a<-1:12

4. If we print the value of a, we'll see the vector's values, all in a single row:RedoComplete

> print(a)

[1] 1 2 3 4 5 6 7 8 9 10 11 12

5. Now call matrix with the vector, the number of rows, and the number of columns:RedoComplete

> matrix(a,3,4)

[,1] [,2] [,3] [,4]

[1,] 1 4 7 10

[2,] 2 5 8 11

[3,] 3 6 9 12

6. The vector's values are copied into the new matrix, one by one. You can also re-shape the vector itself into a matrix. We'll create a new 8-item vector for you:

RedoComplete

> plank<-1:8

7. The dim assignment function sets dimensions for a matrix. It accepts a vector with the number of rows and the number of columns to assign.Assign new dimensions to plank by passing a vector specifying 2 rows and 4 columns (c(2, 4)):RedoComplete

> dim(plank)<-c(2,4)

8. If you print plank now, you'll see that the values have shifted to form 2 rows by 4 columns:RedoComplete

> print(plank)

[,1] [,2] [,3] [,4]

[1,] 1 3 5 7

[2,] 2 4 6 8

9. The vector is no longer one-dimensional. It has been converted, in-place, to a matrix.

Now, use the matrix function to make a 5x5 matrix, with its fields initialized to any values you like.RedoComplete

> matrix(1:25,5,5)

[,1] [,2] [,3] [,4] [,5]

[1,] 1 6 11 16 21

[2,] 2 7 12 17 22

[3,] 3 8 13 18 23

[4,] 4 9 14 19 24

[5,] 5 10 15 20 25

10. Matrix Access3.2Getting values from matrices isn't that different from vectors; you just have to provide two indices instead of one.

Let's take another look at our plank matrix:RedoComplete

> print(plank)

[,1] [,2] [,3] [,4]

[1,] 1 3 5 7

[2,] 2 4 6 8

11. Try getting the value from the second row in the third column ofplank:RedoComplete

> plank[2,3]

[1] 6

12. Now, try getting the value from first row of the fourth column:

RedoComplete

> plank[1,4]

[1] 7

13. As with vectors, to set a single value, just assign to it. Set the previous value to 0:RedoComplete

> plank[1,4]<-0

14. You can get an entire row of the matrix by omitting the column index (but keep the comma). Try retrieving the second row:

RedoComplete

> plank[2,]

[1] 2 4 6 8

15. To get an entire column, omit the row index. Retrieve the fourth column:

RedoComplete

> plank[,4]

[1] 7 8

16. You can read multiple rows or columns by providing a vector or sequence with their indices. Try retrieving columns 2 through 4:

RedoComplete

> plank[,2:4]

[,1] [,2] [,3]

[1,] 3 5 7

[2,] 4 6 8

17. Matrix Plotting3.3Text output is only useful when matrices are small. When working with more complex data, you'll need something better. Fortunately, R includes powerful visualizations for matrix data.

We'll start simple, with an elevation map of a sandy beach.

It's pretty flat - everything is 1 meter above sea level. We'll create a 10 by 10 matrix with all its values initialized to 1 for you:

RedoComplete

> elevation<-matrix(1,10,10)

18. Oh, wait, we forgot the spot where we dug down to sea level to retrieve a treasure chest. At the fourth row, sixth column, set the elevation to 0:RedoComplete

> elevation[4,6]<-0

19. You can now do a contour map of the values simply by passing the matrix to the contour function:RedoComplete

> contour(elevation)

o 0.00.20.40.60.81.00.00.20.40.60.81.020. Or you can create a 3D perspective plot with the persp function:

RedoComplete

> persp(elevation)

o elevationYZ21. The perspective plot looks a little odd, though. This is because persp

automatically expands the view so that your highest value (the beach surface) is at the very top.

We can fix that by specifying our own value for the expandparameter.RedoComplete

> persp(elevation, expand=0.2)

o elevationYZ22. Okay, those examples are a little simplistic. Thankfully, R includes some sample

data sets to play around with. One of these is volcano, a 3D map of a dormant New Zealand volcano.

It's simply an 87x61 matrix with elevation values, but it shows the power of R's matrix visualizations.

Try creating a contour map of the volcano matrix:

RedoComplete

> contour(volcano)

o 0.00.20.40.60.81.00.00.20.40.60.81.023. Try a perspective plot (limit the vertical expansion to one-fifth again):

RedoComplete

> persp(volcano, expand=0.2)

o volcanoYZ24. The image function will create a heat map:

RedoComplete

> image(volcano)

o 0.00.20.40.60.81.00.00.20.40.60.81.0

25. Chapter 3 Completed

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Here we stand on the beach, at the end of Chapter 3. What's this, buried in the sand? It's another badge! Click here to log in over on Code School, if you'd like to add it to your account.In this chapter, we learned how to create matrices from scratch, and how to re-shape a vector into a matrix. We learned how to access values within a matrix one-by-one, or in groups. And we saw just a few of the ways to visualize a matrix's data.

None of the techniques we've used so far will help you describe your data, though. We'll rectify that in the next chapter, where we'll talk about summary statistics.

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Simply throwing a bunch of numbers at your audience will only confuse them. Part of a statistician's job is to explain their data. In this chapter, we'll show you some of the tools R offers to let you do so, with minimum fuss.

2. Mean4.1Determining the health of the crew is an important part of any inventory of the ship. Here's a vector containing the number of limbs each member has left, along with their names.

limbs <- c(4, 3, 4, 3, 2, 4, 4, 4)

names(limbs) <- c('One-Eye', 'Peg-Leg', 'Smitty', 'Hook', 'Scooter', 'Dan', 'Mikey',

'Blackbeard')

A quick way to assess our battle-readiness would be to get the average of the crew's appendage counts. Statisticians call this the "mean". Call the mean function with the limbs vector.RedoComplete

> mean(limbs)

[1] 3.5

An average closer to 4 would be nice, but this will have to do.

3. Here's a barplot of that vector:

RedoComplete

> barplot(limbs)

o One-EyePeg-LegSmittyHookScooterDanMikeyBlackbeard012344. If we draw a line on the plot representing the mean, we can easily compare the

various values to the average. The abline function can take an h parameter with a value at which to draw a horizontal line, or a v parameter for a vertical line. When it's called, it updates the previous plot.Draw a horizontal line across the plot at the mean:

RedoComplete

> abline(h=mean(limbs))

o One-EyePeg-LegSmittyHookScooterDanMikeyBlackbeard01234

5. Median4.2Let's say we gain a crew member that completely skews the mean.

> limbs <- c(4, 3, 4, 3, 2, 4, 4, 14)

> names(limbs) <- c('One-Eye', 'Peg-Leg', 'Smitty', 'Hook',

'Scooter', 'Dan', 'Mikey', 'Davy Jones')

> mean(limbs)

[1] 4.75

Let's see how this new mean shows up on our same graph.

RedoComplete

> barplot(limbs)

> abline(h = mean(limbs))

It may be factually accurate to say that our crew has an average of 4.75 limbs, but it's probably also misleading.

o One-EyePeg-LegSmittyHookScooterDanMikeyDavy Jones024681012146. For situations like this, it's probably more useful to talk about the "median" value. The

median is calculated by sorting the values and choosing the middle one - the third value, in this case. (For sets with an even number of values, the middle two values are averaged.)

Call the median function on the vector:

RedoComplete

> median(limbs)

[1] 4

7. That's more like it. Let's show the median on the plot. Draw a horizontal line across the plot at the median.

RedoComplete

> abline(h=median(limbs))

o One-EyePeg-LegSmittyHookScooterDanMikeyDavy Jones02468101214

8. Standard Deviation4.3Some of the plunder from our recent raids has been worth less than what we're used to. Here's a vector with the values of our latest hauls:

> pounds <- c(45000, 50000, 35000, 40000, 35000, 45000, 10000, 15000)

> barplot(pounds)

> meanValue <- mean(pounds)

Let's see a plot showing the mean value:

RedoComplete

> abline(h=meanValue)

These results seem way below normal. The crew wants to make Smitty, who picked the last couple ships to waylay, walk the plank. But as he dangles over the water, wily Smitty raises a question: what, exactly, is a "normal" haul?

o 010000200003000040000500009. Statisticians use the concept of "standard deviation" from the mean to describe the

range of typical values for a data set. For a group of numbers, it shows how much they typically vary from the average value. To calculate the standard deviation, you calculate the mean of the values, then subtract the mean from each number and square the result, then average those squares, and take the square root of that average.

If that sounds like a lot of work, don't worry. You're using R, and all you have to do is pass a vector to the sd function. Try calling sd on the pounds vector now, and assign the result to the deviation variable:

RedoComplete

> deviation<-sd(pounds)

10. We'll add a line on the plot to show one standard deviation above the mean (the top of the normal range)...

RedoComplete

> abline(h = meanValue + deviation)

Hail to the sailor that brought us that 50,000-pound payday!

o 0100002000030000400005000011. Now try adding a line on the plot to show one standard devation below the

mean (the bottom of the normal range):

RedoComplete

> abline(h = meanValue - deviation)

We're risking being hanged by the Spanish for this? Sorry, Smitty, you're shark bait.

o 01000020000300004000050000

12. Chapter 4 Completed

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Land ho! You've navigated Chapter 4. And what awaits us on the shore? It's another badge!

Summary statistics let you show how your data points are distributed, without the need to look closely at each one. We've shown you the functions for mean, median, and standard deviation, as well as ways to display them on your graphs.

TRY RCHAPTER 5

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Often your data needs to be grouped by category: blood pressure by age range, accidents by auto manufacturer, and so forth. R has a special collection type called a factor to track these categorized values.

2. Creating Factors5.1It's time to take inventory of the ship's hold. We'll make a vector for you with the type of booty in each chest.

To categorize the values, simply pass the vector to the factor function:RedoComplete

> chests <- c('gold', 'silver', 'gems', 'gold', 'gems')

> types <- factor(chests)

3. There are a couple differences between the original vector and the new factor that are worth noting. Print the chests vector:RedoComplete

> print(chests)

[1] "gold" "silver" "gems" "gold" "gems"

4. You see the raw list of strings, repeated values and all. Now print the types factor:RedoComplete

> print(types)

[1] gold silver gems gold gems

Levels: gems gold silver

Printed at the bottom, you'll see the factor's "levels" - groups of unique values. Notice also that there are no quotes around the values. That's because they're not strings; they're actually integer references to one of the factor's levels.

5. Let's take a look at the underlying integers. Pass the factor to the as.integerfunction:RedoComplete

> as.integer(types)

[1] 2 3 1 2 1

6. You can get only the factor levels with the levels function:RedoComplete

> levels(types)

[1] "gems" "gold" "silver"

7. Plots With Factors5.2You can use a factor to separate plots into categories. Let's graph our five chests by weight and value, and show their type as well. We'll create two vectors for you;weights will contain the weight of each chest, and prices will track how much the chests are worth.Now, try calling plot to graph the chests by weight and value.RedoComplete

> weights <- c(300, 200, 100, 250, 150)

> prices <- c(9000, 5000, 12000, 7500, 18000)

> plot(weights, prices)

o 100150200250300600080001000012000140001600018000weightsprices8. We can't tell which chest is which, though. Fortunately, we can use different plot

characters for each type by converting the factor to integers, and passing it to thepch argument of plot.RedoComplete

> plot(weights, prices, pch=as.integer(types))

"Circle", "Triangle", and "Plus Sign" still aren't great descriptions for treasure, though. Let's add a legend to show what the symbols mean.

o 100150200250300600080001000012000140001600018000weightsprices9. The legend function takes a location to draw in, a vector with label names, and a

vector with numeric plot character IDs.RedoComplete

> legend("topright",c("gems","gold","silver"), pch=1:3)

Next time the boat's taking on water, it would be wise to dump the silver and keep the gems!

o 100150200250300600080001000012000140001600018000weightspricesgemsgoldsilvergemsgoldsilver

10. If you hard-code the labels and plot characters, you'll have to update them every time you change the plot factor. Instead, it's better to derive them by using thelevels function on your factor:RedoComplete

> legend("topright", levels(types),pch=1:length(levels(types)))

o 100150200250300600080001000012000140001600018000weightspricesgemsgoldsilvergemsgoldsilver

11. Chapter 5 Completed

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A long inland march has brought us to the end of Chapter 5. We've stumbled across another badge!

Factors help you divide your data into groups. In this chapter, we've shown you how to create them, and how to use them to make plots more readable.

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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.

2. Data Frames6.1Our 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)

3. 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.

4. Data Frame Access6.2Just 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

5. 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

6. 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

7. Now try getting the "types" column:

RedoComplete

> treasure$types

[1] gold silver gems gold gems

Levels: gems gold silver

8. Loading Data Frames6.3Typing 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 theread.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

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

10. Port Infantry

11. Porto Bello 700

12. Cartagena 500

13. Panama City 1500

14. Havana 2000

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

15. 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

16. Merging Data Frames6.4We 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

17. 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!

TRY RCHAPTER 7

Real-World Datao Try R is Sponsored By:

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So far, we've been working purely in the abstract. It's time to take a look at some real data, and see if we can make any observations about it.

2. Some Real World Data7.1Modern pirates plunder software, not silver. We have a file with the software piracy rate, sorted by country. Here's a sample of its format:

Country,Piracy

Australia,23

Bangladesh,90

Brunei,67

China,77

...

We'll load that into the piracy data frame for you:

> piracy <- read.csv("piracy.csv")

We also have another file with GDP per capita for each country (wealth produced, divided by population):

Rank Country GDP

1 Liechtenstein 141100

2 Qatar 104300

3 Luxembourg 81100

4 Bermuda 69900

...

That will go into the gdp frame:

> gdp <- read.table("gdp.txt", sep=" ", header=TRUE)

We'll merge the frames on the country names:

> countries <- merge(x = gdp, y = piracy)

Let's do a plot of GDP versus piracy. Call the plot function, using the "GDP"column of countries for the horizontal axis, and the "Piracy" column for the vertical axis:RedoComplete

> plot(countries$GDP, countries$Piracy)

o 02000040000600008000020406080countries$GDPcountries$Piracy3. It looks like there's a negative correlation between wealth and piracy - generally, the

higher a nation's GDP, the lower the percentage of software installed that's pirated. But do we have enough data to support this connection? Is there really a connection at all?

R can test for correlation between two vectors with the cor.test function. Try calling it on the GDP and Piracy columns of the countries data frame:

RedoComplete

> cor.test(countries$GDP, countries$Piracy)

Pearson's product-moment correlation

data: countries$GDP and countries$Piracy

t = -14.8371, df = 107, p-value < 2.2e-16

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

-0.8736179 -0.7475690

sample estimates:

cor

-0.8203183

The key result we're interested in is the "p-value". Conventionally, any correlation with a p-value less than 0.05 is considered statistically significant, and this sample data's p-value is definitely below that threshold. In other words, yes, these data do show a statistically significant negative correlation between GDP and software piracy.

4. We have more countries represented in our GDP data than we do our piracy rate data. If we know a country's GDP, can we use that to estimate its piracy rate?

We can, if we calculate the linear model that best represents all our data points (with a certain degree of error). The lm function takes a model formula, which is represented by a response variable (piracy rate), a tilde character (~), and a predictor variable (GDP). (Note that the response variable comes first.)Try calculating the linear model for piracy rate by GDP, and assign it to the linevariable:RedoComplete

> line <- lm(countries$Piracy ~ countries$GDP)

5. You can draw the line on the plot by passing it to the abline function. Try it now:

RedoComplete

> abline(line)

Now, if we know a country's GDP, we should be able to make a reasonable prediction of how common piracy is there!

o 02000040000600008000020406080countries$GDPcountries$Piracy

6. ggplot27.2The functionality we've shown you so far is all included with R by default. (And it's pretty powerful, isn't it?) But in case the default installation doesn't include that function you need, there are still more libraries available on the servers of the Comprehensive R Archive Network, or CRAN. They can add anything from new statistical functions to better graphics capabilities. Better yet, installing any of them is just a command away.

Let's install the popular ggplot2 graphics package. Call the install.packagesfunction with the package name in a string:RedoComplete

> install.packages("ggplot2")

7. You can get help for a package by calling the help function and passing the package name in the package argument. Try displaying help for the "ggplot2" package:

RedoComplete

> help(package = "ggplot2")

Information on package 'ggplot2'

Description:

Package: ggplot2

Type: Package

Title: An implementation of the Grammar of Graphics

Version: 0.9.1

...

8. Here's a quick demo of the power you've just added to R. To use it, let's revisit some data from a previous chapter.

9. > weights <- c(300, 200, 100, 250, 150)

10. > prices <- c(9000, 5000, 12000, 7500, 18000)

11. > chests <- c('gold', 'silver', 'gems', 'gold', 'gems')

12. > types <- factor(chests)

The qplot function is a commonly-used part of ggplot2. We'll pass the weights and values of our cargo to it, using the chest types vector for the color argument:

RedoComplete

> qplot(weights, prices, color = types)

Not bad! An attractive grid background and colorful legend, without any of the configuration hassle from before!

ggplot2 is just the first of many powerful packages awaiting discovery on CRAN. And of course, there's much, much more functionality in the standard R libraries. This course has only scratched the surface!

o 80001200016000100150200250300..1..2..3gemsgoldsilver

13. Chapter 7 Completed

Share your plunder:

Captain's Log: The end of chapter 7. Supplies are running low. Luckily, we've spotted another badge!

We've covered how to take some real-world data sets, and test whether they're correlated with `cor.test`. Then we learned how to show that correlation on plots, with a linear model.