Tutorials
finance
+1

# Ordering and Subsetting Factors

This tutorial takes course material from DataCamp's Introduction to R for Finance course and allows you to practice ordering and subsetting factors.

If you want to take our Introduction to R for Finance course, here is the link.

## Create an ordered factor

Look at the plot created over on the right. It looks great, but look at the order of the bars! No order was specified when you created the factor, so, when R tried to plot it, it just placed the levels in alphabetical order. By now, you know that there is an order to credit ratings, and your plots should reflect that!

As a reminder, the order of credit ratings from least risky to most risky is:

AAA, AA, A, BBB, BB, B, CCC, CC, C, D


To order your factor, there are two options.

When creating a factor, specify ordered = TRUE and add unique levels in order from least to greatest:

credit_rating <- c("AAA", "AA", "A", "BBB", "AA", "BBB", "A")

credit_factor_ordered <- factor(credit_rating, ordered = TRUE,
levels = c("AAA", "AA", "A", "BBB"))


For an existing unordered factor like credit_factor, use the ordered() function:

ordered(credit_factor, levels = c("AAA", "AA", "A", "BBB"))


Both ways result in:

credit_factor_ordered

[1] AAA AA  A   BBB AA  BBB A
Levels: AAA < AA < A < BBB


Notice the < specifying the order of the levels that was not there before!

### Instructions

• The character vector credit_rating is in your workspace.
• Use the unique() function with credit_rating to print only the unique words in the character vector. These will be your levels.
• Use factor() to create an ordered factor for credit_rating and store it as credit_factor_ordered. Make sure to list the levels from least to greatest in terms of risk!
• Plot credit_factor_ordered and note the new order of the bars.
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If that makes sense keep going to the next exercise! If not, here is an overview video.

## Subsetting a factor

You can subset factors in a similar way that you subset vectors. As usual, [ ] is the key! However, R has some interesting behavior when you want to remove a factor level from your analysis. For example, what if you wanted to remove the AAA bond from your portfolio?

credit_factor

[1] AAA AA  A   BBB AA  BBB A
Levels: BBB < A < AA < AAA

credit_factor[-1]

[1] AA  A   BBB AA  BBB A
Levels: BBB < A < AA < AAA


R removed the AAA bond at the first position, but left the AAA level behind! If you were to plot this, you would end up with the bar chart over to the right. A better plan would have been to tell R to drop the AAA level entirely. To do that, add drop = TRUE:

credit_factor[-1, drop = TRUE]

[1] AA  A   BBB AA  BBB A
Levels: BBB < A < AA


That's what you wanted!

### Instructions

• Using the same data, remove the "A" bonds from positions 3 and 7 of credit_factor. For now, do not use drop = TRUE. Assign this to keep_level.
• Plot keep_level.
• Now, remove "A" from credit_factor again, but this time use drop = TRUE. Assign this to drop_level.
• Plot drop_level.
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## stringsAsFactors

Do you remember back in the data frame chapter when you used str() on your cash data frame? This was the output:

str(cash)

'data.frame':    3 obs. of  3 variables:
$company : Factor w/ 2 levels "A","B": 1 1 2$ cash_flow: num  100 200 300
$year : num 1 3 2  See how the company column has been converted to a factor? R's default behavior when creating data frames is to convert all characters into factors. This has caused countless novice R users a headache trying to figure out why their character columns are not working properly, but not you! You will be prepared! To turn off this behavior: cash <- data.frame(company, cash_flow, year, stringsAsFactors = FALSE) str(cash) 'data.frame': 3 obs. of 3 variables:$ company  : chr  "A" "A" "B"
$cash_flow: num 100 200 300$ year     : num  1 3 2


### Instructions

• Two variables, credit_rating and bond_owners have been defined for you. bond_owners is a character vector of the names of some of your friends.
• Create a data frame named bonds from credit_rating and bond_owners, in that order, and use stringsAsFactors = FALSE.
• Use str() to confirm that both columns are characters.
• bond_owners would not be a useful factor, but credit_rating could be! Create a new column in bonds called credit_factor using \$ which is created from credit_rating as a correctly ordered factor.
• Use str() again to confirm that credit_factor is an ordered factor.

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