R语言从基础入门到提高（五）factor 因子

第1程序

What's a factor and why would you use it?

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In this chapter you dive into the wonderful world of factors.

The term factor refers to a statistical data type used to store categorical（绝对） variables. The difference between a categorical variable and a continuous（连续） variable is that a categorical variable can belong to a limited number of categories（类别）. A continuous variable, on the other hand, can correspond（符合） to an infinite（无限） number of values.

It is important that R knows whether it is dealing with a continuous or a categorical variable, as the statistical models you will develop in the future treat both types differently. (You will see later why this is the case.)

A good example of a categorical variable is the variable 'Gender'（性别）. A human individual can either be "Male" or "Female", making abstraction（抽象） of inter-sexes. So here "Male" and "Female" are, in a simplified sense, the two values of the categorical variable "Gender", and every observation can be assigned to either the value "Male" of "Female".

要求：

Assign to variable theory the value "factors for categorical variables".

源程序：

# Assign to the variable theory what this chapter is about!
theory <- "factors for categorical variables"
theory
console：
> # Assign to the variable theory what this chapter is about!
> theory <- "factors for categorical variables"
> theory
[1] "factors for categorical variables"

第2程序：

What's a factor and why would you use it? (2)

100xp

To create factors in R, you make use of the function factor(). First thing that you have to do is create a vector that contains（包含） all the observations（观察值） that belong to a limited number of categories. For example, gender_vectorcontains the sex of 5 different individuals（个人）:

gender_vector <- c("Male","Female","Female","Male","Male")

It is clear that there are two categories（类）, or in R-terms 'factor levels', at work here: "Male" and "Female".

The function factor() will encode（编译） the vector as a factor:

factor_gender_vector <- factor(gender_vector)

要求：

• Convert（转变） the character vector gender_vector to a factor with factor() and assign the result to factor_gender_vector
• Print out factor_gender_vector and assert that R prints out the factor levels below the actual values.

源程序：

# Gender vector
gender_vector <- c("Male", "Female", "Female", "Male", "Male")

# Convert gender_vector to a factor
factor_gender_vector <- factor(gender_vector)

# Print out factor_gender_vector
factor_gender_vector
console：
> # Gender vector
> gender_vector <- c("Male", "Female", "Female", "Male", "Male")
> 
> # Convert gender_vector to a factor
> factor_gender_vector <- factor(gender_vector)
> 
> # Print out factor_gender_vector
> factor_gender_vector
[1] Male   Female Female Male   Male  
Levels: Female Male

想要查看函数 factor（），可以直接在console里直接输入

?factor

第3程序：

What's a factor and why would you use it? (3)

100xp

There are two types of categorical variables: a nominal（名义上的） categorical variableand an ordinal（序数） categorical variable.

A nominal variable is a categorical variable without an implied order. This means that it is impossible to say that 'one is worth more than the other'. For example, think of the categorical variable animals_vector with the categories "Elephant", "Giraffe", "Donkey" and "Horse". Here, it is impossible to say that one stands above or below the other. (Note that some of you might disagree ;-) ).

In contrast（相反）, ordinal variables do have a natural ordering（次序）. Consider for example the categorical variable temperature_vector with the categories: "Low","Medium" and "High". Here it is obvious that "Medium" stands above（高于） "Low", and "High" stands above "Medium".

要求：

Click 'Submit Answer' to check how R constructs and prints nominal and ordinal variables. Do not worry if you do not understand all the code just yet, we will get to that.

源程序：

# Animals
animals_vector <- c("Elephant", "Giraffe", "Donkey", "Horse")
factor_animals_vector <- factor(animals_vector)
factor_animals_vector

# Temperature
temperature_vector <- c("High", "Low", "High","Low", "Medium")
factor_temperature_vector <- factor(temperature_vector, order = TRUE, levels = c("Low", "Medium", "High"))
factor_temperature_vector
console：
> # Animals
> animals_vector <- c("Elephant", "Giraffe", "Donkey", "Horse")
> factor_animals_vector <- factor(animals_vector)
> factor_animals_vector
[1] Elephant Giraffe  Donkey   Horse   
Levels: Donkey Elephant Giraffe Horse
> 
> # Temperature
> temperature_vector <- c("High", "Low", "High","Low", "Medium")
> factor_temperature_vector <- factor(temperature_vector, order = TRUE, levels = c("Low", "Medium", "High"))
> factor_temperature_vector
[1] High   Low    High   Low    Medium
Levels: Low < Medium < High

第4程序：

Factor levels（层次）

100xp

When you first get a data set, you will often notice that it contains factors with specific factor levels. However, sometimes you will want to change the names of these levels for clarity（清楚） or other reasons. R allows you to do this with the function levels():

levels(factor_vector) <- c("name1", "name2",...)

A good illustration（解释） is the raw（原始） data that is provided to you by a survey（问卷）. A standard question for every questionnaire is the gender of the respondent. You remember from the previous question that this is a factor and when performing the questionnaire on the streets its levels are often coded as "M" and "F".

survey_vector <- c("M", "F", "F", "M", "M")

Next, when you want to start your data analysis, your main concern is to keep a nice overview of all the variables and what they mean. At that point, you will often want to change the factor levels to "Male" and "Female" instead of "M" and "F" to make your life easier.

Watch out: the order with which you assign the levels is important. If you type levels(factor_survey_vector), you'll see that it outputs [1] "F" "M". If you don't specify（指出） the levels of the factor when creating the vector, R will automatically assign them alphabetically（字母序）. To correctly map "F" to "Female" and "M" to "Male", the levels should be set to c("Female", "Male"), in this order order.

要求：

• Check out the code that builds a factor vector from survey_vector. You should use factor_survey_vector in the next instruction（命令）.
• Change the factor levels of factor_survey_vector to c("Female", "Male"). Mind the order of the vector elements here.

源程序：

# Code to build factor_survey_vector
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)

# Specify the levels of factor_survey_vector
levels(factor_survey_vector) <- c("Female","Male")

factor_survey_vector
console：
> # Code to build factor_survey_vector
> survey_vector <- c("M", "F", "F", "M", "M")
> factor_survey_vector <- factor(survey_vector)
> 
> # Specify the levels of factor_survey_vector
> levels(factor_survey_vector) <- c("Female","Male")
> 
> factor_survey_vector
[1] Male   Female Female Male   Male  
Levels: Female Male
> #这个序列是怎么来的呢，有点迷惑，我换了一下levels的 顺序，序列就改变啦

这个是我从官网给的 levels ( ) 例子中运行的结果：

 ## assign individual levels
> x <- gl(2, 4, 8)
> levels(x)[1] <- "low"
> levels(x)[2] <- "high"
> x
[1] low  low  low  low  high high high high
Levels: low high
> # 根据数值8知道共8个人，然后提供两个等级，low和high，所以得到属性序列
> 
> ## or as a group
> y <- gl(2, 4, 8)
> levels(y) <- c("low", "high")
> y
[1] low  low  low  low  high high high high
Levels: low high
> 
> ## combine some levels
> z <- gl(3, 2, 12, labels = c("apple", "salad", "orange"))
> z
 [1] apple  apple  salad  salad  orange orange apple  apple  salad  salad 
[11] orange orange
Levels: apple salad orange
> levels(z) <- c("fruit", "veg", "fruit")
> z
 [1] fruit fruit veg   veg   fruit fruit fruit fruit veg   veg   fruit fruit
Levels: fruit veg
> 
> ## same, using a named list
> z <- gl(3, 2, 12, labels = c("apple", "salad", "orange"))
> z
 [1] apple  apple  salad  salad  orange orange apple  apple  salad  salad 
[11] orange orange
Levels: apple salad orange
> levels(z) <- list("fruit" = c("apple","orange"),
                  "veg"   = "salad")
> z
 [1] fruit fruit veg   veg   fruit fruit fruit fruit veg   veg   fruit fruit
Levels: fruit veg
> 
> ## we can add levels this way:
> f <- factor(c("a","b"))
> levels(f) <- c("c", "a", "b")
> f
[1] c a
Levels: c a b
> 
> f <- factor(c("a","b"))
> levels(f) <- list(C = "C", A = "a", B = "b")
> f
[1] A B
Levels: C A B

欢迎大家，交流讨论这个地方！！！ 

第5程序：

Summarizing(总结) a factor

100xp

After finishing this course, one of your favorite functions in R will be summary(). This will give you a quick overview of the contents of a variable:

summary(my_var)

Going back to our survey, you would like to know how many "Male"responses you have in your study, and how many "Female" responses. The summary() function gives you the answer to this question.

要求：

Ask a summary() of the survey_vector and factor_survey_vector. Interpret（翻译） the results of both vectors. Are they both equally useful in this case?

源程序：

# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector

# Generate summary for survey_vector

summary(survey_vector)

# Generate summary for factor_survey_vector
summary(factor_survey_vector)
console：
> # Build factor_survey_vector with clean levels
> survey_vector <- c("M", "F", "F", "M", "M")
> factor_survey_vector <- factor(survey_vector)
> levels(factor_survey_vector) <- c("Female", "Male")
> factor_survey_vector
[1] Male   Female Female Male   Male  
Levels: Female Male
> 
> # Generate summary for survey_vector
> 
> summary(survey_vector)
   Length     Class      Mode 
        5 character character 
> 
> # Generate summary for factor_survey_vector
> summary(factor_survey_vector)
Female   Male 
     2      3

第6程序：

Battle（战争） of the sexes

100xp

In factor_survey_vector we have a factor with two levels: Male and Female. But how does R value these relatively to each other? In other words, who does R think is better, males or females?

要求：

Read the code in the editor and click 'Submit Answer' to see whether males are worth more than females.

源程序：

# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")

# Male
male <- factor_survey_vector[1]

# Female
female <- factor_survey_vector[2]

# Battle of the sexes: Male 'larger' than female?
male > female
console：
> # Build factor_survey_vector with clean levels
> survey_vector <- c("M", "F", "F", "M", "M")
> factor_survey_vector <- factor(survey_vector)
> levels(factor_survey_vector) <- c("Female", "Male")
> 
> # Male
> male <- factor_survey_vector[1]
> 
> # Female
> female <- factor_survey_vector[2]
> 
> # Battle of the sexes: Male 'larger' than female?
> male > female
Warning message: '>' not meaningful for factors
[1] NA

第7程序:

Ordered(有序) factors

100xp

Since "Male" and "Female" are unordered (or nominal) factor levels, R returns a warning message, telling you that the greater than operator（操作符） is not meaningful. As seen before, R attaches an equal value to the levels for such factors.

But this is not always the case! Sometimes you will also deal with factors that do have a natural ordering between its categories. If this is the case, we have to make sure that we pass this information to R...

Let us say that you are leading a research team of five data analysts and that you want to evaluate their performance. To do this, you track（跟踪） their speed, evaluate each analyst as "slow", "fast" or "insane", and save the results in speed_vector.

要求：

As a first step, assign speed_vector knowing that:

• Analyst 1 is fast,
• Analyst 2 is slow,
• Analyst 3 is slow,
• Analyst 4 is fast and
• Analyst 5 is insane.

No need to specify（指明） these are factors yet.

源程序：

# Create speed_vector
speed_vector <- c("fast", "slow", "slow", "fast", "insane")
console：
> # Create speed_vector
> speed_vector <- c("fast", "slow", "slow", "fast", "insane")
> speed_vector
[1] "fast"   "slow"   "slow"   "fast"   "insane"

第8程序：

Ordered factors (2)

100xp

speed_vector should be converted（转换） to an ordinal factor since its categories have a natural ordering. By default, the function factor() transforms speed_vector into an unordered factor. To create an ordered factor, you have to add two additional arguments: ordered and levels.

factor(some_vector,
       ordered = TRUE,
       levels = c("lev1", "lev2" ...))

By setting the argument ordered to TRUE in the function factor(), you indicate that the factor is ordered. With the argument levels you give the values of the factor in the correct order.

要求：

From speed_vector, create an ordered factor vector: factor_speed_vector. Set ordered to TRUE, and set levelsto c("slow", "fast", "insane").

源程序：

# Create speed_vector
speed_vector <- c("fast", "slow", "slow", "fast", "insane")

# Convert speed_vector to ordered factor vector
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "fast", "insane"))

# Print factor_speed_vector
factor_speed_vector
summary(factor_speed_vector)
console:
> # Create speed_vector
> speed_vector <- c("fast", "slow", "slow", "fast", "insane")
> 
> # Convert speed_vector to ordered factor vector
> factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "fast", "insane"))
> 
> # Print factor_speed_vector
> factor_speed_vector
[1] fast   slow   slow   fast   insane
Levels: slow < fast < insane
> summary(factor_speed_vector)
  slow   fast insane 
     2      2      1

第9程序：

Comparing ordered factors

100xp

Having a bad day at work, 'data analyst number two' enters your office and starts complaining that 'data analyst number five' is slowing down the entire project. Since you know that 'data analyst number two' has the reputation of being a smarty-pants, you first decide to check if his statement is true.

The fact that factor_speed_vector is now ordered enables us to compare different elements (the data analysts in this case). You can simply do this by using the well-known operators.

要求：

• Use [2] to select from factor_speed_vector the factor value for the second data analyst. Store it as da2.
• Use [5] to select the factor_speed_vector factor value for the fifth data analyst. Store it as da5.
• Check if da2 is greater than da5; simply print out the result. Remember that you can use the > operator to check whether one element is larger than the other.

源程序：

# Create factor_speed_vector
speed_vector <- c("fast", "slow", "slow", "fast", "insane")
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "fast", "insane"))

# Factor value for second data analyst
da2 <- factor_speed_vector[2]

# Factor value for fifth data analyst
da5 <- factor_speed_vector[5]

# Is data analyst 2 faster than data analyst 5?
da2 > da5
console：
> # Create factor_speed_vector
> speed_vector <- c("fast", "slow", "slow", "fast", "insane")
> factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "fast", "insane"))
> 
> # Factor value for second data analyst
> da2 <- factor_speed_vector[2]
> 
> # Factor value for fifth data analyst
> da5 <- factor_speed_vector[5]
> 
> # Is data analyst 2 faster than data analyst 5?
> da2 > da5
[1] FALSE

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