As we have noted, factors are variables in R which take on a limited number of different values; such variables are often referred to as categorical variables.
In a dataset, we can distinguish two types of variables: categorical and continuous.
In a categorical variable, the value is limited and usually based on a particular finite group. For example, a categorical variable can be countries, year, gender, occupation. A continuous variable, however, can take any values, from integer to decimal. For example, we can have the revenue, price of a share, etc.
## Example
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# Question: Lets create a vector v
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# OUR CODE GOES BELOW
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v <- c(1,3,5,8,2,1,3,5,3,5)
# Then determine whether this vector is a factor
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#
is.factor(v)## [1] FALSE## Challenge
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# Question: Calculate the categorical distribution as shown and figure out why the given output
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#levels are unique values.
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factor(v)## [1] 1 3 5 8 2 1 3 5 3 5
## Levels: 1 2 3 5 8## Example
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# Question: Assign factor v to x and print out x
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# OUR CODE GOES BELOW
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x <- factor(v)
x## [1] 1 3 5 8 2 1 3 5 3 5
## Levels: 1 2 3 5 8## Challenge
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# Question: Determine whether x is a factor below.
# Hint: Just like the way you did when you were finding out whether vector v is a factor
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is.factor(x)## [1] TRUE## Example
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# Question: First we create a vector as input, check whether its a factor,
# apply the factor function to create a factor from the vector
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# OUR CODE GOES BELOW
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data <- c("East","West","East","North","North","East","West","West","West","East","North")
# Then print out this vector
data## [1] "East" "West" "East" "North" "North" "East" "West" "West" "West"
## [10] "East" "North"# Now, check whether this is a factor
is.factor(data)## [1] FALSE# Then, apply the factor function to create a factor from the vector
factor_data <- factor(data)
# Then see our newly created factor
factor_data ## [1] East West East North North East West West West East North
## Levels: East North West# Check whether this is a factor
is.factor(factor_data)## [1] TRUE# Example
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# Creating a factor, determine and check the levels
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# OUR CODE GOES BELOW
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sex <- factor(c("male", "female", "female", "male"))
# Determining the levels
levels(sex)## [1] "female" "male"# Then checking the number of levels using nlevels()
nlevels(sex)## [1] 2# Sometimes, the order of the factors does not matter, other times you might want to specify the order
# because it is meaningful (e.g., “low”, “medium”, “high”) or it is required by particular type of analysis.
# Additionally, specifying the order of the levels allows us to compare levels:
food <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
# then print out levels of food
levels(food)## [1] "high" "low" "medium"nlevels(food)## [1] 3A data frame is used for storing data tables. Unlike a matrix in data frame each column can contain different modes of data.
## Example
# ---
# Question: Lets create a data frame BMI
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# OUR CODE GOES BELOW
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BMI <- data.frame(
gender = c("Male", "Male","Female"),
height = c(152, 171.5, 165),
weight = c(81,93, 78),
Age = c(42,38,26)
)
# Then print it out below
BMI## gender height weight Age
## 1 Male 152.0 81 42
## 2 Male 171.5 93 38
## 3 Female 165.0 78 26## Challenge
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# Question: Create a data frame family with column names Name, Age, Gender and Occupation.
# Populate it with 5 your own family members.
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# OUR CODE GOES BELOW
# ## Example
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# Question: Selecting elements from the BMI dataframe
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# OUR CODE GOES BELOW
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# selecting row 1
BMI[1,]## gender height weight Age
## 1 Male 152 81 42# selecting rows 1 to 2
BMI[1:2, ] ## gender height weight Age
## 1 Male 152.0 81 42
## 2 Male 171.5 93 38# selecting column 1
BMI[,1]## [1] "Male" "Male" "Female"# selecting column 1 to 2
BMI[,1:2 ] ## gender height
## 1 Male 152.0
## 2 Male 171.5
## 3 Female 165.0# selecting row 1 in column 2
BMI[1,2] ## [1] 152# selecting row 1 in column 2
BMI[1,2] ## [1] 152## Challenge
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# Question: Select the column 2 from the BMI dataframe
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# OUR CODE GOES BELOW
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# selecting row 1 in column 2
BMI[,2] ## [1] 152.0 171.5 165.0## Challenge
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# Question: Select the second and third members of your family
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# OUR CODE GOES BELOW
# ## Example
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# Question: Sort the BMI dataframe by using the order() function
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#
# Sort in ascending order by gender
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sorted_by_gender <- BMI[order(BMI$gender),]
# Print out sorted_by_gender below
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#
sorted_by_gender## gender height weight Age
## 3 Female 165.0 78 26
## 1 Male 152.0 81 42
## 2 Male 171.5 93 38# Sort in descending order by weight
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#
sorted_by_weight <- BMI[order(-BMI$weight),]
# Print out sorted_by_weight below
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#
sorted_by_weight## gender height weight Age
## 2 Male 171.5 93 38
## 1 Male 152.0 81 42
## 3 Female 165.0 78 26# And sort in descending order by gender below
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# OUR CODE GOES BELOW
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sorted_gender <- BMI[order(-BMI$weight),]As we have mentioned, a data table provides an enhanced version of data.frames.
The data.table R package is considered as the fastest package for data manipulation.
# Example
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# Question: Create a data table DT
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#
library(data.table)
DT = data.table(
ID = c("b","b","b","a","a","c"),
a = 1:6,
b = 7:12,
c = 13:18
)
# Load the data.table package
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#
library(data.table)
# Then print it out to see what happens
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DT## ID a b c
## 1: b 1 7 13
## 2: b 2 8 14
## 3: b 3 9 15
## 4: a 4 10 16
## 5: a 5 11 17
## 6: c 6 12 18# ## Example
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# Question: Select elements from the given datatable DT
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# OUR CODE GOES BELOW
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# Selecting Row 1
DT[1,]## ID a b c
## 1: b 1 7 13# Selecting Rows 1 to 2
DT[1:2,] ## ID a b c
## 1: b 1 7 13
## 2: b 2 8 14# Find out what happens when we print out the following statement
DT[,1]## ID
## 1: b
## 2: b
## 3: b
## 4: a
## 5: a
## 6: c# Find out what happens when we print out the following statement
DT[,1:2] ## ID a
## 1: b 1
## 2: b 2
## 3: b 3
## 4: a 4
## 5: a 5
## 6: c 6# And lastly find out what happens when we print out the following statement
DT[1,2] ## a
## 1: 1# Select the fourth and third rows from the data table
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DT[3:4,]## ID a b c
## 1: b 3 9 15
## 2: a 4 10 16# ## Example
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# Question: Sorting the datatable in ascending order by c
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# OUR CODE GOES BELOW
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# Performing the sort
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sorted_by_c <- DT[order(DT$c),]
# Printing out sorted_by_c
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sorted_by_c## ID a b c
## 1: b 1 7 13
## 2: b 2 8 14
## 3: b 3 9 15
## 4: a 4 10 16
## 5: a 5 11 17
## 6: c 6 12 18#
# Sort in descending order by b, uncommenting the line below
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#
sorted_by_b <- DT[order(-DT$b),]
# Finally printing out sorted_by_b below
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sorted_by_b## ID a b c
## 1: c 6 12 18
## 2: a 5 11 17
## 3: a 4 10 16
## 4: b 3 9 15
## 5: b 2 8 14
## 6: b 1 7 13# Tibbles are data frames, but they tweak some older behaviours to make life a little easier. They also have an enhanced print() method which makes them easier to use with large datasets containing complex objects
You can create a new tibble from individual vectors with tibble(). tibble() will automatically recycle inputs of length 1, and allows you to refer to variables that you just created, as shown below.
## Example
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# Question: Create a tible tb
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# OUR CODE GOES BELOW
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# First, we load the tibble package
library(tibble)
# Then create our tibble tb
tb <- tibble(
x = 1:5,
y = 1,
z = x ^ 2 + y
)
# And finally print the created tibble
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tb## # A tibble: 5 x 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2
## 2 2 1 5
## 3 3 1 10
## 4 4 1 17
## 5 5 1 26# ## Example
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# Question: Find out what happens when we print the following
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# OUR CODE GOES BELOW
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tb[1,]## # A tibble: 1 x 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2tb[1:2, ] ## # A tibble: 2 x 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2
## 2 2 1 5tb[,1]## # A tibble: 5 x 1
## x
## <int>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5tb[,1:2 ] ## # A tibble: 5 x 2
## x y
## <int> <dbl>
## 1 1 1
## 2 2 1
## 3 3 1
## 4 4 1
## 5 5 1# Select the second and third rows
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# OUR CODE GOES BELOW
# ## Example
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# Question: Find out what happens when we sort by doing the following
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#
sorted_by_1 <- tb[order(tb$z),]
sorted_by_1## # A tibble: 5 x 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2
## 2 2 1 5
## 3 3 1 10
## 4 4 1 17
## 5 5 1 26sorted_by_2 <- tb[order(-tb$x),]
sorted_by_2## # A tibble: 5 x 3
## x y z
## <int> <dbl> <dbl>
## 1 5 1 26
## 2 4 1 17
## 3 3 1 10
## 4 2 1 5
## 5 1 1 2# Sort tb in ascending order by x below
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sorted_by_3 <- tb[order(tb$x),]
sorted_by_3## # A tibble: 5 x 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2
## 2 2 1 5
## 3 3 1 10
## 4 4 1 17
## 5 5 1 26