# character vector
c('male', 'female', 'female', 'male', 'female')
c('Math', 'Data Science', 'Math', 'Computer Science', 'Data Science') 2C: Character Vector and Factor
Readings
From R Coding Basics: An Introduction to the Basics of Coding in R by Dr. Gaston Sanchez:
Topics
Character vector
Useful functions for character vector
Factor
Character vector
Data can be in the form of specific categories that describe a certain characteristic.
Categories such as gender, color, nationality, and college major are said to be nominal because they have no natural ordering.
In contrast, categories such as education level, customer satisfaction, and Likert scale responses, are ordinal because they do have a natural ordering.
In R, a character vector is often used to represent nominal categories.
Useful functions for character vector
The nchar() function
nchar()counts the number of characters of each element.
v <- c('Data', 'science', 'is', 'fun', 'and', 'challenging')
nchar(v)[1] 4 7 2 3 3 11strsplit()splits a string into words.
strsplit('Data science is fun and challenging', split = ' ')[[1]]
[1] "Data" "science" "is" "fun" "and"
[6] "challenging"paste()concatenates elements of character vectors
towns <- c('Glassboro', 'Clayton', 'Pitman', 'Deptford')
paste(towns, 'NJ')[1] "Glassboro NJ" "Clayton NJ" "Pitman NJ" "Deptford NJ" paste(towns, 'NJ', sep = ' ')[1] "Glassboro NJ" "Clayton NJ" "Pitman NJ" "Deptford NJ" paste(towns, 'NJ', sep = ', ')[1] "Glassboro, NJ" "Clayton, NJ" "Pitman, NJ" "Deptford, NJ" paste(towns, 'NJ', sep = ' in ')[1] "Glassboro in NJ" "Clayton in NJ" "Pitman in NJ" "Deptford in NJ" - Thanks to vectorization,
paste()allows corresponding elements to be concatenated.
first_names <- c('James', 'Robert', 'Mary')
last_names <- c('Smith', 'Williams', 'Brown')
paste(first_names, last_names)[1] "James Smith" "Robert Williams" "Mary Brown" 💻 Hands-On
Write R code to count the number of characters of the following vector:
towns <- c('Glassboro', 'Clayton', 'Pitman', 'Deptford')
Answer
nchar(towns)[1] 9 7 6 8💻 Hands-On
Write R code extract words in the following string:
#> 'Name,Age,Major,GPA,Hobbies'
Answer
strsplit('Name,Age,Major,GPA,Hobbies', split = ',')[[1]]
[1] "Name" "Age" "Major" "GPA" "Hobbies"The paste() function
💻 Hands-On
Write R code to generate the following output:
#> [1] "Glassboro Township" "Clayton Township" "Pitman Township"
#> [4] "Deptford Township"
Answer
paste(towns, 'Township', sep = ' ')[1] "Glassboro Township" "Clayton Township" "Pitman Township"
[4] "Deptford Township" 💻 Hands-On
What does the paste0() function does?
towns <- c('Glassboro', 'Clayton', 'Pitman', 'Deptford')
paste0(towns, 'NJ')[1] "GlassboroNJ" "ClaytonNJ" "PitmanNJ" "DeptfordNJ"
Answer
paste0 is a shortcut for paste(..., sep = '') (no separator)
towns <- c('Glassboro', 'Clayton', 'Pitman', 'Deptford')
paste0(towns, 'NJ')[1] "GlassboroNJ" "ClaytonNJ" "PitmanNJ" "DeptfordNJ" 💻 Hands-On
Write R code to generate the following output:
#> [1] "Smith, James" "Williams, Robert" "Brown, Mary"
Answer
paste(last_names, first_names, collapse = ', ')[1] "Smith James, Williams Robert, Brown Mary"💻 Hands-On
Write R code to generate the following output:
#> [1] "Glassboro, NJ 08028" "Clayton, NJ 08312" "Pitman, NJ 08071"
#> [4] "Deptford, NJ 08096"
Answer
zip_code <- c('08028', '08312', '08071', '08096')
paste0(towns, ', NJ ', zip_code)[1] "Glassboro, NJ 08028" "Clayton, NJ 08312" "Pitman, NJ 08071"
[4] "Deptford, NJ 08096" Factor
A factor is a specialized data structure for categories, especially ordinal categories.
It behaves like a vector but has additional attributes.
A factor displays categories (levels) but stores them internally as integers.
A factor is created from a character vector using the
factor()function.
### Nominal factor
#> [1] O A AB O O B A B AB
#> Levels: A B AB O
### Ordinal factor
#> [1] extra hot mild hot mild medium extra hot mild
#> [8] medium extra hot medium extra hot mild
#> Levels: mild < medium < hot < extra hot💻 Hands-On
Create a nominal factor from the following character vector:
blood <- c('O', 'A', 'AB', 'O', 'O', 'B', 'A', 'B', 'AB')
Answer
factor(blood, levels = c('O', 'A', 'B', 'AB'))[1] O A AB O O B A B AB
Levels: O A B AB💻 Hands-On
Create an ordinal factor from the following character vector:
pepper <- c('extra hot', 'mild', 'hot', 'mild', 'medium', 'extra hot',
'mild', 'medium', 'extra hot', 'medium', 'extra hot', 'mild')
Answer
factor(pepper, levels = c('mild', 'medium', 'hot', 'extra hot'), ordered = TRUE) [1] extra hot mild hot mild medium extra hot mild
[8] medium extra hot medium extra hot mild
Levels: mild < medium < hot < extra hot💻 Hands-On
Create an ordinal factor from the following character vector:
shirts <- c('L', 'XL', 'XL', 'L', 'M', 'S', 'S', 'XL', 'M', 'S', 'L', 'S')
Answer
factor(shirts, levels = c('S', 'M', 'L', 'XL'),
labels = c('Small', 'Medium', 'Large', 'Extra Large')) [1] Large Extra Large Extra Large Large Medium Small
[7] Small Extra Large Medium Small Large Small
Levels: Small Medium Large Extra Large