Data structures are fundamental for organizing and managing data efficiently in any programming language. Essentially, a data structure is a method to arrange data within a system to facilitate effective access and modification. The main goal is to minimize complexities related to both space and time during various computational tasks.<\/p>\n\n\n\n
When working with programming languages like R, variables serve as the basic containers for storing different types of data. Each variable reserves a specific memory location where its values are stored. Once a variable is created, memory is allocated for it to hold the data.<\/p>\n\n\n\n
In R, data structures are the primary objects that users interact with and manipulate. They provide an organized way to store data, making data manipulation, analysis, and other operations more efficient. R supports several types of data structures, each designed to handle data in specific formats or forms. This part introduces the most basic and widely used data structures in R, starting with vectors.<\/p>\n\n\n\n
A vector is a one-dimensional array that holds elements of the same data type. This homogeneity is a key property: no mixing of types is allowed without automatic coercion.<\/p>\n\n\n\n
R has several atomic vector types:<\/p>\n\n\n\n
You can check the type of any vector with:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
typeof(vec)<\/p>\n\n\n\n
and its class with<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
class(vec)<\/p>\n\n\n\n
Typically, vectors are also considered objects of class numeric, integer, etc., for method dispatch.<\/p>\n\n\n\n
The most common way:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v <- c(1, 2, 3, 4)<\/p>\n\n\n\n
Creates an empty vector of specified type and length:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v <- vector(“numeric”, length = 5)<\/p>\n\n\n\n
print(v) # prints 0 0 0 0 0<\/p>\n\n\n\n
Generates regular sequences:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
seq(1, 10, by = 2) # 1 3 5 7 9<\/p>\n\n\n\n
Repeats values:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
rep(1:3, times = 3) # 1 2 3 1 2 3 1 2 3<\/p>\n\n\n\n
rep(1:3, each = 3) # 1 1 1 2 2 2 3 3 3<\/p>\n\n\n\n
When combining elements of different types in a vector, R coerces them to the most general type according to the hierarchy:<\/p>\n\n\n\n
sql<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
logical < integer < double < complex < character<\/p>\n\n\n\n
For example:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
mixed <- c(TRUE, 1L, 3.14, “hello”)<\/p>\n\n\n\n
typeof(mixed) # “character”<\/p>\n\n\n\n
print(mixed) # “TRUE” “1” “3.14” “hello”<\/p>\n\n\n\n
This implicit coercion can sometimes cause bugs, so be mindful.<\/p>\n\n\n\n
Access elements by position (starting from 1):<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v <- c(10, 20, 30, 40)<\/p>\n\n\n\n
v[2] # 20<\/p>\n\n\n\n
v[c(1, 4)] # 10 40<\/p>\n\n\n\n
Excludes elements:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v[-3] # all except the 3rd element: 10 20 40<\/p>\n\n\n\n
Use a logical vector to pick elements:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v[c(TRUE, FALSE, TRUE, FALSE)] # 10 30<\/p>\n\n\n\n
This must be the same length or recycled accordingly.<\/p>\n\n\n\n
You can assign names to elements and index by name:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v <- c(a = 10, b = 20, c = 30)<\/p>\n\n\n\n
v[“b”] # 20<\/p>\n\n\n\n
Find indices that satisfy a condition:<\/p>\n\n\n\n
r<\/p>\n\n\n\n
CopyEdit<\/p>\n\n\n\n
v <- c(5, 10, 15, 20)<\/p>\n\n\n\n
which(v > 10) # 3 4<\/p>\n\n\n\n
v[which(v > 10)] # 15 20<\/p>\n\n\n\n
Standard arithmetic operations (+, -, *, \/, ^) are element-wise<\/strong>:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n a <- c(1, 2, 3)<\/p>\n\n\n\n b <- c(4, 5, 6)<\/p>\n\n\n\n a + b # 5 7 9<\/p>\n\n\n\n a * b # 4 10 18<\/p>\n\n\n\n If vectors differ in length, the shorter one is recycled:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n a <- c(1, 2, 3, 4)<\/p>\n\n\n\n b <- c(10, 20)<\/p>\n\n\n\n a + b # 11 22 13 24 (b recycled as 10,20,10,20)<\/p>\n\n\n\n Be cautious: if the longer vector length is not a multiple of the shorter vector length, R throws a warning.<\/p>\n\n\n\n One of R\u2019s strengths is vectorization \u2014 functions that operate on vectors element-wise efficiently.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(-1, 0, 1, 4)<\/p>\n\n\n\n abs(v) # 1 0 1 4<\/p>\n\n\n\n sqrt(abs(v)) # 1 0 1 2<\/p>\n\n\n\n Logical vectors store TRUE or FALSE.<\/p>\n\n\n\n You can create them by comparisons:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(2, 5, 8, 1)<\/p>\n\n\n\n v > 4 # FALSE TRUE TRUE FALSE<\/p>\n\n\n\n Logical operations work element-wise:<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n a <- c(TRUE, FALSE, TRUE)<\/p>\n\n\n\n b <- c(FALSE, FALSE, TRUE)<\/p>\n\n\n\n a & b # FALSE FALSE TRUE<\/p>\n\n\n\n a | b # TRUE FALSE TRUE<\/p>\n\n\n\n !a # FALSE TRUE FALSE<\/p>\n\n\n\n Naming vector elements can make your code clearer:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n temps <- c(30, 35, 28, 25)<\/p>\n\n\n\n names(temps) <- c(“Mon”, “Tue”, “Wed”, “Thu”)<\/p>\n\n\n\n temps[“Tue”] # 35<\/p>\n\n\n\n You can also name elements on creation:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n temps <- c(Mon = 30, Tue = 35, Wed = 28, Thu = 25)<\/p>\n\n\n\n Assign new values using indices:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(1, 2, 3)<\/p>\n\n\n\n v[2] <- 10<\/p>\n\n\n\n print(v) # 1 10 3<\/p>\n\n\n\n Assigning with logical indices:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v[v > 2] <- 0<\/p>\n\n\n\n print(v) # 1 10 0<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(1, 2, 2, 3, 4)<\/p>\n\n\n\n unique(v) # 1 2 3 4<\/p>\n\n\n\n duplicated(v) # FALSE FALSE TRUE FALSE FALSE<\/p>\n\n\n\n rev(v) # 4 3 2 2 1<\/p>\n\n\n\n any(v > 3) # TRUE<\/p>\n\n\n\n all(v > 0) # TRUE<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(1, 2, 3)<\/p>\n\n\n\n is.numeric(v) # TRUE<\/p>\n\n\n\n is.integer(v) # FALSE, because the default numeric type is double<\/p>\n\n\n\n v2 <- c(1L, 2L)<\/p>\n\n\n\n is.integer(v2) # TRUE<\/p>\n\n\n\n NA represents missing or undefined values in R.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(1, NA, 3)<\/p>\n\n\n\n sum(v) # returns NA by default<\/p>\n\n\n\n sum(v, na.rm = TRUE) # 4, removes NA<\/p>\n\n\n\n You can test for NA values with is.na():<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n is.na(v) # FALSE TRUE FALSE<\/p>\n\n\n\n Missing values propagate through most operations unless handled explicitly.<\/p>\n\n\n\n Factors are special vectors that represent categorical data with fixed levels.<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- factor(c(“low”, “medium”, “high”, “medium”))<\/p>\n\n\n\n levels(v) # “high” “low” “medium”<\/p>\n\n\n\n typeof(v) # “integer”<\/p>\n\n\n\n class(v) # “factor”<\/p>\n\n\n\n They store the underlying data as integers but print as categories. Factors are essential for statistical modeling.<\/p>\n\n\n\n To extract elements meeting conditions, use:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(3, 6, 9, 12)<\/p>\n\n\n\n v[v > 5] # 6 9 12<\/p>\n\n\n\n v[which(v > 5)] # same as above<\/p>\n\n\n\n Which gives indices; direct logical indexing extracts elements.<\/p>\n\n\n\n You can concatenate vectors with c():<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n a <- c(1, 2, 3)<\/p>\n\n\n\n b <- c(4, 5)<\/p>\n\n\n\n c <- c(a, b) # 1 2 3 4 5<\/p>\n\n\n\n Recycling is a powerful feature, but it must be used with care.<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v1 <- c(1, 2, 3, 4, 5)<\/p>\n\n\n\n v2 <- c(10, 20)<\/p>\n\n\n\n v1 + v2 # 11 22 13 24 15 (v2 recycled as 10 20 10 20 10)<\/p>\n\n\n\n If lengths don\u2019t align evenly, R warns:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v1 <- c(1, 2, 3, 4, 5)<\/p>\n\n\n\n v2 <- c(10, 20, 30)<\/p>\n\n\n\n v1 + v2<\/p>\n\n\n\n # Warning: longer object length is not a multiple of shorter object length<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- c(7, 2, 9, 4)<\/p>\n\n\n\n sort(v) # 2 4 7 9<\/p>\n\n\n\n order(v) # 2 4 1 3 (positions of sorted elements)<\/p>\n\n\n\n v[order(v)] # 2 4 7 9<\/p>\n\n\n\n You can sort descending with:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n sort(v, decreasing = TRUE)<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n x <- c(1, 2, 3)<\/p>\n\n\n\n y <- c(3, 4, 5)<\/p>\n\n\n\n setdiff(x, y) # 1 2<\/p>\n\n\n\n intersect(x, y) # 3<\/p>\n\n\n\n union(x, y) # 1 2 3 4 5<\/p>\n\n\n\n For big data, vectors can become huge. Use functions like:<\/p>\n\n\n\n Example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n v <- rnorm(1000000)<\/p>\n\n\n\n length(v) # 1,000,000<\/p>\n\n\n\n head(v) # first 6 elements<\/p>\n\n\n\n summary(v) # min, max, median, quartiles<\/p>\n\n\n\n Unlike vectors, lists are non-homogeneous data structures, meaning they can contain elements of different types. Lists can hold numbers, characters, vectors, other lists, matrices, and even functions.<\/p>\n\n\n\n Lists are created using the list() function.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n list1 <- list(“Sam”, “Green”, c(8, 2, 67), TRUE, 51.99, 11.78, FALSE)<\/p>\n\n\n\n print(list1)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n [1] “Sam”<\/p>\n\n\n\n [[2]]<\/p>\n\n\n\n [1] “Green”<\/p>\n\n\n\n [[3]]<\/p>\n\n\n\n [1] 8 2 67<\/p>\n\n\n\n [[4]]<\/p>\n\n\n\n [1] TRUE<\/p>\n\n\n\n [[5]]<\/p>\n\n\n\n [1] 51.99<\/p>\n\n\n\n [[6]]<\/p>\n\n\n\n [1] 11.78<\/p>\n\n\n\n [[7]]<\/p>\n\n\n\n [1] FALSE<\/p>\n\n\n\n Elements of a list can be accessed by using their indices.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n list2 <- list(matrix(c(3, 9, 5, 1, -2, 8), nrow = 2), c(“Jan”, “Feb”, “Mar”), list(3, 4, 5))<\/p>\n\n\n\n print(list2[1])<\/p>\n\n\n\n print(list2[2])<\/p>\n\n\n\n print(list2[3])<\/p>\n\n\n\n Output:<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n [,1] [,2] [,3]<\/p>\n\n\n\n [1,] 3 5 -2<\/p>\n\n\n\n [2,] 9 1 8<\/p>\n\n\n\n [[2]]<\/p>\n\n\n\n [1] “Jan” “Feb” “Mar”<\/p>\n\n\n\n [[3]]<\/p>\n\n\n\n [[3]][[1]]<\/p>\n\n\n\n [1] 3<\/p>\n\n\n\n [[3]][[2]]<\/p>\n\n\n\n [1] 4<\/p>\n\n\n\n [[3]][[3]]<\/p>\n\n\n\n [1] 5<\/p>\n\n\n\n You can add elements to the end of a list by assigning a new value to the next index, and remove elements by assigning NULL.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n list2[4] <- “HELLO”<\/p>\n\n\n\n print(list2[4])<\/p>\n\n\n\n list2[4] <- NULL<\/p>\n\n\n\n print(list2[4])<\/p>\n\n\n\n Output:<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n [1] “HELLO”<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n NULL<\/p>\n\n\n\n To update an element, assign a new value to the specific index.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n list2[3] <- “Element Updated”<\/p>\n\n\n\n print(list2[3])<\/p>\n\n\n\n Output:<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n [1] “Element Updated”<\/p>\n\n\n\n Lists are an important and versatile data structure in R. Unlike vectors, which are homogeneous and contain elements of the same data type, lists are non-homogeneous and can store elements of different types together. These elements can include numbers, characters, vectors, other lists, matrices, functions, or any other objects. This flexibility makes lists very useful when you want to store complex or mixed data types in a single structure.<\/p>\n\n\n\n Lists are created using the list() function. You can pass any number of elements to this function, each of any type. For example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n list1 <- list(“Sam”, “Green”, c(8, 2, 67), TRUE, 51.99, 11.78, FALSE)<\/p>\n\n\n\n print(list1)<\/p>\n\n\n\n The output will show each element of the list in its position:<\/p>\n\n\n\n lua<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [[1]]<\/p>\n\n\n\n [1] “Sam”<\/p>\n\n\n\n [[2]]<\/p>\n\n\n\n [1] “Green”<\/p>\n\n\n\n [[3]]<\/p>\n\n\n\n [1] 8 2 67<\/p>\n\n\n\n [[4]]<\/p>\n\n\n\n [1] TRUE<\/p>\n\n\n\n [[5]]<\/p>\n\n\n\n [1] 51.99<\/p>\n\n\n\n [[6]]<\/p>\n\n\n\n [1] 11.78<\/p>\n\n\n\n [[7]]<\/p>\n\n\n\n [1] FALSE<\/p>\n\n\n\n You can access elements in a list by using their indices, enclosed in square brackets. For example, if you have a list:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n list2 <- list(matrix(c(3, 9, 5, 1, -2, 8), nrow = 2), c(“Jan”, “Feb”, “Mar”), list(3, 4, 5))<\/p>\n\n\n\n Accessing the first, second, and third elements can be done as follows:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(list2[1]) # Returns the first element (a matrix)<\/p>\n\n\n\n print(list2[2]) # Returns the second element (a vector of month names)<\/p>\n\n\n\n print(list2[3]) # Returns the third element (a nested list)<\/p>\n\n\n\n If you want to access elements inside the nested list within the list, use double square brackets:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(list2[[3]][[1]]) # Outputs 3<\/p>\n\n\n\n print(list2[[3]][[2]]) # Outputs 4<\/p>\n\n\n\n print(list2[[3]][[3]]) # Outputs 5<\/p>\n\n\n\n You can add elements to a list by assigning a new value at the next index:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n list2[4] <- “HELLO”<\/p>\n\n\n\n print(list2[4])<\/p>\n\n\n\n This will add a new element at position 4. To delete an element, assign NULL to that index:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n list2[4] <- NULL<\/p>\n\n\n\n print(list2[4]) # This will return NULL since the element is removed<\/p>\n\n\n\n To update an existing element in the list, simply assign a new value to the desired index:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n list2[3] <- “Element Updated”<\/p>\n\n\n\n print(list2[3])<\/p>\n\n\n\n This replaces the third element in the list with the string “Element Updated”.<\/p>\n\n\n\n Matrices are two-dimensional data structures in R that hold elements of the same data type. They can be considered as vectors with a dimension attribute. The elements in a matrix are arranged in rows and columns.<\/p>\n\n\n\n You can create a matrix using the matrix() function. The primary arguments include the data elements as a vector, the number of rows, the number of columns, whether to fill the matrix by row or by column, and optional dimension names.<\/p>\n\n\n\n The syntax is:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n matrix(data, nrow, ncol, byrow, dimnames)<\/p>\n\n\n\n Example of creating a 3×3 matrix filled by rows:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n M1 <- matrix(c(1:9), nrow = 3, ncol = 3, byrow = TRUE)<\/p>\n\n\n\n print(M1)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n css<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [,1] [,2] [,3]<\/p>\n\n\n\n [1,] 1 2 3<\/p>\n\n\n\n [2,] 4 5 6<\/p>\n\n\n\n [3,] 7 8 9<\/p>\n\n\n\n Similarly, creating a matrix filled with columns:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n M2 <- matrix(c(1:9), nrow = 3, ncol = 3, byrow = FALSE)<\/p>\n\n\n\n print(M2)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n css<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [,1] [,2] [,3]<\/p>\n\n\n\n [1,] 1 4 7<\/p>\n\n\n\n [2,] 2 5 8<\/p>\n\n\n\n [3,] 3 6 9<\/p>\n\n\n\n You can name the rows and columns by passing a list to the dimnames argument:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n rownames <- c(“row1”, “row2”, “row3”)<\/p>\n\n\n\n colnames <- c(“col1”, “col2”, “col3”)<\/p>\n\n\n\n M3 <- matrix(c(1:9), nrow = 3, byrow = TRUE, dimnames = list(rownames, colnames))<\/p>\n\n\n\n print(M3)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n markdown<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n col1 col2 col3<\/p>\n\n\n\n row1 1 2 3<\/p>\n\n\n\n row2 4 5 6<\/p>\n\n\n\n row3 7 8 9<\/p>\n\n\n\n To access elements of a matrix, you specify the row and column indices within square brackets. The syntax is:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n matrixName[row, column]<\/p>\n\n\n\n For example, using matrix M3:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(M3[1, 1]) # First row, first column<\/p>\n\n\n\n print(M3[3, 3]) # Third row, third column<\/p>\n\n\n\n print(M3[2, 3]) # Second row, third column<\/p>\n\n\n\n The output will be:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] 1<\/p>\n\n\n\n [1] 9<\/p>\n\n\n\n [1] 6<\/p>\n\n\n\n You can also extract entire rows or columns by leaving the other index blank:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(M3[1, ]) # All elements in first row<\/p>\n\n\n\n print(M3[, 2]) # All elements in second column<\/p>\n\n\n\n Factors are a special data structure used for fields that take a limited number of unique values, often called categorical data. Factors are useful in statistical modeling and data analysis because they represent categories and their levels efficiently.<\/p>\n\n\n\n You create factors from vectors using the factor() function. For example:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n data <- c(“Male”, “Female”, “Male”, “Child”, “Child”, “Male”, “Female”, “Female”)<\/p>\n\n\n\n print(data)<\/p>\n\n\n\n factor.data <- factor(data)<\/p>\n\n\n\n print(factor.data)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] “Male” “Female” “Male” “Child” “Child” “Male” “Female” “Female”<\/p>\n\n\n\n Levels: Child, Female, Male<\/p>\n\n\n\n The unique values in the vector become the factor levels.<\/p>\n\n\n\n When a data frame contains text columns, R often treats them as factors by default. For instance, consider a data frame, emp. finalda, ta with a column,umn empdept:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(is.factor(emp.finaldata$empdept))<\/p>\n\n\n\n print(emp.finaldata$empdept)<\/p>\n\n\n\n Output might be:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] TRUE<\/p>\n\n\n\n [1] Sales Marketing HR R&D IT Operations Finance <\/p>\n\n\n\n Levels: HR, Marketing, R&D, Sales, Finance, IT Operations<\/p>\n\n\n\n This shows that the empdept column is treated as a factor, which can be beneficial for grouping and analysis.<\/p>\n\n\n\n Data frames are one of the most widely used data structures in R. They are essentially tables or 2D data structures where each column can be of a different data type (numeric, character, factor, etc.). Data frames are used to store tabular data similar to spreadsheets or SQL tables.<\/p>\n\n\n\n You can create a data frame using the data.frame() function:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n empdata <- data.frame(<\/p>\n\n\n\n empid = c(1001, 1002, 1003, 1004),<\/p>\n\n\n\n empname = c(“Alice”, “Bob”, “Charlie”, “David”),<\/p>\n\n\n\n empdept = c(“Sales”, “Marketing”, “HR”, “IT”),<\/p>\n\n\n\n empsalary = c(50000, 55000, 45000, 60000)<\/p>\n\n\n\n )<\/p>\n\n\n\n print(empdata)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n yaml<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n empid empname empdept empsalary<\/p>\n\n\n\n 1 1001 Alice Sales 50000<\/p>\n\n\n\n 2 1002 Bob Marketing 55000<\/p>\n\n\n\n 3 1003 Charlie HR 45000<\/p>\n\n\n\n 4 1004 David IT 60000<\/p>\n\n\n\n You can access elements of a data frame in several ways:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(empdata$empname) # Prints the empname column<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(empdata[2, 3]) # Row 2, column 3: “Marketing”<\/p>\n\n\n\n print(empdata[ , 2]) # All rows, column 2 (empname)<\/p>\n\n\n\n print(empdata[1, ]) # Row 1, all columns<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n empdata$empbonus <- c(5000, 4000, 3000, 6000)<\/p>\n\n\n\n print(empdata)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n newrow <- data.frame(empid=1005, empname=”Eve”, empdept=”Finance”, empsalary=52000, empbonus=4500)<\/p>\n\n\n\n empdata <- rbind(empdata, newrow)<\/p>\n\n\n\n print(empdata)<\/p>\n\n\n\n Arrays are similar to matrices but can have more than two dimensions. They hold elements of the same data type.<\/p>\n\n\n\n You use the array() function to create an array by specifying the data and dimensions:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n # Create a 3D array with dimensions 2 x 3 x 4<\/p>\n\n\n\n arr <- array(1:24, dim = c(2, 3, 4))<\/p>\n\n\n\n print(arr)<\/p>\n\n\n\n You specify indices for each dimension in square brackets:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(arr[1, 2, 3]) # Element at 1st row, 2nd column, 3rd matrix<\/p>\n\n\n\n print(arr[, , 2]) # The entire 2nd matrix<\/p>\n\n\n\n Arrays are useful for working with multi-dimensional data, like image processing, scientific datasets, etc.<\/p>\n\n\n\n Missing data is common in real-world datasets, and R provides ways to detect and manage missing values.<\/p>\n\n\n\n In R, missing data is represented by the special value NA.<\/p>\n\n\n\n Use the is.na() function to check for missing values:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n data <- c(10, 20, NA, 40, NA)<\/p>\n\n\n\n print(is.na(data))<\/p>\n\n\n\n Output:<\/p>\n\n\n\n graphql<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] FALSE FALSE TRUE FALSE TRUE<\/p>\n\n\n\n You can remove missing values using the na.omit() function or by using logical indexing:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n clean_data <- na.omit(data)<\/p>\n\n\n\n print(clean_data)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] 10 20 40<\/p>\n\n\n\n Alternatively:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n clean_data <- data[!is.na(data)]<\/p>\n\n\n\n print(clean_data)<\/p>\n\n\n\n You can replace missing values with a specific value:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n data[is.na(data)] <- 0<\/p>\n\n\n\n print(data)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] 10 20 0 40 0<\/p>\n\n\n\n Lists are very flexible data structures in R. Unlike vectors or arrays, lists can hold elements of different types and structures \u2014 including vectors, matrices, other lists, data frames, and even functions.<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n mylist <- list(<\/p>\n\n\n\n name = “John”,<\/p>\n\n\n\n age = 28,<\/p>\n\n\n\n scores = c(85, 90, 88),<\/p>\n\n\n\n passed = TRUE<\/p>\n\n\n\n )<\/p>\n\n\n\n print(mylist)<\/p>\n\n\n\n You can access list elements by name or position:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(mylist$name) # “John”<\/p>\n\n\n\n print(mylist[[2]]) # 28 (age)<\/p>\n\n\n\n print(mylist$scores) # c(85, 90, 88)<\/p>\n\n\n\n To access an element inside a list element:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n print(mylist$scores[2]) # 90<\/p>\n\n\n\n Factors are used to handle categorical data \u2014 data that has a fixed number of possible values (levels). They are stored as integers with labels.<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n colors <- c(“red”, “blue”, “red”, “green”, “blue”, “blue”)<\/p>\n\n\n\n color_factor <- factor(colors)<\/p>\n\n\n\n print(color_factor)<\/p>\n\n\n\n Output:<\/p>\n\n\n\n csharp<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n [1] red blue red green blue blue <\/p>\n\n\n\n Levels: blue, green, red<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n levels(color_factor) # “blue” “green” “red”<\/p>\n\n\n\n You can specify the order of levels:<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n ordered_factor <- factor(colors, levels = c(“red”, “green”, “blue”), ordered = TRUE)<\/p>\n\n\n\n print(ordered_factor)<\/p>\n\n\n\n dplyr is a package that makes data manipulation easy and intuitive.<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n install.packages(“dplyr”) # Run once<\/p>\n\n\n\n library(dplyr)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n df <- data.frame(<\/p>\n\n\n\n name = c(“Alice”, “Bob”, “Charlie”, “David”, “Eve”),<\/p>\n\n\n\n age = c(25, 30, 35, 40, 28),<\/p>\n\n\n\n salary = c(50000, 60000, 55000, 65000, 48000)<\/p>\n\n\n\n )<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n young_employees <- filter(df, age < 30)<\/p>\n\n\n\n print(young_employees)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n selected_data <- select(df, name, salary)<\/p>\n\n\n\n print(selected_data)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n df <- mutate(df, bonus = salary * 0.1)<\/p>\n\n\n\n print(df)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n df_sorted <- arrange(df, desc(salary))<\/p>\n\n\n\n print(df_sorted)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n avg_salary <- summarize(df, avg_salary = mean(salary))<\/p>\n\n\n\n print(avg_salary)<\/p>\n\n\n\n r<\/p>\n\n\n\n CopyEdit<\/p>\n\n\n\n df2 <- data.frame(<\/p>\n\n\n\n dept = c(“Sales”, “Sales”, “HR”, “HR”, “IT”),<\/p>\n\n\n\n salary = c(50000, 55000, 45000, 47000, 60000)<\/p>\n\n\n\n )<\/p>\n\n\n\n dept_avg <- df2 %>%<\/p>\n\n\n\n group_by(dept) %>%<\/p>\n\n\n\n summarize(avg_salary = mean(salary))<\/p>\n\n\n\n print(dept_avg)<\/p>\n\n\n\nRecycling rule<\/strong><\/h3>\n\n\n\n
Vectorized Functions and Operations<\/strong><\/h2>\n\n\n\n
Mathematical functions:<\/strong><\/h3>\n\n\n\n
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Modifying Vector Elements<\/strong><\/h2>\n\n\n\n
Useful Vector Functions<\/strong><\/h2>\n\n\n\n
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Any y(v) \u2014 returns TRUE if any element is TRUE (useful with logical vectors)
All l(v) \u2014 returns TRUE if all elements are TRUE.E
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Factors vs. Character Vectors<\/strong><\/h2>\n\n\n\n
Subsetting with <\/strong>which()<\/strong> and Logical Conditions<\/strong><\/h2>\n\n\n\n
Combining Vectors<\/strong><\/h2>\n\n\n\n
Vector Recycling \u2014 Detailed Example<\/strong><\/h2>\n\n\n\n
Sorting and Ordering Vectors<\/strong><\/h2>\n\n\n\n
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Summary y() for quick stats
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Accessing Elements in a List<\/strong><\/h3>\n\n\n\n
Adding and Deleting List Elements<\/strong><\/h3>\n\n\n\n
Updating Elements of a List<\/strong><\/h3>\n\n\n\n
Lists in R Programming<\/strong><\/h2>\n\n\n\n
Creating Lists<\/strong><\/h3>\n\n\n\n
Accessing List Elements<\/strong><\/h3>\n\n\n\n
Adding and Deleting List Elements<\/strong><\/h3>\n\n\n\n
Updating List Elements<\/strong><\/h3>\n\n\n\n
Matrices in R Programming<\/strong><\/h2>\n\n\n\n
Creating Matrices<\/strong><\/h3>\n\n\n\n
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Accessing Matrix Elements<\/strong><\/h3>\n\n\n\n
Factors in R<\/strong><\/h2>\n\n\n\n
Creating Factors<\/strong><\/h3>\n\n\n\n
Using Factors in Data Frames<\/strong><\/h3>\n\n\n\n
Data Frames in R Programming<\/strong><\/h2>\n\n\n\n
Creating a Data Frame<\/strong><\/h3>\n\n\n\n
Accessing Data Frame Elements<\/strong><\/h3>\n\n\n\n
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Creating Arrays<\/strong><\/h3>\n\n\n\n
Accessing Array Elements<\/strong><\/h3>\n\n\n\n
Handling Missing Data in R<\/strong><\/h2>\n\n\n\n
Representing Missing Data<\/strong><\/h3>\n\n\n\n
Detecting Missing Values<\/strong><\/h3>\n\n\n\n
Removing Missing Values<\/strong><\/h3>\n\n\n\n
Replacing Missing Values<\/strong><\/h3>\n\n\n\n
Lists in R Programming<\/strong><\/h2>\n\n\n\n
Creating Lists<\/strong><\/h3>\n\n\n\n
Accessing List Elements<\/strong><\/h3>\n\n\n\n
Factors in R Programming<\/strong><\/h2>\n\n\n\n
Creating Factors<\/strong><\/h3>\n\n\n\n
Why Factors?<\/strong><\/h3>\n\n\n\n
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Basic Data Manipulation with dplyr<\/strong><\/h2>\n\n\n\n
Installing and Loading dplyr<\/strong><\/h3>\n\n\n\n
Sample Data Frame<\/strong><\/h3>\n\n\n\n
Common dplyr Functions<\/strong><\/h3>\n\n\n\n
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1. Practice is Key<\/strong><\/h3>\n\n\n\n
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<\/li>\n<\/ul>\n\n\n\n6. Join the Community<\/strong><\/h3>\n\n\n\n