1. Vectors
Vectors are 1D data structures holding elements of the same type .
Sort : sort() (ascending) or rev(sort()) (descending).
Create : Use c(), seq(from, to, by), or rep(value, times).
Access : Use [index] (positive/negative), logical vectors, or names.
Length : length(vector).
# HW1: Create a vector of even numbers 2, 4, 6 using `seq()`
# HW2: Access the 3rd element of `c(10, 20, 30, 40)`
# HW3: Sort `c(5, 1, 3)` in descending order
# HW4: Check the length of `c("a", "b", "c")`
# HW5: Create a vector with 3 copies of "R" using `rep()` # HW1
vec_seq <- seq(2, 6, by=2) # Output: 2, 4, 6
# HW2
print(c(10, 20, 30, 40)[3]) # Output: 30
# HW3
sorted <- rev(sort(c(5, 1, 3))) # Output: 5, 3, 1
# HW4
print(length(c("a", "b", "c"))) # Output: 3
# HW5
vec_rep <- rep("R", 3) # Output: "R", "R", "R" 2. Lists
Lists store mixed or nested data .
Add/Remove : list[[new_index]] <- value or list[index] <- NULL.
Create : list().
Access : [index] (returns sublist), [[index]] (returns element), or $name.
Modify : Assign new values via [[ ]] or append().
# HW1: Create a list with "apple", 25, and a sub-list `c(1, 2)`
# HW2: Access the sub-list `c(1, 2)` from HW1
# HW3: Change "apple" to "banana" in the list
# HW4: Add `TRUE` to the end of the list
# HW5: Remove the 2nd element (25) # HW1
my_list <- list("apple", 25, list(1, 2))
# HW2
print(my_list[[3]]) # Output: 1, 2
# HW3
my_list[[1]] <- "banana"
# HW4
my_list <- append(my_list, TRUE)
# HW5
my_list[2] <- NULL 3. Matrices
Matrices are 2D, same-type data structures.
Generate Values : seq(), rep().
Create : matrix(data, nrow, ncol).
Access : [row, col], [,] for entire rows/columns.
Add Rows/Columns : rbind(), cbind().
# HW1: Create a 3x2 matrix with 1-6 using `matrix()`
# HW2: Extract the 2nd row
# HW3: Extract the 1st column
# HW4: Add a row `7, 8` to the matrix
# HW5: Create a matrix with 1, 2 repeated 3 times using `rep()` # HW1
mat <- matrix(1:6, nrow=3)
# HW2
print(mat[2, ]) # Output: 2, 5
# HW3
print(mat[, 1]) # Output: 1, 2, 3
# HW4
mat <- rbind(mat, c(7, 8))
# HW5
mat_rep <- matrix(rep(1:2, 3), nrow=3) 4. Arrays
Arrays extend matrices to multi-dimensional data .
Dimensions : dim() to check or set dimensions.
Create : array(data, dim=c(rows, cols, ...)).
Access : [i, j, k] for specific elements.
# HW1: Create a 2x2x2 array with values 1-8
# HW2: Access the 3rd element of the 1st layer
# HW3: Extract the 2nd layer (all rows/columns)
# HW4: Check the total length of the array
# HW5: Convert a vector `1:12` into a 3x4 array # HW1
arr <- array(1:8, dim=c(2, 2, 2))
# HW2
print(arr[3]) # Output: 3
# HW3
print(arr[, , 2])
# HW4
print(length(arr)) # Output: 8
# HW5
arr_3d <- array(1:12, dim=c(3, 4)) 5. Data Frames
Data frames store tabular data (mixed types allowed).
Modify : Add/remove columns via $ or [ ].
Create : data.frame().
Access : $column, [, "column"], or subset().
# HW1: Create a data frame with Name (Alice, Bob), Age (25, 30)
# HW2: Access the "Name" column using `$`
# HW3: Add a column "Salary" with 5000, 6000
# HW4: Remove the "Age" column
# HW5: Check the number of rows # HW1
df <- data.frame(Name=c("Alice", "Bob"), Age=c(25, 30))
# HW2
print(df$Name) # Output: Alice, Bob
# HW3
df$Salary <- c(5000, 6000)
# HW4
df$Age <- NULL
# HW5
print(nrow(df)) # Output: 2 6. Factors
Factors store categorical data with predefined levels.
Ordered Factors : ordered=TRUE for ranking.
Create : factor().
Modify Levels : levels(), factor(..., levels=).
# HW1: Create a factor with "Low", "Medium", "High"
# HW2: Check the levels of the factor
# HW3: Add "Very High" as a new level
# HW4: Remove "Medium" from the factor
# HW5: Convert the factor to an ordered factor # HW1
f <- factor(c("Low", "Medium", "High"))
# HW2
print(levels(f)) # Output: "High", "Low", "Medium"
# HW3
f <- factor(f, levels=c("Low", "Medium", "High", "Very High"))
# HW4
f <- f[f != "Medium"]
# HW5
f_ordered <- factor(f, ordered=TRUE)