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# Define variables with different data types
numeric_var <- 42
character_var <- "Hello, World!"
logical_var <- TRUE
date_time_var <- as.POSIXct("2023-01-15 14:30:00")
# Print variables
cat("Numeric Variable:", numeric_var, "\n")## Numeric Variable: 42cat("Character Variable:", character_var, "\n")## Character Variable: Hello, World!cat("Logical Variable:", logical_var, "\n")## Logical Variable: TRUEcat("Date and Time Variable:", date_time_var, "\n")## Date and Time Variable: 1673773200# Create data structures
vector_example <- c(1, 2, 3, 4, 5)
matrix_example <- matrix(1:6, nrow = 2, ncol = 3)
list_example <- list(1, "apple", TRUE)
data_frame_example <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 22),
Score = c(90, 85, 92)
)
# Print data structures
cat("Vector Example:", vector_example, "\n")## Vector Example: 1 2 3 4 5cat("Matrix Example:\n")## Matrix Example:print(matrix_example)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6cat("List Example:\n")## List Example:print(list_example)## [[1]]
## [1] 1
##
## [[2]]
## [1] "apple"
##
## [[3]]
## [1] TRUEcat("Data Frame Example:\n")## Data Frame Example:print(data_frame_example)## Name Age Score
## 1 Alice 25 90
## 2 Bob 30 85
## 3 Charlie 22 92# a.Create two vectors of integers
vector1 <- c(1, 2, 3)
vector2 <- c(4, 5, 6)
# Add the two vectors element-wise
result_vector <- vector1 + vector2
# Print the result
print(result_vector)## [1] 5 7 9we have added 2 vectors successfully.
# b.Create a vector
my_vector <- c(2, 4, 6, 8, 10)
# Calculate the sum, mean, and product
sum_result <- sum(my_vector)
mean_result <- mean(my_vector)
product_result <- prod(my_vector)
# Print the results
print(paste("Sum:", sum_result))## [1] "Sum: 30"print(paste("Mean:", mean_result))## [1] "Mean: 6"print(paste("Product:", product_result))## [1] "Product: 3840"we have found sum,mean and product of vector elements.
# c.Create a vector
my_vector <- c(3, 1, 7, 2, 9)
# Find the minimum and maximum
min_value <- min(my_vector)
max_value <- max(my_vector)
# Print the results
print(paste("Minimum:", min_value))## [1] "Minimum: 1"print(paste("Maximum:", max_value))## [1] "Maximum: 9"#d. Create a list
my_list <- list(
string_element = "Hello, World",
numeric_element = 42,
vector_element = c(1, 2, 3),
logical_element = TRUE
)
# Print the list
print(my_list)## $string_element
## [1] "Hello, World"
##
## $numeric_element
## [1] 42
##
## $vector_element
## [1] 1 2 3
##
## $logical_element
## [1] TRUEa heterogeneous list is made.
#e. Create a list with named elements
my_list <- list(
vector_element = c(1, 2, 3),
matrix_element = matrix(1:6, nrow = 2),
nested_list = list(a = "apple", b = "banana")
)
# Access the first and second elements of the list
first_element <- my_list$vector_element
second_element <- my_list$matrix_element
# Print the accessed elements
print(first_element)## [1] 1 2 3print(second_element)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6#f. Create a 3x5 matrix filled with zeros
my_matrix <- matrix(0, nrow = 3, ncol = 5)
# Print the matrix
print(my_matrix)## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0#g. Create a sample matrix
my_matrix <- matrix(1:12, nrow = 3)
# Access specific elements
element_1 <- my_matrix[2, 3] # 3rd column, 2nd row
element_2 <- my_matrix[3, ] # 3rd row
element_3 <- my_matrix[, 4] # 4th column
# Print the accessed elements
print(element_1)## [1] 8print(element_2)## [1] 3 6 9 12print(element_3)## [1] 10 11 12#h. Create vectors
name <- c("Alice", "Bob", "Charlie")
age <- c(25, 30, 35)
# Create a DataFrame
df <- data.frame(Name = name, Age = age)
# Display the DataFrame
print(df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35#i. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
# New data to insert
new_data <- data.frame(Name = c("Charlie", "David"), Age = c(35, 40))
# Insert new rows
df <- rbind(df, new_data)
# Display the updated DataFrame
print(df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35
## 4 David 40#j. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
# Add a new column
df$Salary <- c(50000, 60000)
# Display the updated DataFrame
print(df)## Name Age Salary
## 1 Alice 25 50000
## 2 Bob 30 60000#k. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob", "Charlie", "David"), Age = c(25, 30, 35, 40))
# Extract the first 2 rows
first_two_rows <- df[1:2, ]
# Display the extracted rows
print(first_two_rows)## Name Age
## 1 Alice 25
## 2 Bob 30#l. Create a DataFrame
df <- data.frame(Name = c("Charlie", "Alice", "Bob"), Age = c(35, 25, 30))
# Sort the DataFrame by the "Age" column
sorted_df <- df[order(df$Age), ]
# Display the sorted DataFrame
print(sorted_df)## Name Age
## 2 Alice 25
## 3 Bob 30
## 1 Charlie 35#m. Create two DataFrames
df1 <- data.frame(ID = 1:3, Name = c("Alice", "Bob", "Charlie"))
df2 <- data.frame(ID = 2:4, Salary = c(50000, 60000, 70000))
# Merge the DataFrames based on the "ID" column
merged_df <- merge(df1, df2, by = "ID", all = TRUE)
# Display the merged DataFrame
print(merged_df)## ID Name Salary
## 1 1 Alice NA
## 2 2 Bob 50000
## 3 3 Charlie 60000
## 4 4 <NA> 70000#n. Create two DataFrames
df1 <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
df2 <- data.frame(Name = c("Charlie", "David"), Age = c(35, 40))
# Append df2 to the end of df1
appended_df <- rbind(df1, df2)
# Display the appended DataFrame
print(appended_df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35
## 4 David 40#o. Load the dplyr package
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Create a sample DataFrame
df <- data.frame(Group = c("A", "A", "B", "B", "C"),
Value = c(10, 15, 25, 20, 30))
# Select rows with maximum value in each group
result <- df %>%
group_by(Group) %>%
filter(Value == max(Value))
# Display the result
print(result)## # A tibble: 3 × 2
## # Groups: Group [3]
## Group Value
## <chr> <dbl>
## 1 A 15
## 2 B 25
## 3 C 30#p. Create two dataframes
df1 <- data.frame(ID = 1:4, Name = c("Alice", "Bob", "Charlie", "David"))
df2 <- data.frame(ID = 2:5, Salary = c(50000, 60000, 70000, 55000))
# Merge the dataframes based on the "ID" column
merged_df <- merge(df1, df2, by = "ID", all = TRUE)
# Display the merged dataframe
print(merged_df)## ID Name Salary
## 1 1 Alice NA
## 2 2 Bob 50000
## 3 3 Charlie 60000
## 4 4 David 70000
## 5 5 <NA> 55000#q.a. Read data from the console
data <- as.numeric(readline("Enter a number: "))## Enter a number:print(data)## [1] NAx=mtcars
print(x)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2