COPS R Assignment Done

tasks/basics.R

@@ -1,29 +1,35 @@
 # Create a numeric vector with values from 1 to 5
 # Name the vector as "numeric_vector"
-
+numeric_vector <- c(1, 2, 3, 4, 5)
 
 # Function to calculate the sum of a numeric vector
 calculate_sum <- function(vector) {
     # TODO: Complete this function to calculate the sum of 'vector'
+    sum(vector)
 }
 
 # Function to calculate the mean of a numeric vector
 calculate_mean <- function(vector) {
     # TODO: Complete this function to calculate the mean of 'vector'
+    mean(vector)
 }
 
 # Function to calculate the median of a numeric vector
 calculate_median <- function(vector) {
     # TODO: Complete this function to calculate the median of 'vector'
+    median(vector)
 }
 
 # Function to calculate the standard deviation of a numeric vector
 calculate_std_deviation <- function(vector) {
     # TODO: Calculate the standard deviation and round to 3 decimal places
+    round(sd(vector),digits=3)
 }
 
 # Function to calculate the range of a numeric vector
 calculate_range <- function(vector) {
     # TODO: Complete this function to calculate the range of 'vector'
     # To find the range of the vector just subtract the minimum value from the maximum value
+    range <- max(vector)-min(vector)
+    print(range)
 }

tasks/dataset.R

@@ -8,6 +8,10 @@ data(mtcars)
 str(mtcars)
 summary(mtcars)
 
+#installing and loading the ggplot2 package
+install.packages("ggplot2")
+library(ggplot2)
+
 # Task 2: Create a bar plot for average miles per gallon (mpg)
 # - Calculate the average miles per gallon (mpg) for each car model.
 # - Create a bar plot to visualize the average mpg for each car model.
@@ -18,21 +22,42 @@ summary(mtcars)
 # - Display the plot.
 # Hint: You may want to use the ggplot2 package for creating and customizing the plot.
 
+#There is no need to calculate the mean of mpg value for each car model since each car model has only one mpg value
+p <- ggplot(data=mtcars, aes(x=rownames(mtcars), y=mtcars$mpg)) + geom_bar(stat="identity", width=1.0, color='black', fill='purple')
+p <- p+theme(axis.text.x = element_text(angle = 90, hjust=1))
+ggsave("mtcars_barplot1.pdf", p)
+print(p)
+
 # Task 3: Additional Customization
 # - Customize the bar plot further by adding labels, adjusting colors, and improving the appearance.
 # - Save the updated plot as a new image file.
 # - Display the updated plot.
+p <- p + labs(title="AVERAGE MPG OF CAR MODELS", x="Car Model", y="Miles per Gallon(mpg)")+theme(plot.title = element_text(colour = "red"))
+ggsave("mtcars_plot2.pdf", p)
+print(p)
 
 # Task 4: Export Data
 # - Save the modified "mtcars" dataset to a CSV file for further analysis.
 # - Use the "write.csv" function to export the dataset.
+write.csv(mtcars, "mtcars.csv")   #The data set is not modified since no changes were made
 
 # Task 5: Summary and Reporting
 # - Summarize the findings and observations from the data visualization.
 # - Create a brief report explaining the insights gained from the visualization.
+#Summary--
+# The mpg values among the cars are diverse, ranging from just above 10 upto 33.9 miles per gallon.
+# 'Toyota 'Corolla' is the most fuel efficient while 'Cadillac Fleetwood' and 'Lincoln Continental' the least.
 
 # Task 6: Bonus Challenge (5 points)
 # - Create a second type of plot (e.g., scatterplot, boxplot, or line chart) to explore another aspect of the "mtcars" dataset.
 # - Customize the plot.
 # - Save the plot as an image file.
 # - Display the plot.
+
+# Creating a scatterplot of Car weight vs mpg
+s_plot <- ggplot(mtcars, aes(x = wt, y = mpg)) +geom_point(color = 'red', size = 3) + labs(title = "Scatterplot of Car Weight vs. Miles Per Gallon",
+x = "Car Weight",
+y = "Miles Per Gallon") + theme_minimal()
+
+ggsave("scatterplot_mtcars.pdf", s_plot, width = 7, height = 7, units = "in")
+print(s_plot)

tasks/loops.R

@@ -6,6 +6,13 @@
 # - Return the sum of even numbers.
 sum_even_numbers <- function(n) {
     # Your code here
+    sum_even <-0
+    for (i in 1:n) {
+    if (i %% 2 == 0) {
+      sum_even <- sum_even + i
+      }
+    }
+    return (sum_even)
 }
 
 # Task 2: Calculate Factorial Using a Loop
@@ -15,6 +22,11 @@ sum_even_numbers <- function(n) {
 # - Return the factorial.
 factorial_using_loop <- function(n) {
     # Your code here
+    factorial <- 1
+    for (i in 1:n){
+        factorial <- factorial*i
+    }
+    return (factorial)
 }
 
 # Task 3: Reverse a String
@@ -24,7 +36,12 @@ factorial_using_loop <- function(n) {
 # - Return the reversed string.
 reverse_string <- function(input_string) {
     # Your code here
-}
+    rev_string <-""
+    for (i in nchar(input_string):1) {
+    rev_string <- paste0(rev_string, substr(input_string, i, i))
+    }
+    return (rev_string)
+  }
 
 # Task 4: Count the Number of Vowels
 # - Define a function called 'count_vowels' that counts the number of vowels in a string.
@@ -34,6 +51,13 @@ reverse_string <- function(input_string) {
 # - Increment the vowel count and return it.
 count_vowels <- function(input_string) {
     # Your code here
+    vowel_count <- 0
+    for (char in strsplit(input_string, NULL)[[1]]) {
+     if (char %in% c("a", "e", "i", "o", "u","A","E","I","O","U")) {
+      vowel_count <- vowel_count + 1
+     }
+   }
+   return(vowel_count)
 }
 
 # Task 5: Generate a Fibonacci Sequence
@@ -43,4 +67,10 @@ count_vowels <- function(input_string) {
 # - Return the first 'n' terms of the Fibonacci sequence.
 generate_fibonacci <- function(n) {
     # Your code here
+    fibonacci_sequence <- c(1, 1)
+   for (i in 3:n) {
+     next_fibonacci <- fibonacci_sequence[i - 1] + fibonacci_sequence[i - 2]
+     fibonacci_sequence <- c(fibonacci_sequence, next_fibonacci)
+  }
+  return(fibonacci_sequence[1:n])
 }