Module # 2 Assignment

 Evaluate myMean Function

  • Use the vector:
    assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22)
  • Consider this function:
    myMean <- function(assignment2) {
  return(sum(assignment) / length(someData))
}
  • assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22)
  • myMean <- function(assignment2) {
      return(sum(assignment) / length(someData))
    }
  • myMean(assignment2)
  • Error in sum(assignment) : object 'assignment' not found

  • The function fails due to incorrect variable names inside the function body.

    • The function argument is named assignment2

    • Inside the function, the variables assignment and someData are used

    • These variables do not exist within the function or the global environment

    Corrected Version of myMean()

    myMean <- function(assignment2) {
  sum(assignment2) / length(assignment2)
}

    Testing the Corrected Function

    myMean(assignment2)

    Correct Output

    [1] 19.25

  • Reflection

    This exercise helped reinforce how important variable naming and scope are when writing functions in R. Even though the data existed outside the function, R could not recognize incorrectly named variables inside the function body. Debugging this issue made it clear that functions only work with the objects explicitly passed to them. Understanding this concept will help me write cleaner, more reusable code and avoid common errors in future data analysis assignments.

Comments

Post a Comment

Popular posts from this blog

ABC vs. CBS: Comparing Fictional Presidential Poll Data in R

 # Assignment 3 - Fictional 2016 Presidential Poll Data # Note: This data is made up and does NOT reflect real election results # Candidate names Name <- c("Jeb", "Donald", "Ted", "Marco", "Carly", "Hillary", "Bernie") # Poll results from ABC and CBS ABC_poll <- c(4, 62, 51, 21, 2, 14, 15) CBS_poll <- c(12, 75, 43, 19, 1, 21, 19) # Combine data into a data frame poll_data <- data.frame(Name, ABC_poll, CBS_poll) # Add a column to compare differences between polls poll_data$Difference <- CBS_poll - ABC_poll # View the data print(poll_data) # Optional: Simple bar plot comparison barplot(   t(as.matrix(poll_data[, c("ABC_poll", "CBS_poll")])),   beside = TRUE,   names.arg = poll_data$Name,   col = c("gray70", "gray40"),   legend.text = c("ABC Poll", "CBS Poll"),   main = "Fictional 2016 Presidential Poll Results",   ylab = "Poll Per...
Read more

Assignment #10: Building Your Own R Package

 # Proposal for My R Package: Friedman For this project, I am proposing an R package called **Friedman**. The purpose of this package is to provide beginner-friendly tools for simple data analysis and visualization in R. Many students and new R users struggle with repetitive tasks such as cleaning datasets, calculating summary statistics, and creating clear visualizations. This package will help make those tasks easier by grouping useful functions into one simple package. The main audience for this package is students, beginner analysts, and anyone who wants a more straightforward way to explore data in R. Instead of writing long code repeatedly, users will be able to call functions from Friedman to quickly summarize and visualize their data. This makes the package especially useful for class assignments, small research projects, and practice with R programming. Some of the key functions I plan to implement include: - `clean_data()` – removes missing values or standardizes column n...
Read more

Module # 4 Programming structure assignment

 # ------------------------------------------------------------ # hospital_analysis.R # Hospital patient intake dataset: plots + brief summaries # ------------------------------------------------------------ # ---- 1) Create the dataset ---- Freq <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) BP   <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176) # first: bad = 1, good = 0, NA = missing First <- c(1, 1, 1, 1, 0, 0, 0, 0, NA, 1) # second: low = 0, high = 1 Second <- c(0, 0, 1, 1, 0, 0, 1, 1, 1, 1) # finaldecision: low = 0, high = 1 FinalDecision <- c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1) hospital_data <- data.frame(Freq, BP, First, Second, FinalDecision) # Print the dataset print("Hospital dataset:") print(hospital_data) # ---- 2) Boxplot: Blood Pressure by Final Decision ---- # Side-by-side comparison of BP for Low vs High final decision boxplot(   BP ~ FinalDecision,   data = hospital_data,   names = c("Low Priority (0)", "High Priority (...
Read more