LIS 6371 Open Source R Blog - Christine Tran

 Module 12 Assignment GitHub uniqueread package repository:  https://github.com/christilly/uniqueread HTML Markdown:      I created a  Markdown file in R for my package uniqueread. I broke down my functions into sections, explaining the purpose of each.      At first I described the package purpose and the required libraries.       Next I explain how to import data from Goodreads and how to remove unrated books from the analysis.      Next I showed how to calculate the residuals for each book (user rating - average rating). I also showed how to calculate average, standard deviation, highest, and lowest residuals, as well as how to find the associated book titles for the highest/lowest residuals.  Lastly, I showed my function for displaying the residuals data visually in a distribution histogram.       The whole process required a big learning curve. I have been working on the package...

 Module 11 Assignment Identify the bug in example code: tukey_multiple <- function(x) {     outliers <- array(TRUE,dim=dim(x))     for (j in 1:ncol(x))      {      outliers[,j] <- outliers[,j] && tukey.outlier(x[,j])      }  outlier.vec <- vector(length=nrow(x))      for (i in 1:nrow(x))      { outlier.vec[i] <- all(outliers[i,]) } return(outlier.vec) } My first step was to run the code to test the function. I received this error: This helped me narrow down my search of the bug to the second for loop in the function. I wanted to run debug on the function, however, I learned I have to load the function into my local environment first. I changed the working directory to where the file is located on my device and tried to source the R file as shown below.  However, sourcing the R file did not work because the file has an err...

 Module 10 Assignment Github Repository:  https://github.com/christilly/readspeed/blob/main/DESCRIPTION R package idea     My idea for this R package is to glean fun and useful information from Goodreads data. Goodreads is a website/app for books where people can find new books track the books they read. People can download their Goodreads library directly from their accounts in the form of a CSV file. The CSV file contains the aforementioned information and more including ratings given to the books by the readers, books in the read and want to read sections of their accounts, IBSNs, and more. My idea is to create an R package where Goodreads users can upload their data and the program calculates the overall average time it takes for the user to finish a book, as well as the time it takes for the user to finish short, medium, and long books. This information can be useful for the user by giving an understanding of their reading habits.  Creating the R package ...

 Module 9 Assignment Github repository:  https://github.com/christilly/LIS-6371-USF/blob/main/Module%209.R Dataset: Pulse Rates at Various Times of Day - A student measured her pulse in the morning, at noon, at 1:00, and in the evening for each of 26 days. pulse_data has 104 total observations for 26 days and 4 conditions (morning, noon, one, evening).  #1 First visualization: heart rate in the morning     I wanted to make a simple visualization of a scatterplot using base R that displays all the morning heart rate recordings for all 26 days. I first created a subset data frame using the dplyr package that contains the values from pulse_data that contain "morning" in the time column. I then plotted these readings to show the heart rate in the morning graph, with day as the x value and heart rate as the y value. As seen in the graph, the heart rate readings were clustered between 65-75 bpm.  #2 Second visualization: heart rate in the morning compared to...

 Module 8 Assignment GitHub repository:  https://github.com/christilly/LIS-6371-USF/blob/main/Module%208.R Step 1     I successfully imported the data from the "Assignment 6 Dataset" text tile using the read.table function and separating by commas. I then used the plyr package " ddply" where "dd" means it takes in a data frame and returns a data frame. The transform function call creates a new column that calculates the average grade as shown below.      I then found the mean of the sex column. This returns "NA" because the values are male/female, not numerical. I wrote the average grade data frame to a csv file using the write.table function. This returned the following: Step 2     I created a new data frame StudentNames which took a subset of the original data frame. Grepl[iI] finds any instances where the name contains "i" or "I". This was also written to a csv file as shown below: Weekly readings A quick introduction to pl...

 Module 7 GitHub repository:  https://github.com/christilly/LIS-6371-USF/blob/main/Module7.R           The data I downloaded was the CSV format of the amino acid composition of the Human papillomavirus E1 protein. The amino acid composition the file is based on is shown below.  The CSV file formatting is shown below.      Generic functions can be applied to this data. However, the data would have to be formatted in a way that operations are useful. For instance, I could remove the numbers representing the amino acids and keep the letter of the amino acids with their percentage concentration. This could be put in a data frame, and more in-depth analysis could be done. This would likely involve a function that cleans the data using string operations. Since we have not covered string manipulation in-depth yet, I did this manually by creating data frame E1_clean. I applied generic functions on this data frame including head(), summ...