R-programing Journey 2021

This week we learned about debugging and ways we can tackle bugs in our code. Our assignment this week is to debug the following function: 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) } There are deliberate bugs included in this code and our goal is to use our debugging tools to find what possible issues may lie in the function. GitHub link: https://github.com/Ant-nguyen/Intro_r_2021/blob/main/Module%2011.R Let us begin! First thing first we should probably look at our function and have an idea of what it does? This is where it gets tricky. There is no comments in the code (# comments) or inherent restrictions on input that give u...

This week we begin to dive into the procedures needed to create an official R package. Our assignment this week was to create a package in R and create a DESCRIPTION File.  GitHub link:  https://github.com/Ant-nguyen/Intro_r_2021/blob/main/DESCRIPTION Let us begin!:  First thing first, we will be heavily reliant on two main packages: 1. devtools 2. roxygen2 devtools will provide us with most of the functions needed in order to create a package and roxygen2 is a great package that streamlines many of the documentations needed in packages for R.  We simply have to install the packages: >install.packages("devtools") >library("devtools") >install.packages("roxygen2") >library*"roxygen2") Unfortunately for me there was an error in my "backports", that prevented me from installing and using devtools correctly. Upon searching online I found a way to reinstall the backports package and install devtools again. This fixed my issue: ...

This week we will be exploring some of the different visualization options available in R. We will mainly be comparing the built in R plot, lattice package and ggplot2 package. We will discuss the difference syntax and abilities of each method.  Our assignment was to find a dataset from the following link :  https://vincentarelbundock.github.io/Rdatasets/datasets.html   and using the three different visualization methods listed above create some visualization with the data.  GitHub link:  https://github.com/Ant-nguyen/Intro_r_2021/blob/main/Module%209.R Let us begin! The dataset I decided to use was the  Wong   dataset.  https://vincentarelbundock.github.io/Rdatasets/doc/carData/Wong.html This dataset looks at IQ of patients after being in a coma.  I downloaded the csv file into the directory that is the same as where R is installed and ran the following line of code to input the data into R: > wong<- read.table("Wong.CSV",header =...

This week we will be exploring the Input and Out capabilities of R, as well as the usefulness of the tools of the plyr package. For this assignment we are given three different tasks: 1. We must import a text file with that contain a data set of Name, Age, Sex and Grade of 20 students and then using plyr package organize the data based on sex, and include a new column with the grade average of their sex.  2. We need to take our data and output our data into a CSV(Comma Separated Value) format. 3. We will take the original data set and extract the data for only students that have the character "i" in their name. And then output a the results to a file in the CSV format. Github link:  https://github.com/Ant-nguyen/Intro_r_2021/blob/main/Module%208.R Let us begin! First we need to input the text file into R. To do this I used the read.table() function. This is a specific function for files that are already in a table like format. I used the arguments header = TRUE and sep =...