Exploring Input/Output, String Manipulation, and plyr Package in R: A Step-by-Step Guide


Hello all,

In our latest blog post, we'll delve into some important principles of R programming. These include Input/Output operations, string manipulation, and using the plyr package for data manipulation. We'll be following a structured approach outlined by Dr. Friedman, and take a step-by-step approach to explore these concepts using a dataset. By doing so, we aim to gain a deeper understanding of these concepts.

Step 1: Importing Data and Calculating Means by Category

Our first task is to import Assignment 6 dataset into R and calculate the mean grade based on the category of 'Sex' using the plyr package. After importing the data using the read.table() function, we load necessary packages including plyr and perform the mean calculation using ddply(). Finally, we write the resulting output to a file.



Step 2: Filtering Data based on Names containing 'i'

Next, we convert the dataset into a dataframe and filter it to include only the rows where the student names contain the letter 'i'. This is achieved using the subset() function along with grepl(). We then create a new dataset with these filtered names and write them to a file in CSV format.

Step 3: Writing Filtered Data to CSV

In the final step, we use the file.choose() function to choose the location to save our filtered dataset. We employ the subset() function to filter the original dataset and select rows containing names with the letter 'i'. Afterward, we write this filtered dataset to a CSV file using write.table().


Conclusion:

Through this step-by-step guide, we've explored important concepts in R programming such as importing data, manipulating strings, and utilizing packages like plyr for data aggregation. By following Dr. Friedman's instructions, we've successfully executed each step and gained practical experience in handling datasets efficiently. 

Please refer my code via URL | Input/Output/String Manipulation/pylr packages |











Comments

Post a Comment

Popular posts from this blog

Mastering Debugging: A Step-by-Step Guide to Fixing Code Errors

Image
  Debugging is a crucial skill for programmers as it assists them in detecting and fixing errors or bugs in their code. In this detailed guide, we will take you through an example of debugging a code snippet and emphasize the essential tactics and tools needed to master the art of debugging. Understanding the Deliberate Bug Let's begin with the code snippet that contains a deliberate bug, which results in an error message during execution. Here's the faulty code, When this code is executed, it triggers an error message due to the unexpected placement of return(outlier.vec) within the for loop. Step 1: Identifying the Error To identify the error, we can use the traceback function, which prints the list of functions called before the error occurred. Running traceback("tukey_multiple") reveals the location of the error just before return(outlier.vec) . Step 2: Using Debugging Tools After locating the error, we implement the debug function to step through the code an...
Read more

My intro to R programming

 Hey everyone! I just tested a function for calculating the mean using R studio, and I'm excited to share the results with you! Please refer to my   GitHub repository  for my work. After applying the function to my dataset (assignment), the mean value is 19.25.  I followed the instructions given by my professor Dr. Friedman and referred the book "The Art of R Programming" by Norman Matloff. Explanation: This function works by summing up all the values in the given dataset (sum(assignment)) and then dividing the sum by the total number of elements in the dataset (length(assignment)) , it calculates the average value of the dataset. The formula for mean is: Mean = Sum of Values / No. of Values In this case, the function successfully computes the mean of the 'assignment' dataset. Feel free to try it out with your own datasets or explore Matloff's book!                                 ...
Read more

A Visual Analysis of Patient Data : Blood Pressure and Medical Assessments

Image
Hello all ✋   In this blog post, we'll be looking into a dataset obtained from a local hospital. This dataset contains blood pressure readings, evaluations by both general and external doctors, and the final decision regarding immediate care for patients. Our aim is to visually analyze the connection between blood pressure and medical assessments. To accomplish this, we'll be using side-by-side boxplots and histograms to identify patterns and trends within the data. To begin our analysis, we need to prepare the data and generate visualizations as in the URL given below. It will create side-by-side boxplots for general and external doctor assessments, along with a histogram showing the overall distribution of blood pressure values. The x-axis represents the assessment (bad or good), while the y-axis represents the blood pressure values.   Patient Health Assessment The boxplot displays: First Assessment (Bad vs. Good): ...
Read more