All requirments are in the attachments, three simple questions about java and racket

I am on Covid duty in hospital and have no time to work on this. I will need answers to all the questions and report as mentioned in this document. You will need good knowledge about R programming/R Studio. I have attached the document which has all the details. Go through the Merging and Cleaning, Analysis - full questions, Visualizations. Everything should be done in R studio and I need rmd files in pdf format. Questions welcome.

This week our topic shifts to working with data.  In this week’s discussion post, think about the topics that Kirk (2019) briefly went over in the textbook. When you work with data, it can come to you in a variety of formats: scraps of paper, spreadsheets, or databases, to name a few. The way the data is presented in any of the formats also varies significantly. There are two main reasons that you work with data before visualizing it: exploration and cleaning.  More often than not, data is examined for cleanliness with visual analysis. However, that doesn't seem to align with what has been covered so far. When Kirk (2019) discusses the process of visual analysis, most of the processes apply to a finished product, something you'll present to others. The processes are not necessarily important for visualization you use for your own understanding. There are many ways that could explore data; for this forum we're going to focus more on collection and cleaning. Considering the textbook and Zumel (2016), consider the different elements that need to be investigated everytime you clean data. Discuss the folllowing: Provide two different aspects of data that need to be checked for when you clean the data. Provide real examples and explain why it's necessary everytime you clean data and why it's mportant.  Keep in mind there are many things that could be checked or cleaned, but they don't need to be done everytime. For example, analyzing the data distribution is important for certain types of analysis, but it's not necessary something that has to be cleaned. It is not needed when conducting visual analysis. It could serve other benefits, but it's not needed. References Kirk, A. (2019). Data analysis: A handbook for data driven design. Sage. Zumel, N. (2016, March). Preparing data for analysis using R. Github. https://winvector.github.io/DataPrep/EN-CNTNT-Whitepaper-Data-Prep-Using-R.pdf  General forum requirements: Three posts are required: the initial post and two peer responses. Initial posts are needed by Friday at 11:59 PM ET. Peer responses must be sustantive: connect with the discussion topics and what your peer has contributed - Sunday 11:59 PM