Visualization and Data Processing - Science Assignment Help

Assignment Task

Visualization and data processing

Assessment
The following exercises are designed to assess your understanding of concepts, implementation and interpretation of topics in Visualization and Data Processing. Some questions may require you to search and use R functions that we have not used so far. In all following questions submit codes and output.
Note: The questions in this assessment may have multiple correct solutions. Hence submission of R code is essential. Almost no statistical background is presumed knowledge for this assessment. All methods required for solution are available on the content pages of Weeks 2-5 of this subject. Some of them have been covered in detail during collaborate sessions.

Answer sheet and script (code).
All outputs must be followed by a short statement, for full marks. You could use on of the following alternatives in responding to the questions.
Please use a new word document to provide your responses sequentially using Section and Question numbers. You could paste your R code followed by output and discussion in the word document.

A. Visualization: Section
Import the data oneworld.csv into R. Show the class of all variables. Submit code and output. The objective in this question is explore the relationship between the variables GDP vs Industry by regions.
Create a new ordered categorical variable GDPcat - with three categories such that the proportion of countries in each categories is 40%, 40% and 20%, respectively, in increasing order of magnitude of the variable GDPName the three categories to be “Low” “Medium” and “High”. Show your code and the solution.
Hint: Remove any missing observations. There is a function in R that helps find such percentage values for variables. Marks (4)
Use a visualization tool to display the relationship between GDP and Infant Mortality, stratified by Regions, on a single plot. You must use ggplot2 for visualization. For plotting you could use either the variable GDP or the variable GDPcat. Comment on your observations. Hint: There are multiple possible correct responses, but you must justify your choice. Note that your choice of the GDP variable would dictate the choice of the plot function.

B. Data Processing – I: Section
Write an R function to identify the proportion of missing observations in a variable or column of tabular data.
Implement this function across all variables of the dataset airquality. This dataset is available with base R. Show your code and the result— that is, the proportion missing in each variable. Marks (2)
Specify a variable from this dataset that you would select for univariate missing value imputation. Hint: Justify your variable choice based on the count or proportion of missing observations, noting that univariate imputation reduces the natural variation of a variable.
Show your code -in base R - to replace all missing observations in the chosen variable with some value, based on the data. Justify the choice of replacement value. Hint:

Read the appropriate section on your Weekly content page to perform this task.

Summarize the results after imputation and compare with the pre-imputed variable. Hint: In commenting you can use a use a descriptive statistics function or a visualization tool learned on this subject.

C. Data Processing – II: Section
Clinician scientists at Royal Melbourne hospital are investigating the relationship between fecal calprotectin (FC) as a non-invasive diagnostic alternative to Inflammatory Bowel Disease (IBD) and Acute Sever Ulcerative Colitis (ASUC). It would also contribute to standard of care. A subset of the dataset is provided as bowel.csv. The data has several missing values, causes of missing-ness are often unknown.
In the following show questions show your R working and output.

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