How Visualization Provides an Advantage Over Directly Looking at Data - IT Assignment Help

Assignment Task

Exercises :1. Format table 
2. Calculate mean and variance 
3. Plot the data using ggplot

2 and the grammar of graphics
4. Conduct a simple statistical analysis 
5. Summarize visualization of data 
Introduction

• The purpose of this coursework to explore how visualization provides an advantage over directly looking at data.For this reason, your task is to visualize a small dataset to learn something about the data. The data file is called vis-data1.csv and can be downloaded from the course website. The data consists of four datasets of x,y pairs of numbers, Import it as data frame and then use it for the exercises described below.
• The goal of this exercise is to format the datasets as a table and then to find out how they differ from each other. A very useful tool to format dataframes as table is the kable function, which is part of the knitr package.
• If the knitr library is not installed, please install it with install.packages("knitr").
• • Produce a nicely formatted table with the kable() function of the knitr package.
• Calculate mean and variance (1 points)
• • For each of the four datasets, calculate the mean and the variance of all x and y columns with the mean() and sd() functions and provide these numbers either as table or just by printing them out
• • Briefly describe in which respect the four datasets are similar or different from each other.
• 1.3 Plot the data using ggplot2 and the grammar of graphics (2 points)
• The graphics library ggplot2 implements the grammar of graphics, which is defined as a coherent system for describing and building graphs.
• For a concise and very accessible introduction into the grammar of graphics, please consult http: //vita.had.co.nz/papers/layered-grammar.pdf.
• For an introduction to ggplot. 
• The key difference to other graphical systems (i.e., base R) is that plots are build by subsequently adding functions to the plot-generating function, which as a consequence add additional properties to the plots. The commands that construct the plot are easy to understand and are also flexible to use.
   

Based on the data set, the mean and variances and the plotting of the data briefly summarize the work in a few, concise sentences:

•  What are differences and similarities between the data sets?
•  Is a linear model suitable for smoothing all four datasets?
•  What is the advantage of visualization in this particular example of datasets?
   

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