the r programming homework solution

the r programming homework solution

Advanced Data Analysis Techniques in R Programming

1. Introduction to R Programming

R is an ideal data analysis programming language. R is its own language. What makes R unique is that it has been specialized for data analysis. It is not a general programming language. As a consequence, while other programming languages use data analysis packages, R is a data analysis language specifically designed for data analysis. It is used in business, academia, and research.

One important aspect of R is how to use external packages to extend its functionality. There are many external packages available to the R user community. R tends to become a repository for cutting-edge statistical methods. That is, while an academic paper might suggest a sophisticated new data processing approach, if a package is not available, the method might fall by the wayside. R, on the other hand, can become the repository for these methods because they can be made available to the user community.

2. Data Wrangling and Cleaning in R

The data wrangling and cleaning techniques allow users to manipulate data in order to modify or clean complex datasets according to analysis requirements. The aim is to ensure that the data occurs in a readable, user-friendly manner. Data wrangling functions were introduced in the dplyr package, and since R and the Tidyverse are now changing rapidly, there are many newer functions in this package. dplyr, tidyr, and other vital data wrangling functions in R are available in the following section.

Highlight of data wrangling and cleaning functions available in dplyr tell us that dplyr is a widely used data manipulation package written by Hadley Wickham. It is composed of a set of descriptive functions that operate and join data in the “split-apply-combine” regime. They consist mostly of verbs and offer intuitive names which enable users to achieve higher data manipulation through ease, consistency, and readability. dplyr uses data frames, tibbles, and databases. The focus is laid on local data frames, such as for relatively small datasets, which can all be loaded into R or a working script.

3. Statistical Analysis and Visualization in R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment developed at Bell Laboratories. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an open-source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

The R environment allows you to interactively explore data, conduct statistical analysis, and draw sophisticated graphical displays, while the underlying code graphics depict underlying mathematical or computational dependencies. Moreover, it is highly extensible through the use of user-defined functions. This section provides a short introduction to R, its data types, and the standard statistical analyses, as well as a high-level view of the plotting capabilities. Finally, a sample R session allows you to verify the installation of R and introduces you to its capabilities. Note that a more significant review of the features and functions of R is beyond the scope of this chapter, and in order to simplify the presentation and provide the context in which the data from this study was processed, we focused on selected elements of R.

4. Advanced Techniques: Machine Learning and Deep Learning in R

Boosted decision trees and other machine learning techniques Although regression analyses, both the linear and GLM approaches, are very frequently used in data analysis today, the availability of statistical toolsets for a wide variety of other machine learning algorithms is an exciting reality. The use of boosting, for example, offers an approach using decision trees that address non-linear relationships in more complex ways than using non-linear transforms (such as polynomial or spline fits) of independent variables with linear regression. Using boosting, first developed by Freund and Schapire while at AT&T in the early 1990s, and then refined by Friedman at Stanford University during the same period, is surprisingly both computationally efficient and can yield models that beautifully address even complex non-linear relationships.

Random forests is a method that also uses a multiple-tree approach, with an important difference – the trees are not boosted, but are built simultaneously with full depth. Neural networks, support vector machines, and even methods like K nearest neighbors can also be used for machine learning with models built and optimized in R.

Building and comparing decision trees The techniques used in R to build and evaluate regression trees are also used with classification trees. We are, however, now dealing with prediction problems that involve using categorical predictors. Our response variable should be a categorical variable. Our input variables may be categorical or continuous, with no assumptions of linearity or normally distributed residuals. There is an enormous literature devoted to machine learning, often involving tree-based methodologies, that can be used to address higher dimensional and more complex prediction challenges. Data cleansing and pre-processing does tend to take on more importance in more complex problems, and R provides methods for data pre-processing that are the basis for its own boosting functionality.

5. Case Studies and Practical Applications

In this chapter, some advanced data analysis techniques using R are presented through real-world case studies and applications. For each case study, we will provide some context about the corresponding problem, a brief theoretical review of the involved data analytics methods or models, as well as R programming code by discussing relevant R functions and packages. Wherever it is necessary or helpful, we also provide some extra data preprocessing or manipulation steps, including standardization, normalization, or missing value imputation, in advance to analysis based on real-world data set.

A more general way to transform a continuous random variable into a categorical one is to split the entire allowable range of the continuous random variable into several sub-intervals. We can label a probability to each sub-interval based on certain rules, which can be uniform distribution or decision-making methods, and then map the value of the original random variable to the corresponding associated outcome of the categorical response. There can be approaches to labeling a continuous response with a set of penalty weights and outcome for each category, which are applied with respect to non-categorical single penalties, establishing the relevant penalty for a continuous variable associated with a specific outcome. The cut function from R statistical package is used to partition the range of random variable values into a set of semi-open intervals and can be used to segment the data into distinct groups, enabling a single penalty to be assigned to each. The optimal partitioning of the continuous response will usually be solved using an optimization algorithm, such as genetic algorithms, to be dependently performed.