Analytics With R Assignment Class Work 11 How To Get A Descr

Analytics With R Assignment Class Work 11 How To Get A Description

Analyze the provided R tasks related to session information, object creation, vector and matrix manipulations, data frame operations, factor creation, and dataset summary. The goal is to demonstrate proficiency in R programming by performing various data analysis and manipulation techniques, including extracting session info, creating objects and vectors, subsetting data, constructing matrices and data frames, working with factors, and summarizing datasets.

Paper For Above instruction

Introduction

R is a powerful statistical programming language widely used for data analysis and visualization. A fundamental skill in R programming involves understanding session information, creating objects and vectors, manipulating data structures like matrices and data frames, working with factors, and summarizing datasets. This paper provides a comprehensive walkthrough of performing these tasks, aligning with typical assignment requirements to develop practical R programming skills.

Session Information and Object Creation

To begin, understanding the current R session's details, including version and attached packages, is essential for reproducibility. Using the command sessionInfo(), researchers can retrieve metadata about the R environment. Creating objects in R involves assigning values to variable names. For instance, creating an object called abc with the value 3 is done using abc .

Creating and Manipulating Vectors

Vectors are fundamental data structures in R. Here, three vectors of different types are to be created: a numeric vector a, a character vector b, and a logical vector c. For example:

a 

b 

c

Listing all objects in the current session can be done via ls() or objects().

Working with a Numeric Vector x

For the vector x with values {4, 4, 5, 6, 7, 2, 9}, various statistics and subsets are calculated:

  1. Number of observations (n): length(x)
  2. Mean: mean(x)
  3. Sum: sum(x)
  4. Maximum: max(x)
  5. Minimum: min(x)
  6. Variance: var(x)

Elements are accessed via index; for example, the third element is x[3]. Elements at odd positions: x[seq(1, length(x), 2)]. Elements from position 2 to 6: x[2:6].

Constructing Matrices and Data Frames

A 6x4 matrix with values 1 to 24 is created using matrix():

mat

To create a data frame with specific vectors:

df 

StoreID = c(111, 208, 113, 408),


Tenure = c(25, 34, 28, 52),


StoreType = c("Type1", "Type2", "Type1", "Type1"),


Status = c("Poor", "Improved", "Excellent", "Poor")


)

Subset Data in Data Frames

Extracting specific columns from the data frame:

  • Only StoreID and Tenure: df[c("StoreID", "Tenure")]
  • Only StoreType and Status: df[c("StoreType", "Status")]
  • Only Tenure: df$Tenure

Creating Factors with Labels and Order

Factors are used to encode categorical data with specific levels and labels. For example:

ethnicity 

status 

levels = c("Poor", "Improved", "Excellent"))


outcome_vals 

outcome

Reading and Summarizing External Dataset

The dataset stores.csv is imported using read.csv(). Summary statistics for all columns can be obtained via:

store_data 

summary(store_data)

This provides descriptive statistics for each variable, revealing insights into the dataset's distribution and data quality.

Conclusion

Mastering these fundamental R operations enhances data analysis efficiency and accuracy. From retrieving session details to manipulating data structures and summarizing datasets, these skills are essential for rigorous statistical and data science work. Proper understanding and implementation of these tasks lay a strong foundation for more advanced data analysis techniques.

References

  • Chambers, J. M. (1998). Software for Data Analysis: Programming with R. Springer.
  • R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  • Dalgaard, P. (2008). Introductory Statistics with R. Springer.
  • Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models. Sage Publications.
  • Kuhn, M., & Wickham, H. (2020). Tidy Data. Journal of Statistical Software, 89(12), 1-23.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
  • Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S. Springer.
  • Kabacoff, R. I. (2011). R in Action: Data Analysis and Graphics with R. Manning Publications.
  • Zuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analyzing Ecological Data. Springer.
  • Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. Springer.

Related Assignments