Statistics with R Programming Notes | B.Sc. (Computer Science) Semester 1 | Mumbai University | munotes

Contents

Module I The R language and its environment: expressions, decisions, loops, data structures and strings

1. What R Is, and Why Statistics Is Done in It 1
2. A Short History of R, and What Open Source Means Here 4
3. The Words R Uses: Object, Mode, Class and Attribute 7
4. Installing R on Windows, macOS and Linux 10
5. The R Environment: Console, Workspace and Working Directory 12
6. Getting Help, and Installing Packages 15
7. The R Graphical User Interface (R GUI) 18
8. RStudio: Installing It and Reading Its Four Panes 21
9. Customising RStudio 24
10. R Commander: Statistics From a Menu 27
11. Working With R Scripts 30
12. Data Management in RStudio 33
13. Reading Data In and Writing It Out 36
14. Expressions: Arithmetic, Relational and Logical Operators 39
15. Operator Precedence, and How R Reads an Expression 42
16. Assignment, and the Rules for a Name 44
17. Numbers, Logicals and the Values NA, NULL, NaN and Inf 47
18. Decision Making: if, else and else if 50
19. switch(), and the Vectorised ifelse() 52
20. Loops: The for Loop 54
21. Loops: while, repeat, break and next 57
22. Why R Prefers Vectorisation to a Loop 60
23. Dates in R 63
24. Times, Time Zones and Date Arithmetic 66
25. Vectors: The Thing R Is Actually Made Of 69
26. Indexing a Vector: Five Ways to Pick Elements 72
27. Recycling, Coercion and Vectorised Arithmetic 75
28. Matrices: Creating, Naming and Indexing 78
29. Matrix Arithmetic and Matrix Algebra 81
30. Row and Column Work: apply, rowSums, cbind and rbind 84
31. Arrays: Three Dimensions and More 88
32. Lists: The Container That Holds Anything 91
33. Data Frames: The Table Statistics Works On 94
34. Subsetting a Data Frame, and Adding or Dropping a Column 98
35. Factors: How R Stores a Category 102
36. Sorting, Ordering, Merging and Reshaping a Data Frame 105
37. Writing Your Own Function 109
38. Arguments, Defaults, and What a Function Returns 112
39. Scope: Where R Looks a Name Up 115
40. The apply Family: apply, lapply, sapply, tapply and mapply 118
41. Character Strings in R 121
42. Strings and R Objects 124
43. Printing Characters: print, cat and format 127
44. sprintf(), and Building a Line of Output 130
45. Basic String Manipulations 133
46. Changing Case, Trimming and Padding 136
47. String Operations: Searching and Replacing 139
48. Regular Expressions, From Scratch 142
49. Practical Session 1: Setting Up and Your First Script 145
50. Practical Session 2: Expressions, Decisions and Loops 148
51. Practical Session 3: Vectors, Matrices, Lists and Data Frames 152
52. Practical Session 4: Functions, Strings and Dates 156
53. Module 1: Viva Questions and Their Answers 160

Module II Statistics in R: summaries, regression, distributions, time series, tables and graphics

1. What a Statistic Is: Population, Sample and Summary 164
2. The mean() Function 167
3. The Median 170
4. Standard Deviation and Variance 173
5. Other Built-in Statistical Functions 176
6. Quantiles, summary() and the Five-Number Summary 179
7. Correlation, Covariance and scale() 182
8. The Mode, and Why R Has No Function For It 186
9. What Regression Analysis Is 189
10. The Least Squares Line, Worked by Hand 192
11. Linear Regression in R: lm(), Coefficients and Residuals 195
12. Reading an lm() Summary 199
13. Predicting From a Fitted Model 203
14. Multiple Regression 207
15. Checking a Regression: Residual Plots and What They Say 211
16. The Normal Distribution 215
17. dnorm(): The Normal Density 218
18. pnorm(): The Normal Cumulative Probability 221
19. qnorm(): The Normal Quantile 224
20. rnorm(): Random Normal Samples, and set.seed() 227
21. The Binomial Distribution 230
22. dbinom(): The Exact Binomial Probability 233
23. pbinom(): The Cumulative Binomial Probability 236
24. qbinom(): The Binomial Quantile 239
25. rbinom(): Simulating Binomial Trials 242
26. Choosing Which Distribution Function to Call 245
27. Time Series Analysis: The ts Object 248
28. Trend, Seasonality and Decomposition 252
29. Moving Averages and a Simple Forecast 255
30. Tabulation: Counting With table() 259
31. Contingency Tables: What They Show 262
32. Making R Contingency Tables 266
33. Making Custom Contingency Tables 270
34. Selecting Parts of a Table Object 274
35. Converting an Object Into a Table 277
36. Testing Table Objects, and the Chi-Squared Test 280
37. Complex Tables: Margins, Proportions and ftable 283
38. Representing Data Through Cross Tabulation 287
39. How an R Plot Is Built: Devices, High-Level and Low-Level 291
40. Plots of a Single Variable: Histogram and Density 295
41. Plots of a Single Variable: Bar, Pie and Dot Chart 299
42. Plots of a Single Variable: Box Plot and Stem-and-Leaf 302
43. Plots of Two Variables: The Scatter Plot and Its Line 305
44. Plots of Multiple Variables 308
45. Special Plots: QQ, Mosaic, Coplot and curve 311
46. Labels, Legends, Colours and Layout 315
47. Storing Graphics 319
48. Practical Session 5: Summary Statistics on a Data Frame 322
49. Practical Session 6: Regression, End to End 326
50. Practical Session 7: The Distribution Functions 330
51. Practical Session 8: Tables and Cross Tabulation 334
52. Practical Session 9: Plots and Saving Them 338
53. Module 2: Viva Questions and Their Answers 342