1 Data Science: Exercise Solutions
- 1.1 How to Set Up These Materials
- 1.2 How to Use This Guide
2 Reproducibility: Setup
3 Reproducibility: Git and Our Curriculum
4 Setup: RStudio
5 Reproducibility: Tracking Changes with Git
6 Setup: Packages
7 Setup: Function Basics
8 Data: Basics
9 Setup: Documentation
10 Communication: Code Style
11 Setup: RStudio Shortcuts
12 Setup: Vector Basics
- 12.0.1 q1 What single-letter R function do you use to create vectors with specific entries? Use that function to create a vector with the numbers 1, 2, 3 below.
- 12.0.2 q2 Did you know that you can use c() to extend a vector as well? Use this to add the extra entry 4 to vec_q1.
13 Setup: Types
- 13.1 Objects vs Strings
  - 13.1.1 q1 Describe what is wrong with the code below.
- 13.2 Casting
  - 13.2.1 q2 Cast the following vector v_string to integers.
14 Vis: Data Visualization Basics
15 Communication: Active and Constructive Responding
16 Stats: EDA Basics
17 Data: Isolating Data
18 Vis: Bar Charts
19 Data: Deriving Quantities
- 19.0.1 q1 What is the difference between these two versions? How are they the same? How are they different?
- 19.1 Summary Functions
20 Vis: Histograms
21 Reproducibility: Structuring Directories
22 Stats: Intro to Densities
23 Data: Pivoting Data
24 Reproducibility: Collaborating with GitHub
25 Vis: Boxplots and Counts
26 Stats: Probability
27 Data: Separate and Unite Columns
- 27.1 Punnett Square
  - 27.1.1 q1 Use a combination of unite and pivoting to turn genes into the same dataframe as punnett.
- 27.2 Alloys, Revisited
  - 27.2.1 q2 Use a combination of separate and pivoting to tidy alloys_mod.
28 Vis: Scatterplots and Layers
29 Stats: Descriptive Statistics
30 Data: Joining Datasets
- 30.1 Dangers of Binding!
  - 30.1.1 q1 Describe what is wrong with the result of bind_cols above and how it happened.
  - 30.1.2 q2 Use the following beatles3 to correctly join beatles1
- 30.2 Utility of Filtering Joins
  - 30.2.1 q3 Create a “grid” of values
  - 30.2.2 q4 Use the criteria dataframe you produced above to filter flights on dest and month.
31 Vis: Lines
32 Communication: Narrative Basics
- 32.1 Exposition
- 32.2 Exercises: Judging Narrative Content
33 Bibliography
34 Data: Working with strings
35 Vis: Themes
36 Data: Pipes and Placeholders
37 Stats: Populations and Estimation
38 Data: Window Functions
- 38.1 Lead and Lag
  - 38.1.1 q1 Use a window function modify the following visual to color each segment differently based on whether the period of time was increasing or decreasing.
- 38.2 Ranks
  - 38.2.1 q2 Use a rank function to filter the largest 3 hwy values and all vehicles that have those values.
39 Stats: Moment Arithmetic
40 Stats: The Central Limit Theorem and Confidence Intervals
41 Data: Reading Excel Sheets
- 41.1 Wrangling Basics
- 41.2 Danger Zone
  - 41.2.1 q4 I have already provided the while loop below; fill in vars_lagged with the names of the columns where cell values are implicit.
42 Stats: Error and Bias
- 42.1 Errors
43 Data: Map Basics
- 43.1 Formulas
  - 43.1.1 q1 Use map_chr() to prepend the string "N: " to the numbers in v_nums. Use formula notation with str_c() as your map function.
  - 43.1.2 q2 Use map_dbl() to compute the log with base = 2 of the numbers in v_nums. Use formula notation with log() as your map function.
44 Data: Factors
- 44.1 Organization
- 44.2 Visual Tricks
  - 44.2.1 q4 Use fct_reorder() to sort manufacturer to match the order of cty.
  - 44.2.2 q5 Sort the countries by values.
45 Stats: Fitting Distributions
46 Vis: Perceptual Basics
- 46.1 Criticize these graphs!
47 Stats: The Bootstrap, Some Recipies
48 Data: A Simple Data Pipeline
49 Vis: Small Multiples
- 49.1 Small Multiples
  - 49.1.1 q1 Edit the following figure to use the “ghost” point trick above.
- 49.2 Organizing Factors
  - 49.2.1 q2 Create a small multiple plot like ex-mpg-manufacturer above. Keep in mind the idea of “compared to what?” when deciding which variables to place close to one another.
50 Stats: Introduction to Hypothesis Testing
51 Stats: Confidence vs Prediction Intervals
52 Model: Variability Quadrants
53 Vis: Improving Graphs
- 53.1 Improve these graphs!
54 Model: Introduction to Modeling
55 Data: Cleaning
56 Model: Model Selection and the Test-Validate Framework
57 Data: Liberating data with WebPlotDigitizer
58 Model: Warnings when interpreting linear models
59 Data: Liberating data with Tabula
- 59.1 Setup
  - 59.1.1 q1 Install Tabula.
- 59.2 Liberating Data
60 Model: Logistic Regression
61 Stats: Randomization
62 Model: Assessing Classification with ROC
63 Vis: Control Charts

Data Science: Exercise Solutions

33 Bibliography

[1] Olson, “Houston, We Have a Narrative” (2015)