A plot tells a thousand words
Understanding Data Visualization
Richie Cotton
Data Evangelist at DataCamp
What you'll learn
- How do you choose an appropriate plot?
- How do you interpret common types of plots?
- What are best practices for drawing plots?
Three ways of getting insights
Calculating summary statistics
mean, median, standard deviation
Running models
linear and logistic regression
Drawing plots
scatter, bar, histogram
The Datasaurus Dozen
| away_x | away_y | bullseye_x | bullseye_y | ... | x_shape_x | x_shape_y |
|---|---|---|---|---|---|---|
| 32.33 | 61.41 | 51.20 | 83.34 | ... | 38.34 | 92.47 |
| 53.42 | 26.19 | 58.97 | 85.50 | ... | 35.75 | 94.12 |
| 63.92 | 30.83 | 51.87 | 85.83 | ... | 32.77 | 88.52 |
| 70.29 | 82.53 | 48.18 | 85.05 | ... | 33.73 | 88.62 |
| 34.12 | 45.73 | 41.68 | 84.02 | ... | 37.24 | 83.72 |
| 67.67 | 37.11 | 37.89 | 82.57 | ... | 36.03 | 82.04 |
1 Matejka, J., & Fitzmaurice, G. (2017) https://www.autodeskresearch.com/publications/samestats
Mean of x for each dataset
| dataset | mean(x) |
|---|---|
| away | 54.27 |
| bullseye | 54.27 |
| circle | 54.27 |
| dino | 54.26 |
| dots | 54.26 |
| h_lines | 54.26 |
| high_lines | 54.27 |
| dataset | mean(x) |
|---|---|
| slant_down | 54.27 |
| slant_up | 54.27 |
| star | 54.27 |
| v_lines | 54.27 |
| wide_lines | 54.27 |
| x_shape | 54.26 |
Mean of x and y for each dataset
| dataset | mean(x) | mean(y) |
|---|---|---|
| away | 54.27 | 47.83 |
| bullseye | 54.27 | 47.83 |
| circle | 54.27 | 47.84 |
| dino | 54.26 | 47.83 |
| dots | 54.26 | 47.84 |
| h_lines | 54.26 | 47.83 |
| high_lines | 54.27 | 47.84 |
| dataset | mean(x) | mean(y) |
|---|---|---|
| slant_down | 54.27 | 47.84 |
| slant_up | 54.27 | 47.83 |
| star | 54.27 | 47.84 |
| v_lines | 54.27 | 47.84 |
| wide_lines | 54.27 | 47.83 |
| x_shape | 54.26 | 47.84 |
Standard deviations for each dataset
| dataset | std_dev(x) | std_dev(y) |
|---|---|---|
| away | 16.77 | 26.94 |
| bullseye | 16.77 | 26.94 |
| circle | 16.76 | 26.93 |
| dino | 16.77 | 26.94 |
| dots | 16.77 | 26.93 |
| h_lines | 16.77 | 26.94 |
| high_lines | 16.77 | 26.94 |
| dataset | std_dev(x) | std_dev(y) |
|---|---|---|
| slant_down | 16.77 | 26.94 |
| slant_up | 16.77 | 26.94 |
| star | 16.77 | 26.93 |
| v_lines | 16.77 | 26.94 |
| wide_lines | 16.77 | 26.94 |
| x_shape | 16.77 | 26.93 |
Continuous and categorical variables
Continuous: usually numbers
- heights, temperatures, revenues
Continuous and categorical variables
Continuous: usually numbers
- heights, temperatures, revenues
Categorical: usually text
- eye colors, countries, industry
Continuous and categorical variables
Continuous: usually numbers
- heights, temperatures, revenues
Categorical: usually text
- eye colors, countries, industry
Can be either
- age is continuous, but age group is categorical
- time is continuous, month of year is categorical
Let's practice!
Understanding Data Visualization
