Graph Types

Make sure your data is “tidy”:

• Observations are rows.
• Variables are columns.
• First row (and only first row) consists of variable names.
• No extra stuff outside the rectangle.

• Categorical (nominal, qualitative, factor)

Classifies data by category.

color, species, sex

• Quantitative (numeric, scale, interval/ratio)

Numerical measurements, usually with meaningful units.

height, GDP, score

CAREFUL!

Numbers are not always quantitative.

Do you own a car?

0 = “No”, 1 = “Yes”

What is your zip code?

Risk factors associated with low infant birth weight, collected at Baystate Medical Center, Springfield, Mass during 1986.

189 women measured on 10 variables.

• Single variables usually won't answer very interesting questions by themselves.
• Graphs of single variables are often valuable for exploring your data, but generally not suitable for inclusion in the final product.

Mother's race (white, black, or other)

# A tibble: 189 x 1
   race 
   <fct>
 1 Black
 2 Other
 3 White
 4 White
 5 White
 6 Other
 7 White
 8 Other
 9 White
10 White
# ... with 179 more rows

• Frequency table

• Relative frequency table

Click here to see how to improve a pie chart.

• Why do pie charts suck?

“Pie Charts Are the Worst”

“Save the Pies for Dessert”

“In Defense of Pie Charts, and Why You Shouldn't Use Them”

• And to be fair, some praise for pie charts:

“The hate of pie charts harms good data visualization”

Infant birth weight in grams

# A tibble: 189 x 1
     bwt
   <int>
 1  2523
 2  2551
 3  2557
 4  2594
 5  2600
 6  2622
 7  2637
 8  2637
 9  2663
10  2665
# ... with 179 more rows

• Tabular summaries

(“Five-number summary” or other quantiles)

• A bunch of other types I don't prefer:

boxplot, stem-and-leaf plot, dotplot

There are at least six elements of a plot that can be assigned to variables:

• x-axis (horizontal axis)
• y-axis (vertical axis)
• facets
• color/fill
• size
• shape (e.g., dots vs crosses, solid vs dashed lines, etc.)

Mother's race and the number of previous premature labors.

# A tibble: 189 x 2
   race  ptl  
   <fct> <fct>
 1 Black 0    
 2 Other 0    
 3 White 0    
 4 White 0    
 5 White 0    
 6 Other 0    
 7 White 0    
 8 Other 0    
 9 White 0    
10 White 0    
# ... with 179 more rows

Mother's race and infant birth weight in grams

# A tibble: 189 x 2
   race    bwt
   <fct> <int>
 1 Black  2523
 2 Other  2551
 3 White  2557
 4 White  2594
 5 White  2600
 6 Other  2622
 7 White  2637
 8 Other  2637
 9 White  2663
10 White  2665
# ... with 179 more rows

Mother's age and infant birth weight

# A tibble: 189 x 2
     age   bwt
   <int> <int>
 1    19  2523
 2    33  2551
 3    20  2557
 4    21  2594
 5    18  2600
 6    21  2622
 7    22  2637
 8    17  2637
 9    29  2663
10    26  2665
# ... with 179 more rows