Code Along: Data Visualization with ggplot2

Hosted by the Graduate Students of Open Coding Hour

This 1 hour webinar will focus on making three kinds of visualizations: time-series line graphs, bar charts, and distribution plots. For examples of other kinds of plots (in addition to data transformation vignettes), check the class website for the 4 day R course taught by myself and Cory DuPai: https://rachaelcox.github.io/classes/IntroR_summer_2020.html

September 30, 2020

1. Plotting x-y relationships (e.g. time series, correlations)

We will use the brewmats dataset, which is sourced from the US Alcohol and Tobacco Tax and Trade Bureau (TBB, https://www.ttb.gov/beer/statistics), scraped by the TidyTuesday group and cleaned by me. The brewmats dataset contains the following variables:

  • data_type = denotes that all amounts of material used are given in pounds (lbs)
  • material_type = materials belong in one of two categories, grain (e.g., wheat) or non-grain (e.g., sugar)
  • type = specific material details (e.g., malt, corn, rice, barley, wheat, hops, sugar, etc
  • year = year the amount of materials used was recorded (2008-2015)
  • month = month the amount of materials used was recorded (1=January, 2=February, 3=March, etc)
  • month_usage_by_type = total amount of material usage by type per month (lbs)
  • month_sum_all_types = total amount of material used for all types (lbs)
  • year_sum_by_type = total amount of material used by type per year
Beer Components 
# download the `brewmats` dataset
brewmats <- read_csv("https://rachaelcox.github.io/classes/datasets/brewmats.csv")
## Parsed with column specification:
## cols(
##   data_type = col_character(),
##   material_type = col_character(),
##   year = col_double(),
##   month = col_double(),
##   type = col_character(),
##   month_usage_by_type = col_double(),
##   month_sum_all_types = col_double(),
##   year_sum_by_type = col_double()
## )

Code Along: Plot the total monthly usage of all brewing materials (month_sum_all_types) on the y-axis for every month in the dataset on the x-axis, as a line graph colored by year using geom_line().

# R code here

Practice: Plot the yearly usage (year_sum_by_type) of each type of brewing materials on the y-axis, for every year in the dataset on the x-axis, as a line graph colored by type using geom_line(). Remember to map group = type so that ggplot knows how which lines you want to connect.

# R code here

2. Plotting amounts (e.g. bar graphs, heat maps)

For this section, we will use the mushrooms dataset (obtained from Kaggle and cleaned by me), which contains the following information:

  • class = whether the mushroom is edible or poisonous
  • cap_shape = shape of the mushroom cap, e.g., bell, conical, convex, flat, etc
  • cap_color = color of the mushroom cap, e.g., brown, buff, cinnamon, gray, etc
  • odor = smell of the mushroom (almond, anise, creosote, fishy foul, musty, pungent, none)
  • gill_spacing = spacing between mushroom gills, aka the underside of the mushroom cap (close, crowded or distant)
  • gill_size = size of mushroom gills (broad or narrow)
  • gill_color = color of mushroom gills (black, brown, etc)
  • stalk_shape = shape of the mushroom stalk (enlarging or tapering)
  • stalk_root = type of stalk root (bulbous, club, cup, rooted, etc)
  • veil_type = type of veil on the mushroom (partial or universal)
  • veil_color = color of the veil (brown, orange, white or yellow)
  • ring_number = number of rings on the stalk of the mushroom (0, 1 or 2)
  • ring_type = description of the ring(s), if any, found on the stalk, e.g., flaring, large, pendant, etc
  • spore_print_color = color of spores collected on a sheet of paper as a print (black, brown, yellow, etc)
  • population = description of nearby mushrooms of the same species, if any; can be abundant, clustered, numerous, scattered, several, and solitary
  • habitat = where the mushroom was found (grasses, leaves, meadows, paths, urban, waste, woods)
Mushroom Diagram 
# download the `mushrooms` dataset
mushrooms <- read_csv("https://rachaelcox.github.io/classes/datasets/mushrooms.csv")
## Parsed with column specification:
## cols(
##   class = col_character(),
##   cap_shape = col_character(),
##   cap_surface = col_character(),
##   cap_color = col_character(),
##   odor = col_character(),
##   gill_spacing = col_character(),
##   gill_size = col_character(),
##   gill_color = col_character(),
##   stalk_shape = col_character(),
##   stalk_root = col_character(),
##   veil_type = col_character(),
##   veil_color = col_character(),
##   ring_number = col_double(),
##   ring_type = col_character(),
##   spore_print_color = col_character(),
##   population = col_character(),
##   habitat = col_character()
## )

Code Along: Plot a bar graph for counts of mushrooms found in each habitat, colored by class (edible or poisonous) using geom_bar().

# R code here

Practice: Plot a bar graph for counts of mushrooms of each type of odor, colored by class (edible or poisonous) using geom_bar().

# R code here

3. Plotting distributions (e.g., box plots, histograms, density plots)

For this section, we will use the wine dataset (obtained from Kaggle and cleaned by me), which contains the following variables:

  • type: whether the wine is red or white
  • quality: median score between 0 and 10 as blindly graded by wine experts
  • quality_grade: quality category given to each rating based on distribution of ratings (low, med, high)
  • alcohol: the percent alcohol content of the wine (% by volume)
  • alcohol_grade: relative amount of alcohol compared to all wines (low, med, high)
  • pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale
  • acidity_grade: acidity intensity (low, med, higj)
  • fixed_acidity: most acids involved with wine or fixed or nonvolatile/do not evaporate readily (tartaric acid - g / dm^3)
  • volatile_acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste (acetic acid - g / dm^3)
  • citric_acid: found in small quantities, citric acid can add freshness and flavor to wines (g / dm^3)
  • residual_sugar: the amount of sugar remaining after fermentation stops; it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet (g / dm^3)
  • chlorides: the amount of salt in the wine (sodium chloride - g / dm^3)
  • free_sulfur_dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine (mg / dm^3)
  • total_sulfur_dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine (mg / dm^3)
  • density: degree of consistency measured by mass per unit volume (g / cm^3)
  • sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant (potassium sulphate - g / dm^3)
Typical pH of Wine Types

Typical pH of Wine Types

# download the `wine` dataset
wine <- read_csv("https://rachaelcox.github.io/classes/datasets/wine_features.csv")
## Parsed with column specification:
## cols(
##   type = col_character(),
##   quality = col_double(),
##   quality_grade = col_character(),
##   alcohol = col_double(),
##   alcohol_grade = col_character(),
##   pH = col_double(),
##   acidity_grade = col_character(),
##   fixed_acidity = col_double(),
##   volatile_acidity = col_double(),
##   citric_acid = col_double(),
##   residual_sugar = col_double(),
##   chlorides = col_double(),
##   free_sulfur_dioxide = col_double(),
##   total_sulfur_dioxide = col_double(),
##   density = col_double(),
##   sulphates = col_double()
## )

Code Along: Plot the distribution of pH for each wine type (i.e., red or wine) by mapping pH to the x-axis, coloring by type, and calling geom_density(). Then, use geom_boxplot() to visualize the distribution of pH across quality_grade, again coloring by type.

# R code here

Practice: Choose a numeric variable you are interested in. Plot its distribution relative to a categorical variable, e.g., type, quality_grade, alcohol_grade or acidity_grade. Use geom_density(), geom_boxplot(), and/or geom_violin().

# R code here