class: center, middle, inverse, title-slide # Data and visualization <br> 📉 ### Dr. Çetinkaya-Rundel --- layout: true <div class="my-footer"> <span> Dr. Mine Çetinkaya-Rundel - <a href="http://www2.stat.duke.edu/courses/Fall18/sta112.01/schedule" target="_blank">stat.duke.edu/courses/Fall18/sta112.01 </a> </span> </div> --- layout: true <div class="my-footer"> <span> Dr. Mine Çetinkaya-Rundel - <a href="http://www2.stat.duke.edu/courses/Fall18/sta112.01/schedule" target="_blank">stat.duke.edu/courses/Fall18/sta112.01 </a> </span> </div> --- class: center, middle # Exploratory data analysis --- ## What is EDA? - Exploratory data analysis (EDA) is an aproach to analyzing data sets to summarize its main characteristics. - Often, this is visual. That's what we're focusing on today. - But we might also calculate summary statistics and perform data wrangling/manipulation/transformation at (or before) this stage of the analysis. That's what we'll focus on next. --- class: center, middle # Data visualization --- ## Data visualization > *"The simple graph has brought more information to the data analyst’s mind than any other device." — John Tukey* - Data visualization is the creation and study of the visual representation of data. - There are many tools for visualizing data (R is one of them), and many approaches/systems within R for making data visualizations (**ggplot2** is one of them, and that's what we're going to use). --- ## ggplot2 `\(\in\)` tidyverse .pull-left[  ] .pull-right[ - **ggplot2** is tidyverse's data visualization package - The `gg` in "ggplot2" stands for Grammar of Graphics - It is inspired by the book **Grammar of Graphics** by Leland Wilkinson ^† - A grammar of graphics is a tool that enables us to concisely describe the components of a graphic  ] .footnote[ ^† Source: [BloggoType](http://bloggotype.blogspot.com/2016/08/holiday-notes2-grammar-of-graphics.html) ] --- .question[ What are the functions doing the plotting? What is the dataset being plotted? Which variable is on the x-axis and which variable is on the y-axis? What does the warning mean? ] ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + labs(title = "Mass vs. height of Starwars characters", x = "Height (cm)", y = "Weight (kg)") ``` ``` ## Warning: Removed 28 rows containing missing values (geom_point). ``` <!-- --> --- .question[ What does `geom_smooth()` do? What else changed between the previous plot and this one? ] ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + * geom_smooth() + labs(title = "Mass vs. height of Starwars characters", x = "Height (cm)", y = "Weight (kg)") ``` <!-- --> --- ## Hello ggplot2! - `ggplot()` is the main function in ggplot2 and plots are constructed in layers - The structure of the code for plots can often be summarized as ```r ggplot + geom_xxx ``` or, more precisely .small[ ```r ggplot(data = [dataset], mapping = aes(x = [x-variable], y = [y-variable])) + geom_xxx() + other options ``` ] - To use ggplot2 functions, first load tidyverse ```r library(tidyverse) ``` - For help with the ggplot2, see [ggplot2.tidyverse.org](http://ggplot2.tidyverse.org/) --- class: center, middle # Visualizing Star Wars --- ## Dataset terminology .question[ What does each row represent? What does each column represent? ] .small[ ```r starwars ``` ``` ## # A tibble: 87 x 13 ## name height mass hair_color skin_color eye_color birth_year gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> ## 1 Luke… 172 77 blond fair blue 19 male ## 2 C-3PO 167 75 <NA> gold yellow 112 <NA> ## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA> ## 4 Dart… 202 136 none white yellow 41.9 male ## 5 Leia… 150 49 brown light brown 19 female ## 6 Owen… 178 120 brown, gr… light blue 52 male ## 7 Beru… 165 75 brown light blue 47 female ## 8 R5-D4 97 32 <NA> white, red red NA <NA> ## 9 Bigg… 183 84 black light brown 24 male ## 10 Obi-… 182 77 auburn, w… fair blue-gray 57 male ## # ... with 77 more rows, and 5 more variables: homeworld <chr>, ## # species <chr>, films <list>, vehicles <list>, starships <list> ``` ] -- - Each row is an **observation** - Each column is a **variable** --- ## Luke Skywalker  --- ## What's in the Star Wars data? Take a `glimpse` at the data: ```r glimpse(starwars) ``` ``` ## Observations: 87 ## Variables: 13 ## $ name <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", ... ## $ height <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188... ## $ mass <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 8... ## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "b... ## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "l... ## $ eye_color <chr> "blue", "yellow", "red", "yellow", "brown", "blue",... ## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0... ## $ gender <chr> "male", NA, NA, "male", "female", "male", "female",... ## $ homeworld <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alder... ## $ species <chr> "Human", "Droid", "Droid", "Human", "Human", "Human... ## $ films <list> [<"Revenge of the Sith", "Return of the Jedi", "Th... ## $ vehicles <list> [<"Snowspeeder", "Imperial Speeder Bike">, <>, <>,... ## $ starships <list> [<"X-wing", "Imperial shuttle">, <>, <>, "TIE Adva... ``` --- ## What's in the Star Wars data? .question[ How many rows and columns does this dataset have? What does each row represent? What does each column represent? ] Run the following **in the Console** to view the help ```r ?starwars ``` <img src="img/starwars-help.png" width="435" /> --- ## Mass vs. height ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() ``` ``` ## Warning: Removed 28 rows containing missing values (geom_point). ``` <!-- --> --- ## What's that warning? - Not all characters have height and mass information (hence 28 of them not plotted) ``` ## Warning: Removed 28 rows containing missing values (geom_point). ``` - Going forward I'll supress the warning to save room on slides, but it's important to note it --- ## Mass vs. height .question[ How would you describe this relationship? What other variables would help us understand data points that don't follow the overall trend? Who is the not so tall but really chubby character? ] .small[ ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + * labs(title = "Mass vs. height of Starwars characters", * x = "Height (cm)", y = "Weight (kg)") ``` <!-- --> ] --- ## Jabba! <img src="img/jabbaplot.png" width="768" /> --- ## Additional variables We can map additional variables to various features of the plot: - aesthetics - shape - colour - size - alpha (transparency) - faceting: small multiples displaying different subsets --- class: center, middle # Aesthetics --- ## Aesthetics options Visual characteristics of plotting characters that can be **mapped to a specific variable** in the data are - `color` - `size` - `shape` - `alpha` (transparency) --- ## Mass vs. height + gender ```r ggplot(data = starwars, mapping = aes(x = height, y = mass, color = gender)) + geom_point() ``` <!-- --> --- ## Mass vs. height + gender Let's map the size to birth_year: ```r ggplot(data = starwars, mapping = aes(x = height, y = mass, color = gender, * size = birth_year )) + geom_point() ``` <!-- --> --- ## Mass vs. height + gender Let's now increase the size of all points **not** based on the values of a variable in the data: ```r ggplot(data = starwars, mapping = aes(x = height, y = mass, color = gender)) + * geom_point(size = 2) ``` <!-- --> --- ## Aesthetics summary - Continuous variable are measured on a continuous scale - Discrete variables are measured (or often counted) on a discrete scale aesthetics | discrete | continuous ------------- | ------------------------ | ------------ color | rainbow of colors | gradient size | discrete steps | linear mapping between radius and value shape | different shape for each | shouldn't (and doesn't) work - Use aesthetics for mapping features of a plot to a variable, define the features in the geom for customization **not** mapped to a variable --- class: center, middle # Faceting --- ## Faceting options - Smaller plots that display different subsets of the data - Useful for exploring conditional relationships and large data .small[ ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + * facet_grid(. ~ gender) + geom_point() + labs(title = "Mass vs. height of Starwars characters", * subtitle = "Faceted by gender", x = "Height (cm)", y = "Weight (kg)") ``` <!-- --> ] --- ## Dive further... .question[ In the next few slides describe what each plot displays. Think about how the code relates to the output. ] -- <br><br><br> .alert[ The plots in the next few slides do not have proper titles, axis labels, etc. because we want you to figure out what's happening in the plots. But you should always label your plots! ] --- ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + facet_grid(gender ~ .) ``` <!-- --> --- ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + facet_grid(. ~ gender) ``` <!-- --> --- ```r ggplot(data = starwars, mapping = aes(x = height, y = mass)) + geom_point() + facet_wrap(~ eye_color) ``` <!-- --> --- ## Facet summary - `facet_grid()`: - 2d grid - `rows ~ cols` - use `.` for no split - `facet_wrap()`: 1d ribbon wrapped into 2d --- class: center, middle # Identifying variables --- ## Number of variables involved * Univariate data analysis - distribution of single variable * Bivariate data analysis - relationship between two variables * Multivariate data analysis - relationship between many variables at once, usually focusing on the relationship between two while conditioning for others --- ## Types of variables - **Numerical variables** can be classified as **continuous** or **discrete** based on whether or not the variable can take on an infinite number of values or only non-negative whole numbers, respectively. - If the variable is **categorical**, we can determine if it is **ordinal** based on whether or not the levels have a natural ordering. --- class: center, middle # Visualizing numerical data --- ## Describing shapes of numerical distributions * shape: * skewness: right-skewed, left-skewed, symmetric (skew is to the side of the longer tail) * modality: unimodal, bimodal, multimodal, uniform * center: mean (`mean`), median (`median`), mode (not always useful) * spread: range (`range`), standard deviation (`sd`), inter-quartile range (`IQR`) * unusal observations --- ## Histograms .small[ ```r ggplot(data = starwars, mapping = aes(x = height)) + geom_histogram(binwidth = 10) ``` ``` ## Warning: Removed 6 rows containing non-finite values (stat_bin). ``` <!-- --> ] --- ## Density plots .small[ ```r ggplot(data = starwars, mapping = aes(x = height)) + geom_density() ``` ``` ## Warning: Removed 6 rows containing non-finite values (stat_density). ``` <!-- --> ] --- ## Side-by-side box plots .small[ ```r ggplot(data = starwars, mapping = aes(y = height, x = gender)) + geom_boxplot() ``` ``` ## Warning: Removed 6 rows containing non-finite values (stat_boxplot). ``` <!-- --> ] --- class: center, middle # Visualizing categorical data --- ## Bar plots .small[ ```r ggplot(data = starwars, mapping = aes(x = gender)) + geom_bar() ``` <!-- --> ] --- ## Segmented bar plots, counts .small[ ```r ggplot(data = starwars, mapping = aes(x = gender, fill = hair_color)) + geom_bar() ``` <!-- --> ] --- ## Segmented bar plots, proportions .small[ ```r ggplot(data = starwars, mapping = aes(x = gender, fill = hair_color)) + geom_bar(position = "fill") + labs(y = "proportion") ``` <!-- --> ] --- ## Which bar plot is more appropriate? .question[ Which of the previous two bar plots is a more useful representation for visualizing the relationship between gender and hair color? ]