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Basic Data Analysis

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Summary Statistics in R

Because R is mainly a statistical processing software, summary statistics come standard with base R functionality.

  • Use mean() and median() to calculate average of a vector.
  • Use min(), max(), and range() to see the range of a vector.
  • Use sd() or var() to calculate the spread of a vector.
  • Use table() to view the frequency of each value in a vector.
## AVERAGE
mean(dat) #mean
median(dat) #median
## RANGE
min(dat) #minimum value
max(dat) #maximum value
range(dat) #minimum and maximum
## SPREAD
sd(dat) #standard deviation
var(dat) #variance
## FREQUENCY
table(dat) #frequency of each value

ggplot() Initializes a ggplot Object

Invoking the ggplot() function returns an object that serves as the base of a ggplot2 visualization.

viz <- ggplot()
viz # renders blank plot

Data is bound to a ggplot2 visualization by passing a data frame as the first argument in the ggplot() function call. Layers can be added to the plot object by adding function calls after ggplot() with a + plus sign. These functions have access to the data frame and can use the column names as variables.

For example, consider a data frame sales with the columns cost and profit. To assign the data frame sales to the ggplot() object that is initialized:

viz <- ggplot(data=sales) +
geom_point(aes(x=cost, y=profit))
viz # renders plot

In the example above:

  • The ggplot object or canvas was initialized with the data frame sales assigned to it
  • The subsequent geom_point layer used the cost and profit columns to define the scales of the axes for that particular geom. Notice that it referred to those columns with their column names.
  • The variable name of the ggplot object is stated so the plot is viewable.

ggplot2 Aesthetics

In ggplot2 aesthetics are the instructions that determine the visual properties of a plot and its geometries.

Examples of ggplot2 aesthetics include:

  • scales for the x and y axes
  • color of the data points on the plot based on a property or on a color preference
  • the size or shape of different geometries

Aesthetics are set either manually or by aesthetic mappings. Aesthetic mappings “map” variables from the bound data frame to visual properties in the plot. These mappings are provided in two ways using the aes() mapping function:

  1. At the canvas level: All subsequent layers on the canvas will inherit the aesthetic mappings defined when the ggplot object was created with ggplot().
  2. At the geom level: Only that layer will use the aesthetic mappings provided.

For example, the following code assigns aes() mappings for the x and y scales at the canvas level:

viz <- ggplot(data=airquality, aes(x=Ozone, y=Temp)) +
geom_point() +
geom_smooth()

Aesthetics Inheritance Example

In the example above:

  • The aesthetic mapping is wrapped in the aes() aesthetic mapping function as an additional argument to ggplot().
  • Both of the subsequent geom layers, geom_point() and geom_smooth() use the scales defined inside the aesthetic mapping assigned at the canvas level.

You could create the same plot by setting the aesthetics at the geom level, as follows:

viz <- ggplot(data=airquality) +
geom_point(aes(x=Ozone, y=Temp)) +
geom_smooth(aes(x=Ozone, y=Temp))

Creating Regression Models in R

The lm() function creates a linear regression model in R. The glm() function creates a logistic regression model in R.

These functions take a formula Y ~ X where Y is the outcome variable and X is the predictor variable. We can add additional predictor variables using +.

A summary of these models can be printed using the summary() function.

## Linear regression model
temp_lm <- lm(temp ~ month + region, data = world)
summary(temp_lm) #print summary
## Logistic regression model
winning_glm <- glm(win ~ ranking + home + starting_players, data = team)
summary(winning_glm) #print summary

Making Predictions from Regression Objects in R

To make predictions of the outcome variable using a regression model, we need a dataset whose column names match the names of the coefficients in the model. Once establishing the data to make predictions about, we can use the predict() function to generate predictions. This will produce 1 predicted outcome for each observation in this new dataset.

## Create linear regression model
lm1 <- lm(y ~ x1 + x2 + x3, data = dat)
## Establish data to make predictions about
pred_data <- data.frame(
x1 = c(0, 1, -1),
x2 = c(1, 6, 5),
x3 = c(10, -4, 9)
)
## Make predictions
predict(lm1, pred_data)