Data Visualization With R: Introduction
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The document serves as an introduction to data visualization using R, specifically focusing on the 'plot()' function and its applications with the mtcars dataset. It covers six different cases demonstrating how to plot one or two continuous and categorical variables, emphasizing the interpretation of various plot types created. Additional resources for learning about R programming and data visualization are available on the datacrunch website.
![dataCrunchCase 2: One categorical variable
Slide 8
Let us use the cyl (number of cylinders) variable for this data as we need a categorical variable.
But before that we need to convert it to type factor using as.factor
# check the data type of cyl
class(mtcars$cyl)
[1] "numeric"
# coerce to type factor
mtcars$cyl <- as.factor(mtcars$cyl)
# plot a single categorical variable
plot(mtcars$cyl)
The plot() function creates a bar
plot when the data is categorical in
nature.](https://image.slidesharecdn.com/datavisualizationwithr-introduction-150826074109-lva1-app6891/85/Data-Visualization-With-R-Introduction-8-320.jpg)
Data Visualization With R: Introduction
- 1. www.r-squared.in/git-hub dataCrunch Data Visualization With R: Introduction
- 2. dataCrunchCourse Material Slide 2 All the material related to this course are available at our website Slides can be viewed at SlideShare Scripts can be downloaded from GitHub Videos can be viewed on our Youtube Channel
- 4. dataCrunchplot() Slide 4 The plot() function is the fundamental tool to build plots in the Graphics package. It is a generic function and creates the appropriate plot based on the input received from the user. In this section, we will explore the plot() function by inputting different types of data and observing the corresponding plots created. We will use the mtcars data set throughout this section. The documentation for the plot() function and the mtcars data set can be viewed using the help function. 1 2 3 4 help(plot) help(mtcars)
- 5. dataCrunchmtcars Slide 5 Let us take a quick look at the mtcars data set as we will be using it throughout this section: > head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 > str(mtcars) 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
- 6. dataCrunchExplore plot() Slide 6 Next, we will begin exploring the plot() function. The following data will be used as an input: ● Case 1: One continuous variable ● Case 2: One categorical variable ● Case 3: Two continuous variables ● Case 4: Two categorical variables ● Case 5: One continuous & one categorical variable ● Case 6: One categorical & one continuous variable Case 5 and 6 might look similar but the difference lies in the variables being assigned to the X and Y axis.
- 7. dataCrunchCase 1: One continuous variable Slide 7 We will use the variable mpg (Miles Per Gallon) for this example. # plot a single continuous variable plot(mtcars$mpg) The plot() function creates a Scatter Plot when a single continuous variable is used as the input. We cannot infer anything from the above plot as it represents the data points of the mpg variable in the XY coordinate. Let us plot a categorical variable and see what happens.
- 8. dataCrunchCase 2: One categorical variable Slide 8 Let us use the cyl (number of cylinders) variable for this data as we need a categorical variable. But before that we need to convert it to type factor using as.factor # check the data type of cyl class(mtcars$cyl) [1] "numeric" # coerce to type factor mtcars$cyl <- as.factor(mtcars$cyl) # plot a single categorical variable plot(mtcars$cyl) The plot() function creates a bar plot when the data is categorical in nature.
- 9. dataCrunchCase 3: Two continuous variables Slide 9 Till now we had used only one variable as the input but from this example, we will be using two variables; one for the X axis and another for the Y axis. In this example, we will look at the relationship between the displacement and mileage of the cars. The disp and mpg variables are used and disp is plotted on X axis while mpg is plotted on the Y axis. # plot two continuous variables plot(mtcars$disp, mtcars$mpg) A Scatter plot is created when we use two continuous variables as the input for the plot function but in this case, we can interpret the plot as it represents the relationship between two variables.
- 10. dataCrunchCase 4: Two categorical variables Slide 10 In this example, we will use two categorical variables am (transmission type) and cyl (number of cylinders). We will convert am to type factor before creating the plot. Transmission type will be plotted on X axis and number of cylinders on Y axis. # coerce am to type factor mtcars$am <- as.factor(mtcars$am) # coerce cyl to type factor mtcars$cyl <- as.factor(mtcars$cyl) # plot two categorical variables plot(mtcars$am, mtcars$cyl) A stacked bar plot is created when we use two categorical variables as the input for the plot function. In the next two examples, we will use both continuous and categorical variables.
- 11. dataCrunchCase 5: Continuous/Categorical Variables Slide 11 In this example, we will plot a categorical variable cyl on the X axis and a continuous variable mpg on the Y axis. # coerce cyl to type factor mtcars$cyl <- as.factor(mtcars$cyl) # categorical vs continuous variables plot(mtcars$cyl, mtcars$mpg) A box plot is created when we use a categorical variable and continuous variable as input for the plot function. But in this case, the categorical variable was plotted on the X axis and the continuous variable on the Y axis. What happens if we flip this?
- 12. dataCrunchCase 6: Categorical/Continuous Variables Slide 12 In this example, the continuous variable is plotted on the X axis and the categorical variable on the Y axis. # coerce cyl to type factor mtcars$cyl <- as.factor(mtcars$cyl) # continuous vs categorical variables plot(mtcars$mpg, mtcars$cyl) A scatter plot is created but since the Y axis variable is discrete, we can observe lines of points for each level of the discrete variable. We can compare the range of the X axis variable for each level of the Y axis variable.
- 13. dataCrunch Slide 13 Visit dataCrunch for tutorials on: → R Programming → Business Analytics → Data Visualization → Web Applications → Package Development → Git & GitHub