Advanced Statistics 1. Lab 11 - Charts for three or more variables. Systems modelling and data analysis 2016/2017

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1 Advanced Statistics 1 Lab 11 - Charts for three or more variables 1 Preparing the data 1. Run RStudio Systems modelling and data analysis 2016/ Set your Working Directory using the setwd() command. 3. If you didn t do it before, clean up your workspace using rm command 2 Creating Charts Creating clustered bar charts for frequencies 1. Load the data from warpbreaks data set to looking at how often different kinds of wool break under different kinds of tension. Here we have three variables: the outcome variable which is the number of breaks and two predictor variables: the kind of wool (a or b) and the level of tension (low, medium or high).?warpbreaks 2. Use the barplot function to chart breaks as a function of wool and tension. Is it works? Why? barplot(breaks ~ wool*tension, data = warpbreaks) 3. Restructure the data using the function tapply to create double matrix. data <- tapply(warpbreaks$breaks, list(warpbreaks$wool, warpbreaks$tension), mean) 4. Now create a chart using barplot function for the data. barplot(data, beside = TRUE, col = c( steelblue3, thistle3 ), bor = NA, main = Mean Number of Warp Breaks\nby Tension and Wool, xlab = Tension, ylab = Mean Number of Breaks )

2 Advanced Statistics 2 5. Add legend to the created chart. Notice that if we use locator(1) this legend will be interactive and lets you click where you want to put the legend. You can also specify legend location with coordinates. legend(locator(1), rownames(data), fill = c( steelblue3, thistle3 )) Creating scatter plots for grouped data Variations of scatter plots are most common choices to show the relationship between several quantitative variables. 1. Load and look at the first five observations of iris file, which is very well known data set in the statistic world. Edgar Anderson s Iris Data has the measurement of sepal lenght and width and petal length and width on three species of irises.?iris data(iris) iris[1:5, ] 2. Load car package that is companion to applied regression (if you don t have it installed already, you will need to do install.packages and then car. require(car) 3. Create a single scatter plot with groups marked by using the sp function from car sp(sepal.width ~ Sepal.Length Species, data = iris, xlab = Sepal Width, ylab = Sepal Length, main = Iris Data, labels = row.names(iris)) 4. Analyze created chart. Creating scatter plot matrices Scatter plot matrix is one of the options to look at the association of several quantitative variables with each other. 1. Create the basic scatter plot matrix for the iris data set. Use the pairs function. Analyze the created plot. pairs(iris[1:4])

3 Advanced Statistics 3 2. Create palette with RColorBrewer package to color the scatter plot. require( RColorBrewer ) display.brewer.pal(3, Pastel1 ) 3. Put histograms on the diagonal. For this - create the function panel.hist. panel.hist <- function(x,...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nb <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nb], 0, breaks[-1], y,...) # Removed "col = "cyan" from code block; original below # rect(breaks[-nb], 0, breaks[-1], y, col = "cyan",...) } 4. Again create the scatter plot matrix by using the pairs function, but now with smoother (panel.smooth) and with histograms on the diagonal (panel.hist function) pairs(iris[1:4], panel = panel.smooth, # Optional smoother main = "Scatterplot Matrix for Iris Data Using pairs Function", diag.panel = panel.hist, pch = 16, col = brewer.pal(3, "Pastel1")[unclass(iris$Species)]) 5. Create new scatter plot by using the scatterplotmatrix function from car package. Compare all created plots. library(car) scatterplotmatrix(~petal.length + Petal.Width + Sepal.Length + Sepal.Width Species, data = iris, col = brewer.pal(3, "Dark2"), main="scatterplot Matrix for Iris Data Using \"car\" Package") 6. Clean up the workspace. palette("default") # Return to default detach("package:rcolorbrewer", unload = TRUE) detach("package:car", unload=true)

4 Advanced Statistics 4 Creating 3D scatter plots 1. Load the iris data into the work space. data(iris) 2. To produce a static 3D scatter plot, first install and load the scatterplot3d package. Then, create the basic static 3D scatter plot by choosing the first three variables from iris data set with the function scatterplot3d. install.packages( scatterplot3d ) require( scatterplot3d ) scatterplot3d(iris[1:3]) 3. Now, create the modified static 3D scatter plot by adding coloring and vertical lines that connect each points to the floor of the scatter plot. Save the plot to the object s3d. s3d <- scatterplotd(iris[1:3], pch = 16, highlight.3d = TRUE, type = h, main = 3D Scatter plot ) 4. Calculate the regression plane and add this plane to the s3d object. Try to analyze this graph. plane <- lm(iris$petal.length ~ iris$sepal.length + iris$sepal.width) s3d$plane3d(plane) 5. Produce an interactive (here: dynamic spinning) 3D scatter plot, which is much easier to see the pattern because you can move it around and see where things are located in space. To do this, install and load the rgl package ( 3D visualization device system (OpenGL) ). Notice that unfortunately, this is not compatible with the RStudio (it will cause RStudio to crash when you close the graphic window). Because of that, you need to go to the standard console version of R and run it from there. install.packages( rgl ) require( rgl ) require( RColorBrewer ) plot3d(iris$petal.length, # x variable iris$petal.width, # y variable iris$sepal.length, # z variable xlab = "Petal.Length", ylab = "Petal.Width",

5 Advanced Statistics 5 zlab = "Sepal.Length", col = brewer.pal(3, "Dark2")[unclass(iris$Species)], size = 8) 6. Clean up your workspace. detach("package:scatterplot3d", unload = TRUE) detach("package:rgl", unload = TRUE) detach("package:rcolorbrewer", unload = TRUE) 3 Exercise Creating a scatter plot matrix Use the data from Lab 8 (searchdata.csv file) - This external data set contains information about Google searches. 1. Download, extract and load into R Studio the data from the file searchdata.zip 2. Look for the five variablesfrom this data set: nba, nfl, fifa, degree (demographic information about the percentage of adults with degrees), age (average). 3. Graph this using pairs. 4. Graph this using scatterplotmatrix from package cars which is for companion to applied regression.