plots Chris Parrish August 20, 2015

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1 plots Chris Parrish August 20, 2015 plots We construct some of the most commonly used types of plots for numerical data. dotplot A stripchart is most suitable for displaying small data sets. data <- rnorm(15) stripchart(data, pch=19, col="darkred", method="stack", main="stripchart") Stripchart boxplot Boxplots give a quick impression of the spread of the data by systematically incorporating its quartiles, and flagging outliers. data <- rnorm(50) summary(data) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## boxplot(data, horizontal=true, col="mediumturquoise", main="boxplot") 1

2 Boxplot beanplot Beanplots are imaginative variants of the boxplot idea. library(beanplot) beanplot(data, horizontal=true, col="green", main="beanplot") Beanplot Side-by-side boxplot and beanplot give complementary views of the data. par(mfrow=c(1,2)) boxplot(data, col="mediumturquoise", main="boxplot") beanplot(data, col="green", main="beanplot") 2

3 Boxplot Beanplot par(mfrow=c(1,2)) histogram The histogram is one of the most commonly used plots for displaying the distribution of numerical data. data <- rnorm(200) hist(data, las=1, xlim=c(-3,3), col="wheat", main="histogram") Histogram 30 Frequency data In this case, the data was generated by a normal density function, so it is natural to compare the histogram with the graph of the normal curve that created it. We set freq=false to get a histogram of densities. 3

4 hist(data, freq=false, las=1, xlim=c(-3,3), ylim=c(0, 0.40), col="orange", main="histogram with Normal Curve") curve(dnorm(x), from=-3, to=3, add=true, col="darkred") Histogram with Normal Curve Density data bar chart If subjects are assigned to categories, the number or proportions of objects in each category can be displayed with a bar chart. Peck (ex. 2.20) mentions that the environmental organization Heal the Bay issues annual report cards on water quality for certain beaches in California. For the wet season, we have the following data. wet <- c("a+", "C", "B", "A", "A+", "A+", "A", "A+", "B", "D", "C", "D", "F", "F") Display a relative frequency distribution. table(wet) ## wet ## A A+ B C D F ## Construct a bar chart. barplot(table(wet), names.arg=names(table(wet)), col=c("steelblue", "skyblue", "springgreen", "forestgreen", "orange", "red"), main="heal the Bay, Wet Weather") 4

5 Heal the Bay, Wet Weather A A+ B C D F Here is a different set of data for the dry season. dry <- c("a", "B", "B", "A+", "A", "F", "A", "A", "A", "A", "A", "A", "B", "A") Display a relative frequency distribution. table(dry) ## dry ## A A+ B F ## Construct a bar chart. Is the water quality in these California beaches better in the wet or dry season? barplot(table(dry), names.arg=names(table(dry)), col=c("steelblue", "skyblue", "springgreen", "red"), main="heal the Bay, Dry Weather") 5

6 Heal the Bay, Dry Weather A A+ B F contingency table A 2x2 contingency table is a powerful device for summarizing certain kinds of data. In 1867, Joseph Lister performed a pioneering experiment on the use of sterile surgical technique. Here is his data. data <- read.table("lister.txt", header=true, sep="\t") head(data) ## Group Outcome ## 1 Sterile Survived ## 2 Sterile Survived ## 3 Sterile Survived ## 4 Sterile Survived ## 5 Sterile Survived ## 6 Sterile Survived table(data) ## Outcome ## Group Died Survived ## Control ## Sterile 6 34 mosaicplot Make a mosaicplot to visualize these results. Did sterile surgical technique improve outcomes? mosaicplot(table(data), col=c("yellow", "green"), main="joseph Lister's Experiment\non Sterile Surgical Technique, 1867") 6

7 Joseph Lister's Experiment on Sterile Surgical Technique, 1867 Control Sterile Survived Outcome Died Group scatterplot Here is a scatterplot which suggests a linear relationship between the x and y values, so we add a regression line to the plot. x <- seq(from=0, to=1, by=0.06) jitter <- rnorm(length(x), sd=0.2) y <- x + jitter plot(x, y, pch=19, col="darkred", main="scatterplot") xy.lm <- lm(y ~ x) abline(xy.lm, col="orange") 7

8 Scatterplot y The regression line has the formula ŷ = a + bx, where a is the first coefficient in the linear model, b is the second, and ŷ is the predicted value of y for that value of x. ŷ = a + bx = x coefficients(xy.lm) ## (Intercept) x ## x 8