The Basics of Plotting in R
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1 The Basics of Plotting in R
2 R has a built-in Datasets Package: iris mtcars precip faithful state.x77 USArrests presidents ToothGrowth USJudgeRatings You can call built-in functions like hist() or plot() on a built-in data set to quickly produce a chart
3 Basic plotting in R hist(nhtemp) plot(nhtemp)
4 Basic plotting in R attach(iris) plot(petal.length ~ Petal.Width)
5 Basic plotting in R attach(iris) plot(petal.length ~ Petal.Width, col = Species)
6 Basic plotting in R plot(petal.length ~ Petal.Width, col = Species, main = Iris: Petal width vs. length )
7 Legends legend("topleft", legend = unique(species), fill = unique(species))
8 Basic plotting in R attach(faithful) duration = eruptions plot(duration, waiting, xlab = "Eruption duration in minutes", ylab = "Time waited in minutes") plot(duration, waiting, xlab = "Eruption duration in minutes", ylab = "Time waited in minutes", col = "seagreen")
9 Histograms hist(waiting) hist(eruptions)
10 Histograms hist(waiting, breaks = 5) hist(waiting, breaks = 20)
11 Histograms hist(waiting, main = "Old Faithful", cex.main = 1.75, xlab = "Waiting Time in Minutes", ylab = "Frequency", cex.lab = 1.25)
12 Histograms hist(waiting, main = "Old Faithful", cex.main = 1.75, xlab = "Waiting Time in Minutes", ylab = "Frequency", cex.lab = 1.25 col = "blue", border = "orange")
13 Stem and leaf plot > stem(waiting) The decimal point is 1 digit(s) to the right of the
14 > stem(eruptions) The decimal point is 1 digit(s) to the left of the
![Bar charts > races_younger [1] "Black" "Hispanic" "Other" "White" > population_in_millions_younger [1] 10.76 19.03 7.76 40.](/docs-images/80/81222210/images/15-0.jpg "50 > barplot(population_in_millions_younger, names.")
15 Bar charts > races_younger [1] "Black" "Hispanic" "Other" "White" > population_in_millions_younger [1] > barplot(population_in_millions_younger, names.arg = races_younger, main = "Younger than 18, 2014", ylab = "Population in Millions")
16 Bar charts > races_all [1] "Native" "Asian" "Black" "Hispanic" "Two" "White" > population_in_millions_all [1] barplot(population_in_millions_all, names.arg = races_all, main = "All ages, 2014", ylab = "Population in Millions")
![Bar charts > races_all_other [1] "Black" "Hispanic" "White" "Other" > population_in_millions_all_other [1] 38.60 55.61 195.35 25.](/docs-images/80/81222210/images/17-0.jpg "19 barplot(population_in_millions_all_other, names.")
17 Bar charts > races_all_other [1] "Black" "Hispanic" "White" "Other" > population_in_millions_all_other [1] barplot(population_in_millions_all_other, names.arg = races_all_other, main = "All ages, 2014", ylab = "Population in Millions")
18 Stacked and grouped bar charts
19 Pie charts pie_labels_younger <- paste(races_younger, population_in_millions_younger, sep = "\n") pie(population_in_millions_younger, main = "Younger than 18\nPopulation in Millions", labels = pie_labels_younger)
20 Pie charts pie_labels_all_other <- paste(races_all_other, population_in_millions_all_other, sep = "\n") pie(population_in_millions_all_other, main = "All Ages\nPopulation in Millions", labels = pie_labels_all_other)
21 Pie charts
22 Dot plots states <- data.frame(state.x77) sorted_states <- states[order(states$income), ] dotchart(sorted_states$income, rownames(sorted_states), cex.lab =.25, main = "Income per capita")
23 Box plots boxplot(petal.length ~ Species, main = "Iris Petal Length by Species", xlab = "Species", ylab = "Petal Length")
24 Notched Box plots attach(toothgrowth) boxplot(len ~ supp * dose, data = Toothgrowth, notch = TRUE, col = (c("gold", "darkgreen")), main = "Tooth Growth", xlab = "Supplement and Dose") If the notches do not overlap, then the medians of the groups are different (because their confidence intervals do not overlap).
25 Simple Scatterplots plot(usjudgeratings$rten ~ USJudgeRatings$FAMI, xlab = "Familiarity with Law", ylab = "Worthy of Retention", main = "Law Familiarity vs. Worthy of Retention", pch = "+")
26 Scatterplot Matrices CONT Number of contacts of lawyer with judge. INTG Judicial integrity. DMNR Demeanor. DILG Diligence. pairs(~usjudgeratings$cont + USJudgeRatings$INTG + USJudgeRatings$DMNR + USJudgeRatings$DILG, main = "US Judge Ratings")
27 Scatterplot Matrices features <- c("contacts", "Integrity", "Demeanor", "Diligence") pairs(~usjudgeratings$cont + USJudgeRatings$INTG + USJudgeRatings$DMNR + USJudgeRatings$DILG, labels = features, main = "US Judge Ratings")
28 3D Scatterplots library(scatterplot3d) scatterplot3d(mtcars$wt, mtcars$disp, mtcars$mpg, main = "3D Scatterplot")
29 Graphical parameters Text and symbol size (cex:.axis,.lab,.main) Fonts (font:.axis,.lab,.main): 1=plain, 2=bold, 3=italic, 4=bold italic, 5=symbol Colors (col): colors, and more colors
30 Graphical parameters (cont d) Plotting symbols (pch) Line type (lty)
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