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RDataMining slides-data-exploration-visualisation

This document discusses exploring and visualizing data in R. It introduces the iris dataset and explores its structure and variables. Various summary statistics and charts are demonstrated, including histograms, density plots, scatter plots, boxplots and heat maps to explore individual and relationships between multiple variables. Multivariate exploration techniques like 3D scatter plots and pairwise plots are also covered. The document demonstrates how to save charts to files.

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Data Exploration and Visualisation with R ∗ Yanchang Zhao http://www.RDataMining.com R and Data Mining Course Beijing University of Posts and Telecommunications, Beijing, China July 2019 ∗ Chapter 3: Data Exploration, in R and Data Mining: Examples and Case Studies. http://www.rdatamining.com/docs/RDataMining-book.pdf 1 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 2 / 45
Data Exploration and Visualisation with R Data Exploration and Visualisation Summary and stats Various charts like pie charts and histograms Exploration of multiple variables Level plot, contour plot and 3D plot Saving charts into files 3 / 45
Quiz: What’s the Name of This Flower? Oleg Yunakov [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons. 4 / 45
The Iris Dataset The iris dataset [Frank and Asuncion, 2010] consists of 50 samples from each of three classes of iris flowers. There are five attributes in the dataset: sepal length in cm, sepal width in cm, petal length in cm, petal width in cm, and class: Iris Setosa, Iris Versicolour, and Iris Virginica. Detailed desription of the dataset can be found at the UCI Machine Learning Repository †. † https://archive.ics.uci.edu/ml/datasets/Iris 5 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 6 / 45
Size and Variables Names of Data # number of rows nrow(iris) ## [1] 150 # number of columns ncol(iris) ## [1] 5 # dimensionality dim(iris) ## [1] 150 5 # column names names(iris) ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Wid... ## [5] "Species" 7 / 45
Structure of Data Below we have a look at the structure of the dataset with str(). str(iris) ## 'data.frame': 150 obs. of 5 variables: ## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1... ## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1... ## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0... ## $ Species : Factor w/ 3 levels "setosa","versicolor",.... 150 observations (records, or rows) and 5 variables (or columns) The first four variables are numeric. The last one, Species, is categoric (called “factor” in R) and has three levels of values. 8 / 45
Attributes of Data attributes(iris) ## $names ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Wid... ## [5] "Species" ## ## $class ## [1] "data.frame" ## ## $row.names ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 ... ## [16] 16 17 18 19 20 21 22 23 24 25 26 27 28 ... ## [31] 31 32 33 34 35 36 37 38 39 40 41 42 43 ... ## [46] 46 47 48 49 50 51 52 53 54 55 56 57 58 ... ## [61] 61 62 63 64 65 66 67 68 69 70 71 72 73 ... ## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 ... ## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 1... ## [106] 106 107 108 109 110 111 112 113 114 115 116 117 118 1... ## [121] 121 122 123 124 125 126 127 128 129 130 131 132 133 1... ## [136] 136 137 138 139 140 141 142 143 144 145 146 147 148 1... 9 / 45
First/Last Rows of Data iris[1:3, ] ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa head(iris, 3) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa tail(iris, 3) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Spe... ## 148 6.5 3.0 5.2 2.0 virgi... ## 149 6.2 3.4 5.4 2.3 virgi... ## 150 5.9 3.0 5.1 1.8 virgi... 10 / 45
A Single Column The first 10 values of Sepal.Length iris[1:10, "Sepal.Length"] ## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 iris$Sepal.Length[1:10] ## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 11 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 12 / 45
Summary of Data Function summary() numeric variables: minimum, maximum, mean, median, and the first (25%) and third (75%) quartiles categorical variables (i.e., factors): frequency of every level summary(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Wid... ## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.... ## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.... ## Median :5.800 Median :3.000 Median :4.350 Median :1.... ## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.... ## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.... ## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.... ## Species ## setosa :50 ## versicolor:50 ## virginica :50 ## ## ## 13 / 45
library(Hmisc) # describe(iris) # check all columns describe(iris[, c(1, 5)]) # check columns 1 and 5 ## iris[, c(1, 5)] ## ## 2 Variables 150 Observations ## -----------------------------------------------------------... ## Sepal.Length ## n missing distinct Info Mean Gmd ... ## 150 0 35 0.998 5.843 0.9462 4.... ## .10 .25 .50 .75 .90 .95 ## 4.800 5.100 5.800 6.400 6.900 7.255 ## ## lowest : 4.3 4.4 4.5 4.6 4.7, highest: 7.3 7.4 7.6 7.7 7.9 ## -----------------------------------------------------------... ## Species ## n missing distinct ## 150 0 3 ## ## Value setosa versicolor virginica ## Frequency 50 50 50 ## Proportion 0.333 0.333 0.333 ## -----------------------------------------------------------... 14 / 45
Mean, Median, Range and Quartiles Mean, median and range: mean(), median(), range() Quartiles and percentiles: quantile() range(iris$Sepal.Length) ## [1] 4.3 7.9 quantile(iris$Sepal.Length) ## 0% 25% 50% 75% 100% ## 4.3 5.1 5.8 6.4 7.9 quantile(iris$Sepal.Length, c(0.1, 0.3, 0.65)) ## 10% 30% 65% ## 4.80 5.27 6.20 15 / 45
Variance and Histogram var(iris$Sepal.Length) ## [1] 0.6856935 hist(iris$Sepal.Length) Histogram of iris$Sepal.Length iris$Sepal.Length Frequency 4 5 6 7 8 051015202530 16 / 45
Density library(magrittr) ## for pipe operations iris$Sepal.Length %>% density() %>% plot(main='Density of Sepal.Length') 4 5 6 7 8 0.00.10.20.30.4 Density of Sepal.Length N = 150 Bandwidth = 0.2736 Density 17 / 45
Pie Chart Frequency of factors: table() library(dplyr) iris2 <- iris %>% sample_n(50) iris2$Species %>% table() %>% pie() # add percentages tab <- iris2$Species %>% table() precentages <- tab %>% prop.table() %>% round(3) * 100 txt <- paste0(names(tab), 'n', precentages, '%') pie(tab, labels=txt) setosa versicolor virginica setosa 38% versicolor 36% virginica 26% 18 / 45
Bar Chart iris2$Species %>% table() %>% barplot() # add colors and percentages bb <- iris2$Species %>% table() %>% barplot(axisnames=F, main='Species', ylab='Frequency', col=c('pink', 'lightblue', 'lightgreen')) text(bb, tab/2, labels=txt, cex=1.5) setosa versicolor virginica 051015 Species Frequency 051015 setosa 38% versicolor 36% virginica 26% 19 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 20 / 45
Correlation Covariance and correlation: cov() and cor() cov(iris$Sepal.Length, iris$Petal.Length) ## [1] 1.274315 cor(iris$Sepal.Length, iris$Petal.Length) ## [1] 0.8717538 cov(iris[, 1:4]) ## Sepal.Length Sepal.Width Petal.Length Petal.Width ## Sepal.Length 0.6856935 -0.0424340 1.2743154 0.5162707 ## Sepal.Width -0.0424340 0.1899794 -0.3296564 -0.1216394 ## Petal.Length 1.2743154 -0.3296564 3.1162779 1.2956094 ## Petal.Width 0.5162707 -0.1216394 1.2956094 0.5810063 # cor(iris[,1:4]) 21 / 45
Aggreation Stats of Sepal.Length for every Species with aggregate() aggregate(Sepal.Length ~ Species, summary, data = iris) ## Species Sepal.Length.Min. Sepal.Length.1st Qu. ## 1 setosa 4.300 4.800 ## 2 versicolor 4.900 5.600 ## 3 virginica 4.900 6.225 ## Sepal.Length.Median Sepal.Length.Mean Sepal.Length.3rd Qu. ## 1 5.000 5.006 5.200 ## 2 5.900 5.936 6.300 ## 3 6.500 6.588 6.900 ## Sepal.Length.Max. ## 1 5.800 ## 2 7.000 ## 3 7.900 22 / 45
Boxplot The bar in the middle is median. The box shows the interquartile range (IQR), i.e., range between the 75% and 25% observation. boxplot(Sepal.Length ~ Species, data = iris) setosa versicolor virginica 4.55.05.56.06.57.07.58.0 23 / 45
Scatter Plot with(iris, plot(Sepal.Length, Sepal.Width, col = Species, pch = as.numeric(Species))) 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 2.02.53.03.54.0 Sepal.Length Sepal.Width 24 / 45
Scatter Plot with Jitter Function jitter(): add a small amount of noise to the data with(iris, plot(jitter(Sepal.Length), jitter(Sepal.Width), col=Species,pch=as.numeric(Species))) 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 2.02.53.03.54.0 jitter(Sepal.Length) jitter(Sepal.Width) 25 / 45
A Matrix of Scatter Plots pairs(iris) Sepal.Length2.03.04.00.51.52.5 4.5 5.5 6.5 7.5 2.0 3.0 4.0 Sepal.Width Petal.Length 1 2 3 4 5 6 7 0.5 1.5 2.5 Petal.Width 4.55.56.57.51234567 1.0 2.0 3.0 1.02.03.0 Species 26 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 27 / 45
3D Scatter plot library(scatterplot3d) scatterplot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width) 0.0 0.5 1.0 1.5 2.0 2.5 2.02.53.03.54.04.5 4 5 6 7 8 iris$Petal.Width iris$Sepal.Length iris$Sepal.Width 28 / 45
Interactive 3D Scatter Plot Package rgl supports interactive 3D scatter plot with plot3d(). library(rgl) plot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width) 29 / 45
Heat Map Calculate the similarity between different flowers in the iris data with dist() and then plot it with a heat map dist.matrix <- as.matrix(dist(iris[, 1:4])) heatmap(dist.matrix) 422314943394132463674831634154561921322425274417333749112247202631303510385411250284082918111910612313211813110811013613010312610114412114561999458658081826383936860709054107855667627291899796100955276665755598869987586797492641091371051251411461421401131041381171161491291331151351121111487853518777841501471241341271281397173120122114102143 422314943394132463674831634154561921322425274417333749112247202631303510385411250284082918111910612313211813110811013613010312610114412114561999458658081826383936860709054107855667627291899796100955276665755598869987586797492641091371051251411461421401131041381171161491291331151351121111487853518777841501471241341271281397173120122114102143 30 / 45
Level Plot Function rainbow() creates a vector of contiguous colors. rev() reverses a vector. library(lattice) levelplot(Petal.Width ~ Sepal.Length * Sepal.Width, data=iris, cuts=8) Sepal.Length Sepal.Width 2.0 2.5 3.0 3.5 4.0 5 6 7 0.0 0.5 1.0 1.5 2.0 2.5 31 / 45
Contour contour() and filled.contour() in package graphics contourplot() in package lattice filled.contour(volcano, color=terrain.colors, asp=1, plot.axes=contour(volcano, add=T)) 100 120 140 160 180 100 100 100 110 110 110 110 120 130 140 150 160 160 170 170 180 180 190 32 / 45
3D Surface persp(volcano, theta = 25, phi = 30, expand = 0.5, col = "lightblue") volcano Y Z 33 / 45
Parallel Coordinates Visualising multiple dimensions library(MASS) parcoord(iris[1:4], col = iris$Species) Sepal.Length Sepal.Width Petal.Length Petal.Width 34 / 45
Parallel Coordinates with Package lattice library(lattice) parallelplot(~iris[1:4] | Species, data = iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min Max setosa versicolor Sepal.Length Sepal.Width Petal.Length Petal.Width virginica 35 / 45
Visualisation with Package ggplot2 library(ggplot2) qplot(Sepal.Length, Sepal.Width, data = iris, facets = Species ~ .) setosaversicolorvirginica 5 6 7 8 2.0 2.5 3.0 3.5 4.0 4.5 2.0 2.5 3.0 3.5 4.0 4.5 2.0 2.5 3.0 3.5 4.0 4.5 Sepal.Length Sepal.Width 36 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 37 / 45
Save Charts to Files Save charts to PDF and PS files: pdf() and postscript() BMP, JPEG, PNG and TIFF files: bmp(), jpeg(), png() and tiff() Close files (or graphics devices) with graphics.off() or dev.off() after plotting # save as a PDF file pdf("myPlot.pdf") x <- 1:50 plot(x, log(x)) graphics.off() # Save as a postscript file postscript("myPlot2.ps") x <- -20:20 plot(x, x^2) graphics.off() 38 / 45
Save ggplot Charts to Files ggsave(): by defult, saving the last plot that you displayed. It also guesses the type of graphics device from the extension. ggsave("myPlot3.png") ggsave("myPlot4.pdf") ggsave("myPlot5.jpg") ggsave("myPlot6.bmp") ggsave("myPlot7.ps") ggsave("myPlot8.eps") 39 / 45
Contents Introduction Have a Look at Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 40 / 45
Further Readings Examples of ggplot2 plotting: https://ggplot2.tidyverse.org/ Package iplots: interactive scatter plot, histogram, bar plot, and parallel coordinates plot (iplots) http://rosuda.org/software/iPlots/ Package googleVis: interactive charts with the Google Visualisation API http://cran.r-project.org/web/packages/googleVis/vignettes/ googleVis_examples.html Package ggvis: interactive grammar of graphics http://ggvis.rstudio.com/ Package rCharts: interactive javascript visualisations from R https://ramnathv.github.io/rCharts/ 41 / 45
Online Resources Book titled R and Data Mining: Examples and Case Studies http://www.rdatamining.com/docs/RDataMining-book.pdf R Reference Card for Data Mining http://www.rdatamining.com/docs/RDataMining-reference-card.pdf Free online courses and documents http://www.rdatamining.com/resources/ RDataMining Group on LinkedIn (27,000+ members) http://group.rdatamining.com Twitter (3,300+ followers) @RDataMining 42 / 45
The End Thanks! Email: yanchang(at)RDataMining.com Twitter: @RDataMining 43 / 45
How to Cite This Work Citation Yanchang Zhao. R and Data Mining: Examples and Case Studies. ISBN 978-0-12-396963-7, December 2012. Academic Press, Elsevier. 256 pages. URL: http://www.rdatamining.com/docs/RDataMining-book.pdf. BibTex @BOOK{Zhao2012R, title = {R and Data Mining: Examples and Case Studies}, publisher = {Academic Press, Elsevier}, year = {2012}, author = {Yanchang Zhao}, pages = {256}, month = {December}, isbn = {978-0-123-96963-7}, keywords = {R, data mining}, url = {http://www.rdatamining.com/docs/RDataMining-book.pdf} } 44 / 45
References I Frank, A. and Asuncion, A. (2010). UCI machine learning repository. university of california, irvine, school of information and computer sciences. http://archive.ics.uci.edu/ml. 45 / 45

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RDataMining slides-data-exploration-visualisation

  • 1.
    Data Exploration andVisualisation with R ∗ Yanchang Zhao http://www.RDataMining.com R and Data Mining Course Beijing University of Posts and Telecommunications, Beijing, China July 2019 ∗ Chapter 3: Data Exploration, in R and Data Mining: Examples and Case Studies. http://www.rdatamining.com/docs/RDataMining-book.pdf 1 / 45
  • 2.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 2 / 45
  • 3.
    Data Exploration andVisualisation with R Data Exploration and Visualisation Summary and stats Various charts like pie charts and histograms Exploration of multiple variables Level plot, contour plot and 3D plot Saving charts into files 3 / 45
  • 4.
    Quiz: What’s theName of This Flower? Oleg Yunakov [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons. 4 / 45
  • 5.
    The Iris Dataset Theiris dataset [Frank and Asuncion, 2010] consists of 50 samples from each of three classes of iris flowers. There are five attributes in the dataset: sepal length in cm, sepal width in cm, petal length in cm, petal width in cm, and class: Iris Setosa, Iris Versicolour, and Iris Virginica. Detailed desription of the dataset can be found at the UCI Machine Learning Repository †. † https://archive.ics.uci.edu/ml/datasets/Iris 5 / 45
  • 6.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 6 / 45
  • 7.
    Size and VariablesNames of Data # number of rows nrow(iris) ## [1] 150 # number of columns ncol(iris) ## [1] 5 # dimensionality dim(iris) ## [1] 150 5 # column names names(iris) ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Wid... ## [5] "Species" 7 / 45
  • 8.
    Structure of Data Belowwe have a look at the structure of the dataset with str(). str(iris) ## 'data.frame': 150 obs. of 5 variables: ## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1... ## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1... ## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0... ## $ Species : Factor w/ 3 levels "setosa","versicolor",.... 150 observations (records, or rows) and 5 variables (or columns) The first four variables are numeric. The last one, Species, is categoric (called “factor” in R) and has three levels of values. 8 / 45
  • 9.
    Attributes of Data attributes(iris) ##$names ## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Wid... ## [5] "Species" ## ## $class ## [1] "data.frame" ## ## $row.names ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 ... ## [16] 16 17 18 19 20 21 22 23 24 25 26 27 28 ... ## [31] 31 32 33 34 35 36 37 38 39 40 41 42 43 ... ## [46] 46 47 48 49 50 51 52 53 54 55 56 57 58 ... ## [61] 61 62 63 64 65 66 67 68 69 70 71 72 73 ... ## [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 ... ## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 1... ## [106] 106 107 108 109 110 111 112 113 114 115 116 117 118 1... ## [121] 121 122 123 124 125 126 127 128 129 130 131 132 133 1... ## [136] 136 137 138 139 140 141 142 143 144 145 146 147 148 1... 9 / 45
  • 10.
    First/Last Rows ofData iris[1:3, ] ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa head(iris, 3) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa tail(iris, 3) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Spe... ## 148 6.5 3.0 5.2 2.0 virgi... ## 149 6.2 3.4 5.4 2.3 virgi... ## 150 5.9 3.0 5.1 1.8 virgi... 10 / 45
  • 11.
    A Single Column Thefirst 10 values of Sepal.Length iris[1:10, "Sepal.Length"] ## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 iris$Sepal.Length[1:10] ## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 11 / 45
  • 12.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 12 / 45
  • 13.
    Summary of Data Functionsummary() numeric variables: minimum, maximum, mean, median, and the first (25%) and third (75%) quartiles categorical variables (i.e., factors): frequency of every level summary(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Wid... ## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.... ## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.... ## Median :5.800 Median :3.000 Median :4.350 Median :1.... ## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.... ## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.... ## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.... ## Species ## setosa :50 ## versicolor:50 ## virginica :50 ## ## ## 13 / 45
  • 14.
    library(Hmisc) # describe(iris) #check all columns describe(iris[, c(1, 5)]) # check columns 1 and 5 ## iris[, c(1, 5)] ## ## 2 Variables 150 Observations ## -----------------------------------------------------------... ## Sepal.Length ## n missing distinct Info Mean Gmd ... ## 150 0 35 0.998 5.843 0.9462 4.... ## .10 .25 .50 .75 .90 .95 ## 4.800 5.100 5.800 6.400 6.900 7.255 ## ## lowest : 4.3 4.4 4.5 4.6 4.7, highest: 7.3 7.4 7.6 7.7 7.9 ## -----------------------------------------------------------... ## Species ## n missing distinct ## 150 0 3 ## ## Value setosa versicolor virginica ## Frequency 50 50 50 ## Proportion 0.333 0.333 0.333 ## -----------------------------------------------------------... 14 / 45
  • 15.
    Mean, Median, Rangeand Quartiles Mean, median and range: mean(), median(), range() Quartiles and percentiles: quantile() range(iris$Sepal.Length) ## [1] 4.3 7.9 quantile(iris$Sepal.Length) ## 0% 25% 50% 75% 100% ## 4.3 5.1 5.8 6.4 7.9 quantile(iris$Sepal.Length, c(0.1, 0.3, 0.65)) ## 10% 30% 65% ## 4.80 5.27 6.20 15 / 45
  • 16.
    Variance and Histogram var(iris$Sepal.Length) ##[1] 0.6856935 hist(iris$Sepal.Length) Histogram of iris$Sepal.Length iris$Sepal.Length Frequency 4 5 6 7 8 051015202530 16 / 45
  • 17.
    Density library(magrittr) ## forpipe operations iris$Sepal.Length %>% density() %>% plot(main='Density of Sepal.Length') 4 5 6 7 8 0.00.10.20.30.4 Density of Sepal.Length N = 150 Bandwidth = 0.2736 Density 17 / 45
  • 18.
    Pie Chart Frequency offactors: table() library(dplyr) iris2 <- iris %>% sample_n(50) iris2$Species %>% table() %>% pie() # add percentages tab <- iris2$Species %>% table() precentages <- tab %>% prop.table() %>% round(3) * 100 txt <- paste0(names(tab), 'n', precentages, '%') pie(tab, labels=txt) setosa versicolor virginica setosa 38% versicolor 36% virginica 26% 18 / 45
  • 19.
    Bar Chart iris2$Species %>%table() %>% barplot() # add colors and percentages bb <- iris2$Species %>% table() %>% barplot(axisnames=F, main='Species', ylab='Frequency', col=c('pink', 'lightblue', 'lightgreen')) text(bb, tab/2, labels=txt, cex=1.5) setosa versicolor virginica 051015 Species Frequency 051015 setosa 38% versicolor 36% virginica 26% 19 / 45
  • 20.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 20 / 45
  • 21.
    Correlation Covariance and correlation:cov() and cor() cov(iris$Sepal.Length, iris$Petal.Length) ## [1] 1.274315 cor(iris$Sepal.Length, iris$Petal.Length) ## [1] 0.8717538 cov(iris[, 1:4]) ## Sepal.Length Sepal.Width Petal.Length Petal.Width ## Sepal.Length 0.6856935 -0.0424340 1.2743154 0.5162707 ## Sepal.Width -0.0424340 0.1899794 -0.3296564 -0.1216394 ## Petal.Length 1.2743154 -0.3296564 3.1162779 1.2956094 ## Petal.Width 0.5162707 -0.1216394 1.2956094 0.5810063 # cor(iris[,1:4]) 21 / 45
  • 22.
    Aggreation Stats of Sepal.Lengthfor every Species with aggregate() aggregate(Sepal.Length ~ Species, summary, data = iris) ## Species Sepal.Length.Min. Sepal.Length.1st Qu. ## 1 setosa 4.300 4.800 ## 2 versicolor 4.900 5.600 ## 3 virginica 4.900 6.225 ## Sepal.Length.Median Sepal.Length.Mean Sepal.Length.3rd Qu. ## 1 5.000 5.006 5.200 ## 2 5.900 5.936 6.300 ## 3 6.500 6.588 6.900 ## Sepal.Length.Max. ## 1 5.800 ## 2 7.000 ## 3 7.900 22 / 45
  • 23.
    Boxplot The bar inthe middle is median. The box shows the interquartile range (IQR), i.e., range between the 75% and 25% observation. boxplot(Sepal.Length ~ Species, data = iris) setosa versicolor virginica 4.55.05.56.06.57.07.58.0 23 / 45
  • 24.
    Scatter Plot with(iris, plot(Sepal.Length,Sepal.Width, col = Species, pch = as.numeric(Species))) 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 2.02.53.03.54.0 Sepal.Length Sepal.Width 24 / 45
  • 25.
    Scatter Plot withJitter Function jitter(): add a small amount of noise to the data with(iris, plot(jitter(Sepal.Length), jitter(Sepal.Width), col=Species,pch=as.numeric(Species))) 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 2.02.53.03.54.0 jitter(Sepal.Length) jitter(Sepal.Width) 25 / 45
  • 26.
    A Matrix ofScatter Plots pairs(iris) Sepal.Length2.03.04.00.51.52.5 4.5 5.5 6.5 7.5 2.0 3.0 4.0 Sepal.Width Petal.Length 1 2 3 4 5 6 7 0.5 1.5 2.5 Petal.Width 4.55.56.57.51234567 1.0 2.0 3.0 1.02.03.0 Species 26 / 45
  • 27.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 27 / 45
  • 28.
    3D Scatter plot library(scatterplot3d) scatterplot3d(iris$Petal.Width,iris$Sepal.Length, iris$Sepal.Width) 0.0 0.5 1.0 1.5 2.0 2.5 2.02.53.03.54.04.5 4 5 6 7 8 iris$Petal.Width iris$Sepal.Length iris$Sepal.Width 28 / 45
  • 29.
    Interactive 3D ScatterPlot Package rgl supports interactive 3D scatter plot with plot3d(). library(rgl) plot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width) 29 / 45
  • 30.
    Heat Map Calculate thesimilarity between different flowers in the iris data with dist() and then plot it with a heat map dist.matrix <- as.matrix(dist(iris[, 1:4])) heatmap(dist.matrix) 422314943394132463674831634154561921322425274417333749112247202631303510385411250284082918111910612313211813110811013613010312610114412114561999458658081826383936860709054107855667627291899796100955276665755598869987586797492641091371051251411461421401131041381171161491291331151351121111487853518777841501471241341271281397173120122114102143 422314943394132463674831634154561921322425274417333749112247202631303510385411250284082918111910612313211813110811013613010312610114412114561999458658081826383936860709054107855667627291899796100955276665755598869987586797492641091371051251411461421401131041381171161491291331151351121111487853518777841501471241341271281397173120122114102143 30 / 45
  • 31.
    Level Plot Function rainbow()creates a vector of contiguous colors. rev() reverses a vector. library(lattice) levelplot(Petal.Width ~ Sepal.Length * Sepal.Width, data=iris, cuts=8) Sepal.Length Sepal.Width 2.0 2.5 3.0 3.5 4.0 5 6 7 0.0 0.5 1.0 1.5 2.0 2.5 31 / 45
  • 32.
    Contour contour() and filled.contour()in package graphics contourplot() in package lattice filled.contour(volcano, color=terrain.colors, asp=1, plot.axes=contour(volcano, add=T)) 100 120 140 160 180 100 100 100 110 110 110 110 120 130 140 150 160 160 170 170 180 180 190 32 / 45
  • 33.
    3D Surface persp(volcano, theta= 25, phi = 30, expand = 0.5, col = "lightblue") volcano Y Z 33 / 45
  • 34.
    Parallel Coordinates Visualising multipledimensions library(MASS) parcoord(iris[1:4], col = iris$Species) Sepal.Length Sepal.Width Petal.Length Petal.Width 34 / 45
  • 35.
    Parallel Coordinates withPackage lattice library(lattice) parallelplot(~iris[1:4] | Species, data = iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min Max setosa versicolor Sepal.Length Sepal.Width Petal.Length Petal.Width virginica 35 / 45
  • 36.
    Visualisation with Packageggplot2 library(ggplot2) qplot(Sepal.Length, Sepal.Width, data = iris, facets = Species ~ .) setosaversicolorvirginica 5 6 7 8 2.0 2.5 3.0 3.5 4.0 4.5 2.0 2.5 3.0 3.5 4.0 4.5 2.0 2.5 3.0 3.5 4.0 4.5 Sepal.Length Sepal.Width 36 / 45
  • 37.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 37 / 45
  • 38.
    Save Charts toFiles Save charts to PDF and PS files: pdf() and postscript() BMP, JPEG, PNG and TIFF files: bmp(), jpeg(), png() and tiff() Close files (or graphics devices) with graphics.off() or dev.off() after plotting # save as a PDF file pdf("myPlot.pdf") x <- 1:50 plot(x, log(x)) graphics.off() # Save as a postscript file postscript("myPlot2.ps") x <- -20:20 plot(x, x^2) graphics.off() 38 / 45
  • 39.
    Save ggplot Chartsto Files ggsave(): by defult, saving the last plot that you displayed. It also guesses the type of graphics device from the extension. ggsave("myPlot3.png") ggsave("myPlot4.pdf") ggsave("myPlot5.jpg") ggsave("myPlot6.bmp") ggsave("myPlot7.ps") ggsave("myPlot8.eps") 39 / 45
  • 40.
    Contents Introduction Have a Lookat Data Explore Individual Variables Explore Multiple Variables More Explorations Save Charts to Files Further Readings and Online Resources 40 / 45
  • 41.
    Further Readings Examples ofggplot2 plotting: https://ggplot2.tidyverse.org/ Package iplots: interactive scatter plot, histogram, bar plot, and parallel coordinates plot (iplots) http://rosuda.org/software/iPlots/ Package googleVis: interactive charts with the Google Visualisation API http://cran.r-project.org/web/packages/googleVis/vignettes/ googleVis_examples.html Package ggvis: interactive grammar of graphics http://ggvis.rstudio.com/ Package rCharts: interactive javascript visualisations from R https://ramnathv.github.io/rCharts/ 41 / 45
  • 42.
    Online Resources Book titledR and Data Mining: Examples and Case Studies http://www.rdatamining.com/docs/RDataMining-book.pdf R Reference Card for Data Mining http://www.rdatamining.com/docs/RDataMining-reference-card.pdf Free online courses and documents http://www.rdatamining.com/resources/ RDataMining Group on LinkedIn (27,000+ members) http://group.rdatamining.com Twitter (3,300+ followers) @RDataMining 42 / 45
  • 43.
  • 44.
    How to CiteThis Work Citation Yanchang Zhao. R and Data Mining: Examples and Case Studies. ISBN 978-0-12-396963-7, December 2012. Academic Press, Elsevier. 256 pages. URL: http://www.rdatamining.com/docs/RDataMining-book.pdf. BibTex @BOOK{Zhao2012R, title = {R and Data Mining: Examples and Case Studies}, publisher = {Academic Press, Elsevier}, year = {2012}, author = {Yanchang Zhao}, pages = {256}, month = {December}, isbn = {978-0-123-96963-7}, keywords = {R, data mining}, url = {http://www.rdatamining.com/docs/RDataMining-book.pdf} } 44 / 45
  • 45.
    References I Frank, A.and Asuncion, A. (2010). UCI machine learning repository. university of california, irvine, school of information and computer sciences. http://archive.ics.uci.edu/ml. 45 / 45