enter the tidyversewhat is the “tidyverse”? a collection of r packages largely developed by...

EntertheTidyverseBIO5312FALL2017

STEPHANIE J. SPIELMAN,PHD

Whatisthe“tidyverse”?AcollectionofRpackageslargelydevelopedbyHadleyWickhamandothersatRstudio

Haveemergedasstaplesofmodern-daydatascienceinthepast5—10years

Wewillfocuson:• Visualization/plottingwithggplot2• Datamanagementand”wrangling”withdplyr andtidyr•DocumentpresentationwithRMarkdown

FocusisontidydataframesEachvariableformsacolumn.Eachobservationformsarow.Eachtypeofobservationalunitformsatable.

Tidydataprovidesaconsistentapproachtodatamanagementthatgreatlyfacilitatesdownstreamanalysisandviz

WorkingwithtidydataThepackagedplyr canmanipulateandmanagetidydata

Thepackagetidyr canrearrangedatatoconvertto/fromtidydata

Thepackageggplot2 isusedforvisualization/plotting

Thefundamentalverbsofdplyrfilter() selectrowsselect() selectcolumnsmutate() createnewcolumnsgroup_by() establish adatagroupingtally() count observationsinagroupingsummarize() calculate summarystatisticarrange() arrangerows

Therearemorefunctionsbuttheseonesarekey!

Thepipeoperator%>%“Pipes”outputfromonefunction/operationasinputtothenext

## Find the mean of iris sepal lengthsmean.sepal <- mean(iris$Sepal.Length)

## Using %>%mean.sepal <- iris$Sepal.Length %>% mean()

iris$Sepal.Length %>% mean() -> mean.sepal

iris %>% mean(Sepal.Length) -> mean.sepal

“forwardassignment”operatorfollowsthelogicalflowofpiping

## Start simple: display datahead(iris)

## Using %>%iris %>% head()

dplyr demoCommandsindemoareonsjspielman.org/bio5312_fall2017/day2_tidyverse1

Visualizingwithggplot2Thepackageggplot2 isagraphicspackagethatimplementsagrammarofgraphics◦ Operatesondataframes,notvectorslikeBaseR◦ Explicitlydifferentiatesbetweenthedataandtherepresentationofthedata

Theggplot2 grammar

Grammar element* What isit

Data Thedataframebeingplotted

Geometrics Thegeometricshapethatwillrepresentthedata• Point,boxplot,histogram, violin,bar,etc.

Aesthetics Theaesthetics ofthegeometricobject• Color,size,shape,etc.

*Tableistinysubsetofwhatggplot2hastooffer

Example:scatterplot> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point()

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Example:scatterplot> ggplot( iris, aes(x = Sepal.Length, y = Petal.Length) ) + geom_point()

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• Passinthedataframeasyourfirstargument

• Aestheticsmapthedataontoplotcharacteristics,herexandyaxes

• Displaythedatageometricallyaspoints

Example:scatterplotwithcolor> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(color = "red" )

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Example:scatterplotwithaes color> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point()

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setosa

versicolor

virginica

• Placingcolorinsideaesethetic mapsittothedata.

Example:scatterplotwithaes color,shape

> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species, shape = Species)) + geom_point()

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● setosa

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virginica

Aestheticsmaybeplacedinsidetherelevantgeom

> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species, shape = Species))

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● setosa

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> ## Remember dplyr!> iris %>% ggplot(aes(x = Sepal.Length, y =

Petal.Length)) + geom_point(aes(color = Species, shape = Species))

Aestheticsareformappingonly> ### Color all points blue?> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = "blue")) + geom_point()

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colour● blue

Aestheticsareformappingonly> ### Color all points blue?> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = "blue")) + geom_point()

> ### Correctly color all points blue> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(color = "blue")

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colour● blue

Example:multiplegeoms> ### Use some fake data:> fake.data <- data.frame(t = 1:10, y = runif(10, 1, 100))

> ggplot(fake.data, aes(x = t, y = y)) + geom_point() + geom_line()

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> ggplot(fake.data, aes(x = t, y = y, size = y)) + geom_point() + geom_line()

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> ggplot(fake.data, aes(x = t, y = y, size = y)) + geom_point() + geom_line()

> ggplot(fake.data, aes(x = t, y = y)) + geom_point( aes(size=y) ) + geom_line()

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Histograms> ggplot(iris, aes(x = Sepal.Length)) + geom_histogram()

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count

Histograms> ggplot(iris, aes(x = Sepal.Length)) + geom_histogram( fill = "orange" )

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Histograms> ggplot(iris, aes(x = Sepal.Length)) + geom_histogram( fill = "orange", color = "brown" )

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Histograms> ggplot(iris, aes(x = Sepal.Length)) + geom_histogram( fill = "orange", color = "brown" )

+ xlab("Sepal Length") + ylab("Count") + ggtitle("Histogram of iris sepal lengths")

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Cou

nt

Histogram of iris sepal lengths

Boxplots> ggplot(iris, aes(x = "", y = Sepal.Length)) + geom_boxplot()

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Boxplots> ggplot(iris, aes(x = "", y = Sepal.Length)) + geom_boxplot(fill = "green")

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Boxplots> ggplot(iris, aes(x = Species, y = Sepal.Length)) + geom_boxplot(fill = "green")

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setosa versicolor virginicaSpecies

Sepal.Length

Boxplots> ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + geom_boxplot()

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Sepal.Length Species

setosa

versicolor

virginica

Boxplots:Customizingthefillmappings

> ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + geom_boxplot() + scale_fill_manual(values=c("red", "blue", "purple"))

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setosa

versicolor

virginica

scale_fill_manual()alsotweakslegend

> ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + geom_boxplot() + scale_fill_manual(values=c("red", "blue", "purple"), name = "Species name", labels=c("SETOSA", "VIRGINICA", "VERSICOLOR"))

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Sepa

l.Len

gth Species name

SETOSA

VIRGINICA

VERSICOLOR

Changingtheorder> ### Ordering depends on factor levels> levels(iris$Species)

[1] "setosa" "versicolor" "virginica"

> ### Change order of levels> iris$Species <- factor(iris$Species, levels=c("virginica", "setosa", "versicolor"))

[1] "virginica" "setosa" "versicolor"

> ### Replot> ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +

geom_boxplot() + scale_fill_manual(values=c("red", "blue", "purple"))

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virginica setosa versicolorSpecies


virginica

setosa

versicolor

GroupedboxplotsThiswillapplytoviolinplotsaswell.> ## Create another categorical variable for grouping purpopses> iris %>%

group_by(Species) %>%mutate(size = ifelse( Sepal.Width > median(Sepal.Width) , "big" , "small" )) -> iris2

> head(iris2) Source: local data frame [150 x 6]Groups: Species [3]

Sepal.Length Sepal.Width Petal.Length Petal.Width Species size<dbl> <dbl> <dbl> <dbl> <fctr> <chr>

1 5.1 3.5 1.4 0.2 setosa big2 4.9 3.0 1.4 0.2 setosa small3 4.7 3.2 1.3 0.2 setosa small4 4.6 3.1 1.5 0.2 setosa small5 5.0 3.6 1.4 0.2 setosa big6 5.4 3.9 1.7 0.4 setosa big

Condition ValueifTRUE

ValueifFALSE

Groupedboxplots> ggplot(iris2, aes( x = Species, fill=size, y=Sepal.Width)) + geom_boxplot()

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idth size

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small

Groupedboxplots> ggplot(iris2, aes( x = size, fill = Species, y=Sepal.Width)) + geom_boxplot()

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big smallsize

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idth Species

setosa

versicolor

virginica

Detour:scale_color_manual()customizescolor

> ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species)) + scale_color_manual(values=c("cornflowerblue", "deepskyblue4", "lightcyan4"))

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Detourround2:scale_<fill/color>_??Therearemany scalestousebesidesdefaultandcustom.◦ scale_<fil/color>_brewer()usespre-madecolorschemesfromcolorbrewer.org

◦ scale_color_gradient()cantakealowandhightofillalongaspectrum

Seehere:http://ggplot2.tidyverse.org/reference/#scales

Violinplot> ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + geom_violin()

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virginica

setosa

versicolor

Barplot> ggplot(iris, aes(x = Species, fill = Species)) + geom_bar()

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count

Speciesvirginica

setosa

versicolor

Stacked/groupedbarplot> head(iris2)

Source: local data frame [150 x 6]Groups: Species [3]

Sepal.Length Sepal.Width Petal.Length Petal.Width Species size<dbl> <dbl> <dbl> <dbl> <fctr> <chr>

1 5.1 3.5 1.4 0.2 setosa big2 4.9 3.0 1.4 0.2 setosa small3 4.7 3.2 1.3 0.2 setosa small4 4.6 3.1 1.5 0.2 setosa small5 5.0 3.6 1.4 0.2 setosa big6 5.4 3.9 1.7 0.4 setosa big

Stacked/groupedbarplot> ggplot(iris, aes(x = Species, fill = size)) + geom_bar()

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big

small

Stacked/groupedbarplot> ggplot(iris, aes(x = Species, fill = size)) + geom_bar( position = "dodge" )

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small

Densityplot> ggplot(iris, aes(x = Sepal.Length, fill = Species)) + geom_density()

Whatdoesthetailofthesetosa distributionlooklike?

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density

Speciessetosa

versicolor

virginica

Densityplot> ggplot(iris, aes(x = Sepal.Length, fill = Species)) + geom_density( alpha = 0.5 )

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virginica

ThemesGraybackgroundandgridnotworkingforyou?Meneither.

◦ Built-inotherthemes:http://ggplot2.tidyverse.org/reference/ggtheme.html

◦ Customizeyourtheme:http://ggplot2.tidyverse.org/reference/theme.html

◦ Usesomebodyelse'sthemes:◦ https://cran.r-project.org/web/packages/ggthemes/vignettes/ggthemes.html◦ https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html

enter the tidyversewhat is the “tidyverse”? a collection of r packages largely developed by...

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