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<!-- README.md is generated from README.Rmd. Please edit that file -->
< p > < a href = "http://www.repostatus.org/#active" > < img src = "data:image/svg+xml;base64,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" alt = "Project Status: Active - The project has reached a stable, usable state and is being actively developed." / > < / a > < a href = "http://cran.r-project.org/web/packages/ggalt" > < img src = "data:image/svg+xml; charset=utf-8;base64,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" alt = "CRAN_Status_Badge" / > < / a > < img src = "data:image/svg+xml; charset=utf-8;base64,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" alt = "downloads" / > < / p >
< p > < code > ggalt< / code > : Alternate/Extra ‘Geoms’, ‘Stats’ and ‘Coords’ for ‘ggplot2’< / p >
< p > A package containing additional/alternate ‘geoms’, ‘coords’ and ‘stats’ for use with ggplot2 2.0+.< / p >
< p > The first three forays into this brave, new < code > ggplot2< / code > world are < em > splines< / em > ! and being able to use the (much better) < code > KernSmooth::bkde< / code > and < code > KernSmooth::bkde2D< / code > for density plots and an initial port of the (still needing work) < code > coord_proj< / code > .< / p >
< p > The following functions are implemented:< / p >
< ul >
< li > < code > coord_proj< / code > : Like < code > coord_map< / code > only better 😜< / li >
< li > < code > geom_xspline< / code > : Connect control points/observations with an X-spline< / li >
< li > < code > stat_xspline< / code > : Connect control points/observations with an X-spline< / li >
< li > < code > geom_bkde< / code > : Display a smooth density estimate (uses < code > KernSmooth::bkde< / code > )< / li >
< li > < code > stat_bkde< / code > : Display a smooth density estimate (uses < code > KernSmooth::bkde< / code > )< / li >
< li > < code > geom_bkde2d< / code > : Contours from a 2d density estimate. (uses < code > KernSmooth::bkde2D< / code > )< / li >
< li > < code > stat_bkde2d< / code > : Contours from a 2d density estimate. (uses < code > KernSmooth::bkde2D< / code > )< / li >
< li > < code > stat_ash< / code > : Compute and display a univariate averaged shifted histogram (polynomial kernel) (uses < code > ash::ash1< / code > /< code > ash::bin1< / code > )< / li >
< / ul >
< div id = "news" class = "section level3" >
< h3 > News< / h3 >
< ul >
< li > Version 0.1.1 released - CRAN!< / li >
< li > Version 0.1.0.9000 released - Tweaks for ggplot2 2.0 release< / li >
< li > Version 0.0.4.9000 released - < code > stat_ash< / code > < / li >
< li > Version 0.0.3.9000 released - < code > coord_proj< / code > ! (requires my github copy of ggplot2 for now)< / li >
< li > Version 0.0.2.9005 released - cleanup before blog post< / li >
< li > Version 0.0.2.9002 released - working 2D density plots< / li >
< li > Version 0.0.2.9000 released< / li >
< / ul >
< / div >
< div id = "installation" class = "section level3" >
< h3 > Installation< / h3 >
< pre class = "r" > < code > # you'll want to see the vignettes, trust me
install.packages(" ggplot2" )
install.packages(" ggalt" )
# OR: devtools::install_github(" hrbrmstr/ggalt" )< / code > < / pre >
< / div >
< div id = "usage" class = "section level3" >
< h3 > Usage< / h3 >
< pre class = "r" > < code > library(ggplot2)
library(gridExtra)
library(ggalt)
# current verison
packageVersion(" ggalt" )
#> [1] '0.1.1'
set.seed(1492)
dat < - data.frame(x=c(1:10, 1:10, 1:10),
y=c(sample(15:30, 10), 2*sample(15:30, 10), 3*sample(15:30, 10)),
group=factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))
)< / code > < / pre >
< / div >
< div id = "splines" class = "section level3" >
< h3 > Splines!< / h3 >
< pre class = "r" > < code > ggplot(dat, aes(x, y, group=group, color=group)) +
geom_point() +
geom_line()< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point() +
geom_line() +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(spline_shape=-0.4, size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(spline_shape=0.4, size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(spline_shape=1, size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(spline_shape=0, size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color=" black" ) +
geom_smooth(se=FALSE, linetype=" dashed" , size=0.5) +
geom_xspline(spline_shape=-1, size=0.5)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< div id = "alternate-better-density-plots" class = "section level4" >
< h4 > Alternate (better) density plots< / h4 >
< pre class = "r" > < code > # bkde
data(geyser, package=" MASS" )
ggplot(geyser, aes(x=duration)) +
stat_bkde(alpha=1/2)
#> Bandwidth not specified. Using '0.14', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(geyser, aes(x=duration)) +
geom_bkde(alpha=1/2)
#> Bandwidth not specified. Using '0.14', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(geyser, aes(x=duration)) +
stat_bkde(bandwidth=0.25)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(geyser, aes(x=duration)) +
geom_bkde(bandwidth=0.25)< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
set.seed(1492)
dat < - data.frame(cond = factor(rep(c(" A" ," B" ), each=200)),
rating = c(rnorm(200),rnorm(200, mean=.8)))
ggplot(dat, aes(x=rating, color=cond)) + geom_bkde(fill=" #00000000" )
#> Bandwidth not specified. Using '0.36', via KernSmooth::dpik.
#> Bandwidth not specified. Using '0.31', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
ggplot(dat, aes(x=rating, fill=cond)) + geom_bkde(alpha=0.3)
#> Bandwidth not specified. Using '0.36', via KernSmooth::dpik.
#> Bandwidth not specified. Using '0.31', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
# ash
set.seed(1492)
dat < - data.frame(x=rnorm(100))
grid.arrange(ggplot(dat, aes(x)) + stat_ash(),
ggplot(dat, aes(x)) + stat_bkde(),
ggplot(dat, aes(x)) + stat_density(),
nrow=3)
#> Estimate nonzero outside interval ab.
#> Bandwidth not specified. Using '0.43', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
cols < - RColorBrewer::brewer.pal(3, " Dark2" )
ggplot(dat, aes(x)) +
stat_ash(alpha=1/3, fill=cols[3]) +
stat_bkde(alpha=1/3, fill=cols[2]) +
stat_density(alpha=1/3, fill=cols[1]) +
geom_rug() +
labs(x=NULL, y=" density/estimate" ) +
scale_x_continuous(expand=c(0,0)) +
theme_bw() +
theme(panel.grid=element_blank()) +
theme(panel.border=element_blank())
#> Estimate nonzero outside interval ab.
#> Bandwidth not specified. Using '0.43', via KernSmooth::dpik.< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< / div >
< / div >
< div id = "alternate-2d-density-plots" class = "section level3" >
< h3 > Alternate 2D density plots< / h3 >
< pre class = "r" > < code > m < - ggplot(faithful, aes(x = eruptions, y = waiting)) +
geom_point() +
xlim(0.5, 6) +
ylim(40, 110)
m + geom_bkde2d(bandwidth=c(0.5, 4))< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< pre class = "r" > < code >
m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = " polygon" )< / code > < / pre >
< p > < img src = "data:image/png;base64,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
< / div >
< div id = "coord_proj-lives-still-needs-work" class = "section level3" >
< h3 > < code > coord_proj< / code > LIVES! (still needs work)< / h3 >
< pre class = "r" > < code > world < - map_data(" world" )
world < - world[world$region != " Antarctica" ,]
gg < - ggplot()
gg < - gg + geom_map(data=world, map=world,
aes(x=long, y=lat, map_id=region))
gg < - gg + coord_proj(" +proj=wintri" )
gg< / code > < / pre >
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< / div >
< div id = "code-of-conduct" class = "section level3" >
< h3 > Code of Conduct< / h3 >
< p > Please note that this project is released with a < a href = "CONDUCT.md" > Contributor Code of Conduct< / a > . By participating in this project you agree to abide by its terms.< / p >
< / div >
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