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Examples For ggplot2

Author: Mitch Richling
Updated: 2022-06-04 16:17:46

Copyright 2020-2021 Mitch Richling. All rights reserved.

Table of Contents

1. Metadata

The home for this HTML file is: https://richmit.github.io/ex-R/ggplot.html

Files related to this document may be found on github: https://github.com/richmit/ex-R

Directory contents:

src - The org-mode file that generated this HTML document
docs - This html document
data - Data files
tangled - Tangled R code from this document

2. Setup

2.1. Load libraries

ggplot2 is the graphics library, and gridExtra helps us lay things out. 

library(ggplot2)
library(gridExtra)
library(reshape2)
library(scales)

2.2. Create some data sets we can graph

numPnt <- 1000
numCat <- 4
someData <- data.frame(xi=(1:numPnt)/numPnt,
                       xi2=(1:numPnt)/numPnt+2,
                       xd=seq(as.POSIXct('2012-01-01'), by='day', length.out=numPnt),
                       rn=runif(numPnt, -.6, .6),
                       m=rep(1:numCat,(numPnt/numCat)))
someData$rm <- someData$rn*someData$m
someData$y  <- someData$m*(someData$xi-0.5)+0.5+someData$rm
someData$y2 <- 2.5*(someData$xi-0.5)+0.5
someData$k1 <- paste('K1F', someData$m, sep='_')

someMoreData <- read.table(header=T, text='
 quarter factors   value emin emax
      Q1 factor1      15   14   16
      Q2 factor1      25   22   29
      Q3 factor1       5    1    6
      Q4 factor1      10    8   11
      Q1 factor2      25   22   23
      Q2 factor2      20   19   21
      Q3 factor2      25   20   26
      Q4 factor2      10   10   11
 ')

someLessData <- subset(someMoreData, someMoreData$factors=='factor1')
someLessData$factors <- factor(someLessData$factors)

twoDimData  <-expand.grid(x=seq(from=-4, to=4, length.out=30), y=seq(from=-3, to=5, length.out=30))
twoDimData$z<-sin(twoDimData$x*twoDimData$x+twoDimData$y*twoDimData$y)
twoDimData$p<-sign(twoDimData$z)

volcanoDF   <- stack(as.data.frame(volcano))
volcanoDF$x <- rep(seq_len(nrow(volcano)), ncol(volcano))
volcanoDF$y <- rep(seq_len(ncol(volcano)), rep(nrow(volcano), ncol(volcano)))

twoDimNormal <- data.frame(x=rnorm(10000, sd=2.0),
                           y=rnorm(10000))
twoDimNormal$y <- twoDimNormal$y+twoDimNormal$x*.75

3. Two Corrilated Variables

3.1. x-y scatter plot

ggplot(data=someData, aes(x=xi, y=y)) +
  geom_point(col='red') ## Make the points RED -- very diffrent: geom_point(aes(col='red'))

2c-basic.png

3.2. Time Series scatter plot broken down by a factor (Fancy Axis)

ggplot(data=someData, aes(x=xd, y=y, col=k1)) +
  scale_x_datetime(breaks       = scales::date_breaks("1 year"),  ## Set major break lines to 1 year
                   minor_breaks = scales::date_breaks("1 month"), ## Set minor break lines to 1 year
                   labels       = scales::date_format("%Y")) +    ## Set the date format
  geom_point(size=3, pch=21) +                                    ## BIG Circles for points
  scale_colour_discrete(name ="Legend Title\nLine 2",
                        breaks=c("K1F_4", "K1F_1", "K1F_2", "K1F_3"),
                        labels=c("k1f_4", "k1f_1", "k1f_2", "k1f_3"))

2dc-cats1.png

3.3. x-y scatter plot broken down by a factor (Fancy Axis)

ggplot(data=someData, aes(x=xi, y=y, col=k1)) + geom_point() +
  #theme(panel.grid.major=element_blank())          ## Zap major grid lines
  #theme(panel.grid.minor=element_blank())          ## Zap minor grid lines
  #theme(axis.ticks=element_blank())                ## x-axis: Zap ticks
  #theme(axis.text.x=element_blank())               ## x-axis: Zap Labels
  scale_y_continuous(breaks=c(1, 1.5, 2.5, 5, 6,8)) ## y-axis: Set location for ticks and grid lines
  #scale_y_continuous(breaks=NULL)                  ## Zap y tick and grid lines
  #scale_x_continuous(breaks=NULL)                  ## Zap x tick and grid lines
  #scale_x_continuous(limits=c(-1, 2))              ## Remove data outside the limits
  #xlim(NA, 2)                                      ## Like above.  Use NA to not set limit.
  #coord_cartesian(xlim = c(-1, 2))                 ## Simply set the visible region.

2dc-cats2.png

3.4. x-y scallter plot broken down by factor (fancy titles)

Adding a 'ribbon' of color to a plot (useful for things like confidence intervals and ranges around a smoother). Also demo some crazy title and legend stuff. 

ggplot(data=someData, aes(x=xd)) + 
  geom_ribbon(aes(ymin=y2-1, ymax=y2+1), alpha=.3, fill='pink', col='grey') +  ## alpha is important
  geom_point(aes(y=y, col=k1)) +                                               ## Colorful points
  geom_line(aes(y=y2), col='black') +                                          ## Center line
  labs(title    = "Title\nLine 2 Of Title",
       subtitle = "Subtitle",
       tag      = 'TAG TAG\nTAG TAG\nTAG TAG\n',
       x        = 'x-title\nLine 2 Of x-title',
       y        = 'y-title\nLine 2 Of y-title',
       caption  = "Caption Line 1 Caption Line 1 Caption Line 1\nCaption Line 3\nCaption Line 4") +
  theme(plot.title    = element_text(lineheight=0.7, face="bold",   colour="#aa0000", size=20, angle=0))  +
  theme(plot.subtitle = element_text(lineheight=0.7, face="bold",   colour="#fa0000", size=18, angle=0))  +
  theme(plot.caption  = element_text(lineheight=0.8, face="plain",  colour="#0000aa", size=12, angle=0))  +
  theme(plot.tag      = element_text(lineheight=0.7, face="plain",  colour="#00aa00", size=12, angle=0))  +
  theme(axis.title.x  = element_text(lineheight=0.7, face="plain",  colour="#ffa000", size=18, angle=0))  +
  theme(axis.title.y  = element_text(lineheight=0.7, face="italic", colour="#ff00a0", size=18, angle=90)) +
  theme(axis.text.x   = element_text(angle=0, color='red',   vjust=0.5, size=10))                         +
  theme(axis.text.y   = element_text(angle=0, color='brown', vjust=0.5, size=10))                         +
  #theme(legend.title=element_blank()) +  ## This is how you nix a legend title entirely
  guides(col=guide_legend(title="HELLO")) +                                    
  theme(legend.title = element_text(colour="#00ffa0", size=10, face="bold.italic"),
        legend.text  = element_text(colour="#60ff00", size=8,  face="plain"),
        legend.position="right") ## Put the legend on the right side of the graph

2dc-cats3.png

3.5. x-y scatter plot with facets instead of color.

ggplot(data=someData, aes(x=xd, y=y, col=k1)) +  ## col=k1 has nothing to do with facets
  geom_point() +
  facet_wrap(~k1, ncol=2) +   ## Gets facets in a grid
  #facet_grid(.~k1)  +        ## Gets facets arranged left to right
  #facet_grid(k1~.)  +        ## Gets facets arranged top to bottom
  labs(title = 'Main Title',  
       x     = 'X Title',     
       y     = 'Y Title') +   
  theme(strip.text.x = element_text(size=8, face="plain", colour='red',  angle=0),  ## y-axis strip text
        strip.text.y = element_text(size=8, face="bold",  colour='blue', angle=90), ## y-axis strip text
        strip.background = element_rect(colour="blue", fill="pink"))                ## Strip border and fill

2dc-facet1.png

3.6. x-y scatter plots with facets and trends (lowess & linear regression)

ggplot(data=someData, aes(x=xd, y=y)) +
  geom_point(col='pink') +                             ## Draw the points in pink
  #geom_smooth(method="loess") +                       ## DEFAULT. lowess.  Show confidence interval. 
  #geom_smooth(method="lm") +                          ## Linear model
  geom_smooth(method="lm", level=0.9999) +             ## Linear model with explicit confidence level
  #geom_smooth(method="lm", se=FALSE) +                ## Don't show the confidence interval
  #geom_smooth(method="gam", formula = y~s(x)) +       ## Generalised additive model.  Needs library(mgcv)
  #geom_smooth(method="rlm") +                         ## Robust linear regression. Needs library(MASS)
  facet_wrap(~k1, ncol=2)

2dc-facet2.png

3.7. Simple x-y graphs with linear regression lines

ggplot(data=someData, aes(x=xd, y=y, col=k1)) +
  geom_point() +                                      ## Draw points
  geom_smooth(method="lm", se=FALSE)                  ## Don't show the confidence interval
  #geom_smooth(method="loess", span=.2, se=FALSE)     ## lowess.  No confidence interval

2dc-lr1.png

3.8. linear regression used for future prediction

expandedRange <- c(min(someData$xi),                     ## Range from min to max+1/2 the range.
                   max(someData$xi) +
                       diff(range(someData$xi))/2) 
ggplot(data=someData, aes(x=xi, y=y)) +
  scale_x_continuous(limits = expandedRange) +         ## Extend the x-axis. coord_cartesian won't work here.
  #geom_line() +                                       ## Add this if you want to connect the dots. ;)
  geom_point() +                                       ## You can also use points!
  geom_smooth(method="lm", fullrange=TRUE, level=0.99) ## Linear model with a .99 confidence interval

2dc-lrf.png

4. Distribution Comparison

4.1. Box-n-Wisker

ggplot(data=someData, aes(x=k1, y=y)) + 
  geom_boxplot(col='red', fill='pink')

dc-baw.png

4.2. Colorful Box-n-Wisker

ggplot(data=someData, aes(x=k1, y=y, fill=k1))+
  geom_boxplot(show.legend=FALSE)                  ## Suppress the legend

dc-bawcolor.png

4.3. Category labels on the axis with no legend

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) +  
  geom_boxplot(col='black', alpha=.4, show.legend=FALSE) +
  scale_x_discrete(labels=c("x1", "x2", "x3", "x4")) +
  scale_fill_discrete(name="Title\nSecond Line Of Title",  ## Set title of legend
                      labels=c("x1", "x2", "x3", "x4"))    ## Set labels of legend

dc-leg1.png

4.4. Category labels on the axis with no legend

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) +  
  geom_boxplot(col='black', alpha=.4) +
  scale_x_discrete(breaks=NULL) +                          ## x-axis: Zap the lables all togehter
  scale_fill_discrete(name="Title\nSecond Line Of Title",  ## Set title of legend
                      labels=c("x1", "x2", "x3", "x4"))    ## Set labels of legend

dc-leg2.png

4.5. A standard violin plot

Note: The white borders help the regions stand out 

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) + 
  geom_violin(col='white',             ## Add white border on the violins
              show.legend=FALSE)       ## Suppress the legend

dc-v.png

4.6. Combine a violin and box-n-wisker plot

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) + 
  geom_boxplot(col='black', alpha=.4) +
  geom_violin(alpha=.25, col=NA) +
  theme(legend.position="none")

dc-vpbnw.png

5. 2D Data

5.1. Images

5.1.1. Simple Example

ggplot(data=twoDimData, aes(x=x, y=y, fill=z)) +
  geom_tile() 
  #geom_raster() ## geom_raster() is faster, but requires length(x)==length(y)

2di-basic.png

5.1.2. A dot in each cell scaled to abs(z)

ggplot(data=twoDimData, aes(x=x, y=y)) +
  geom_tile(aes(fill=z)) +
  geom_point(aes(size=abs(z)), col='red')

2di-dots.png

5.1.3. White text in each cell

ggplot(data=twoDimData, aes(x=x, y=y)) +
  geom_tile(aes(fill=z)) +
  geom_text(aes(label=p), col='white', size=4)

2di-text.png

5.1.4. Text in each cell with a color set by the z value

ggplot(data=twoDimData, aes(x=x, y=y)) +
  geom_tile(aes(fill=z)) +
  geom_text(aes(label=p), col=c('red', 'black', 'green')[sign(twoDimData$z)+2], size=4)

2di-txtcol.png

5.1.5. Contours in white

ggplot(data=volcanoDF, aes(x=x, y=y)) +
  geom_raster(aes(fill=values), interpolate=TRUE) +  # tile has no "interpolate" option.
  geom_contour(aes(z=values), col='white', size=1)

2di-cont1.png

5.1.6. Contours in white (via stat_contour instead of geom_tile)

ggplot(data=volcanoDF, aes(x=x, y=y, z=values)) +
  stat_contour(geom="polygon", aes(fill=..level..))  + 
  stat_contour(col='white', size=1)

2di-cont2.png

5.1.7. Just contour lines colored determined by contour level

ggplot(data=volcanoDF, aes(x=x, y=y, z=values)) +
  geom_contour(aes(col=..level..), size=2)              ##  Fatten up the line so the color shows up

2di-ccont.png

5.2. Histograms

5.2.1. Rectangular or hexagon bins

ggplot(data=twoDimNormal, aes(x=x,y=y)) + 
  #geom_rug() +                           ## Add a rug (dot-plot) to each axis for lower density plots
  stat_bin2d(aes(fill=..count..))         ## Use this for rectangular bins!
  #stat_binhex(aes(fill=..count..))       ## Use this for hexagon bins.

2d-hexhist.png

5.2.2. Density Estimation via scatterplot with semi-transparent data points

ggplot(data=twoDimNormal, aes(x=x,y=y)) +
  geom_point(alpha=.2, col='red') +           ## Alpha to visually indicate density
  #geom_rug() +                               ## Add a rug (dot-plot) to each axis for lower density plots
  geom_density2d(col='black', size=1)         ## Put contour lines after points to make sure we can see them.

2d-densa.png

5.2.3. Density Estimation via a filled contour graph

ggplot(data=twoDimNormal, aes(x=x,y=y)) +
  geom_point(alpha=.5, col='black') +         ## Show outlier with dots (must be first)
  #geom_rug() +                               ## Add a rug (dot-plot) to each axis for lower density plots
  stat_density2d(aes(fill = ..level..),       ## Fill in the contour graph -- covering up non-outlier points.
                 geom="polygon", col='white')

2d-densc.png

5.2.4. Scatter plot with marginal histograms

histTop <- ggplot(twoDimNormal) +                                     ## Create histogram that goes at the top
  geom_histogram(aes(x=x),
                 col='white',
                 fill='red',
                 binwidth=diff(range(twoDimNormal$x))/50) +
  theme(axis.ticks = element_blank(),
        axis.text.x = element_text(margin=margin(0,0,0,0,"pt")),
        plot.margin = unit(c(0,0,0,0),"lines"),
        axis.title.x = element_blank(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.length = unit(0,"null")) +
  scale_x_continuous(limits=range(twoDimNormal$x))

histRight <- ggplot(twoDimNormal) +                                   ## Create histogram that goes at the right
  geom_histogram(aes(x=y),
                 col='white',
                 fill='red',
                 binwidth=diff(range(twoDimNormal$y))/50) +
  coord_flip() +
  theme(axis.text.x = element_blank(),
        axis.text.y = element_text(margin=margin(0,0,0,0,"pt")),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0,0,0,0),"lines"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.length = unit(0,"null")) +
  scale_x_continuous(limits=range(twoDimNormal$y))

maxCount = max(c(max(ggplot_build(histTop)$data[[1]]$count),          ## Set idential scales for histograms
                 max(ggplot_build(histRight)$data[[1]]$count)))
histTop   <- histTop + scale_y_continuous(limits=c(0,maxCount+1))
histRight <- histRight + scale_y_continuous(limits=c(0,maxCount+1))

scatter <- ggplot(twoDimNormal)+                                      ## Create scatter plot in the center.  
  geom_point(aes(x=x,y=y), col=rgb(1,0,0,.05)) +
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0,0,0,0),"lines"),
        axis.title.x = element_blank(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.length = unit(0,"null")) +
  scale_x_continuous(limits=range(twoDimNormal$x)) + 
  scale_y_continuous(limits=range(twoDimNormal$y))

aGrob <- arrangeGrob(histTop,                                         ## Put it all together
                     grob(),
                     scatter,
                     histRight,
                     ncol=2,
                     nrow=2,
                     widths=c(3, 1),
                     heights=c(1, 3))
grid.newpage()
grid.draw(aGrob)

scat-hist.png

6. Barcharts

6.1. With pre-computed data

ggplot(data=someLessData, aes(x=quarter, y=value)) +
  geom_bar(stat='identity', col='black', fill='red') +       ## Draws red bars with black borders
  geom_text(aes(label=value), vjust='top', nudge_y=-0.25) +  ## Adds the numerical label to each bar
  theme(panel.grid.minor.x=element_blank(),                  ## Get rid of the vertical grid lines
        panel.grid.major.x=element_blank())

bar-basic.png

6.2. Barcharts with pre-computed data with color filled bars

ggplot(data=someLessData, aes(x=quarter, y=value, fill=quarter)) +
  geom_bar(stat='identity', col='black', show.legend=FALSE) +        ## col for black lines between bars
  geom_label(aes(label=value), fill='white', vjust='center') +       ## Number on each bar
  theme(panel.grid.minor.x=element_blank(),                          ## Get rid of the vertical grid lines
        panel.grid.major.x=element_blank())

bars-fill.png

6.3. Stacked barchart

Note: This form of barchart is easily misread by may people. Not generally recommended. 

ggplot(data=someMoreData, aes(x=quarter, y=value, fill=factors)) +
  guides(fill=guide_legend(override.aes=list(colour=NA))) +       ## Zap slash across the legend color boxes
  geom_bar(stat='identity', col='black', position="stack") +
  theme(panel.grid.minor.x=element_blank(),                       ## Get rid of the vertical grid lines
        panel.grid.major.x=element_blank())

bar-stack.png

6.4. Side by side barchart

ggplot(data=someMoreData, aes(x=quarter, y=value, fill=factors)) +
  guides(fill=guide_legend(override.aes=list(colour=NA))) +       ## Zap slash across the legend color boxes
  geom_bar(stat='identity', col='black', position="dodge") +
  theme(panel.grid.minor.x=element_blank(),                       ## Get rid of the vertical grid lines
        panel.grid.major.x=element_blank())

bar-sbs.png

6.5. Stacked with constant height

Note: With more than two colors per bar, this form of barchart is easily misread by may people. Not generally recommended. 

ggplot(data=someMoreData, aes(x=quarter, y=value, fill=factors)) +
  guides(fill=guide_legend(override.aes=list(colour=NA))) +       ## Zap slash across the legend color boxes
  geom_bar(stat='identity', col='black', position="fill") +
  theme(panel.grid.minor.x=element_blank(),                       ## Get rid of the vertical grid lines
        panel.grid.major.x=element_blank())

bar-stackunit.png

6.6. Circular bar chart

Note: Yhis form of chart is easily misread by may people. Not generally recommended. 

ggplot(someLessData, aes(x=factor(1), y=value, fill=quarter)) +
  geom_bar(width=1, stat='identity', col='black') +         ## col puts black lines between slices
  guides(fill=guide_legend(override.aes=list(colour=NA))) + ## Get rid of the slash across the legend color boxes
  coord_polar(theta='y') +                                  ## This is how it gets round
  theme(axis.ticks=element_blank(),                         ## Get rid of axis ticks and labels
        axis.text.y=element_blank(),
        axis.text.x=element_text(colour='black')) +
  labs(title='Main Title',                                  ## You can set the title, but the x & y are ignored
       x='', y='')                                          ## Ignored!

bar-pie.png

7. 1D Histograms

7.1. Basic

ggplot(data=someData, aes(x=rn)) +
  geom_histogram(col = "black", fill = "red", binwidth=diff(range(someData$rn))/20)

1dh-basic.png

7.2. With percentage instead of count

ggplot(data=someData, aes(x=rn, y = (..count..)/sum(..count..))) +
  geom_histogram(col = "black", fill = "red", binwidth=diff(range(someData$rn))/20) + 
  scale_y_continuous(labels=percent) +
  labs(y='%')

1dh-perc.png

7.3. Hard coded, inconsistantly sized, bin breaks

ggplot(data=someData, aes(x=rn)) +
  geom_histogram(col = "black", fill = "red", breaks=c(-1.0,-0.5,-0.25,-0.1,0.0,0.1,0.25,0.5,1.0))

1dh-badbins.png

7.4. Fill color determined by bin count

ggplot(data=someData, aes(x=rn, fill=..count..)) + 
  geom_histogram(col = "black", binwidth = .1) +   ## 'col' gets us black lines separating bars
  theme(legend.position="none")                    ## legend provides no new information (y-axis shows bar height)

1dh-cbc.png

7.5. With a density curve

ggplot(data=someData, aes(x=rn, y=..density..)) +
  geom_histogram(col = "black", fill = "red", binwidth = .11) + ## First so we always see density line.
  geom_density(col = "blue", size=2)                            ## Fatten up line so we can see it

1dh-dens.png

8. Pallets

8.1. Completely custom

For example, a color blind safe palette from http://jfly.iam.u-tokyo.ac.jp/color/ 

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) +
  geom_boxplot(col='black', alpha=.4) +
  scale_fill_manual(values=cbPalette)

pal-custom1.png 

ggplot(data=someData, aes(x=xd, y=y, col=k1)) + geom_point() +
  scale_colour_manual(values=cbPalette)

pal-custom2.png

8.2. Colorbrewer

8.2.1. Sequential palettes

display.brewer.all(type="div")

pal-cb-div.png

8.2.2. Diverging palettes

display.brewer.all(type="seq")

pal-cb-seq.png

8.2.3. Qualitative palettes

display.brewer.all(type="qual")

pal-cb-qual.png

8.2.4. With ggplot2

ggplot(data=someData, aes(x=k1, y=y, fill=k1)) +
  geom_boxplot(col='black', alpha=.4) +
  scale_fill_brewer(palette="Set2")

pal-cb1.png 

ggplot(data=someData, aes(x=xd, y=y, col=k1)) + 
  geom_point() +
  scale_colour_brewer(palette="Set1")

pal-cb2.png

9. Miscellaneous Stuff

9.1. Working without data.frame

9.1.1. Sequence Plot of ONE Vector

Note: Like plot(x) 

ggplot(data=someData, aes(y=y, x=seq_along(y))) + geom_point()

basic-seq.png

9.1.2. Scatterplot with a two vectors

Note: Like plot(x, y) 

ggplot() + geom_point(aes(x=rnorm(100), y=rnorm(100)))

basic-scat.png

9.2. Annotate within the plot region

qplot(data=someData,x=xi, y=y, color=k1) +
  geom_abline(intercept=0, slope=1, col='blue', size=3) +                                      ## line
  geom_hline(yintercept=-.5, col='red') +                                                      ## horizontal line
  geom_vline(xintercept=.55, col='red') +                                                      ## vertical line
  annotate("text", x=.25, y=.75, label="HI", col='red', size=14) +                             ## Text
  annotate("rect", xmin=0, xmax=.5, ymin=0, ymax=1.5, alpha=.1, fill='red', col='black') +     ## Rectangles
  annotate("segment", x=0.0, xend=0.25, y=-2.0, yend=-1.0) +                                   ## Segments
  annotate("segment", x=1.0, xend=0.75, y=-2.0, yend=-1.0, arrow=arrow(length=unit(0.5,"cm"))) ## Arrows!

anno.png

9.3. Combine Graphs

9.3.1. From potentially from different data frames on the same set of axes

ggplot() +
  geom_point(data=twoDimNormal, aes(x=x,  y=y, col='dots'),    size=2, alpha=0.25) +
  geom_line(data=someData,     aes(x=xi2+3, y=y2, col='line'), size=1, alpha=1.0) +
  scale_colour_manual(name='foo', 
                      values=c('dots'='red',     'line'='blue'), 
                      labels=c('line'='Da Line', 'dots'='Da Dots'))

misc-combine.png

10. Area graphs

10.1. Standard

ggplot(data=subset(someData, k1=='K1F_1'), aes(x=xi, y=abs(y))) +
  geom_area(col = "black", fill='red')

area-basic.png

10.2. Stacked

Note: Yhis form of chart is easily misread by may people. Not generally recommended. 

ggplot(data=someData, aes(x=xi, y=abs(y), fill=k1)) +
  geom_area(stat='identity', position="stack")

area-stack.png