5. Data Visualization

5.1. Plot time series

Let us come back to Currency dataset and improve the quality of its visualization.

Currency = read.table("http://edu.modas.lu/data/txt/currency.txt",header=T,as.is=T)

First, we can initialize graphical window using x11().

You can draw your image directly to the figure file using special devices: pdf(), png(), tiff(), jpeg(). Always finalize drawing to file by calling dev.off() function (no parameters needed).

Please run line-by-line:

x11()  # try x11(width=8, height=5)

plot(Currency$EUR, pch=19, col = "blue", cex=2)

# pre-defined colors in R
colors()

# custom transparent color in #RRGGBB format:
plot(Currency$EUR, pch=19, col = "#00FF0033", cex=2)

There are many graphical parameters which you can tweak using par() functions. Please, have a look by ?par

Now we will produce a more plot with smoothed interpolation and average rate for each year. We will add lines to existing plot using lines() and abline() functions. text() will write some text on the plot.

pch - dot shape code. Can be set to character, like "a"
cex - size of the dots
main - title
xlab, ylab - axis names
lwd - line thickness
lty - line type (solid, dashed, etc)
font - normal=1, bold=2, etc

plot(Currency$EUR,col="#00FF0033",pch=19,
     main="EUR/USD ratio for 1999-2017",
     ylab="EUR/USD",
     xlab="Measures (working days)")

## add smoothing. Try different "f"
smooth = lowess(Currency$EUR,f=0.1)
lines(smooth,col="red",lwd=2)
## add 1 level
abline(h=1,col="blue",lty=2)

## (*) add years
year=1999  # an initial year
while (year<=2017){  # loop for all the years up to now
  # take the indexes of the measures for the "year"
  idx=grep(paste("^",year,sep=""),Currency$Date)  
  # calculate the average ratio for the "year"
  average=mean(Currency$EUR[idx]) 
  # draw the year separator
  abline(v=min(idx),col=1,lty=3)  
  # draw the average ratio for the "year"
  lines(x=c(min(idx),max(idx)),y=c(average,average),col=2)  
  # write the years
  text(median(idx),max(Currency$EUR),sprintf("%d",year),font=2,cex=0.7) 
  # write the average ratio
  text(median(idx),average+0.05,sprintf("%.2f",average),col=2,font=2,cex=0.8) 
  year=year+1;
}

5.2. Visualize categorical data

Here we will work with a dataset from Mouse Phenome project. ‘Tordoff3’ dataset was downloaded and prepared for the analysis. See the file here: http://edu.modas.lu/data/txt/mice.txt

Let’s first load the data.

Mice = read.table("http://edu.modas.lu/data/txt/mice.txt", header=T, sep="\t",as.is=FALSE)  
## as.is=FALSE => we transform strings to factors
str(Mice)

## 'data.frame':    790 obs. of  14 variables:
##  $ ID                  : int  1 2 3 368 369 370 371 372 4 5 ...
##  $ Strain              : Factor w/ 40 levels "129S1/SvImJ",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex                 : Factor w/ 2 levels "f","m": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Starting.age        : int  66 66 66 72 72 72 72 72 66 66 ...
##  $ Ending.age          : int  116 116 108 114 115 116 119 122 109 112 ...
##  $ Starting.weight     : num  19.3 19.1 17.9 18.3 20.2 18.8 19.4 18.3 17.2 19.7 ...
##  $ Ending.weight       : num  20.5 20.8 19.8 21 21.9 22.1 21.3 20.1 18.9 21.3 ...
##  $ Weight.change       : num  1.06 1.09 1.11 1.15 1.08 ...
##  $ Bleeding.time       : int  64 78 90 65 55 NA 49 73 41 129 ...
##  $ Ionized.Ca.in.blood : num  1.2 1.15 1.16 1.26 1.23 1.21 1.24 1.17 1.25 1.14 ...
##  $ Blood.pH            : num  7.24 7.27 7.26 7.22 7.3 7.28 7.24 7.19 7.29 7.22 ...
##  $ Bone.mineral.density: num  0.0605 0.0553 0.0546 0.0599 0.0623 0.0626 0.0632 0.0592 0.0513 0.0501 ...
##  $ Lean.tissues.weight : num  14.5 13.9 13.8 15.4 15.6 16.4 16.6 16 14 16.3 ...
##  $ Fat.weight          : num  4.4 4.4 2.9 4.2 4.3 4.3 5.4 4.1 3.2 5.2 ...

If you would like to have an overview of the data, and you have a reasonable number of columns, you can just plot() all possible scatter plots for your data.frame

plot(Mice, pch=".", col="blue")

Let us plot some factorial data. For example, we can check how well was the experiment designed with respect to mouse strain.

plot(Mice$Strain)

Or do it more beautifully:

las - axis text direction (2 for perpendicular)
cex.names - size of the category names on the horizontal axis

plot(Mice$Strain, las=2, col=rainbow(nlevels(Mice$Strain)), cex.names =0.7)
title("Number of mice from each strain")

Pie-chart

pie(summary(Mice$Sex), col=c("pink","lightblue"))
title("Gender composition (f:female, m:male)")

5.3. Distributions and box-plot

Distributions can be shown using hist() or plot(density()) functions. If you need to add line on the existing plot, you could use lines() (or points()) instead of plot()

hist(Mice$Starting.weight,probability = T, main="Histogram and p.d.f. approximation", xlab="weight, g")
lines(density(Mice$Starting.weight),lwd=2,col=4)

Now let us build a standard boxplot that shows starting weight for male and female mice. We will use formula here: ‘variable’ ~ ‘factor’. It is the easiest way.

boxplot(Starting.weight ~ Sex, data=Mice, 
        col=c("pink","lightblue"), 
        main="Weight by sex (f:female,b m:male)",  
        ylab="weight, g",xlab="sex")

5.4. Heatmaps

Heatmaps are a useful 2D representation of the data that have 3 dimensions. Example: expression (dim 1) is measured over n genes (dim 2) in m samples (dim 3). Heatmaps will automatically cluster the data (see Lecture 9). Let us make a heatmap for internal Iris dataset. We should also transform data.frame to a matrix

# see iris dataset
str(iris)

# let us transform the data to a matrix
X = as.matrix(iris[,-5])
heatmap(t(X))

# add species as colors
color = as.integer(iris[,5])
str(color)

##  int [1:150] 1 1 1 1 1 1 1 1 1 1 ...

color = rainbow(4)[color]
str(color)

##  chr [1:150] "#FF0000" "#FF0000" "#FF0000" "#FF0000" "#FF0000" "#FF0000" ...

heatmap(t(X),ColSideColors = color)

More advanced heatmaps can be built using pheatmap package.

## uncomment to install pheatmap
#install.packages("pheatmap")
library(pheatmap)
pheatmap(t(iris[,-5]))

## we can control many things in pheatmap.
## let's make annotation table
rownames(iris)=paste("flower",1:nrow(iris)) ## we need to annotate flowers
ann = data.frame(species = iris[,5])        ## data.frame with 1 column
rownames(ann) = rownames(iris)              ## same annotation in ann as in data

pheatmap(t(iris[,-5]), annotation_col = ann, fontsize_col = 3, fontsize_row=15)

5.5. Plot into PNG and PDF

R can directly plot into PDF, PNG or other graphic files.

5.5.1 `pdf()`

pdf(file="test.pdf",...) opens file “test.pdf” and start drawing to it. To stop and save the file, call dev.off()

onefile - set to TRUE to put multiple pages into a single PDF
width,height - width & height in inches (A4: width=8.3, height=11.7)

pdf("test.pdf",onefile=TRUE,width=8.3, height=11.7) ## open file
pheatmap(iris[,-5])
plot(iris, col=as.integer(iris[,5]))
dev.off()  ## close file

5.5.2 `png()`

png(file="test.png",...) opens file “test.png” and start drawing to it. To stop and save the file, call dev.off(). Multiple PNGs can be generated if file names contains %d substring, i.e. file="test%d.png"

width,height - width & height in pixels
pointsize - scale. Default = 12. Larger the image is, lager pointsize should be selected.

5.6. 3D visualization

There are options for 3D plotting in R. See static images in demo(persp) And here is an interactive 3D plot using rgl package:

## uncomment to install pheatmap
#install.packages("rgl")

library(rgl)

# define coordinates
x=Mice$Starting.weight
y=Mice$Ending.weight
z=Mice$Fat.weight

# plot in 3D
plot3d(x,y,z)

Exercises

Use mice dataset. Build distributions for male and female body weights in one plot. Hint: first draw density plot for female plot(density()) and then add line for male lines(density())

plot, lines, density

Draw boxplot, showing variability of bleeding time for mice of different strains.

boxplot

Save results into a PDF.

pdf

Prev Home

By Petr Nazarov

5. Data Visualization

Petr V. Nazarov, LIH

2022-10-03

5.1. Plot time series

5.2. Visualize categorical data

5.3. Distributions and box-plot

5.4. Heatmaps

5.5. Plot into PNG and PDF

5.5.1 `pdf()`

5.5.2 `png()`

5.6. 3D visualization

Exercises

5. Data Visualization

Petr V. Nazarov, LIH

2022-10-03

5.1. Plot time series

5.2. Visualize categorical data

5.3. Distributions and box-plot

5.4. Heatmaps

5.5. Plot into PNG and PDF

5.5.1 pdf()

5.5.2 png()

5.6. 3D visualization

Exercises

5.5.1 `pdf()`

5.5.2 `png()`