ÚVOD DO GRAFIKY V R
Vít Gabrhel
vit.gabrhel@mail.muni.cz
FSS MU,
24. 10. 2016
Harmonogram
Graphing and figures – general principles
Guidelines for different kinds of graphs and publications:
Specialist (colour) publications
Presentations
Journal papers (APA guidelines)
Quick plots using qplot (plus, a review on saving graphs)
More flexible graphs using ggplot
Final formatting using ggplot
Plotting packages in R
Graphics functions in the base package:
plot( ), hist( ), etc.
Specialised packages:
ggplot2
ggvis
lattice
Specialised functions inside other
packages:
plotrix, car, plotmeans, etc.
Plotting packages in R
General principles: Graphs and figures should...
1.Summarise and/or reveal data, making large datasets coherent
2.Encourage the viewer to think about the data being presented
Rather than some aspect of the graph, like how pink, “pretty” or
poorly visible it is
3.Avoid distorting the data
4.Encourage the viewer to compare different pieces of data
Plotting packages in R
A that satisfies all four criteria despite its complexitygraph
Plotting packages in R
A graph that satisfies all four criteria despite its simplicity
Guidelines for different types of graphs
All graphs:
Think about when to start scales at the origin (0)
Bar graphs:
If needing colour, use colours to avoid overwhelming the viewer.
Best to use some shading (e.g., grey or patterns)
Histograms:
Consider displaying the mean/median value as well.
Graphs involving points (and possibly lines):
Vibrant colours work well for points and lines, making them quite visible.
Best to start y-axis at 0.
Use a line if there is a flow of time from one point to the next.
Patterns disappear if you stretch the x-axis too far.
Scatter plots:
Simplify the look by not using a thick border.
Any line through the dots must be thicker than the dots or in a brighter colour.
pastel
Guidelines for different publication types
Think about:
What story do you want to tell?
Who are you telling the story to?
Journal articles Specialist (colour) articles Conference presentations
•Follow APA guidelines – e.g.,
page 4 in this style manual.
(https://intranet.ecu.edu.au/__
data/assets/pdf_file/0010/2061
1/APAstyle.pdf)
•Follow APA guidelines as
much as possible, but use
colour if an additional
explanatory tool is needed.
•Colours: think about whether
they should be vibrant or
neutral, given (a) your topic,
and (b) whether you are
plotting points or bars (see
previous slide).
•No caption available, so more
labelling in the title or inside
the graph is needed.
•Figures can be useful for a
non-specialist audience.
•Same advice as for specialist
articles regarding colour.
•For small sample sizes, you
might not even need an x-axis
and y-axis. Can instead label
points of interest directly.
Highlighting individual data points
Quick plots using qplot
The qplot function is in the ggplot2 package.
The function is very useful for data exploration, as it is possible to draw
fairly complex plots with one or two lines of code.
The function is not useful for final plots for presentations and publications
because the overall appearance of the plots is difficult to change.
Basic principle: "geoms" (representations of data) have "aesthetics“
(properties) that can be "mapped" to variables in the dataset or “set” to a
desired value
geom examples: point, histogram, smooth (regression line)
aesthetics (aes) examples: x and y (the variables being plotted), colour,
size, shape, alpha (transparency), group
additional arguments to do with avoiding overplotting: position and
facet (facet_grid and facet_wrap)
Packages + data
ggplot
install.packages("ggplot2")
library("ggplot2")
data
setwd()
NPAS = read.csv2("NPAS.csv", header = TRUE)
NPAS_Clean <- na.omit(NPAS)
qplot
option description
alpha Alpha transparency for overlapping elements expressed as a fraction between 0 (complete transparency) and
1 (complete opacity)
color, shape,
size, fill
Associates the levels of variable with symbol color, shape, or size. For line plots, color associates levels of a
variable with line color. For density and box plots, fill associates fill colors with a variable. Legends are drawn
automatically.
data Specifies a data frame
facets Creates a trellis graph by specifying conditioning variables. Its value is expressed asrowvar ~ colvar. To create
trellis graphs based on a single conditioning variable, userowvar~. or .~colvar)
geom Specifies the geometric objects that define the graph type. The geom option is expressed as a character
vector with one or more entries. geom values include "point", "smooth", "boxplot", "line", "histogram",
"density", "bar", and "jitter".
main, sub Character vectors specifying the title and subtitle
method,
formula
If geom="smooth", a loess fit line and confidence limits are added by default. When the number of
observations is greater than 1,000, a more efficient smoothing algorithm is employed. Methods include "lm"
for regression, "gam" for generalized additive models, and "rlm" for robust regression. The formula parameter
gives the form of the fit.
For example, to add simple linear regression lines, you'd specify geom="smooth", method="lm", formula=y~x.
Changing the formula to y~poly(x,2) would produce a quadratic fit. Note that the formula uses the letters x
and y, not the names of the variables.
For method="gam", be sure to load the mgcv package. For method="rml", load the MASS package.
x, y Specifies the variables placed on the horizontal and vertical axis. For univariate plots (for example,
histograms), omit y
xlab, ylab Character vectors specifying horizontal and vertical axis labels
xlim,ylim Two-element numeric vectors giving the minimum and maximum values for the horizontal and vertical axes,
respectively
qplot(x, y, data=, color=, shape=, size=, alpha=, geom=, method=,
formula=, facets=, xlim=, ylim= xlab=, ylab=, main=, sub=)
Bar plot
qplot
# Data
qplot(data = NPAS, x = urban, geom = "bar") # bar chart with categories on x
axis
# Faktorizace proměnné "urban"
NPAS_Clean$urbanFACTOR = NPAS_Clean$urban
NPAS_Clean$urbanFACTOR <-
factor(NPAS_Clean$urbanFACTOR,levels=c(1,2,3),
labels=c("Rural","Suburban","Urban"))
# Jednoduchý barplot
qplot(data = NPAS_Clean, x = urbanFACTOR, color = urbanFACTOR, fill =
urbanFACTOR, geom = c("bar"), alpha=I(1), main="Respondenti dle typu
osídlení", xlab="Typ osídlení", ylab="Počet obyvatel")
Histogram
qplot
# Jednoduchý histogram
qplot(NPAS_Clean$age, geom="histogram")
# Stanovení rozsahu
min(NPAS_Clean$age)
max(NPAS_Clean$age)
qplot(NPAS_Clean$age, geom="histogram", xlim = c(14, 90))
# Šířka jednotlivých sloupců v histogramu
qplot(NPAS_Clean$age, geom="histogram", binwidth = 1, xlim = c(0, 100))
qplot(NPAS_Clean$age,
geom="histogram",
binwidth = 1,
main = "Histogram for věk",
xlab = "Věk",
ylab = "Počet",
fill=I("blue"),
col=I("red"),
alpha=I(.5),
xlim=c(14,90))
Scatterplot
qplot
# Index nerdství
NPAS_Clean$NerdyPersona = rowSums(NPAS_Clean[, 1:26])
# Jednoduchý scatterplot
qplot(age, NerdyPersona, data = NPAS_Clean, geom = c("point"))
# Data dle vybrané proměnné
qplot(age, NerdyPersona, data = NPAS_Clean, colour = urbanFACTOR, geom = c("point"))
# Scatterplot proložený křivkou
qplot(age, NerdyPersona, data = NPAS_Clean, geom = c("point", "smooth"))
# Scatterplot proložený křivkou dle vybrané proměnné
qplot(age, NerdyPersona, data = NPAS_Clean, geom = c("point", "smooth"), colour = urbanFACTOR)
# Scatterplot se spojnicemi bodů dle vybrané proměnné
qplot(age, NerdyPersona, data = NPAS_Clean, colour = urbanFACTOR, geom = "line")
Boxplot
qplot
# Jednoduchý boxplot s "integer" třídou třídící proměnné
qplot(gender, NerdyPersona, data=NPAS_Clean, geom="boxplot")
# Faktorizace proměnné gender
NPAS_Clean$genderFACTOR = NPAS_Clean$urban
NPAS_Clean$genderFACTOR <- factor(NPAS_Clean$genderFACTOR,levels=c(1,2,3),
labels=c("Muž","Žena","Ostatní"))
# Jednoduchý boxplot
qplot(genderFACTOR, NerdyPersona, data=NPAS_Clean, geom="boxplot")
# Jednoduchý boxplot s legendou dané proměnné
qplot(genderFACTOR, NerdyPersona, fill=genderFACTOR, data=NPAS_Clean,
geom="boxplot")
Saving graphs: a review
In the Plots tab, Export -> Save Plot As Image... Then choose Image
Format and Size
By default, graphs are saved to your working directory, but you can
choose any folder by clicking “Directory” after clicking “Save Plot As
Image”.
More flexible graphs using ggplot
The ggplot function is also in the ggplot2 package.
Key concepts, apart from the already mentioned geoms, aesthetics, position,
facet, setting and mapping:
Layers (+): The graph is not displayed until you add a layer, but it is
customary to specify the aesthetics that apply to all layers at the very
beginning.
Stats: Stats have default geoms, while geoms have default stats.
Each plot is treated as a variable.
The aesthetics in each layer override any aesthetics specified at the
beginning.
Search for these terms in the script and in the book for concrete examples.
We covered: overlaying of histograms and regression lines, adding error bars
to line plots and bar plots, setting axis limits (coord_cartesian), faceting, and
free scales.
Final formatting using ggplot
​Adjusted through ​Terms to look for
Overall colour-scheme:
•black and white?
•settings for colours
theme_set
scale_colour_manual
theme_bw()
scale_colour_hue()
scale_colour_grey
Appearance of points geom_point
geom_params
Appearance of error bars stat_summary (in our script)
Other possibilities:
geom_errorbar,
geom_params
colour = "gray41"
Appearance of lines scale_linetype_manual scale_linetype_manual(value
s=c("dotted", "solid",
"longdash", "dotdash"))
Gridlines theme(panel.grid.major =
element_line( ))
theme(panel.grid.minor =
element_line( ))
panel.grid.major =
element_line(colour =
"gray41", size = 1)
panel.grid.minor.y =
element_blank()
Final formatting using ggplot
​Adjusted through ​Terms to look for
Labels along the axes scale_x_continuous (when x is not a
factor variable)
scale_x_discrete
scale_y_continous
scale_y_discrete
scale_x_continuous(breaks = 1:2,
labels = c("Trials 1-24", "Trials 25-
48“, name = "Time period")
scale_y_continuous(name = "")
Facet labels theme(strip.text = ___)
Changes to name of factor levels
strip.text.y = element_text(size=14,
face = "bold")
levels(longsub2$Measure) <- c("Kick
Dir Entropy", "No. of
Player\nChanges")
Text size theme( ___ = element_text( )) theme(axis.title.x =
element_text(size= 20), axis.text.y =
element_text(size=14, colour =
"black")
Legend scale_linetype_manual
scale_colour_hue
etc. Depending on what aesthetic
(colour, linetype, shape) you have
mapped the variable to
theme(legend.text = ___)
scale_linetype_manual(values=c("do
tted", "solid", "longdash", "dotdash"),
name="Success Slope",
breaks=c("Descending", "U-shaped",
"Ascending", "Flat"), labels=c("Desc.",
"U-shaped", "Ascending", "Flat"))
legend.text = element_text(size=12)
ggplot2
Bar chart
Data + formula
mtc <- mtcars
ggplot(mtc, aes(x = factor(gear))) + geom_bar(stat = "count")
Aggregate data for barplot
summary.mtc <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean))
summary.mtc
ggplot2
Bar chart
Horizontal bars, colors, width of bars
#1. horizontal bars
p1<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar") +
coord_flip()
p1
#2. change colors of bars
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(gear))) + stat_summary(fun.y=mean,geom="bar") +
scale_fill_manual(values=c("purple", "blue", "darkgreen"))
p2
#3. change width of bars
p3<-ggplot(mtc,aes(x=factor(gear),y=wt)) + stat_summary(fun.y=mean,geom="bar", aes(width=0.5))
p3
Split and color by another variable
#1. next to each other
p1<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position=position_dodge(),geom="bar")
p1
#2. stacked
p2<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean,position="stack",geom="bar")
p2
#3. with facets
p3<-ggplot(mtc,aes(x=factor(gear),y=wt,fill=factor(vs)), color=factor(vs)) +
stat_summary(fun.y=mean, geom="bar") +
facet_wrap(~vs)
p3
ggplot2
Bar chart
Add text to the bars, label axes, and label legend
ag.mtc<-aggregate(mtc$wt, by=list(mtc$gear,mtc$vs), FUN=mean)
colnames(ag.mtc)<-c("gear","vs","meanwt")
ag.mtc
g1<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs),color=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=meanwt),position= position_dodge(width=0.9),
vjust=-.5)
g1
#2. fixing the yaxis problem, changing the color of text, legend labels, and rounding to 2 decimals
g2<-ggplot(ag.mtc, aes(x = factor(gear), y = meanwt, fill=factor(vs))) +
geom_bar(stat = "identity", position=position_dodge()) +
geom_text(aes(y=meanwt, ymax=meanwt, label=round(meanwt,2)), position=
position_dodge(width=0.9), vjust=-.5, color="black") +
scale_y_continuous("Mean Weight",limits=c(0,4.5),breaks=seq(0, 4.5, .5)) +
scale_x_discrete("Number of Gears") +
scale_fill_discrete(name ="Engine", labels=c("V-engine", "Straight engine"))
g2
ggplot2
Bar chart
Add error bars
summary.mtc2 <- data.frame(
gear=levels(as.factor(mtc$gear)),
meanwt=tapply(mtc$wt, mtc$gear, mean),
sd=tapply(mtc$wt, mtc$gear, sd))
summary.mtc2
ggplot(summary.mtc2, aes(x = factor(gear), y = meanwt)) +
geom_bar(stat = "identity", position="dodge", fill="lightblue") +
geom_errorbar(aes(ymin=meanwt-sd, ymax=meanwt+sd), width=.3,
color="darkblue")
ggplot2
Histogram
# Histogram s nastavením hodnot na osách X a Y
ggplot(data=NPAS_Clean, aes(NPAS_Clean$age)) +
geom_histogram(breaks=seq(14, 100, by = 2),
col="red",
fill="green",
alpha = .2) +
labs(title="Histogram for Age") +
labs(x="Age", y="Count") +
xlim(c(14,90)) +
ylim(c(0,250))
# Barva jako intenzita
ggplot(data=NPAS_Clean, aes(NPAS_Clean$age)) +
geom_histogram(breaks=seq(14, 100, by = 2),
col="red",
aes(fill=..count..))
# Dvě barvy pro vyjádření intenzity
ggplot(data=NPAS_Clean, aes(NPAS_Clean$age)) +
geom_histogram(breaks=seq(14, 100, by = 2),
col="red",
aes(fill=..count..)) +
scale_fill_gradient("Count", low = "green", high = "red")
ggplot2
Histogram
# Manipulace s backgroundem:
ggplot(data=NPAS_Clean, aes(NPAS_Clean$age)) +
geom_histogram(breaks=seq(14, 100, by = 2),
col="red",
aes(fill=..count..)) +
scale_fill_gradient("Count", low = "green", high = "red") +
theme(plot.background = element_blank(),
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=.4))
# You can easily add a trendline to your histogram by adding geom_density to your code:
ggplot(data=NPAS_Clean, aes(NPAS_Clean$age)) +
geom_histogram(aes(y =..density..),
breaks=seq(14, 100, by = 2),
col="red",
fill="green",
alpha = .2) +
geom_density(col=2) +
labs(title="Histogram for Age") +
labs(x="Age", y="Count")
ggplot2
Scatterplot
mtc <- mtcars
# Basic scatterplot
p1 <- ggplot(mtc, aes(x = hp, y = mpg))
# Print plot with default points
p1 + geom_point()
Change color of points
p2 <- p1 + geom_point(color="red") #set one color for all points
p2
p3 <- p1 + geom_point(aes(color = wt)) #set color scale by a continuous variable
p3
p4 <- p1 + geom_point(aes(color=factor(am))) #set color scale by a factor variable
p4
Change default colors in color scale
p1 + geom_point(aes(color=factor(am))) + scale_color_manual(values = c("orange", "purple"))
ggplot2
Scatterplot
Change shape or size of points
p2 <- p1 + geom_point(size = 5) #increase all points to size 5
p2
p3 <- p1 + geom_point(aes(size = wt)) #set point size by continuous variable
p3
p4 <- p1 + geom_point(aes(shape = factor(am))) #set point shape by factor
variable p4
ggplot2
Scatterplot
Add lines to scatterplot
p1 + geom_point(aes(shape = factor(am))) + scale_shape_manual(values=c(0,2))
#connect points with line
p2 <- p1 + geom_point(color="blue") + geom_line()
p2
#add regression line
p3 <- p1 + geom_point(color="red") + geom_smooth(method = "lm", se = TRUE)
p3
#add vertical line
p4 <- p1 + geom_point() + geom_vline(xintercept = 100, color="red")
p4
ggplot2
Scatterplot
Change axis labels
#label all axes at once
p2 <- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
p3 <- p2 + labs(x="Horsepower",
y = "Miles per Gallon")
p3
#label and change font size
p4 <- p2 + theme(axis.title.x = element_text(face="bold", size=20)) +
labs(x="Horsepower")
p4
#adjust axis limits and breaks
p5 <- p2 + scale_x_continuous("Horsepower",
limits=c(0,400),
breaks=seq(0, 400, 50))
p5
ggplot2
Scatterplot
Change legend options
g1<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(aes(color=factor(vs)))
#move legend inside
g2 <- g1 + theme(legend.position=c(1,1),legend.justification=c(1,1))
g2
#move legend bottom
g3 <- g1 + theme(legend.position = "bottom")
g3
#change labels
g4 <- g1 + scale_color_discrete(name ="Engine",
labels=c("V-engine", "Straight engine"))
g4
ggplot2
Scatterplot
Change legend options
If we had changed the shape of the points, we would use scale_shape_discrete() with the same
options. We can also remove the entire legend altogether by using
theme(legend.position=“none”)
g5<-ggplot(mtc, aes(x = hp, y = mpg)) + geom_point(size=2, aes(color = wt))
g5 + scale_color_continuous(name="Weight", #name of legend
breaks = with(mtc, c(min(wt), mean(wt), max(wt))), #choose breaks of variable
labels = c("Light", "Medium", "Heavy"), #label
low = "pink", #color of lowest value
high = "red") #color of highest value
ggplot2
Scatterplot
Change background color and style
g2<- ggplot(mtc, aes(x = hp, y = mpg)) + geom_point()
g2
#Completely clear all lines except axis lines and make background white
t1<-theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size=.4))
#Use theme to change axis label style
t2<-theme(
axis.title.x = element_text(face="bold", color="black", size=10),
axis.title.y = element_text(face="bold", color="black", size=10),
plot.title = element_text(face="bold", color = "black", size=12))
g3 <- g2 + t1
g3
g4 <- g2 + theme_bw()
g4
g5 <- g2 + theme_bw() + t2 + labs(x="Horsepower", y = "Miles per Gallon", title= "MPG vs Horsepower")
g5
ggplot2
Scatterplot
Change background color and style
g2<- ggplot(mtc, aes(x = hp, y = mpg)) +
geom_point(size=2, aes(color=factor(vs), shape=factor(vs))) +
geom_smooth(aes(color=factor(vs)),method = "lm", se = TRUE) +
scale_color_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c("red","blue")) +
scale_shape_manual(name ="Engine",
labels=c("V-engine", "Straight engine"),
values=c(0,2)) +
theme_bw() +
theme(
axis.title.x = element_text(face="bold", color="black", size=12),
axis.title.y = element_text(face="bold", color="black", size=12),
plot.title = element_text(face="bold", color = "black", size=12),
legend.position=c(1,1),
legend.justification=c(1,1)) +
labs(x="Horsepower",
y = "Miles per Gallon",
title= "Linear Regression (95% CI) of MPG vs Horsepower by Engine type")
g2
ggplot2
Boxplot
# Prostý boxplot
bp1 <- ggplot(NPAS_Clean, aes(urbanFACTOR, NerdyPersona))
bp1 + geom_boxplot()
# Barva dle proměnné (bez legendy)
bp1 + geom_boxplot(aes(color=urbanFACTOR)) + theme(legend.position='none')
bp1 + geom_boxplot(aes(fill=urbanFACTOR), alpha=I(0.5)) + theme(legend.position='none')
# Manipulace s rozložením bodů
bp1 + geom_boxplot(aes(fill=urbanFACTOR), alpha=I(0.5)) +
geom_point(aes(color=urbanFACTOR), size=3) +
theme(legend.position='none')
bp1 + geom_boxplot(aes(fill=urbanFACTOR), alpha=I(0.5)) +
geom_point(position="jitter", alpha=0.5) +
geom_boxplot(outlier.size=0, alpha=0.5)
ggplot2
Pirate plot
# To use the install_github function, you also need to have the devtools library
installed and loaded!
# install.packages("devtools")
library(devtools)
install_github("ndphillips/yarrr")
library("yarrr")
pirateplot(NerdyPersona ~ urbanFACTOR, data = NPAS_Clean, main = "Index
nerdství dle typu osídlení")
For each interval, we can state that there is a 95% probability that the true
population mean falls within that interval
ggplot2
Pirate plot
# 1 IV
pirateplot(NerdyPersona ~ urbanFACTOR, data = NPAS_Clean,
main = "Index nerdství dle typu osídlení",
pal = "southpark",
theme = 2,
point.o = 1, # Add points
point.col = "black",
point.bg = "purple",
point.pch = 21,
bean.f.o = 0.2, # Turn down bean filling
inf.f.o = 1, # Turn up inf filling
gl.col = "gray", # gridlines
gl.lwd = c(.5, 0)) # turn off minor grid lines)
# 2 IV
pirateplot(formula = NerdyPersona ~ gender + urbanFACTOR,
data = NPAS_Clean,
main = "Index nerdství dle typu osídlení a genderu",
point.pch = 1, # Point specifications...
point.col = "black",
point.o = .7,
inf.f.o = .9, # inference band opacity
gl.col = "gray")
Základní literatura
Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis.
Available online:
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R.
Sage: UK. Chapter 4. Exploring data with graphs.
Support website:
http://moderngraphics11.pbworks.com/f/ggplot2-
Book09hWickham.pdf
http://docs.ggplot2.org/current/