ÚVOD DO GRAFIKY V R

Vít Gabrhel

vit.gabrhel@mail.muni.cz

FSS MU,

30. 10. 2017

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 graph that satisfies all four criteria despite its complexity 

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 pastel 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.

Guidelines for different publication types

Think about:

• What story do you want to tell?
• Who are you telling the story to?

Highlighting individual data points

ggplot2: From Hadley with love

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

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

Final formatting using ggplot

Bar chart

ggplot2

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

Bar chart

ggplot2

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

Bar chart

ggplot2

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

Bar chart

ggplot2

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")

Histogram

ggplot2

# 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")

Histogram

ggplot2

 # 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(),
      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")

Scatterplot

ggplot2

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"))

Scatterplot

ggplot2

 

 

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

Scatterplot

ggplot2

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

Scatterplot

ggplot2

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

Scatterplot

ggplot2

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

Scatterplot

ggplot2

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

Scatterplot

ggplot2

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

Scatterplot

ggplot2

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

Boxplot

ggplot2

# 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)

Pirate plot

ggplot2

# 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

Pirate plot

ggplot2

# 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: http://moderngraphics11.pbworks.com/f/ggplot2-Book09hWickham.pdf

 

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. Sage: UK. Chapter 4. Exploring data with graphs.

 

Support website: http://docs.ggplot2.org/current/