Analyzing and visualizing Data - Overview with 12 slides
srinivas15
ITS530RAdvancedGraphsggplot.pptxSchool of Computer & Information Sciences
ITS530 Analyzing and Visualizing Data
Introduction: R Advanced Graphs
ITS530 R Advanced Graphs
1
Revision Data Import & Entry
ITS530 R Advanced Graphs
2
2
Importing csv dataframes
setwd(“/Users/…./UC/ITS530/Rprog”)
pcd <- read.csv("dataset_price_personal_computers.csv", na.string="")
Commands:
read.csv(file, header = TRUE, sep = ",", quote = "\"", dec = ".", fill = TRUE, comment.char = "", ...)
na.string=“”
a character vector of strings which are to be interpreted as NA values. Blank fields are also considered to be missing values in logical, integer, numeric and complex fields. Note that the test happens after white space is stripped from the input, so na.strings values may need their own white space stripped in advance.
ITS530 R Advanced Graphs
3
ggplot2
Advantages of ggplot2
consistent underlying grammar of graphics (Wilkinson, 2005)
plot specification at a high level of abstraction
very flexible
theme system for polishing plot appearance
mature and complete graphics system
many users, active mailing list
That said, there are some things you cannot (or should not) do With ggplot2:
3-dimensional graphics (see the rgl package)
Graph-theory type graphs (nodes/edges layout; see the igraph package)
Interactive graphics (see the ggvis package)
ITS530 R Advanced Graphs
4
What is ggplot2?
ITS530 R Advanced Graphs
5
Grammar of graphics:
Independently specify plot building blocks
combine them to create a graphical display to your liking.
Building blocks of a graph include:
data
aesthetic mapping
geometric object
statistical transformations
scales
coordinate system
position adjustments
faceting
What is a geom
Use a geom to
represent data points,
geom’s aesthetic properties to represent variables.
each function returns a layer.
ITS530 R Advanced Graphs
6
ITS530 R Advanced Graphs
7
ITS530 R Advanced Graphs
8
2 Variable
3 variable
ITS530 R Advanced Graphs
9
2 variable contd.
Example graph in ggplot2
You can reproduce this graphic from the Economist
ITS530 R Advanced Graphs
10
ggplot2 VS Base Graphics in R
Compared to base graphics, ggplot2
is more verbose for simple / canned graphics
is less verbose for complex / custom graphics
does not have methods (data should always be in a data.frame)
uses a different system for adding plot elements
ITS530 R Advanced Graphs
11
Simple Analysis - Base Graphs Ok
housing <- read.csv("landdata-states.csv")
hist(housing$Home.Value)
ITS530 R Advanced Graphs
12
library(ggplot2)
ggplot(housing, aes(x = Home.Value)) +
geom_histogram()
Ggplot2 - complex
https://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html#introduction
Complex graphs; ggplot easier!
ggplot(subset(housing, State %in% c("MA", "TX")), aes(x=Date, y=Home.Value, color=State))+ geom_point()
ITS530 R Advanced Graphs
13
plot(Home.Value ~ Date,
data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
data=subset(housing, State == "TX"))
legend(1975, 400000,
c("MA", "TX"), title="State",
col=c("black", "red"),
pch=c(1, 1))
Geometric Objects And Aesthetics
Aesthetic Mapping
In ggplot land aesthetic means “something you can see”. Examples include:
position (i.e., on the x and y axes)
color (“outside” color)
fill (“inside” color)
shape (of points)
linetype
Size
Each geom accepts only a subset of all aesthetics
ITS530 R Advanced Graphs
14
Geometric Objects (geom)
are the actual marks we put on a plot.
Examples include:
points (geom_point, for scatter plots, dot plots, etc)
lines (geom_line, for time series, trend lines, etc)
boxplot (geom_boxplot, for, well, boxplots!)
A plot must have at least one geom;
there is no upper limit.
can add a geom to a plot using the + operator
Different Geoms
You can get a list of available geometric objects using the code below:
>help.search("geom_", package = "ggplot2")
ITS530 R Advanced Graphs
15
| ggplot2::geom_abline | Reference lines: horizontal, vertical, and diagonal |
| ggplot2::geom_bar | Bars charts |
| ggplot2::geom_bin2d | Heatmap of 2d bin counts |
| ggplot2::geom_blank | Draw nothing |
| ggplot2::geom_boxplot | A box and whiskers plot (in the style of Tukey) |
| ggplot2::geom_contour | 2d contours of a 3d surface |
| ggplot2::geom_count | Count overlapping points |
| ggplot2::geom_density | Smoothed density estimates |
| ggplot2::geom_density_2d | Contours of a 2d density estimate |
| ggplot2::geom_dotplot | Dot plot |
| ggplot2::geom_errorbarh | Horizontal error bars |
| ggplot2::geom_hex | Hexagonal heatmap of 2d bin counts |
| ggplot2::geom_freqpoly | Histograms and frequency polygons |
| ggplot2::geom_jitter | Jittered points |
| ggplot2::geom_crossbar | Vertical intervals: lines, crossbars & errorbars |
| ggplot2::geom_map | Polygons from a reference map |
| ggplot2::geom_path | Connect observations |
| ggplot2::geom_point | Points |
| ggplot2::geom_polygon | Polygons |
| ggplot2::geom_qq | A quantile-quantile plot |
| ggplot2::geom_quantile | Quantile regression |
| ggplot2::geom_ribbon | Ribbons and area plots |
| ggplot2::geom_rug | Rug plots in the margins |
| ggplot2::geom_segment | Line segments and curves |
| ggplot2::geom_smooth | Smoothed conditional means |
| ggplot2::geom_spoke | Line segments parameterised by location, direction and distance |
| ggplot2::geom_label | Text |
| ggplot2::geom_raster | Rectangles |
| ggplot2::geom_violin | Violin plot |
| ggplot2::update_geom_defaults | Modify geom/stat aesthetic defaults for future plots |
Points (Scatterplot) geom_point()
hp2001Q1 <- subset(housing, Date == 2001.25)
> ggplot(hp2001Q1, aes(y = Structure.Cost, x = Land.Value)) + geom_point()
ITS530 R Advanced Graphs
16
> ggplot(hp2001Q1,
aes(y = Structure.Cost, x = log(Land.Value))) +
geom_point()
Lines (Prediction Line)
#Prediction
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
ITS530 R Advanced Graphs
17
# scatter plot
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
#prediction + scatter plot
p1 + geom_point(aes(color = Home.Value)) +
geom_line(aes(y = pred.SC))
Smoothers
the smooth geom includes a line and a ribbon.
p1 + geom_point(aes(color = Home.Value)) + geom_smooth()
ITS530 R Advanced Graphs
18
Text (Label Points)
ITS530 R Advanced Graphs
19
p1 + geom_text(aes(label=State), size = 3)
## install.packages("ggrepel")
library("ggrepel")
p1 + geom_point() + geom_text_repel(aes(label=State), size = 3)
Aesthetic Mapping VS Assignment
ITS530 R Advanced Graphs
20
p1 + geom_point(aes(size = 2),# incorrect! 2 is not a variable
+ color="red") # this is fine -- all points red
p1 + geom_point(aes(color=Home.Value, shape = region))
Exercise I
The data for the exercises is available in the EconomistData.csv file. Read it in with read.csv()
It consist of the following scores for several countries:
Human Development Index (HDI) and
Corruption Perception Index (CPI) .
Create a scatter plot with CPI on the x axis and HDI on the y axis.
Color the points blue.
Map the color of the the points to Region.
Make the points bigger by setting size to 2
Map the size of the points to HDI.Rank
ITS530 R Advanced Graphs
21
Solution I
setwd("~/Desktop/Training/UC/ITS530/RProg")
dat <- read.csv("EconomistData.csv")
# Create a scatter plot with CPI on the x axis and HDI on the y axis.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point()
# Color the points blue.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point(color = "blue")
# Color the points in the previous plot according to Region.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point(aes(color = Region))
# Make the points bigger by setting size to 2
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point(aes(color = Region), size = 2)
# Map the size of the points to HDI.Rank
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point(aes(color = Region, size = HDI.Rank))
ITS530 R Advanced Graphs
22
Exercise II
Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
Overlay a smoothing line on top of the scatter plot using geom_smooth.
Overlay a smoothing line on top of the scatter plot using geom_smooth, but use a linear model for the predictions. Hint: see ?stat_smooth.
Overlay a smoothing line on top of the scatter plot using geom_line. Hint: change the statistical transformation.
BONUS: Overlay a smoothing line on top of the scatter plot using the default loess method, but make it less smooth. Hint: see ?loess.
ITS530 R Advanced Graphs
23
Solution II
1. Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point()
2. Overlay a smoothing line on top of the scatter plot using geom_smooth
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth()
3. Overlay a smoothing line on top of the scatter plot using geom_smooth, but use a linear model for the predictions. Hint: see ?stat_smooth.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(method = "lm")
4. Overlay a loess (method = “loess”) smoothling line on top of the scatter plot using geom_line. Hint: change the statistical transformation.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_line(stat = "smooth", method = "loess")
5. Overlay a smoothing line on top of the scatter plot using the loess method, but make it less smooth. Hint: see ?loess.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(span = .4)
ITS530 R Advanced Graphs
24
5. Overlay a smoothing line on top of
The scatter plot using loess method
Scales: Controlling Aesthetic Mapping
Describing what colors/shapes/sizes etc. to use is done by modifying the corresponding scale.
In ggplot2 scales include
position
color and fill
size
shape
line type
Scales are modified with a series of functions using a scale_<aesthetic>_<type> naming scheme.
Common Scale Arguments
name: the first argument gives the axis or legend title
limits: the minimum and maximum of the scale
breaks: the points along the scale where labels should appear
labels: the labels that appear at each break
ITS530 R Advanced Graphs
25
Exercise III
Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate region.
Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of “HDI”).
Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual.
ITS530 R Advanced Graphs
26
Solution Exercise III
Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate region.
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point()
2. Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of “HDI”).
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_discrete(name = "Region of the world")
ITS530 R Advanced Graphs
27
3. Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual.
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_manual(name = "Region of the world",
values = c("#24576D",
"#099DD7",
"#28AADC",
"#248E84",
"#F2583F",
"#96503F"))
Exercise IV – Recreate this Graph
To complete the basic scatter plot:
Add a trend line
Change the point shape to open circle
Label select points
Change the order & labels of Region;
Add title and format axes
Theme tweaks
ITS530 R Advanced Graphs
28
1. Add a trend line
Creating the basic scatter plot,
dat <- read.csv("EconomistData.csv")
library(ggplot2)
pc1 <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
pc1 + geom_point()
Adding the trend line
we need to guess at the model being on the graph.
A little bit of trial and error leads to:
pc2 <- pc1 +
geom_smooth(mapping = aes(linetype = "r2"),
method = "lm",
formula = y ~ x + log(x), se = FALSE,
color = "red")
pc2 + geom_point()
ITS530 R Advanced Graphs
29
2 Change the point shape to open circle
ITS530 R Advanced Graphs
30
pc2 + geom_point(shape = 1, size = 4)
(pc3 <- pc2 + geom_point(shape = 1, size = 2.5, stroke = 1.25))
3. Label select points
ITS530 R Advanced Graphs
31
pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
"Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
"India", "Italy", "China", "South Africa", "Spane",
"Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
"United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
"New Zealand", "Singapore")
(pc4 <- pc3 +
geom_text(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel)))
library("ggrepel")
(pc4 <- pc3 +
geom_text_repel(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel),
force = 10))
4. Change the order and labels of Region;
ITS530 R Advanced Graphs
32
dat$Region <- factor(dat$Region,
levels = c("EU W. Europe",
"Americas",
"Asia Pacific",
"East EU Cemt Asia",
"MENA",
"SSA"),
labels = c("OECD",
"Americas",
"Asia &\nOceania",
"Central &\nEastern Europe",
"Middle East &\nNorth Africa",
"Sub-Saharan\nAfrica"))
pc4$data <- dat
pc4
5. Add title and format axes
ITS530 R Advanced Graphs
33
library(grid)
(pc5 <- pc4 +
scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)",
limits = c(.9, 10.5),
breaks = 1:10) +
scale_y_continuous(name = "Human Development Index, 2011 (1=Best)",
limits = c(0.2, 1.0),
breaks = seq(0.2, 1.0, by = 0.1)) +
scale_color_manual(name = "",
values = c("#24576D",
"#099DD7",
"#28AADC",
"#248E84",
"#F2583F",
"#96503F")) +
ggtitle("Corruption and Human development"))
6. Theme tweaks
ITS530 R Advanced Graphs
34
library(grid) # for the 'unit' function
(pc6 <- pc5 +
theme_minimal() + # start with a minimal theme and add what we need
theme(text = element_text(color = "gray20"),
legend.position = c("top"), # position the legend in the upper left
legend.direction = "horizontal",
legend.justification = 0.1, # anchor point for legend.position.
legend.text = element_text(size = 11, color = "gray10"),
axis.text = element_text(face = "italic"),
axis.title.x = element_text(vjust = -1), # move title away from axis
axis.title.y = element_text(vjust = 2), # move away for axis
axis.ticks.y = element_blank(), # element_blank() is how we remove elements
axis.line = element_line(color = "gray40", size = 0.5),
axis.line.y = element_blank(),
panel.grid.major = element_line(color = "gray50", size = 0.5),
panel.grid.major.x = element_blank()
))
7. Final tweaks
mR2 <- summary(lm(HDI ~ CPI + log(CPI), data = dat))$r.squared mR2 <- paste0(format(mR2, digits = 2), "%")
See this text file:
ITS530 R Advanced Graphs
35
Questions?
ITS530 R Advanced Graphs
36
Microsoft Word Document
Microsoft
Word Document
p <- ggplot(dat,
mapping = aes(x = CPI, y = HDI)) +
geom_smooth(mapping = aes(linetype = "r2"),
method = "lm",
formula = y ~ x + log(x), se = FALSE,
color = "red") +
geom_point(mapping = aes(color = Region),
shape = 1,
size = 4,
stroke = 1.5) +
geom_text_repel(mapping = aes(label = Country, alpha = labels),
color = "gray20",
data = transform(dat,
labels = Country %in% c("Russia",
"Venezuela",
"Iraq",
"Mayanmar",
"Sudan",
"Afghanistan",
"Congo",
"Greece",
"Argentinia",
"Italy",
"Brazil",
"India",
"China",
"South Africa",
"Spain",
"Cape Verde",
"Bhutan",
"Rwanda",
"France",
"Botswana",
"France",
"US",
"Germany",
"Britain",
"Barbados",
"Japan",
"Norway",
"New Zealand",
"Sigapore"))) +
scale_x_continuous(name = "Corruption Perception Index, 2011 (10=least corrupt)",
limits = c(1.0, 10.0),
breaks = 1:10) +
scale_y_continuous(name = "Human Development Index, 2011 (1=best)",
limits = c(0.2, 1.0),
breaks = seq(0.2, 1.0, by = 0.1)) +
scale_color_manual(name = "",
values = c("#24576D",
"#099DD7",
"#28AADC",
"#248E84",
"#F2583F",
"#96503F"),
guide = guide_legend(nrow = 1, order=1)) +
scale_alpha_discrete(range = c(0, 1),
guide = FALSE) +
scale_linetype(name = "",
breaks = "r2",
labels = list(bquote(R^2==.(mR2))),
guide = guide_legend(override.aes = list(linetype = 1, size = 2, color = "red"), order=2)) +
ggtitle("Corruption and human development") +
labs(caption="Sources: Transparency International; UN Human Development Report") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid = element_blank(),
panel.grid.major.y = element_line(color = "gray"),
text = element_text(color = "gray20"),
axis.title.x = element_text(face="italic"),
axis.title.y = element_text(face="italic"),
legend.direction = "horizontal",
legend.box = "horizontal",
legend.text = element_text(size = 12),
plot.caption = element_text(hjust=0),
plot.title = element_text(size = 16, face = "bold"))
p
