A few useful links with many examples:
http://r-statistics.co/ggplot2-Tutorial-With-R.html
http://shinyapps.org/apps/RGraphCompendium/index.php
http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual
http://www.cookbook-r.com/Graphs/
Installing Packages
The following packages from R-CRAN will be used in this session. Use install.packages() function to install them:
install.packages(c("ggplot2", "RColorBrewer", "dplyr"))If the package comes from Bioconductor, then to install it:
#To build Bioconductor package:
#source("http://bioconductor.org/biocLite.R")
#biocLite("annotate")
#biocLite("geneplotter")
#biocLite("hgu95av2.db")
#biocLite("ggbio")
#biocLite("Homo.sapiens")To load packages in R use library() function. It’s a common practice to have all library() statements at the beginning of R script:
library(ggplot2)
library(RColorBrewer)
library(annotate)## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, as.vector, cbind,
## colnames, do.call, duplicated, eval, evalq, Filter, Find, get,
## grep, grepl, intersect, is.unsorted, lapply, lengths, Map,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
## setdiff, sort, table, tapply, union, unique, unlist, unsplit## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors## Loading required package: XML#library(geneplotter)
#library(hgu95av2.db)
#library(Homo.sapiens)
#library(ggbio)Reading input datasets;
salaries <- read.csv( "http://scv.bu.edu/classes/BI594/Salaries.csv")Functions like head(), str(), summary() are a great way to explore the dataset, learn about the size of the data, column names, type of each column and get a quick summary for each variable there.
head( salaries )## rank discipline yrs.since.phd yrs.service sex salary
## 1 Prof B 56 49 Male 186960
## 2 Prof A 12 6 Male 93000
## 3 Prof A 23 20 Male 110515
## 4 Prof A 40 31 Male 131205
## 5 Prof B 20 18 Male 104800
## 6 Prof A 20 20 Male 122400str( salaries )## 'data.frame': 78 obs. of 6 variables:
## $ rank : Factor w/ 3 levels "AssocProf","AsstProf",..: 3 3 3 3 3 3 1 3 3 3 ...
## $ discipline : Factor w/ 2 levels "A","B": 2 1 1 1 2 1 1 1 1 1 ...
## $ yrs.since.phd: int 56 12 23 40 20 20 20 18 29 51 ...
## $ yrs.service : int 49 6 20 31 18 20 17 18 19 51 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
## $ salary : int 186960 93000 110515 131205 104800 122400 81285 126300 94350 57800 ...summary( salaries )## rank discipline yrs.since.phd yrs.service sex
## AssocProf:13 A:36 Min. : 1.00 Min. : 0.00 Female:39
## AsstProf :19 B:42 1st Qu.:10.25 1st Qu.: 5.25 Male :39
## Prof :46 Median :18.50 Median :14.50
## Mean :19.71 Mean :15.05
## 3rd Qu.:27.75 3rd Qu.:20.75
## Max. :56.00 Max. :51.00
## salary
## Min. : 57800
## 1st Qu.: 88612
## Median :104671
## Mean :108024
## 3rd Qu.:126775
## Max. :186960Exercise Read and explore Ecoli dataset:
## sample generation clade strain cit run genome_size
## 1 REL606 0 <NA> REL606 unknown 4.62
## 2 REL1166A 2000 unknown REL606 unknown SRR098028 4.63
## 3 ZDB409 5000 unknown REL606 unknown SRR098281 4.60
## 4 ZDB429 10000 UC REL606 unknown SRR098282 4.59
## 5 ZDB446 15000 UC REL606 unknown SRR098283 4.66
## 6 ZDB458 20000 (C1,C2) REL606 unknown SRR098284 4.63## 'data.frame': 30 obs. of 7 variables:
## $ sample : Factor w/ 30 levels "CZB152","CZB154",..: 7 6 18 19 20 21 22 23 24 25 ...
## $ generation : int 0 2000 5000 10000 15000 20000 20000 20000 25000 25000 ...
## $ clade : Factor w/ 7 levels "C1","(C1,C2)",..: NA 7 7 6 6 2 2 2 1 4 ...
## $ strain : Factor w/ 1 level "REL606": 1 1 1 1 1 1 1 1 1 1 ...
## $ cit : Factor w/ 3 levels "minus","plus",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ run : Factor w/ 30 levels "","SRR097977",..: 1 5 22 23 24 25 26 27 28 29 ...
## $ genome_size: num 4.62 4.63 4.6 4.59 4.66 4.63 4.62 4.61 4.65 4.59 ...## sample generation clade strain cit
## CZB152 : 1 Min. : 0 Cit+ :9 REL606:30 minus : 9
## CZB154 : 1 1st Qu.:21250 C2 :6 plus : 9
## CZB199 : 1 Median :31750 C1 :5 unknown:12
## REL10979: 1 Mean :27350 (C1,C2):3
## REL10988: 1 3rd Qu.:33750 C3 :2
## REL1166A: 1 Max. :40000 (Other):4
## (Other) :24 NA's :1
## run genome_size
## : 1 Min. :4.590
## SRR097977: 1 1st Qu.:4.610
## SRR098026: 1 Median :4.625
## SRR098027: 1 Mean :4.663
## SRR098028: 1 3rd Qu.:4.740
## SRR098029: 1 Max. :4.800
## (Other) :24Sometimes categorical variables are coded as string (“male”, “female”), numeric (0, 1) or logical (TRUE, FALSE). When R reads the data by default it will treat all string variables as categorical variables. Note: To disable this behaviour use stringAsFactors=FALSE argument in read.csv() and other similar functions.
Use factor() function to convert any type of a vector to a factor:
gender <- c(0,1,1,1,1,0,0,0) # define numeric variable
summary(gender ) # summary will calculate basic statistics for a numeric variable## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.5 0.5 1.0 1.0gender <- factor(gender , labels=c("male", "female")) # convert vector to a categorical (factor) variable
summary(gender ) # summary produces appropriate output ## male female
## 4 4In case of ecoli dataset some columns are not categorical variables but were read as such. We need to convert them back to “character” type:
str(ecoli)## 'data.frame': 30 obs. of 7 variables:
## $ sample : Factor w/ 30 levels "CZB152","CZB154",..: 7 6 18 19 20 21 22 23 24 25 ...
## $ generation : int 0 2000 5000 10000 15000 20000 20000 20000 25000 25000 ...
## $ clade : Factor w/ 7 levels "C1","(C1,C2)",..: NA 7 7 6 6 2 2 2 1 4 ...
## $ strain : Factor w/ 1 level "REL606": 1 1 1 1 1 1 1 1 1 1 ...
## $ cit : Factor w/ 3 levels "minus","plus",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ run : Factor w/ 30 levels "","SRR097977",..: 1 5 22 23 24 25 26 27 28 29 ...
## $ genome_size: num 4.62 4.63 4.6 4.59 4.66 4.63 4.62 4.61 4.65 4.59 ...ecoli$sample <- as.character(ecoli$sample)
ecoli$run <- as.character(ecoli$run)
str(ecoli)## 'data.frame': 30 obs. of 7 variables:
## $ sample : chr "REL606" "REL1166A" "ZDB409" "ZDB429" ...
## $ generation : int 0 2000 5000 10000 15000 20000 20000 20000 25000 25000 ...
## $ clade : Factor w/ 7 levels "C1","(C1,C2)",..: NA 7 7 6 6 2 2 2 1 4 ...
## $ strain : Factor w/ 1 level "REL606": 1 1 1 1 1 1 1 1 1 1 ...
## $ cit : Factor w/ 3 levels "minus","plus",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ run : chr "" "SRR098028" "SRR098281" "SRR098282" ...
## $ genome_size: num 4.62 4.63 4.6 4.59 4.66 4.63 4.62 4.61 4.65 4.59 ...#Notice a different kind of summary output for character, factor and numeric columns
summary(ecoli)## sample generation clade strain cit
## Length:30 Min. : 0 Cit+ :9 REL606:30 minus : 9
## Class :character 1st Qu.:21250 C2 :6 plus : 9
## Mode :character Median :31750 C1 :5 unknown:12
## Mean :27350 (C1,C2):3
## 3rd Qu.:33750 C3 :2
## Max. :40000 (Other):4
## NA's :1
## run genome_size
## Length:30 Min. :4.590
## Class :character 1st Qu.:4.610
## Mode :character Median :4.625
## Mean :4.663
## 3rd Qu.:4.740
## Max. :4.800
## dplyr package
dplyr - R package that provide easy tools for data manipulation
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:AnnotationDbi':
##
## select## The following objects are masked from 'package:IRanges':
##
## collapse, desc, intersect, setdiff, slice, union## The following objects are masked from 'package:S4Vectors':
##
## intersect, rename, setdiff, union## The following object is masked from 'package:Biobase':
##
## combine## The following objects are masked from 'package:BiocGenerics':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Chose specific columns from a dataframe
select(ecoli, sample, clade, cit, genome_size)## sample clade cit genome_size
## 1 REL606 <NA> unknown 4.62
## 2 REL1166A unknown unknown 4.63
## 3 ZDB409 unknown unknown 4.60
## 4 ZDB429 UC unknown 4.59
## 5 ZDB446 UC unknown 4.66
## 6 ZDB458 (C1,C2) unknown 4.63
## 7 ZDB464* (C1,C2) unknown 4.62
## 8 ZDB467 (C1,C2) unknown 4.61
## 9 ZDB477 C1 unknown 4.65
## 10 ZDB483 C3 unknown 4.59
## 11 ZDB16 C1 unknown 4.61
## 12 ZDB357 C2 unknown 4.62
## 13 ZDB199* C1 minus 4.62
## 14 ZDB200 C2 minus 4.63
## 15 ZDB564 Cit+ plus 4.74
## 16 ZDB30* C3 minus 4.61
## 17 ZDB172 Cit+ plus 4.77
## 18 ZDB158 C2 minus 4.63
## 19 ZDB143 Cit+ plus 4.79
## 20 CZB199 C1 minus 4.59
## 21 CZB152 Cit+ plus 4.80
## 22 CZB154 Cit+ plus 4.76
## 23 ZDB83 Cit+ minus 4.60
## 24 ZDB87 C2 plus 4.75
## 25 ZDB96 Cit+ plus 4.74
## 26 ZDB99 C1 minus 4.61
## 27 ZDB107 Cit+ plus 4.79
## 28 ZDB111 C2 minus 4.62
## 29 REL10979 Cit+ plus 4.78
## 30 REL10988 C2 minus 4.62# Use "pipeline synax" to perform several operations at once:
ecoli %>% select( sample, clade, cit, genome_size)## sample clade cit genome_size
## 1 REL606 <NA> unknown 4.62
## 2 REL1166A unknown unknown 4.63
## 3 ZDB409 unknown unknown 4.60
## 4 ZDB429 UC unknown 4.59
## 5 ZDB446 UC unknown 4.66
## 6 ZDB458 (C1,C2) unknown 4.63
## 7 ZDB464* (C1,C2) unknown 4.62
## 8 ZDB467 (C1,C2) unknown 4.61
## 9 ZDB477 C1 unknown 4.65
## 10 ZDB483 C3 unknown 4.59
## 11 ZDB16 C1 unknown 4.61
## 12 ZDB357 C2 unknown 4.62
## 13 ZDB199* C1 minus 4.62
## 14 ZDB200 C2 minus 4.63
## 15 ZDB564 Cit+ plus 4.74
## 16 ZDB30* C3 minus 4.61
## 17 ZDB172 Cit+ plus 4.77
## 18 ZDB158 C2 minus 4.63
## 19 ZDB143 Cit+ plus 4.79
## 20 CZB199 C1 minus 4.59
## 21 CZB152 Cit+ plus 4.80
## 22 CZB154 Cit+ plus 4.76
## 23 ZDB83 Cit+ minus 4.60
## 24 ZDB87 C2 plus 4.75
## 25 ZDB96 Cit+ plus 4.74
## 26 ZDB99 C1 minus 4.61
## 27 ZDB107 Cit+ plus 4.79
## 28 ZDB111 C2 minus 4.62
## 29 REL10979 Cit+ plus 4.78
## 30 REL10988 C2 minus 4.62# Chose rows (filter the dataframe based on specific condition):
filter(ecoli, cit == "plus")## sample generation clade strain cit run genome_size
## 1 ZDB564 31500 Cit+ REL606 plus SRR098289 4.74
## 2 ZDB172 32000 Cit+ REL606 plus SRR098042 4.77
## 3 ZDB143 32500 Cit+ REL606 plus SRR098040 4.79
## 4 CZB152 33000 Cit+ REL606 plus SRR097977 4.80
## 5 CZB154 33000 Cit+ REL606 plus SRR098026 4.76
## 6 ZDB87 34000 C2 REL606 plus SRR098035 4.75
## 7 ZDB96 36000 Cit+ REL606 plus SRR098036 4.74
## 8 ZDB107 38000 Cit+ REL606 plus SRR098038 4.79
## 9 REL10979 40000 Cit+ REL606 plus SRR098029 4.78#Pipes (filter and then select columns)
ecoli %>%
filter(cit == "plus") %>%
select(sample, generation, clade)## sample generation clade
## 1 ZDB564 31500 Cit+
## 2 ZDB172 32000 Cit+
## 3 ZDB143 32500 Cit+
## 4 CZB152 33000 Cit+
## 5 CZB154 33000 Cit+
## 6 ZDB87 34000 C2
## 7 ZDB96 36000 Cit+
## 8 ZDB107 38000 Cit+
## 9 REL10979 40000 Cit+#To save the resulting object into a dataframe""
ecoli_citplus <- ecoli %>%
filter(cit == "plus") %>%
select(sample, generation, clade)
# View the result:
str(ecoli_citplus )## 'data.frame': 9 obs. of 3 variables:
## $ sample : chr "ZDB564" "ZDB172" "ZDB143" "CZB152" ...
## $ generation: int 31500 32000 32500 33000 33000 34000 36000 38000 40000
## $ clade : Factor w/ 7 levels "C1","(C1,C2)",..: 5 5 5 5 5 3 5 5 5# Split - apply - combine ( n() is a function within dplyr package to find count for each group )
ecoli %>%
group_by(cit) %>%
summarize( n() )## # A tibble: 3 × 2
## cit `n()`
## <fctr> <int>
## 1 minus 9
## 2 plus 9
## 3 unknown 12#To calculate mean for each subgroup
ecoli %>%
group_by(cit, clade) %>%
summarize( mean_size = mean(genome_size, na.rm = TRUE) )## Source: local data frame [13 x 3]
## Groups: cit [?]
##
## cit clade mean_size
## <fctr> <fctr> <dbl>
## 1 minus C1 4.606667
## 2 minus C2 4.625000
## 3 minus C3 4.610000
## 4 minus Cit+ 4.600000
## 5 plus C2 4.750000
## 6 plus Cit+ 4.771250
## 7 unknown C1 4.630000
## 8 unknown (C1,C2) 4.620000
## 9 unknown C2 4.620000
## 10 unknown C3 4.590000
## 11 unknown UC 4.625000
## 12 unknown unknown 4.615000
## 13 unknown NA 4.620000R graphics
There are 3 main environments in R to create graphics:
- R base utilities
- lattice package
- ggplot2 package
There is also powerful “grid” package and a couple in-development interactive packages
Base R Graphics
Generic(high level) plot functions:
- -plot: generic x-y plots, scatterplots
- -matplot
- -barplot
- -boxplot
- -hist: histogram
- -pie: pie charts
- -dotchart: cleveland dot plots
- -image, heatmap, contour, persp: image-like plots
- -qqnorm, qqline, qqplot: distribution analysis plots
- -pairs, coplot: display of multivariant data
- -dotchart
- -mosaicplot
- -stripchart
- -contour
- -hclust: hierarchial clustering
Low level - functions that add to the existing plot created with a high-level plot functions:
- -points
- -lines
- -abline
- -segments
- -text
- -mtext
- -title
- -axis
- -grid
- -legend
- -rug
- -polygon
- -box
# create points for sqrt(x) and log(x) functions
x <- 1:10
y1 <- log(x)
y2 <- sqrt(x)
# First attempt to draw the points:
plot(x,y1)
lines(x,y2)
There are many problems with this plot: Line did not fit into the plot, log() line is displayed using points, while sqrt() is displayed as a line, there is no title or annotations.
R provides many options to every plot function to control various parameters:
# Using R graphics parameters we can convert this plot into high quality one:
plot(x, y1, # specify the coordinates
type = "b", # type of the plot
ylim = c(min(c(y1,y2)), max(c(y1,y2))), # set the axis limits
main = "Logarithm and Square root functions", # title
ylab = "y", # # y label
col = "darkgreen", # line color
lwd = 2, # line tickness
pch=15, # symbol to display the point
cex.lab=1.5, # size of the font used for labels
font.lab = 3, # font type
cex.main = 2) # size of the font used for the title
#Additional line
lines(x,y2, # coordinates
col = "steelblue", # color
lwd =2, # line width
type="b", # line type (b - both, lines and points)
pch=19, # symbol used to display points
cex.lab=1.5) # font size for labels
#Add grid to the plot
abline(h = seq(0,3,by=0.5), v = c(0:10), col='lightgray',lty="dotted")
#Add legend
legend("topleft", # position
col=c("darkgreen","steelblue"), # colors
lty=1, # draw line
pch=c(15,19), # draw points
cex=1.5, # text size
legend=c("logarithm","square root")) # text
# Add text to the graph
text(x = 6, y = log(6), pos = 2, labels=expression(paste("y = ",ln(x))), cex=2, offset=0.75)
text(x = 6, y = sqrt(6), pos = 2, labels=expression(paste("y = ",sqrt(x))), cex=2, offset=0.75)
For our ecoli dataset
plot(ecoli$genome_size,
pch=8,
main="Scatter plot of genome sizes",
cex=2,
col="steelblue",
ylab="Genome Size",
xlab="Sample")
Barplot
# Basic Barplot
ranks <- table(salaries$rank)
barplot(ranks)
# Improved barplot
# Notice how the main plot returns the position of each bar which we will store in the variable b.
b<-barplot(ranks,
col = "darkgreen",
main = "Teaching Faculty",
sub = "Source: University Administration",
names.arg = names(ranks),
ylim = c(0, max(ranks) * 1.1),
axes = FALSE)
# Values stored in the variable b are used as x coordinates to place the text on top of each bar
text(b,ranks,
labels = ranks,
pos = 3)
Colors in R
There are many ways to specify a color in R:
- specify an index within a colorscheme
- by name
- using rgb, hsv, hex values
#1. Using index within colorscheme (palettes)
barplot(c(1:10), col=c(1:10))
#list colors in the current palette:
palette()## [1] "black" "red" "green3" "blue" "cyan" "magenta" "yellow"
## [8] "gray" pie(rep(1,length(palette())),
col=palette())
Some available palettes:
- rainbow()
- heat.colors()
- terrain.colors()
- topo.colors()
- cm.colors()
- gray.colors()
num.col=15
barplot(c(1:num.col), col=rainbow(num.col))
barplot(c(1:num.col), col=heat.colors(num.col))
barplot(c(1:num.col), col=terrain.colors(num.col))
barplot(c(1:num.col), col=topo.colors(num.col))
barplot(c(1:num.col), col=cm.colors(num.col))
barplot(c(1:num.col), col=gray.colors(num.col))
A popular palettes could be found in package RColorBrewer:
#display.brewer.all()
barplot(c(1:8), col=brewer.pal(8,"Dark2") )
barplot(c(1:9), col=brewer.pal(9,"Set1") )
# 2. Specify colors by name
colors() # list all colornamesbarplot(c(21,19,16,14,12,10,8,5,4,3,2,1), col=c("steelblue4",
"darkred",
"dodgerblue",
"forestgreen",
"gold",
"steelblue",
"firebrick",
"darkorange",
"darkgrey",
"tomato",
"olivedrab",
"dimgray"))
A lits of some of my favorite colors:
- steelblue, steelblue4
- firebrick
- tomato
- skyblue
- olivedrab
- forestgreen
- gold
- dodgerblue
- dimgray
- darkred
- darkorange
- darkgray
- springgreen
- violetred
- slateblue
- sienna
- seagreen
- sandybrown
- salmon
- saddlebrown
- maroon
- limegreen
barplot(c(21,19,17,14,12,8,5,4,3,2,1), col=c("springgreen",
"skyblue",
"violetred",
"slateblue",
"sienna",
"seagreen",
"sandybrown",
"salmon",
"saddlebrown",
"maroon",
"limegreen"))
You can find a color table in this pdf document: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
# 3. Specify rgb values (4th argument is used to control transparency)
col1 <- rgb(0.1, 0.5, 0.1, alpha=0.8)
col2 <- rgb(0.9, 0.7, 0.0)
barplot(c(7,11), col=c(col1, col2))
#Conversion one color format to another
col2rgb(), rgb2hsv()
# Additional help related to colors
help(package=colorspace)Boxplot
#Simple boxplot
boxplot(salaries$salary)
#let's display 2 boxplots: one for male and one for female names
boxplot( formula = salary ~ discipline,
data = salaries,
names = c("A","B"),
col = c("gold","forestgreen"),
horizontal = TRUE,
main = "Salary range for Disciplines A and B")
legend("topright",
legend=c("A","B"),
pch=15,
col = c("gold","forestgreen"))
# Side by side boxplot for ecoli dataset
boxplot(genome_size ~ cit, ecoli) #simple
#Add colors, title, labels
boxplot(genome_size ~ cit, ecoli, col=c("pink","purple", "darkgrey"),
main="Average expression differences between celltypes", ylab="Expression")
Explore various symbols View help of points() function:
?points#Draw a table with all symbols
plot(c(-1, 26), 0:1, type = "n", axes = FALSE, xlab="", ylab="")
text(0:25, 0.6, 0:25, cex=0.75)
points(0:25, rep(0.5, 26), pch = 0:25, bg = "grey")
graphics parameters:
- pch: plotting symbol (default - open circle)
- lty: line type (default - solid line)
- lwd: line width
- las: orientation of the axis labels on the plot
- bg: background color
- fg: foreground color
- mar: margin size
- oma: outer margin size
levels(salaries$rank)## [1] "AssocProf" "AsstProf" "Prof"col3 <- c("steelblue4","firebrick","forestgreen")
cols <- col3 [ as.numeric(salaries$rank) ]
plot(x = salaries$yrs.service,
y = salaries$salary,
pch = 19,
main = "Analysis of dependency of 2 variables",
xlab = "Years of Service",
ylab = "Salary",
col = cols,
cex=1.5)
lm.fit <- lm(salary ~ yrs.service, data = salaries)
abline(lm.fit, lty = 4, lwd = 2, col="darkgray")
legend("topleft",levels(salaries$rank),fill=col3 )
eq <- substitute(italic(salary) == a + b %.% italic(yrs.service)*","~~italic(r)^2~"="~r2,
list(a = format(coef(lm.fit)[1], digits = 2),
b = format(coef(lm.fit)[2], digits = 2),
r2 = format(summary(lm.fit)$r.squared, digits = 3)))
#R-squared: what proportion of the variation in the outcome can be explained by the covariates/predictors
# if R-squared is low, than the outcome is poorly predicted by the covariates/
text(15,170000,labels=as.expression(eq), pos=4, cex=.8 )
Histogram
#plot simple histogram
hist(salaries$salary)
# Add color and title:
hist(salaries$salary,
main="Histogram of Salaries of professors in Disciplines A and B",
col="steelblue3",
xlab="Salary, dollars")
# Same plot with probability along the y axes
hist(salaries$salary,
main="Histogram of Salaries of professors in Disciplines A and B",
col="steelblue3",
xlab="Salary, dollars",
prob=TRUE)
#Add rugplot
rug(salaries$salary)
#Add density line
lines(density(salaries$salary), col="darkblue", lwd=2)
Pairs
# Delays per airport
pairs( salaries[ ,c("yrs.since.phd", "yrs.service","salary") ] )
#An alternative way using formula
pairs( ~ yrs.since.phd + yrs.service + salary,
data = salaries) 
Improve the plot appearance using custom function:
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y))
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste0(prefix, txt)
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * r)
}
pairs( ~ yrs.since.phd + yrs.service + salary,
data = salaries,
lower.panel = panel.smooth,
upper.panel = panel.cor) 
Multiple plots in one graph
#par.save <- par() # save current graphics parameters
#print( par() ) # print current graphics parameters
par( mfrow = c(1,2) ) # 1 row and 2 columns
# first graph
hist(salaries$salary,
main="Histogram of Salaries of professors in Disciplines A and B",
col="steelblue3",
xlab="Salary, dollars",
prob=TRUE)
#Add rugplot
rug(salaries$salary)
#Add density line
lines(density(salaries$salary), col="darkblue", lwd=2)
# second graph
b<-barplot(table(salaries$rank),
col=c("steelblue4","firebrick","forestgreen"),
main = "Professor Ranks",
names.arg = c("Associate Professor", "Assistant Professor", "Professor"),
ylim = c(0, max(table(salaries$rank)) * 1.25),
axes=FALSE)
text(b,table(salaries$rank),table(salaries$rank), pos=3)
mtext("Disciplines A and B")
par ( mfrow = c(1,1) )#basic plot with points
ggplot(salaries, aes( yrs.service, salary)) + geom_point() 
ggplot(salaries, aes( yrs.service, salary)) +
geom_point() + # type of geom objects
theme_bw() + # theme
ggtitle(" Salary analysis") # plot title
# color the points according to the faculty type
ggplot(salaries, aes( yrs.service, salary)) +
geom_point( aes(color = factor(rank) ), size=5) + # type of geom objects
ggtitle(" Salary analysis") + # plot title
theme_bw( ) + # theme
theme ( plot.title=element_text(size=14,face="bold" ) )
#Add a regression line to the plot
ggplot(salaries, aes( yrs.service, salary)) +
geom_point( aes(color = factor(rank) ), size=5) + # type of geom objects
ggtitle(" Salary analysis") + # plot title
geom_smooth() +
theme_bw( ) + # theme
theme ( plot.title=element_text(size=14,face="bold" ) )## `geom_smooth()` using method = 'loess'
# Change the line to a simple lm line
ggplot(salaries, aes( yrs.service, salary)) +
geom_point( aes(color = factor(rank) ), size=5) + # type of geom objects
ggtitle(" Salary analysis") + # plot title
geom_smooth( method='lm' , col = "black") +
theme_bw( ) + # theme
theme ( plot.title=element_text(size=14,face="bold" ) )
ggplot(ecoli) +
geom_point(aes(x = sample, y= genome_size,
color = generation,
shape = cit), size = rel(3.0)) +
theme(axis.text.x = element_text(angle=45, hjust=1))
#boxplot
ggplot(salaries, aes( x=discipline, y=salary ) ) + geom_boxplot() 
#adding addional features to the boxplot
ggplot(salaries, aes( x=discipline, y=salary ) ) +
geom_boxplot() +
geom_jitter(width = 0.2)
ggplot(salaries, aes( x=rank, y=salary) ) +
geom_boxplot( varwidth = TRUE ) + #width is proportional to the square roots of the number of observations
geom_jitter(width = 0.2) +
ggtitle(" Salary analysis") +
coord_flip() +
theme ( plot.title=element_text(size=14,face="bold" ) )
ggplot(ecoli) +
geom_boxplot(aes(x = cit, y = genome_size, fill = cit)) +
ggtitle('Boxplot of genome size by citrate mutant type') +
xlab('citrate mutant') +
ylab('genome size') +
theme(panel.grid.major = element_line(size = .5, color = "grey"),
axis.text.x = element_text(angle=45, hjust=1),
axis.title = element_text(size = rel(1.5)),
axis.text = element_text(size = rel(1.25)))
graph <- ggplot(ecoli) +
geom_boxplot(aes(x = cit, y = genome_size, fill = cit))
graph +ggtitle('Boxplot of genome size by citrate mutant type') +
xlab('citrate mutant') +
ylab('genome size') +
theme(panel.grid.major = element_line(size = .5, color = "grey"),
axis.text.x = element_text(angle=45, hjust=1),
axis.title = element_text(size = rel(1.5)),
axis.text = element_text(size = rel(1.25)))
#histogram
ggplot(salaries, aes(x=salary)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(salaries, aes(x=salary)) + geom_histogram(binwidth=10000)
#improving histogram
ggplot(salaries, aes(x=salary)) +
geom_histogram(binwidth=10000,
color="black", fill="white")
ggplot(salaries, aes(x=salary)) +
geom_histogram(binwidth=10000,
color="black", fill="white", aes(y=..density..)) +
geom_density(alpha=.3, fill="darkorange")