Scatter Plot Matrices - r

I have a matrix mat[n,m] and I'd like to use splom to plot the scatterplots of mat[,"col4"] as a function of all other column values. Also I'd like to add different colors to points of certain row numbers which are stored in rownID[]. I've seen examples using splom but they plot all variables against all variables and use group of columns to change the color of points. Is it possible to do what I want using splom (or other R function)?
Example:
set.seed(1)
mat <- matrix(sample(0:100, 16), ncol=4)
dimnames(mat) <- list(rownames(mat, do.NULL = FALSE, prefix = "row"),
colnames(mat, do.NULL = FALSE, prefix = "col"))
mat
col1 col2 col3 col4
row1 26 19 58 61
row2 37 86 5 33
row3 56 97 18 66
row4 89 62 15 42
rowID <- matrix(c(1,3), ncol=1, nrow=2)
Thanks to https://stackoverflow.com/a/16033003/1262767
I've been using featurePlot function of caret package but I don't know how to change color of some specific points (that's why I'm interested in splom):
featurePlot(mat, mat$col4, plot = "scatter",
## Add some space between the panels
between = list(x = 1, y = 1), main = "testSet",
## Add a background grid ('g') and a smoother ('smooth')
type = c("g", "p", "s"))


This doesn't really seem like a good fit for splom. I think you're be better off reshaping your data and using a standard xyplot. For example
library(reshape2)
mm<-melt(cbind(data.frame(mat), high=1:nrow(mat) %in% rowID), c("col4","high"))
xyplot(col4~value|variable, mm, groups=high)
which gives

Related

Heatmap in ggplot2 issue with fill

I'm trying to make a heatmap using ggplot2. What I want to be plotted is in the form of a matrix which is the result of a function.
Here is the data:
Image A B C D E F
1 3 23 45 23 45 90
2 4 34 34 34 34 89
3 34 33 24 89 23 67
4 3 45 234 90 12 78
5 78 89 34 23 12 56
6 56 90 56 67 34 45
Here is the function:
vector_a <- names(master)[2:4]
vector_b <- names(master)[5:6]
heatmap_prep <- function(dataframe, vector_a,vector_b){
dummy <- as.data.frame(matrix(0, nrow=length(vector_a), ncol=length(vector_b)))
for (i in 1:length(vector_a)){
first_value <- dataframe[[ vector_a[i] ]]
# print(first_value)
for(j in 1:length(vector_b)){
second_value <- dataframe[[ vector_b[j] ]]
result <- cor(first_value, second_value, method = "spearman")
dummy [i,j] <- result
}
}
rownames(dummy) <- vector_a
return(as.matrix(dummy))
heatmap_data_matrix1 <- heatmap_prep(master,vector_a, vector_b)
Using the data in heatmap_data_matrix1, I want to create a heatmap using the following code:
library(ggplot2)
if (length(grep("ggplot2", (.packages() ))) == 0){
library(ggplot2)
}
p <- ggplot(data = heatmap_data_matrix1, aes(x = vector_a, y = vector_b)
+ geom_tile(aes(fill = ))
However, this does not work. How should I reformat my data/code so this heatmap can be created? What should I put under "fill="?
Thanks!

Due to many of R functions being vectorized and that, for the most part, you don't need to pre-allocate or define a vector the for loop is unnecessary. You can simply run corr(x,y, method = "spearman") without the complications of the loop.
Regarding your question of what to put in for fill, you'll need to reshape your data to the configuration that ggplot2 uses (long format).
The gather function from tidyr does this, placing the rows/columns of the correlation into separate columns, and then using the r value for fill.
library(tidyverse) # for tidyr, tibble, ggplot2, and magrittr
heatmap_function <- function(df, a, b) {
cor_data <- cor(df[a], df[b], method = "spearman") %>%
as.data.frame(rownames = a) %>%
rownames_to_column("x") %>%
gather(y, fill, -x)
ggplot(cor_data, aes(x = x, y = y, fill = fill)) +
geom_tile()
}
This results in:
heatmap_function(master, c("A","B","C"), c("D","E"))

R tableGrob heatmap or conditional formating in column

Is there a way to create a similar effect to excel's conditional formating -> color scales in order to present a table in grid.table/tablegrob object? The color indicator should be red for the lower values and green for the higher values in the column.
That object format is needed so the table can be presented in grid format along with plots.
Thank you.

You can do this within tableGrob. You create a vector of colours, and then assign these to the cells.
So using the data from clemens's answer:
library(gridExtra)
library(grid)
# define colour vector
# change `vec` argument of `findInterval` to suit your cut-points
cols <- c("red" ,"orange", "green") [findInterval(my_data$Balance, c(-Inf, 1e4, 2e4, Inf))]
# or
# https://stackoverflow.com/questions/34517031/red-amber-green-sequential-palette-for-treemap-in-r
cols <- colorRampPalette(c("red", "yellow", "green"))(nrow(my_data))[rank(my_data$Balance)]
# create tales individually for each column
# this make it easy to assign colours to rows
t1 <- tableGrob(my_data["Balance"],
theme=ttheme_default(
core=list(bg_params = list(fill=cols)),
colhead = list(bg_params=list(fill="white", col="grey90"))),
rows = NULL)
t2 <- tableGrob(my_data["ID"],
theme=ttheme_default(
core=list(bg_params = list(fill="white", col="grey90")),
colhead = list(bg_params=list(fill="white", col="grey90"))),
rows = NULL)
# join tables
tab <- gtable_combine(t2, t1)
# grid.newpage() ; grid.draw(tab)
# if also want to add black border
# https://stackoverflow.com/questions/31506294/gtable-put-a-black-line-around-all-cells-in-the-table-body
library(gtable)
tab <- gtable::gtable_add_grob(tab,
grobs = rectGrob(gp=gpar(fill=NA, lwd=2)),
t = 1, b = nrow(tab), l = 1, r = ncol(tab))
grid.newpage() ; grid.draw(tab)

You could use tableHTML for that:
library(tableHTML)
for the dataset:
set.seed(666)
my_data <- data.frame(ID = 101:117,
Balance = sample(-1000:60000, 17))
ID Balance
1 101 46237
2 102 11030
3 103 58657
4 104 11280
5 105 21034
6 106 44296
7 107 58697
8 108 29381
9 109 -188
10 110 14854
11 111 46322
12 112 -2
13 113 4839
14 114 7670
15 115 11875
16 116 48475
17 117 1228
You can than create an HTML table using the tableHTML() function. Then apply a colour rank with theme RAG to the 2nd column of the table:
my_data %>%
tableHTML(rownames = FALSE,
widths = c(50, 100)) %>%
add_css_conditional_column(columns = 2,
colour_rank_theme = 'RAG',
decreasing = TRUE)
The result looks like this:

The most natural solution for that is to use a heatmap()?
heatmap(data.matrix(mtcars))
Would yield a heatmap with some default color options. You can change the color using an additional parameter (e.g col = cm.colors(256)) or your own color palette to achieve the desired output.
,

A solution I found was to do the following.. this only works if the data is in order and you list the count of rows(17 based on your screenshot):
theme=ttheme_default(
core=list(bg_params = list(fill=blues9[1:17]) or
theme=ttheme_default(
core=list(bg_params = list(fill=blues9[1:17])
Hope that helps. I am also seeking for alternatives myself

Accessing the values by their rowname and columnname,instead of numbers

I have a table which has multiple columns and rows. I want to access the each value by its column name and rowname, and make a plot with these values.
The table looks like this with 101 columns:
IDs Exam1 Exam2 Exam3 Exam4 .... Exam100
Ellie 12 48 33 64
Kate 98 34 21 76
Joe 22 53 49 72
Van 77 40 12
Xavier 88 92
What I want is to be able to reach the marks for given row (IDs),and given column(exams) as:
table[Ellie,Exam3] --> 48
table[Ellie,Exam100] --> 64
table[Ellie,Exam2] --> (empty)
Then with these numbers, I want to see the distribution of how Ellie did comparing the rest of exams to Exam2,3 and 100.
I have almost figured out this part with R:
library(data.table)
library(ggplot2)
pdf("distirbution_given_row.pdf")
selectedvalues <- c(table[Ellie,Exam3] ,table[Ellie,Exam100])
library(plyr)
cdat <- ddply(selected values, "IDs", summarise, exams.mean=mean(exams))
selectedvaluesggplot <- ggplot(selectedvalues, aes(x=IDs, colour=exams)) + geom_density() + geom_vline(data=cdat, aes(xintercept=exams.mean, colour=IDs), linetype="dashed", size=1)
dev.off()
Which should generate the Ellie's marks for exams of interests versus the rest of the marks ( if it is a blank, then it should not be seen as zero. It is still a blank.)
Red: Marks for Exam3, 100 and 2 , Blue: The marks for the remaining 97 exams
(The code and the plot are taken as an example of ggplot2 from this link.)
All ideas are appreciated!

For accessing your data at least you can do the following:
df=data.frame(IDs=c("Ellie","Kate","Joe","Van","Xavier"),Exam1=c(12,98,22,77,NA),Exam2=c(NA,34,53,NA,NA),
Exam3=c(48,21,49,40,NA),Exam4=c(33,76,NA,12,88))
row.names(df)=df$IDs
df=df%>%select(-IDs)
> df['Joe','Exam2']
[1] 53
Now I prepared an example with random created numbers to illustrate a bit what you could do. First let us create an example data frame
df=as.data.frame(matrix(rnorm(505,50,10),ncol=101))
colnames(df)=c("IDs",paste0("Exam",as.character(1:100)))
df$IDs=c("Ellie","Kate","Joe","Van","Xavier")
To work with ggplot it is recomended to convert it to long format:
df0=df%>%gather(key="exams",value="score",-IDs)
From here on you can play with your variables as desired. For instance plotting the density of the score per ID:
ggplot(df0, aes(x=score,col=IDs)) + geom_density()
or selecting only Exams 2,3,100 and plotting density for different exams
df0=df0%>%filter(exams=="Exam2"|exams=="Exam3"|exams=="Exam100")
ggplot(df0, aes(x=score,col=exams)) + geom_density()

IIUC - you want to plot each IDs select exams with all else exams. Consider the following steps:
Reshape your data to long format even replace NAs with zero as needed.
Run by() to subset data by IDs and build mean aggregrate data and ggplots.
Within by, create a SelectValues indicator column on the select exams then graph with vertical line mean summation.
Data
txt = 'IDs Exam1 Exam2 Exam3 Exam4 Exam100
Ellie 12 NA 48 33 64
Kate 98 34 21 76 NA
Joe 22 53 49 NA 72
Van 77 NA 40 12 NA
Xavier NA NA NA 88 92'
exams_df <- read.table(text=txt, header = TRUE)
# ADD OTHER EXAM COLUMNS (SEEDED FOR REPRODUCIBILITY)
set.seed(444)
exams_df[paste0("Exam", seq(5:99))] <- replicate(99-4, sample(100, 5))
Reshape and Graph
library(ggplot2) # ONLY PACKAGE NEEDED
# FILL NA
exams_df[is.na(exams_df)] <- 0
# RESHAPE (BASE R VERSION)
exams_long_df <- reshape(exams_df,
timevar = "Exam",
times = names(exams_df)[grep("Exam", names(exams_df))],
v.names = "Score",
varying = names(exams_df)[grep("Exam", names(exams_df))],
new.row.names = 1:1000,
direction = "long")
# GRAPH BY EACH ID
by(exams_long_df, exams_long_df$IDs, FUN=function(df) {
df$SelectValues <- ifelse(df$Exam %in% c("Exam1", "Exam3", "Exam100"), "Select Exams", "All Else")
cdat <- aggregate(Score ~ SelectValues, df, FUN=mean)
ggplot(df, aes(Score, colour=SelectValues)) +
geom_density() + xlim(-50, 120) +
labs(title=paste(df$IDs[[1]], "Density Plot of Scores"), x ="Exam Score", y = "Density") +
geom_vline(data=cdat, aes(xintercept=Score, colour=SelectValues), linetype="dashed", size=1)
})
Output

Coloring Rarefaction curve lines by metadata (vegan package) (phyloseq package)

First time question asker here. I wasn't able to find an answer to this question in other posts (love stackexchange, btw).
Anyway...
I'm creating a rarefaction curve via the vegan package and I'm getting a very messy plot that has a very thick black bar at the bottom of the plot which is obscuring some low diversity sample lines.
Ideally, I would like to generate a plot with all of my lines (169; I could reduce this to 144) but make a composite graph, coloring by Sample Year and making different types of lines for each Pond (i.e: 2 sample years: 2016, 2017 and 3 ponds: 1,2,5). I've used phyloseq to create an object with all my data, then separated my OTU abundance table from my metadata into distinct objects (jt = OTU table and sampledata = metadata). My current code:
jt <- as.data.frame(t(j)) # transform it to make it compatible with the proceeding commands
rarecurve(jt
, step = 100
, sample = 6000
, main = "Alpha Rarefaction Curve"
, cex = 0.2
, color = sampledata$PondYear)
# A very small subset of the sample metadata
Pond Year
F16.5.d.1.1.R2 5 2016
F17.1.D.6.1.R1 1 2017
F16.1.D15.1.R3 1 2016
F17.2.D00.1.R2 2 2017
enter image description here

Here is an example of how to plot a rarefaction curve with ggplot. I used data available in the phyloseq package available from bioconductor.
to install phyloseq:
source('http://bioconductor.org/biocLite.R')
biocLite('phyloseq')
library(phyloseq)
other libraries needed
library(tidyverse)
library(vegan)
data:
mothlist <- system.file("extdata", "esophagus.fn.list.gz", package = "phyloseq")
mothgroup <- system.file("extdata", "esophagus.good.groups.gz", package = "phyloseq")
mothtree <- system.file("extdata", "esophagus.tree.gz", package = "phyloseq")
cutoff <- "0.10"
esophman <- import_mothur(mothlist, mothgroup, mothtree, cutoff)
extract OTU table, transpose and convert to data frame
otu <- otu_table(esophman)
otu <- as.data.frame(t(otu))
sample_names <- rownames(otu)
out <- rarecurve(otu, step = 5, sample = 6000, label = T)
Now you have a list each element corresponds to one sample:
Clean the list up a bit:
rare <- lapply(out, function(x){
b <- as.data.frame(x)
b <- data.frame(OTU = b[,1], raw.read = rownames(b))
b$raw.read <- as.numeric(gsub("N", "", b$raw.read))
return(b)
})
label list
names(rare) <- sample_names
convert to data frame:
rare <- map_dfr(rare, function(x){
z <- data.frame(x)
return(z)
}, .id = "sample")
Lets see how it looks:
head(rare)
sample OTU raw.read
1 B 1.000000 1
2 B 5.977595 6
3 B 10.919090 11
4 B 15.826125 16
5 B 20.700279 21
6 B 25.543070 26
plot with ggplot2
ggplot(data = rare)+
geom_line(aes(x = raw.read, y = OTU, color = sample))+
scale_x_continuous(labels = scales::scientific_format())
vegan plot:
rarecurve(otu, step = 5, sample = 6000, label = T) #low step size because of low abundance
One can make an additional column of groupings and color according to that.
Here is an example how to add another grouping. Lets assume you have a table of the form:
groupings <- data.frame(sample = c("B", "C", "D"),
location = c("one", "one", "two"), stringsAsFactors = F)
groupings
sample location
1 B one
2 C one
3 D two
where samples are grouped according to another feature. You could use lapply or map_dfr to go over groupings$sample and label rare$location.
rare <- map_dfr(groupings$sample, function(x){ #loop over samples
z <- rare[rare$sample == x,] #subset rare according to sample
loc <- groupings$location[groupings$sample == x] #subset groupings according to sample, if more than one grouping repeat for all
z <- data.frame(z, loc) #make a new data frame with the subsets
return(z)
})
head(rare)
sample OTU raw.read loc
1 B 1.000000 1 one
2 B 5.977595 6 one
3 B 10.919090 11 one
4 B 15.826125 16 one
5 B 20.700279 21 one
6 B 25.543070 26 one
Lets make a decent plot out of this
ggplot(data = rare)+
geom_line(aes(x = raw.read, y = OTU, group = sample, color = loc))+
geom_text(data = rare %>% #here we need coordinates of the labels
group_by(sample) %>% #first group by samples
summarise(max_OTU = max(OTU), #find max OTU
max_raw = max(raw.read)), #find max raw read
aes(x = max_raw, y = max_OTU, label = sample), check_overlap = T, hjust = 0)+
scale_x_continuous(labels = scales::scientific_format())+
theme_bw()

I know this is an older question but I originally came here for the same reason and along the way found out that in a recent (2021) update vegan has made this a LOT easier.
This is an absolutely bare-bones example.
Ultimately we're going to be plotting the final result in ggplot so you'll have full customization options, and this is a tidyverse solution with dplyr.
library(vegan)
library(dplyr)
library(ggplot2)
I'm going to use the dune data within vegan and generate a column of random metadata for the site.
data(dune)
metadata <- data.frame("Site" = as.factor(1:20),
"Vegetation" = rep(c("Cactus", "None")))
Now we will run rarecurve, but provide the argument tidy = TRUE which will export a dataframe rather than a plot.
One thing to note here is that I have also used the step argument. The default step is 1, and this means by default you will get one row per individual per sample in your dataset, which can make the resulting dataframe huge. Step = 1 for dune gave me over 600 rows. Reducing the step too much will make your curves blocky, so it will be a balance between step and resolution for a nice plot.
Then I piped a left join right into the rarecurve call
dune_rare <- rarecurve(dune,
step = 2,
tidy = TRUE) %>%
left_join(metadata)
Now it will be plottable in ggplot, with a color/colour call to whatever metadata you attached.
From here you can customize other aspects of the plot as well.
ggplot(dune_rare) +
geom_line(aes(x = Sample, y = Species, group = Site, colour = Vegetation)) +
theme_bw()
dune-output
(Sorry it says I'm not allowed to embed the image yet :( )

Decrease number of x-axis ticks (labels) in barchart

I am using barchart from the lattice package. I have time series data going back 10 years, and I would like the x-axis to be displayed in the format %b-%Y, in six month intervals. This is trivially accomplished in xyplot (given vector of dates DateVector) with syntax such as:
scales=list(x=list(format = "%b-%Y",tick.number = length(DateVector)/2))
barchart ignores the tick.number option for factors by design, however, so the x axis becomes unreadable for large number of data labels. How can I reduce the number of ticks and/or tick labels?
Here is a simple example that reproduces my problem, with data following (save the data as Testrr.csv to run, apologies if this is the wrong format, it's my first time ;)
library(lattice)
inptTrans <- read.csv("Testrr.csv")
inptTrans$NotherTime <- as.Date(as.character(inptTrans$TransDateS),"%m/%d/%Y")
xyp2 <- barchart (NumE+NumF~ NotherTime, data=inptTrans, main = sprintf("Total")
,type='r',xlab = '',ylab='',col=c('red','black')
,horizontal=FALSE
,scales=list(x=list( rot=45,cex=1.0 ,
labels=format(inptTrans$NotherTime,"%b-%Y"),tick.number=2)
)
,key=list(text = list(c("Num F","Num E"))
,rectangle=list(col=c('black','red') ),columns = 2
,corner = c(0.05,-0.11),lty = c(1),lwd=3)
,stack=TRUE
)
png(sprintf('Testrr.png'),width = 900, height = 750)
print(xyp2)
dev.off()
Testrr.csv:
TransDateS,NumTot,NumF,NumE
01/15/2003,339486,18478,293879
02/15/2003,343761,16430,295272
03/15/2003,413700,17924,356004
04/15/2003,432741,18260,377046
05/15/2003,465439,18721,406632
06/15/2003,490699,18276,424773
07/15/2003,507818,18354,440237
08/15/2003,506530,17391,437386
09/15/2003,481039,17809,411568
10/15/2003,483364,19507,412902
11/15/2003,392189,16308,329675
12/15/2003,444249,18342,367489
01/15/2004,364662,20009,308456
02/15/2004,389718,16894,329946
03/15/2004,511386,20207,434052
04/15/2004,525563,19867,452646
05/15/2004,538570,17557,463289
06/15/2004,620868,20386,535523
07/15/2004,581368,20844,500511
08/15/2004,580773,19687,495930
09/15/2004,523992,21322,442304
10/15/2004,507288,18907,427265
11/15/2004,489571,19135,409818
12/15/2004,510904,21213,419328
01/15/2005,419417,21930,351257
02/15/2005,446402,19600,374778
03/15/2005,581109,22655,486727
04/15/2005,574275,21263,487661
05/15/2005,617399,21033,524466
06/15/2005,688876,26999,584782
07/15/2005,610606,20712,516127
08/15/2005,670746,23171,562919
09/15/2005,601594,23769,499257
10/15/2005,537107,21057,446620
11/15/2005,517340,22378,421351
12/15/2005,507610,22002,403392
01/15/2006,409802,25032,334607
02/15/2006,429196,23383,350498
03/15/2006,567056,27528,461193
04/15/2006,502403,24994,415797
05/15/2006,578793,27765,480468
06/15/2006,603134,31641,501012
07/15/2006,507408,25796,423143
08/15/2006,557500,31435,460225
09/15/2006,475884,30448,386443
10/15/2006,469092,31648,385751
11/15/2006,432720,36363,349832
12/15/2006,422369,30619,335706
01/15/2007,365297,38993,302488
02/15/2007,372276,34855,308599
03/15/2007,455525,39038,377113
04/15/2007,431043,36892,363764
05/15/2007,473539,42371,401959
06/15/2007,483341,44540,408415
07/15/2007,441046,43735,373058
08/15/2007,446111,48509,375242
09/15/2007,341554,45252,283456
10/15/2007,365869,55746,304820
11/15/2007,333946,57399,274292
12/15/2007,309551,51137,252391
01/15/2008,270806,70554,229161
02/15/2008,289606,66395,245970
03/15/2008,328369,65904,279960
04/15/2008,353531,78510,308293
05/15/2008,375080,81284,328119
06/15/2008,390034,80175,342170
07/15/2008,378648,85689,334255
08/15/2008,363756,83357,321556
09/15/2008,350942,79301,309385
10/15/2008,330164,75568,293867
11/15/2008,252408,67388,222510
12/15/2008,296037,65057,261619
01/15/2009,222048,69380,201462
02/15/2009,247591,77532,224236
03/15/2009,305516,57897,277544
04/15/2009,322308,65778,295160
05/15/2009,335134,78854,305599
06/15/2009,386702,93204,353285
07/15/2009,391393,91153,358731
08/15/2009,361150,77043,329656
09/15/2009,360568,79893,327923
10/15/2009,374886,90766,339969
11/15/2009,342502,77895,305321
12/15/2009,330770,81255,300899
01/15/2010,238132,94309,220064
02/15/2010,261558,81167,241123
03/15/2010,363432,103052,335172
04/15/2010,385535,100984,355852
05/15/2010,381303,95408,350883
06/15/2010,416893,94812,376190
07/15/2010,297810,94574,278054
08/15/2010,313494,98536,291501
09/15/2010,301782,110154,280312
10/15/2010,279117,80204,260114
11/15/2010,270178,67700,250713
12/15/2010,301323,67770,279877
01/15/2011,232283,78998,219762
02/15/2011,236917,64257,223213
03/15/2011,312669,80275,295648
04/15/2011,317720,77881,301463
05/15/2011,332220,76017,315955
06/15/2011,364962,79413,347253
07/15/2011,320199,67812,305785
08/15/2011,352555,74725,337283
09/15/2011,316858,82664,303357
10/15/2011,293425,69684,281407
11/15/2011,279320,73735,267671
12/15/2011,295369,70498,282722
01/15/2012,238417,73051,229405
02/15/2012,267105,65015,256719
03/15/2012,328104,63709,315019
04/15/2012,330508,58175,318258
05/15/2012,369418,65886,356174
06/15/2012,361304,59515,348708
07/15/2012,305613,49700,295448
08/15/2012,227541,35801,219883

The argument tick.number is really only a suggestion about placing tick marks. In this case, using the at argument is likely to produce more consistent results.
If you want to plot every sixth label, first create a vector to index this sequence:
okLabs <- seq(1, nrow(inptTrans), by = 6)
okLabs
[1] 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115
Then you can pass okLabs to the at argument and use it to subset your labels (here the scales list is assigned to an object that you can pass to the scales argument):
scalesList <- list(x = list(rot = 45, cex = 1.0,
labels = format(inptTrans$NotherTime, "%b-%Y")[okLabs], at = okLabs))
barchart (NumE+NumF~ NotherTime, data=inptTrans, main = sprintf("Total")
,type='r',xlab = '',ylab='',col=c('red','black')
,horizontal=FALSE
,scales= scalesList
,key=list(text = list(c("Num F","Num E"))
,rectangle=list(col=c('black','red') ),columns = 2
,corner = c(0.05,-0.11),lty = c(1),lwd=3)
,stack=TRUE
)
NOTE that this code won't work well when there are missing (NA) values in the data you're trying to plot. For that, you might try omitting the rows with missing data and creating a new data.frame from that subset.

Resources