attempting to produce a stacked barplot (something like the plot below, except for multiple years and stacked bars). Ideally the x-axis would be months J, F, M...repeating (realise row names cannot duplicate but I wondered if there was a way to label the axis and then group by year) and there would be 2 y-axes - same as the example. I'm trying to plot the 2 * 'RainAvg' columns as stacked bars against the right-hand axis, and then the 2 * 'GRACEAnom' columns as 2 lines relating to the left axis. Not sure where to begin....any help appreciated as always - hopefully this is clear. I've added the first few rows of my data below the image:
> head(Figures, 34)
DecimDate GRACEAnomLVB RainAvgLVB GRACEAnomVNB RainAvgVNB
1 2003.000 13.46956583 5.749109 6.15705017 3.478762
2 2003.083 6.31473051 5.331211 0.97906465 2.873399
3 2003.167 3.63883171 10.363173 0.77220028 8.090037
4 2003.250 6.49458212 17.210327 1.24673188 17.405001
5 2003.333 11.33909662 14.840302 5.56158736 15.673977
6 2003.417 9.38271799 7.536387 6.00824271 9.961779
7 2003.500 7.42633936 7.322593 6.45489806 9.617705
8 2003.583 3.60612356 11.447746 5.60098976 15.430943
9 2003.667 3.44546767 7.968092 6.63687748 8.056800
10 2003.750 2.75612873 8.769927 5.22673658 8.333266
11 2003.833 5.30475366 9.782655 6.91241363 9.305419
12 2003.917 8.68239955 7.474251 7.37673817 5.731811
13 2004.000 5.48150209 9.109684 4.04360382 5.772269
14 2004.083 2.62570392 6.976879 -0.71817402 3.780555
15 2004.167 1.45723630 10.559618 -2.23807975 6.471265
16 2004.250 5.98037042 17.895779 0.04639658 17.677118
17 2004.333 7.35279067 7.203534 3.23732162 8.284600
18 2004.417 1.41878133 4.536058 0.41008077 6.321057
19 2004.500 -0.89443672 5.439750 0.09167621 7.704055
20 2004.583 -3.98526800 9.248759 -0.22851368 12.973643
21 2004.667 -4.91880694 12.214854 -0.30143818 12.626995
22 2004.750 -4.13842871 10.903502 1.08566462 11.491835
23 2004.833 1.04833693 15.731056 4.50875694 12.300916
24 2004.917 2.93758790 8.431368 3.10471313 3.997466
...and so on until December 2012.
I'm not quite clear on a couple of items in the description of your chart such as whether you're looking for one chart for all years or one for each year but the following code might help get you started. The basic idea is to draw the bar chart and then rescale the plot window for the line plots. Chart titles and labels are added as required.
org_mar <- par()$mar
par(mar=c(5,4,4,5)+.1)
Figures <- as.matrix(Figures)
nrow_F <- nrow(Figures)
x_labs <- cbind(1:nrow_F,c("J","F","M","A","M","J","J","A","S","O","N","D") )[,2]
# make bar chart
barplot(t(Figures[,c("RainAvgLVB","RainAvgVNB")]), yaxt="n", names.arg=x_labs,
xlab = "Monthly", font.lab=2, xlim= 1.2*c(1,nrow_F)-.5)
axis(side=4)
mtext("Mean Monthly Rainfall (mm)", side=4, line=2.5, font=2)
abline(h=0)
# rescale the plot window and draw the line plots
plot.window(xlim=c(1,nrow_F), ylim=range(Figures[,c("GRACEAnomLVB","GRACEAnomVNB")]))
axis(side=2)
mtext("Water Storage Anomalay (cm)", side=2, line=2.5, font=2)
abline(v=par()$usr[1])
lines( Figures[,2], col="black", lty=1, lwd=2)
lines( Figures[,4], col="blue", lty=2, lwd=2)
par(mar=org_mar)
This should make a chart like the following:
Related
I want to follow up on this thread, as it's over 2 years old and I also can't comment because I do not have enough posts, yet.
plot acf of several timeseries in one plot
I would like to understand why all additional lines start at lag=1 and not 0. How can I have them start at lag=0 like the first one?
Also, is there a way to extend the x-axis to negative values? When I do pairwise ccf, I get values from -10 to +10, which nicely shows the pattern I'm looking for, but with acf I only get lags of either -6 or +6.
Also, please apologize my ignorance, but what does the dashed blue line at 0.4 represent? Significance? I've seen the line at various values in different examples.
Thank you in advance.
Here the code, I basically used the same as in the link above.
> data3
Maui8 Maui7 Maui6 Olowalu Maalaea
1 1.01532397 0.7583463 -1.45102480 0.37355214 0.093384619
2 0.84997103 0.7802248 -1.47906584 0.57370139 0.000741584
3 0.65297103 0.9325412 -1.31256709 0.29211557 0.077706758
4 0.42029456 0.8041302 -1.36599992 0.15763796 0.018583624
5 0.15063769 0.5932333 -1.00933326 -0.03478742 0.073490340
6 0.14522593 0.4739607 -0.82896012 0.22469641 0.226357256
7 0.03779456 0.4774847 -0.09524122 0.42900612 0.194261484
8 -0.39651917 -0.2433839 0.07535580 -0.03204488 0.384578649
9 -0.99220544 -1.3080379 0.07143167 -0.57821403 0.012594818
10 -1.58116623 -1.3739277 -0.28876112 -1.34129239 -0.543698715
11 -1.68365642 -1.5527201 0.35511326 -0.99125508 -0.574656426
12 -1.67555838 -1.6044574 0.21679237 -1.05519787 -0.731770854
13 -1.64012701 -1.6975577 0.68442918 -1.20809587 -0.888636526
14 -1.22618583 -1.3975012 0.94365182 -0.84284090 -0.611341749
15 -1.12916623 -0.8248387 1.05953344 -0.86989314 -0.242448715
16 -1.11394684 -0.3294150 1.41744881 -0.45954904 -0.331766245
17 -0.41821140 -0.4312582 1.19811924 -0.45322699 -0.384893352
18 0.22428860 -0.2696410 1.14340119 -0.28008162 -0.323007387
19 0.69397114 -0.1249800 1.12954154 0.48571412 0.074298377
20 1.55118345 1.1953590 0.91711047 1.47251236 0.802606648
21 1.76527075 1.6837135 0.50540620 1.30325798 0.951992613
22 1.34356440 1.6247940 -0.09836573 1.21764394 0.794730708
23 1.59601480 0.9492149 -0.69564643 0.87988078 0.490006397
24 1.41023107 0.8847163 -1.09236948 0.73676048 0.436886096
> ACF<-acf(data3)
> plot(ACF, type="l", max.mfrow=1)
> lines(ACF$acf[-1, 2,3], lty=1, col="red", lwd=1)
> lines(ACF$acf[-1, 2,4], lty=1, col="green", lwd=1)
I have a dataset like the following:
derv Season Date
1 -32.497029042 2008sp 2008-01-02
2 -32.309188148 2008sp 2008-01-03
......
3 -32.121614690 2008au 2008-07-04
4 -31.934310377 2008au 2008-07-07
......
5 -31.747276914 2009sp 2009-01-08
6 -31.560516012 2009sp 2009-01-09
......
7 -31.374029378 2009au 2009-07-10
8 -31.187818719 2009au 2009-07-11
......
9 -31.001885744 2014sp 2014-01-14
10 -30.816232161 2014sp 2014-01-15
I used the following code to create a plot:
plot(d1, type = "l",main = "derivative", xlim = c(0,1800))
abline(h = 0)
abline(h = 2.578269274, col = "red")
abline(h = -1.4242559021, col = "purple")
And I want to know at which date or which season, d1 (derivative) crosses the three horizontal lines. But the x-axis value does not really allow to trace the date or season when d1 (derivative) crosses those horizontal lines.
So, I am wondering if there is a way that I can put the Season as in the x-axis or if there is a way that I can trace values on the plot.
New to R, would greatly appreciate any help!
I have a monthly weather dataset and I want to plot a line graph.
My dataset is here:
weather.data2:
date mtemp mrh ah1 ah2 vaporpressure
1 31/01/2008 15.95161 74.96774 10.463958 10.376739 12.60586
2 29/02/2008 13.32759 71.96552 8.506296 8.457573 10.32157
3 31/03/2008 19.98065 76.00000 13.461108 13.301972 16.07004
4 30/04/2008 23.06667 85.06667 17.884817 17.612111 21.20251
5 31/05/2008 25.34194 82.96774 19.904886 19.548480 23.47831
6 30/06/2008 26.67000 88.13333 22.655861 22.217597 26.65403
7 31/07/2008 28.37097 82.16129 23.216533 22.715155 27.21262
8 31/08/2008 28.38387 79.45161 22.520920 22.034029 26.39536
9 30/09/2008 28.96667 74.56667 21.834234 21.345684 25.55925
10 31/10/2008 26.50000 77.03226 19.685226 19.308482 23.16607
11 30/11/2008 21.94667 65.33333 13.473522 13.271739 15.98306
12 31/12/2008 18.43548 63.38710 10.184461 10.081156 12.20581
13 31/01/2009 15.32258 63.87097 8.663397 8.597653 10.45324
14 28/02/2009 20.51071 81.28571 14.778456 14.596660 17.62418
15 31/03/2009 19.69032 83.09677 14.448571 14.280276 17.25859
16 30/04/2009 22.02333 77.13333 15.350085 15.134001 18.23880
17 31/05/2009 25.53548 78.29032 19.013323 18.669040 22.41749
18 30/06/2009 28.14333 81.36667 22.795169 22.309445 26.72967
19 31/07/2009 29.04839 80.77419 23.784844 23.249975 27.83724
20 31/08/2009 29.43226 79.96774 24.035433 23.482366 28.10789
21 30/09/2009 28.82667 78.46667 22.788483 22.282172 26.68366
22 31/10/2009 26.16774 73.06452 18.258184 17.917379 21.50479
23 30/11/2009 20.48000 72.20000 13.498049 13.315853 16.06684
24 31/12/2009 17.31290 78.06452 11.815604 11.705578 14.19231
Here is my plot:
weather.data2$date=as.Date(as.character(weather.data$date),format="%d/%m/%Y")
windows(width=7*1.5,height=12/2)
par(mar=c(4,4,2,5))
plot(weather.data2$date,weather.data2$ah1,ylim=c(-2,30),type='l',col="blue", xlab="month", ylab=NA)
par(new=TRUE)
plot(weather.data2$date,weather.data2$ah2,ylim=c(-2,30),type='l',col="green", xlab="", ylab=NA)
par(new=TRUE)
plot(weather.data2$date,weather.data2$mtemp,ylim=c(-2,30),type='l',col="red", xlab="", ylab=NA)
par(new=TRUE)
plot(weather.data2$date,weather.data2$mrh,ylim=c(-2,100),type='l',col="orange", axes=F, xlab=NA, ylab=NA)
axis(side=4)
mtext(side=4,line=3,"Relative Humiditiy (%)")
par(new=TRUE)
plot(weather.data2$date,weather.data2$vaporpressure,ylim=c(-2,30),type='l',col="steelblue", xlab="", ylab=NA)
mtext(side=2,line=3,"Temperature (C)/Vapour Pressure (mb)/Absolute humidity(g/m^3)")
legend("bottomright", c("Relative Humidiity","Temperature","Vapour Pressure","Absolute Humidity 1","Absolute Humidity 2"),lty=1,col = c("orange","red","steelblue","blue","green"),bty="n")
legend("bottomleft",c("Household Contact Enrollment Date"),pch=19,col=c("red"),bty="n")
But then my when I plotted it, it looked like this...
I want it to be looking like this, not this exactly though (this is a loess regression fitted to a daily average, that is why I calculated the monthly average so that it will hopefully look better than the one below)
Is the following helping you at all?
x$date=as.Date(x$date, format='%d/%m/%Y')
library(reshape2)
library(ggplot2)
x=melt(x,id='date',value.name='VALUE',variable.name='FACTOR')
x$VALUE=as.numeric(x$VALUE)
ggplot(x, aes(date, VALUE, group=FACTOR, color=FACTOR))+geom_line()
Using ggplot2 you cannot have two axes, but you can do faceting and improve how it looks. Is this at the right direction?
I have some data as following:
xlab ylab xval yval class
1 2 6015 10500 3
1 7 6015 9696 1
3 5 6632 15626 3
3 6 6632 6074 1
3 5 6632 4189 2
1 3 6015 6632 1
1 5 6015 15626 1
I want to create a scatter plot using with x and y coordinate specified in xval and yval columns. Each point is colored based on the value in class column. This part I could do as following:
<!-- language: lang-R -->
data <- read.table(filename, header=TRUE);
df <- data.frame(data["xval"], data["yval"], data["class"]);
plot(xval, yval, col=c("red", "blue", "green")[class],
main="Title here",
xlab="Ox title",
ylab="Oy title");
Now, I want to have a label for each node using the xlab and ylab columns in the form of (xlab, ylab). Eg. The first node is labeled as (1,2). However, I couldn't figured out how to do it.
Can someone please show me how to add labels to nodes pleases? I'm a newbie in R so I'd really appreciate if you could show me the sample code.
Many thanks,
Use the text function as follows:
with(df, plot(xval, yval, col=c("red", "blue", "green")[class],
main="Title here",
xlab="Ox title",
ylab="Oy title"))
with(df, text(xval, yval, sprintf("(%s, %s)", xlab, ylab)))
I use sprint here but you could use paste/paste0 as well.
How to create a categorical bubble plot, using GNU R, similar to that used in systematic mapping studies (see below)?
EDIT: ok, here's what I've tried so far. First, my dataset (Var1 goes to the x-axis, Var2 goes to the y-axis):
> grid
Var1 Var2 count
1 Does.Not.apply Does.Not.apply 53
2 Not.specified Does.Not.apply 15
3 Active.Learning..general. Does.Not.apply 1
4 Problem.based.Learning Does.Not.apply 2
5 Project.Method Does.Not.apply 4
6 Case.based.Learning Does.Not.apply 22
7 Peer.Learning Does.Not.apply 6
10 Other Does.Not.apply 1
11 Does.Not.apply Not.specified 15
12 Not.specified Not.specified 15
21 Does.Not.apply Active.Learning..general. 1
23 Active.Learning..general. Active.Learning..general. 1
31 Does.Not.apply Problem.based.Learning 2
34 Problem.based.Learning Problem.based.Learning 2
41 Does.Not.apply Project.Method 4
45 Project.Method Project.Method 4
51 Does.Not.apply Case.based.Learning 22
56 Case.based.Learning Case.based.Learning 22
61 Does.Not.apply Peer.Learning 6
67 Peer.Learning Peer.Learning 6
91 Does.Not.apply Other 1
100 Other Other 1
Then, trying to plot the data:
# Based on http://flowingdata.com/2010/11/23/how-to-make-bubble-charts/
grid <- subset(grid, count > 0)
radius <- sqrt( grid$count / pi )
symbols(grid$Var1, grid$Var2, radius, inches=0.30, xlab="Research type", ylab="Research area")
text(grid$Var1, grid$Var2, grid$count, cex=0.5)
Here's the result:
Problems: axis labels are wrong, the dashed grid lines are missing.
Here is ggplot2 solution. First, added radius as new variable to your data frame.
grid$radius <- sqrt( grid$count / pi )
You should play around with size of the points and text labels inside the plot to perfect fit.
library(ggplot2)
ggplot(grid,aes(Var1,Var2))+
geom_point(aes(size=radius*7.5),shape=21,fill="white")+
geom_text(aes(label=count),size=4)+
scale_size_identity()+
theme(panel.grid.major=element_line(linetype=2,color="black"),
axis.text.x=element_text(angle=90,hjust=1,vjust=0))
This will get you started by adding the tick marks to your xaxis.
To add the lines, just add a line at each level
ggs <- subset(gg, count > 0)
radius <- sqrt( ggs$count / pi )
# ggs$Var1 <- as.character(ggs$Var1)
# set up your tick marks
# (this can all be put into a single line in `axis`, but it's placed separate here to be more readable)
#--------------
# at which values to place the x tick marks
x_at <- seq_along(levels(gg$Var1))
# the string to place at each tick mark
x_labels <- levels(gg$Var1)
# use xaxt="n" to supress the standard axis ticks
symbols(ggs$Var1, ggs$Var2, radius, inches=0.30, xlab="Research type", ylab="Research area", xaxt="n")
axis(side=1, at=x_at, labels=x_labels)
text(ggs$Var1, ggs$Var2, ggs$count, cex=0.5)
also, notice that instead of calling the object grid I called it gg, and then ggs for the subset. grid is a function in R. While it is "allowed" to overwrite the function with an object, it is not recommended and can lead to annoying bugs down the line.
Here a version using levelplot from latticeExtra.
library(latticeExtra)
levelplot(count~Var1*Var2,data=dat,
panel=function(x,y,z,...)
{
panel.abline(h=x,v=y,lty=2)
cex <- scale(z)*3
panel.levelplot.points(x,y,z,...,cex=5)
panel.text(x,y,label=z,cex=0.8)
},scales=(x=list(abbreviate=TRUE))) ## to get short labels
To get the size of bubble proprtional to the count , you can do this
library(latticeExtra)
levelplot(count~Var1*Var2,data=dat,
panel=function(x,y,z,...)
{
panel.abline(h=x,v=y,lty=2)
cex <- scale(z)*3
panel.levelplot.points(x,y,z,...,cex=5)
panel.text(x,y,label=z,cex=0.8)
})
I don't display it since the render is not clear as in the fix size case.