I am running the following R code:
plot(datereal, casesimm8lock7,
type = "l", lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
And I want the y-axis, which currently displays 50000 100000 150000 etc. to display 50,000 100,000 150,000 etc.
I am trying to use the scales package but haven't figured it out yet.
Start by making an empty plot with no y axis. Then plot the data.
Then plot the y axis with axis(2, ...). In order to have the labels formatted like the question asks for, use help("formatC").
# test data
datereal <- Sys.Date() - 10:0
casesimm8lock7 <- seq(0,250000, length.out = 11)
# the plot
plot(datereal, casesimm8lock7, type = "n", yaxt = "n")
lines(datereal, casesimm8lock7,
lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
axis(2, at = seq(0, 250000, by = 50e3),
labels = formatC(seq(0, 250000, by = 50e3),
format = "d", big.mark = ","))
With package scales, it could be with either label_comma, like below, or label_number. Thse functions return a labeller function, to be applied to the vector of axis marks.
labeller <- scales::label_comma()
plot(datereal, casesimm8lock7, type = "n", yaxt = "n")
lines(datereal, casesimm8lock7,
lwd = 1, col = "red",
main = "a Sensitivity to time delays: 8 month immunity",
xaxt = "n", xlab = "Month",
ylab = "Daily new cases", ylim = c(0,250000))
axis(2, at = seq(0, 250000, by = 50e3),
labels = labeller(seq(0, 250000, by = 50e3)))
I am trying to plot return against date, and I would like the line to start and end at the line border. The plot code I am using -
minX = min(hf_instl$date)
maxX = max(hf_instl$date)
plot(df$date, cumsum(df$return), type = "l", col = rgb(0, 0.447, 0.741), xlim = c(minX, maxX), ylim = c(minY, maxY), yaxt = "n", xlab = NA, ylab = NA)
What I get is this -
Any solution to this in the base plot package? ggplot2 solutions are also okay.
You can use the xaxs property for that:
# create some fake data for the example
hf_instl = data.frame(date=seq(Sys.Date()-200,Sys.Date(),by='day'),return=runif(201,-1,1.5))
df = hf_instl
minX = min(hf_instl$date)
maxX = max(hf_instl$date)
minY=0
maxY=sum(df$return)+10
# your plot
plot(df$date, cumsum(df$return), type = "l", col = rgb(0, 0.447, 0.741),
xlim = c(minX, maxX), ylim = c(minY, maxY), yaxt = "n",
xlab = NA, ylab = NA,xaxs='i')
Hope this helps!
In the plot below, you probably find the distance between x-aixs and the date (Jan-01-60 to Mar-31-16) is large.
Is there anyway I could make the distance smaller?
Here is my example code:
plot(dates, data, type = "l", lwd = 3, ylab = " ",
xlab = " ", col = "gray35", xaxt='n', ann = FALSE)
axis(side=1, at=dates_ten, labels=format(dates_ten, "%b-%d-%y"), las =
1, cex.axis=0.5, las = 1, font = 2, tcl = -0.2)
Many thanks.
You can use the padj argument to axis
# make a reproducible example
dates <- seq(as.Date("2016/1/1"), as.Date("2016/4/1"), "days")
dates_ten <- seq(as.Date("2016/1/1"), as.Date("2016/4/1"), "10 days")
set.seed(42)
data <- rnorm(seq_along(dates))
Vary padj as needed:
plot(dates, data, type = "l", lwd = 3, ylab = " ",
xlab = " ", col = "gray35", xaxt='n', ann = FALSE)
axis(side=1, at=dates_ten, labels=format(dates_ten, "%b-%d-%y"), las = 1,
cex.axis=0.5, las = 1, font = 2, tcl = -0.2, padj = -2)
I have a set of code, below, and I need to run it 120 times, with the variables modified, of course, for each run. I tried a for loop and I tried to create a function. I just want to do this process faster. Can someone please help me? The variables I need to have modified are score and seconds. So, it would be score1, seconds1 for the first run, score2, seconds2 for the second run, and so on. Thank you.
pre <- aggregate(score~seconds, dataframe, FUN = sum)
freqsdf <- data.frame(table(SecsOnly$seconds))
condp <- pre$score/freqsdf$Freq
condp <- data.frame(condp)
par(mar = c(5,5,5,5))
hist(dataframe$seconds, xlab = "", ylab = "", ylim = c(0, 25), axes = FALSE, col = "grey", main = 'title here', breaks = 100)
axis(4, ylim = c(0, 25), col = "black", col.axis = "black", las = 2, cex.axis = 1)
par(new = TRUE)
plot(pres$seconds, condp$condp, col = "red" , type = "l", xlab = "Time", ylab = "y axis label", ylim=c(0,1.0))
abline(h = 0.20)
In the followup of the discussion on stackexchange I tried to implement the following plot
from
Cumming, G., & Finch, S. (2005). [Inference by Eye: Confidence Intervals and How to Read Pictures of Data][5]. American Psychologist, 60(2), 170–180. doi:10.1037/0003-066X.60.2.170
I share some people's dislike of double axis, but I think this is a fair use.
Below my partial attempt, the second axis is still missing. I am looking for more elegant alternatives, intelligent variations are welcome.
library(lattice)
library(latticeExtra)
d = data.frame(what=c("A","B","Difference"),
mean=c(75,105,30),
lower=c(50,80,-3),
upper = c(100,130,63))
# Convert Differences to left scale
d1 = d
d1[d1$what=="Difference",-1] = d1[d1$what=="Difference",-1]+d1[d1=="A","mean"]
segplot(what~lower+upper,centers=mean,data=d1,horizontal=FALSE,draw.bands=FALSE,
lwd=3,cex=3,ylim=c(0,NA),pch=c(16,16,17),
panel = function (x,y,z,...){
centers = list(...)$centers
panel.segplot(x,y,z,...)
panel.abline(h=centers[1:2],lty=3)
} )
## How to add the right scale, close to the last bar?
par(mar=c(3,5,3,5))
plot(NA, xlim=c(.5,3.5), ylim=c(0, max(d$upper[1:2])), bty="l", xaxt="n", xlab="",ylab="Mean")
points(d$mean[1:2], pch=19)
segments(1,d$mean[1],5,d$mean[1],lty=2)
segments(2,d$mean[2],5,d$mean[2],lty=2)
axis(1, 1:3, d$what)
segments(1:2,d$lower[1:2],1:2,d$upper[1:2])
axis(4, seq((d$mean[1]-30),(d$mean[1]+50),by=10), seq(-30,50,by=10), las=1)
points(3,d$mean[1]+d$mean[3],pch=17, cex=1.5)
segments(3,d$lower[3]+d$lower[2],3,d$lower[3]+d$upper[2], lwd=2)
mtext("Difference", side=4, at=d$mean[1], line=3)
As a starting point another base R solution with Hmisc:
library(Hmisc)
with(d1,
errbar(as.integer(what),mean,upper,lower,xlim=c(0,4),xaxt="n",xlab="",ylim=c(0,150))
)
points(3,d1[d1$what=="Difference","mean"],pch=15)
axis(1,at=1:3,labels=d1$what)
atics <- seq(floor(d[d$what=="Difference","lower"]/10)*10,ceiling(d[d$what=="Difference","upper"]/10)*10,by=10)
axis(4,at=atics+d1[d1=="A","mean"],labels=atics,pos=3.5)
I would also go with base graph, as it includes the possibility to actually have two y-axis, see the answer here:
Here is my soultion that uses only d:
xlim <- c(0.5, 3.5)
plot(1:2, d[d$what %in% LETTERS[1:2], "mean"], xlim = xlim, ylim = c(0, 140),
xlab = "", ylab = "", xaxt = "n", bty = "l", yaxs = "i")
lines(c(1,1), d[1, 3:4])
lines(c(2,2), d[2, 3:4])
par(new = TRUE)
plot(3, d[d$what == "Difference", "mean"], ylim = c(-80, 130), xlim = xlim,
yaxt = "n", xaxt = "n", xlab = "", ylab = "", bty = "n")
lines(c(3,3), d[3, 3:4])
Axis(x = c(-20, 60), at = c(-20, 0, 20, 40, 60), side = 4)
axis(1, at = c(1:3), labels = c("A", "B", "Difference"))
Which gives:
To make it clearer that the difference is something different, you can increase the distance from the other two points:
xlim <- c(0.5, 4)
plot(1:2, d[d$what %in% LETTERS[1:2], "mean"], xlim = xlim, ylim = c(0, 140),
xlab = "", ylab = "", xaxt = "n", bty = "l", yaxs = "i")
lines(c(1,1), d[1, 3:4])
lines(c(2,2), d[2, 3:4])
par(new = TRUE)
plot(3.5, d[d$what == "Difference", "mean"], ylim = c(-80, 130), xlim = xlim,
yaxt = "n", xaxt = "n", xlab = "", ylab = "", bty = "n")
lines(c(3.5,3.5), d[3, 3:4])
Axis(x = c(-20, 60), at = c(-20, 0, 20, 40, 60), side = 4)
axis(1, at = c(1,2,3.5), labels = c("A", "B", "Difference"))
I think you can do that also with base R, what about:
d = data.frame(what=c("A","B","Difference"),
mean=c(75,105,30),
lower=c(50,80,-3),
upper = c(100,130,63))
plot(-1,-1,xlim=c(1,3),ylim=c(0,140),xaxt="n")
lines(c(1,1),c(d[1,3],d[1,4]))
points(rep(1,3),d[1,2:4],pch=4)
lines(c(1.5,1.5),c(d[2,3],d[2,4]))
points(rep(1.5,3),d[2,2:4],pch=4)
lines(c(2,2),c(d[3,3],d[3,4]))
points(rep(2,3),d[3,2:4],pch=4)
lines(c(1.5,2.2),c(d[2,2],d[2,2]),lty="dotted")
axis(1, at=c(1,1.5,2), labels=c("A","B","Difference"))
axis(4,at=c(40,80,120),labels=c(-1,0,1),pos=2.2)
I simplified some things and didn't wrote it as function, but I think the idea is clear and could easily be extended to a function.