search for: free_y

...ply(precip.1$precipitation, .(precip.1$gauge_name), cumsum) DF.precip <- precip.1 DF.precip$precipitation <- b$.data #discharge if(input=="data"){ return(DF) }else{ qplot(Date, discharge, data=DF, geom="line", ylab="Date")+facet_wrap(~gauge_name, scales="free_y")+coord_trans(y="log10")} if(input=="precipitation"){ #precipitation qplot(Date, precipitation, data=DF.precip, geom="line")+facet_wrap(~gauge_name, scales="free_y") }else{ qplot(Date, discharge, data=DF, geom="line", ylab="Date")+...

...o parts, using facet_grid. I would like the vertical axis (labelled 'place') to be different for each location (=part). So in the upper part, only places 'n' through 'z' are shown, while in the lower part, only places 'a' through 'm' are shown. I thought 'free_y' would do the trick. I also tried converting variable place into class 'factor'. require(ggplot2) DF <- data.frame(place=letters, value=runif(26), location=c(rep(1, 13), rep(0, 13))) qplot(data=DF, x=place, y=value, geom="bar", stat="identity") +   coord_flip()...

...h[,"time"]) date_time <- as.chron(paste(dts, tms), "%Y-%m-%d %H:%M") DF <- data.frame(date_time, both) library(ggplot2) #discharge if(input=="discharge"){ qplot(as.POSIXct(date_time), discharge, data=DF, geom="line")+facet_wrap(~gauge_name, scales="free_y")+coord_trans(y="log10") }else{ #precipitation qplot(as.POSIXct(date_time), precipitation, data=subset(DF, precipitation!="NA"), geom="line")+facet_wrap(~gauge_name, scales="free_y") } } USGS("precip") I would like to have the cumsum based o...

...= list(seed = 1234)) lda22 %>% tidy() %>% group_by(topic) %>% top_n(10, beta) %>% ungroup() %>% mutate(term = reorder_within(term, beta, topic)) %>% ggplot(aes(term, beta, fill = factor(topic))) + geom_col(show.legend = FALSE) + facet_wrap(~ topic, scales = "free_y") + coord_flip() + scale_x_reordered() Muchas gracias

...ut I fail to see how one could control ylim and xlim ranges for each facet separately. For instance, I would like to set the ylim = c(0,10) for facet "A" and ylim = c(42,102) for facet "B". Since the data is out of these ranges, setting facet_grid(factor ~ ., scales = "free_y") does not achieve the goal . Is there a decent way to achieve this or not? or I have to drop data points outside y-ranges as a quick hack? Swen Laur

...spaces along the y-axis in each panel. drop.unused.levels=TRUE will drop conditioning variables, but does not affect the levels in the y-variable. I can get something like what I want with ggplot2:library(ggplot2)ggplot(data=dfr,mapping=aes(x=B,y=A)) + geom_point() + facet_wrap( ~ C,scales="free_y") I wonder if this is possible in lattice? Thanks! Stephen [[alternative HTML version deleted]]

In page http://had.co.nz/ggplot2/scale_date.html , there is a time series plot for multiple variables at the bottom. i.e qplot(date, value, data = em, geom = "line", group = variable) + + facet_grid(variable ~ ., scale = "free_y") How can I make different color for different series? Thanks -- View this message in context: http://www.nabble.com/ggplot-question-tp24641347p24641347.html Sent from the R help mailing list archive at Nabble.com.

...= unemploy,position = "lower")) lower #upper$date <- as.Date(upper$date,"%m/%d/%Y") #lower$date <- as.Date(lower$date,"%m/%d/%Y") ggplot(mapping = aes(x=date, y=value)) + geom_line(data = upper) + geom_area(data = lower) + facet_wrap(~ position, scale = "free_y",ncol=1) Felipe D. Carrillo Supervisory Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA

...;, c('psavert','uempmed','unemploy')) mcsm <- melt(data.frame(date=economics$date, q=quarters(economics$date)), id='date') mcsm$value <- factor(mcsm$value) ggplot(subm, aes(date, value, col=variable, group=1)) + geom_line() + facet_grid(variable~., scale='free_y') + geom_step(data=mcsm, aes(date, value)) + scale_y_discrete(breaks=levels(mcsm$value)) If I leave out scale_y_discrete, R complains that I'm trying to combine discrete value with continuous scale. If I include scale_y_discreate my continuous series miss their scale. Is there any neat wa...

...goes on like this for another 11 subjects. I can plot a 3 row (A,B,C) x 4 column (Sub 1,2,3,4) grid of time courses with the y labels for the facets being 1,2,3,4 and the x labels for the facets being A,B,C. qplot(Day,Value,data="data above") + facet_grid(Variable~Subject, scales="free_y") But, how do I rearrange the columns so that all the Yeses to Ab are together and the Nos to Ab are together while keeping the same labeling? Thanks in advance. George Chen

...uot;,"italic(\u03b4)^13*C")) #So this is what I want my plot to look like: plt <- ggplot(data = dat, aes(x = yy, y = x)) + geom_point(aes(x= yy, y=x, shape=z, group=z), alpha=0.4,position = position_dodge(width = 0.8)) + facet_grid(gltr~.,labeller= label_parseall, scales="free_y") plt #So then I exported my plot as a PDF like so: pdf("Times_regular.pdf", family='Times') plt dev.off() #The problem with this was that the delta symbols turned into dots. #I solved this problem using Cairo library(Cairo) cairo_pdf("Cairo.pdf") plt dev.off()...

2017-06-08 12:54 GMT-04:00 Javier Marcuzzi <javier.ruben.marcuzzi en gmail.com> : > ¿me hice entender? ?No. Para salir del escollo lo convertiré a gráficos? base y continuaré con mi vida ?... ?Au revoir.? -- «Pídeles sus títulos a los que te persiguen, pregúntales cuándo nacieron, diles que te demuestren su existencia.» Rafael Cadenas [[alternative HTML version deleted]]

...eight", "discharge", "precipitation", "gauge_name"), row.names = c(NA, -266L), class = "data.frame") qplot(as.POSIXct(date_time), precipitation, data=subset(DF, precipitation!="NA"), geom="line")+facet_wrap(~gauge_name, scales="free_y") #if I issue: qplot(as.POSIXct(date_time), cumsum(precipitation), data=subset(DF, precipitation!="NA"), geom="line")+facet_wrap(~gauge_name, scales="free_y") #ggplot will plot the total cumulative sum and not the sum based no the facet(factor) I am trying to p...

...ts.melt <- melt(stats.df, id.var=c("date", "search")) str(stats.melt) # stats.melt <- within(stats.melt, counts=value) library(ggplot2) p <- ggplot(data = subset(stats.melt, date>1960 ), mapping = aes(x = date,y = value)) + facet_wrap(~search,ncol=2, scale="free_y") + layer(colour="grey", geom = c( "histogram"), stat = "identity" ) + scale_y_continuous("number of publications") p _____________________________ Baptiste Augui? School of Physics University of Exeter Stocker Road, Exeter, Devon, EX4 4QL, UK Phon...

I am trying to plot this dataset using ggplot2: df <- data.frame( sid = c(rep('11',30),rep('22',30)), time = rep(ISOdate(year = 2010, month = 1, day = 1:30),2), sales = c(rnorm(30, 1000, 20),rnorm(30, 900, 10)), price = c(rnorm(30, 2, 0.5),rnorm(30, 3,0.5)) ) Plotting just the sales can be done easily: ggplot(data=df, aes(x=time, y=sales, group=sid, color=sid)) +

Hello R-Group, I am trying plotting simple time-series with ggplot2 because the output looks better and I've heard its richer in features. I have the following dataset. > dput(dat) structure(list(Date = structure(c(14970, 14971, 14972, 14973, 14974, 14977, 14978, 14979, 14980, 14981), class = "Date"), Close = c(5998.1, 5996, 6060.35, 6101.85, 6134.5, 6157.6, 6146.35, 6079.8,

In lattice #toy data library(ggplot2) library(lattice) x <- rnorm(100) y <- rnorm(100) k <- sample(c("Weak","Strong"),100,replace=T) j <- sample(c("Tall","Short"),100,replace=T) w <- data.frame(x,y,j,k) xyplot(y~x|j+k,scales=list(y=list(relation="free"))) will give you a scale in each subplot with a range equal to the range of y

...ng ribbon p <- p + stat_summary(aes(group = 1), subset(ecg, trt == 'Placebo'), fun.ymin = 'q25', fun.ymax = 'q75', geom = 'ribbon', fill = 'gray80', colour = NA) # Basic plot p <- p + geom_boxplot() p <- p + facet_grid(variable ~ trt, scales = 'free_y') # Median line - again, want to reflect across rows p <- p + stat_summary(aes(group = 1), subset(ecg, trt == 'Placebo'), fun.y = 'median', geom = 'line', colour = 'gray60') # Formatting p <- p + labs(x = '', y = '') p <- p + theme(leg...

Hello! I need to make a plot with whispers that does the following. Reads in 50 files, each file containing 200 data points. A file looks like this: base100.log Send Receive 10.5 100.3 15.0 102.4 ... There are 100 lines, each with two data points. I need to read in the 50 files, and plot three lines The first line is the mean of the send column with whiskers indicating standard deviation

Dear Users, please help with the following DF test: ===== library(tseries) library(timeSeries) Y=c(3519,3803,4332,4251,4661,4811,4448,4451,4343,4067,4001,3934,3652,3768 ,4082,4101,4628,4898,4476,4728,4458,4004,4095,4056,3641,3966,4417,4367 ,4821,5190,4638,4904,4528,4383,4339,4327,3856,4072,4563,4561,4984,5316 ,4843,5383,4889,4681,4466,4463,4217,4322,4779,4988,5383,5591,5322,5404