search for: geom_pointrang

...aes(x = Month, y = med, ymin = min, ymax = max, subset = CATCHMENT_ == subbasin_select)) + ylab("Precipitation (mm)\n") + opts(title = paste("Projected Precipitation 2030 - 2049 (Sub-Basin ", subbasin_select, ")\n", sep = "")) + scale_colour_grey() p + geom_pointrange(linetype = 2, colour = "navyblue") + opts(axis.text.x = theme_text(colour = "black")) + opts(axis.text.y = theme_text(colour = "black")) + opts(axis.ticks = theme_segment(colour = "white")) ggsave(paste(subbasin_select,"2100.jpeg", sep = &q...

...x,y,g) se <- with(dat,tapply(y,list(g,x),sd)/table(g,x)) means <- with(dat,tapply(y,list(g,x),mean)) limits <- aes(ymax=means+se,ymin=means-se) gg <- factor(rep(1:ngrp,nx)) xx <- rep(1:nx,each=ngrp) p <- ggplot(dat,aes(y=c(means),x=xx, group=gg,colour=gg)) p + geom_pointrange(aes(ymin=c(means-se),ymax=c(means+se))) xdodge <- xx + rep(seq(-0.1,0.1,length=ngrp),nx) p + geom_line() + geom_pointrange(aes(x=xdodge, ymin=c(means-se), ymax=c(means+se))) + scale_x_continuous(name="Year",...

Dear R Users: I have the individual mortality rate and 95% CI of 100 hospitals, how to do the plot with the individual hospital in the Yaxis, and the mortality rate and 95% CI in the Xais and a overall mean as a reference line? Thanks and regards, Xin [[alternative HTML version deleted]]

...ot;1","2","2"), var2 = c("A","B","A","B"), var1var2 = c("1A","1B","2A","2B")) ggplot(figure4, aes(var1var2, cen, ymin = min, ymax=max, linetype=var1)) + scale_linetype_manual(values = c(1,3)) + geom_pointrange() + geom_errorbar(width=0.2) + opts(title="Title goes here") + labs(x="Var2", y="Y units", linetype="Var1") Thanks Matt [[alternative HTML version deleted]]

...in.IgM Merlin.IgM 64.50167 60.30376 68.69958 Pb.IgM Pb.IgM 65.38667 61.81591 68.95742 AFRIMS AFRIMS 88.29958 84.84922 91.74995 I am currently using the code as suggested on web: credplot.gg<- function(d){require(ggplot2) p <- ggplot(d,aes(x=x,y=y,ymin=ylo,ymax=yhi))+geom_pointrange()+coord_flip()+ xlab('RDT tests')+ylab('Sensitivity') return(p)} d <- data.frame(x=names(ragama.meta)[2:13],y=xx[2:13],ylo=xx[2:13]-1.96*ss[2:13], yhi=xx[2:13]+1.96*ss[2:13]) credplot.gg(d) How should I correct this so that I can fix the order of the rapid tests the...

Hi folks, Can someone please help me in identifying the type of plot shown here? Sample R code or specific package name would be of help as well. Thanks, -Girish http://www.nabble.com/file/p24477714/example_plot.jpg -- View this message in context: http://www.nabble.com/Help-needed-in-identifying-type-of-plot-tp24477714p24477714.html Sent from the R help mailing list archive at Nabble.com.

...ucb, and group, and I'm faceting by group. If you want the plots horizontal, add coord_flip() to the command --- as it stands the plots are vertically oriented. > > require(ggplot2) > p <- ggplot(x, aes(x=drug, y = hr, ymin=hr.lcb, ymax=hr.ucb))+ > geom_pointrange()+ facet_grid(.~group) > p <- p + xlab('Drug') + ylab('Hazard ratio')+ > geom_hline(y=1, col='red', lty=2) > > > Abhijit > On Oct 30, 2010, at 5:31 PM, Matt Shotwell wrote: > >> Here is a small function for forest plots...

Hello: I am plotting some data using the box and whisker plot. However, I only want to plot the median, max and min, as I only have these values and not the quartile values. It seems R arbitrarily constructs the box margins to be halfway between the median and the max/min. How do I make the box and whisker plots without the box values? Thanks, Michael [[alternative HTML version

Hi, I want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same

Hi, I have the following data: > est sch190 sch107 sch290 sch256 sch287 sch130 sch139 4.16656026 2.64306071 4.22579866 6.12024789 4.49624748 11.12799127 1.17353917 sch140 sch282 sch161 sch193 sch156 sch288 sch352 3.48197696 -0.29659410 -1.99194986 10.23489859 7.77342138 6.77624539 9.66795001 sch368

Like this? # example using qplot library(ggplot2) meanprice <- tapply(diamonds$price, diamonds$cut, mean);meanprice cut <- factor(levels(diamonds$cut), levels = levels(diamonds$cut)) qplot(cut, meanprice, geom="bar", stat="identity", fill = I("grey50")) dev.new() # create a new graph to compare with qplot # Example using ggplot ggdata <-

...is now considerably more sophisticated and will attempt to merge together legends for the same variable * also, legends are drawn based on the geoms used (instead of the scales used, as previously) so should match the plot much better (e.g. for geom_smooth, geom_boxplot, geom_vline, geom_abline, geom_pointrange). These features are new, so there are likely to be a few bugs that I haven't discovered. Please me know if you do find any. Other additions and corrections * coord_equal: should now work correctly in all situations * coord_polar: add start and direction parameters, giving more control...

...is now considerably more sophisticated and will attempt to merge together legends for the same variable * also, legends are drawn based on the geoms used (instead of the scales used, as previously) so should match the plot much better (e.g. for geom_smooth, geom_boxplot, geom_vline, geom_abline, geom_pointrange). These features are new, so there are likely to be a few bugs that I haven't discovered. Please me know if you do find any. Other additions and corrections * coord_equal: should now work correctly in all situations * coord_polar: add start and direction parameters, giving more control...

...t_wrap if formula looks like ~ a + b (as opposed to a ~ b) Aesthetic tweaks * geom_bar, geom_polygon, geom_rect, ...: default fill colour is now much closer to black to match the defaults in other geoms (point, line, etc) * geom_line, geom_path, geom_segment: lines have squared ends * geom_point, geom_pointrange and geom_boxplot: now use shape = 16 instead of 19. This shape does not have a border from R 2.8 on, and so will look better when displayed transparently. * geom_contour, geom_density2d, geom_quantile and geom_smooth use a bright blue colour for lines, to make them stand out when used with black...

...t_wrap if formula looks like ~ a + b (as opposed to a ~ b) Aesthetic tweaks * geom_bar, geom_polygon, geom_rect, ...: default fill colour is now much closer to black to match the defaults in other geoms (point, line, etc) * geom_line, geom_path, geom_segment: lines have squared ends * geom_point, geom_pointrange and geom_boxplot: now use shape = 16 instead of 19. This shape does not have a border from R 2.8 on, and so will look better when displayed transparently. * geom_contour, geom_density2d, geom_quantile and geom_smooth use a bright blue colour for lines, to make them stand out when used with black...

Hi all, I want to plot the grouped means of some variables. The dependent variables and the grouping factor are stored in different columns. I want to draw a simple line-plot of means, in which the x-axis represents the variables and y-axis represents the means. The means of the groups should be connected by lines. So far, the only function that I could find comes closest to what I'm

Hi group, I found a module for adaptive kernel density estimation for Stata users, but unfortunetly I don't have access to Stata, can I find a similar approach using R? Thank u so much 4 ur time. [[alternative HTML version deleted]]