similar to: plotting time series using ggplots

Hi, I've tried to figure this out using Intro to R and help(), to no avail - I am new at this. I'm trying to write a script that will read multiple files from a directory and then merge them into a single new data frame. The original data are in a tree-ring specific format, and so I've first used a function (read.rwl) from the dplR package to read each file, translate each into a

Colleagues, Here is my reproducible code for a graph using geom_smooth set.seed(55) scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) library(ggplot2) library(cowplot) ggplot(scatter_data,aes(x=x_var,y=y_var))+ ? geom_point()+ ? geom_smooth(se=TRUE,fill="blue",color="black",linetype="dashed")+

?s 05:17 de 12/08/2023, Thomas Subia via R-help escreveu: > Colleagues, > > Here is my reproducible code for a graph using geom_smooth > set.seed(55) > scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) > ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) > > library(ggplot2) > library(cowplot) > > ggplot(scatter_data,aes(x=x_var,y=y_var))+

Hi everyone, it looks like geom_ribbon removes missing values and plots a single ribbon over the whole interval of x values. However, I'd rather want it to act like geom_line, that is, interrupt the ribbon for the interval of missing values and continue once there are new values. Here's an example: library(ggplot2) df <- data.frame( date = seq(from = as.Date("2010-05-15"),

Consider the following situation: we have quantified algal concentrations for a variety of species using many samples at each of three years. It seems to make sense to generate a line plot (matplot-like), with each species plotted as a separate line, with the points connected to emphasize the temporal pattern. The problem: lots of overlapping error bars. The question: from both a

G'day Thomas, On Sat, 12 Aug 2023 04:17:42 +0000 (UTC) Thomas Subia via R-help <r-help at r-project.org> wrote: > Here is my reproducible code for a graph using geom_smooth The call "library(tidyverse)" was missing. :) > I'd like to add a black boundary around the shaded area. I suspect > this can be done with geom_ribbon but I cannot figure this out. Some >

Dear R-helpers, the problem I'm facing today is to convince lattice to paint some areas in gray. The areas I would like to have in gray, are confidence bands I've googled around in the mailing list archives and eventually find some clues. This link is my starting point http://tolstoy.newcastle.edu.au/R/e2/help/07/04/15595.html I'm reproducing here the code for your convenience est

Hi, I have a data set that I'd like to run logistic regressions on, using ddply to speed up the computation of many models with different combinations of variables. I would like to run regressions on every unique two-variable combination in a portion of my data set, but I can't quite figure out how to do using ddply. The data set looks like this, with "status" as

Hi Did you try google? I got several answers using your question e.g. https://stackoverflow.com/questions/54687321/fill-area-between-lines-using-g gplot-in-r Cheers Petr > -----Original Message----- > From: R-help <r-help-bounces at r-project.org> On Behalf Of Luigi Marongiu > Sent: Friday, October 23, 2020 9:59 AM > To: r-help <r-help at r-project.org> > Subject:

Thank you, but this split the area into two and distorts the shape of the plot. (compared to ``` p + geom_abline(slope = slope_1, intercept = intercept_1 - 1/w[2], linetype = "dashed", col = "royalblue") + geom_abline(slope = slope_1, intercept = intercept_1 + 1/w[2], linetype = "dashed", col = "royalblue") ``` Why there

Hello, I am running SVM and showing the results with ggplot2. The results include the decision boundaries, which are two dashed lines parallel to a solid line. I would like to remove the dashed lines and use a shaded area instead. How can I do that? Here is the code I wrote.. ``` library(e1071) library(ggplot2) set.seed(100) x1 = rnorm(100, mean = 0.2, sd = 0.1) y1 = rnorm(100, mean = 0.7, sd =

Hi, I'm trying to plot a longitudinal data set, using ggplot and adding some summary info (eg. mean, 1 sd bounds) using geom=ribbon. The summary info is based on a subset of the original data (eg. less an outlier). But I'm having trouble getting the ribbons to show up correctly. It's probably something obvious that I'm missing as a novice at ggplot2, and any help is much

Hi Put fill outside aes p+geom_ribbon(aes(ymin = slope_1*x + intercept_1 - 1/w[2], ymax = slope_1*x + intercept_1 + 1/w[2]), fill = "blue", alpha=0.1) The "hole" is because you have two levels of data (red and blue). To get rid of this you should put new data in ribbon call. Something like newdat <- trainset newdat$z <- factor(0) p+geom_ribbon(data=newdat, aes(ymin =

I'm trying to make a loop with many files... > library(dplR) > > files <- system("ls *.rwl", intern=TRUE) > > files [1] "cimfasy.rwl" "rocquce.rwl" > for (i in files) {a <- read.rwl(i,header=0)} There are 70 series There are 21 series > class(a) [1] "data.frame" This loop import all the files rwl in a single data.frame ( a

I'm trying to make a loopo with some file.... > > library(dplR) > files.rwl <- system("ls *.rwl", intern=TRUE) > files.rwl [1] "cimfasy.rwl" "rocquce.rwl" > for (i in files.rwl) assign(gsub("\\.rwl$", "_rwl", i), read.rwl(i, header=0)) There are 70 series There are 21 series > > cimfasy_rwl 1990 1.55 0.00

also from this site: https://plotly.com/ggplot2/geom_ribbon/ I get the answer is geom_ribbon but I am still missing something ``` #! plot p = ggplot(data = trainset, aes(x=x, y=y, color=z)) + geom_point() + scale_color_manual(values = c("red", "blue")) # show support vectors df_sv = trainset[svm_model$index, ] p = p + geom_point(data = df_sv, aes(x=x, y=y),

Hi, With this data.frame: > class(rwl) [1] "data.frame" >rwl 0028002F 0028013F 0028032F 1833 3.39 NA NA 1834 3.09 NA NA 1835 3.05 NA NA 1836 3.31 NA NA 1837 2.26 NA NA > colnames(rwl) [1] "0028002F" "0028013F" "0028032F" Ok.... > colnames(rwl[,1]) NULL why??

Dear useRs, I'm struggling with the new version of ggplot2. In the previous version I did something like this. But now this yield an error (object "fill" not found). library(ggplot2) dummy <- data.frame(x = rep(1:10, 4), group = gl(4, 10)) dummy$y <- dummy$x * rnorm(4)[dummy$group] + 5 * rnorm(4)[dummy$group] dummy$min <- dummy$y - 5 dummy$max <- dummy$y + 5 ggplot(data

Hi, I have a 5-paneled figure that i made using the facet function in qplot (ggplot). I've managed to arrange the panels into two rows/ three columns, but for the sake of easy visual comparisons between panels in my particular dataset, I want to have the two plots on the bottom align on the right hand side of the figure instead of the left. Here's an example: m <-

Hi everyone, Hadley, it seems there's a bug in geom_ribbon() when using it in a log-scaled plot: d<-data.frame(x=c(1:20),y1=rnorm(20)+3,y2=rnorm(20)+5) p<-ggplot() p<-p+geom_ribbon(data=d,aes(x=d[["x"]],min=d[["y1"]],max=d[["y2"]])) p<-p+geom_line(data=d,aes(x=d[["x"]],y=d[["y1"]]),colour="blue")