similar to: ggplot2 -- scale_colour_manual()

Hello All, I am trying to create a legend for a black-white graph. The package I use is ggplot2. It can add colors to the legend key but not line types. Can you please help? # example from Wickman (2009, ggplot2 - elegant graphics for data analysis, page 109) library(ggplot2) huron <- data.frame(year=1875:1972, level=LakeHuron) ggplot(huron, aes(year)) +

Dear R-list, I would like to show the implications of estimating a linear trend to time series, which contain significant serial correlation. I want to demonstrate this, comparing lm() and an gls() fits, using the LakeHuron data set, available in R. Now in my particular case I would like to draw confidence bands on the plot and show that there are differences. Unfortunately, I do not know how to

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

hi, i am trying to teach myself about ARIMA models. i have followed examples from a number of sources and have more or less got the hang of how it works. i would like to compare the output from the fitted model to the original data. is this possible? or even a meaningful thing to do? to be clear, for example, having generated a fit to some data using > fit <- arima(LakeHuron, order = c(1,

Hi, To my knowledge, an excellent of ggplot with a second y-axis is https://rpubs.com/MarkusLoew/226759 In this example, the author uses two colors for the two lines, but the line shapes are the same -- both are solid. Could each line have its own color as well as its own shape? For example, can I make the red line with the linetype "twodash", while the blue line with the

Hi John, You can try the following: override.linetype=c("twodash","solid") p <- ggplot(obs, aes(x = Timestamp)) p <- p + geom_line(aes(y = air_temp, colour = "Temperature", linetype ="Temperature")) p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity", linetype="Humidity")) p <- p +

Sorry let me clarify. If I modify the line p <- p + geom_line(aes(y = air_temp, colour = "Temperature")) by p <- p + geom_line(aes(y = air_temp, colour = "Temperature", linetype ="Temperature")) and p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity")) by p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity",

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents

Say I have a dataset that looks like Location Year GW_Elv MW01 1999 546.63 MW02 1999 474.21 MW03 1999 471.94 MW04 1999 466.80 MW01 2000 545.90 MW02 2000 546.10 The whole dataset is at http://doylesdartden.com/ExampleData.csv and I use the code below to do the graph but I want to do it without MW01. How can I

Hi, I really feel I've looked everywhere, although I know this can't be a hard problem. I'd like to be able to call the graph below as a function, but I can't get the function to recognize variables beyond 'dframe'. I've read through many papers on writing functions in R, but I can't get this to work. data <- data.frame('date' = as.Date(rep(c(15101,

Hi, Color of my step plot is now by default. Now I'd like to change the color as the grey scale I specified. I don't know why I got three black plot. Here I attach two version of codes. The first one produces a step plot with color by default. The second one, modified from the first one, is not producing the grey scale I want. Any help would be highly appreciated. [version 1- working]

Good afternoon! I'm trying to learn time series but i have a bit of of a problem using R packages for this. 1. > LakeHuron > sample(500:600, 98) > sample(500:600, 98)->t > fit<-arima(LakeHuron, order=c(2,1,1), xreg=t) > fit > predict(fit, n.ahead=1, newxreg=t) Now, my problem is this: is it ok to use the same t in predict function or should my newxreg contain 99

Hello - I am trying to plot flows in a number of rivers within the same plot, and need to colour the lines differently, using a colour-blind palette. Code beneath works but has colours assigned by the program I have made some simple dummy data: ## code 1 cb8<- c("#000000", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2",

Hello, this is probably a recurrent question, but I couldn't find any answers that didn't involve the expression "data frame"... so perhaps I'm looking for something new here. I wanted to find a code equivalent to > x=sqrt(1:10) > y=log(1:10) > plot(1:10, x, type="lines", col="darkgreen") > lines(1:10, y, col="red") to use with

Hello, I am drawing some data with ggplot2 and would like to reverse the order of the y axis and set a custom range for it. I can do the former but when I add the key `limits` to `scale_y_reverse` I get an error as shows below and, worse, no data shown in the plot. How can I properly set a reverse y scale with a defined range in ggplot2? Thank you ``` A <- rep(c(0, 3, 6, 12, 24), 3) B <-

Hi, I often use plotmeans() from the gplots package to quickly visualize a pattern of change. I would like to be able to plot separate lines for different groups, but the function gives an error when a grouping variable is included in the formula argument. For instance, > require(gplots) > x <- data.frame(Score=rnorm(100), Time=rep(1:10, 10),

On 05/03/2024 7:16 a.m., Luigi Marongiu wrote: > A <- rep(c(0, 3, 6, 12, 24), 3) > B <- c(rep(0,5), rep(1,5), rep(2,5)) > V <- c(27.5, 27.01, 27.75, 27.98, 27.4, 25.69, > 26.17, 27.78, 26.08, > 24.97, 23.18, 21.78, 22.49, 21.85, 22.2) > df <- data.frame(Conc = A, Time = B, Value = V) > df$Conc = as.factor(df$Conc) >

Greetings This is a long email. I'm struggling with a data set comprising 2,278 hydroacoustic estimates of fish biomass density made along line transects in two lakes (lakes Michigan and Huron, three years in each lake). The data represent lakewide surveys in each year and each data point represents the estimate for a horizontal interval 1 km in length. I'm interested in comparing

Reading in the data from the file x <- read.csv( "ExampleData.csv", header = TRUE, stringsAsFactors = FALSE ) Subsetting as you want x <- x[ x$Location != "MW01", ] This selects all rows where the value in column 'Location' is not equal to "MW01". The comma after that ensures that all columns are copied into the amended data.frame. Rgds,