ggplot2

[ad_1] With ggplot, it is essential to have a single data frame with everything in it (at least for a single plotting layer, e.g., all the bars in a plot). You create a data frame of the column sums, and then try to use external vectors for the id and the grouping, which makes things … Read more

[ad_1] First define a function with the elements that need to be changed as arguments. Set those arguments to defaults in the graph you really like. To change the defaults, pass them on to the function in its calls. library(ggplot2) my_theme <- function(plot_title_size = 30, colour = “black”, size = 20, angle= 0){ theme(plot.title = … Read more

[ad_1] I got some help after asking the same question on the comp.lang.r.general mailing list (http://permalink.gmane.org/gmane.comp.lang.r.general/313656). I ended up adding the following: nodelist <- sort(levels(flatdata$value)) and then using geom_rect(aes(ymin=as.Date(“8-Apr-2014″, format=”%d-%b-%Y”), ymax=as.Date(“30-Apr-2014″, format=”%d-%b-%Y”), xmin=which(nodelist==”node004″)-0.5, xmax=which(nodelist==”node004″)+0.5, fill=”red”, alpha=0.25)) [ad_2] solved Using factor levels with geom_rect

[ad_1] First I loaded ggplot and added mtcars library to explain. library(ggplot2) cars <- mtcars This is a normal ggplot graph with geom_smooth and the confidence interval. ggplot(cars, aes(x = mpg, y= disp)) + geom_point() + geom_smooth() within geom_smooth is an option se which is your confidence interval. se is default set to TRUE by … Read more

[ad_1] I think this what you want. Having access to your data was really useful. if you look at the structure of your dataset, you will see that Cost is a factor. Just change it to numeric and it will work. it will easier to change your tick if still necessary str(uws) ‘data.frame’: 72 obs. … Read more

[ad_1] The following should work, I have stored your example input as a dataframe called ‘dat’: library(ggplot2) library(cowplot) plt = ggplot(dat, aes(Income_group, percentage)) + geom_bar(stat=”identity”) + facet_grid(Airport ~.) + background_grid(major=”y”, minor = “none”) + panel_border() plt + theme(axis.text.x = element_text(angle = 90, hjust = 1)) 2 [ad_2] solved In ggplot, how can I plot multiple … Read more

[ad_1] You should post the output from dput(Ft_max), that would make it easier for people to load your example. Use functions from the tidyverse package to gather your data in two columns: “key” for the grouping and “values” for the actual values require(tidyverse) Ft_max %>% gather(“key”, “values”) %>% ggplot(aes(x = values, fill = as.factor(key))) + … Read more

[ad_1] You need to combine DATA2 and DATA3 to one vector and create a vector to define your data. library(ggplot2) x1 <- c(0.2, 0.27, 0.4, 0.37, 0.42, 0.45, 0.70) x2 <- c(0.25, 0.32, 0.28, 0.24, 0.20, 0.19, 0.20) data <- data.frame(x = rep(c(1:7), 2), label = rep(c(“x1”, “x2”), each = 7), y = c(x1, x2)) … Read more

[ad_1] The reason for the observed behavior is the fact, that the factor levels are handled as string. Because of that, the sort is done in an alphabetical order. This results in “100” being before “99” in an ascending order. The workaround was a bit tricky, I have used the stringr package for easier manipulation … Read more

[ad_1] I used solution provided by NelsonGon, but maked it a bit shorter. Besides i always try to avoid of using third party libraries if it’s possible. So the code working for me was: ggplot(df_test,aes(x=factor(genotype),fill=factor(type)))+geom_bar(position=”fill”)+xlab(“Genotype”)+scale_y_continuous(labels=scales::percent_format()) [ad_2] solved Stacked barplot with percentage in R ggplot2 for categorical variables from scratch