boxplot

Closed . This question needs details or clarity . It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post . Closed 5 years ago . I would like to make a boxplot that shows how time spent doing a behaviour(Alert) is affected by two variables (Period= Morning/Afternoon and Visitor Level= High/Low). Alert ~ Period + Vis.Level 'Alert' is a set of 12 numbers that show the amount of time spent awake with the other two as the significant categorical variables. I have looked at other examples but none seem to fit this type of

I understood how I have to plot multiple boxplots in one graph from the several others posts. But I have this situation where am unable to plot multiple conditions together. I applied the same idea as my former post ( Multiple boxplots in R ) but does not work for this case. I have this dataset Control Treatment L1 L2 L3 L4 L5 S1 S2 S3 S4 S5 g1 10.5 12 10 11 12 13 14 10 11 12 g2 11 13 10 10 11 10.5 12 8 9 10 g3 10 9 9 8 9 11 10 11 9 11 g4 9 8 8 9 8 6 5 5 7 6 g5 16 4 6.5 6.8 5 4 6 6 8 9 g6 11 12 7.8 7.5 6 5 4 9 10 11 g7 10 6 8.9 6.4 7.2 13 12 12 12 10 g8 5 4 9.0 5.6 7.8 12 12 9 8 7 g9 11 12 11

I am trying to create a boxplot that shows all of the factors of a variable, along with sample size, and at eh end of the plot also want an overall boxplot that combines all of the values into one. I am using the following line of code to do everything except making the overall plot: library(ggplot2) library(plyr) xlabels <- ddply(extract8, .(Fuel), summarize, xlabels = paste(unique(Fuel), '\n(n = ', length(Fuel),')')) ggplot(extract8, aes(x = Fuel, y = Exfiltration.Fraction.Percentage))+geom_boxplot()+ stat_boxplot(geom='errorbar', linetype=1) + geom_boxplot(fill="pink") + geom_hline

I have two groups with mean scores and standard deviations which represent how confident we are with the mean estimates. Note: I do not have raw scores, just mean estimates outputted from a model and the SD of the estimates outputted from the model, around that mean. I have a feature set around 20, and I want to compare for each feature the mean +/- standard deviations of each of my 2 groups. It will essentially look like this: ggplot() seems to work with data that has the raw data and it calculates the mean and standard deviation from the arrays of each feature. boxplot() works similarly. Can

I have a count table that I have generated with another tool, and I would like to get a boxplot from it with ggplot2. For instance, suppose that I have: df1 = data.frame(nSiblings = c(0, 1, 2), count = c(10, 15, 12)) instead of df2 = data.frame(nSiblings = c(rep(0, 10), rep(1, 15), rep(2, 12))) I know how to produce a boxplot from the second data frame: qplot(y=df2$nSiblings, x=1, geom = "boxplot") I know how to produce a histogram from the first data frame: ggplot(df1, aes(x = nSiblings, y = count)) + geom_bar(stat = "identity") But how can I get a boxplot from the first data frame? Ggplot is

We can draw box plot as below: qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot") and point as: qplot(factor(cyl), mpg, data = mtcars, geom = "point") How would you combine both - but just to show a few specific points(say when wt is less than 2) on top of the box? Use + geom_point(...) on your qplot (just add a + geom_point() to get all the points plotted). To plot selectively just select those points that you want to plot: n <- nrow(mtcars) # plot every second point idx <- seq(1,n,by=2) qplot( factor(cyl), mpg, data=mtcars, geom="boxplot" ) + geom_point( aes(x=factor(cyl)[idx],y=mpg