boxplot

Does anyone know how to create a scatterplot in R to create plots like these in PRISM's graphpad: I tried using boxplots but they don't display the data the way I want it. These column scatterplots that graphpad can generate show the data better for me. Any suggestions would be appreciated. csgillespie As @smillig mentioned, you can achieve this using ggplot2. The code below reproduces the plot that you are after pretty well - warning it is quite tricky. First load the ggplot2 package and generate some data: library(ggplot2) dd = data.frame(values=runif(21), type = c("Control", "Treated",

i am trying to create a set of day-of-week boxplots for a timeseries (e.g. 5-minute temperature observations). My code: # ts is our timeseries ts = df.SomeColumn dow_map = {} days = ['MON','TUE','WED','THU','FRI','SAT','SUN'] dow_idx = ts.index.dayofweek i = 0 for d in days: dow_map[d] = ts[dow_idx == i] i = i + 1 df = pd.DataFrame(dow_map) df.boxplot() results in: --------------------------------------------------------------------------- Exception Traceback (most recent call last) <ipython-input-898-6070c45e4c4b> in <module>() 41 i = i + 1 42 ---> 43 df = pd.DataFrame(dow_map) 44 df.boxplot(

I would like to add lines between "mean" in my boxplot. My code: library(ggplot2) library(ggthemes) Gp=factor(c(rep("G1",80),rep("G2",80))) Fc=factor(c(rep(c(rep("FC1",40),rep("FC2",40)),2))) Z <-factor(c(rep(c(rep("50",20),rep("100",20)),4))) Y <- c(0.19 , 0.22 , 0.23 , 0.17 , 0.36 , 0.33 , 0.30 , 0.39 , 0.35 , 0.27 , 0.20 , 0.22 , 0.24 , 0.16 , 0.36 , 0.30 , 0.31 , 0.39 , 0.33 , 0.25 , 0.23 , 0.13 , 0.16 , 0.18 , 0.20 , 0.16 , 0.15 , 0.09 , 0.18 , 0.21 , 0.20 , 0.14 , 0.17 , 0.18 , 0.22 , 0.16 , 0.14 , 0.11 , 0.18 , 0.21 , 0.30 , 0.36 , 0.40 , 0.42 , 0.26 , 0.23 , 0.25 , 0.30 , 0.27 , 0.15 ,

This question already has answers here : Changing the outlier rule in a boxplot (2 answers) Possible Duplicate: Changing the outlier rule in a boxplot I need to visualize my result using box-plot. x<-rnorm(10000) boxplot(x,horizontal=TRUE,axes=FALSE) How can i filter outliers during visualisation? (1) So that i can have full image on screen without having ugly outliers. http://postimage.org/image/szzbez0h1/a610666d/ (2) Is there any way to show outliers upto certain range? http://postimage.org/image/np28oee0b/8251d102/ Regards See ?boxplot for all the help you need. outline: if ‘outline’ is

I want to (got suggestion to do so) create boxplot with different look and added labels to it. The expected (not complete) output will look like the following (with every box has quatile labeles) and sample size. boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, col=(c("gold","darkgreen")), main="Tooth Growth", xlab="Suppliment and Dose", names = supp ) # some unsuccessful trials # to add names boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, col=(c("gold","darkgreen")), main="Tooth Growth", xlab="Suppliment and Dose", names = supp*dose) # to remove the plot outline boxplot(len~supp

I would like to compare a set of distributions of scores ( score ), grouped by some categories ( centrality ) and colored by some other ( model ). I've tried the following with seaborn: plt.figure(figsize=(14,6)) seaborn.boxplot(x="centrality", y="score", hue="model", data=data, palette=seaborn.color_palette("husl", len(models) +1)) seaborn.despine(offset=10, trim=True) plt.savefig("/home/i11/staudt/Eval/properties-replication-test.pdf", bbox_inches="tight") There are some problems I have with this plot: There is a large amount of outliers and I don't like how they are drawn here. Can I remove

I have some data here [in a .txt file] which I read into a data frame df, df <- read.table("data.txt", header=T,sep="\t") I remove the negative values in the column x (since I need only positive values) of the df using the following code, yp <- subset(df, x>0) Now I want plot multiple box plots in the same layer. I first melt the data frame df , and the plot which results contains several outliers as shown below. # Melting data frame df df_mlt <-melt(df, id=names(df)[1]) # plotting the boxplots plt_wool <- ggplot(subset(df_mlt, value > 0), aes(x=ID1,y=value)) + geom_boxplot(aes(color=factor