outliers

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 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

I have a DataFrame df with 40 columns and many records. df: User_id | Col1 | Col2 | Col3 | Col4 | Col5 | Col6 | Col7 |...| Col39 For each column except the user_id column I want to check for outliers and remove the whole record, if an outlier appears. For outlier detection on each row I decided to simply use 5th and 95th percentile (I know it's not the best statistical way): Code what I have so far: P = np.percentile(df.Col1, [5, 95]) new_df = df[(df.Col1 > P[0]) & (df.Col1 < P[1])] Question : How can I apply this approach to all columns (except User_id ) without doing this by hand? My goal is

I tried to normalize the data by using Gaussian function 2 times on both positive and negative numbers of each parameter of this dataset . The dataset includes missing data as well. The problem is I want to highlight outliers via scatter graph by using cmap='coolwarm' for parameters A, B and specifically T so that: outliers outside of that interval can be marked by (x) or (*) with cmap='coolwarm' on the right side of the graph cbar is suppose to be available. my aim is to highlight them in an elegant way before applying cleaning data then compare the raw data and processed data before & after

I am currently trying to remove outliers in R in a very easy way. I know there are functions you can create on your own for this but I would like some input on this simple code and why it does not seem to work? outliers <- boxplot(okt$pris)$out okt_no_out <- okt[-c(outliers),] boxplot(okt_no_out$pris) so first row I create a vector with the outliers, the second I create a new dataframe omitting the values in that vector. But... When I check the new dataframe only about 400 of the 750 outliers were removed? So, the vector outliers contain roughly 750 rows, but when doing this it only remove

Thanks for your help in advance. Regards, Raj Adding the values to MAX() values would skew the data even if it were possible. There are two hacks to do this though. Right Click > Properties > Y-Axis > set the MIN range value and MAX range values to something that would eliminate all outliers. This is really only suitable for box plots that are close in all values to each other (all percentiles) On your toolbar click Insert > Calculated Column > choose the correct data table and paste in the expression below. You will need to replace the [x-axisColumn] and the [y-axisColumn] with what ever is