how to detect outliers in the columns of a dataframe? in R -

i have data frame, suppose this:

names<-c("a","a","a","a","a","b","b","b","b","b","c","c","c","c","c","c","c","c") var1<-c(0.942999593,0.935507266,0.973589623,0.969415912,0.95230801,0.935507266,0.888740961,0.91750551,0.944482672,0.945468585,1.457579147,0.922206277,0.941511433,0.954724791,0.941014244,0.941511433,0.941511433,1.50511433) var2<-c(-0.012678088,0.014313763,0.001138275,-0.020568206,0.012987126,0.001217192,0.03360358,0.009758172,0.015066932,-0.037879492,0.020471157,0.010738162,0.010952531,0.019377213,0.027140572,0.031116892,-0.018530676,-8.90e-05) as.data.frame(cbind(names,var1,var2))->df 

i convert outliers na in columns var1 , var2. calculate outliers independently each category in column "names". outliers "a" in var1, outliers found using first 5 rows in var1.

the way in detect outlier values, below or above quantiles 0.25 , 0.75 respectively.

is there easy way in r?

thank in advance.

tina.

here's how can var1:

quantiles<-tapply(var1,names,quantile) minq <- sapply(names, function(x) quantiles[[x]]["25%"]) maxq <- sapply(names, function(x) quantiles[[x]]["75%"]) var1[var1<minq | var1>maxq] <- na 

repeat same var2 (or df$var2).