I have a data frame that has 5 variables and 800 rows:
head(df)
V1 variable value element OtolithNum
1 24.9835 V7 130230.0 Mg 25
2 24.9835 V8 145844.0 Mg 25
3 24.9835 V9 126126.0 Mg 25
4 24.9835 V10 103152.0 Mg 25
5 24.9835 V11 129571.9 Mg 25
6 24.9835 V12 114214.0 Mg 25
I need to perform the following:
I have been using dplyr package and have used the following code to group by the "element" variable, and provide the mean values:
df1=df %>%
group_by(element) %>%
summarise_each(funs(mean), value)
Can you please help me manipulate or add to the code above in order to remove outliers (defined above, as >2 sd from the median) grouped by the "element" variable, before I extract the means.
I have tried the following code from another posting (thats why the data names don't match with my personal data above), without luck:
#standardize each column (we use it in the outdet function)
scale(dat)
#create function that looks for values > +/- 2 sd from mean
outdet <- function(x) abs(scale(x)) >= 2
#index with the function to remove those values
dat[!apply(sapply(dat, outdet), 1, any), ]
Here's a method using base R:
element <- sample(letters[1:5], 1e4, replace=T)
value <- rnorm(1e4)
df <- data.frame(element, value)
means.without.ols <- tapply(value, element, function(x) {
mean(x[!(abs(x - median(x)) > 2*sd(x))])
})
And using dplyr
df1 = df %>%
group_by(element) %>%
filter(!(abs(value - median(value)) > 2*sd(value))) %>%
summarise_each(funs(mean), value)
Comparison of results:
> means.without.ols
a b c d e
-0.008059215 -0.035448381 -0.013836321 -0.013537466 0.021170663
> df1
Source: local data frame [5 x 2]
element value
1 a -0.008059215
2 b -0.035448381
3 c -0.013836321
4 d -0.013537466
5 e 0.021170663