How to remove the 'minimum' and 'maximum' values in a data frame and compute the-CodePudding

Data

I have a data like this. There are six entries for the code 5005132#2000, in which '0' is the minimum and maximum is '22' and for the code 5008568#2000, there are 7 entries, in which '0' is the minimum and '11' is the maximum. I have to remove these minimum and maximum values related to the particular codes and compute the 'Average' for the particular code.

The avg of 5005132#2000 should be 7.75 and The avg of 5008568#2000 should be 7.8

CodePudding user response：

One solution is to use data.table. The data.table is like a data.frame but with added fuctionality. You will first need to load the data.table package and convert your data.frame (df) to a data.table

library(data.table)
setDT(df)

From there, filter out the values at the extremes for each group using by, then get the mean of the remaining values.

# Solution: 
df[, 
    # ID rows where value is min/max
    .(Cycle.Time, "drop" = Cycle.Time %in% range(Cycle.Time)), by=Code][
    # Filter to those where value is not mon/max and get mean per Code
    drop==FALSE, mean(Cycle.Time), by=Code]

An alternative is to use dplyr

df %>% 
  group_by(Code) %>% 
  filter(!Cycle.Time %in% range(Cycle.Time)) %>% 
  summarize(mean(Cycle.Time))

And to store that in a data.frame:

df %>% 
  group_by(Code) %>% 
  filter(!Cycle.Time %in% range(Cycle.Time)) %>% 
  summarize(mean(Cycle.Time)) %>% 
  data.frame -> averages