missing-data

I am trying to write a function in R which takes 3 inputs: Directory pollutant id I have a directory on my computer full of CSV's files i.e. over 300. What this function would do is shown in the below prototype: pollutantmean <- function(directory, pollutant, id = 1:332) { ## 'directory' is a character vector of length 1 indicating ## the location of the CSV files ## 'pollutant' is a character vector of length 1 indicating ## the name of the pollutant for which we will calculate the ## mean; either "sulfate" or "nitrate". ## 'id' is an integer vector indicating the monitor ID numbers ## to be

I am not sure how to loop over each column to replace the NA values with the column mean. When I am trying to replace for one column using the following, it works well. Column1[is.na(Column1)] <- round(mean(Column1, na.rm = TRUE)) The code for looping over columns is not working: for(i in 1:ncol(data)){ data[i][is.na(data[i])] <- round(mean(data[i], na.rm = TRUE)) } the values are not replaced. Can someone please help me with this? A relatively simple modification of your code should solve the issue: for(i in 1:ncol(data)){ data[is.na(data[,i]), i] <- mean(data[,i], na.rm = TRUE) } If DF is

I have a data frame which is arranged by descending order of date. ps1 = data.frame(userID = c(21,21,21,22,22,22,23,23,23), color = c(NA,'blue','red','blue',NA,NA,'red',NA,'gold'), age = c('3yrs','2yrs',NA,NA,'3yrs',NA,NA,'4yrs',NA), gender = c('F',NA,'M',NA,NA,'F','F',NA,'F') ) I wish to impute(replace) NA values with previous values and grouped by userID In case the first row of a userID has NA then replace with the next set of values for that userid group. I am trying to use dplyr and zoo packages something like this...but its not working cleanedFUG <- filteredUserGroup %>% group_by(UserID)

I have a huge vector which has a couple of NA values, and I'm trying to find the max value in that vector (the vector is all numbers), but I can't do this because of the NA values. How can I remove the NA values so that I can compute the max? Trying ?max , you'll see that it actually has a na.rm = argument, set by default to FALSE . (That's the common default for many other R functions, including sum() , mean() , etc.) Setting na.rm=TRUE does just what you're asking for: d <- c(1, 100, NA, 10) max(d, na.rm=TRUE) If you do want to remove all of the NA s, use this idiom instead: d <- d[!is.na(d)

This question is related to a post with a similar title ( replace NA in an R vector with adjacent values ). I would like to scan a column in a data frame and replace NA's with the value in the adjacent cell. In the aforementioned post, the solution was to replace the NA not with the value from the adjacent vector (e.g. the adjacent element in the data matrix) but was a conditional replace for a fixed value. Below is a reproducible example of my problem: UNIT <- c(NA,NA, 200, 200, 200, 200, 200, 300, 300, 300,300) STATUS <-c('ACTIVE','INACTIVE','ACTIVE','ACTIVE','INACTIVE','ACTIVE','INACTIVE',