missing-data

I have data frame where some of the values are missing A 1 A NA A NA B NA B 2 B NA C NA C NA C NA How can I fill in groups where I have data? Alternative solution, though perhaps a bit flawed in how many assumptions it makes: library(dplyr) y %>% group_by(V1) %>% arrange(V2) %>% mutate(V2 = V2[1]) # Source: local data frame [9 x 2] # Groups: V1 [3] # V1 V2 # (chr) (int) # 1 A 1 # 2 A 1 # 3 A 1 # 4 B 2 # 5 B 2 # 6 B 2 # 7 C NA # 8 C NA # 9 C NA You can also use fill from tidyr : library(dplyr) library(tidyr) df1 %>% group_by(ID) %>% fill(v1) %>% fill(v1, .direction = "up") Result: # A tibble: 9

This question already has an answer here: How to connect dots where there are missing values? 4 answers I'm trying to plot a dataset consisting of numbers and some NA entries in R. V1,V2,V3 2, 4, 3 NA, 5, 4 NA,NA,NA NA, 7, 3 6, 6, 9 Should return the same lines in the plot, as if I had entered: V1,V2,V3 2, 4, 3 3, 5, 4 4, 6, 3.5 5, 7, 3 6, 6, 9 What I need R to do is basically plotting the dataset as points, an then connect these points by straight lines, which - due to the size of the dataset - would be much more efficient then the actual calculation of each interpolated value within the

I want to replace missing values in columns of a dataframe. I have written the following code MedianImpute <- function(data=data) { for(i in 1:ncol(data)) { if(class(data[,i]) %in% c("numeric","integer")) { if(sum(is.na(data[,i]))) { data[is.na(data[,i]),i] <- median(data[,i],na.rm = TRUE) } } } return(data) } This returns the dataframe with the NAs replaced by the column median. I do no want to use for loop, how can I get the same result using any of the apply functions in R? This is actually a subtle problem, so worth a bit of discussion (IMO). You have a data frame and want to impute

I have a categorical variable with three levels ( A , B , and C ). I also have a continuous variable with some missing values on it. I would like to replace the NA values with the mean of its group. This is, missing observations from group A has to be replaced with the mean of group A . I know I can just calculate each group's mean and replace missing values, but I'm sure there's another way to do so more efficiently with loops. A <- subset(data, group == "A") mean(A$variable, rm.na = TRUE) A$variable[which(is.na(A$variable))] <- mean(A$variable, na.rm = TRUE) Now, I understand I could do the