It didn’t work because status was a factor. When you mix factor with numeric then numeric is the least restrictive. By forcing status to be character you get the results you’re after and the column is now a character vector: TEST$UNIT[is.na(TEST$UNIT)] <- as.character(TEST$STATUS[is.na(TEST$UNIT)]) ## UNIT STATUS TERMINATED START STOP ## 1 ACTIVE ACTIVE 1999-07-06 2007-04-23 … Read more
require(tidyverse) #fill is part of tidyr ps1 %>% group_by(userID) %>% fill(color, age, gender) %>% #default direction down fill(color, age, gender, .direction = “up”) Which gives you: Source: local data frame [9 x 4] Groups: userID [3] userID color age gender <dbl> <fctr> <fctr> <fctr> 1 21 blue 3yrs F 2 21 blue 2yrs F 3 … Read more
This is an old question, but I just wanted to post a dplyr way of handling this, as I came across this post while searching for an answer to a similar problem. I find it more intuitive and easier on the eyes than the zoo approach. library(dplyr) ts <- seq.POSIXt(as.POSIXct(“2001-09-01 0:00”,’%m/%d/%y %H:%M’), as.POSIXct(“2001-09-01 0:07”,’%m/%d/%y %H:%M’), … Read more
Convert your variable to a factor, and set the categories you wish to include in the result using levels. Values with a count of zero will then also appear in the result: y <- c(0, 0, 1, 3, 4, 4) table(factor(y, levels = 0:5)) # 0 1 2 3 4 5 # 2 1 0 … Read more
See my comment in @gsk3 answer. A simple example: > m <- matrix(sample(c(NA, 1:10), 100, replace = TRUE), 10) > d <- as.data.frame(m) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 1 4 3 NA 3 7 6 6 10 6 5 2 9 8 9 5 10 NA 2 1 7 2 … Read more
Also check complete.cases : > final[complete.cases(final), ] gene hsap mmul mmus rnor cfam 2 ENSG00000199674 0 2 2 2 2 6 ENSG00000221312 0 1 2 3 2 na.omit is nicer for just removing all NA‘s. complete.cases allows partial selection by including only certain columns of the dataframe: > final[complete.cases(final[ , 5:6]),] gene hsap mmul mmus … Read more