na

I have a dataframe with an NA row: df = data.frame(c("classA", NA, "classB"), t(data.frame(rep("A", 5), rep(NA, 5), rep("B", 5)))) rownames(df) <- c(1,2,3) colnames(df) <- c("class", paste("Year", 1:5, sep = "")) > df class Year1 Year2 Year3 Year4 Year5 1 classA A A A A A 2 <NA> <NA> <NA> <NA> <NA> <NA> 3 classB B B B B B I introduced the empty row (NA row) on purpose because I wanted to have some space between classA row and classB row. Now, I would like to substitute the <NA> by blank, so that the second row looks like an empty row. I tried: df[is.na(df)] <- "" and df[df == "NA"] <- "" but

I have got a map with a legend gradient and I would like to add a box for the NA values. My question is really similar to this one and this one . Also I have read this topic , but I can't find a "nice" solution somewhere or maybe there isn't any? Here is an reproducible example: library(ggplot2) map <- map_data("world") map$value <- setNames(sample(-50:50, length(unique(map$region)), TRUE), unique(map$region))[map$region] map[map$region == "Russia", "value"] <- NA ggplot() + geom_polygon(data = map, aes(long, lat, group = group, fill = value)) + scale_fill_gradient2(low = "brown3", mid =

At first sight this seems a duplicate of Combine/merge columns while avoiding NA? but in fact it isn't. I am dealing sometimes with more than two columns instead of just two. My dataframe looks like this: col1 col2 col3 col4 col5 [1,] 1 NA NA 13 NA [2,] NA NA 10 NA 18 [3,] NA 7 NA 15 NA [4,] 4 NA NA 16 NA Now I want to "collapse" this dataframe into a dataframe with less columns and with removed NA's. In fact I am looking for and "excel way of doing": removing one cell and the whole row will move one cell to the left. The result in this example case would be: col1 col2 [1,] 1 13 [2,] 10 18 [3,

When I use filter from the dplyr package to drop a level of a factor variable, filter also drops the NA values. Here's an example: library(dplyr) set.seed(919) (dat <- data.frame(var1 = factor(sample(c(1:3, NA), size = 10, replace = T)))) # var1 # 1 <NA> # 2 3 # 3 3 # 4 1 # 5 1 # 6 <NA> # 7 2 # 8 2 # 9 <NA> # 10 1 filter(dat, var1 != 1) # var1 # 1 3 # 2 3 # 3 2 # 4 2 This does not seem ideal -- I only wanted to drop rows where var1 == 1 . It looks like this is occurring because any comparison with NA returns NA , which filter then drops. So, for example, filter(dat, !(var1 %in% 1)) produces