The default for matrix is to have 1 column. To explicitly have 0 columns, you need to write matrix(, nrow = 15, ncol = 0) A better way would be to preallocate the entire matrix and then fill it in mat <- matrix(, nrow = 15, ncol = n.columns) for(column in 1:n.columns){ mat[, column] <- … Read more
This: df[df == “foo”] <- NA
I don’t think desc takes an na.rm argument… I’m actually surprised it doesn’t throw an error when you give it one. If you just want to remove NAs, use na.omit (base) or tidyr::drop_na: outcome.df %>% na.omit() %>% group_by(Hospital, State) %>% arrange(desc(HeartAttackDeath)) %>% head() library(tidyr) outcome.df %>% drop_na() %>% group_by(Hospital, State) %>% arrange(desc(HeartAttackDeath)) %>% head() If … Read more
You can do: x[, 1:2][is.na(x[, 1:2])] <- 0 or better (IMHO), use the variable names: x[c(“a”, “b”)][is.na(x[c(“a”, “b”)])] <- 0 In both cases, 1:2 or c(“a”, “b”) can be replaced by a pre-defined vector.
One way of doing it: df[, colSums(is.na(df)) != nrow(df)] If the count of NAs in a column is equal to the number of rows, it must be entirely NA. Or similarly df[colSums(!is.na(df)) > 0]
Never use ==’NA’ to test for missing values. Use is.na() instead. This should do it: new_DF <- DF[rowSums(is.na(DF)) > 0,] or in case you want to check a particular column, you can also use new_DF <- DF[is.na(DF$Var),] In case you have NA character values, first run Df[Df==’NA’] <- NA to replace them with missing values.
Use suppressWarnings(): suppressWarnings(as.numeric(c(“1”, “2”, “X”))) [1] 1 2 NA This suppresses warnings.
Use is.na DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA), z=c(NA, 33, 22)) DF[!is.na(DF$y),]