df <- data.frame(A = 1:10, B = 11:20, c = 21:30) head(df) ## A B c ## 1 1 11 21 ## 2 2 12 22 ## 3 3 13 23 ## 4 4 14 24 ## 5 5 15 25 ## 6 6 16 26 as.data.frame(lapply(df, function(cc) cc[ sample(c(TRUE, NA), prob = c(0.85, 0.15), … Read more
You don’t need to wrap anything in a function – the following works a = c(T,F,NA) a %in% TRUE [1] TRUE FALSE FALSE
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$y),]
Good question, but in my opinion, this shouldn’t have caused a major debugging headache because it is documented quite clearly in multiple places in the manual page for aggregate. First, in the usage section: ## S3 method for class ‘formula’ aggregate(formula, data, FUN, …, subset, na.action = na.omit) Later, in the description: na.action: a function … Read more
You could try na.omit() to omit the values, then paste. Also, you could use toString(), as it is the equivalent of paste(…, collapse = “, “). apply(dd2, 1, function(x) toString(na.omit(x))) # [1] “A, AK2, PPT” “B, HFM1, PPT” “C, TRR” # [4] “D, TRR, RTT, GGT” “E, RTT” If you have specific columns you are … Read more
Use suppressWarnings(): suppressWarnings(as.numeric(c(“1”, “2”, “X”))) [1] 1 2 NA This suppresses warnings.
Never use ==’NA’ to test for missing values. Use is.na() instead. This should do it: new_DF <- DF[rowSums(is.na(DF)) > 0,] or in case you want to check a particular column, you can also use new_DF <- DF[is.na(DF$Var),] In case you have NA character values, first run Df[Df==’NA’] <- NA to replace them with missing values.
You can use this mydf %>% group_by(col_1) %>% summarise(non_na_count = sum(!is.na(col_2))) # A tibble: 2 x 2 col_1 non_na_count <fctr> <int> 1 A 1 2 B 2
You can count the NAs in each row with this command: rowSums(is.na(dat)) where dat is the name of your data frame.
Wrap the condition in which: df[which(df$number1 < df$number2), ] How it works: It returns the row numbers where the condition matches (where the condition is TRUE) and subsets the data frame on those rows accordingly. Say that: which(df$number1 < df$number2) returns row numbers 1, 2, 3, 4 and 5. As such, writing: df[which(df$number1 < df$number2), … Read more