在R中一次从数据框中的多个变量中删除Na

Question

So I have a data frame: df and I plot it but there are too many Na's and it is not nice.

So I try to remove Na's with 1):

 df <- na.omit(df)

But my data are getting messed up. 2):

 df <- df[!is.na(df$column_name),]

This work for a specific column name but in the plot I have multiple column names with Na's and when I try to use the same command but for other column name it changes my data complitely. So can anyone help me? Is there a way to !is.na (Multiple column names) Or Ignore NA's In a ggplot?

I am using this:

df<-Ass1MatrixNoNa %>% gather(test, value, 3:5)
ggplot(df,aes(x=test,fill=value)) +
  geom_bar(position=position_dodge(preserve="single"))

And I get a plot but with NA's
Then I try to remove the NA's:

Ass1MatrixNoNa <- Ass1Matrix[!is.na(Ass1Matrix$Ass_1_hearingA),]

Removes the Ass_1_hearingA Na's But I want also hearingB but ovverides the first one and the NA's are removed only in the second one:

Ass1MatrixNoNa <- Ass1Matrix[!is.na(Ass1Matrix$Ass_1_hearingB]

Answer 1

I'm unsure if this is what you want. But if you are trying to deal with warnings from geom_bar regarding NA s, you may notice from the documentation ( help("geom_bar") ) that that the function has the argument na.rm . So the function can remove the NA s for you. Try

ggplot(df,aes(x=test,fill=value)) +
  geom_bar(position=position_dodge(preserve="single"), na.rm = TRUE)

Does that do what you want?

So you may not necessarily need to remove the NAs in df .

EDIT : Otherwise, the complete.cases function might help you:

df <- data.frame(x = c(1, NA, 3, 4), value = c(1, 2, 3, 4), fill= c(1, 2, NA, 4))
print(df)
#   x value fill
#1  1     1    1
#2 NA     2    2
#3  3     3   NA
#4  4     4    4

ccol <- c("value", "fill") # Cols to keep 'complete'
df_complete <- df[complete.cases(df[, ccol]), ]
print(df_complete)
#   x value fill
#1  1     1    1
#2 NA     2    2
#4  4     4    4

Running complete.cases(x) returns a logical vector with TRUE where no NA s appear in the rows of x .

Alternatively, using the tidyverse / dplyr , something like the following

df_complete2 <- df %>% filter(!is.na(fill) & !is.na(value))

should do it too.