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R is simply trying to distinguish between a string whose value are the two letters "NA" and an actual missing value, NA Thus the difference you see when displaying df versus df$y. The RStudio console returns NA – not as we wanted. Fortunately, the mean function comes with the na.rm (i.e. NA remove) option, which can be used to ignore NA values. Let’s do this in practice: mean (x2, na.rm = TRUE) # Use na.rm option # 4.625 The NA can actually be due to 2 reasons.

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NA is a valid logical object. Where a component of x or y is NA, the result will be NA if the outcome is ambiguous. In other words NA & TRUE evaluates to NA, but NA & FALSE evaluates to FALSE. See the examples below.

# A vector with missing values x <- c ( 1 : 4 , NA , 6 : 7 , NA ) # including NA values will produce an NA output mean ( x ) ## [1] NA # excluding NA values will calculate the mathematical operation for all non-missing values mean ( x , na.rm = TRUE ) ## [1] 3.833333 If you include the NA value in a calculation it will result in an NA value. While this may be okay sometimes in other cases you need a number. The two remove NA values in r is by the na.omit() function that deletes the entire row, and the na.rm logical perimeter which tells the function to skip that value. What does na.rm mean in r?

If you include the NA value in a calculation it will result in an NA value. While this may be okay sometimes in other cases you need a number. The two remove NA values in r is by the na.omit() function that deletes the entire row, and the na.rm logical perimeter which tells the function to skip that value. What does na.rm mean in r?

When dealing with missing values, you might want to replace values with a missing values (NA).This is useful in cases when you know the origin of the data and can be certain which values should be missing. R is simply trying to distinguish between a string whose value are the two letters "NA" and an actual missing value, NA Thus the difference you see when displaying df versus df$y.

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The generic function is.na indicates which elements are missing. The generic function is.na<- sets elements to NA . The generic function anyNA implements any(is.na(x)) in a possibly faster way (especially for atomic … is.na_replace_mean <- data$x_num # Duplicate first column x_num_mean <- mean (is.na_replace_mean, na.rm = TRUE) # Calculate mean is.na_replace_mean [is.na (is.na_replace_mean)] <- x_num_mean # Replace by mean. In case of characters or factors, it is also possible in R to set NA to blank: test$ID <- ifelse(is.na(test$time) | test$type == "A", NA, "1") This gives the result as .

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Appending a data frame with for if and else statements or how do put print in dataframe. r,loops,data.frame,append. It's generally not a good idea to try to add rows one-at-a-time to a data.frame. it's better to generate all the column data at once and then throw it into a data.frame.

There are two possible choices that are na.omit and na.exclude. na.omit that will omit all of the rows from the calculations.

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2016-07-18 R: filtering with NA values. NA - Not Available/Not applicable is R’s way of denoting empty or missing values.