How to Ignore Na in Ifelse Statement

How to ignore NA in ifelse statement

This syntax is easier to read:

x <- c(NA, 1, 0, -1)

(x > 0) & (!is.na(x)) 
# [1] FALSE  TRUE FALSE FALSE

(The outer parentheses aren't necessary, but will make the statement easier to read for almost anyone other than the machine.)

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Edit:

## If you want 0s and 1s
((x > 0) & (!is.na(x))) * 1
# [1] 0 1 0 0

Finally, you can make the whole thing into a function:

isPos <- function(x) {
    (x > 0) & (!is.na(x)) * 1
}

isPos(x)
# [1] 0 1 0 0

Direct way of telling ifelse to ignore NA

You can use %in% instead of == to sort-of ignore NAs.

ifelse(df$a %in% 1, "a==1", 
       ifelse(df$b %in% 1, "b==1", 
              ifelse(df$c %in% 1, "c==1", NA)))

Unfortunately, this does not give any performance gain compared to the original while @arkun's solution is about 3 times faster.

solution_original <- function(){
  ifelse(df$a==1 &  !is.na(df$a), "a==1", 
         ifelse(df$b==1 & !is.na(df$b), "b==1", 
                ifelse(df$c==1 & !is.na(df$c), "c==1", NA)))
}

solution_akrun <- function(){
  v1 <- names(df)[max.col(!is.na(df)) * NA^!rowSums(!is.na(df))]
  i1 <- !is.na(v1)
  v1[i1] <- paste0(v1[i1], "==1")
}

solution_mine <- function(x){
  ifelse(df$a %in% 1, "a==1", 
         ifelse(df$b %in% 1, "b==1", 
                ifelse(df$c %in% 1, "c==1", NA)))
}
set.seed(1)
df <- data.frame(a = sample(c(1, rep(NA, 4)), 1e6, T),
                 b = sample(c(1, rep(NA, 4)), 1e6, T),
                 c = sample(c(1, rep(NA, 4)), 1e6, T))
microbenchmark::microbenchmark(
  solution_original(),
  solution_akrun(),
  solution_mine()
)
## Unit: milliseconds
##                expr      min       lq     mean   median       uq       max neval
## solution_original() 701.9413 839.3715 845.0720 853.1960 875.6151 1051.6659   100
##    solution_akrun() 217.4129 242.5113 293.2987 253.2144 387.1598  564.3981   100
##     solution_mine() 698.7628 845.0822 848.6717 858.7892 877.9676 1006.2872   100

Was inspired by this: R: Dealing with TRUE, FALSE, NA and NaN

Edit

Following the comment by @arkun, I redid the benchmark and revised the statement.

Ignoring NAs in an Ifelse statement R to be applied over a list of dataframes R

I think you should use if(any(...)) as condition since you want to check if any of the value in ZScore is greater than ZMax. NA values can be ignored with na.rm = TRUE in any.

ZMax <- 3.5

FinalStats <- function(x,...){ 
  unlistdata <- unlist(x[-1])
  GrandMean <- mean(unlistdata,na.rm = T)
  GrandSD <- sd(unlistdata,na.rm=T)
  ZScore <- abs(((x[-1])-GrandMean)/GrandSD)
  if(any(ZScore > ZMax, na.rm = TRUE)){
    LabMean <- mapply(mean, x[-1], na.rm = T) #Calculate Mean by columns
    SD.All <- unlist(x[-1])
    ConsensusValue <- mean(LabMean)
    Uncertainty <- sd(SD.All, na.rm = T)
  }else{ 
    LabMedian <- mapply(median, x[-1], na.rm = T) #Calculate Median by columns
    LabMedian[is.infinite(LabMedian)] <- NA #convert any Inf values to NA
    SD.All <- unlist(x[-1])
    ConsensusValue <- LabMedian
    Uncertainty <- sd(SD.All, na.rm = T)
  }
  
  FinalValues <- cbind(ConsensusValue,Uncertainty) #combined the desired Info
  
  return(FinalValues)
}

This returns -

CatergoreisStats <- lapply(df,FinalStats)
CatergoreisStats
#$Al2O3
#     ConsensusValue Uncertainty
#[1,]       2.088453  0.03880474

#$As
#   ConsensusValue Uncertainty
#2          0.0010 0.001475832
#3          0.0020 0.001475832
#4          0.0020 0.001475832
#5          0.0010 0.001475832
#7              NA 0.001475832
#8          0.0010 0.001475832
#10             NA 0.001475832
#12             NA 0.001475832
#36         0.0053 0.001475832

#$Ba
#   ConsensusValue Uncertainty
#2              NA 0.001303559
#3      0.00100000 0.001303559
#4      0.00300000 0.001303559
#5      0.00300000 0.001303559
#7              NA 0.001303559
#8      0.00200000 0.001303559
#10             NA 0.001303559
#12             NA 0.001303559
#36     0.00089566 0.001303559

Ignore NA in Ifelse statement- R

You could solve this in two ways I think:

1) Another ifelse before this to check for NAs - something like:

ww.LIG = ifelse( is.na(Accel2$wk.VWD) | is.na(Accel2$we.VWD), NA,
             ifelse( (Accel2$wk.VWD >= 3 & Accel2$we.VWD >= 0 ) 
                 | ( Accel2$wk.VWD >=2 & Accel2$we.VWD >=1 ) 
                 | ( Accel2$wk.VWD >=1 & Accel2$we.VWD >=2) , 
                 (Accel2$wk.LIG + Accel2$we.LIG)/2, NA))

2) Remove the NA rows to start with - something like:

df = complete.cases(data.frame(wkVWD = Accel2$wk.VWD, weVWD = Accel2$we.VWD, Accel2$wk.LIG, weLIG = Accel2$we.LIG))
df$wwLIG = ifelse( (df$wkVWD >= 3 & df$weVWD >= 0 ) 
         | ( df$wkVWD >=2 & df$weVWD >=1 ) 
         | ( df$wkVWD >=1 & df$weVWD >=2) , 
         (df$wkLIG + df$weLIG)/2, NA)

Does that work for you?

R handling NA values while doing a comparison ifelse

Please see the following SO post: How to ignore NA in ifelse statement

With respect to your question:

df$counting <- ifelse(df$age > 5 & df$age < 8 & !is.na(df$age), 1, 0) + ifelse(df$marks > 60 & df$marks < 70, 1, 0)
> df
  sex occupation age marks counting
1   M    Student  NA    34        0
2   F    Analyst   6    65        2
3   M    Analyst   9    21        0

How to make an ifelse statement ignore NAs?

First solution looks like:

df %>%
  mutate(prev_PC = case_when(changed_PC == "No" &
                               is.na(prev_PC) ~ new_PC,
                             TRUE ~ prev_PC))

but this is probably better:

df %>% 
  mutate(prev_PC = if_else(is.na(prev_PC) & 
                               changed_PC == "No", new_PC, prev_PC))

Resulting in:

> df %>% 
+   print() %>% 
+   mutate(prev_PC = if_else(is.na(prev_PC) & 
+                                changed_PC == "No", new_PC, prev_PC))
  prev_PC new_PC changed_PC
1    5039   5039         No
2    1402   1402       <NA>
3    3050   3050         No
4      NA   3021        Yes
5      NA   2154       <NA>
6      NA   4853       <NA>
7      NA   1252         No
8      NA   2954         No
  prev_PC new_PC changed_PC
1    5039   5039         No
2    1402   1402       <NA>
3    3050   3050         No
4      NA   3021        Yes
5      NA   2154       <NA>
6      NA   4853       <NA>
7    1252   1252         No
8    2954   2954         No

(see the before and after)

How to include NA in ifelse?

You can't really compare NA with another value, so using == would not work. Consider the following:

NA == NA
# [1] NA

You can just change your comparison from == to %in%:

ifelse(is.na(test$time) | test$type %in% "A", NA, "1")
# [1] NA  "1" NA  "1"

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Regarding your other question,

I could get this to work with my existing code if I could somehow change the result of is.na(test$type) to return FALSE instead of TRUE, but I'm not sure how to do that.

just use ! to negate the results:

!is.na(test$time)
# [1]  TRUE  TRUE FALSE  TRUE

How to handle or ignore NAs when using ifelse to mutate a new column with multiple conditions (solved)

You can use rowMeans() in place of if_else() which will handle cases that are all NA.

z %>% 
  mutate(age_event = +(rowMeans(. < 18, na.rm = TRUE) > 0))

   j6 j7 j8 age_event
1   6 27  8         1
2  19 20 22         0
3  NA NA NA        NA
4  NA  7 20         1
5  NA 19 NA         0
6  NA NA  8         1
7  NA NA 30         0
8   8 20 NA         1
9  20 30 NA         0
10 20  9 NA         1
11 NA NA  3         1

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