How to count the missing value in R

r

#1

I am currently working on a data set and I want to count number of missing value in my Ozone column but I am not able to count it
str(z)
‘data.frame’: 153 obs. of 6 variables:
Ozone : int 41 36 12 18 NA 28 23 19 8 NA ... Solar.R: int 190 118 149 313 NA NA 299 99 19 194 …
Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... Temp : int 67 72 74 62 56 66 65 59 61 69 …
Month : int 5 5 5 5 5 5 5 5 5 5 ... Day : int 1 2 3 4 5 6 7 8 9 10 …


Experiments with Data - Query
#2

sum(is.na(z$Ozone)) should work. The command is.na will return a vector of length z$Ozone with 1 at all the entries that are NA. Summing those will give the total number of NAs.


#3

Or summary(z).


#4

table(z$Ozone, exclude=NULL) or table(is.na(z$Ozone)) also work (although the first one is not so nice to read if column has many different values).


#5

Hi @harry

Hope you got the answer from Above replies .If your not getting have a look at this might be helpful!!!

If You need NA count of all — table(is.na(z))
If you need NA count Column wise – sapply(z, function(x) sum(is.na(x)))
If you need NA count Row wise — rowSums(is.na(z))

Hope it Useful

Regards
Raghavendra


How to find missing values?
#6

Hello Harry,

Just use summary(z), this will give you the missing values in each column.

Using sum(is.na(z$columnname)) can be misleading since missing values are essentially taken as Null values and not NA and sum(is.na) only sums those where your value is assigned NA in the dataset


#7

if you look at data for each of the months(5 through 9) in solar, how to find which month had the greatest inter quatile range for Ozone readings.

‘data.frame’: 153 obs. of 6 variables:
Ozone : int 41 36 12 18 NA 28 23 19 8 NA ... Solar.R: int 190 118 149 313 NA NA 299 99 19 194 …
Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... Temp : int 67 72 74 62 56 66 65 59 61 69 …
Month : int 5 5 5 5 5 5 5 5 5 5 ... Day : int 1 2 3 4 5 6 7 8 9 10 …


#8

if you look at data for each of the months(5 through 9) in solar, how to find which month had the greatest inter quatile range for Ozone readings.

‘data.frame’: 153 obs. of 6 variables:
Ozone : int 41 36 12 18 NA 28 23 19 8 NA ... Solar.R: int 190 118 149 313 NA NA 299 99 19 194 …
Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... Temp : int 67 72 74 62 56 66 65 59 61 69 …
Month : int 5 5 5 5 5 5 5 5 5 5 ... Day : int 1 2 3 4 5 6 7 8 9 10 …


#9

If you want to see the number of rwos with Miss value you can use:
sum(complete.cases(data))
sum(!complete.cases(data))


#10

Try this:

sum(is.na(HERE THE NAME OF YOUR IMPORTED DATA**$**HERE THE VARIABLE YOU ARE LOOKING FOR))
For example:
If you upload your CSV. and you assigned the name my data, will look like.

mydata <- read.CSV(“hw1_data.csv”)

To see the result of the NA in the Ozone column.

sum(is.na(mydata$Ozone))


#11

colsum(is.na(Z))


#12

sapply(trainset,function(x)sum(is.na(x)))


#13

using for loop:
res<-NULL
tes<-function(x){
for (i in 1:ncol(x)){
temp<-sum(is.na(x[,i]))
temp<-as.data.frame(temp)
temp$var<-colnames(x)[i]

res<-rbind(res,temp)
}
return(res)
}


#14

Hi
You could also user the below function
sapply(data, function(x) sum(is.na(x)))


#15

It will count all NA in each column

na_count <-sapply(data, function(y) sum(length(which(is.na(y)))))
na_count <- data.frame(na_count)