I am building an App using shiny and openair to analyze wind data.
Right now the data needs to be “cleaned” before uploading by the user.
I am interested in doing this automatically.
Some of the data is empty, some of is not numeric, so it is not possible to build a wind rose.
I want to:
here is an example of the data:
the "NO2.mg" is read as a factor and not int becuse it does not consist only numbers
OK
here is a reproducible example:
no2<-factor(c(5,4,"c1",54,"c5",seq(2:50)))
no2
[1] 5 4 c1 54 c5 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[20] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[39] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
52 Levels: 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 ... c5
> as.numeric(no2)
[1] 45 34 51 46 52 1 12 23 34 45 47 48 49 50 2 3 4 5 6
[20] 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22 24 25 26 27
[39] 28 29 30 31 32 33 35 36 37 38 39 40 41 42 43 44
To convert a factor to numeric, you need to convert to character first:
no2<-factor(c(5,4,"c1",54,"c5",seq(2:50)))
no2_num <- as.numeric(as.character(no2))
#Warning message:
# NAs introduced by coercion
no2_clean <- na.omit(no2_num) #remove NAs resulting from the bad data
# [1] 5 4 54 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
# [40] 37 38 39 40 41 42 43 44 45 46 47 48 49
# attr(,"na.action")
# [1] 3 5
# attr(,"class")
# [1] "omit"
length(attr(no2_clean,"na.action"))/length(no2)*100
#[1] 3.703704