Dear all,
I have partial data set with four colums. First column is "site" with three
factors (i.e., A, B, and C). From second to fourth columns (v1 ~ v3) are my
observations. In the observations of the data set, "." indicates missing
value. I replaced "." with NA. To replace "." with NA, I used two steps.
First, I replaced "." with NA, and then, changed each variable from factor
to numeric using "df1$v1 <- as.numeric(df1$v1)". The second step was OK
when I have low numbers of variables, however, it is painful when I have a
lot of variables.
My question is: Is there any much more efficient way to convert this kind of
large scale data? In short, I am looking for an alternative way of STEP 2.
Or whole procedure if there is.
Any comment will be highly appreciated.
Thank you in advance!!
Steve
P.S.: Below is an example of what I did.
STEP 1
> df1
site v1 v2 v3
1 A 10 5 .
2 A 22 54 .
3 A 44 214 2
4 A 521 14 4
5 A 5 73 1
6 A 1654 0.4 4
7 B 16 1 .
8 B . 4 5
9 B . . 4
10 B . 4 1
11 B 51 . 2
12 B 5 . .
13 C 1 0.4 .
14 C 0 4 .
15 C 1 1 4
16 C 40 . 7
17 C 4 . 7
18 C 10 . 1
> str(df1)
'data.frame': 18 obs. of 4 variables:
$ site: Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 2 2 2 2 ...
$ v1 : Factor w/ 13 levels ".","0","1","10",..: 4 7 10 13 11 6 5 1 1 1 ...
$ v2 : Factor w/ 9 levels ".","0.4","1",..: 7 8 5 4 9 2 3 6 1 6 ...
$ v3 : Factor w/ 6 levels ".","1","2","4",..: 1 1 3 4 2 4 1 5 4 2 ...
> df1[df1=="."] <- "NA"
Warning messages:
1: In `[<-.factor`(`*tmp*`, thisvar, value = "NA") :
invalid factor level, NAs generated
2: In `[<-.factor`(`*tmp*`, thisvar, value = "NA") :
invalid factor level, NAs generated
3: In `[<-.factor`(`*tmp*`, thisvar, value = "NA") :
invalid factor level, NAs generated
> df1
site v1 v2 v3
1 A 10 5 <NA>
2 A 22 54 <NA>
3 A 44 214 2
4 A 521 14 4
5 A 5 73 1
6 A 1654 0.4 4
7 B 16 1 <NA>
8 B <NA> 4 5
9 B <NA> <NA> 4
10 B <NA> 4 1
11 B 51 <NA> 2
12 B 5 <NA> <NA>
13 C 1 0.4 <NA>
14 C 0 4 <NA>
15 C 1 1 4
16 C 40 <NA> 7
17 C 4 <NA> 7
18 C 10 <NA> 1
> str(df1)
'data.frame': 18 obs. of 4 variables:
$ site: Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 2 2 2 2 ...
$ v1 : Factor w/ 13 levels ".","0","1","10",..: 4 7 10 13 11 6 5 NA NA NA
...
$ v2 : Factor w/ 9 levels ".","0.4","1",..: 7 8 5 4 9 2 3 6 NA 6 ...
$ v3 : Factor w/ 6 levels ".","1","2","4",..: NA NA 3 4 2 4 NA 5 4 2 ...
STEP 2.
> df1$v1 <- as.numeric(df1$v1)
> df1$v2 <- as.numeric(df1$v2)
> df1$v3 <- as.numeric(df1$v3)
> df1
site v1 v2 v3
1 A 4 7 NA
2 A 7 8 NA
3 A 10 5 3
4 A 13 4 4
5 A 11 9 2
6 A 6 2 4
7 B 5 3 NA
8 B NA 6 5
9 B NA NA 4
10 B NA 6 2
11 B 12 NA 3
12 B 11 NA NA
13 C 3 2 NA
14 C 2 6 NA
15 C 3 3 4
16 C 9 NA 6
17 C 8 NA 6
18 C 4 NA 2
> str(df1)
'data.frame': 18 obs. of 4 variables:
$ site: Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 2 2 2 2 ...
$ v1 : num 4 7 10 13 11 6 5 NA NA NA ...
$ v2 : num 7 8 5 4 9 2 3 6 NA 6 ...
$ v3 : num NA NA 3 4 2 4 NA 5 4 2 ...
>
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