Hi Jim
Thanks a lot. Its really helpful but actually I have around 10,000 columns
& 8000 rows. I simply want to get the weight of each observation in a group
(group is identified by ID). Therefore, I am looking for an easy way to
divide each observation in a row with the sum of all values in its group.
How can I apply this on such a huge data set?
Regards
Saba
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On Mon, 25 Apr, 2016 at 3:38 AM, jim holtman
<jholtman at gmail.com> wrote:
You can use 'dplyr':
library(dplyr)
df1 <- read.table(text = "ID A B
+ 1 1 2
+ 1 0 3
+ 2 5 NA
+ 2 1 3
+ 3 1 4
+ 4 NA NA
+ 4 0 1
+ 4 3 0
+ 5 2 5
+ 5 7 NA", header = TRUE)
+ group_by(ID) %>%
+ mutate(new_A = A / sum(A, na.rm = TRUE)
+ , new_B = B / sum(B, na.rm = TRUE)
+ )
Source: local data frame [10 x 5]
Groups: ID [5]
ID A B new_A new_B
(int) (int) (int) (dbl) (dbl)
1 1 1 2 1.0000000 0.4
2 1 0 3 0.0000000 0.6
3 2 5 NA 0.8333333 NA
4 2 1 3 0.1666667 1.0
5 3 1 4 1.0000000 1.0
6 4 NA NA NA NA
7 4 0 1 0.0000000 1.0
8 4 3 0 1.0000000 0.0
9 5 2 5 0.2222222 1.0
10 5 7 NA 0.7777778 NA
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Sun, Apr 24, 2016 at 12:46 AM, Saba Sehrish via R-help <
r-help at r-project.org> wrote:
Hi
I have two data frames as shown below (second one is obtained by
aggregating rows of similar IDs in df1.). They both have similar number of
columns but rows of df2 are lesser than rows of df1.
df1:
ID A B
1 1 2
1 0 3
2 5 NA
2 1 3
3 1 4
4 NA NA
4 0 1
4 3 0
5 2 5
5 7 NA
df2:
ID A B
1 1 5
2 6 3
3 1 4
4 3 1
5 9 5
Now, to obtain weight of each value of df1, I want to divide each row of
df1 by the row of df2 having similar ID. What I want is as below:
ID A B
1 1 0.4
1 0 0.6
2 0.83 NA
2 0.17 1
3 1 4
4 NA NA
4 0 1
4 1 0
5 0.22 1
5 0.78 NA
Kindly guide me in this regard.
Thanks
Saba