CODEWITHSUNDEEP
rdataframedata.table
Shiv
Shiv

Reputation: 51

Replace values in each column based on conditions according to groups (by rows) data.frame

I have a data.frame, dim = 400 rows and 15000 columns. I would like to apply a condition where for rows belonging to each group, defined by df$Group, I have to check if the group has values in more than 50% of the rows. If yes, then keep then existing values, else replace all by 0.

for example, for group a df[1:6,1], if sum(df[1:6,1] == 0)/length(df[1:6,1]) >50%, then all values in df[1:6,1] will be replace with 0. Else the existing values will remain.

Sample input:

df <- read.table(text= "DATA  r1    r2  r3  Group
a1  6835    256 0   a
a2  5395    0   67  a
a3  7746    0   30  a
a4  7496    556 50  a
a5  5780    255 0   a
a6  6060    603 0   a
b1  0   0   0   b
b2  0   258 0   b
b3  0   0   0   b
b4  0   0   0   b
b5  5099    505 0   b
b6  0   680 0   b
c1  8443    4900    280 c
c2  8980    4949    0   c
c3  7828    0   0   c
c4  6509    3257    0   c
c5  6563    0   49  c
", header=TRUE, na.strings=NA,row.name=1)
dt <- as.data.table(df) #or data.frame

Expected output:

>df
DATA   r1     r2    r3  Group
 a1   6835   256    0     a
 a2   5395     0   67     a
 a3   7746     0   30     a
 a4   7496   556   50     a
 a5   5780   255    0     a
 a6   6060   603    0     a
 b1      0     0    0     b
 b2      0   258    0     b
 b3      0     0    0     b
 b4      0     0    0     b
 b5      0   505    0     b
 b6      0   680    0     b
c1    8443  4900    0     c
c2    8980  4949    0     c
c3    7828     0    0     c
c4    6509  3257    0     c
c5    6563     0    0     c

Upvotes: 2

Views: 2660

Answers (2)

juba
juba

Reputation: 49053

Update: This bug, #4957 is now fixed in v1.8.11. From NEWS:

Fixing #5007 also fixes #4957, where .N was not visible during lapply(.SD, function(x) ...) in j. Thanks to juba for noticing it here on SO: Replace values in each column based on conditions according to groups (by rows) data.frame

Here is a way with data.table :

dt[, lapply(.SD, function(v) {
    len <- length(v)
    if((sum(v==0)/len)>0.5) rep(0L,len) else v
}), by="Group", .SDcols=c("r1","r2","r3")]

Which gives :

   Group   r1   r2 r3
 1:     a 6835  256  0
 2:     a 5395    0 67
 3:     a 7746    0 30
 4:     a 7496  556 50
 5:     a 5780  255  0
 6:     a 6060  603  0
 7:     b    0    0  0
 8:     b    0  258  0
 9:     b    0    0  0
10:     b    0    0  0
11:     b    0  505  0
12:     b    0  680  0
13:     c 8443 4900  0
14:     c 8980 4949  0
15:     c 7828    0  0
16:     c 6509 3257  0
17:     c 6563    0  0

Upvotes: 5

mrip
mrip

Reputation: 15163

Quick and dirty: 

ff<-function(x){
  if(is.numeric(x)){
    b<-by(x==0,df$Group,mean)
    x[df$Group %in% names(b)[b>0.5]]<-0 
  }
  x
}

data.frame(lapply(df,ff))

Upvotes: 1

Related Questions