Question

I have a data frame like this (specifically the data.frame contains 50 columns):

  "G1"            "G2"  
  SEP11          ABCC1   
  0.1365         0.1858   
  214223_at      ADAM19     
  0.1305         0.131   
  COPS4          BIK 
  0.1271         0.1143
  ACE            ALG3
  0.1333         0.119
  EMP3           GGH
  0.1246         0.1214

and another data.frame like this(specifically the data.frame contains 50 columns):

   "G1"           "G2"  
  0.1365         0.1858   
  0.1271         0.1143    
  0.1246         0.1214 

I would like the following output:

  "G1"           "G2"  
 SEP11          ABCC1  
 0.1365         0.1858  
 COPS4          BIK     
 0.1271         0.1143   
 EMP3           GGH
 0.1246         0.1214 

Can anyone help me please?

Basically, after R find the match between “0.1365” in the data.frame 1 and “0.1365” in the data.frame2 it will extract the corresponding name from the data.frame1 associated to that number for which the match exist and the number too because I would like to answer the question: which element from the data.frame1 is associated to that number?

Answer 1

df1 <- read.table(text=" G1            G2  
  SEP11          ABCC1   
  0.1365         0.1858   
  214223_at      ADAM19     
  0.1305         0.131   
  COPS4          BIK 
  0.1271         0.1143
  ACE            ALG3
  0.1333         0.119
  EMP3           GGH
  0.1246         0.1214",header=TRUE,stringsAsFactors=FALSE)

df2 <- read.table(text="G1           G2  
      0.1365         0.1858   
      0.1271         0.1143    
      0.1246         0.1214 
 ",header=TRUE,stringsAsFactors=FALSE)

#separate names and numbers
df1a <- df1[seq(from=1,to=nrow(df1)-1,by=2),]
df1b <- df1[seq(from=2,to=nrow(df1),by=2),]

#look up and merge again
df <- rbind(df1b[apply(df1b,1,paste,collapse=",") %in% apply(df2,1,paste,collapse=","),],
            df1a[apply(df1b,1,paste,collapse=",") %in% apply(df2,1,paste,collapse=","),])
df <- df[order(as.numeric(rownames(df))),]
#       G1     G2
#1   SEP11  ABCC1
#2  0.1365 0.1858
#5   COPS4    BIK
#6  0.1271 0.1143
#9    EMP3    GGH
#10 0.1246 0.1214