[R] Combine columns having same column name from multiple data frames

[arun]

Hi,
You may try:
lapply(seq_len(ncol(data1)),function(i) {x1 <- do.call(cbind,lapply(lapply(as.list(paste0("data",1:3)),get),`[`,i)); write.csv(x1,paste0("new",i,".csv"),quote=FALSE) })

A.K.


Dear All, 

I am trying to combine columns having same name from 3 different
 data frames and create new data frame with combined columns. The 
challenging thing is each data-frame has 100 columns so I have to create
 100 new data frames and write each data-frame as new text file with 
unique column name. 

Example: 

> data1 
                 1         2         3         4         5         6         7         8         9 
x         -39532.0  -39472.0  -39472.0  -39592.0  -39532.0  -39472.0  -39412.0  -39592.0  -39412.0 
y        2015408.1 2015348.1 2015288.1 2015228.1 2015228.1 2015228.1 2015228.1 2015168.1 2015168.1 
id             1.0       2.0       3.0       4.0       5.0       6.0       7.0       8.0       9.0 
srad_120     496.0     496.0     496.0     496.0     496.0     496.0     496.0     496.0     496.0 
srad_121     441.6     441.6     441.6     441.6     441.6     441.6     441.6     441.6     441.6 
srad_122     150.4     150.4     150.4     150.4     150.4     150.4     150.4     150.4     150.4 
srad_123     249.6     249.6     249.6     249.6     249.6     249.6     249.6     249.6     249.6 
srad_124     272.0     272.0     272.0     272.0     272.0     272.0     272.0     272.0     272.0 
srad_125     153.6     153.6     153.6     153.6     153.6     153.6     153.6     153.6     153.6 

> data2 
                 1         2         3         4         5         6         7         8         9 
x         -39532.0  -39472.0  -39472.0  -39592.0  -39532.0  -39472.0  -39412.0  -39592.0  -39412.0 
y        2015408.1 2015348.1 2015288.1 2015228.1 2015228.1 2015228.1 2015228.1 2015168.1 2015168.1 
id             1.0       2.0       3.0       4.0       5.0       6.0       7.0       8.0       9.0 
srad_120     542.0     542.0     542.0     542.0     542.0     542.0     542.0     542.0     542.0 
srad_121     487.6     487.6     487.6     487.6     487.6     487.6     487.6     487.6     487.6 
srad_122     196.4     196.4     196.4     196.4     196.4     196.4     196.4     196.4     196.4 
srad_123     295.6     295.6     295.6     295.6     295.6     295.6     295.6     295.6     295.6 
srad_124     318.0     318.0     318.0     318.0     318.0     318.0     318.0     318.0     318.0 
srad_125     199.6     199.6     199.6     199.6     199.6     199.6     199.6     199.6     199.6 

> data3 
                 1         2         3         4         5         6         7         8         9 
x         -39532.0  -39472.0  -39472.0  -39592.0  -39532.0  -39472.0  -39412.0  -39592.0  -39412.0 
y        2015408.1 2015348.1 2015288.1 2015228.1 2015228.1 2015228.1 2015228.1 2015168.1 2015168.1 
id             1.0       2.0       3.0       4.0       5.0       6.0       7.0       8.0       9.0 
srad_120     578.0     578.0     578.0     578.0     578.0     578.0     578.0     578.0     578.0 
srad_121     523.6     523.6     523.6     523.6     523.6     523.6     523.6     523.6     523.6 
srad_122     232.4     232.4     232.4     232.4     232.4     232.4     232.4     232.4     232.4 
srad_123     331.6     331.6     331.6     331.6     331.6     331.6     331.6     331.6     331.6 
srad_124     354.0     354.0     354.0     354.0     354.0     354.0     354.0     354.0     354.0 
srad_125     235.6     235.6     235.6     235.6     235.6     235.6     235.6     235.6     235.6 

write.csv(cbind(data1[,1], data2[,1], data3[,1]), "new1.csv") 
Above statement generates new csv file that has first column of each data frame (data1, data2, data3). 
        V1	V2	V3 
x	-39531.99524	-39531.99524	-39531.99524 
y	2015408.131	2015408.131	2015408.131 
id	1	                      1	                           1 
srad_120	496	            542	                  578 
srad_121	441.6000061	487.6000061	523.6000061 
srad_122	150.3999939	196.3999939	232.3999939 
srad_123	249.6000061	295.6000061	331.6000061 
srad_124	272	                     318	             354 
srad_125	153.6000061	199.6000061	235.6000061 

Similarly I need to create 9 csv files using 9 columns in each data frame. If possible, please help in this. 

Thank you very much.