R: cbind dataframes with common column names -

cbinding 2 dataframes (equal number of rows) few columns having common names results in data.frame common names altered (e.g. namea.1, nameb.1, etc) avoid issues.

i noticed though names have changed, there data substitutions. specifically, resulting data.frame had data first data.frame in columns same name, in supposed have data second data.frame.

this 1 easy overcome, since 1 can change names prior cbind might sneak errors in results.

------edit---- i'll try provide example:

df1 is:

    row     seqnames    start   end     width   strand  region  extra1     1       chr10       8111    8111    172      *      123      456     2       chr11       8112    8112    173      *      123b     456b 

and df2 is:

    row     seqnames    start   end     width   strand  whatever1 whatever2     1       chr12       9111    9111    174      +      abc      efg     2       chr13       9112    9112    175      +      abcb     efgb 

i perform cbind , get:

    row     seqnames    start   end     width   strand  region  extra1  seqnames.1  start.1 end.1   width.1 strand.1 whatever1 whatever2     1       chr10       8111    8111    172      *      123      456    chr10       8111    8111    172      *        abc        efg     2       chr11       8112    8112    173      *      123b     456b   chr11       8112    8112    173      *        abcb       efgb 

the values in second part belong df1 instead of df2. happens in columns had same name in df1 , df2. have been automatically renamed data have been repeated first df.

question: normal behavior?

i hope helps

thank again

not sure question, can specify own column prefix columns of each merged object named arguments of cbind:

data('cars') cars2=cbind(dataset1=cars, dataset2=cars) head(cars2) # dataset1.speed dataset1.dist dataset2.speed dataset2.dist # 1              4             2              4             2 # 2              4            10              4            10 # 3              7             4              7             4 # 4              7            22              7            22 # 5              8            16              8            16 # 6              9            10              9            10