tidyr::separate should prevent duplicated column names

[holgerbrandl]

Example:

iris %>% separate(Species, c("Petal.Length"), remove=F) 
Observations: 150
Variables: 6
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4,...
$ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7,...
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5,...
$ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2,...
$ Species      <fctr> setosa, setosa, setosa, setosa, setosa, setosa, setos...
$ Petal.Length <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "set...

The example does not really separate anything, but just illustrates the problem of tidyr creating ansemantically invalid data-frame with a duplicated column name.

tidyr::separate should throw an error in such a case. Or at least overwrite the existing column while printing a warning.

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_0.5.0     purrr_0.2.2     readr_1.0.0     tidyr_0.6.0    
[5] tibble_1.2      ggplot2_2.1.0   tidyverse_1.0.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.7      assertthat_0.1   R6_2.1.3         grid_3.3.1      
 [5] plyr_1.8.4       gtable_0.2.0     DBI_0.5-1        magrittr_1.5    
 [9] scales_0.4.0     stringi_1.1.1    tools_3.3.1      munsell_0.4.3   
[13] colorspace_1.2-6

[pkopps]

Having same issue with gather(), not sure if I could be something differently

> sessionInfo()
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] tidyr_0.6.0    stringr_1.1.0  dplyr_0.5.0    openxlsx_3.0.0 reshape2_1.4.1 ggplot2_2.1.0  pander_0.6.0   readr_1.0.0   
[9] knitr_1.14    

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.7      magrittr_1.5     munsell_0.4.3    colorspace_1.2-6 R6_2.2.0         highr_0.6        plyr_1.8.4      
 [8] tools_3.3.0      grid_3.3.0       gtable_0.2.0     DBI_0.5-1        yaml_2.1.13      lazyeval_0.2.0   assertthat_0.1  
[15] digest_0.6.10    tibble_1.1       formatR_1.4      rsconnect_0.5    evaluate_0.9     labeling_0.3     stringi_1.1.1   
[22] scales_0.4.0   

> names(final)
 [1] "csa"                         "WK_NUM_IN_YR"                "opening_percent_score"       "procedures_percent_score"   
 [5] "resolution_percent_score"    "account_promo_percent_score" "customer_care_percent_score" "documentation_percent_score"
 [9] "hold_transfer_percent_score" "closing_percent_score"       "total_percent_score"

filter(final, csa == agent, WK_NUM_IN_YR == week) %>% gather(csa, WK_NUM_IN_YR) %>% names()
[1] "csa"          "WK_NUM_IN_YR" "csa"          "WK_NUM_IN_YR"

> filter(final, csa == agent, WK_NUM_IN_YR == week) %>% gather(csa, WK_NUM_IN_YR)
        csa WK_NUM_IN_YR                         csa WK_NUM_IN_YR
1  clkjedel           12       opening_percent_score    100.00000
2  clkjedel           12       opening_percent_score    100.00000
3  clkjedel           12    procedures_percent_score     58.82353
4  clkjedel           12    procedures_percent_score     64.70588
5  clkjedel           12    resolution_percent_score     68.42105
6  clkjedel           12    resolution_percent_score     21.05263
7  clkjedel           12 account_promo_percent_score      0.00000
8  clkjedel           12 account_promo_percent_score      0.00000
9  clkjedel           12 customer_care_percent_score    100.00000
10 clkjedel           12 customer_care_percent_score    100.00000
11 clkjedel           12 documentation_percent_score     80.00000
12 clkjedel           12 documentation_percent_score     80.00000
13 clkjedel           12 hold_transfer_percent_score      0.00000
14 clkjedel           12 hold_transfer_percent_score      0.00000
15 clkjedel           12       closing_percent_score    100.00000
16 clkjedel           12       closing_percent_score    100.00000
17 clkjedel           12         total_percent_score     68.00000
18 clkjedel           12         total_percent_score     60.00000