Merge df and print


Please, help me fix this data.merge script, and figure out how to use the variable y in the output filename. If I can fix these two things, I can then quickly format many dozens of data frames (each with variable dimensions in terms of rows/columns), so that:

  • each of these data frames has the SAME number of rows as x.
  • each row from y that matches RID and VISCODE is added (in its entirety) to x on the same row at the right (added columns).
  • extra rows that are contained in y but not in x are discarded.
  • if y lacks a row that matches RID and VISCODE in x, then NA is added to each of the added columns so that there is still a regular/rectangular data frame.

Finally, Iā€™d like outfile to have the same name as the y input df. For example, instead of "ADNI_TEST.csv" in the data.merge function, I would like the function to exit 'y'.csv where y is the actual name of the input dataframe specified by y.

Suggestions please! And thank you very much for any help you can offer.


Here is the reprex of my sample data and example data.merge script:

data.merge <- function(x,y,n){

x: Main Data file to which columns are added.

y: New data file used to add columns to x

(y has to have "RID" and "VISCODE" columns for proper matching)

(y may need to alter VISCODE to VISCODE2 for some data files)

n: the column(s) in y you want to add to x -- may select ranges, too.

RID <- y$RID
y <- data.frame(RID,VISCODE,y[,n])
ADNI <- merge(x,y, by = c("RID","VISCODE") ,all.x=TRUE,all.y=FALSE);

return(ADNI) # Stop printing, so as not to overrun memory buffer.

write.csv(ADNI, file = "ADNI_TEST.csv", quote = TRUE)

firstfile_x <- tibble::tribble(
123L, "v11", "m84", "3/23/2013", 1L, "ADNI2 Year 1 Visit", 28L, 369744L, 369769L, 369819L, 369794L, "5/2/2013", "Complete", 0.00114663, 0.00116237, 0.00161111,
123L, "v21", "m96", "2/14/2014", 1L, "ADNI2 Year 2 Visit", 30L, 420946L, 420987L, 421069L, 421028L, "5/2/2014", "Complete", 0.00130536, 0.00133773, 0.00135224,
123L, "v31", "m108", "3/5/2015", 1L, "ADNI2 Year 3 Visit", 8L, 487576L, 487587L, 487635L, 487624L, "8/21/2019", "Complete", 0.00116516, 0.00117003, 0.00146521,
610L, "v51", "m120", "7/8/2016", 1L, "ADNI2 Year 5 Visit", 14L, 786152L, 786164L, 786829L, 786176L, "8/21/2019", "Complete", 0.00160112, 0.00166888, 0.00192628,
671L, "v31", "m108", "7/19/2015", 1L, "ADNI2 Year 3 Visit", 13L, 537878L, 537904L, 537956L, 537930L, "8/21/2019", "Complete", 0.00138872, 0.00122101, 0.00133198,
778L, "v21", "m84", "9/6/2013", 1L, "ADNI2 Year 2 Visit", 30L, 397234L, 397299L, 397444L, 397363L, "11/2/2013", "Complete", 0.00207073, 0.00170183, 0.00188191,
778L, "v31", "m96", "9/25/2014", 1L, "ADNI2 Year 3 Visit", 12L, 471183L, 471200L, 471235L, 471217L, "8/21/2019", "Complete", 0.00208479, 0.00205418, 0.00172855,
908L, "v21", "m84", "1/8/2014", 1L, "ADNI2 Year 2 Visit", 30L, 420931L, 420972L, 421054L, 421013L, "5/2/2014", "Complete", 0.00129875, 0.00116119, 0.00122157,
934L, "v11", "m84", "10/24/2013", 1L, "ADNI2 Year 1 Visit", 28L, 413297L, 413362L, 413492L, 413427L, "2/2/2014", "Complete", 0.0012088, 0.0011105, 0.0014697,
934L, "v31", "m108", "10/16/2015", 1L, "ADNI2 Year 3 Visit", 13L, 614650L, 614692L, 614748L, 614720L, "8/21/2019", "Complete", 0.00118444, 0.00116324, 0.00126697,
1074L, "v31", "m96", "4/24/2015", 1L, "ADNI2 Year 3 Visit", 12L, 537875L, 537901L, 537953L, 537927L, "8/21/2019", "Complete", 0.00117554, 0.00115228, 0.00132158,
1346L, "v31", "m96", "4/2/2015", 1L, "ADNI2 Year 3 Visit", 12L, 537879L, 537905L, 537957L, 537931L, "8/21/2019", "Complete", 0.00125772, 0.00126526, 0.00140356,
1352L, "v31", "m96", "4/12/2015", 1L, "ADNI2 Year 3 Visit", 12L, 786153L, 786165L, 786830L, 786177L, "8/21/2019", "Complete", 0.00138516, 0.00130067, 0.00164973,
2007L, "scmri", "scmri", "6/19/2010", 1L, "ADNIGO Screening MRI", 14L, 299634L, 299885L, 359704L, 359115L, "10/3/2012", "Complete", 0.0012181, 0.00109861, 0.00113204,
2007L, "v11", "m24", "3/28/2012", 1L, "ADNI2 Year 1 Visit", 28L, 335804L, 335683L, 360065L, 359414L, "10/3/2012", "Complete", 0.00143382, 0.00130726, 0.00157676,
2007L, "m03", "m03", "9/1/2010", 1L, "ADNIGO Month 3 MRI", 15L, 299635L, 299888L, 359835L, 359263L, "10/3/2012", "Complete", 0.00139614, 0.00118858, 0.00126052,
2007L, "v06", "m12", "3/26/2011", 1L, "ADNI2 Initial Visit-Cont Pt", 26L, 299636L, 299891L, 359935L, 359365L, "10/3/2012", "Complete", 0.00146508, 0.0014392, 0.00145967,
2031L, "scmri", "scmri", "8/13/2010", 1L, "ADNIGO Screening MRI", 14L, 299637L, 299894L, 359705L, 359116L, "10/3/2012", "Complete", 0.00118117, 0.00110229, 0.00132531,
2031L, "v11", "m24", "7/25/2012", 1L, "ADNI2 Year 1 Visit", 28L, 356984L, 358870L, 360240L, 359559L, "2/1/2013", "Complete", 0.00129732, 0.00120146, 0.00148761,
2031L, "m03", "m03", "11/12/2010", 1L, "ADNIGO Month 3 MRI", 15L, 299638L, 299897L, 359836L, 359264L, "10/3/2012", "Complete", 0.00122562, 0.00111628, 0.0011707,
2031L, "v21", "m36", "7/24/2013", 1L, "ADNI2 Year 2 Visit", 30L, 397236L, 397301L, 397446L, 397365L, "11/2/2013", "Complete", 0.00124111, 0.0012689, 0.00157564,
2031L, "v06", "m12", "7/21/2011", 1L, "ADNI2 Initial Visit-Cont Pt", 26L, 299640L, 299903L, 360066L, 359415L, "10/3/2012", "Complete", 0.00140551, 0.00132471, 0.00175401,
2031L, "m06", "m06", "2/10/2011", 1L, "ADNI1/GO Month 6", 3L, 299639L, 299900L, 359936L, 359366L, "10/3/2012", "Complete", 0.0013122, 0.00135983, 0.00144974,
2047L, "v11", "m24", "9/28/2012", 1L, "ADNI2 Year 1 Visit", 28L, 356988L, 358875L, 360245L, 359564L, "2/1/2013", "Complete", 0.00131261, 0.00120259, 0.00139444,
2047L, "m06", "m06", "3/4/2011", 1L, "ADNI1/GO Month 6", 3L, 299732L, 299951L, 359969L, 359392L, "10/3/2012", "Complete", 0.00125712, 0.00117188, 0.00138467,
2047L, "m03", "m03", "12/8/2010", 1L, "ADNIGO Month 3 MRI", 15L, 299731L, 299950L, 359874L, 359315L, "10/3/2012", "Complete", 0.00127272, 0.00127151, 0.00140615,
2047L, "v06", "m12", "9/15/2011", 1L, "ADNI2 Initial Visit-Cont Pt", 26L, 299733L, 299952L, 360077L, 359427L, "10/3/2012", "Complete", 0.00121407, 0.00121281, 0.00130178,
2047L, "scmri", "scmri", "8/20/2010", 1L, "ADNIGO Screening MRI", 14L, 299730L, 299949L, 360286L, 359188L, "10/3/2012", "Complete", 0.00125326, 0.0012075, 0.00139156,
2047L, "v21", "m36", "9/14/2013", 1L, "ADNI2 Year 2 Visit", 30L, 397237L, 397302L, 397447L, 397366L, "11/2/2013", "Complete", 0.00124826, 0.00119243, 0.00143786

secondfile_y <- tibble::tribble(
610L, "m120", "005_S_0610", 1L, 1L, 5L, "ADNI2", "ADNI1", "7/6/2016", "CN", 79, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, 0.946164, NA, NA, NA, 0, 2L,
778L, "m84", "129_S_0778", 1L, 1L, 129L, "ADNI2", "ADNI1", "9/4/2013", "CN", 72.4, "Male", 13L, "Not Hisp/Latino", "White", "Married", 1L, NA, NA, NA, NA, NA, NA, 6, 18L,
778L, "m96", "129_S_0778", 1L, 1L, 129L, "ADNI2", "ADNI1", "9/23/2014", "CN", 72.4, "Male", 13L, "Not Hisp/Latino", "White", "Married", 1L, NA, NA, 1.39235, "646.2", 226.2, 19.25, 6, 22L,
908L, "m84", "003_S_0908", 1L, 1L, 3L, "ADNI2", "ADNI1", "1/6/2014", "LMCI", 62.9, "Female", 16L, "Not Hisp/Latino", "White", "Married", 0L, 1.2554, NA, 1.01167, NA, NA, NA, 1.5, 8L,
2077L, "m03", "021_S_2077", 1L, 1L, 21L, "ADNIGO", "ADNIGO", "12/8/2010", "EMCI", 81, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, NA, NA,
2077L, "m24", "021_S_2077", 1L, 1L, 21L, "ADNI2", "ADNIGO", "10/17/2012", "EMCI", 81, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, 1.2748, NA, NA, NA, 0.5, 14L,
2077L, "m48", "021_S_2077", 1L, 1L, 21L, "ADNI2", "ADNIGO", "10/21/2014", "EMCI", 81, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, 0.5, 6L,
2347L, "m12", "129_S_2347", 1L, 1L, 129L, "ADNI2", "ADNIGO", "3/28/2012", "EMCI", 71, "Male", 18L, "Not Hisp/Latino", "White", "Married", 1L, NA, NA, NA, NA, NA, NA, 1.5, 9L,
2347L, "m24", "129_S_2347", 1L, 1L, 129L, "ADNI2", "ADNIGO", "3/27/2013", "EMCI", 71, "Male", 18L, "Not Hisp/Latino", "White", "Married", 1L, 1.421, NA, 0.959929, "1081", 300.4, 25.62, 2, 5L,
2347L, "m36", "129_S_2347", 1L, 1L, 129L, "ADNI2", "ADNIGO", "4/7/2014", "EMCI", 71, "Male", 18L, "Not Hisp/Latino", "White", "Married", 1L, NA, NA, NA, NA, NA, NA, 2, 4L,
4185L, "m03", "005_S_4185", 1L, 1L, 5L, "ADNI2", "ADNI2", "1/5/2012", "EMCI", 80.4, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, NA, NA,
4185L, "m06", "005_S_4185", 1L, 1L, 5L, "ADNI2", "ADNI2", "4/2/2012", "EMCI", 80.4, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, 0.5, 5L,
4185L, "m12", "005_S_4185", 1L, 1L, 5L, "ADNI2", "ADNI2", "9/27/2012", "EMCI", 80.4, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, 1, 6L,
4185L, "m24", "005_S_4185", 1L, 1L, 5L, "ADNI2", "ADNI2", "9/26/2013", "EMCI", 80.4, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, 1.11068, NA, 1.05932, NA, NA, NA, 0.5, 4L,
4185L, "m48", "005_S_4185", 1L, 1L, 5L, "ADNI2", "ADNI2", "9/24/2015", "EMCI", 80.4, "Male", 20L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, 1.11009, NA, NA, NA, 0.5, 9L,
4371L, "m03", "129_S_4371", 1L, 1L, 129L, "ADNI2", "ADNI2", "2/13/2012", "CN", 67.7, "Male", 18L, "Not Hisp/Latino", "White", "Married", 1L, NA, NA, NA, NA, NA, NA, NA, NA,
4371L, "m24", "129_S_4371", 1L, 1L, 129L, "ADNI2", "ADNI2", "11/26/2013", "CN", 67.7, "Male", 18L, "Not Hisp/Latino", "White", "Married", 1L, 1.10519, NA, 1.17295, "769.8", 273.6, 24.27, 0, 5L,
4494L, "m03", "126_S_4494", 1L, 1L, 126L, "ADNI2", "ADNI2", "4/30/2012", "AD", 71.1, "Male", 12L, "Not Hisp/Latino", "White", "Married", 2L, NA, NA, NA, NA, NA, NA, NA, NA,
4494L, "m06", "126_S_4494", 1L, 1L, 126L, "ADNI2", "ADNI2", "8/13/2012", "AD", 71.1, "Male", 12L, "Not Hisp/Latino", "White", "Married", 2L, NA, NA, NA, NA, NA, NA, 4.5, 19L,
4494L, "m12", "126_S_4494", 1L, 1L, 126L, "ADNI2", "ADNI2", "2/7/2013", "AD", 71.1, "Male", 12L, "Not Hisp/Latino", "White", "Married", 2L, NA, NA, NA, NA, NA, NA, 4.5, 19L,
4494L, "m24", "126_S_4494", 1L, 1L, 126L, "ADNI2", "ADNI2", "2/10/2014", "AD", 71.1, "Male", 12L, "Not Hisp/Latino", "White", "Married", 2L, 1.05921, NA, 1.40822, "300.8", 510.2, 50.2, 8, 22L,
4626L, "m03", "052_S_4626", 1L, 1L, 52L, "ADNI2", "ADNI2", "7/27/2012", "LMCI", 69.2, "Male", 18L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, NA, NA,
4626L, "m06", "052_S_4626", 1L, 1L, 52L, "ADNI2", "ADNI2", "10/29/2012", "LMCI", 69.2, "Male", 18L, "Not Hisp/Latino", "White", "Married", 0L, NA, NA, NA, NA, NA, NA, 0.5, 11L



Welcome to the RStudio community!

I'm afraid your request is a bit unclear and the code you provided can't be run as it is missing data to recreate the issue. I suggest you read the reprex guide to learn how to best write a post that will help folk understanding the issue and recreating, then solving it. A reprex consists of the minimal code and data needed to recreate the issue/question you're having. You can find instructions how to build and share one here:

Judging from wat you are trying to do, it seems you are trying to join two tables together. You might want to have a look at the left_join function of the dpyr package!

Hope this helps, and create a reprex if you need more help

Thank you for your suggestions. I've provided the reprex for this post. Also, I'll see if the dplyr tricks will work. I had already looked at it an attempted to use it before posting, but hadn't yet been able to figure out how to use it. Will keep trying. Thanks.



Thanks for updating the post and crating a reprex!

Here is the code I came up with (I did not include the data in here to save space, so copy paste that part from your original post):


# Left-Join firstfile_x and secondfile_y
mergedFile = firstfile_x %>% left_join(secondfile_y, by = c("RID", "VISCODE"))

# Example where there are matches of RID and VISCODE in the two files
thirdfile_z = secondfile_y
thirdfile_z[c(1,2,3), c("RID", "VISCODE")] = firstfile_x[c(1,2,3), c("RID", "VISCODE")]
mergedFile = firstfile_x %>% left_join(thirdfile_z, by = c("RID", "VISCODE"))

# As a function (if needed to repeat many times)
mergeFiles = function(currentData, dataToAdd, joinBy = c("RID", "VISCODE")){
  currentData %>% left_join(dataToAdd, by = joinBy)
mergedFile = mergeFiles(firstfile_x, thirdfile_z)


  • First of all, your description says the number of rows are the same, but this is not the case here. I assume this is because of the reprex and not including the full dataset (which is smart), but just pointing this out.
  • Also, since you provided a sample of data, there are no RID, VISCOSE matches between the datasets, ending up with adding all empty columns to the data frame. To solve this for the example, I created thirdfile_z that has the same first 3 RID, VISCOSE as firstfile_x, just to be clear.
  • The function I used for the 'magic' part is the left_join from dplyr. This will add any column from a dataframe to an existing one if there is a match in a specific row value (or more).
  • RID, VISCOSE are the matching criteria, so these columns will NOT be repeated when added to the original data. This can be forced if you really wanted to, but now went for default behaviour. Also, if there is any column in both datasets with the same name, it will get an extension .x and .y to disambiguate them. Again, you can change this beforehand by renaming columns to be unique, but haven't don't that now.
  • I created a function mergeFiles where you can join any two data frames, and specify the columns to join on (default now set on c("RID", "VISCODE") )
  • I did not understand your request for changing the name of the save-file, so please elaborate if needed.

Hope this helps,

1 Like

Thanks, this works nicely. I really appreciate your detailed explanation, too.


What I mean is this, let's say I have 100 variables in a "package." Each variable is fed into the function as "thirdfile_z". The output I'd like to see is 100 .csv files, with the 100 variables as the stem, followed by .csv, something like write.csv(thirdfile_z,file = 'var1_name'.csv) ... At the end of the day, I'd have a hundred .csv files each of which would have firstfile_x data in the first ~100 columns, and thirdfile_z data [matched] in the last ~100-500 columns.

The way I approached this was through brute force. I created separate lines to extract each of 100 variables from the package (ADNIMERGE) and put the data into a separate variable. Now, you've helped me merge those data to a target file that I'd like to match (DTIROI). The next step is to output all the data as csv files.

This approach should allow me to run through thousands of variables to screen for correlations that arise from the noise -- correlations between the data represented in the ~100 thirdfile_z variables, and the ~100 or so columns in firstfile_x.

I'm a novice at this, but just know there must be some very simple trick, probably using paste...

Thanks for your help!



This is still a bit confusing. Where do the 100 'variables in a package' come from? Are these all separate data frames you read in to R? Or is this a larger data frame you split into parts. Or does other code generate them? This will define the best way how to rename the columns. It is indeed simple, but if I know the origin of the data, I might be able to improve the whole function instead of just using the variable name (which you'll have to type in every time).


Hi, PJ,

Here's the top of my "brute force" script file:

loaded the library


First, I'll create separate variables for each ADNIMERGE datafile:

adas <- ADNIMERGE::adas
addcomm <- ADNIMERGE::addcomm
admcba <- ADNIMERGE::admcba
admcbapp <- ADNIMERGE::admcbapp
admcbarcelonapurine <- ADNIMERGE::admcbarcelonapurine
admcdukep180fia <- ADNIMERGE::admcdukep180fia
admcdukep180fiaadni2go <- ADNIMERGE::admcdukep180fiaadni2go
admcdukep180uplc <- ADNIMERGE::admcdukep180uplc
admcdukep180uplcadni2go <- ADNIMERGE::admcdukep180uplcadni2go

There are actually 226 dataframes in the ADNIMERGE package.

Once I've extracted each dataframe from ADNIMERGE, labelled as its own name,

then I will run a loop using your mergeFiles function, and write out the combined,

formatted data as .csv files, each with the corresponding name to make it easy to

trace back to the exact data within ADNIMERGE.

Currently, I've been using "brute force" to run each of these variables separately, using

an old flawed version of data.merge function. Once I fix it using your suggestions, I

hope to create a loop that will turn 214 lines of output code into a short loop...

To do this, I need to understand how to use the 214 df names (above) to name each

of the 214 .csv files I am trying to extract....



If you are planning on merging all data frames, how will you end up with 214 output files? There will ony be one if you merge.

I have no access to the ADNIMERGE package (data is only available with login to website), but I made an example on how to extract all data from a package and loop over it.

PART 1 - Extract all data from a package (example with ggplot2)

library(ggplot2) # Replace with ADNIMERGE

#Get the names of all data sets in a package
datasetNames = data(package='ggplot2')$results[,3]

#Put all data sets in one list
datasets = lapply(datasetNames, get)
names(datasets) = datasetNames

This will generate a list in which each entry is a data set from that package (named by the original name of the data frame).

PART 2 - Run the merge loop (dummy data used)

#Dummy data
datasetNames = c("a", "b")
datasets = list(a = data.frame(id = 1:5, x = letters[1:5]), b = data.frame(id = 1:5, y = 1:5))

#Don't forget to change the 'joinBy' to c("RID", "VISCODE") in your case
mergeFiles = function(currentData, dataToAdd, joinBy = c("id")){
  currentData %>% left_join(dataToAdd, by = joinBy)

#Get the first dataset to join all the rest to
finalData = datasets[[1]]

#Join all the others
for(name in datasetNames[-1]){
  finalData = mergeFiles(finalData, datasets[[name]])
  • I had to use dummy data because the datasets in ggplot2 do not have columns I can join all data on (which should be the case in your data).
  • Note that there is only 1 output data frame, since you merge everything
  • Beware that the first dataset you use will be the basis for all other joins. If an ID ("RID", "VISCODE") is not in that fist set, it will be ignored in all joins because you always left join to the last one.

I don't think this is exactly what you want, but it's progress I think...


This is helpful, PJ, and thank you, again.

To answer your first question about the output files... I first extract each data frame from the ADNIMERGE package, and get 214 dataframe objects (as I created with my brute force script, and you did more elegantly with your loop).

When I sequentially merge a "target" dataframe (firstfile_x, in your initial response) with each separate dataframe, the result will be 214 objects with exactly the same number of rows as the target file, and all the variables (columns) represented within BOTH firstfile_x and secondfile_y.

I then want to save each of these 214 merged dataframes as separate .csv files for analysis. These 214 dataframes contain clinical, lab, imaging, and cerebrospinal fluid data; the same technique can be extended to genetic data to screen for correlations between each firstfile_x variable (column) and many thousands of genetic loci.

I appreciate your patience and your help.



I think I know what you mean. You want the 214 files to be merged individually with a custom data frame of which you have set the IDs ("RID", "VISCODE") ? So if firstfile_x is your custom data frame. you want a combination of firstfile_x and ADNIMERGE1, firstfile_x and ADNIMERGE2, firstfile_x and ADNIMERGE3, ... ? In that case my code does something different, but is easy fix.


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