Last week, I was working on an R script in which I generated a list of data frames and was looking for a way to join them quickly. I thought I would be able to leverage do.call() but I found this thread on StackOverflow that suggested using Reduce() to do that and it worked very well.

Reduce() is one of the “Common Higher-Order Functions in Functional Programming Languages”, a set of functions that are more advanced, maybe not super friendly to use at first, but very powerful!

‘Reduce’ uses a binary function to successively combine the elements of a given vector and a possibly given initial value

Here is a simple example, a simplified version of what I was working on:

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vrbl <- c("pH", "salinity", "temperature")
site <- paste0("site", 1:6)

ls_df <- vrbl |> lapply(\(x) {
    out <- data.frame(site = site)
    out[[x]] <- runif(6)
    out
})
ls_df

[[1]]
   site         pH
1 site1 0.40696251
2 site2 0.09590183
3 site3 0.68285947
4 site4 0.46012124
5 site5 0.92826767
6 site6 0.94730994

[[2]]
   site   salinity
1 site1 0.93431986
2 site2 0.75389578
3 site3 0.89646509
4 site4 0.31757034
5 site5 0.84771590
6 site6 0.03696824

[[3]]
   site temperature
1 site1  0.95577654
2 site2  0.06747292
3 site3  0.49588110
4 site4  0.03272121
5 site5  0.76317572
6 site6  0.33745719

Note that I often work with lists and lapply(), this is very powerful and here it is again: to do the join of all data frames, I call Reduce() and inner_join() (from dplyr) to do the join:

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ls_df |> Reduce(f = dplyr::inner_join)

Joining with `by = join_by(site)`
Joining with `by = join_by(site)`

   site         pH   salinity temperature
1 site1 0.40696251 0.93431986  0.95577654
2 site2 0.09590183 0.75389578  0.06747292
3 site3 0.68285947 0.89646509  0.49588110
4 site4 0.46012124 0.31757034  0.03272121
5 site5 0.92826767 0.84771590  0.76317572
6 site6 0.94730994 0.03696824  0.33745719

Done, I love this!

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sessionInfo()

R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 25.10

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.1 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] 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   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Toronto
tzcode source: system (glibc)

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

loaded via a namespace (and not attached):
 [1] vctrs_0.7.1         cli_3.6.5           knitr_1.51         
 [4] rlang_1.1.7         xfun_0.56           otel_0.2.0         
 [7] processx_3.8.6      targets_1.11.4      generics_0.1.4     
[10] jsonlite_2.0.0      data.table_1.18.2.1 glue_1.8.0         
[13] prettyunits_1.2.0   backports_1.5.0     htmltools_0.5.9    
[16] ps_1.9.1            rmarkdown_2.30      evaluate_1.0.5     
[19] tibble_3.3.1        fastmap_1.2.0       base64url_1.4      
[22] yaml_2.3.12         lifecycle_1.0.5     compiler_4.5.1     
[25] dplyr_1.2.0         codetools_0.2-20    igraph_2.1.4       
[28] pkgconfig_2.0.3     digest_0.6.39       R6_2.6.1           
[31] tidyselect_1.2.1    pillar_1.11.1       callr_3.7.6        
[34] magrittr_2.0.4      tools_4.5.1         secretbase_1.2.0