How to divide combinations of rows using dplyr or another method in R?

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site <- rep(1:4, each = 8, len = 32)
rep <- rep(1:8, times = 4, len = 32)
treatment <- rep(c("A.low","A.low","A.high","A.high","A.mix","A.mix","B.mix","B.mix"), 4)
sp.1 <- sample(0:3,size=32,replace=TRUE)
sp.2 <- sample(0:2,size=32,replace=TRUE)
df.dummy <- data.frame(site, rep, treatment, sp.1, sp.2)

The final dataframe looks like this

enter image description here

For each site, I want to summarize various groups. Two for example: "A.low / A.high" = "sp.1/sp.1"; "A.low/ A.mix" = "sp.1/sp.2". As you will notice, there are two for each site and I want all permutations of that in my final columns. My final product would resemble something like:

site  rep   treatment      value
  1.  1/3.  A.low/A.high.   Inf
  1.  1/4.  A.low/A.high.   1

I started to use dplyr but I am really not sure how to proceed especially with all the combinations

  df.dummy %>% 
  group_by(site) %>% 
  summarise(value.1 = sp.1[treatment = "A.low"] / sp.1[treatment = "A.high"])
dplyr combinations permutation r
1 Answers

You could use reshape2 to get the data in a format that is easier to work with.

The code below separates out the sp.1 and sp.2 data. acast is used so that each dataframe consists of a single row per site, and each column is a unique sample with the values being from sp.1 and sp.2.

Name the columns something unique and combine the dataframes with cbind.

Now each column can be compared based on your requirements.

library(dplyr)
library(reshape2)

##your setup
site <- rep(1:4, each = 8, len = 32)
rep <- rep(1:8, times = 4, len = 32)
treatment <- rep(c("A.low","A.low","A.high","A.high","A.mix","A.mix","B.mix","B.mix"), 4)
sp.1 <- sample(0:3,size=32,replace=TRUE)
sp.2 <- sample(0:2,size=32,replace=TRUE)
df.dummy <- data.frame(site, rep, treatment, sp.1, sp.2)

##create unique ids and create a dataframe containing 1 value column
sp1 <- df.dummy %>% mutate(id = paste(rep, treatment, sep = "_")) %>% select(id, site, rep, treatment, sp.1)
sp2 <- df.dummy %>% mutate(id = paste(rep, treatment, sep = "_")) %>% select(id, site, rep, treatment, sp.2)

##reshape the data so that each treament and replicate is assigned a single column
##each row will be a single site
##each column will contain the values from sp.1 or sp.2
sp1 <- reshape2::acast(data = sp1, formula = site ~ id)
sp2 <- reshape2::acast(data = sp2, formula = site ~ id)

##rename columns something sensible and unique
colnames(sp1) <- c("low.1.sp1", "low.2.sp1", "high.3.sp1", "high.4.sp1",
                   "mix.5.sp1", "mix.6.sp1", "mix.7.sp1", "mix.8.sp1")
colnames(sp2) <- c("low.1.sp2", "low.2.sp2", "high.3.sp2", "high.4.sp2",
                   "mix.5.sp2", "mix.6.sp2", "mix.7.sp2", "mix.8.sp2")

##combine datasets
dat <- sp1 %>% cbind(sp2)

##choose which columns to compare. Some examples shown below
dat <-  dat %>% mutate(low.1.sp1/high.3.sp1, low.1.sp1/high.4.sp1,
                       low.2.sp1/high.3.sp2)
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