Merge 4 data objects with different columns (variables) in R

Question

So initially I had the following object:

> head(gs)
  year disturbance lek_id  complex tot_male
1 2006           N     3T  Diamond        3
2 2007           N         


        

Answer 1

mystat <- function(x) c(mi=min(x), ma=max(x))
aggregate(Sepal.Length~Species, FUN=mystat, data=iris)

for you:

mystat <- function(x) c(mi=min(x), ma=max(x), m=mean(x), s=sd(x), l=length(x))
aggregate(tot_male~year+complex, FUN=mystat, data=gs)

Answer 2

This might work (but hard to check without a reproducible example):

gsnew <- Reduce(function(...) merge(..., all = TRUE, by = c("year","complex")), 
                list(tyc_min, tyc_max, tyc_mean, tyc_sd))

But instead of aggregating for the separate statistics and then merging, you can also aggregate everything at once into a new dataframe / datatable with for example data.table, dplyr or base R. Then you don't have to merge afterwards (for a base R solution see the other answer):

library(data.table)
gsnew <- setDT(gs)[, .(male_min = min(tot_male),
                       male_max = max(tot_male),
                       male_mean = mean(tot_male),
                       male_sd = sd(tot_male), by = .(year, complex)]

library(dplyr)
gsnew <- gs %>% group_by(year, complex) %>%
  summarise(male_min = min(tot_male),
            male_max = max(tot_male),
            male_mean = mean(tot_male),
            male_sd = sd(tot_male))