One possible reason for the impossible percent values I’ve found in assemblages data is that taxa have been merged in Excel after percent were calculated. Doing anything in Excel is to invite disaster, if nothing else, it is very difficult to check what has been done.
Merging and renaming taxa is an almost inevitable step in the workflow for processing community and assemblages data. We need a reproducible method: here I show how can it be done with R.
I’m going to assume that the assemblage data are in wide format (one column per taxa) and that there are meta data (depths, ages etc) in one or more columns. If the meta data are in the rownames (which is very convenient for the ‘rioja’ and ‘vegan’ packages, less so for ‘dplyr’ as tibbles don’t have rownames), they can be moved into a column with rownames_to_column.
Here is a small artificial assemblage dataset.
library("tidyverse")
set.seed(1)
spp <- data_frame(
depth_cm = 1:3,
sp_A = rpois(3, 5),
sp_b = rpois(3, 5),
sp.C = rpois(3, 5),
sp_D = rpois(3, 5))
spp_save <- spp # keep copy for later
spp
## # A tibble: 3 x 5 ## depth_cm sp_A sp_b sp.C sp_D ## ## 1 1 4 8 9 2 ## 2 2 4 3 6 3 ## 3 3 5 8 6 3
If we just want to rename a couple of taxa, the simplest solution is to use rename, where we set new_name = old_name. rename can take pairs of new and old names, separated by commas.
spp %>% rename(sp_B = sp_b, sp_C = sp.C)
## # A tibble: 3 x 5 ## depth_cm sp_A sp_B sp_C sp_D ## ## 1 1 4 8 9 2 ## 2 2 4 3 6 3 ## 3 3 5 8 6 3
If there are many names that need altering, or we need to make the same changes to multiple data.frames, we need a different solution as rename gets tedious.
I like to make a data.frame of the old and new names and then use plyr::mapvalues to change the old into the new names. (plyr is a useful package but has several conflicts with dplyr so it is safer to use the :: notation than loading it).
changes <- read.csv(stringsAsFactors = FALSE, text = "old, new sp_b, sp_B sp.C, sp_C", strip.white = TRUE)#this can go in an separate file names(spp) <- plyr::mapvalues(names(spp), from = changes$old, to = changes$new) spp
## # A tibble: 3 x 5 ## depth_cm sp_A sp_B sp_C sp_D ## ## 1 1 4 8 9 2 ## 2 2 4 3 6 3 ## 3 3 5 8 6 3
Merging taxa is possible in the wide format, but much easier in a thin format. We can convert from a wide format to a thin format with gather, and back with spread.
spp <- spp_save#original version spp_thin <- spp %>% gather(key = taxa, value = count, -depth_cm)#don't gather depth_cm spp_thin
## # A tibble: 12 x 3 ## depth_cm taxa count ## ## 1 1 sp_A 4 ## 2 2 sp_A 4 ## 3 3 sp_A 5 ## 4 1 sp_b 8 ## 5 2 sp_b 3 ## 6 3 sp_b 8 ## 7 1 sp.C 9 ## 8 2 sp.C 6 ## 9 3 sp.C 6 ## 10 1 sp_D 2 ## 11 2 sp_D 3 ## 12 3 sp_D 3
If there are just a few taxa that need merging, we can use recode within mutate followed by summarise. Note that in contrast with rename, recode expects “old_name” = “new_name”
spp_thin %>% mutate(taxa = recode(taxa, "sp.C" = "sp_D")) %>% group_by(depth_cm, taxa) %>% summarise(count = sum(count)) %>% spread(key = taxa, value = count)
## # A tibble: 3 x 4 ## # Groups: depth_cm [3] ## depth_cm sp_A sp_b sp_D ## * ## 1 1 4 8 11 ## 2 2 4 3 9 ## 3 3 5 8 9
If there are many taxa that need merging (or some that need merging and some renaming) we can use mapvalues again.
changes <- read.csv(stringsAsFactors = FALSE, text = "old, new sp_b, sp_B sp.C, sp_D", strip.white = TRUE)#this can go in an separate file spp_thin %>% mutate(taxa = plyr::mapvalues(taxa, from = changes$old, to = changes$new)) %>% group_by(depth_cm, taxa) %>% summarise(count = sum(count)) %>% spread(key = taxa, value = count)
## # A tibble: 3 x 4 ## # Groups: depth_cm [3] ## depth_cm sp_A sp_B sp_D ## * ## 1 1 4 8 11 ## 2 2 4 3 9 ## 3 3 5 8 9
This can also be done with a left_join.
spp2 <- spp_thin %>%
left_join(changes, by = c("taxa" = "old")) %>%
mutate(taxa = coalesce(new, taxa)) %>% #takes original name if no new one.
select(-new) %>%
group_by(depth_cm, taxa) %>%
summarise(count = sum(count)) %>%
spread(key = taxa, value = count)
spp2
## # A tibble: 3 x 4 ## # Groups: depth_cm [3] ## depth_cm sp_A sp_B sp_D ## * ## 1 1 4 8 11 ## 2 2 4 3 9 ## 3 3 5 8 9
Now the data are ready for further analysis – remember some functions will want you to remove the meta_data first. For example
cca(select(spp2, -depth_cm))