问题
Problem:
I have a number of sites, with 10 sampling points at each site.
Site Time Sample Species1 Species2 Species3 etc
Home A 1 1 0 4 ...
Home A 2 0 0 2 ...
Work A 1 0 1 1 ...
Work A 2 1 0 1 ...
Home B 1 1 0 4 ...
Home B 2 0 0 2 ...
Work B 1 0 1 1 ...
Work B 2 1 0 1 ...
...
I would like to obtain the richness and abundance of each site. Richness is the total number of species at a site, and abundance is the total number of all individuals of all species at a site, like this:
Site Time Richness Abundance
Home A 2 7
Work A 3 4
Home B 2 7
Work B 3 4
I can get there with two functions (below). However, I would like both in one dplyr function. The range 7:34
refers to my species matrix (each row a site/sample, species as columns).
df1 <- df %>% mutate(Abundance = rowSums(.[,4:30])) %>%
group_by(Site,Time) %>%
summarise_all(sum)
df1$Richness <- apply(df1[,4:30]>0, 1, sum)
If I try to do both in one function, I get the following error
df1 <- df %>% mutate(Abundance = rowSums(.[,4:30]) ) %>%
group_by(Site, Time) %>%
summarise_all(sum) %>%
mutate(Richness = apply(.[,4:30]>0, 1, sum))
Error in mutate_impl(.data, dots) :
Column `Richness` must be length 5 (the group size) or one, not 19
The Richness part has to come after the summarise function, since it has to operate on summed and grouped data.
How do I make this function work?
(Note: This was previously marked as a duplicate of this question: Manipulating seperated species quantity data into a species abundance matrix
It is a completely different question, however - that question is essentially about transposing a dataset and summing within a single species/column. This is about summing all species across columns (multiple columns). In addition, I actually think the answer to this question is very helpful - ecologists like me calculate richness and abundance all the time, and I'm sure they'll appreciate a dedicated question.)
回答1:
After the summarise
, we need to ungroup
library(tidyverse)
df %>%
mutate(Abundance = rowSums(.[4:ncol(.)])) %>%
group_by(Site, Time) %>%
summarise_all(sum) %>%
ungroup %>%
mutate(Richness = apply(.[4:(ncol(.)-1)] > 0, 1, sum)) %>%
#or
#mutate(Richness = rowSums(.[4:(ncol(.)-1)] > 0)) %>%
select(Site, Time, Abundance, Richness)
# A tibble: 4 x 4
# Site Time Abundance Richness
# <chr> <chr> <dbl> <int>
#1 Home A 7 2
#2 Home B 7 2
#3 Work A 4 3
#4 Work B 4 3
It can also be written by first doing the group_by
sum
and then transmute
df %>%
group_by(Site, Time) %>%
summarise_at(vars(matches("Species")), sum) %>%
ungroup %>%
transmute(Site, Time, Abundance = rowSums(.[3:ncol(.)]),
Richness = rowSums(.[3:ncol(.)] > 0))
Or another option is sum
with map
df %>%
group_by(Site, Time) %>%
summarise_at(vars(matches("Species")), sum) %>%
group_by(Time, add = TRUE) %>%
nest %>%
mutate(data = map(data, ~
tibble(Richness = sum(.x > 0),
Abundance = sum(.x)))) %>%
unnest
data
df <- structure(list(Site = c("Home", "Home", "Work", "Work", "Home",
"Home", "Work", "Work"), Time = c("A", "A", "A", "A", "B", "B",
"B", "B"), Sample = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), Species1 = c(1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L), Species2 = c(0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L), Species3 = c(4L, 2L, 1L, 1L, 4L, 2L, 1L, 1L)),
class = "data.frame", row.names = c(NA,
-8L))
来源:https://stackoverflow.com/questions/52547792/how-to-obtain-species-richness-and-abundance-for-sites-with-multiple-samples-usi