I have a dataframe like below, which contains the body lengths and the observed counts of species A and B at each station
> set.seed(10)
> df <- data.frame(
species = c(rep("A",4), rep("B",4)),
station = rep(1:2, 4),
length = round(rnorm(8, 15, 2)),
count = round(rnorm(8, 5, 2))
)
> df
species station length count
1 A 1 15 2
2 A 2 15 4
3 A 1 12 7
4 A 2 14 7
5 B 1 16 5
6 B 2 16 7
7 B 1 13 6
8 B 2 14 5
What I want to do is to transform this into a table with 2cm bins like this. But how can I get this?
> cnt_table <- data.frame(
species = c("A","A","B","B"),
station = c(1,2,1,2),
L11_12 = c(0,0,0,0),
L13_14 = c(7,7,6,5),
L15_16 = c(2,4,5,7),
L17_18 = c(0,0,0,0),
L19_20 = c(0,0,0,0)
)
> cnt_table
species station L11_12 L13_14 L15_16 L17_18 L19_20
1 A 1 0 7 2 0 0
2 A 2 0 7 4 0 0
3 B 1 0 6 5 0 0
4 B 2 0 5 7 0 0
CodePudding user response：
First, you need to use cut to bin the lengths. Then you can use complete to fill the missing counts with 0. Then, group_by species, station and bin and use summarize to add the counts per group. Last, use pivot_wider to make the bins column labels.
Note 1: The result differs from your expected output, but I think you have a typo.
Note 2: I don't know if teh grouping and summing is necessary. In your example it's not, but logically I would include it.
library(tidyverse)
set.seed(10)
df <- data.frame(
species = c(rep("A",4), rep("B",4)),
station = rep(1:2, 4),
length = round(rnorm(8, 15, 2)),
count = round(rnorm(8, 5, 2))
)
df
#---------------------
df %>%
mutate(length = cut(length,
breaks = seq(10.5, 20.5, by = 2),
labels = c("L11_12", "L13_14", "L15_16", "L17_18", "L19_20"))) %>%
complete(species, station, length, fill = list(count = 0)) %>%
group_by(species, station, length) %>%
summarize(count = sum(count)) %>%
pivot_wider(names_from = length, values_from = count)
#---------------------
# A tibble: 4 x 7
# Groups: species, station [4]
species station L11_12 L13_14 L15_16 L17_18 L19_20
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A 1 7 0 2 0 0
2 A 2 0 7 4 0 0
3 B 1 0 6 5 0 0
4 B 2 0 5 7 0 0
CodePudding user response：
a data.table approach that should scale well
library(data.table)
# create alookup table with categories
lookup <- data.table(
name = paste0("L", seq(11, 19, 2), "_", seq(12,20, 2)),
from = seq(11, 19, 2),
to = seq(12,20, 2)
)
lookup
# join with a non-equi join
setDT(df)[lookup, name := i.name, on = .(length >= from, length <= to)]
# spreak to wide
dcast(df, species station ~ name, value.var = "count", fun.aggregate = sum)
# species station L11_12 L13_14 L15_16
# 1: A 1 7 0 2
# 2: A 2 0 7 4
# 3: B 1 0 6 5
# 4: B 2 0 5 7
CodePudding user response：
You can do something like:
library(dplyr)
library(tidyr)
# Create the bins
df$bin <- cut(df$length, breaks = seq(from = min(df$length-1), to = max(df$length) 1, by = 2))
# Get the data frame into shape
df |>
mutate(
bin_start = substr(bin, 2,3),
bin_end = as.numeric(substr(bin, 5,6))-1,
bin_name = paste0("L", bin_start, "_", bin_end)) |>
group_by(species, station, bin_name) |>
summarise(n = n()) |>
pivot_wider(
names_from = bin_name,
values_from = n,
values_fill = list(n = 0)
)
# A tibble: 4 x 5
# Groups: species, station [4]
# species station L11_12 L13_14 L15_16
# <chr> <int> <int> <int> <int>
# 1 A 1 1 1 0
# 2 A 2 0 2 0
# 3 B 1 1 0 1
# 4 B 2 0 1 1
CodePudding user response：
Define your desired range r, i.e. 11:20 as shown in OP (length should be even!). Then match df$lengh with a helper-matrix m to get bins, calculate aggregated sums and reshape to wide format, e.g. using reshape2::dcast.
r <- 11:20; m <- matrix(r, ncol=2, byrow=TRUE)
transform(df, bin=factor(sapply(df$length, \(z) which(rowSums(z == m) > 0)),
levels=seq_along(x), labels=apply(m, 1, paste, collapse='_'))) |>
aggregate(count ~ bin species station, sum) |>
reshape2::dcast(species station ~ bin, value.var='count', drop=FALSE, fill=0)
# species station 11_12 13_14 15_16 17_18 19_20
# 1 A 1 7 0 2 0 0
# 2 A 2 0 7 4 0 0
# 3 B 1 0 6 5 0 0
# 4 B 2 0 5 7 0 0