如何解决使用Tidyr 数据
我有一个包含植被数据的数据框。列是物种名称，行是它们在每个站点的相对丰度。地点，地块代码和年份也是变量。数据如下：
Site Code Year speca specb specc
A A1 2001 0 1 10
A A2 2001 5 5 15
B B1 2001 0 5 20
B B1 2004 15 75 0
C C1 2006 50 0 15
我希望数据表看起来像这样：
species A1_2001 A2_2001 B1_2001 B1_2004 C1_2006
speca 0 5 0 15 50
specb 1 5 5 75 0
specc 10 15 20 0 15
我尝试使用tidyr:pivot_longer函数，但这没有得到我想要的结果。
tidyr::pivot_longer(df,4:length(df),names_to = "species",values_to = "abundance")
有没有一种方法可以以代码友好的方式实现，最好使用tidyr（tidyverse）？
解决方法
我们将其重塑为“长”格式，然后使用<!DOCTYPE html>
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pivot_wider数据
library(dplyr)
library(tidyr)
df %>%
pivot_longer(cols = starts_with('spec'),names_to = 'species') %>%
unite(CodeYear,Code,Year) %>%
select(-Site) %>%
pivot_wider(names_from = CodeYear,values_from = value)
# A tibble: 3 x 6
# species A1_2001 A2_2001 B1_2001 B1_2004 C1_2006
# <chr> <int> <int> <int> <int> <int>
#1 speca 0 5 0 15 50
#2 specb 1 5 5 75 0
#3 specc 10 15 20 0 15
在data.table中：
library(data.table)
DT <- data.table(Site = c('A1','A2','B1','C1'),Year = c(2001,2001,2004,2006),speca = c(0,5,15,50),specb = c(1,75,0),specc = c(10,20,15))
DT <- melt(DT,id.vars = c('Site','Year'),measure.vars = c('speca','specb','specc'),variable.name = 'species')
DT <- dcast(DT,species ~ Site + Year,value.var = c('value'))
> DT
species A1_2001 A2_2001 B1_2001 B1_2004 C1_2006
1: speca 0 5 0 15 50
2: specb 1 5 5 75 0
3: specc 10 15 20 0 15
您主要需要pivot_wider()才能跟随pivot_longer()：
library(tidyverse)
df <- tribble(~Site,~Code,~Year,~speca,~specb,~specc,"A","A1",1,10,"A2","B","B1","C","C1",2006,50,15)
df %>%
mutate(Code = paste(Code,Year,sep = "_")) %>%
select(-Site,-Year) %>%
pivot_longer(starts_with("spec"),names_to = "species",values_to = "abundance") %>%
pivot_wider(names_from = Code,values_from = abundance)
结果是
# A tibble: 3 x 6
species A1_2001 A2_2001 B1_2001 B1_2004 C1_2006
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 speca 0 5 0 15 50
2 specb 1 5 5 75 0
3 specc 10 15 20 0 15
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