我想根據識別符號計算幾列之間的差異（有關可重現的示例和目標資料框，請參見下面的腳本）。
這個問題在某種程度上類似，但僅針對識別符號對。我想不出如何適應它。我也可以有多個資料框，每個識別符號一個，但我也不知道在這種情況下如何計算多列差異。
下面的代碼允許創建一個示例資料集，并包含我當前使用的代碼。它給了我我想要的東西，我只想知道是否有辦法不說明我想要計算的所有差異（在我的資料集中，我有比樣本資料更多的引數和深度）。
在此先感謝您的幫助！
library(tidyverse)
# sample data
create.dt <- function(t = 0) {
data.frame(parameter = rep(c("temperature","oxygen"), each = 3),
date = rep(c(Sys.Date() t), each = 6),
depth = rep(1:3, times = 2),
value = c(data.frame(x = rnorm(3, 16, 2)) %>%
arrange(-x) %>% pull,
data.frame(x = rnorm(3, 7, 1)) %>%
arrange(-x) %>% pull
))
}
# Multi-site dataset
dt <- rbind(
cbind(site = "A", create.dt(t = c(-3:0))),
cbind(site = "B", create.dt(t = c(-3:0))),
cbind(site = "C", create.dt(t = c(-3:0))),
cbind(site = "D", create.dt(t = c(-3:0))),
cbind(site = "E", create.dt(t = c(-3:0))))
# Reshape the data and compute differences
dt %>% pivot_wider(id_cols = c(site,date), names_from = c(parameter,depth), values_from = value, names_sep = "_") %>%
# do the difference, depth to depth, parameter by parameter
# What I would like is not have to write manually each differences pair
mutate(temperature_1_2 = temperature_1 - temperature_2,
temperature_1_3 = temperature_1 - temperature_3,
temperature_2_3 = temperature_2 - temperature_3,
oxygen_1_2 = oxygen_1 - oxygen_2,
oxygen_1_3 = oxygen_1 - oxygen_3,
oxygen_2_3 = oxygen_2 - oxygen_3)
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library(tidyverse)
library(rlang)
create.dt <- function(t = 0) {
data.frame(parameter = rep(c("temperature","oxygen"), each = 3),
date = rep(c(Sys.Date() t), each = 6),
depth = rep(1:3, times = 2),
value = c(data.frame(x = rnorm(3, 16, 2)) %>%
arrange(-x) %>% pull,
data.frame(x = rnorm(3, 7, 1)) %>%
arrange(-x) %>% pull
))
}
# Multi-site dataset
dt <- rbind(
cbind(site = "A", create.dt(t = c(-3:0))),
cbind(site = "B", create.dt(t = c(-3:0))),
cbind(site = "C", create.dt(t = c(-3:0))),
cbind(site = "D", create.dt(t = c(-3:0))),
cbind(site = "E", create.dt(t = c(-3:0))))
# result
temperature <- str_c("temperature_", 1:3)
oxygen <- str_c("oxygen_", 1:3)
temperature_frml <- combn(temperature, m = 2, FUN = function(x) str_c(x, collapse = " - "))
oxygen_frml <- combn(oxygen, m = 2, FUN = function(x) str_c(x, collapse = " - "))
all_frml <- c(temperature_frml, oxygen_frml)
df_wider <- dt %>% pivot_wider(
id_cols = c(site, date),
names_from = c(parameter, depth),
values_from = value,
names_sep = "_"
)
bind_cols(df_wider,
map_dfc(
.x = all_frml,
.f = ~ transmute(.data = df_wider,!!.x := eval(parse_expr(.x)))
))
#> # A tibble: 20 x 14
#> site date temperature_1 temperature_2 temperature_3 oxygen_1 oxygen_2
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A 2021-12-11 17.6 17.1 12.9 7.34 6.86
#> 2 A 2021-12-12 17.6 17.1 12.9 7.34 6.86
#> 3 A 2021-12-13 17.6 17.1 12.9 7.34 6.86
#> 4 A 2021-12-14 17.6 17.1 12.9 7.34 6.86
#> 5 B 2021-12-11 17.1 15.6 13.7 8.52 7.58
#> 6 B 2021-12-12 17.1 15.6 13.7 8.52 7.58
#> 7 B 2021-12-13 17.1 15.6 13.7 8.52 7.58
#> 8 B 2021-12-14 17.1 15.6 13.7 8.52 7.58
#> 9 C 2021-12-11 17.7 15.5 13.6 7.66 7.31
#> 10 C 2021-12-12 17.7 15.5 13.6 7.66 7.31
#> 11 C 2021-12-13 17.7 15.5 13.6 7.66 7.31
#> 12 C 2021-12-14 17.7 15.5 13.6 7.66 7.31
#> 13 D 2021-12-11 16.5 16.4 14.5 7.50 7.27
#> 14 D 2021-12-12 16.5 16.4 14.5 7.50 7.27
#> 15 D 2021-12-13 16.5 16.4 14.5 7.50 7.27
#> 16 D 2021-12-14 16.5 16.4 14.5 7.50 7.27
#> 17 E 2021-12-11 16.7 16.1 15.7 7.52 7.51
#> 18 E 2021-12-12 16.7 16.1 15.7 7.52 7.51
#> 19 E 2021-12-13 16.7 16.1 15.7 7.52 7.51
#> 20 E 2021-12-14 16.7 16.1 15.7 7.52 7.51
#> # ... with 7 more variables: oxygen_3 <dbl>,
#> # temperature_1 - temperature_2 <dbl>, temperature_1 - temperature_3 <dbl>,
#> # temperature_2 - temperature_3 <dbl>, oxygen_1 - oxygen_2 <dbl>,
#> # oxygen_1 - oxygen_3 <dbl>, oxygen_2 - oxygen_3 <dbl>
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