R 比较列表并识别个别更改
R Compare Lists and Identify Individual Changes
这是 R 中的一个冰淇淋店数据框，它显示了商店每个月的口味。
df <- data.frame(date = as.Date(c(rep("2022-01-01", 3),
rep("2022-02-01", 3),
rep("2022-03-01", 4))),
flavor = c("Almond", "Apple", "Apricot",
"Almond", "Maple", "Mint",
"Almond", "Maple", "Mint", "Pumpkin"))
#> date flavor
#> 1 2022-01-01 Almond
#> 2 2022-01-01 Apple
#> 3 2022-01-01 Apricot
#> 4 2022-02-01 Almond
#> 5 2022-02-01 Maple
#> 6 2022-02-01 Mint
#> 7 2022-03-01 Almond
#> 8 2022-03-01 Maple
#> 9 2022-03-01 Mint
#> 10 2022-03-01 Pumpkin
我创建了一个脚本来显示任何特定月份添加的冰淇淋口味。您还可以看到已删除的口味，作为列表。请注意，3 月份没有删除任何口味（2022-03-01，flavors.removed 等于 <chr [0]>）。
library(dplyr)
library(tidyr)
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
unnest_longer(flavors.added)
#> # A tibble: 6 x 3
#> date flavors.added flavors.removed
#> <date> <chr> <list>
#> 1 2022-01-01 Almond <NULL>
#> 2 2022-01-01 Apple <NULL>
#> 3 2022-01-01 Apricot <NULL>
#> 4 2022-02-01 Maple <chr [2]>
#> 5 2022-02-01 Mint <chr [2]>
#> 6 2022-03-01 Pumpkin <chr [0]>
当我试图通过调用 unnest_longer(flavors.removed) 来捕获有关删除的口味的信息时，我最终无意中过滤掉了 2022-03-01 的所有信息，因为 flavors.removed 列表是空的（<chr [0]>) 对于这个 2022-03-01 时间段。
library(dplyr)
library(tidyr)
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
unnest_longer(flavors.added) %>%
unnest_longer(flavors.removed) %>%
pivot_longer(-date, names_to = "type", values_to = "flavor") %>%
arrange(date, type) %>%
unique()
#> # A tibble: 8 x 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 flavors.added Almond
#> 2 2022-01-01 flavors.added Apple
#> 3 2022-01-01 flavors.added Apricot
#> 4 2022-01-01 flavors.removed NA
#> 5 2022-02-01 flavors.added Maple
#> 6 2022-02-01 flavors.added Mint
#> 7 2022-02-01 flavors.removed Apple
#> 8 2022-02-01 flavors.removed Apricot
是否有更好的方法来逐月单独识别添加和删除的口味？我需要重新获得第九行，如下所示，利用我有缺陷的方法将其过滤掉。
#> # A tibble: 9 x 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 flavors.added Almond
#> 2 2022-01-01 flavors.added Apple
#> 3 2022-01-01 flavors.added Apricot
#> 4 2022-01-01 flavors.removed NA
#> 5 2022-02-01 flavors.added Maple
#> 6 2022-02-01 flavors.added Mint
#> 7 2022-02-01 flavors.removed Apple
#> 8 2022-02-01 flavors.removed Apricot
#> 9 2022-03-01 flavors.added Pumpkin
如果您不需要第 4 行的 NA，可能的解决方案：
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
pivot_longer(-date, names_to = "type", values_to = "flavor") %>%
unnest(flavor)
# A tibble: 8 × 3
date type flavor
<date> <chr> <chr>
1 2022-01-01 flavors.added Almond
2 2022-01-01 flavors.added Apple
3 2022-01-01 flavors.added Apricot
4 2022-02-01 flavors.added Maple
5 2022-02-01 flavors.added Mint
6 2022-02-01 flavors.removed Apple
7 2022-02-01 flavors.removed Apricot
8 2022-03-01 flavors.added Pumpkin
我发现单独找到添加和删除的风味更直接，然后在需要时将它们绑定在一起。
在这种情况下，您可以使用 tidyr::unchop(keep_empty = TRUE) 来避免删除空行。
library(tidyverse)
df <- tibble(
date = as.Date(c(
rep("2022-01-01", 3),
rep("2022-02-01", 3),
rep("2022-03-01", 4)
)),
flavor = c(
"Almond", "Apple", "Apricot",
"Almond", "Maple", "Mint",
"Almond", "Maple", "Mint", "Pumpkin"
)
)
df
#> # A tibble: 10 × 2
#> date flavor
#> <date> <chr>
#> 1 2022-01-01 Almond
#> 2 2022-01-01 Apple
#> 3 2022-01-01 Apricot
#> 4 2022-02-01 Almond
#> 5 2022-02-01 Maple
#> 6 2022-02-01 Mint
#> 7 2022-03-01 Almond
#> 8 2022-03-01 Maple
#> 9 2022-03-01 Mint
#> 10 2022-03-01 Pumpkin
flavors <- df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
ungroup()
flavors
#> # A tibble: 3 × 2
#> date flavors
#> <date> <list>
#> 1 2022-01-01 <chr [3]>
#> 2 2022-02-01 <chr [3]>
#> 3 2022-03-01 <chr [4]>
# Find added flavors
added <- flavors %>%
mutate(added = mapply(setdiff, flavors, lag(flavors)), .keep = "unused") %>%
unchop(added, keep_empty = TRUE) %>%
pivot_longer(added, names_to = "type", values_to = "flavor")
# Find removed flavors
removed <- flavors %>%
mutate(removed = mapply(setdiff, lag(flavors), flavors), .keep = "unused") %>%
unchop(removed, keep_empty = TRUE) %>%
pivot_longer(removed, names_to = "type", values_to = "flavor")
added
#> # A tibble: 6 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 added Almond
#> 2 2022-01-01 added Apple
#> 3 2022-01-01 added Apricot
#> 4 2022-02-01 added Maple
#> 5 2022-02-01 added Mint
#> 6 2022-03-01 added Pumpkin
removed
#> # A tibble: 4 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 removed <NA>
#> 2 2022-02-01 removed Apple
#> 3 2022-02-01 removed Apricot
#> 4 2022-03-01 removed <NA>
bind_rows(added, removed) %>%
arrange(date, type)
#> # A tibble: 10 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 added Almond
#> 2 2022-01-01 added Apple
#> 3 2022-01-01 added Apricot
#> 4 2022-01-01 removed <NA>
#> 5 2022-02-01 added Maple
#> 6 2022-02-01 added Mint
#> 7 2022-02-01 removed Apple
#> 8 2022-02-01 removed Apricot
#> 9 2022-03-01 added Pumpkin
#> 10 2022-03-01 removed <NA>
由 reprex package (v2.0.1)
创建于 2022-06-02
这是 R 中的一个冰淇淋店数据框，它显示了商店每个月的口味。
df <- data.frame(date = as.Date(c(rep("2022-01-01", 3),
rep("2022-02-01", 3),
rep("2022-03-01", 4))),
flavor = c("Almond", "Apple", "Apricot",
"Almond", "Maple", "Mint",
"Almond", "Maple", "Mint", "Pumpkin"))
#> date flavor
#> 1 2022-01-01 Almond
#> 2 2022-01-01 Apple
#> 3 2022-01-01 Apricot
#> 4 2022-02-01 Almond
#> 5 2022-02-01 Maple
#> 6 2022-02-01 Mint
#> 7 2022-03-01 Almond
#> 8 2022-03-01 Maple
#> 9 2022-03-01 Mint
#> 10 2022-03-01 Pumpkin
我创建了一个脚本来显示任何特定月份添加的冰淇淋口味。您还可以看到已删除的口味，作为列表。请注意，3 月份没有删除任何口味（2022-03-01，flavors.removed 等于 <chr [0]>）。
library(dplyr)
library(tidyr)
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
unnest_longer(flavors.added)
#> # A tibble: 6 x 3
#> date flavors.added flavors.removed
#> <date> <chr> <list>
#> 1 2022-01-01 Almond <NULL>
#> 2 2022-01-01 Apple <NULL>
#> 3 2022-01-01 Apricot <NULL>
#> 4 2022-02-01 Maple <chr [2]>
#> 5 2022-02-01 Mint <chr [2]>
#> 6 2022-03-01 Pumpkin <chr [0]>
当我试图通过调用 unnest_longer(flavors.removed) 来捕获有关删除的口味的信息时，我最终无意中过滤掉了 2022-03-01 的所有信息，因为 flavors.removed 列表是空的（<chr [0]>) 对于这个 2022-03-01 时间段。
library(dplyr)
library(tidyr)
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
unnest_longer(flavors.added) %>%
unnest_longer(flavors.removed) %>%
pivot_longer(-date, names_to = "type", values_to = "flavor") %>%
arrange(date, type) %>%
unique()
#> # A tibble: 8 x 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 flavors.added Almond
#> 2 2022-01-01 flavors.added Apple
#> 3 2022-01-01 flavors.added Apricot
#> 4 2022-01-01 flavors.removed NA
#> 5 2022-02-01 flavors.added Maple
#> 6 2022-02-01 flavors.added Mint
#> 7 2022-02-01 flavors.removed Apple
#> 8 2022-02-01 flavors.removed Apricot
是否有更好的方法来逐月单独识别添加和删除的口味？我需要重新获得第九行，如下所示，利用我有缺陷的方法将其过滤掉。
#> # A tibble: 9 x 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 flavors.added Almond
#> 2 2022-01-01 flavors.added Apple
#> 3 2022-01-01 flavors.added Apricot
#> 4 2022-01-01 flavors.removed NA
#> 5 2022-02-01 flavors.added Maple
#> 6 2022-02-01 flavors.added Mint
#> 7 2022-02-01 flavors.removed Apple
#> 8 2022-02-01 flavors.removed Apricot
#> 9 2022-03-01 flavors.added Pumpkin
如果您不需要第 4 行的 NA，可能的解决方案：
df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
mutate(flavors.added = mapply(setdiff, flavors, lag(flavors)),
flavors.removed = mapply(setdiff, lag(flavors), flavors)) %>%
ungroup %>%
select(-flavors) %>%
pivot_longer(-date, names_to = "type", values_to = "flavor") %>%
unnest(flavor)
# A tibble: 8 × 3
date type flavor
<date> <chr> <chr>
1 2022-01-01 flavors.added Almond
2 2022-01-01 flavors.added Apple
3 2022-01-01 flavors.added Apricot
4 2022-02-01 flavors.added Maple
5 2022-02-01 flavors.added Mint
6 2022-02-01 flavors.removed Apple
7 2022-02-01 flavors.removed Apricot
8 2022-03-01 flavors.added Pumpkin
我发现单独找到添加和删除的风味更直接，然后在需要时将它们绑定在一起。
在这种情况下，您可以使用 tidyr::unchop(keep_empty = TRUE) 来避免删除空行。
library(tidyverse)
df <- tibble(
date = as.Date(c(
rep("2022-01-01", 3),
rep("2022-02-01", 3),
rep("2022-03-01", 4)
)),
flavor = c(
"Almond", "Apple", "Apricot",
"Almond", "Maple", "Mint",
"Almond", "Maple", "Mint", "Pumpkin"
)
)
df
#> # A tibble: 10 × 2
#> date flavor
#> <date> <chr>
#> 1 2022-01-01 Almond
#> 2 2022-01-01 Apple
#> 3 2022-01-01 Apricot
#> 4 2022-02-01 Almond
#> 5 2022-02-01 Maple
#> 6 2022-02-01 Mint
#> 7 2022-03-01 Almond
#> 8 2022-03-01 Maple
#> 9 2022-03-01 Mint
#> 10 2022-03-01 Pumpkin
flavors <- df %>%
group_by(date) %>%
summarize(flavors = list(flavor)) %>%
ungroup()
flavors
#> # A tibble: 3 × 2
#> date flavors
#> <date> <list>
#> 1 2022-01-01 <chr [3]>
#> 2 2022-02-01 <chr [3]>
#> 3 2022-03-01 <chr [4]>
# Find added flavors
added <- flavors %>%
mutate(added = mapply(setdiff, flavors, lag(flavors)), .keep = "unused") %>%
unchop(added, keep_empty = TRUE) %>%
pivot_longer(added, names_to = "type", values_to = "flavor")
# Find removed flavors
removed <- flavors %>%
mutate(removed = mapply(setdiff, lag(flavors), flavors), .keep = "unused") %>%
unchop(removed, keep_empty = TRUE) %>%
pivot_longer(removed, names_to = "type", values_to = "flavor")
added
#> # A tibble: 6 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 added Almond
#> 2 2022-01-01 added Apple
#> 3 2022-01-01 added Apricot
#> 4 2022-02-01 added Maple
#> 5 2022-02-01 added Mint
#> 6 2022-03-01 added Pumpkin
removed
#> # A tibble: 4 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 removed <NA>
#> 2 2022-02-01 removed Apple
#> 3 2022-02-01 removed Apricot
#> 4 2022-03-01 removed <NA>
bind_rows(added, removed) %>%
arrange(date, type)
#> # A tibble: 10 × 3
#> date type flavor
#> <date> <chr> <chr>
#> 1 2022-01-01 added Almond
#> 2 2022-01-01 added Apple
#> 3 2022-01-01 added Apricot
#> 4 2022-01-01 removed <NA>
#> 5 2022-02-01 added Maple
#> 6 2022-02-01 added Mint
#> 7 2022-02-01 removed Apple
#> 8 2022-02-01 removed Apricot
#> 9 2022-03-01 added Pumpkin
#> 10 2022-03-01 removed <NA>
由 reprex package (v2.0.1)
创建于 2022-06-02