如何删除基于特定日期的特定值
How to remove a specific value that is based on a specific date
下面是我的数据框的一部分：
Year Date Location Tag Section domsec atDominantLocation
2017 2017-08-13 a wnd n n 1
2017 2017-08-14 a wnd n n 1
2017 2017-08-15 a wnd n n 1
2017 2017-08-16 a wnd n n 1
2017 2017-08-17 a wnd n n 1
2017 2017-08-18 a wnd n n 1
2017 2017-08-19 a wnd n n 1
2017 2017-08-20 a wnd n n 1
2017 2017-08-21 a wnd b n 0
2017 2017-08-21 a wnd u n 0
2017 2017-08-23 a wnd f n 0
2018 2018-08-21 a wnd f s 0
2018 2018-08-18 a wnd h s 0
2018 2018-08-19 a wnd o s 0
2018 2018-08-15 a wnd s s 1
2018 2018-08-17 a wnd s s 1
2018 2018-08-14 c wnd ss s 0
2018 2018-08-16 a wnd t s 0
2018 2018-08-21 a wnd t s 0
2018 2018-08-13 c wnd ww s 0
2018 2018-08-20 a wnd y s 0
“atDominantLocation”列包含“1”和“0”。我想保留所有的“0”，但只保留最早日期的“1”。因此，每个标签和年份应该只有一个“1”，并且“1”应该是那一年的最早日期。
下面是我想要的输出：
Year Date Location Tag Section domsec atDominantLocation
2017 2017-08-13 a wnd n n 1
2017 2017-08-21 a wnd b n 0
2017 2017-08-21 a wnd u n 0
2017 2017-08-23 a wnd f n 0
2018 2018-08-21 a wnd f s 0
2018 2018-08-18 a wnd h s 0
2018 2018-08-19 a wnd o s 0
2018 2018-08-15 a wnd s s 1
2018 2018-08-14 c wnd ss s 0
2018 2018-08-16 a wnd t s 0
2018 2018-08-21 a wnd t s 0
2018 2018-08-13 c wnd ww s 0
2018 2018-08-20 a wnd y s 0
我试过重复函数和唯一函数都没有成功。感谢您的帮助。
我们可以按 'Year'、'Tag' 分组并在 filter 中创建逻辑表达式以对行进行子集化 - 创建单独的逻辑表达式并将它们与 | 连接（或者）即..因为我们想要所有 0 值 (atDominantLocation == 0) 并且只有 minimum 'Date' 的行，其中 'atDominantLocation' 是 1 (Date == min(Date[atDominantLocation == 1]))
library(dplyr)
df1 %>%
mutate(Date = as.Date(Date)) %>%
group_by(Year, Tag) %>%
filter(atDominantLocation == 0|
( Date == min(Date[atDominantLocation == 1]))) %>%
ungroup
-输出
# A tibble: 13 × 7
Year Date Location Tag Section domsec atDominantLocation
<int> <date> <chr> <chr> <chr> <chr> <int>
1 2017 2017-08-13 a wnd n n 1
2 2017 2017-08-21 a wnd b n 0
3 2017 2017-08-21 a wnd u n 0
4 2017 2017-08-23 a wnd f n 0
5 2018 2018-08-21 a wnd f s 0
6 2018 2018-08-18 a wnd h s 0
7 2018 2018-08-19 a wnd o s 0
8 2018 2018-08-15 a wnd s s 1
9 2018 2018-08-14 c wnd ss s 0
10 2018 2018-08-16 a wnd t s 0
11 2018 2018-08-21 a wnd t s 0
12 2018 2018-08-13 c wnd ww s 0
13 2018 2018-08-20 a wnd y s 0
数据
df1 <- structure(list(Year = c(2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L), Date = c("2017-08-13",
"2017-08-14", "2017-08-15", "2017-08-16", "2017-08-17", "2017-08-18",
"2017-08-19", "2017-08-20", "2017-08-21", "2017-08-21", "2017-08-23",
"2018-08-21", "2018-08-18", "2018-08-19", "2018-08-15", "2018-08-17",
"2018-08-14", "2018-08-16", "2018-08-21", "2018-08-13", "2018-08-20"
), Location = c("a", "a", "a", "a", "a", "a", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "c", "a", "a", "c", "a"),
Tag = c("wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd"), Section = c("n", "n",
"n", "n", "n", "n", "n", "n", "b", "u", "f", "f", "h", "o",
"s", "s", "ss", "t", "t", "ww", "y"), domsec = c("n", "n",
"n", "n", "n", "n", "n", "n", "n", "n", "n", "s", "s", "s",
"s", "s", "s", "s", "s", "s", "s"), atDominantLocation = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-21L))
另一个 tidyverse 选项是 filter atDominantLocation 到 1。然后，按 Year 和 Tag 排序和分组，然后使用 slice 获取最早日期的行。然后，我们可以将这些行绑定回原始数据框，但只绑定到 atDominantLocation 为 0.
的行
library(tidyverse)
df %>%
filter(atDominantLocation == 1) %>%
arrange(Date) %>%
group_by(Year, Tag) %>%
slice(1) %>%
bind_rows(df %>% filter(atDominantLocation == 0)) %>%
arrange(Date)
输出
Year Date Location Tag Section domsec atDominantLocation
<int> <chr> <chr> <chr> <chr> <chr> <int>
1 2017 2017-08-13 a wnd n n 1
2 2017 2017-08-21 a wnd b n 0
3 2017 2017-08-21 a wnd u n 0
4 2017 2017-08-23 a wnd f n 0
5 2018 2018-08-13 c wnd ww s 0
6 2018 2018-08-14 c wnd ss s 0
7 2018 2018-08-15 a wnd s s 1
8 2018 2018-08-16 a wnd t s 0
9 2018 2018-08-18 a wnd h s 0
10 2018 2018-08-19 a wnd o s 0
11 2018 2018-08-20 a wnd y s 0
12 2018 2018-08-21 a wnd f s 0
13 2018 2018-08-21 a wnd t s 0
数据
df <- structure(list(Year = c(2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L),
Date = c("2017-08-13", "2017-08-14", "2017-08-15", "2017-08-16", "2017-08-17", "2017-08-18",
"2017-08-19", "2017-08-20", "2017-08-21", "2017-08-21", "2017-08-23",
"2018-08-21", "2018-08-18", "2018-08-19", "2018-08-15", "2018-08-17",
"2018-08-14", "2018-08-16", "2018-08-21", "2018-08-13", "2018-08-20"
), Location = c("a", "a", "a", "a", "a", "a", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "c", "a", "a", "c", "a"),
Tag = c("wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd"),
Section = c("n", "n", "n", "n", "n", "n", "n", "n", "b", "u", "f", "f", "h", "o",
"s", "s", "ss", "t", "t", "ww", "y"),
domsec = c("n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "s", "s", "s",
"s", "s", "s", "s", "s", "s", "s"),
atDominantLocation = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA, -21L))
library(data.table)
library(magrittr)
setDT(df)
rbindlist(
list(df[, .SD[atDominantLocation == 1][1], by = list(Year, Tag)],
df[atDominantLocation == 0,]), use.names=TRUE) %>%
.[order(Tag, Date)]
#> Year Tag Date Location Section domsec atDominantLocation
#> 1: 2017 wnd 2017-08-13 a n n 1
#> 2: 2017 wnd 2017-08-21 a b n 0
#> 3: 2017 wnd 2017-08-21 a u n 0
#> 4: 2017 wnd 2017-08-23 a f n 0
#> 5: 2018 wnd 2018-08-13 c ww s 0
#> 6: 2018 wnd 2018-08-14 c ss s 0
#> 7: 2018 wnd 2018-08-15 a s s 1
#> 8: 2018 wnd 2018-08-16 a t s 0
#> 9: 2018 wnd 2018-08-18 a h s 0
#> 10: 2018 wnd 2018-08-19 a o s 0
#> 11: 2018 wnd 2018-08-20 a y s 0
#> 12: 2018 wnd 2018-08-21 a f s 0
#> 13: 2018 wnd 2018-08-21 a t s 0
由 reprex package (v2.0.1)
于 2022-02-09 创建
下面是我的数据框的一部分：
Year Date Location Tag Section domsec atDominantLocation
2017 2017-08-13 a wnd n n 1
2017 2017-08-14 a wnd n n 1
2017 2017-08-15 a wnd n n 1
2017 2017-08-16 a wnd n n 1
2017 2017-08-17 a wnd n n 1
2017 2017-08-18 a wnd n n 1
2017 2017-08-19 a wnd n n 1
2017 2017-08-20 a wnd n n 1
2017 2017-08-21 a wnd b n 0
2017 2017-08-21 a wnd u n 0
2017 2017-08-23 a wnd f n 0
2018 2018-08-21 a wnd f s 0
2018 2018-08-18 a wnd h s 0
2018 2018-08-19 a wnd o s 0
2018 2018-08-15 a wnd s s 1
2018 2018-08-17 a wnd s s 1
2018 2018-08-14 c wnd ss s 0
2018 2018-08-16 a wnd t s 0
2018 2018-08-21 a wnd t s 0
2018 2018-08-13 c wnd ww s 0
2018 2018-08-20 a wnd y s 0
“atDominantLocation”列包含“1”和“0”。我想保留所有的“0”，但只保留最早日期的“1”。因此，每个标签和年份应该只有一个“1”，并且“1”应该是那一年的最早日期。
下面是我想要的输出：
Year Date Location Tag Section domsec atDominantLocation
2017 2017-08-13 a wnd n n 1
2017 2017-08-21 a wnd b n 0
2017 2017-08-21 a wnd u n 0
2017 2017-08-23 a wnd f n 0
2018 2018-08-21 a wnd f s 0
2018 2018-08-18 a wnd h s 0
2018 2018-08-19 a wnd o s 0
2018 2018-08-15 a wnd s s 1
2018 2018-08-14 c wnd ss s 0
2018 2018-08-16 a wnd t s 0
2018 2018-08-21 a wnd t s 0
2018 2018-08-13 c wnd ww s 0
2018 2018-08-20 a wnd y s 0
我试过重复函数和唯一函数都没有成功。感谢您的帮助。
我们可以按 'Year'、'Tag' 分组并在 filter 中创建逻辑表达式以对行进行子集化 - 创建单独的逻辑表达式并将它们与 | 连接（或者）即..因为我们想要所有 0 值 (atDominantLocation == 0) 并且只有 minimum 'Date' 的行，其中 'atDominantLocation' 是 1 (Date == min(Date[atDominantLocation == 1]))
library(dplyr)
df1 %>%
mutate(Date = as.Date(Date)) %>%
group_by(Year, Tag) %>%
filter(atDominantLocation == 0|
( Date == min(Date[atDominantLocation == 1]))) %>%
ungroup
-输出
# A tibble: 13 × 7
Year Date Location Tag Section domsec atDominantLocation
<int> <date> <chr> <chr> <chr> <chr> <int>
1 2017 2017-08-13 a wnd n n 1
2 2017 2017-08-21 a wnd b n 0
3 2017 2017-08-21 a wnd u n 0
4 2017 2017-08-23 a wnd f n 0
5 2018 2018-08-21 a wnd f s 0
6 2018 2018-08-18 a wnd h s 0
7 2018 2018-08-19 a wnd o s 0
8 2018 2018-08-15 a wnd s s 1
9 2018 2018-08-14 c wnd ss s 0
10 2018 2018-08-16 a wnd t s 0
11 2018 2018-08-21 a wnd t s 0
12 2018 2018-08-13 c wnd ww s 0
13 2018 2018-08-20 a wnd y s 0
数据
df1 <- structure(list(Year = c(2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L), Date = c("2017-08-13",
"2017-08-14", "2017-08-15", "2017-08-16", "2017-08-17", "2017-08-18",
"2017-08-19", "2017-08-20", "2017-08-21", "2017-08-21", "2017-08-23",
"2018-08-21", "2018-08-18", "2018-08-19", "2018-08-15", "2018-08-17",
"2018-08-14", "2018-08-16", "2018-08-21", "2018-08-13", "2018-08-20"
), Location = c("a", "a", "a", "a", "a", "a", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "c", "a", "a", "c", "a"),
Tag = c("wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd"), Section = c("n", "n",
"n", "n", "n", "n", "n", "n", "b", "u", "f", "f", "h", "o",
"s", "s", "ss", "t", "t", "ww", "y"), domsec = c("n", "n",
"n", "n", "n", "n", "n", "n", "n", "n", "n", "s", "s", "s",
"s", "s", "s", "s", "s", "s", "s"), atDominantLocation = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-21L))
另一个 tidyverse 选项是 filter atDominantLocation 到 1。然后，按 Year 和 Tag 排序和分组，然后使用 slice 获取最早日期的行。然后，我们可以将这些行绑定回原始数据框，但只绑定到 atDominantLocation 为 0.
library(tidyverse)
df %>%
filter(atDominantLocation == 1) %>%
arrange(Date) %>%
group_by(Year, Tag) %>%
slice(1) %>%
bind_rows(df %>% filter(atDominantLocation == 0)) %>%
arrange(Date)
输出
Year Date Location Tag Section domsec atDominantLocation
<int> <chr> <chr> <chr> <chr> <chr> <int>
1 2017 2017-08-13 a wnd n n 1
2 2017 2017-08-21 a wnd b n 0
3 2017 2017-08-21 a wnd u n 0
4 2017 2017-08-23 a wnd f n 0
5 2018 2018-08-13 c wnd ww s 0
6 2018 2018-08-14 c wnd ss s 0
7 2018 2018-08-15 a wnd s s 1
8 2018 2018-08-16 a wnd t s 0
9 2018 2018-08-18 a wnd h s 0
10 2018 2018-08-19 a wnd o s 0
11 2018 2018-08-20 a wnd y s 0
12 2018 2018-08-21 a wnd f s 0
13 2018 2018-08-21 a wnd t s 0
数据
df <- structure(list(Year = c(2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L),
Date = c("2017-08-13", "2017-08-14", "2017-08-15", "2017-08-16", "2017-08-17", "2017-08-18",
"2017-08-19", "2017-08-20", "2017-08-21", "2017-08-21", "2017-08-23",
"2018-08-21", "2018-08-18", "2018-08-19", "2018-08-15", "2018-08-17",
"2018-08-14", "2018-08-16", "2018-08-21", "2018-08-13", "2018-08-20"
), Location = c("a", "a", "a", "a", "a", "a", "a", "a", "a",
"a", "a", "a", "a", "a", "a", "a", "c", "a", "a", "c", "a"),
Tag = c("wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd", "wnd",
"wnd", "wnd", "wnd", "wnd", "wnd"),
Section = c("n", "n", "n", "n", "n", "n", "n", "n", "b", "u", "f", "f", "h", "o",
"s", "s", "ss", "t", "t", "ww", "y"),
domsec = c("n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "s", "s", "s",
"s", "s", "s", "s", "s", "s", "s"),
atDominantLocation = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA, -21L))
library(data.table)
library(magrittr)
setDT(df)
rbindlist(
list(df[, .SD[atDominantLocation == 1][1], by = list(Year, Tag)],
df[atDominantLocation == 0,]), use.names=TRUE) %>%
.[order(Tag, Date)]
#> Year Tag Date Location Section domsec atDominantLocation
#> 1: 2017 wnd 2017-08-13 a n n 1
#> 2: 2017 wnd 2017-08-21 a b n 0
#> 3: 2017 wnd 2017-08-21 a u n 0
#> 4: 2017 wnd 2017-08-23 a f n 0
#> 5: 2018 wnd 2018-08-13 c ww s 0
#> 6: 2018 wnd 2018-08-14 c ss s 0
#> 7: 2018 wnd 2018-08-15 a s s 1
#> 8: 2018 wnd 2018-08-16 a t s 0
#> 9: 2018 wnd 2018-08-18 a h s 0
#> 10: 2018 wnd 2018-08-19 a o s 0
#> 11: 2018 wnd 2018-08-20 a y s 0
#> 12: 2018 wnd 2018-08-21 a f s 0
#> 13: 2018 wnd 2018-08-21 a t s 0
由 reprex package (v2.0.1)
于 2022-02-09 创建