R dplyr 用第一个非 "missing" 值替换 "missing" 列数据
R dplyr replace "missing" column data with first non-"missing" value
这是一个很难在标题（或 google）中进行简洁描述的问题。我有一个分类法 table，其中某些列可能会根据置信度列为“已删除”。我想用“未识别”替换任何显示“已删除”的列，后跟第一列中的值这并没有以 row-wise 的方式说“掉线”。所以，输入看起来像这样：
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota dropped dropped dropped
#> 2 Eukaryota dropped dropped dropped
#> 3 Eukaryota dropped dropped dropped
#> 4 Eukaryota dropped dropped dropped
#> 5 Eukaryota dropped dropped dropped
#> 6 Eukaryota dropped dropped dropped
#> 7 Eukaryota Hexanauplia Calanoida dropped
#> 8 Eukaryota dropped dropped dropped
#> 9 Eukaryota Dinophyceae Syndiniales dropped
#> 10 Animals Polychaeta Terebellida dropped
#> 11 Eukaryota Acantharia Chaunacanthida dropped
#> 12 Eukaryota dropped dropped dropped
#> 13 Animals Ascidiacea Stolidobranchia dropped
#> 14 Eukaryota Haptophyta dropped dropped
#> 15 Eukaryota dropped dropped dropped
#> 16 Eukaryota dropped dropped dropped
#> 17 Eukaryota dropped dropped dropped
#> 18 Animals Ascidiacea Stolidobranchia dropped
#> 19 Eukaryota dropped dropped dropped
#> 20 Eukaryota dropped dropped dropped
输出应该是这样的：
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 2 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 3 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 4 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 5 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 6 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
#> 8 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
#> 10 Animals Polychaeta Terebellida Unidentified Terebellida
#> 11 Eukaryota Acantharia Chaunacanthida Unidentified Chaunacanth…
#> 12 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 13 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 14 Eukaryota Haptophyta Unidentified Haptop… Unidentified Haptophyta
#> 15 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 16 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 17 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 18 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 19 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 20 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
我使用 purrr::pmap_dfr 想出了一个很好的解决方案，但我很想知道是否有更“纯粹”的 dplyr 方法来做到这一点？我的方法中的一个缺陷是它不适用于第一个非“丢弃”列位于一个或多个“丢弃”列之后的列（请参见下面输出中的第 21 行）。这是我目前的解决方案：
library(tidyverse)
otu_table <- structure(list(domain = c("Eukaryota", "Eukaryota", "Eukaryota",
"Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota",
"Eukaryota", "Animals", "Eukaryota", "Eukaryota", "Animals",
"Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota", "Animals",
"Eukaryota", "Eukaryota", "dropped"), class = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "Hexanauplia", "dropped",
"Dinophyceae", "Polychaeta", "Acantharia", "dropped", "Ascidiacea",
"Haptophyta", "dropped", "dropped", "dropped", "Ascidiacea",
"dropped", "dropped", "not dropped"), order = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "Calanoida", "dropped",
"Syndiniales", "Terebellida", "Chaunacanthida", "dropped", "Stolidobranchia",
"dropped", "dropped", "dropped", "dropped", "Stolidobranchia",
"dropped", "dropped", "dropped"), species = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped")), row.names = c(NA, -21L), class = c("tbl_df", "tbl",
"data.frame"))
tax_data <- otu_table %>%
pmap_dfr(~{
items <- list(...)
first_dropped = match("dropped",items)
if (first_dropped > 1) {
dropped_name <- str_c("Unidentified ",items[first_dropped-1])
} else {
dropped_name <- "Unidentified"
}
items[-c(1:first_dropped-1)] <- dropped_name
items
})
print(tax_data,n=30)
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 2 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 3 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 4 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 5 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 6 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
#> 8 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
#> 10 Animals Polychaeta Terebellida Unidentified Terebellida
#> 11 Eukaryota Acantharia Chaunacanthida Unidentified Chaunacanth…
#> 12 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 13 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 14 Eukaryota Haptophyta Unidentified Haptop… Unidentified Haptophyta
#> 15 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 16 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 17 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 18 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 19 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 20 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 21 dropped not dropped dropped dropped
更新:
下面有一些很好的答案。我接受了投票最多的那个，但事实证明，在 运行 通过 microbenchmark 的所有建议之后，purrr 解决方案的速度几乎快了一个数量级。
这是一种使用 dplyr + tidyr::pivot_longer/wider 的方法。我认为它读起来很干净，但肯定有更简洁的方法。
otu_table %>%
mutate(across(class:species, ~if_else(.x == "dropped", NA_character_, .x))) %>%
mutate(row = row_number()) %>%
pivot_longer(cols = -row) %>%
group_by(row) %>%
mutate(value = if_else(is.na(value) & !is.na(lag(value)), paste("Unidentified", lag(value)), value)) %>%
fill(value) %>%
ungroup() %>%
pivot_wider(names_from = name, values_from = value)
我觉得执行时间还算可以，不过你可以自己试试。我要感谢@IRTFM 关于将 dropped 值更改为 NA 的评论。我实际上使用了这个想法，但我决定在 dplyr 方面犯错而不是 zoo 所以我使用 coalesce 而不是 na.locf 来达到这个目的。
library(dplyr)
library(tidyr)
otu_table %>%
mutate(across(!domain, ~ replace(.x, .x == "dropped", NA))) %>%
rowwise() %>%
mutate(output = list(coalesce(c_across(everything()),
str_c("Unidentified",
last(c_across(everything())[!is.na(c_across(everything()))]), sep = " ")))) %>%
select(output) %>%
unnest_wider(output) %>%
set_names(colnames(otu_table))
# A tibble: 21 x 4
domain class order species
<chr> <chr> <chr> <chr>
1 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
2 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
3 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
4 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
5 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
6 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
8 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
10 Animals Polychaeta Terebellida Unidentified Terebellida
# ... with 11 more rows
这是另一种方法，结合使用 rowwise() 和 across()。
- 我们正在使用
rowwise，因为它有助于通过 cur_data() 将行用作单个向量
across(everything(), ~) 帮助我们一次性改变所有列max.col(cur_data() != 'dropped', ties.method = 'last') 将检索值 != 'dropped'- 我们将其列名存储在一个临时变量中，比如
x
- 最后我们使用来自基数 R 的
if()..else 来仅改变值为 dropped 的那些列
希望答案够清楚
library(tidyverse)
otu_table %>% rowwise() %>%
mutate(across(everything(), ~ {x<- names(cur_data())[max.col(cur_data() != 'dropped', ties.method = 'last')];
if (. == 'dropped') paste0('unidentified ', get(x)) else . }))
#> # A tibble: 21 x 4
#> # Rowwise:
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 2 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 3 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 4 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 5 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 6 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida unidentified Calanoida
#> 8 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales unidentified Syndinial~
#> 10 Animals Polychaeta Terebellida unidentified Terebelli~
#> # ... with 11 more rows
由 reprex package (v2.0.0)
于 2021-06-19 创建
这是一个很难在标题（或 google）中进行简洁描述的问题。我有一个分类法 table，其中某些列可能会根据置信度列为“已删除”。我想用“未识别”替换任何显示“已删除”的列，后跟第一列中的值这并没有以 row-wise 的方式说“掉线”。所以，输入看起来像这样：
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota dropped dropped dropped
#> 2 Eukaryota dropped dropped dropped
#> 3 Eukaryota dropped dropped dropped
#> 4 Eukaryota dropped dropped dropped
#> 5 Eukaryota dropped dropped dropped
#> 6 Eukaryota dropped dropped dropped
#> 7 Eukaryota Hexanauplia Calanoida dropped
#> 8 Eukaryota dropped dropped dropped
#> 9 Eukaryota Dinophyceae Syndiniales dropped
#> 10 Animals Polychaeta Terebellida dropped
#> 11 Eukaryota Acantharia Chaunacanthida dropped
#> 12 Eukaryota dropped dropped dropped
#> 13 Animals Ascidiacea Stolidobranchia dropped
#> 14 Eukaryota Haptophyta dropped dropped
#> 15 Eukaryota dropped dropped dropped
#> 16 Eukaryota dropped dropped dropped
#> 17 Eukaryota dropped dropped dropped
#> 18 Animals Ascidiacea Stolidobranchia dropped
#> 19 Eukaryota dropped dropped dropped
#> 20 Eukaryota dropped dropped dropped
输出应该是这样的：
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 2 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 3 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 4 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 5 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 6 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
#> 8 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
#> 10 Animals Polychaeta Terebellida Unidentified Terebellida
#> 11 Eukaryota Acantharia Chaunacanthida Unidentified Chaunacanth…
#> 12 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 13 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 14 Eukaryota Haptophyta Unidentified Haptop… Unidentified Haptophyta
#> 15 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 16 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 17 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 18 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 19 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 20 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
我使用 purrr::pmap_dfr 想出了一个很好的解决方案，但我很想知道是否有更“纯粹”的 dplyr 方法来做到这一点？我的方法中的一个缺陷是它不适用于第一个非“丢弃”列位于一个或多个“丢弃”列之后的列（请参见下面输出中的第 21 行）。这是我目前的解决方案：
library(tidyverse)
otu_table <- structure(list(domain = c("Eukaryota", "Eukaryota", "Eukaryota",
"Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota",
"Eukaryota", "Animals", "Eukaryota", "Eukaryota", "Animals",
"Eukaryota", "Eukaryota", "Eukaryota", "Eukaryota", "Animals",
"Eukaryota", "Eukaryota", "dropped"), class = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "Hexanauplia", "dropped",
"Dinophyceae", "Polychaeta", "Acantharia", "dropped", "Ascidiacea",
"Haptophyta", "dropped", "dropped", "dropped", "Ascidiacea",
"dropped", "dropped", "not dropped"), order = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "Calanoida", "dropped",
"Syndiniales", "Terebellida", "Chaunacanthida", "dropped", "Stolidobranchia",
"dropped", "dropped", "dropped", "dropped", "Stolidobranchia",
"dropped", "dropped", "dropped"), species = c("dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped", "dropped", "dropped", "dropped", "dropped", "dropped",
"dropped")), row.names = c(NA, -21L), class = c("tbl_df", "tbl",
"data.frame"))
tax_data <- otu_table %>%
pmap_dfr(~{
items <- list(...)
first_dropped = match("dropped",items)
if (first_dropped > 1) {
dropped_name <- str_c("Unidentified ",items[first_dropped-1])
} else {
dropped_name <- "Unidentified"
}
items[-c(1:first_dropped-1)] <- dropped_name
items
})
print(tax_data,n=30)
#> # A tibble: 21 x 4
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 2 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 3 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 4 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 5 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 6 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
#> 8 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
#> 10 Animals Polychaeta Terebellida Unidentified Terebellida
#> 11 Eukaryota Acantharia Chaunacanthida Unidentified Chaunacanth…
#> 12 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 13 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 14 Eukaryota Haptophyta Unidentified Haptop… Unidentified Haptophyta
#> 15 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 16 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 17 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 18 Animals Ascidiacea Stolidobranchia Unidentified Stolidobran…
#> 19 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 20 Eukaryota Unidentified Eukary… Unidentified Eukary… Unidentified Eukaryota
#> 21 dropped not dropped dropped dropped
更新:
下面有一些很好的答案。我接受了投票最多的那个，但事实证明，在 运行 通过 microbenchmark 的所有建议之后，purrr 解决方案的速度几乎快了一个数量级。
这是一种使用 dplyr + tidyr::pivot_longer/wider 的方法。我认为它读起来很干净，但肯定有更简洁的方法。
otu_table %>%
mutate(across(class:species, ~if_else(.x == "dropped", NA_character_, .x))) %>%
mutate(row = row_number()) %>%
pivot_longer(cols = -row) %>%
group_by(row) %>%
mutate(value = if_else(is.na(value) & !is.na(lag(value)), paste("Unidentified", lag(value)), value)) %>%
fill(value) %>%
ungroup() %>%
pivot_wider(names_from = name, values_from = value)
我觉得执行时间还算可以，不过你可以自己试试。我要感谢@IRTFM 关于将 dropped 值更改为 NA 的评论。我实际上使用了这个想法，但我决定在 dplyr 方面犯错而不是 zoo 所以我使用 coalesce 而不是 na.locf 来达到这个目的。
library(dplyr)
library(tidyr)
otu_table %>%
mutate(across(!domain, ~ replace(.x, .x == "dropped", NA))) %>%
rowwise() %>%
mutate(output = list(coalesce(c_across(everything()),
str_c("Unidentified",
last(c_across(everything())[!is.na(c_across(everything()))]), sep = " ")))) %>%
select(output) %>%
unnest_wider(output) %>%
set_names(colnames(otu_table))
# A tibble: 21 x 4
domain class order species
<chr> <chr> <chr> <chr>
1 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
2 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
3 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
4 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
5 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
6 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
7 Eukaryota Hexanauplia Calanoida Unidentified Calanoida
8 Eukaryota Unidentified Eukaryota Unidentified Eukaryota Unidentified Eukaryota
9 Eukaryota Dinophyceae Syndiniales Unidentified Syndiniales
10 Animals Polychaeta Terebellida Unidentified Terebellida
# ... with 11 more rows
这是另一种方法，结合使用 rowwise() 和 across()。
- 我们正在使用
rowwise，因为它有助于通过cur_data() 将行用作单个向量
across(everything(), ~)帮助我们一次性改变所有列max.col(cur_data() != 'dropped', ties.method = 'last')将检索值!= 'dropped' 的最后一列索引
- 我们将其列名存储在一个临时变量中，比如
x - 最后我们使用来自基数 R 的
if()..else来仅改变值为dropped 的那些列
希望答案够清楚
library(tidyverse)
otu_table %>% rowwise() %>%
mutate(across(everything(), ~ {x<- names(cur_data())[max.col(cur_data() != 'dropped', ties.method = 'last')];
if (. == 'dropped') paste0('unidentified ', get(x)) else . }))
#> # A tibble: 21 x 4
#> # Rowwise:
#> domain class order species
#> <chr> <chr> <chr> <chr>
#> 1 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 2 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 3 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 4 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 5 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 6 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 7 Eukaryota Hexanauplia Calanoida unidentified Calanoida
#> 8 Eukaryota unidentified Eukaryo~ unidentified Eukaryo~ unidentified Eukaryota
#> 9 Eukaryota Dinophyceae Syndiniales unidentified Syndinial~
#> 10 Animals Polychaeta Terebellida unidentified Terebelli~
#> # ... with 11 more rows
由 reprex package (v2.0.0)
于 2021-06-19 创建