棘手的条件插补，理想情况下使用Tidyverse
我有一个问题，我需要对缺失的值进行一些棘手的条件插补，同时标记这些插补值，但我不太明白如何处理它 我的数据是整齐（长）的格式。我想做的是生成一个完整的数据集，其中每个“州”都有一组完整的行，其中包含“男性”、“女性”和“总数”的“出生”值。如果某一州缺少“总计”，则该“州”的“总计”由“男性”+“女性”估算。如果我们有“总计”，但没有“男性”或“女性”，则缺少的“出生”值是根据“总计”-“男性”（或“女性”，取决于缺少的值） 但是，只有当该状态的所有当前行的“源”相同时，才能插补缺少的值我们不能基于合并来自不同来源的数据进行插补。最后，所有插补行应具有其父状态和来源，并且二进制“聚合”列应具有“1”标志 reprex在下面，期望的结果示例在下面，并附有快速解释。如果可能的话，我想用Tidyverse来做这件事，但我愿意接受更好的解决方案。提前谢谢你棘手的条件插补，理想情况下使用Tidyverse,r,tidyverse,aggregation,imputation,R,Tidyverse,Aggregation,Imputation,我有一个问题，我需要对缺失的值进行一些棘手的条件插补，同时标记这些插补值，但我不太明白如何处理它 我的数据是整齐（长）的格式。我想做的是生成一个完整的数据集，其中每个“州”都有一组完整的行，其中包含“男性”、“女性”和“总数”的“出生”值。如果某一州缺少“总计”，则该“州”的“总计”由“男性”+“女性”估算。如果我们有“总计”，但没有“男性”或“女性”，则缺少的“出生”值是根据“总计”-“男性”（或“女性”，取决于缺少的值） 但是，只有当该状态的所有当前行的“源”相同时，才能插补缺少的值我们不能
sex <- c("Male", "Female", "Total", "Male", "Female", "Male", "Female", "Male", "Total")
state <- c("New Jersey", "New Jersey", "New Jersey", "Vermont", "Vermont", "Washington", "Washington", "Montana", "Montana")
source <- c("WHO", "WHO", "WHO", "CDC", "CDC", "UN", "CDC", "UN", "UN")
aggregated <- c(0, 0, 0, 0, 0, 0, 0, 0, 0)
births <- c(20, 30, 50, 15, 16, 20, 27, 15, 33)
df <- data.frame(sex, state, source, aggregated, births)
df
sex state source aggregated births
1 Male New Jersey WHO 0 20
2 Female New Jersey WHO 0 30
3 Total New Jersey WHO 0 50
4 Male Vermont CDC 0 15
5 Female Vermont CDC 0 16
6 Male Washington UN 0 20
7 Female Washington CDC 0 27
8 Male Montana UN 0 15
9 Total Montana UN 0 33
更新03 现在我可以休息了 我知道这与亲爱的@akrun提出的两个绝妙解决方案相比算不了什么。但我不能在这里留下一个不产生预期输出的解决方案。因此，我做了一些修改，结果如下：在
出生男性library(dplyr)
library(tidyr)
df %>%
pivot_wider(names_from = sex, values_from = births) %>%
pivot_longer(Male:Total, names_to = "sex", values_to = "births") %>%
group_split(state, source) %>%
map_dfr(~ if(sum(is.na(.x$births)) > 1 ) drop_na(.x) else .x) %>%
group_by(state, source) %>%
mutate(aggregated = ifelse(is.na(births), 1, 0),
births = ifelse(sex == "Female" & is.na(births), births[sex == "Total"] -
births[sex == "Male"],
ifelse(sex == "Total" & is.na(births),
births[sex == "Female"] + births[sex == "Male"],
ifelse(sex == "Male" & is.na(births),
births[sex == "Total"] - births[sex == "Female"],
births)))) %>%
relocate(state, source, sex)
# A tibble: 11 x 5
# Groups: state, source [5]
state source sex aggregated births
<chr> <chr> <chr> <dbl> <dbl>
1 Montana UN Male 0 15
2 Montana UN Female 1 18
3 Montana UN Total 0 33
4 New Jersey WHO Male 0 20
5 New Jersey WHO Female 0 30
6 New Jersey WHO Total 0 50
7 Vermont CDC Male 0 15
8 Vermont CDC Female 0 16
9 Vermont CDC Total 1 31
10 Washington CDC Female 0 27
11 Washington UN Male 0 20
df %>%
group_by(state, source) %>%
complete(sex = unique(df$sex)) %>%
arrange(state, source, factor(sex, levels = c('Male', 'Female', 'Total'))) %>%
filter(sum(is.na(aggregated)) > 1 & !is.na(aggregated)|sum(is.na(aggregated)) <= 1) %>%
mutate(aggregated = replace(aggregated, is.na(aggregated), 1),
births = case_when(is.na(births) & row_number() == n() ~ sum(births, na.rm = TRUE),
is.na(births) ~ last(births) - na.omit(births)[1], TRUE ~ births))
# A tibble: 11 x 5
# Groups: state, source [5]
state source sex aggregated births
<chr> <chr> <chr> <dbl> <dbl>
1 Montana UN Male 0 15
2 Montana UN Female 1 18
3 Montana UN Total 0 33
4 New Jersey WHO Male 0 20
5 New Jersey WHO Female 0 30
6 New Jersey WHO Total 0 50
7 Vermont CDC Male 0 15
8 Vermont CDC Female 0 16
9 Vermont CDC Total 1 31
10 Washington CDC Female 0 27
11 Washington UN Male 0 20
df%>%
分组依据（州、来源）%>%
完成（性别=唯一（df$sex））%>%
排列（状态、来源、因素（性别、等级=c（‘男性’、‘女性’、‘总数’）））%>%
过滤器（总和（is.na（聚合））>1&！is.na（聚合）|总和（is.na（聚合））%
突变（聚合=替换（聚合，为.na（聚合），1），
出生率=案例时（is.na（出生率）&行数（）==n（）~sum（出生率，na.rm=TRUE），
is.na（出生）~最后一次（出生）-na.省略（出生）[1]，TRUE~出生）
#A tibble:11 x 5
#分组：国家，来源[5]
国家来源性别合计出生数
1蒙大拿州联合国男0 15
2蒙大拿州联合国女1 18
3蒙大拿州联合国总计0 33
4新泽西州男子0 20
5新泽西州女性0 30
6新泽西州，共0.50人
7佛蒙特州疾病预防控制中心男性0 15
8佛蒙特州疾病预防控制中心女性0 16
9佛蒙特州疾病预防控制中心总计1 31
10华盛顿疾控中心女性0 27
11华盛顿联合国男0 20
df%>%group_split（state，source）%>%map_-dfr（~if（所有（c（'Male'，'femal'）%in%.x$sex）&&！'Total'%in%.x$sex）{添加行（.x，sex='Total'，state=first（.x$state），source=first（.x$source），aggregated=1，birtions=sum（.x$birtions））}else.x）条件，或者你可以使用附加条件，否则，你可以在后面用以前的非NAIt填充。你可以提到我的名字
library(dplyr)
library(tibble)
df %>%
group_split(state, source) %>%
map_dfr(~ if(all(c('Male', 'Female') %in% .x$sex) && !'Total' %in% .x$sex)
{ add_row(.x, sex = 'Total', state = first(.x$state), source = first(.x$source), aggregated = 1, births = sum(.x$births)) }
else if(all(c('Male', 'Total') %in% .x$sex) && !'Female' %in% .x$sex)
{ add_row(.x, sex = 'Female', state = first(.x$state), source = first(.x$source), aggregated = 1, births = sum(.x$births)) }
else .x)
# A tibble: 11 x 5
sex state source aggregated births
<chr> <chr> <chr> <dbl> <dbl>
1 Male Montana UN 0 15
2 Total Montana UN 0 33
3 Female Montana UN 1 48
4 Male New Jersey WHO 0 20
5 Female New Jersey WHO 0 30
6 Total New Jersey WHO 0 50
7 Male Vermont CDC 0 15
8 Female Vermont CDC 0 16
9 Total Vermont CDC 1 31
10 Female Washington CDC 0 27
11 Male Washington UN 0 20
df %>%
group_by(state, source) %>%
complete(sex = unique(df$sex)) %>%
arrange(state, source, factor(sex, levels = c('Male', 'Female', 'Total'))) %>%
filter(sum(is.na(aggregated)) > 1 & !is.na(aggregated)|sum(is.na(aggregated)) <= 1) %>%
mutate(aggregated = replace(aggregated, is.na(aggregated), 1),
births = case_when(is.na(births) & row_number() == n() ~ sum(births, na.rm = TRUE),
is.na(births) ~ last(births) - na.omit(births)[1], TRUE ~ births))
# A tibble: 11 x 5
# Groups: state, source [5]
state source sex aggregated births
<chr> <chr> <chr> <dbl> <dbl>
1 Montana UN Male 0 15
2 Montana UN Female 1 18
3 Montana UN Total 0 33
4 New Jersey WHO Male 0 20
5 New Jersey WHO Female 0 30
6 New Jersey WHO Total 0 50
7 Vermont CDC Male 0 15
8 Vermont CDC Female 0 16
9 Vermont CDC Total 1 31
10 Washington CDC Female 0 27
11 Washington UN Male 0 20