如何在 R Dplyr 中解释 NA

Question

下面是软件包列表、示例数据和我正在运行ning 的脚本。下面是模式。您会注意到其中两个值高于 500，因此不符合架构。期望的结果将只考虑那些符合模式的人（雇用少于 500 人）。当我运行在我的较大数据集（不是下面的示例数据集）上执行此操作时，我得到的结果类似于在底部找到的结果。简而言之，我将如何修改脚本以使其忽略大于 500 的条目，因此不会 return NA 的第五行？

library(dplyr)
library(data.table)
library(odbc)
library(DBI)
library(stringr)

firm <- c("firm1","firm2","firm3","firm4","firm5","firm6","firm7","firm8","firm9","firm10","firm11")
employment <- c(1,50,90,249,499,115,145,261,210,874,1140)
small <- c(1,1,1,3,4,2,2,4,3,NA,NA)

smbtest <- data.frame(firm,employment,small)

smbsummary2<-smbtest %>% 
select(employment,small) %>%
group_by(small) %>%
summarise(employment = sum(employment), worksites = n(), 
        .groups = 'drop') %>% 
mutate(employment = cumsum(employment),
     worksites = cumsum(worksites))

smb1     >= 0 and <100
smb2     >= 0 and <150
smb3     >= 0 and <250
smb4     >= 0 and <500

smb      employment   worksites
 1           1000         20
 2           1500         22
 3           2500         25
 4           10000        29
 5           25000        NA

Answer 1

我相信这会有所帮助

firm <- c("firm1","firm2","firm3","firm4","firm5","firm6","firm7","firm8","firm9","firm10","firm11")
employment <- c(1,50,90,249,499,115,145,261,210,874,1140)
small <- c(1,1,1,3,4,2,2,4,3,NA,NA)

smbtest <- data.frame(firm,employment,small)

smbtest %>% 
select(employment,small) %>%
group_by(small) %>%
summarise(employment = sum(employment), worksites = n(), 
        .groups = 'drop') %>% 
 mutate(employment = cumsum(employment),
     worksites = cumsum(worksites)) %>% drop_na() %>% filter(employment < 500)

我刚刚添加了两行语法

"drop_na"
"过滤器（就业 < 500）

如何在 R Dplyr 中解释 NA

How to account for NA's in R Dplyr

r

cumsum

dplyr