R 使用聚合函数以特定方式处理NA

我有一个如下所示的数据框：

Project Week Number
Project1   01  46.0
Project2   01  46.4
Project3   01 105.0
Project1   02  70.0
Project2   02  84.0
Project3   02  34.8
Project1   03  83.0
Project3   03  37.9

编辑：

我想计算每周每个项目的总和

因此，我使用聚合函数：

aggregate(Number ~ Project + Week, data = my.df, sum)

如您所见，第3周的项目2没有价值

使用聚合函数只会将其保留为空。 我想要的是在行中填入0

我试过：

aggregate(Number ~ Project + Week, data = my.df, sum, na.action = 0)

及

但都不管用。 有什么想法吗？

您可以使用xtabs：

我们还可以使用tidyr包中的完整函数在第3周填写Project2的值。然后，我们可以聚合数据

library(tidyr)

my.df2 <- my.df %>% 
  complete(Project, Week, fill = list(Number = 0))

my.df2

# # A tibble: 9 x 3
#    Project  Week Number
#      <chr> <chr>  <dbl>
# 1 Project1    01   46.0
# 2 Project1    02   70.0
# 3 Project1    03   83.0
# 4 Project2    01   46.4
# 5 Project2    02   84.0
# 6 Project2    03    0.0
# 7 Project3    01  105.0
# 8 Project3    02   34.8
# 9 Project3    03   37.9

资料

或者，您可以使用填充为0的tidyr排列

然后使用“聚集”将其恢复为原始形式

aggregate(Number ~ Project + Week, data = my.df, sum) %>% 
  spread(key = Week,value = Number,fill = 0) %>% 
  gather(key = Week, value = Number,`1`,`2`,`3`)

---

您可以在BaseR中实现这一点，它相当于用BaseR翻译的tidyr:：complete代码，请参见@www的答案

df <- merge(
  setNames(expand.grid(unique(df$Project),unique(df$Week)),c("Project","Week")),
  df, all.x=TRUE)
df$Number[is.na(df$Number)] <- 0

---

请使用dputwell显示您的数据，agregation函数不会神奇地创建最初不存在的数据！：-您需要首先明确地创建缺失组合的行，或者将输出与包含所有组合的data.Frame合并

library(tidyr)

my.df2 <- my.df %>% 
  complete(Project, Week, fill = list(Number = 0))

my.df2

# # A tibble: 9 x 3
#    Project  Week Number
#      <chr> <chr>  <dbl>
# 1 Project1    01   46.0
# 2 Project1    02   70.0
# 3 Project1    03   83.0
# 4 Project2    01   46.4
# 5 Project2    02   84.0
# 6 Project2    03    0.0
# 7 Project3    01  105.0
# 8 Project3    02   34.8
# 9 Project3    03   37.9

my.df <- read.table(text = "Project Week Number
Project1   '01'  46.0
                 Project2   01  46.4
                 Project3   01 105.0
                 Project1   02  70.0
                 Project2   02  84.0
                 Project3   02  34.8
                 Project1   03  83.0
                 Project3   03  37.9",
                 header = TRUE, stringsAsFactors = FALSE)

my.df$Week <- paste0("0", my.df$Week)

aggregate(Number ~ Project + Week, data = my.df, sum) %>% 
  spread(key = Week,value = Number,fill = 0)

aggregate(Number ~ Project + Week, data = my.df, sum) %>% 
  spread(key = Week,value = Number,fill = 0) %>% 
  gather(key = Week, value = Number,`1`,`2`,`3`)

df <- merge(
  setNames(expand.grid(unique(df$Project),unique(df$Week)),c("Project","Week")),
  df, all.x=TRUE)
df$Number[is.na(df$Number)] <- 0