如何计算 R 中每天的事件，包括没有事件发生的日期？

Question

我想创建一个数据框，其中第一列中包含特定时间段的所有日期，第二列中包含每个日期发生的事件数，包括未发生事件的日期。我还想统计分配了特定因素的事件

第一个数据框，其中包含给定日期的事件：

Row Sex Age      Date
1    2   36   2004-01-05
2    1   47   2004-01-06
3    1   26   2004-01-10
4    2   23   2004-01-20
5    1   50   2004-01-27
6    2   35   2004-01-28
7    1   35   2004-01-30
8    1   38   2004-02-06
9    2   29   2004-02-11

在“性别”列中，1 表示女性，2 表示男性。

第二个数据框，其中包含检查期间的日期：

行日期

1  2004-01-05
 2  2004-01-06
 3  2004-01-07
 4  2004-01-08
 5  2004-01-09
 6  2004-01-10
 7  2004-01-11
 8  2004-01-12
 9  2004-01-13
10  2004-01-14

我想得到一个如下所示的数据框：

Row    Date       Events (All)   Events (Female)   Events (Male)
 1  2004-01-05         1                0                1
 2  2004-01-06         1                1                0
 3  2004-01-07         0                0                0
 4  2004-01-08         0                0                0
 5  2004-01-09         0                0                0
 6  2004-01-10         0                1                0
 7  2004-01-11         0                0                0
 8  2004-01-12         0                0                0
 9  2004-01-13         0                0                0
10  2004-01-14         0                0                0

谁能帮忙？

Answer 1

这是一种方法：

library(data.table)
library(magrittr) # just for %>%
out <- dat1 %>%
  dcast(Date ~ Sex, data = ., fun.aggregate = length) %>%
  setnames(., c("1", "2"), c("Female", "Male")) %>%
  .[ dat2[ , .(Date)], on = "Date" ] %>%
  .[, lapply(.SD, function(a) replace(a, is.na(a), 0)), ] %>%
  .[, All := Female + Male ]
out
#           Date Female Male All
#  1: 2004-01-05      0    1   1
#  2: 2004-01-06      1    0   1
#  3: 2004-01-07      0    0   0
#  4: 2004-01-08      0    0   0
#  5: 2004-01-09      0    0   0
#  6: 2004-01-10      1    0   1
#  7: 2004-01-11      0    0   0
#  8: 2004-01-12      0    0   0
#  9: 2004-01-13      0    0   0
# 10: 2004-01-14      0    0   0

请注意，使用lapply 可能不是将NA 替换为0 的整体最快方法，但它明白了这一点。另外，我使用magrittr::%>% 只是为了突破步骤，不用%>% 也可以轻松完成。

数据：

dat1 <- fread(text = "
Row Sex Age      Date
1    2   36   2004-01-05
2    1   47   2004-01-06
3    1   26   2004-01-10
4    2   23   2004-01-20
5    1   50   2004-01-27
6    2   35   2004-01-28
7    1   35   2004-01-30
8    1   38   2004-02-06
9    2   29   2004-02-11")

dat2 <- fread(text = "
Row Date
 1  2004-01-05
 2  2004-01-06
 3  2004-01-07
 4  2004-01-08
 5  2004-01-09
 6  2004-01-10
 7  2004-01-11
 8  2004-01-12
 9  2004-01-13
10  2004-01-14")

---

tidyversion：

dat1 <- read.table(header = TRUE, stringsAsFactors = FALSE, text = "
Row Sex Age      Date
1    2   36   2004-01-05
2    1   47   2004-01-06
3    1   26   2004-01-10
4    2   23   2004-01-20
5    1   50   2004-01-27
6    2   35   2004-01-28
7    1   35   2004-01-30
8    1   38   2004-02-06
9    2   29   2004-02-11")

dat2 <- read.table(header = TRUE, stringsAsFactors = FALSE, text = "
Row Date
 1  2004-01-05
 2  2004-01-06
 3  2004-01-07
 4  2004-01-08
 5  2004-01-09
 6  2004-01-10
 7  2004-01-11
 8  2004-01-12
 9  2004-01-13
10  2004-01-14")

library(dplyr)
library(tidyr)
as_tibble(dat1) %>%
  group_by(Date, Sex) %>%
  tally() %>%
  ungroup() %>%
  pivot_wider(id_cols = "Date", names_from = "Sex", values_from = "n",
              values_fill = list(n = 0)) %>%
  rename(Female = "1", Male = "2") %>%
  left_join(select(dat2, Date), ., by = "Date") %>%
  mutate_at(vars(Female, Male), ~ replace(., is.na(.), 0)) %>%
  mutate(All = Female + Male)