Question

我是新来的，所以如果我错过任何约定，我会道歉。

我有一个~2000行数据集，其中包含三年内发生的独特案例数据。每个案例都有一个开始日期和结束日期。我希望能够获得一个新的数据框，显示在这三年期间每周发生的案例数量。

我拥有的数据集的结构如下：

ID  Start_Date  End_Date      
1   2015-01-04  2017-11-02    
2   2015-01-05  2015-10-26    
3   2015-01-07  2015-03-04     
4   2015-01-12  2016-05-17  
5   2015-01-15  2015-04-08
6   2015-01-21  2016-07-31 
7   2015-01-21  2015-07-16
8   2015-01-22  2015-03-03

`

Answer 1

欢迎来到SO！

在解决问题之前，一定要安装一些软件包并运行

install.packages(c("tidyr","dplyr","lubridate"))

如果你还没有安装这些软件包。

接下来我将向您展示一个现代化的R解决方案，这些软件包很神奇。

这是一种解决方法：

library(readr)
library(dplyr)
library(lubridate)

raw_data <- 'id start_date  end_date
1   2015-01-04  2017-11-02
2   2015-01-05  2015-10-26
3   2015-01-07  2015-03-04
4   2015-01-12  2016-05-17
5   2015-01-15  2015-04-08
6   2015-01-21  2016-07-31
7   2015-01-21  2015-07-16
8   2015-01-22  2015-03-03'

curated_data <- read_delim(raw_data, delim = "\t") %>% 
  mutate(start_date = as.Date(start_date)) %>% # convert column 2 to date format assuming the date is yyyy-mm-dd
  mutate(weeks_lapse = as.integer((start_date - min(start_date))/dweeks(1))) # count how many weeks passed since the lowest date in the data

curated_data %>% 
  group_by(weeks_lapse) %>%  # I group to count by week
  summarise(cases_per_week = n()) # now count by group by week

解决方案是：

# A tibble: 3 x 2
  weeks_lapse cases_per_week
        <int>          <int>
1           0              3
2           1              2
3           2              3

Answer 2

使用sqldf包可以更轻松地解决这个问题，但我想坚持使用dplyr包。

方法：

library(dplyr)
library(lubridate)

# First create a data frame having all weeks from chosen start date to end date. 
# 2015-01-01 to 2017-12-31
df_week <- data.frame(weekStart = seq(floor_date(as.Date("2015-01-01"), "week"),
                                     as.Date("2017-12-31"), by = 7))
df_week <- df_week %>% 
  mutate(weekEnd = weekStart + 7,
                   weekNum = as.character(weekStart, "%V-%Y"), 
                   dummy = TRUE)
# The dummy column is only for joining purpose. 
# Header looks like
#> head(df_week)
#   weekStart    weekEnd weekNum dummy
#1 2014-12-28 2015-01-04 52-2014  TRUE
#2 2015-01-04 2015-01-11 01-2015  TRUE
#3 2015-01-11 2015-01-18 02-2015  TRUE
#4 2015-01-18 2015-01-25 03-2015  TRUE
#5 2015-01-25 2015-02-01 04-2015  TRUE
#6 2015-02-01 2015-02-08 05-2015  TRUE

# Prepare the data as mentioned in OP
df <- read.table(text = "ID  Start_Date  End_Date      
1   2015-01-04  2017-11-02    
2   2015-01-05  2015-10-26    
3   2015-01-07  2015-03-04     
4   2015-01-12  2016-05-17  
5   2015-01-15  2015-04-08
6   2015-01-21  2016-07-31 
7   2015-01-21  2015-07-16
8   2015-01-22  2015-03-03", header = TRUE, stringsAsFactors = FALSE)

df$Start_Date <- as.Date(df$Start_Date)
df$End_Date <- as.Date(df$End_Date)
df <- df %>% mutate(dummy = TRUE)     # just for joining

# Use dplyr to join, filter and then group on week to find number of cases
# in each week
df_week %>%
  left_join(df, by = "dummy") %>%
  select(-dummy) %>%
  filter((weekStart >= Start_Date & weekStart <= End_Date) | 
           (weekEnd >= Start_Date & weekEnd <= End_Date)) %>%
  group_by(weekStart, weekEnd, weekNum) %>%
  summarise(cases = n())

# Result
#   weekStart  weekEnd    weekNum cases
#   <date>     <date>     <chr>   <int>
# 1 2014-12-28 2015-01-04 52-2014     1
# 2 2015-01-04 2015-01-11 01-2015     3
# 3 2015-01-11 2015-01-18 02-2015     5
# 4 2015-01-18 2015-01-25 03-2015     8
# 5 2015-01-25 2015-02-01 04-2015     8
# 6 2015-02-01 2015-02-08 05-2015     8
# 7 2015-02-08 2015-02-15 06-2015     8
# 8 2015-02-15 2015-02-22 07-2015     8
# 9 2015-02-22 2015-03-01 08-2015     8
#10 2015-03-01 2015-03-08 09-2015     8
# ... with 139 more rows

根据每个案例的开始和结束日期，计算每周存在多少案例

2 个答案: