總和如果時間差大於按id的值和類型

Question

在這個問題上更小： Sum if the date difference is smaller than a value 現在我必須計算出以前12H感謝@Davis沃恩在發生事件數的可能性：

df <- tribble(
    ~fechayhora,  ~id,  ~tipo, 
    "2017-03-17 08:03:00", "A", "APF", 
    "2017-05-17 10:34:00", "A", "APF", 
    "2017-05-17 12:52:00", "A", "APF", 
    "2017-05-17 08:52:00", "A", "APP", 
    "2017-05-17 10:52:00", "A", "APP", 
    "2017-05-17 10:46:00", "B", "APP", 
    "2017-05-17 14:23:00", "B", "APP", 
    "2017-05-17 17:29:00", "B", "APF" 
) 

df <- df %>% 
    mutate(fechayhora = as.POSIXct(fechayhora), 
     minus_12 = fechayhora - hours(12)) 

df <- df %>% mutate(
    number_of_APF_12h = map2_dbl(.x = fechayhora, 
           .y = minus_12, 
           .f = ~sum(between(df$fechayhora, .y, .x)) - 
1)) 

然後，我試圖做同樣的事情，但通過「id」和「tipo」（類型）進行分組。我tryed與數據表和數據幀，沒有贏得成功：

df=df[,number_of_failures_12h = map2_dbl(.x = fechayhora, 
           .y = minus_12, 
           .f = ~sum(between(df$fechayhora, .y, .x)) - 
1)),by=.(tipo,id)] 

或

df <- df %>% 
group_by(id,tipo) 
%>% mutate(
    number_of_failure = map2_dbl(.x = fechayhora, 
           .y = minus_12, 
           .f = ~sum(between(df$fechayhora, .y, .x)) - 
1)) %>% 
ungroup() 

結果預計：

fechayhora    id tipo  n_APP n_APF 
    "2017-03-17 08:03:00", "A", "APF", 0  0 
    "2017-05-17 10:34:00", "A", "APF", 0  1 
    "2017-05-17 12:52:00", "A", "APF", 0  2 
    "2017-05-17 08:52:00", "A", "APP", 0  2 
    "2017-05-17 10:52:00", "A", "APP", 1  2 
    "2017-05-17 10:46:00", "B", "APP", 0  0 
    "2017-05-17 14:23:00", "B", "APP", 1  0 
    "2017-05-17 17:29:00", "B", "APF"  0  0 

謝謝!!

Answer 1

必須有一個分類器的方式，但是這應該這樣做：

# Auxiliary function 
count_failures <- function(group, last_12, rowid, type) { 
    group[1:rowid-1, ] %>% 
    filter(tipo %in% type & fechayhora >= last_12) %>% 
    nrow() 
} 

split_by_group <- df %>% 
    group_by(id) %>% 
    do(data = (.)) %>% 
    select(data) %>% 
    map(identity) %>% 
    .[[1]] 

df_s <- split_by_group %>% 
    map(arrange, fechayhora) %>% 
    map(.f = function(x) { 
    x %>% 
     rowid_to_column() %>% 
     rowwise() %>% 
     mutate(n_APP = count_failures(x, minus_12, rowid, "APP"), 
      n_APF = count_failures(x, minus_12, rowid, "APF")) %>% 
     ungroup() %>% 
     select(-rowid) 
     }) %>% 
    bind_rows() 

輸出：

# A tibble: 8 x 6 
      fechayhora id tipo   minus_12 n_APP n_APF 
       <dttm> <chr> <chr>    <dttm> <int> <int> 
1 2017-03-17 08:03:00  A APF 2017-03-16 20:03:00  0  0 
2 2017-05-17 08:52:00  A APP 2017-05-16 20:52:00  0  0 
3 2017-05-17 10:34:00  A APF 2017-05-16 22:34:00  1  0 
4 2017-05-17 10:52:00  A APP 2017-05-16 22:52:00  1  1 
5 2017-05-17 12:52:00  A APF 2017-05-17 00:52:00  2  1 
6 2017-05-17 10:46:00  B APP 2017-05-16 22:46:00  0  0 
7 2017-05-17 14:23:00  B APP 2017-05-17 02:23:00  1  0 
8 2017-05-17 17:29:00  B APF 2017-05-17 05:29:00  2  0