樣品n個連續的日期從隨機起始日期爲數據幀

Question

考慮下面的數據幀的每個索引：

DF = structure(list(c_number = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L), date = c("2001-01-06", "2001-01-07", "2001-01-08", 
"2001-01-09", "2001-01-10", "2001-01-11", "2001-01-12", "2001-01-13", 
"2001-01-14", "2001-01-15", "2001-01-16", "2001-01-17", "2001-01-18", 
"2001-01-19", "2001-01-20", "2001-01-21", "2001-01-22", "2001-01-23", 
"2001-01-24", "2001-01-25", "2001-01-26", "2001-01-11", "2001-01-12", 
"2001-01-13", "2001-01-14", "2001-01-15", "2001-01-16", "2001-01-17", 
"2001-01-18", "2001-01-19", "2001-01-20", "2001-01-21", "2001-01-22", 
"2001-01-23", "2001-01-24", "2001-01-25", "2001-01-26", "2001-01-27", 
"2001-01-28", "2001-01-12", "2001-01-13", "2001-01-14", "2001-01-15", 
"2001-01-16", "2001-01-17", "2001-01-18", "2001-01-19", "2001-01-20", 
"2001-01-21", "2001-01-22", "2001-01-23", "2001-01-24", "2001-01-25", 
"2001-01-26", "2001-01-27", "2001-01-28", "2001-01-29", "2001-01-30", 
"2001-01-21", "2001-01-22", "2001-01-23", "2001-01-24", "2001-01-25", 
"2001-01-26", "2001-01-27", "2001-01-28", "2001-01-29", "2001-01-30", 
"2001-01-31", "2001-01-24", "2001-01-25", "2001-01-26", "2001-01-27", 
"2001-01-28", "2001-01-29", "2001-01-30", "2001-01-31", "2001-02-01" 
), value = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), .Names = c("c_number", 
"date", "value"), row.names = c(NA, -78L), class = "data.frame") 

我有5客戶連續日期的銷售數據;對於客戶1，我有連續21日的銷售數據....客戶＃5，我已經連續9日的銷售數據...：

> table(DF[, 1]) 

1 2 3 4 5 
21 18 19 11 9 

對每一個客戶我想品嚐子DF （如果該客戶至少有15個連續日期）或該客戶的所有日期（如果我沒有爲該客戶連續15個日期），那麼這個連續15天的日期。

關鍵部分是在情況1（如果我有至少15個連續日期的情況下）那些連續15天應該有一個隨機的開始日期（例如，並非總是客戶的第一個或最後15個日期）避免在分析中引入偏見。

在純R I會做：

library(dplyr) 

slow_function <- function(i, DF, length_out = 15){ 
    sub_DF = DF[DF$c_number == i, ] 
    if(nrow(sub_DF) <= length_out){ 
    out_DF = sub_DF 
    } else { 
    random_start = sample.int(nrow(sub_DF) - length_out, 1) 
    out_DF = sub_DF[random_start:(random_start + length_out - 1), ] 
    } 
} 
a_out = lapply(1:nrow(a_1), slow_function, DF = DF, length_out = 15) 
a_out = dplyr::bind_rows(a_out) 


table(a_out[, 1]) 
1 2 3 4 5 
15 15 15 11 9 

但我的數據大得多，上面不能忍受緩慢的操作。在data.table/dplyr中獲得相同結果的方法有多快嗎？

編輯：生成數據的代碼。

num_customer = 10 
m = 2 * num_customer 
a_0 = seq(as.Date("2001-01-01"), as.Date("2001-12-31"), by = "day") 
a_1 = matrix(sort(sample(as.character(a_0), m)), nc = 2) 
a_2 = list() 
for(i in 1:nrow(a_1)){ 
    a_3 = seq(as.Date(a_1[i, 1]), as.Date(a_1[i, 2]), by = "day") 
    a_4 = data.frame(i, as.character(a_3), round(runif(length(a_3), 1))) 
    colnames(a_4) = c("c_number", "date", "value") 
    a_2[[i]] = a_4 
} 
DF = dplyr::bind_rows(a_2) 
dim(DF) 
table(DF[, 1]) 
dput(DF) 

EDIT2：

在100K客戶DF，克里斯托夫·沃爾克的解決方案是最快的。 接下來是G.GTothendieck的（大約4倍的時間），接下來是 Nathan Werth的（另一個比G格洛騰迪克慢2倍）。 其他解決方案明顯較慢。儘管如此，所有的提案都比我的試探性的'慢'功能更快，所以感謝大家！

Answer 1

試試這個：

sample15consecutive <- function(DF) { 
runs <- rle(DF$c_number)$lengths 
start <- ifelse(runs > 15, sapply(pmax(runs-15, 1), sample.int, size=1), 1) 
end <- ifelse(runs >= 15, 15, runs) 
previous <- cumsum(c(0, head(runs, -1))) 
DF[unlist(mapply(seq, previous + start, previous + start + end - 1), length),] 
} 

這是根據微基準快約4倍。 c_numbers和日期必須進行排序。

Answer 2

這與tidyverse包（即，dplyr和tidyr）非常簡單。

library(tidyverse) 

df.sample <- arrange(DF, date) %>% 
    group_by(c_number) %>% 
    do(head(., 15)) 

輸出（第一個30行/ 2員工）：

# A tibble: 65 x 3 
    c_number  date value 
     <int>  <chr> <dbl> 
1  1 2001-01-06  1 
2  1 2001-01-07  1 
3  1 2001-01-08  1 
4  1 2001-01-09  1 
5  1 2001-01-10  1 
6  1 2001-01-11  1 
7  1 2001-01-12  1 
8  1 2001-01-13  1 
9  1 2001-01-14  1 
10  1 2001-01-15  1 
11  1 2001-01-16  1 
12  1 2001-01-17  1 
13  1 2001-01-18  1 
14  1 2001-01-19  1 
15  1 2001-01-20  1 
16  2 2001-01-11  1 
17  2 2001-01-12  1 
18  2 2001-01-13  1 
19  2 2001-01-14  1 
20  2 2001-01-15  1 
21  2 2001-01-16  1 
22  2 2001-01-17  1 
23  2 2001-01-18  1 
24  2 2001-01-19  1 
25  2 2001-01-20  1 
26  2 2001-01-21  1 
27  2 2001-01-22  1 
28  2 2001-01-23  1 
29  2 2001-01-24  1 
30  2 2001-01-25  1 
# ... with 35 more rows 

編輯：以下選擇每個僱員的隨機開始日期，然後將隨機選擇的點之後，選擇最多連續15天數：

df.sample <- arrange(DF, date) %>% 
    group_by(c_number) %>% 
    mutate(date = as.Date(date), start = sample(date, 1)) %>% 
    filter(date >= start & date <= (start + 14)) 

Answer 3

在基礎R中加速的一種方法可能是在子集化之前只使用索引而不是整個數據框架。

output = DF[unlist(lapply(
      split(1:NROW(DF), DF$c_number), #Split indices along rows of DF 
      function(x){ 
       if(length(x) < 15){   #Grab all indices if there are less than 15 
        x 
       } else{ 
        #Grab an index randomly such that there will be 14 more left after it 
        x[sample(0:(length(x) - 15), 1) + sequence(15)] 
       } 
      })), 
      ] 

sapply(split(output, output$c_number), NROW) 
# 1 2 3 4 5 
#15 15 15 11 9 

Answer 4

samp生成0（樣品）的1的矢量（在樣品）和，我們由該子集。我沒有對它進行基準測試，但它不會將DF分解爲子數據幀，但僅將c_number矢量分開，然後在原始DF上執行一個子集。

samp <- function(x) { 
    n <- length(x) 
    replace(0*x, seq(sample(max(n - 15, 1), 1), length = min(n, 15)), 1) 
} 
s <- subset(DF, ave(c_number, c_number, FUN = samp) == 1) 

Answer 5

試試這個：

library(data.table) 

setDT(DF) 

DF[ 
    , 
    { 
    if (.N <= 15) { 
     # 15 or fewer rows? Grab them all. 
     .SD 
    } else { 
     # Grab a random starting row not too close to the end 
     random_start <- sample(seq_len(.N - 14), size = 1) 
     .SD[random_start + 0:14] 
    } 
    }, 
    by = c_number 
]