如何根據另一個變量的值計算 R 中的變量？

Question

我想根據第三個變量的值計算另一個變量在數據框中的變量出現次數。 這是我的數據：

Name Store        Purchase    Date
John CVS          Shampoo     1/1/2001
John CVS          Toothpaste  1/1/2001
John Whole Foods  Kombucha    1/1/2005
John Kroger       Ice Cream   1/1/2002
Jane CVS          Soap        1/1/2001
Jane Whole Foods  Crackers    1/1/2004

對於每次購買，我想要計算指定人之前購買的次數以及之前的購物次數，如下所示：

Name Store        Purchase    Date     Prev_Purchase Prev_trip
John CVS          Shampoo     1/1/2001 0             0
John CVS          Toothpaste  1/1/2001 0             0
John Whole Foods  Kombucha    1/1/2005 3             2          
John Kroger       Ice Cream   1/1/2002 2             1
Jane CVS          Soap        1/1/2001 0             0
Jane Whole Foods  Crackers    1/1/2004 1             1

如果我想要每個人的購買/旅行總數，我會使用 count 或 tapply——有沒有辦法調整這些函數，以便輸出以第三個變量（日期）為條件？

Answer 1

也許您可以使用ave嘗試基本的 R 代碼

transform(df,
  Prev_Purchase = ave(as.numeric(as.Date(Date, "%d/%m/%Y")), Name, FUN = function(x) sapply(x, function(p) sum(p > x))),
  Prev_trip = ave(as.numeric(as.Date(Date, "%d/%m/%Y")), Name, FUN = function(x) sapply(x, function(p) length(unique(x[p > x]))))
)

這使

  Name       Store   Purchase     Date Prev_Purchase Prev_trip
1 John         CVS    Shampoo 1/1/2001             0         0
2 John         CVS Toothpaste 1/1/2001             0         0
3 John Whole Foods   Kombucha 1/1/2005             3         2
4 John      Kroger  Ice Cream 1/1/2002             2         1
5 Jane         CVS       Soap 1/1/2001             0         0
6 Jane Whole Foods   Crackers 1/1/2004             1         1

數據

df <- structure(list(Name = c("John", "John", "John", "John", "Jane", 
"Jane"), Store = c("CVS", "CVS", "Whole Foods", "Kroger", "CVS",
"Whole Foods"), Purchase = c("Shampoo", "Toothpaste", "Kombucha",
"Ice Cream", "Soap", "Crackers"), Date = c("1/1/2001", "1/1/2001", 
"1/1/2005", "1/1/2002", "1/1/2001", "1/1/2004")), class = "data.frame", row.names = c(NA,       
-6L))

Answer 2

我認為它應該可以解決您的問題。 如果您的數據很大，最好優化此代碼塊。

# load environment
library(lubridate)
# base function
AddInfo = function(name, date, df) {
  prev_purchase = sum(df$Name == name & df$Date < date)
  prev_trip = length(unique(filter(df, Name == name & Date < date)$Date))
  data = data.frame(
    Prev_purchase = prev_purchase,
    Prev_trip = prev_trip
  )
  return(data)
}
# define data frame
df = data.frame(
  Name = c(rep('John', 4), rep('Jane', 2)),
  Store = c('CVS', 'CVS', 'Whole Foods', 'Kroger', 'CVS', 'Whole Foods'),
  Purchase = c('Shampoo', 'Toothpaste', 'Kombucha', 'Ice Cream', 'Soap', 'Crackers'),
  Date = c('1/1/2001', '1/1/2001', '1/1/2005', '1/1/2002', '1/1/2001', '1/1/2004')
)
# transform date to POSIXct
df$Date = dmy(df$Date)
# apply function and bind the results
cols = mapply(AddInfo, df$Name, df$Date, MoreArgs = list(df), SIMPLIFY = FALSE)
cols = bind_rows(cols)
df = cbind(df, cols)

這是輸出：

  Name       Store   Purchase     Date Prev_purchase Prev_trip
1 John         CVS    Shampoo 1/1/2001             0         0
2 John         CVS Toothpaste 1/1/2001             0         0
3 John Whole Foods   Kombucha 1/1/2005             3         2
4 John      Kroger  Ice Cream 1/1/2002             2         1
5 Jane         CVS       Soap 1/1/2001             0         0
6 Jane Whole Foods   Crackers 1/1/2004             1         1

Answer 3

我們也可以使用outer

library(dplyr)
library(lubridate)
df %>% 
   mutate(Date = dmy(Date)) %>%
   group_by(Name) %>% 
   mutate(Prev_Purchase = colSums(outer(Date, Date, FUN = "<")), 
    Prev_trip = colSums(outer(unique(Date), Date, FUN = "<")))
# A tibble: 6 x 6
# Groups:   Name [2]
#  Name  Store       Purchase   Date       Prev_Purchase Prev_trip
#  <chr> <chr>       <chr>      <date>             <dbl>     <dbl>
#1 John  CVS         Shampoo    2001-01-01             0         0
#2 John  CVS         Toothpaste 2001-01-01             0         0
#3 John  Whole Foods Kombucha   2005-01-01             3         2
#4 John  Kroger      Ice Cream  2002-01-01             2         1
#5 Jane  CVS         Soap       2001-01-01             0         0
#6 Jane  Whole Foods Crackers   2004-01-01             1         1