Question

我有一个数据集

dput(Data)
    structure(list(Date = c(20170120L, 20170120L, 20170120L, 20170120L, 
    20170120L, 20170121L, 20170121L, 20170121L, 20170121L, 20170121L, 
    20170121L, 20170122L, 20170122L, 20170122L, 20170122L, 20170122L, 
    20170123L, 20170123L, 20170123L, 20170123L), Card = c(1207330L, 
    1873230L, 1556250L, 1395950L, 1395950L, 1393220L, 1058940L, 1556250L, 
    1395950L, 1395950L, 1058940L, 1207330L, 1058940L, 1700880L, 1395950L, 
    1055360L, 1395950L, 1556250L, 1207330L, 1395950L)), .Names = c("Date", 
    "Card"), class = "data.frame", row.names = c(NA, -20L))

我试图得到这个统计数据

business_date     New Card
20170120             4
20170121             2
20170122             2
20170123             0

在第一天 - 所有独特的新卡(1207330,1873230,1556250,1395950)都将是新卡。在第二天 - 第二天的所有唯一卡片将与第一天进行比较，而不重复的那些卡片将是新卡片( 1393220,1058940)。在第三天 - 需要第一天和第二天(1700880,1055360)不存在的新卡片等等。

Answer 1

在基数R中，这将通过aggregate每个日期的非重复卡片来完成：

aggregate(!duplicated(df$Card), by = list(df$Date), FUN = sum)
#   Group.1 x
#1 20170120 4
#2 20170121 2
#3 20170122 2
#4 20170123 0

或者在dplyr：

library(dplyr)
df %>% 
  mutate(count = !duplicated(Card)) %>% 
  group_by(Date) %>% 
  summarise(n = sum(count))

## A tibble: 4 × 2
#      Date     n
#     <int> <int>
#1 20170120     4
#2 20170121     2
#3 20170122     2
#4 20170123     0

计算列的非重复次数

1 个答案: