Question

假设我的数据集如下：

library(tidyverse)

df_raw <- data.frame(id = paste0('id', sample(c(1:13), replace = TRUE)), startTime = as.Date(rbeta(13, 0.7, 10) * 100, origin = "2016-01-01"), Channel = paste0('c', sample(c(1:3), 13, replace = TRUE, prob = c(0.2, 0.12, 0.3))) ) %>%
  group_by(id) %>%
  mutate(totals_transactions = sample(c(0, 1), n(), prob = c(0.9, 0.1), replace = TRUE)) %>%
  ungroup() %>%
  arrange(id, startTime)

现在，我想将相同的ID汇总到一起，并在此新数据框中添加列，指示该ID是否使用了某个频道。我这样做了：

seq_summaries <- df_raw %>%
  group_by(id) %>%
  summarize(
    c1_touches = max(ifelse(Channel == "c1",1,0)),
    c2_touches = max(ifelse(Channel == "c2",1,0)),
    c3_touches = max(ifelse(Channel == "c3",1,0)),
    conversions = sum(totals_transactions)
  ) %>% ungroup()

但是，我正在寻找一种不必为每个频道手动创建列的方式，因为频道数量可能超过三个，这导致了很多工作。

Answer 1

这是一个想法。请注意，您的数据框中没有任何c2。要使用complete功能，您仍需提供c（c1至c3）的完整列表。

library(tidyverse)

df2 <- df_raw %>%
  group_by(id, Channel) %>%
  summarize(
    touches = 1L,
    conversions = as.integer(sum(totals_transactions))
  ) %>% 
  ungroup() %>%
  complete(Channel = paste0("c", 1:3)) %>%
  spread(Channel, touches, fill = 0L) %>%
  drop_na(id) %>%
  select(id, paste0("c", 1:3), conversions)
df2
# # A tibble: 8 x 5
#   id       c1    c2    c3 conversions
#   <fct> <int> <int> <int>       <int>
# 1 id10      1     0     0           0
# 2 id11      0     0     1           0
# 3 id12      0     0     1           1
# 4 id2       0     0     1           0
# 5 id3       0     0     1           0
# 6 id6       1     0     0           0
# 7 id8       1     0     0           1
# 8 id9       0     0     1           0

根据R

1 个答案: