【发布时间】：2020-07-28 17:37:32
【问题描述】：
我想做一个简单的操作，并为此使用 dplyr mutate 添加一个新列。基本上我有一个包含很多列的 DF，我想选择其中的一些，只是包含 hist_avg、tgt_ 和 monthyl_X_ly 的那些。这应该很简单，添加一个以“fct_”+ metric 开头的新列应该不是问题。但是，正如您在下面看到的那样，它添加了列但名称很奇怪（fct_visits$hist_avg_visits 和 fct_revenue$hist_avg_revenue_lcy）。
另外，不确定，但我尝试使用 mutate + cross 来实现，因为它可以为我节省很多代码行，并且无法弄清楚如何做到这一点。
library(tidyverse)
(example <- tibble(brand = c("Brand A", "Brand A", "Brand A", "Brand A", "Brand A"),
country = c("Country A", "Country A", "Country A", "Country A", "Country A"),
date = c("2020-08-01", "2020-08-02", "2020-08-03", "2020-08-04", "2020-08-05"),
visits = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
visits_ly = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
tgt_visits = c(2491306, 2491306, 2491306, 2491306, 2491306),
hist_avg_visits = c(177185, 175758, 225311, 210871, 197405),
monthly_visits_ly = c(3765612, 3765612, 3765612, 3765612, 3765612),
revenue_lcy = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
revenue_ly = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
tgt_revenue_lcy = c(48872737, 48872737, 48872737, 48872737, 48872737),
hist_avg_revenue_lcy = c(231101, 222236, 276497, 259775, 251167),
monthly_revenue_lcy_ly = c(17838660, 17838660, 17838660, 17838660, 17838660))) %>%
print(width = Inf)
#> # A tibble: 5 x 13
#> brand country date visits visits_ly tgt_visits hist_avg_visits
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Brand A Country A 2020-08-01 NA NA 2491306 177185
#> 2 Brand A Country A 2020-08-02 NA NA 2491306 175758
#> 3 Brand A Country A 2020-08-03 NA NA 2491306 225311
#> 4 Brand A Country A 2020-08-04 NA NA 2491306 210871
#> 5 Brand A Country A 2020-08-05 NA NA 2491306 197405
#> monthly_visits_ly revenue_lcy revenue_ly tgt_revenue_lcy hist_avg_revenue_lcy
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3765612 NA NA 48872737 231101
#> 2 3765612 NA NA 48872737 222236
#> 3 3765612 NA NA 48872737 276497
#> 4 3765612 NA NA 48872737 259775
#> 5 3765612 NA NA 48872737 251167
#> monthly_revenue_lcy_ly
#> <dbl>
#> 1 17838660
#> 2 17838660
#> 3 17838660
#> 4 17838660
#> 5 17838660
first_forecast <- function(dataset, metric) {
avg_metric <- select(dataset, paste0("hist_avg_", metric))
tgt_metric <- select(dataset, paste0("tgt_", metric))
monthly_metric <- select(dataset, paste0("monthly_", metric, "_ly"))
output <- avg_metric * (tgt_metric / monthly_metric)
return(output)
}
example %>%
mutate(fct_visits = first_forecast(., "visits"),
fct_revenue = first_forecast(., "revenue_lcy")) %>%
print(width = Inf)
#> # A tibble: 5 x 15
#> brand country date visits visits_ly tgt_visits hist_avg_visits
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Brand A Country A 2020-08-01 NA NA 2491306 177185
#> 2 Brand A Country A 2020-08-02 NA NA 2491306 175758
#> 3 Brand A Country A 2020-08-03 NA NA 2491306 225311
#> 4 Brand A Country A 2020-08-04 NA NA 2491306 210871
#> 5 Brand A Country A 2020-08-05 NA NA 2491306 197405
#> monthly_visits_ly revenue_lcy revenue_ly tgt_revenue_lcy hist_avg_revenue_lcy
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3765612 NA NA 48872737 231101
#> 2 3765612 NA NA 48872737 222236
#> 3 3765612 NA NA 48872737 276497
#> 4 3765612 NA NA 48872737 259775
#> 5 3765612 NA NA 48872737 251167
#> monthly_revenue_lcy_ly fct_visits$hist_avg_visits
#> <dbl> <dbl>
#> 1 17838660 117225.
#> 2 17838660 116280.
#> 3 17838660 149064.
#> 4 17838660 139511.
#> 5 17838660 130602.
#> fct_revenue$hist_avg_revenue_lcy
#> <dbl>
#> 1 633149.
#> 2 608862.
#> 3 757521.
#> 4 711708.
#> 5 688124.
由reprex package (v0.3.0) 于 2020 年 7 月 28 日创建
【问题讨论】：
-
你正在从你的函数中返回一个 tibble。而不是变异，使用
cbind