我正在嘗試轉換在某個日期有多個產品銷售的資料集。最后，我只想保留包含每天產品銷售額總和的唯一列。
我的 MRE：
df <- data.frame(created = as.Date(c("2020-01-01", "2020-01-01", "2020-01-02", "2020-01-02", "2020-01-03", "2020-01-03"), "%Y-%m-%d", tz = "GMT"),
soldUnits = c(1, 1, 1, 1, 1, 1),
Weekday = c("Mo","Mo","Tu","Tu","Th","Th"),
Sunshinehours = c(7.8,7.8,6.0,6.0,8.0,8.0))
看起來像這樣：
Date soldUnits Weekday Sunshinehours
2020-01-01 1 Mo 7.8
2020-01-01 1 Mo 7.8
2020-01-02 1 Tu 6.0
2020-01-02 1 Tu 6.0
2020-01-03 1 We 8.0
2020-01-03 1 We 8.0
轉換后應該是這樣的：
Date soldUnits Weekday Sunshinehours
2020-01-01 2 Mo 7.8
2020-01-02 2 Tu 6.0
2020-01-03 2 We 8.0
我試過aggregate()和group_by，但沒有成功，因為我的資料下降了。
有沒有人有想法，我如何根據我提到的規范轉換和清理我的資料集？
uj5u.com熱心網友回復：
這可以作業：
library(tidyverse)
df %>%
group_by(created) %>%
count(Weekday, Sunshinehours, wt = soldUnits,name = "soldUnits")
#> # A tibble: 3 × 4
#> # Groups: created [3]
#> created Weekday Sunshinehours soldUnits
#> <date> <chr> <dbl> <dbl>
#> 1 2020-01-01 Mo 7.8 2
#> 2 2020-01-02 Tu 6 2
#> 3 2020-01-03 Th 8 2
由reprex 包(v2.0.1)于 2021 年 12 月 4 日創建
uj5u.com熱心網友回復：
可以將不同的函式應用于不同的列（或一組列） collap
library(collapse)
collap(df, ~ created Weekday,
custom = list(fmean = "Sunshinehours", fsum = "soldUnits"))
created soldUnits Weekday Sunshinehours
1 2020-01-01 2 Mo 7.8
2 2020-01-02 2 Tu 6.0
3 2020-01-03 2 Th 8.0
uj5u.com熱心網友回復：
另一種dplyr方法：
df %>%
group_by(created, Weekday, Sunshinehours) %>%
summarise(soldUnits = sum(soldUnits))
created Weekday Sunshinehours soldUnits
<date> <chr> <dbl> <dbl>
1 2020-01-01 Mo 7.8 2
2 2020-01-02 Tu 6 2
3 2020-01-03 Th 8 2
uj5u.com熱心網友回復：
使用base和dplyrR
df1 = aggregate(df["Sunshinehours"], by=df["created"], mean)
df2 = aggregate(df["soldUnits"], by=df["created"], sum)
df3 = inner_join(df1, df2)
#converting `Weekday` to factors
df$Weekday = as.factor(df$Weekday)
df3$Weekday = levels(df$Weekday)
created Sunshinehours soldUnits Weekday
1 2020-01-01 7.8 2 Mo
2 2020-01-02 6.0 2 Th
3 2020-01-03 8.0 2 Tu
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