R每天更新的列的总平均值

gk7wooem  于 2022-12-20  发布在  其他
关注(0)|答案(2)|浏览(111)

这是我的数据框。

structure(list(date = structure(c(18993, 18994, 18995, 18996, 
18997, 18998, 18999, 19000, 19001, 19002, 19003, 19004, 19005, 
19006), class = "Date"), sales = c(10, 40, 30, 20, 50, 20, 10, 
20, 10, 30, 60, 10, 10, 50)), class = c("tbl_ts", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -14L), key = structure(list(
    .rows = structure(list(1:14), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), index = structure("date", ordered = TRUE), index2 = "date", interval = structure(list(
    year = 0, quarter = 0, month = 0, week = 0, day = 1, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")))

我正在寻找这个输出:

structure(list(date = structure(c(18993, 18994, 18995, 18996, 
18997, 18998, 18999, 19000, 19001, 19002, 19003, 19004, 19005, 
19006), class = "Date"), sales = c(10, 40, 30, 20, 50, 20, 10, 
20, 10, 30, 60, 10, 10, 50), average_total_sales_at_date = c(10, 
25, 26.66667, 25, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA, 
-14L), key = structure(list(.rows = structure(list(1:14), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), index = structure("date", ordered = TRUE), index2 = "date", interval = structure(list(
    year = 0, quarter = 0, month = 0, week = 0, day = 1, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")), class = c("tbl_ts", "tbl_df", "tbl", 
"data.frame"))

我没有把数字一路往下填,但希望这能说明我的想法。我试图得到当前日期的总平均销售额,每个新日期都需要额外一天的数据来计算总平均销售额。

fcy6dtqo

fcy6dtqo1#

dplyr中使用cummean

data%>%mutate(cummulative_average_sales=cummean(sales))
# A tibble: 14 x 3
   date       sales cummulative_average_sales
   <date>     <dbl>                     <dbl>
 1 2022-01-01    10                      10  
 2 2022-01-02    40                      25  
 3 2022-01-03    30                      26.7
 4 2022-01-04    20                      25  
 5 2022-01-05    50                      30  
 6 2022-01-06    20                      28.3
 7 2022-01-07    10                      25.7
 8 2022-01-08    20                      25  
 9 2022-01-09    10                      23.3
10 2022-01-10    30                      24  
11 2022-01-11    60                      27.3
12 2022-01-12    10                      25.8
13 2022-01-13    10                      24.6
14 2022-01-14    50                      26.4
fcy6dtqo

fcy6dtqo2#

library(tidyverse)
df$avg_tot_sales<-cummean(df$sales)

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