Hi there,
Here is an example with your data where we are grouping on each day. You can extract the month from whole_date
and then group_by
on that and then sum that to get the answer you want (in your example it would not have led to an interesting result). You can also add ItemId
on which to group in addition to the date.
df <- structure(list(ItemId = c("AS1020", "AS1020", "AS1020", "AS1020",
"AS1020", "AS1020"), SalesQty = c(1, 6, 1, 1, 1, 1), CustAccount = c("14013",
"305009", "17184", "EVET", "10486", "10800"), createdDateTime = structure(c(1577979900,
1577984448, 1578296412, 1578311977, 1578321138, 1578406933), tzone = "UTC", class = c("POSIXct",
"POSIXt"))), row.names = c(NA, -6L), class = c("tbl_df", "tbl","data.frame"))
library(tidyverse)
library(lubridate)
df2 <- df %>%
dplyr::mutate(whole_date = lubridate::as_date(df$createdDateTime)) %>%
dplyr::group_by(whole_date) %>%
dplyr::summarise(sum_whole = sum(SalesQty))
df2
#> # A tibble: 3 x 2
#> whole_date sum_whole
#> <date> <dbl>
#> 1 2020-01-02 7
#> 2 2020-01-06 3
#> 3 2020-01-07 1
Created on 2022-02-02 by the reprex package (v2.0.1)