aggregate by date sequences and id variables in R-CodePudding

Im strugling to aggregate hourly temperatures into 3-hourly while keeping the station ID.Here is the df:

ID	Date	temp
1155	2012-01-01 00:00:00	-0.8
1155	2012-01-01 01:00:00	0.1
1155	2012-01-01 02:00:00	0.5

and Im striving to get smth like:

ID	Date	temp
1155	2012-01-01	-0.2

Ive elaborated this code:

library(dplyr)
  Temp_3h<- df %>%
    group_by(ID)%>%
    aggregate(.,by=list(Date=cut(as.POSIXct(df$Date), "3 hour")),mean)

but beside the "temp" variable it also tend to aggregate IDs (categorical), so they become NAs. And I dont know how to integrate ID into "by=" argument. Any help would be appreciated

CodePudding user response：

You may use floor_date/ceiling_date to combine timestamp every 3 hours into one and take average of temp values for each ID.

library(dplyr)
library(lubridate)

Temp_3h <- df %>%
  group_by(ID, Date = floor_date(ymd_hms(Date), '3 hours')) %>%
  summarise(temp = mean(temp, na.rm = TRUE), .groups = 'drop')

Temp_3h

CodePudding user response：

I actually like the cut approach.

d |>
  transform(date_s=cut(as.POSIXct(d$Date), breaks="3 hours")) |>
  with(aggregate(list(mn_temp=temp), list(date=date_s, ID=ID), FUN=mean))
#                  date   ID     mn_temp
# 1 2012-01-01 00:00:00 1155 -0.06666667
# 2 2012-01-01 03:00:00 1155  0.56666667
# 3 2012-01-01 06:00:00 1155  0.93333333
# 4 2012-01-01 09:00:00 1155  3.70000000

If instead of the start time we rather want to display the end of the time interval, we could do

d |>
  transform(date_s=cut(
    as.POSIXct(d$Date), breaks="3 hours", 
    labels=(as.POSIXct(Date)   10800)[(seq(Date) - 1) %% 3 == 0])) |>
  with(aggregate(list(mn_temp_lst3=temp), list(date=date_s, ID=ID), FUN=mean))
#                  date   ID mn_temp_lst3
# 1 2012-01-01 03:00:00 1155  -0.06666667
# 2 2012-01-01 06:00:00 1155   0.56666667
# 3 2012-01-01 09:00:00 1155   0.93333333
# 4 2012-01-01 12:00:00 1155   3.70000000

Data

d <- structure(list(ID = c(1155L, 1155L, 1155L, 1155L, 1155L, 1155L, 
1155L, 1155L, 1155L, 1155L), Date = c("2012-01-01 00:00:00", 
"2012-01-01 01:00:00", "2012-01-01 02:00:00", "2012-01-01 03:00:00", 
"2012-01-01 04:00:00", "2012-01-01 05:00:00", "2012-01-01 06:00:00", 
"2012-01-01 07:00:00", "2012-01-01 08:00:00", "2012-01-01 09:00:00"
), temp = c(-0.8, 0.1, 0.5, 0.6, 0.6, 0.5, 0.7, 0.9, 1.2, 3.7
)), row.names = c(NA, -10L), class = "data.frame")

CodePudding user response：

You could floor the dates and use the group_by and summarize functions:

library(lubridate)
library(dplyr)
library(plyr)
summarise(group_by(df, ID, Date = floor_date(ymd_hms(Date), '3 hours')), first(Date), first(ID), sum(temp))

Output:

  first(Date) first(ID) sum(temp)
1  2012-01-01      1155      -0.2