R group by date, and summarize the values
using data.table
Test$Datetime <- as.Date(Test$Datetime)
DT<- data.table(Test )
DT[,sum(value),by = Datetime]
Datetime V1
1: 2015-04-27 46.1
2: 2015-04-28 3.0
Use as.Date()
then aggregate()
.
energy$Date <- as.Date(energy$Datetime)
aggregate(energy$value, by=list(energy$Date), sum)
EDIT
Emma made a good point about column names. You can preserve column names in aggregate
by using the following instead:
aggregate(energy["value"], by=energy["Date"], sum)
Using the tidyverse, specifically lubridate and dplyr:
library(lubridate)
library(tidyverse)
set.seed(10)
df <- tibble(Datetime = sample(seq(as.POSIXct("2015-04-27"), as.POSIXct("2015-04-29"), by = "min"), 10),
value = sample(1:100, 10)) %>%
arrange(Datetime)
df
#> # A tibble: 10 x 2
#> Datetime value
#> <dttm> <int>
#> 1 2015-04-27 04:04:00 35
#> 2 2015-04-27 10:48:00 41
#> 3 2015-04-27 13:02:00 25
#> 4 2015-04-27 13:09:00 5
#> 5 2015-04-27 14:43:00 57
#> 6 2015-04-27 20:29:00 12
#> 7 2015-04-27 20:34:00 77
#> 8 2015-04-28 00:22:00 66
#> 9 2015-04-28 05:29:00 37
#> 10 2015-04-28 09:14:00 58
df %>%
mutate(date_col = date(Datetime)) %>%
group_by(date_col) %>%
summarize(value = sum(value))
#> # A tibble: 2 x 2
#> date_col value
#> <date> <int>
#> 1 2015-04-27 252
#> 2 2015-04-28 161
Created on 2018-08-01 by the reprex package (v0.2.0).