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).