How to aggregate/ sum values by time in r - r

Question

There are 2 issues:
I have time data in factor format and I want to change it into date format for later manipulation.
The goal is to sum values of precipitation of the same time unit, eg. precipitation per hour.
I tried to convert the time using as.POSIXct() or as.date() in lubridate but always get NA values after defining the format. This is the code I used:
tt=as.POSIXct(FixUseNew$StartTimestamp, )
df$time <- as.Date(df$time, "%d-%m-%Y")
If I leave out the format and do the following :
tt=as.POSIXct(df$time)
tt
hour(tt)
The date data looks like this now: "0010-07-14 00:38:00 LMT"
I wanted to use aggregate function to sum the precipitation in the same hour interval or day but couldn't do it as I am stuck with the date format.
Just a brain dump. I was going to change the factor date in to character then to date format as following. Please advise if that is a good idea.
df$time <-paste(substr(df$time,6,7),
substr(df$time,9,10),
substr(df$time,1,4),sep="/")
Here is a subset of the data, hope this helps to illustrate the question better:
Id <- c(1,2,3,4)
Time <- c("10/7/2014 12:30:00 am", "10/7/2014 01:00:05 am","10/7/2014 01:30:10 am", "10/7/2014 02:00:15 am")
Precipitation <- c(0.06, 0.02,0,0.25)
cbind(Id, Time, Precipitation)
Thank you so much.
Here is the outcome:
It seems like the order is distorted:
6 1/1/15 0:35 602
7 1/1/15 0:36 582
8 1/1/15 0:37 958
9 1/1/15 0:38 872
10 1/10/14 0:31 500
11 1/10/14 0:32 571
12 1/10/14 0:33 487
13 1/10/14 0:34 220
14 1/10/14 0:35 550
15 1/10/14 0:36 582
16 1/10/14 0:37 524
17 1/10/14 0:38 487
⋮
106 10/10/14 15:16 494
107 10/10/14 7:53 37
108 10/10/14 7:56 24
109 10/10/14 8:01 3
110 10/11/14 0:30 686
111 10/11/14 0:31 592
112 10/11/14 0:32 368
113 10/11/14 0:33 702
114 10/11/14 0:34 540
115 10/11/14 0:35 564

Answer 1

Using dplyr and lubridate packages we can extract the hour from each Time and sum.
library(dplyr)
library(lubridate)
df %>%
mutate(hour = hour(dmy_hms(Time))) %>%
group_by(hour) %>%
summarise(Precipitation = sum(Precipitation, na.rm = TRUE))
For aggregation by date, we can do
df %>%
mutate(day = as.Date(dmy_hms(Time))) %>%
group_by(day) %>%
summarise(Precipitation = sum(Precipitation, na.rm = TRUE))
Using base R, we could do
df$Hour <- format(as.POSIXct(df$Time, format = "%d/%m/%Y %I:%M:%S %p"), "%H")
df$Day <- as.Date(as.POSIXct(df$Time, format = "%d/%m/%Y %I:%M:%S %p"))
#Aggregation by hour
aggregate(Precipitation~Hour, df, sum, na.rm = TRUE)
#Aggregation by date
aggregate(Precipitation~Day, df, sum, na.rm = TRUE)
EDIT
Based on updated data and information, we can do
df <- readxl::read_xlsx("/path/to/file/df (1).xlsx")
hour_df <- df %>%
group_by(hour = hour(Time)) %>%
summarise(Precipitation = sum(Precipitation, na.rm = TRUE))
day_df <- df %>%
mutate(day = as.Date(Time)) %>%
group_by(day) %>%
summarise(Precipitation = sum(Precipitation, na.rm = TRUE))
So hour_df has got hourly sum of values without taking into consideration the date and day_df has got sum of Precipitation for each day.
data
Id <- c(1,2,3,4)
Time <- c("10/7/2014 12:30:00 am", "10/7/2014 01:00:05 am",
"10/7/2014 01:30:10 am", "10/7/2014 02:00:15 am")
Precipitation <- c(0.06, 0.02,0,0.25)
df <- data.frame(Id, Time, Precipitation)