CODEWITHSUNDEEP
r
Ophir
Ophir

Reputation: 1

Calculating specified Hourly Averages in R

I have real-time data on NO2 that was collected with an NO2 sensor. The data has 48-hour NO2 logged per second. NO2 was measured for 48 hours in different homes. There are over 50,000 rows and three columns. Here a snapshot.

Homes   Time      ppb
Home 1  12:00 AM    6
Home 1  12:01 AM    6
Home 1  12:02 AM    7
Home 1  12:03 AM    6
Home 1  12:04 AM    7
Home 1  12:05 AM    9
Home 1  12:06 AM    8
Home 2  12:00 AM    2
Home 2  12:01 AM    3
Home 2  12:02 AM    4
Home 2  12:03 AM    1
Home 2  12:04 AM    6
Home 2  12:05 AM    4
Home 2  12:06 AM    8

I want to create a new dataset which will have hourly (12 am, 1 am, etc) averages of NO2 instead of per second. I want to make 1 am to be the average of 12:01 am - 1:00 am and continue with this trend.

I tried a number of methods, but I'm yet to figure how to do this. The group_by and summarize functions gave me averages of time (e.g. 1 am, 2 am) in the entire dataset, but I want hourly averages according to the home numbers.

I tried using a loop which didn't work: 

Avg = data.frame(Sensor_analysis)    
head(Avg)    
Hourly = rep(0, 48)    
for (i in 1:48) {
   Hourly[i] = mean(Avg$ppb[60*(i-1)+1:60*i])
}
Hourly

Upvotes: 0

Views: 903

Answers (1)

RoyalTS
RoyalTS

Reputation: 10203

Your question is really far, far away from the ideal of a fully reproducible example but try this on for size:

library(dplyr)
library(lubridate)

Sensor_analysis %>%
    mutate(hour = ceiling_date(Time, 'hour')) %>%
    group_by(Homes, hour) %>%
    summarize(hourly = mean(ppb)) -> Hourly

Depending on how your Time variable is stored you may first have to convert it either to POSIXct or to hms for this to work

Upvotes: 1

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