I am quite new to R, and I am trying to find a way to average continuous data into a specific period of time.
My data is a month recording of several parameters with 1s time steps
The table via read.csv has a date and time in one column and several other columns with values.
TimeStamp UTC Pitch Roll Heave(m)
05-02-13 6:45 0 0 0
05-02-13 6:46 0.75 -0.34 0.01
05-02-13 6:47 0.81 -0.32 0
05-02-13 6:48 0.79 -0.37 0
05-02-13 6:49 0.73 -0.08 -0.02
So I want to average the data in specific intervals: 20 min for example in a way that the average for hour 7:00, takes all the points from hour 6:41 to 7:00 and returns the average in this interval and so on for the entire dataset.
The time interval will look like this :
TimeStamp
05-02-13 19:00 462
05-02-13 19:20 332
05-02-13 19:40 15
05-02-13 20:00 10
05-02-13 20:20 42
Here is a reproducible dataset similar to your own.
meteorological <- data.frame(
TimeStamp = rep.int("05-02-13", 1440),
UTC = paste(
rep(formatC(0:23, width = 2, flag = "0"), each = 60),
rep(formatC(0:59, width = 2, flag = "0"), times = 24),
sep = ":"
),
Pitch = runif(1440),
Roll = rnorm(1440),
Heave = rnorm(1440)
)
The first thing that you need to do is to combine the first two columns to create a single (POSIXct) date-time column.
library(lubridate)
meteorological$DateTime <- with(
meteorological,
dmy_hm(paste(TimeStamp, UTC))
)
Then set up a sequence of break points for your different time groupings.
breaks <- seq(ymd("2013-02-05"), ymd("2013-02-06"), "20 mins")
Finally, you can calculate the summary statistics for each group. There are many ways to do this. ddply from the plyr package is a good choice.
library(plyr)
ddply(
meteorological,
.(cut(DateTime, breaks)),
summarise,
MeanPitch = mean(Pitch),
MeanRoll = mean(Roll),
MeanHeave = mean(Heave)
)
Please see if something simple like this works for you:
myseq <- data.frame(time=seq(ISOdate(2014,1,1,12,0,0), ISOdate(2014,1,1,13,0,0), "5 min"))
myseq$cltime <- cut(myseq$time, "20 min", labels = F)
> myseq
time cltime
1 2014-01-01 12:00:00 1
2 2014-01-01 12:05:00 1
3 2014-01-01 12:10:00 1
4 2014-01-01 12:15:00 1
5 2014-01-01 12:20:00 2
6 2014-01-01 12:25:00 2
7 2014-01-01 12:30:00 2
8 2014-01-01 12:35:00 2
9 2014-01-01 12:40:00 3
10 2014-01-01 12:45:00 3
11 2014-01-01 12:50:00 3
12 2014-01-01 12:55:00 3
13 2014-01-01 13:00:00 4
Related
I have a dataset of temperature values taken at specific datetimes across five locations. For whatever reason, sometimes the readings are every hour, and some every four hours. Another issue is that when the time changed as a result of daylight savings, the readings are off by one hour. I am interested in the readings taken every four hours and would like to subset these by day and night to ultimately get daily and nightly mean temperatures.
To summarise, the readings I am interested in are either:
0800, 1200, 1600 =day
2000, 0000, 0400 =night
Recordings between 0800-1600 and 2000-0400 each day should be averaged.
During daylight savings, the equivalent times are:
0900, 1300, 1700 =day
2100, 0100, 0500 =night
Recordings between 0900-1700 and 2100-0500 each day should be averaged.
In the process, I am hoping to subset by site.
There are also some NA values or blank cells which should be ignored.
So far, I tried to subset by one hour of interest just to see if it worked, but haven't got any further than that. Any tips on how to subset by a series of times of interest? Thanks!
temperature <- read.csv("SeaTemperatureData.csv",
stringsAsFactors = FALSE)
temperature <- subset(temperature, select=-c(X)) #remove last column that contains comments, not needed
temperature$Date.Time < -as.POSIXct(temperature$Date.Time,
format="%d/%m/%Y %H:%M",
tz="Pacific/Auckland")
#subset data by time, we only want to include temperatures recorded at certain times
temperature.goat <- subset(temperature, Date.Time==c('01:00:00'), select=c("Goat.Island"))
Date.Time Goat.Island Tawharanui Kawau Tiritiri Noises
1 2019-06-10 16:00:00 16.820 16.892 16.749 16.677 15.819
2 2019-06-10 20:00:00 16.773 16.844 16.582 16.654 15.796
3 2019-06-11 00:00:00 16.749 16.820 16.749 16.606 15.819
4 2019-06-11 04:00:00 16.487 16.796 16.654 16.558 15.796
5 2019-06-11 08:00:00 16.582 16.749 16.487 16.463 15.867
6 2019-06-11 12:00:00 16.630 16.773 16.725 16.654 15.867
One possible solution is to extract hours from your DateTime variable, then filter for particular hours of interest.
Here a fake example over 4 days:
library(lubridate)
df <- data.frame(DateTime = seq(ymd_hms("2020-02-01 00:00:00"), ymd_hms("2020-02-05 00:00:00"), by = "hour"),
Value = sample(1:100,97, replace = TRUE))
DateTime Value
1 2020-02-01 00:00:00 99
2 2020-02-01 01:00:00 51
3 2020-02-01 02:00:00 44
4 2020-02-01 03:00:00 49
5 2020-02-01 04:00:00 60
6 2020-02-01 05:00:00 56
Now, you can extract hours with hour function of lubridate and subset for the desired hour:
library(lubridate)
subset(df, hour(DateTime) == 5)
DateTime Value
6 2020-02-01 05:00:00 56
30 2020-02-02 05:00:00 31
54 2020-02-03 05:00:00 65
78 2020-02-04 05:00:00 80
EDIT: Getting mean of each sites per subset of hours
Per OP's request in comments, the question is to calcualte the mean of values for various sites for different period of times.
Basically, you want to have two period per days, one from 8:00 to 17:00 and the other one from 18:00 to 7:00.
Here, a more elaborated example based on the previous one:
df <- data.frame(DateTime = seq(ymd_hms("2020-02-01 00:00:00"), ymd_hms("2020-02-05 00:00:00"), by = "hour"),
Site1 = sample(1:100,97, replace = TRUE),
Site2 = sample(1:100,97, replace = TRUE))
DateTime Site1 Site2
1 2020-02-01 00:00:00 100 6
2 2020-02-01 01:00:00 9 49
3 2020-02-01 02:00:00 86 12
4 2020-02-01 03:00:00 34 55
5 2020-02-01 04:00:00 76 29
6 2020-02-01 05:00:00 41 1
....
So, now you can do the following to label each time point as daily or night, then group by this category for each day and calculate the mean of each individual sites using summarise_at:
library(lubridate)
library(dplyr)
df %>% mutate(Date = date(DateTime),
Hour= hour(DateTime),
Category = ifelse(between(hour(DateTime),8,17),"Daily","Night")) %>%
group_by(Date, Category) %>%
summarise_at(vars(c(Site1,Site2)), ~ mean(., na.rm = TRUE))
# A tibble: 9 x 4
# Groups: Date [5]
Date Category Site1 Site2
<date> <chr> <dbl> <dbl>
1 2020-02-01 Daily 56.9 63.1
2 2020-02-01 Night 58.9 46.6
3 2020-02-02 Daily 54.5 47.6
4 2020-02-02 Night 36.9 41.7
5 2020-02-03 Daily 42.3 56.9
6 2020-02-03 Night 44.1 55.9
7 2020-02-04 Daily 54.3 50.4
8 2020-02-04 Night 54.8 34.3
9 2020-02-05 Night 75 16
Does it answer your question ?
I have a large dataset over many years which has several variables, but the one I am interested in is wind speed and dateTime. I want to find the time of the max wind speed for every day in the data set. I have hourly data in Posixct format, with WS as a numeric with occasional NAs. Below is a short data set that should hopefully illustrate my point, however my dateTime wasn't working out to be hourly data, but it provides enough for a sample.
dateTime <- seq(as.POSIXct("2011-01-01 00:00:00", tz = "GMT"),
as.POSIXct("2011-01-29 23:00:00", tz = "GMT"),
by = 60*24)
WS <- sample(0:20,1798,rep=TRUE)
WD <- sample(0:390,1798,rep=TRUE)
Temp <- sample(0:40,1798,rep=TRUE)
df <- data.frame(dateTime,WS,WD,Temp)
df$WS[WS>15] <- NA
I have previously tried creating a new column with just a posix date (minus time) to allow for day isolation, however all the things I have tried have only returned a shortened data frame with date and WS (aggregate, splitting, xts). Aggregate was only one that didn't do this, however, it gave me 23:00:00 as a constant time which isn't correct.
I have looked at How to calculate daily means, medians, from weather variables data collected hourly in R?, https://stats.stackexchange.com/questions/7268/how-to-aggregate-by-minute-data-for-a-week-into-hourly-means and others but none have answered this question, or the solutions have not returned an ideal result.
I need to compare the results of this analysis with another data frame, so hence the reason I need the actual time when the max wind speed occurred for each day in the dataset. I have a feeling there is a simple solution, however, this has me frustrated.
A dplyr solution may be:
library(dplyr)
df %>%
mutate(date = as.Date(dateTime)) %>%
left_join(
df %>%
mutate(date = as.Date(dateTime)) %>%
group_by(date) %>%
summarise(max_ws = max(WS, na.rm = TRUE)) %>%
ungroup(),
by = "date"
) %>%
select(-date)
# dateTime WS WD Temp max_ws
# 1 2011-01-01 00:00:00 NA 313 2 15
# 2 2011-01-01 00:24:00 7 376 1 15
# 3 2011-01-01 00:48:00 3 28 28 15
# 4 2011-01-01 01:12:00 15 262 24 15
# 5 2011-01-01 01:36:00 1 149 34 15
# 6 2011-01-01 02:00:00 4 319 33 15
# 7 2011-01-01 02:24:00 15 280 22 15
# 8 2011-01-01 02:48:00 NA 110 23 15
# 9 2011-01-01 03:12:00 12 93 15 15
# 10 2011-01-01 03:36:00 3 5 0 15
Dee asked for: "I want to find the time of the max wind speed for every day in the data set." Other answers have calculated the max(WS) for every day, but not at which hour that occured.
So I propose the following solution with dyplr:
library(dplyr)
set.seed(12345)
dateTime <- seq(as.POSIXct("2011-01-01 00:00:00", tz = "GMT"),
as.POSIXct("2011-01-29 23:00:00", tz = "GMT"),
by = 60*24)
WS <- sample(0:20,1738,rep=TRUE)
WD <- sample(0:390,1738,rep=TRUE)
Temp <- sample(0:40,1738,rep=TRUE)
df <- data.frame(dateTime,WS,WD,Temp)
df$WS[WS>15] <- NA
df %>%
group_by(Date = as.Date(dateTime)) %>%
mutate(Hour = hour(dateTime),
Hour_with_max_ws = Hour[which.max(WS)])
I want to highlight out, that if there are several hours with the same maximal windspeed (in the example below: 15), only the first hour with max(WS) will be shown as result, though the windspeed 15 was reached on that date at the hours 0, 3, 4, 21 and 22! So you might need a more specific logic.
For the sake of completeness (and because I like the concise code) here is a "one-liner" using data.table:
library(data.table)
setDT(df)[, max.ws := max(WS, na.rm = TRUE), by = as.IDate(dateTime)][]
dateTime WS WD Temp max.ws
1: 2011-01-01 00:00:00 NA 293 22 15
2: 2011-01-01 00:24:00 15 55 14 15
3: 2011-01-01 00:48:00 NA 186 24 15
4: 2011-01-01 01:12:00 4 300 22 15
5: 2011-01-01 01:36:00 0 120 36 15
---
1734: 2011-01-29 21:12:00 12 249 5 15
1735: 2011-01-29 21:36:00 9 282 21 15
1736: 2011-01-29 22:00:00 12 238 6 15
1737: 2011-01-29 22:24:00 10 127 21 15
1738: 2011-01-29 22:48:00 13 297 0 15
I have some data which is formatted in the following way:
time count
00:00 17
00:01 62
00:02 41
So I have from 00:00 to 23:59hours and with a counter per minute. I'd like to group the data in intervals of 15 minutes such that:
time count
00:00-00:15 148
00:16-00:30 284
I have tried to do it manually but this is exhausting so I am sure there has to be a function or sth to do it easily but I haven't figured out yet how to do it.
I'd really appreciate some help!!
Thank you very much!
For data that's in POSIXct format, you can use the cut function to create 15-minute groupings, and then aggregate by those groups. The code below shows how to do this in base R and with the dplyr and data.table packages.
First, create some fake data:
set.seed(4984)
dat = data.frame(time=seq(as.POSIXct("2016-05-01"), as.POSIXct("2016-05-01") + 60*99, by=60),
count=sample(1:50, 100, replace=TRUE))
Base R
cut the data into 15 minute groups:
dat$by15 = cut(dat$time, breaks="15 min")
time count by15
1 2016-05-01 00:00:00 22 2016-05-01 00:00:00
2 2016-05-01 00:01:00 11 2016-05-01 00:00:00
3 2016-05-01 00:02:00 31 2016-05-01 00:00:00
...
98 2016-05-01 01:37:00 20 2016-05-01 01:30:00
99 2016-05-01 01:38:00 29 2016-05-01 01:30:00
100 2016-05-01 01:39:00 37 2016-05-01 01:30:00
Now aggregate by the new grouping column, using sum as the aggregation function:
dat.summary = aggregate(count ~ by15, FUN=sum, data=dat)
by15 count
1 2016-05-01 00:00:00 312
2 2016-05-01 00:15:00 395
3 2016-05-01 00:30:00 341
4 2016-05-01 00:45:00 318
5 2016-05-01 01:00:00 349
6 2016-05-01 01:15:00 397
7 2016-05-01 01:30:00 341
dplyr
library(dplyr)
dat.summary = dat %>% group_by(by15=cut(time, "15 min")) %>%
summarise(count=sum(count))
data.table
library(data.table)
dat.summary = setDT(dat)[ , list(count=sum(count)), by=cut(time, "15 min")]
UPDATE: To answer the comment, for this case the end point of each grouping interval is as.POSIXct(as.character(dat$by15)) + 60*15 - 1. In other words, the endpoint of the grouping interval is 15 minutes minus one second from the start of the interval. We add 60*15 - 1 because POSIXct is denominated in seconds. The as.POSIXct(as.character(...)) is because cut returns a factor and this just converts it back to date-time so that we can do math on it.
If you want the end point to the nearest minute before the next interval (instead of the nearest second), you could to as.POSIXct(as.character(dat$by15)) + 60*14.
If you don't know the break interval, for example, because you chose the number of breaks and let R pick the interval, you could find the number of seconds to add by doing max(unique(diff(as.POSIXct(as.character(dat$by15))))) - 1.
The cut approach is handy but slow with large data frames. The following approach is approximately 1,000x faster than the cut approach (tested with 400k records.)
# Function: Truncate (floor) POSIXct to time interval (specified in seconds)
# Author: Stephen McDaniel # PowerTrip Analytics
# Date : 2017MAY
# Copyright: (C) 2017 by Freakalytics, LLC
# License: MIT
floor_datetime <- function(date_var, floor_seconds = 60,
origin = "1970-01-01") { # defaults to minute rounding
if(!is(date_var, "POSIXct")) stop("Please pass in a POSIXct variable")
if(is.na(date_var)) return(as.POSIXct(NA)) else {
return(as.POSIXct(floor(as.numeric(date_var) /
(floor_seconds))*(floor_seconds), origin = origin))
}
}
Sample output:
test <- data.frame(good = as.POSIXct(Sys.time()),
bad1 = as.Date(Sys.time()),
bad2 = as.POSIXct(NA))
test$good_15 <- floor_datetime(test$good, 15 * 60)
test$bad1_15 <- floor_datetime(test$bad1, 15 * 60)
Error in floor_datetime(test$bad, 15 * 60) :
Please pass in a POSIXct variable
test$bad2_15 <- floor_datetime(test$bad2, 15 * 60)
test
good bad1 bad2 good_15 bad2_15
1 2017-05-06 13:55:34.48 2017-05-06 <NA> 2007-05-06 13:45:00 <NA>
You can do it in one line by using trs function from FQOAT, just like:
df_15mins=trs(df, "15 mins")
Below is a repeatable example:
library(foqat)
head(aqi[,c(1,2)])
# Time NO
#1 2017-05-01 01:00:00 0.0376578
#2 2017-05-01 01:01:00 0.0341483
#3 2017-05-01 01:02:00 0.0310285
#4 2017-05-01 01:03:00 0.0357016
#5 2017-05-01 01:04:00 0.0337507
#6 2017-05-01 01:05:00 0.0238120
#mean
aqi_15mins=trs(aqi[,c(1,2)], "15 mins")
head(aqi_15mins)
# Time NO
#1 2017-05-01 01:00:00 0.02736549
#2 2017-05-01 01:15:00 0.03244958
#3 2017-05-01 01:30:00 0.03743626
#4 2017-05-01 01:45:00 0.02769419
#5 2017-05-01 02:00:00 0.02901817
#6 2017-05-01 02:15:00 0.03439455
I have flow data for a year. I want to get the 2 hourly averages of the data and make a timeseries that records the average flow for the two hours along with the timestamp.
The data look like this:
2005-01-01 00:00:00 18
2005-01-01 00:15:00 18
2005-01-01 00:30:00 18
2005-01-01 00:45:00 18
2005-01-01 01:00:00 18
2005-01-01 01:15:00 18
2005-01-01 01:30:00 18
2005-01-01 01:45:00 19
So at the end I would like something that looks like:
2005-01-01 00:00:00 18.125
This is what I'm doing right now:
for (i in seq(1,length(streamflow),8)){
streamflow2hr[i] <- mean(streamflow[i:i+7])
}
valid2hr <- complete.cases(streamflow2hr)
validIndex <- which(valid2hr,arr.ind = TRUE)
streamflow2hrvalid <- streamflow2hr[validIndex]
streamflow2hrvalidTime <- streamflowDateTime[validIndex]
data2hr <- data.frame(streamflow2hrvalidTime,streamflow2hrvalid)
names(data2hr) <- c("DateTime","Flow")
But since I'm using relative positions it isn't consistent with the 2 hourly timestamp!
You can adjust this code for your needs:
# Generate a sample dataset
set.seed(1)
z <- as.POSIXct("2015-01-31 13:00:00") + 900*0:23
d <- data.frame(t=z,v=sample(length(z)))
d$cut <- cut(d$t,breaks="2 hours")
aggregate(v~cut,d,mean)
# cut v
#1 2015-01-31 13:00:00 12.875
#2 2015-01-31 15:00:00 12.125
#3 2015-01-31 17:00:00 12.500
This solution doesn't rely on 15-minute intervals between timestamps. Instead, it divides the time range into 2-hour intervals and uses them to calculate per-interval means.
I am quite new to R and have been struggling with trying to convert my data and could use some much needed help.
I have a dataframe which is approx. 70,000*2. This data covers a whole year (52 weeks/365 days). A portion of it looks like this:
Create.Date.Time Ticket.ID
1 2013-06-01 12:59:00 INCIDENT684790
2 2013-06-02 07:56:00 SERVICE684793
3 2013-06-02 09:39:00 SERVICE684794
4 2013-06-02 14:14:00 SERVICE684796
5 2013-06-02 17:20:00 SERVICE684797
6 2013-06-03 07:20:00 SERVICE684799
7 2013-06-03 08:02:00 SERVICE684839
8 2013-06-03 08:04:00 SERVICE684841
9 2013-06-03 08:04:00 SERVICE684842
10 2013-06-03 08:08:00 SERVICE684843
I am trying to get the number of tickets in every hour of the week (that is, hour 1 to hour 168) for each week. Hour 1 would start on Monday at 00.00, and hour 168 would be Sunday 23.00-23.59. This would be repeated for each week. I want to use the Create.Date.Time data to calculate the hour of the week the ticket is in, say for:
2013-06-01 12:59:00 INCIDENT684790 - hour 133,
2013-06-03 08:08:00 SERVICE684843 - hour 9
I am then going to do averages for each hour and plot those. I am completely at a loss as to where to start. Could someone please point me to the right direction?
Before addressing the plotting aspect of your question, is this the format of data you are trying to get? This uses the package lubridate which you might have to install (install.packages("lubridate",dependencies=TRUE)).
library(lubridate)
##
Events <- paste(
sample(c("INCIDENT","SERVICE"),20000,replace=TRUE),
sample(600000:900000,20000)
)
t0 <- as.POSIXct(
"2013-01-01 00:00:00",
format="%Y-%m-%d %H:%M:%S",
tz="America/New_York")
Dates <- sort(t0 + sample(0:(3600*24*365-1),20000))
Weeks <- week(Dates)
wDay <- wday(Dates,label=TRUE)
Hour <- hour(Dates)
##
hourShift <- function(time,wday){
hShift <- sapply(wday, function(X){
if(X=="Mon"){
0
} else if(X=="Tues"){
24*1
} else if(X=="Wed"){
24*2
} else if(X=="Thurs"){
24*3
} else if(X=="Fri"){
24*4
} else if(X=="Sat"){
24*5
} else {
24*6
}
})
##
tOut <- hour(time) + hShift + 1
return(tOut)
}
##
weekHour <- hourShift(time=Dates,wday=wDay)
##
Data <- data.frame(
Event=Events,
Timestamp=Dates,
Week=Weeks,
wDay=wDay,
dayHour=Hour,
weekHour=weekHour,
stringsAsFactors=FALSE)
##
This gives you:
> head(Data)
Event Timestamp Week wDay dayHour weekHour
1 SERVICE 783405 2013-01-01 00:13:55 1 Tues 0 25
2 INCIDENT 860015 2013-01-01 01:06:41 1 Tues 1 26
3 INCIDENT 808309 2013-01-01 01:10:05 1 Tues 1 26
4 INCIDENT 835509 2013-01-01 01:21:44 1 Tues 1 26
5 SERVICE 769239 2013-01-01 02:04:59 1 Tues 2 27
6 SERVICE 762269 2013-01-01 02:07:41 1 Tues 2 27