R - Getting Hourly Average from not constant frequency data acquisition - r

I have tons of meteorological data gathered from a Hobo station, which has it's own software, but is hard to post process it there. So, I just appended all the information in a descent data.frame but now I've been reading to learn how to get hourly results. Have tried with the Plyr and lubridate package with no succeed yet. I'm a novice programmer in R, usually building codes from patchs of work find on the internet.
So, What I already have is the following:
data<-read.csv("file.txt",header=TRUE,sep=";",dec=".",stringsAsFactors=FALSE)
data<-data[-1,]
data$TIMETAMP <- strptime(data$TIMETAMP, format="%d-%m-%y %H:%M", tz=Sys.timezone(location=TRUE))
data$Vel_VIENTO<-as.numeric(as.character(data$Vel_VIENTO))
data$Vel_RAFAGAS <-as.numeric(as.character(data$Vel_RAFAGAS))
data$Temp_Amb <-as.numeric(as.character(data$Temp_Amb))
data$HR <-as.numeric(as.character(data$HR))
data$Temp_Agua <-as.numeric(as.character(data$Temp_Agua))
data$Presion <-as.numeric(as.character(data$Presion))
data$Radiacion <-as.numeric(as.character(data$Radiacion))
data$Dir_VIENTO <-as.numeric(as.character(data$Dir_VIENTO))
the data looks like the following:
REGISTRO FECHA Vel_VIENTO Vel_RAFAGAS Temp_Amb HR Temp_Agua
2 1 2015-01-08 15:03:00 6.30 7.55 20.579 58.5 23.472
3 2 2015-01-08 15:18:00 6.55 9.07 20.412 57.5 22.609
4 3 2015-01-08 15:33:00 6.80 8.56 21.413 54.7 23.761
5 4 2015-01-08 15:48:00 6.30 8.31 20.222 59.5 22.705
6 5 2015-01-08 16:03:00 6.55 8.31 20.246 58.6 22.298
7 6 2015-01-08 16:18:00 7.30 9.57 19.008 63.5 21.366
Presion Radiacion Dir_VIENTO
2 906.55 NA NA
3 906.15 NA NA
4 905.95 NA NA
5 906.05 NA 202.2
6 906.05 966.9 210.6
7 905.75 919.4 227.4
So each column has one parameter with a TIMESTAMP on data$TIMESTAMP. The frequency of the data ranges from every 15 mins to every 30 mins within the data.frame. I would like to get the same table with the hourly averages for each of the parameters in the data.frame.

Here is a function that I have been using for similar applications. The main things to watch out for are using trunc instead of round and that you need to convert datetime into POSIXct for dplyr and trunc returns POSIXlt.
library(lubridate)
library(dplyr)
hourly_ave <- function(timeseries_data){
# Convert the "FECHA" column into datetime
timeseries_data$FECHA <- mdy_hm(timeseries_data$FECHA)
# Add an Hourly column (use trunc instead of round)
# Remember the "as.POSIXct() since trunc() returns POSIXlt which dplyr does not support
timeseries_data$Hourly = trunc(timeseries_data$FECHA, "hours") %>% as.POSIXct()
# Then group the data and summarize using dplyr
# I did not include all the variables, but you should get the idea
data_hr <- timeseries_data %>%
group_by(Hourly) %>%
summarize(Vel_RAFAGAS = mean(Vel_RAFAGAS), Temp_Am = mean(Temp_Am), HR = mean(HR), Temp_Ag = mean(Temp_Ag))
data_hr
}

First of all it would be great if you could (in the future) include a code snippet so we could provide you with a reproducable solution.
As one possible solution I would suggest to have a look at the dplyr package, have separate columns for the date, the time and the hour and finally group everything by date and hour to calculate the hourly mean:
library(stringr)
library(dplyr)
FECHA <- c("2015-01-08 15:03:00", "2015-01-08 15:18:00","2015-01-08 15:33:00","2015-01-08 15:48:00","2015-01-08 16:03:00","2015-01-08 16:18:00")
Temp_Aqua <- c("23.472","22.609", "23.761","22.705", "22.298", "21.366")
date_time <- matrix(unlist(str_split(FECHA, " ")), ncol = 2, byrow = T)
x <- as.data.frame(cbind(date_time, Temp_Aqua), stringsAsFactors = F)
names(x) <- c("date", "time", "temp_aqua")
x$temp_aqua <- as.numeric(x$temp_aqua)
x$hour <- str_extract(x$time, "^[0-9]{2}")
x %>% group_by(date, hour) %>% summarise(hourly_temp_aqua = mean(temp_aqua))
Source: local data frame [2 x 3]
Groups: date [?]
date hour hourly_temp_aqua
<chr> <chr> <dbl>
1 2015-01-08 15 23.13675
2 2015-01-08 16 21.83200
In your solution you should have the timestamp correctly stored/formatted and potentially consider whether the simple mean is the right solution (e.g., if the intervalls are not equally distributed you could consider giving them a different weight)
Hope this helps!

Related

subset data by time interval if I have all data between time interval

I have a data frame that looks like this:
X id mat.1 mat.2 mat.3 times
1 1 1 Anne 1495206060 18.5639404 2017-05-19 11:01:00
2 2 1 Anne 1495209660 9.0160321 2017-05-19 12:01:00
3 3 1 Anne 1495211460 37.6559161 2017-05-19 12:31:00
4 4 1 Anne 1495213260 31.1218856 2017-05-19 13:01:00
....
164 164 1 Anne 1497825060 4.8098351 2017-06-18 18:31:00
165 165 1 Anne 1497826860 15.0678781 2017-06-18 19:01:00
166 166 1 Anne 1497828660 4.7636241 2017-06-18 19:31:00
What I would like is to subset the data set by time interval (all data between 11 AM and 4 PM) if there are data points for each hour at least (11 AM, 12, 1, 2, 3, 4 PM) within each day. I want to ultimately sum the values from mat.3 per time interval (11 AM to 4 PM) per day.
I did tried:
sub.1 <- subset(t,format(times,'%H')>='11' & format(times,'%H')<='16')
but this returns all the data from any of the times between 11 AM and 4 PM, but often I would only have data for e.g. 12 and 1 PM for a given day.
I only want the subset from days where I have data for each hour from 11 AM to 4 PM. Any ideas what I can try?
A complement to #Henry Navarro answer for solving an additional problem mentioned in the question.
If I understand in proper way, another concern of the question is to find the dates such that there are data points at least for each hour of the given interval within the day. A possible way following the style of #Henry Navarro solution is as follows:
library(lubridate)
your_data$hour_only <- as.numeric(format(your_data$times, format = "%H"))
your_data$days <- ymd(format(your_data$times, "%Y-%m-%d"))
your_data_by_days_list <- split(x = your_data, f = your_data$days)
# the interval is narrowed for demonstration purposes
hours_intervals <- 11:13
all_hours_flags <- data.frame(days = unique(your_data$days),
all_hours_present = sapply(function(Z) (sum(unique(Z$hour_only) %in% hours_intervals) >=
length(hours_intervals)), X = your_data_by_days_list), row.names = NULL)
your_data <- merge(your_data, all_hours_flags, by = "days")
There is now the column "all_hours_present" indicating that the data for a corresponding day contains at least one value for each hour in the given hours_intervals. And you may use this column to subset your data
subset(your_data, all_hours_present)
Try to create a new variable in your data frame with only the hour.
your_data$hour<-format(your_data$times, format="%H:%M:%S")
Then, using this new variable try to do the next:
#auxiliar variable with your interval of time
your_data$aux_var<-ifelse(your_data$hour >"11:00:00" || your_data$hour<"16:00:00" ,1,0)
So, the next step is filter your data when aux_var==1
your_data[which(your_data$aux_var ==1),]

Moving average over 5 years with irregular dates

I have a large number of files (~1200) which each contains a large timeserie with data about the height of the groundwater. The starting date and length of the serie is different for each file. There can be large data gaps between dates, for example (small part of such a file):
Date Height (cm)
14-1-1980 7659
28-1-1980 7632
14-2-1980 7661
14-3-1980 7638
28-3-1980 7642
14-4-1980 7652
25-4-1980 7646
14-5-1980 7635
29-5-1980 7622
13-6-1980 7606
27-6-1980 7598
14-7-1980 7654
28-7-1980 7654
14-8-1980 7627
28-8-1980 7600
12-9-1980 7617
14-10-1980 7596
28-10-1980 7601
14-11-1980 7592
28-11-1980 7614
11-12-1980 7650
29-12-1980 7670
14-1-1981 7698
28-1-1981 7700
13-2-1981 7694
17-3-1981 7740
30-3-1981 7683
14-4-1981 7692
14-5-1981 7682
15-6-1981 7696
17-7-1981 7706
28-7-1981 7699
28-8-1981 7686
30-9-1981 7678
17-11-1981 7723
11-12-1981 7803
18-2-1982 7757
16-3-1982 7773
13-5-1982 7753
11-6-1982 7740
14-7-1982 7731
15-8-1982 7739
14-9-1982 7722
14-10-1982 7794
15-11-1982 7764
14-12-1982 7790
14-1-1983 7810
28-3-1983 7836
28-4-1983 7815
31-5-1983 7857
29-6-1983 7801
28-7-1983 7774
24-8-1983 7758
28-9-1983 7748
26-10-1983 7727
29-11-1983 7782
27-1-1984 7801
28-3-1984 7764
27-4-1984 7752
28-5-1984 7795
27-7-1984 7748
27-8-1984 7729
28-9-1984 7752
26-10-1984 7789
28-11-1984 7797
18-12-1984 7781
28-1-1985 7833
21-2-1985 7778
22-4-1985 7794
28-5-1985 7768
28-6-1985 7836
26-8-1985 7765
19-9-1985 7760
31-10-1985 7756
26-11-1985 7760
20-12-1985 7781
17-1-1986 7813
28-1-1986 7852
26-2-1986 7797
25-3-1986 7838
22-4-1986 7807
27-5-1986 7785
24-6-1986 7787
26-8-1986 7744
23-9-1986 7742
22-10-1986 7752
1-12-1986 7749
17-12-1986 7758
I want to calculate the average height over 5 years. So, in case of the example 14-1-1980 + 5 years, 14-1-1985 + 5 years, .... The amount of datapoints is different for each calculation of the average. It is very likely that the date 5 years later will not be in the dataset as a datapoint. Hence, I think I need to tell R somehow to take an average in a certain timespan.
I searched on the internet but didn't find something that fitted my needs. A lot of useful packages like uts, zoo, lubridate and the function aggregate passed by. Instead of getting closer to the solution I get more and more confused about which approach is the best for my problem.
Thanks a lot in advance!
As #vagabond points out, you'll want to combine your 1200 files into a single data frame (the plyr package would allow you to do something simple like: data.all <- adply(dir([DATA FOLDER]), 1, read.csv).
Once you have the data, the first step would be to transform the Date column into proper POSIXct date data. Right now the data appear to be strings, and we want them to have an underlying numerical representation (which POSIXct does):
library(lubridate)
df$date.new <- as.Date(dmy(df$Date))
Date Height date.new
1 14-1-1980 7659 1980-01-14
2 28-1-1980 7632 1980-01-28
3 14-2-1980 7661 1980-02-14
4 14-3-1980 7638 1980-03-14
5 28-3-1980 7642 1980-03-28
6 14-4-1980 7652 1980-04-14
Note that the date.new column looks like a string, but is in fact Date data, and can be handled with numerical operations (addition, comparison, etc.).
Next, we might construct a set of date periods, over which we want to compute averages. Your example mentions 5 years, but with the data you provided, that's not a very illustrative example. So here I'm creating 1-year periods starting at every day between Jan 14 1980 and Jan 14 1985
date.start <- as.Date(as.Date('1980-01-14') : as.Date('1985-01-14'), origin = '1970-01-01')
date.end <- date.start + years(1)
dates <- data.frame(start = date.start, end = date.end)
start end
1 1980-01-14 1981-01-14
2 1980-01-15 1981-01-15
3 1980-01-16 1981-01-16
4 1980-01-17 1981-01-17
5 1980-01-18 1981-01-18
6 1980-01-19 1981-01-19
Then we can use the dplyr package to move through each row of this data frame and compute a summary average of Height:
library(dplyr)
df.mean <- dates %>%
group_by(start, end) %>%
summarize(height.mean = mean(df$Height[df$date.new >= start & df$date.new < end]))
start end height.mean
<date> <date> <dbl>
1 1980-01-14 1981-01-14 7630.273
2 1980-01-15 1981-01-15 7632.045
3 1980-01-16 1981-01-16 7632.045
4 1980-01-17 1981-01-17 7632.045
5 1980-01-18 1981-01-18 7632.045
6 1980-01-19 1981-01-19 7632.045
The foverlaps function is IMHO the perfect candidate for such a situation:
library(data.table)
library(lubridate)
# convert to a data.table with setDT()
# convert the 'Date'-column to date-format
# create a begin & end date for the required period
setDT(dat)[, Date := as.Date(Date, '%d-%m-%Y')
][, `:=` (begindate = Date, enddate = Date + years(1))]
# set the keys (necessary for the foverlaps function)
setkey(dat, begindate, enddate)
res <- foverlaps(dat, dat, by.x = c(1,3))[, .(moving.average = mean(i.Height)), Date]
the result:
> head(res,15)
Date moving.average
1: 1980-01-14 7633.217
2: 1980-01-28 7635.000
3: 1980-02-14 7637.696
4: 1980-03-14 7636.636
5: 1980-03-28 7641.273
6: 1980-04-14 7645.261
7: 1980-04-25 7644.955
8: 1980-05-14 7646.591
9: 1980-05-29 7647.143
10: 1980-06-13 7648.400
11: 1980-06-27 7652.900
12: 1980-07-14 7655.789
13: 1980-07-28 7660.550
14: 1980-08-14 7660.895
15: 1980-08-28 7664.000
Now you have for each date an average of all the values that lie the date and one year ahead of that date.
Hey I just tried after seeing your question!!! Ran on a sample data frame. Try it on yours after understanding the code and then let me know!
Bdw instead of having an interval of 5 years, I used just 2 months (2*30 = approx 2 months) as the interval!
df = data.frame(Date = c("14-1-1980", "28-1-1980", "14-2-1980", "14-3-1980", "28-3-1980",
"14-4-1980", "25-4-1980", "14-5-1980", "29-5-1980", "13-6-1980:",
"27-6-1980", "14-7-1980", "28-7-1980", "14-8-1980"), height = 1:14)
# as.Date(df$Date, "%d-%m-%Y")
df1 = data.frame(orig = NULL, dest = NULL, avg_ht = NULL)
orig = as.Date(df$Date, "%d-%m-%Y")[1]
dest = as.Date(df$Date, "%d-%m-%Y")[1] + 2*30 #approx 2 months
dest_final = as.Date(df$Date, "%d-%m-%Y")[14]
while (dest < dest_final){
m = mean(df$height[which(as.Date(df$Date, "%d-%m-%Y")>=orig &
as.Date(df$Date, "%d-%m-%Y")<dest )])
df1 = rbind(df1,data.frame(orig=orig,dest=dest,avg_ht=m))
orig = dest
dest = dest + 2*30
print(paste("orig:",orig, " + ","dest:",dest))
}
> df1
orig dest avg_ht
1 1980-01-14 1980-03-14 2.0
2 1980-03-14 1980-05-13 5.5
3 1980-05-13 1980-07-12 9.5
I hope this works for you as well
This is my best try, but please keep in mind that I am working with the years instead of the full date, i.e. based on the example you provided I am averaging over beginning of 1980- end of 1984.
dat<-read.csv("paixnidi.csv")
install.packages("stringr")
library(stringr)
dates<-dat[,1]
#extract the year of each measurement
years<-as.integer(str_sub(dat[,1], start= -4))
spread_y<-years[length(years)]-years[1]
ind<-list()
#find how many 5-year intervals there are
groups<-ceiling(spread_y/4)
meangroups<-matrix(0,ncol=2,nrow=groups)
k<-0
for (i in 1:groups){
#extract the indices of the dates vector whithin the 5-year period
ind[[i]]<-which(years>=(years[1]+k)&years<=(years[1]+k+4),arr.ind=TRUE)
meangroups[i,2]<-mean(dat[ind[[i]],2])
meangroups[i,1]<-(years[1]+k)
k<-k+5
}
colnames(meangroups)<-c("Year:Year+4","Mean Height (cm)")

R aggregate a dataframe by hours from a date with time field

I'm relatively new to R but I am very familiar with Excel and T-SQL.
I have a simple dataset that has a date with time and a numeric value associated it. What I'd like to do is summarize the numeric values by-hour of the day. I've found a couple resources for working with time-types in R but I was hoping to find a solution similar to is offered excel (where I can call a function and pass-in my date/time data and have it return the hour of the day).
Any suggestions would be appreciated - thanks!
library(readr)
library(dplyr)
library(lubridate)
df <- read_delim('DateTime|Value
3/14/2015 12:00:00|23
3/14/2015 13:00:00|24
3/15/2015 12:00:00|22
3/15/2015 13:00:00|40',"|")
df %>%
mutate(hour_of_day = hour(as.POSIXct(strptime(DateTime, "%m/%d/%Y %H:%M:%S")))) %>%
group_by(hour_of_day) %>%
summarise(meanValue = mean(Value))
breakdown:
Convert column of DateTime (character) into formatted time then use hour() from lubridate to pull out just that hour value and put it into new column named hour_of_day.
> df %>%
mutate(hour_of_day = hour(as.POSIXct(strptime(DateTime, "%m/%d/%Y %H:%M:%S"))))
Source: local data frame [4 x 3]
DateTime Value hour_of_day
1 3/14/2015 12:00:00 23 12
2 3/14/2015 13:00:00 24 13
3 3/15/2015 12:00:00 22 12
4 3/15/2015 13:00:00 40 13
The group_by(hour_of_day) sets the groups upon which mean(Value) is computed in the via the summarise(...) call.
this gives the result:
hour_of_day meanValue
1 12 22.5
2 13 32.0

Split Date to Day, Month and Year for ffdf Data in R

I'm using R's ff package with ffdf objects named MyData, (dim=c(10819740,16)). I'm trying to split the variable Date into Day, Month and Year and add these 3 variables into ffdf existing data MyData.
For instance: My Date column named SalesReportDate with VirtualVmode and PhysicalVmode = double after I've changed SalesReportDate to as.date(,format="%m/%d/%Y").
Example of SalesReportDate are as follow:
> B
SalesReportDate
1 2013-02-01
2 2013-05-02
3 2013-05-04
4 2013-10-06
5 2013-15-10
6 2013-11-01
7 2013-11-03
8 2013-30-02
9 2013-12-12
10 2014-01-01
I've refer to Split date into different columns for year, month and day and try to apply it but keep getting error warning.
So, is there any way for me to do this? Thanks in advance.
Credit to #jwijffels for this great solution:
require(ffbase)
MyData$SalesReportDateYear <- with(MyData["SalesReportDate"], format(SalesReportDate, "%Y"), by = 250000)
MyData$SalesReportDateMonth <- with(MyData["SalesReportDate"], format(SalesReportDate, "%m"), by = 250000)
MyData$SalesReportDateDay <- with(MyData["SalesReportDate"], format(SalesReportDate, "%d"), by = 250000)

How to get sum of values every 8 days by date in data frame in R

I don't often have to work with dates in R, but I imagine this is fairly easy. I have daily data as below for several years with some values and I want to get for each 8 days period the sum of related values.What is the best approach?
Any help you can provide will be greatly appreciated!
str(temp)
'data.frame':648 obs. of 2 variables:
$ Date : Factor w/ 648 levels "2001-03-24","2001-03-25",..: 1 2 3 4 5 6 7 8 9 10 ...
$ conv2: num -3.93 -6.44 -5.48 -6.09 -7.46 ...
head(temp)
Date amount
24/03/2001 -3.927020472
25/03/2001 -6.4427004
26/03/2001 -5.477592528
27/03/2001 -6.09462162
28/03/2001 -7.45666902
29/03/2001 -6.731540928
30/03/2001 -6.855206184
31/03/2001 -6.807210228
1/04/2001 -5.40278802
I tried to use aggregate function but for some reasons it doesn't work and it aggregates in wrong way:
z <- aggregate(amount ~ Date, timeSequence(from =as.Date("2001-03-24"),to =as.Date("2001-03-29"), by="day"),data=temp,FUN=sum)
I prefer the package xts for such manipulations.
I read your data, as zoo objects. see the flexibility of format option.
library(xts)
ts.dat <- read.zoo(text ='Date amount
24/03/2001 -3.927020472
25/03/2001 -6.4427004
26/03/2001 -5.477592528
27/03/2001 -6.09462162
28/03/2001 -7.45666902
29/03/2001 -6.731540928
30/03/2001 -6.855206184
31/03/2001 -6.807210228
1/04/2001 -5.40278802',header=TRUE,format = '%d/%m/%Y')
Then I extract the index of given period
ep <- endpoints(ts.dat,'days',k=8)
finally I apply my function to the time series at each index.
period.apply(x=ts.dat,ep,FUN=sum )
2001-03-29 2001-04-01
-36.13014 -19.06520
Use cut() in your aggregate() command.
Some sample data:
set.seed(1)
mydf <- data.frame(
DATE = seq(as.Date("2000/1/1"), by="day", length.out = 365),
VALS = runif(365, -5, 5))
Now, the aggregation. See ?cut.Date for details. You can specify the number of days you want in each group using cut:
output <- aggregate(VALS ~ cut(DATE, "8 days"), mydf, sum)
list(head(output), tail(output))
# [[1]]
# cut(DATE, "8 days") VALS
# 1 2000-01-01 8.242384
# 2 2000-01-09 -5.879011
# 3 2000-01-17 7.910816
# 4 2000-01-25 -6.592012
# 5 2000-02-02 2.127678
# 6 2000-02-10 6.236126
#
# [[2]]
# cut(DATE, "8 days") VALS
# 41 2000-11-16 17.8199285
# 42 2000-11-24 -0.3772209
# 43 2000-12-02 2.4406024
# 44 2000-12-10 -7.6894484
# 45 2000-12-18 7.5528077
# 46 2000-12-26 -3.5631950
rollapply. The zoo package has a rolling apply function which can also do non-rolling aggregations. First convert the temp data frame into zoo using read.zoo like this:
library(zoo)
zz <- read.zoo(temp)
and then its just:
rollapply(zz, 8, sum, by = 8)
Drop the by = 8 if you want a rolling total instead.
(Note that the two versions of temp in your question are not the same. They have different column headings and the Date columns are in different formats. I have assumed the str(temp) output version here. For the head(temp) version one would have to add a format = "%d/%m/%Y" argument to read.zoo.)
aggregate. Here is a solution that does not use any external packages. It uses aggregate based on the original data frame.
ix <- 8 * ((1:nrow(temp) - 1) %/% 8 + 1)
aggregate(temp[2], list(period = temp[ix, 1]), sum)
Note that ix looks like this:
> ix
[1] 8 8 8 8 8 8 8 8 16
so it groups the indices of the first 8 rows, the second 8 and so on.
Those are NOT Date classed variables. (No self-respecting program would display a date like that, not to mention the fact that these are labeled as factors.) [I later noticed these were not the same objects.] Furthermore, the timeSequence function (at least the one in the timeDate package) does not return a Date class vector either. So your expectation that there would be a "right way" for two disparate non-Date objects to be aligned in a sensible manner is ill-conceived. The irony is that just using the temp$Date column would have worked since :
> z <- aggregate(amount ~ Date, data=temp , FUN=sum)
> z
Date amount
1 1/04/2001 -5.402788
2 24/03/2001 -3.927020
3 25/03/2001 -6.442700
4 26/03/2001 -5.477593
5 27/03/2001 -6.094622
6 28/03/2001 -7.456669
7 29/03/2001 -6.731541
8 30/03/2001 -6.855206
9 31/03/2001 -6.807210
But to get it in 8 day intervals use cut.Date:
> z <- aggregate(temp$amount ,
list(Dts = cut(as.Date(temp$Date, format="%d/%m/%Y"),
breaks="8 day")), FUN=sum)
> z
Dts x
1 2001-03-24 -49.792561
2 2001-04-01 -5.402788
A more cleaner approach extended to #G. Grothendieck appraoch. Note: It does not take into account if the dates are continuous or discontinuous, sum is calculated based on the fixed width.
code
interval = 8 # your desired date interval. 2 days, 3 days or whatevea
enddate = interval-1 # this sets the enddate
nrows = nrow(z)
z <- aggregate(.~V1,data = df,sum) # aggregate sum of all duplicate dates
z$V1 <- as.Date(z$V1)
data.frame ( Start.date = (z[seq(1, nrows, interval),1]),
End.date = z[seq(1, nrows, interval)+enddate,1],
Total.sum = rollapply(z$V2, interval, sum, by = interval, partial = TRUE))
output
Start.date End.date Total.sum
1 2000-01-01 2000-01-08 9.1395926
2 2000-01-09 2000-01-16 15.0343960
3 2000-01-17 2000-01-24 4.0974712
4 2000-01-25 2000-02-01 4.1102645
5 2000-02-02 2000-02-09 -11.5816277
data
df <- data.frame(
V1 = seq(as.Date("2000/1/1"), by="day", length.out = 365),
V2 = runif(365, -5, 5))

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