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Converting data to xts changes the timestamp data
How to join (merge) data frames (inner, outer, left, right)Drop data frame columns by nameChanging column names of a data frameHow to calculate daily means, medians, from weather variables data collected hourly in R?create timeseries based on start and end dateR date origin for time in secondsARIMA ForecastingExtract day and month from dateHow to create full date column using “Month_Year” character string and join three different data frames ordered by the date column in Ri am preparing time series data for building a rnn
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I am currently doing research on soil moisture and have to get hourly and daily means from my time series data.
When I convert the dataframe into an xts object, the time series changes and I can't figure out why.
The data in the data frame looks like this:
time MC temp
1 2018-06-27 11:30:00 17.1 15.8
2 2018-06-27 11:45:00 17.0 15.8
3 2018-06-27 12:00:00 17.0 15.8
4 2018-06-27 12:15:00 17.0 15.9
5 2018-06-27 12:30:00 17.2 15.9
6 2018-06-27 12:45:00 17.0 16.0
But when I convert it, the time stamp begins at 2018-01-09 00:00 and proceeds to make 5 min time increments. This is the code I am using:
sm_xts <- xts(sm.data[,2:3], as.Date(sm.data$time))
sm_zoo <- read.zoo(sm.data, index.column = 1)
dat_xts <- as.xts(sm_zoo)
I have already converted the time stamp to a as.POSIXct class and checked for duplicates in my time series.
> anyDuplicated(sm.data$time)
[1] 0
r time-series xts
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
add a comment |
I am currently doing research on soil moisture and have to get hourly and daily means from my time series data.
When I convert the dataframe into an xts object, the time series changes and I can't figure out why.
The data in the data frame looks like this:
time MC temp
1 2018-06-27 11:30:00 17.1 15.8
2 2018-06-27 11:45:00 17.0 15.8
3 2018-06-27 12:00:00 17.0 15.8
4 2018-06-27 12:15:00 17.0 15.9
5 2018-06-27 12:30:00 17.2 15.9
6 2018-06-27 12:45:00 17.0 16.0
But when I convert it, the time stamp begins at 2018-01-09 00:00 and proceeds to make 5 min time increments. This is the code I am using:
sm_xts <- xts(sm.data[,2:3], as.Date(sm.data$time))
sm_zoo <- read.zoo(sm.data, index.column = 1)
dat_xts <- as.xts(sm_zoo)
I have already converted the time stamp to a as.POSIXct class and checked for duplicates in my time series.
> anyDuplicated(sm.data$time)
[1] 0
r time-series xts
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
add a comment |
I am currently doing research on soil moisture and have to get hourly and daily means from my time series data.
When I convert the dataframe into an xts object, the time series changes and I can't figure out why.
The data in the data frame looks like this:
time MC temp
1 2018-06-27 11:30:00 17.1 15.8
2 2018-06-27 11:45:00 17.0 15.8
3 2018-06-27 12:00:00 17.0 15.8
4 2018-06-27 12:15:00 17.0 15.9
5 2018-06-27 12:30:00 17.2 15.9
6 2018-06-27 12:45:00 17.0 16.0
But when I convert it, the time stamp begins at 2018-01-09 00:00 and proceeds to make 5 min time increments. This is the code I am using:
sm_xts <- xts(sm.data[,2:3], as.Date(sm.data$time))
sm_zoo <- read.zoo(sm.data, index.column = 1)
dat_xts <- as.xts(sm_zoo)
I have already converted the time stamp to a as.POSIXct class and checked for duplicates in my time series.
> anyDuplicated(sm.data$time)
[1] 0
r time-series xts
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
I am currently doing research on soil moisture and have to get hourly and daily means from my time series data.
When I convert the dataframe into an xts object, the time series changes and I can't figure out why.
The data in the data frame looks like this:
time MC temp
1 2018-06-27 11:30:00 17.1 15.8
2 2018-06-27 11:45:00 17.0 15.8
3 2018-06-27 12:00:00 17.0 15.8
4 2018-06-27 12:15:00 17.0 15.9
5 2018-06-27 12:30:00 17.2 15.9
6 2018-06-27 12:45:00 17.0 16.0
But when I convert it, the time stamp begins at 2018-01-09 00:00 and proceeds to make 5 min time increments. This is the code I am using:
sm_xts <- xts(sm.data[,2:3], as.Date(sm.data$time))
sm_zoo <- read.zoo(sm.data, index.column = 1)
dat_xts <- as.xts(sm_zoo)
I have already converted the time stamp to a as.POSIXct class and checked for duplicates in my time series.
> anyDuplicated(sm.data$time)
[1] 0
r time-series xts
r time-series xts
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
edited Mar 24 at 12:31
Joshua Ulrich
141k23280363
141k23280363
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
asked Mar 22 at 16:32
Danielle CrawfordDanielle Crawford
11
11
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Maybe you have some problems with your initial data input process.
df
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
Now the date and time values are stored in a single variable as a char
str(df$time)
# chr [1:6] "2018-06-27 11:30:00" "2018-06-27 11:45:00" ...
Let's convert it into real date and time so as not to lose time information:
strptime(df$time, "%Y-%m-%d %H:%M:%S")
# [1] "2018-06-27 11:30:00 EEST" "2018-06-27 11:45:00 EEST"
# [3] "2018-06-27 12:00:00 EEST" "2018-06-27 12:15:00 EEST"
# [5] "2018-06-27 12:30:00 EEST" "2018-06-27 12:45:00 EEST"
Seems like it works. The possible problems with timezone are beyond the scope of this answer.
Now let's convert the data frame to xts. In the finished xts, we do not need date and time in the symbolic form. So we exclude the first column.
df2xts <- xts(df[,2:3], order.by=strptime(df$time, "%Y-%m-%d %H:%M:%S"))
df2xts
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
answered Apr 5 at 7:42
user3139228user3139228
265
add a comment |
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1 Answer
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Maybe you have some problems with your initial data input process.
df
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
Now the date and time values are stored in a single variable as a char
str(df$time)
# chr [1:6] "2018-06-27 11:30:00" "2018-06-27 11:45:00" ...
Let's convert it into real date and time so as not to lose time information:
strptime(df$time, "%Y-%m-%d %H:%M:%S")
# [1] "2018-06-27 11:30:00 EEST" "2018-06-27 11:45:00 EEST"
# [3] "2018-06-27 12:00:00 EEST" "2018-06-27 12:15:00 EEST"
# [5] "2018-06-27 12:30:00 EEST" "2018-06-27 12:45:00 EEST"
Seems like it works. The possible problems with timezone are beyond the scope of this answer.
Now let's convert the data frame to xts. In the finished xts, we do not need date and time in the symbolic form. So we exclude the first column.
df2xts <- xts(df[,2:3], order.by=strptime(df$time, "%Y-%m-%d %H:%M:%S"))
df2xts
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
answered Apr 5 at 7:42
user3139228user3139228
265
add a comment |
Maybe you have some problems with your initial data input process.
df
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
Now the date and time values are stored in a single variable as a char
str(df$time)
# chr [1:6] "2018-06-27 11:30:00" "2018-06-27 11:45:00" ...
Let's convert it into real date and time so as not to lose time information:
strptime(df$time, "%Y-%m-%d %H:%M:%S")
# [1] "2018-06-27 11:30:00 EEST" "2018-06-27 11:45:00 EEST"
# [3] "2018-06-27 12:00:00 EEST" "2018-06-27 12:15:00 EEST"
# [5] "2018-06-27 12:30:00 EEST" "2018-06-27 12:45:00 EEST"
Seems like it works. The possible problems with timezone are beyond the scope of this answer.
Now let's convert the data frame to xts. In the finished xts, we do not need date and time in the symbolic form. So we exclude the first column.
df2xts <- xts(df[,2:3], order.by=strptime(df$time, "%Y-%m-%d %H:%M:%S"))
df2xts
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
answered Apr 5 at 7:42
user3139228user3139228
265
add a comment |
Maybe you have some problems with your initial data input process.
df
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
Now the date and time values are stored in a single variable as a char
str(df$time)
# chr [1:6] "2018-06-27 11:30:00" "2018-06-27 11:45:00" ...
Let's convert it into real date and time so as not to lose time information:
strptime(df$time, "%Y-%m-%d %H:%M:%S")
# [1] "2018-06-27 11:30:00 EEST" "2018-06-27 11:45:00 EEST"
# [3] "2018-06-27 12:00:00 EEST" "2018-06-27 12:15:00 EEST"
# [5] "2018-06-27 12:30:00 EEST" "2018-06-27 12:45:00 EEST"
Seems like it works. The possible problems with timezone are beyond the scope of this answer.
Now let's convert the data frame to xts. In the finished xts, we do not need date and time in the symbolic form. So we exclude the first column.
df2xts <- xts(df[,2:3], order.by=strptime(df$time, "%Y-%m-%d %H:%M:%S"))
df2xts
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
answered Apr 5 at 7:42
user3139228user3139228
265
Maybe you have some problems with your initial data input process.
df
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
Now the date and time values are stored in a single variable as a char
str(df$time)
# chr [1:6] "2018-06-27 11:30:00" "2018-06-27 11:45:00" ...
Let's convert it into real date and time so as not to lose time information:
strptime(df$time, "%Y-%m-%d %H:%M:%S")
# [1] "2018-06-27 11:30:00 EEST" "2018-06-27 11:45:00 EEST"
# [3] "2018-06-27 12:00:00 EEST" "2018-06-27 12:15:00 EEST"
# [5] "2018-06-27 12:30:00 EEST" "2018-06-27 12:45:00 EEST"
Seems like it works. The possible problems with timezone are beyond the scope of this answer.
Now let's convert the data frame to xts. In the finished xts, we do not need date and time in the symbolic form. So we exclude the first column.
df2xts <- xts(df[,2:3], order.by=strptime(df$time, "%Y-%m-%d %H:%M:%S"))
df2xts
# time MC temp
# 1 2018-06-27 11:30:00 17.1 15.8
# 2 2018-06-27 11:45:00 17.0 15.8
# 3 2018-06-27 12:00:00 17.0 15.8
# 4 2018-06-27 12:15:00 17.0 15.9
# 5 2018-06-27 12:30:00 17.2 15.9
# 6 2018-06-27 12:45:00 17.0 16.0
answered Apr 5 at 7:42
user3139228user3139228
265
edited Apr 5 at 21:42
answered Apr 5 at 7:42
user3139228user3139228
265
answered Apr 5 at 7:42
user3139228user3139228
265
answered Apr 5 at 7:42
user3139228user3139228
265
265
add a comment |
add a comment |
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