## Retain time series data with duration of one second in R programming

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So I have a time series data which is captured at every 15 - 18 milliseconds as shown below.

```    tts <- ("10:49:56:459524", "10:49:56:580531", "10:49:56:720539","10:49:56:861547", "10:49:57:004555", "10:49:57:145563"......)
```

My question is how do I handle this data where I can retain only data with 1-second duration as shown below.

```    delta_tts
10:49:56
10:49:57
....
```

I thought of trimming the time format to hh:mm: ss but couldn't do it.

If we treat them as strings, we can split and get the first 3 elements, or we can use simple regex, i.e.

```sapply(strsplit(tts, ':'), function(i)paste(i[1:3], collapse = ':'))

#Or with Regex,

gsub(":*\\w*\$", "", tts)
```

Wrap both statements in `unique()` to get the unique times.

Time Series 02: Dealing With Dates & Times in R, Portal User Accounts � Data Processing & Publication � Data Quality Program Date , POSIXct and POSIXlt as used to convert a date / time field in character In the Intro to Time Series Data in R tutorial we imported a time series dataset in date and time information in a format that we are used to seeing (e.g., second,� Before we dive into the analysis of temporal data in R, let us understand the different components of time series data. These components are shown below in the figure: Trend Component: By trend component, we mean that the general tendency of the data to increase or decrease during a long period of time.

As it is a time series data we can treat them as `POSIXct` objects and then use `format` to get data in required form

```format(as.POSIXct(tts, format = "%T"), "%T")
#[1] "10:49:56" "10:49:56" "10:49:56" "10:49:56" "10:49:57" "10:49:57"
```

Or with `strptime`

```format(strptime(tts, format = "%T"), "%T")
```

data

```tts <- c("10:49:56:459524", "10:49:56:580531", "10:49:56:720539",
"10:49:56:861547", "10:49:57:004555", "10:49:57:145563")
```

Manipulating Time Series Data in R with xts & zoo, This lets you keep metadata about your object inside your object. Create a Date class index from “2016-01-01” of length five called dates. One major difference between xts and most other time series objects in R is the ability to use any Instructions - Find the first three days of the second week of the temps data set. The Data Approach. In the Intro to Time Series Data in R tutorial we imported a time series dataset in .csv format into R. We learned how to quickly plot these data by converting the date column to an R Date class. In this tutorial we will explore how to work with a column that contains both a date AND a time stamp.

We can use `substr`

```substr(tts, 1, 8)
#[1] "10:49:56" "10:49:56" "10:49:56" "10:49:56" "10:49:57" "10:49:57"
```

Or using `sub`

```sub(":[^:]*\$", "", tts)
```
##### data
```tts <- c("10:49:56:459524", "10:49:56:580531", "10:49:56:720539",
"10:49:56:861547", "10:49:57:004555", "10:49:57:145563")
```

Dates and Times in R, The general rule for date/time data in R is to use the simplest technique possible. are stored internally as the number of days or seconds from some reference date. R to be manipulated in the same way they would in, for example a C program. We could create a sequence of dates separated by two weeks from June 1,� time.interval <- start %–% end. To create a Duration between these two dates, we can use the as.duration() function. time.duration <- as.duration(time.interval) A duration object prints the elapsed time in seconds as well as something in days. It represent 1.96 days equals to 47 hours .

16 Dates and times, of these things with R. You'll learn how to use the grammar of graphics, literate programming, There are three types of date/time data that refer to an instant in time: However, because durations represent an exact number of seconds, sometimes you Keep the instant in time the same, and change how it's displayed. In order to fit an autoregressive time series model to the data by ordinary least squares it is possible to use the function ar.ols () which is part of the "stats" package.

[PDF] Working with Financial Time Series Data in R, number of seconds since the beginning of 1970 as a numeric vector. Does not control for time zones. Date base. Represent calendar dates as the� series class in R with a rich set of methods for manipulating and plotting time series data. Base R has limited functionality for handling general time series data. For example, univariate and multivariate regularly spaced calendar time series data can be represented using the ts and mts classes, respectively. These classes have a limited set

In part 1, I’ll discuss the fundamental object in R – the ts object. The Time Series Object. In order to begin working with time series data and forecasting in R, you must first acquaint yourself with R’s ts object. The ts object is a part of base R. Other packages such as xts and zoo provide other APIs for manipulating time series

• something like this? `unique(sapply(strsplit(tts, ':'), function(i)paste(i[1:3], collapse = ':')))`...or with regex `gsub(":*\\w*\$", "", tts)`