R Programming in Data Science: Dates and Times

2h 18mIntermediate2019-07-24

Authors

Mark Niemann-Ross

Technologist experienced in hardware, software, and science fiction

Course details

One of the fundamental difficulties of data science is working with dates and times. This course shows data engineers, DevOps practitioners, and data-science programmers the most common (and many not so common!) problems and how to use R-based tools to implement solutions. Learn how dates and times are stored and retrieved in base R. Find out how to format, compare, add and subtract, and extract dates and times using built-in R functions. Then discover how to incorporate specialized R packages, such as lubridate, busdater, zoo, timelineR, anytime, datetime, and more, to perform some of the heavy lifting. Instructor Mark Niemann-Ross walks you through each package, so you can appreciate the advantages and best uses of each one.

Learning objectives
Choosing the right tool
Dates and times in base R
Dealing with time zones
Adding and subtracting dates and times
Formatting dates and times
Rounding dates and times
Using lubridate for dates and times
Business and finance packages for R
Working with time-series data
Specialized data and time packages

Skills covered

RStudioRStatisticsData Science FoundationsProgramming LanguagesData ScienceOpen SourceSoftware DevelopmentDeep Dive (X:Y)

Concepts

0. Introduction

01 - Calculating times and dates with R
02 - Course organization

1. Why Are Dates and Times in R Confusing

03 - Typical date calculations
04 - How dates and times are stored in R
05 - Choose the right date and time tool

2. Dates and Times in Base R

06 - The base R Date class
07 - Use formatters to recognize dates in character strings
08 - Dealing with time zones and daylight savings time
09 - Use operators to compare date objects
10 - Adding and subtracting dates and times
11 - Create sequences of dates, cut dates, and round dates
12 - Extract parts of a date
13 - Presenting formatted dates and times
14 - Use read.csv() to import CSV date information

3. Lubridate and the Tidyverse

15 - Advantages of the Lubridate package
16 - Parsing date and time with Lubridate
17 - Getting and setting time components with Lubridate
18 - Rounding dates and time with Lubridate
19 - Lubridate math with durations
20 - Lubridate math with periods
21 - Lubridate math with intervals
22 - Time zones with Lubridate

4. Dates and Times for Business and Finance

23 - The busdater package
24 - The BusinessDuration package
25 - The fmdates package

5. Working with Time-Series Data

26 - Time-series data
27 - The base R ts class
28 - The zoo package
29 - The xts package
30 - The tsibble and tibbletime packages
31 - Time-series rolling statistics
32 - Time-series graphics
33 - The timelineR package
34 - The timelineS package
35 - The CRAN task view for time-series analysis

6. Specialized Date and Time Packages

36 - The anytime package
37 - The hms package
38 - The mondate package
39 - The datetime package
40 - The datetimeutils package
41 - The padr package

Conclusion

42 - Next steps