How To Extract Year From Date in R - ProgrammingR (2023)

I love dates. Not really, they tend to be a common cause of programming problems and random frustration.

If you’re working with time-series data in R, you might need to extract the year from a date value to analyze trends and patterns over time. However, extracting the year from a date value can be tricky, especially when the date is stored in different formats or has missing or invalid values. In this article, we’ll guide you through the different ways to extract the year from a date value under multiple circumstances.

The article explains how to extract the year from a date value stored in a vector or a dataframe column using theformat()function. It also shows how to extract the year from a date value stored as a character string using theas.Date()andformat()functions. The article provides examples for different date formats, such as “YYYY-MM-DD” and “MM/DD/YYYY”, and how to convert between them using theas.Date()function. Additionally, the article provides tips on how to handle missing or invalid dates, such as “NA” or “0000-00-00”, and how to convert them into a valid date format before extracting the year. By following these tips and examples, you’ll be able to extract the year from a date value in R with ease, regardless of the circumstances.

A Basic Example of Extracting A Date In R

This is particularly useful if you need to roll up daily data into a higher level aggregate, such as a yearly number. Consider the following list of orders from various customers for some cute teddy bears at Christmas:

# How To Extract Year From Date in R - setup> bears <- data.frame(orderdate=c('2019-12-01','2019-12-03','2019-12-04','2019-12-06','2019-12-09','2019-12-10','2019-12-11','2019-12-12','2019-12-12','2019-12-12','2019-12-12','2019-12-12','2019-12-15','2019-12-19','2019-12-23','2019-12-24','2020-01-01','2020-01-15','2020-01-21','2020-01-21','2020-01-21','2020-01-21'),amount= c(1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,10,3,5,15))> head(bears) orderdate amount1 2019-12-01 12 2019-12-03 23 2019-12-04 14 2019-12-06 15 2019-12-09 16 2019-12-10 1

So we sell a trickle of bears (presumably at high prices) in 2019 and then need to dump the rest (at a greatly reduced price) in 2020. Typical holiday product, in other words. So – how did we do, in terms of total sales?

For this example, we’re going to do a couple of things. First, we’re going to show you how to extract year from date in R using the as function. Next, we’re going to roll up the amount of units sold by year using the aggregate function in R. The aggregate function can group and sum data by the levels of a variable. The final result will be a report on our sales of bears by year, in a nice date format that can tell us the value of the date object for a specified date year- that is, how many orders were placed in that year interval.

How To Extract Year From Date in R – The Code

Out of all of the datetime functions available for use in your R code, the extract year function is one of the most useful. There are many other extract date function options, including an extract month function, extract month number or week number, find date value, find current date, or convert date. Each of these has a different effect on the given datetime object, and can help you perform a wide variety of data analysis tasks on your specified date value or date range.

# How To Extract Year From Date in R - example# make sure the date is indeed a date> bears$orderdate <- as.date(bears$orderdate)# extract the year and convert to numeric format> bears$year <- as.numeric(format(bears$orderdate, "%Y"))# spot check for quality; yes, showing the year> head(bears) orderdate amount year1 2019-12-01 1 20192 2019-12-03 2 20193 2019-12-04 1 20194 2019-12-06 1 20195 2019-12-09 1 20196 2019-12-10 1 2019

We’re prepped. Time to tally the results.

How To Extract Year From Date in R – Denouement

We’re going to use the aggregate year function in R to roll up data by year.

> aggregate(bears$amount, by=list(year=bears$year), FUN=sum) year x1 2019 172 2020 35

Hmmm. We sold 17 toy bears in the month before Christmas and 35 toy bears when we liquidated the remaining inventory in the month after the holidays. Not a great year for bears. I’m thinking we should try another product next year. We may have made decent margins on the first 17 bears sold during the holiday season, but dumping 2/3 of our inventory at the end of the season has got to hurt…

Anyone want to give the Christmas Ferret a go?

Going Deeper: Different Date Formats in R

Dates in R can be represented in different formats, depending on the needs of the user. The most common date formats in R are “YYYY-MM-DD” and “MM/DD/YYYY”, but there are many other formats that can be used as well. Here is an overview of the most common date formats in R:

• “YYYY-MM-DD” format: This is the most common date format in R. It represents the year, month, and day in a four-digit year format, followed by a hyphen and then the two-digit month and day. For example, “2022-10-31” represents October 31, 2022.
• “MM/DD/YYYY” format: This is another common date format in R. It represents the month, day, and year in a two-digit month format, followed by a forward slash and then the two-digit day and year. For example, “10/31/2022” represents October 31, 2022.
• Other formats: There are many other date formats that can be used in R, such as “DD-MM-YYYY” and “YYYY/MM/DD”. The specific format used will depend on the needs of the user and the data being analyzed.

When working with dates in R, it is important to ensure that the date format is consistent across all data sources and analyses. If the date format is inconsistent, it can lead to errors and incorrect data analysis.

Tips on How to Convert Between Different Date Formats

Converting between different date formats in R can be done using theas.Date()function. This function takes a character string as input and converts it to a date object in the specified format. For example, to convert a date in “MM/DD/YYYY” format to “YYYY-MM-DD” format, use the following code:

> date <- "10/31/2022"> new_date <- as.Date(date, format = "%m/%d/%Y")> new_date[1] "2022-10-31"

In this example, theas.Date()function takes thedatecharacter string and converts it to a date object in “YYYY-MM-DD” format using theformatargument.

To convert a date object to a character string in a specific format, use theformat()function. This function takes a date object as input and returns a character string in the specified format. For example, to convert a date object to a character string in “DD/MM/YYYY” format, use the following code:

> date <- as.Date("2022-10-31")> new_date <- format(date, format = "%d/%m/%Y")> new_date[1] "31/10/2022"

In this example, theformat()function takes thedateobject and converts it to a character string in “DD/MM/YYYY” format using theformatargument.

It is important to note that when converting between different date formats, the resulting date may be different from the original date due to differences in the way dates are represented. For example, if you convert a date in “MM/DD/YYYY” format to “YYYY-MM-DD” format, the resulting date will be different because the month and day are switched. Therefore, it is important to carefully consider the implications of converting between different date formats and to ensure that the resulting date is still valid and meaningful.

By understanding the different date formats in R and how to convert between them, ensure that your date data is consistent and meaningful across all data sources and analyses.

Examples of Extracting the Year from a Date in Different Contexts

Extracting the year from a date is a common operation when working with time-series data in R. Here are some examples of how to extract the year from a date in different contexts:

Extracting the Year from a Date Stored in a Vector or a Dataframe Column

To extract the year from a date stored in a vector or a dataframe column, use theformat()function. This function takes a date object as input and returns a character string in the specified format. For example, to extract the year from a vector of dates, use the following code:

> dates <- c("2022-10-31", "2023-01-01", "2023-06-30")> years <- format(as.Date(dates), "%Y")> years[1] "2022" "2023" "2023"

In this example, theas.Date()function is used to convert the character string dates to date objects, and then theformat()function is used to extract the year in “YYYY” format.

To extract the year from a date stored in a dataframe column, use the same approach. For example, to extract the year from a “Date” column in a dataframe called “mydata”, use the following code:

> mydata$Year <- format(as.Date(mydata$Date), "%Y")

In this example, a new column called “Year” is created in the “mydata” dataframe, and theformat()function is used to extract the year in “YYYY” format from the “Date” column.

Extracting the Year from a Date that is Stored as a Character String

If the date is stored as a character string, you can still extract the year by converting the string to a date object using theas.Date()function. For example, to extract the year from a character string date, use the following code:

> date <- "2022-10-31"> year <- format(as.Date(date), "%Y")> year[1] "2022"

In this example, theas.Date()function is used to convert the character string date to a date object, and then the format()function is used to extract the year in “YYYY” format.

If the character string date is not in a recognized date format, specify the format using theformatargument in theas.Date()function. For example, to extract the year from a character string date in “MM/DD/YYYY” format, use the following code:

> date <- "10/31/2022"> year <- format(as.Date(date, format = "%m/%d/%Y"), "%Y")> year[1] "2022"

In this example, theas.Date()function is used to convert the character string date to a date object, and theformatargument is used to specify the date format as “MM/DD/YYYY”.

Strategies for Handling Missing or Invalid Dates

Dealing with missing or invalid dates can be a challenge when working with time-series data in R. Here are some strategies for handling missing or invalid dates:

Tips on How to Convert Missing or Invalid Dates into a Valid Date Format

If you encounter missing or invalid dates in your data, convert them into a valid date format before extracting the year. Use the as.Date()function to convert a character string to a date object in a specific format. For example, to convert a missing date represented by “NA” to a valid date format, use the following code:

> date <- "NA"> new_date <- as.Date(date, format = "%Y-%m-%d")> new_date[1] NA

In this example, theas.Date()function takes thedatecharacter string and converts it to a date object in “YYYY-MM-DD” format using theformatargument. The resulting date isNA, which indicates a missing value.

Similarly, if you encounter invalid dates represented by “0000-00-00”, convert them to a valid date format using the same approach. For example:

> date <- "0000-00-00"> new_date <- as.Date(date, format = "%Y-%m-%d")> new_date[1] NA

In this example, thedatecharacter string is converted to a date object in “YYYY-MM-DD” format using theformatargument. The resulting date isNA, which indicates a missing value.

Explanation of How to Handle Missing or Invalid Dates

When working with missing or invalid dates, it is important to first identify the cause of the missing or invalid value. Missing dates can be caused by a variety of factors, such as data entry errors, incomplete data, or technical issues. Invalid dates can be caused by incorrect date formats, data entry errors, or other issues.

Once you have identified the cause of the missing or invalid dates, take appropriate steps to handle them. For example, if the missing dates are caused by incomplete data, you may need to impute the missing values using a statistical method. If the invalid dates are caused by incorrect date formats, you may need to convert them to a valid date format with the as.Date()function.

In some cases, it may not be possible to handle missing or invalid dates. In these cases, you may need to exclude the affected data points from your analysis or consider alternative methods for handling missing or invalid data.

It is important to note that when handling missing or invalid dates, you should ensure that the resulting data is still meaningful and accurate. For example, if you impute missing dates using a statistical method, you should ensure that the imputed values are plausible and consistent with the rest of the data.