Trending February 2024 # How To Measure Branding Ppc Campaigns # Suggested March 2024 # Top 11 Popular

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Rule #1 in every paid search campaign (PPC) is Identify and Track a Measurable Conversion. For 95% of campaigns this is a must, but there is a new breed of paid search campaign: the branding campaign. These are based on traffic to a site only and intentionally do not rely on traditional tracked metrics (sales, forms) for results.

Before I get into the new breed let me make one thing very clear, paid campaigns are almost impossible to optimize if there is no trackable conversion to measure. Paid search managers rely on the conversion to determine where they are losing the searcher. The funnel of paid search starts with the impression, or the search query.

Another change is coming to the online marketing space. Trust is still a big part of the consumer’s decision-making process and with all of the scams online, it makes sense that a return to some form of branding was going to happen. How do we make this work though?

As much as branding campaigns cannot be tied directly to specific sales, every paid search campaign needs to have metrics and benchmarks. If you are tasked with a paid search campaign with no conversion metrics and a focus on traffic, dig deeper for what they are looking for from the traffic. Most branding campaigns should be measurable through a marked increase in overall traffic (given), but here are a few more metrics you might want to track:

Time on Page – This metric will allow you to see how long people are spending on the initial page. This is one of the metrics to determine landing page relevance to keyword and ad. In most cases, the higher the better.

Bounce Rate – Are your visitors leaving quickly? You might be on the wrong keywords, need a page redesign, or new ad copy if the traffic from your paid campaign has a high bounce rate. The main goal though is to get your branding campaign to drop your bounce rate over the entire site. An increase in return visitors will have an affect across the board if the branding campaign if effective.

Total Time on Site – If your intent is to drive traffic and get more people interested in your brand, then the entire site needs to be targeted to them. If the total time on site goes up (be sure you are not tracking yourself), this trust and branding goal is being met.

Remember that the online marketing world changes everyday. Roll with it and use your analytical brain to tie old media with new metrics. Everything is trackable and measurable online. Find benchmarks from prior years and forge a new path.

The guest post is by Kate Morris. You can find her on her blog or on twitter @katemorris.

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How To Track Social Media Campaigns Using Google Analytics

Understanding how GA ‘UTM’ values can give you more insight into your social media marketing

This form of media has now become an integral part of our lives and continues to evolve. A few years ago the emphasis was on B2B companies being active and creating pages on sites such as Facebook, LinkedIn, and Twitter… now the conversation has swung and is moving towards the idea that every marketing campaign must be social.

With every month that passes, there seems to be an endless stream of new channels, terminologies, and the dialogue can be fascinating. But it can be very easy to get caught up with the new innovations and forget about what you’re currently doing. It seems that before you have got to grips with one channel there is another one to topple that, and it requires your undivided attention.

The evolving digital landscape

The latest research on the use of different social networks shows that there is a huge choice of existing social media channels and a stream of new channels. We need to keep an eye on the new options, but this can sometimes get in the way of making social media marketing measurable in a meaningful way. We are too busy ‘doing’ social and trying to figure the best way of using them so measurement can be neglected.

As the digital world keeps expanding (let’s not forget you are dealing with email, website, PPC, SEO, remarketing, and content to name a few) it is very easy to get overloaded and overlook the key reason you attempted social in the first place.

Interestingly the ‘social’ nature of these channels are really useful for engaging prospects and customers (here’s a really interesting infographic summing up the 6 major social channels). Rather than the dodgy reputation that haunts sales departments (‘why are they not listening to me? Maybe it’s end of the month and they need to hit their commission’), social media allows you to build a relationship in an informal, personable, low-pressured way. Sounds like a good thing to do right….?

Measuring social engagement

So you need a way to measure engagement and how this translates to business results on your site.

When we talk to customers we find that most B2B marketers are naturally cynical and fall into one of two camps when it comes to social media marketing:

1. Those not doing it and thinking it is a waste of time and

2. Those doing it and wondering if it is a waste of time.

There’s that niggling feeling that there must be a way to make social media work that just won’t go away. In our experience that uncertainty is born out of a total lack of meaningful measurement.

Social media has a whole range of self-fulfilling metrics that enable those charging for their social media services to justify their own existence. The value of a retweet to the bottom line of your business is quite intangible.

Like any marketing activity, we must be able to track and measure its ROI. What is it delivering to the business in terms of opportunities? You cannot improve what you cannot measure after all.

Introducing how to use Google Analytics UTMs for measuring social media marketing

Do you know what UTMs are and do you use them? If you answer ‘no’ I would suggest you’re missing out since tracking campaigns with them is one of the most underused and undervalued things in digital marketing.

At a practical level UTM parameters are bits of text added to the end of your URL, technically called a query string since they’re separated by a question mark from the web address.

For example, a URL with UTM values from this post taking you to CommuniGator’s GatorSocial page could be tagged as:

It helps you track where your links are coming from but more importantly the actual source and content. Once you have goals setup in Google Analytics you can use them to track all of your links and measure the success of marketing activities, like social media and guest blog posting.

To explain the full details of measuring social media, download our social media measurement whitepaper which will help you get the most out of you social media and make sure you are able to ascertain what value it is providing to your business.

The paper covers the five key areas below – I hope you enjoyed the read and find it useful.

1. Social Profiles: First Impressions Count

2. Audiences: Follow You, Follow Me

3. Content: A Kingsize Challenge

3. Analytics: Meaningful Measurement

4.Conclusion: Managing the Marketing Mix

Image Credit / Copyright: Marcel De Grijs/ 123RF Stock Photo.

Thanks to Simon Moss for sharing his opinions and thoughts in this blog post. Simon Moss is a Chartered Marketer with over eight years’ marketing experience gained primarily in the B2B marketplace. He currently looks after the marketing for CommuniGator and WOW Analytics, a leading digital marketing agency providing email marketing solutions and cutting edge technology that enables you to maximise the value of every visit to your website – identifying and naming prospects visiting corporate websites. You can follow him on LinkedIn or connect on Twitter. For more information on lead scoring and to receive a demonstration and trial, visit the WOW Analytics website or call us on 0844 880 2899.

How To Measure Credit Risk With Types And Uses?

Definition of Credit Risk

Credit risk infers the possibility of a loss emerging from a borrower’s downfall to pay back a loan or meet contractual commitments. Conventionally, it pertains to the risk arising from lenders’ inability to return the owed interest and principal, impacting the cash flows and increasing assemblage costs. It’s inconceivable to predict who will default on agreements accurately. Still, a proper assessment and risk management can help you mitigate such credit risk to a remarkable extent by reducing the stringency of losses.

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When any lender extends loans such as mortgages, credit cards, or other similar loans, there is an avoidable risk that the borrower will not repay the loan amounts. Furthermore, if a company offers such credit to the customer, there’s the same risk that the customer will not pay back. It also incorporates other related risks, such as that the bond issuer may not make payment at the time of maturity and the risk occurring out of the incapacity of the insurance company to compensate for the claim. A higher level of credit risk in a profitable market will correlate with the elevated borrowing cost. Because of this, it is evaluated technically to mitigate such risk to a certain level.

How to Measure Credit Risk?

One of the modest ways to calculate credit risk loss is to compute expected loss which is calculated as the product of the Probability of default(PD), exposure at default(EAD), and loss given default(LGD) minus one.

Mathematically, it is depicted as follows-

Expected Loss = PD * EAD * (1 – LGD)

Where PD= Probability of default

EAD= exposure at default

LGD=Loss given default

Example of Credit Risk

Suppose that a bank, XYZ Bank Ltd, has given a loan of $250,000 to a real estate company. As per the bank credibility assessment, the company was rated “A” based on the industry cyclicality witnessed.

Let us formulate the expected loss for XYZ Ltd based on the details below:



Exposure at Default (EAD) $250,000

Probability of Default (PD) 1%

Loss Given Default (LGD) 68%


Expected Loss = PD * EAD * (1 – LGD)

Given, PD= 1%, EAD = $250,000, LGD = 68%

PD = 0.10% * $250,000 * (1 – 68%)

Expected Loss = $800

Types of Credit Risk

Credit risks are the reason why lending institutions undergo a lot of creditability assessments before providing credit. It can be considerably classified into three types.

Credit Default Risk: It includes losses incurred by the lender when the borrower is incapacitated from returning such amount in entire or when the borrower has exceeded 90 days from the due date but hasn’t made any payment.

Concentration Risk: Concentration risks emerge from substantial exposure to any individual or group because any unfavorable incident will likely impose significant losses. It is mainly concerned with any individual industry or company.

Causes of Credit Risk

Most lenders prefer to give loans to specific borrowers only. This causes credit concentration, including lending to a single borrower, a group of related borrowers, a particular industry, or a sector.

Credit Risk Mitigation

Institutions providing loans must consider the following points to mitigate credit risks, including:

Risk-Based Pricing: Pricing should be based on the amount of risk undertaken. Lenders can charge a high-interest rate to those more likely to default. Such practice can mitigate loss from default to a much extent.

Covenants: Lenders can inscribe stipulates on the borrower in the form of an agreement called covenants. Such as,

Periodically reporting the financial status of the borrower.

Pre-payment in case of an unfavorable change in the borrower’s debt-equity ratio or interest coverage ratio.

Diversification lenders face a high probability level in the case of small borrowers with an inevitable risk of default. Lenders can mitigate credit risks by diversifying the borrower funding pool.

Credit Insurance and Credit Alternative: Credit insurance is widely operated to mitigate credit risks. These contracts transmit the risk from the lender to the insurer in exchange for payment. The most general form of a credit derivative is a credit default swap.

Uses of Credit Risk

Credit risk analysis is a type of scrutiny to acknowledge the borrower’s ability to pay back.

Credit risks infer the ability of the individual to pay back what he owes; lenders usually perform various assessments to mitigate any loss that would arrive in the foreseeable future.

Lenders can arrive at a less quantifiable loss probability by properly evaluating such credit risks to curb the chances of loss.


A good credit risk management scheme improves the capacity to foresee, which helps evaluate the potential risk in every transaction.

The banks use the credit risks model to examine the degree of lending which can be financed to prospected or new borrowers.

Credit risk management is an alternative to traditional techniques for pricing options.

Risk management can be a very expensive liaison.

Although there are some quantitative techniques to evaluate credit risk, such decisions are inaccurate as assessing risk thoroughly is impossible.

Generally, lenders apply one rigid model to all mitigation approaches, which is wrong.


Nowadays, technical innovations have improved credit risk management. Such techniques have increased the proficiency in measuring, identifying, and regulating credit risks as a popartBasel III execution.

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How To Plan, Measure And Achieve Your Marketing Goals

Without setting clear marketing objectives, you’re basically directionless – and working to achieve vanity metrics that don’t necessarily help you achieve your overall business goals

Digital marketing objectives underpin all marketing strategies. After all, how do you know what you’re working towards without clear, actionable, and achievable metrics? Setting marketing goals should always be the priority as they will be the basis of your entire strategy.

By achieving your digital marketing objectives, you’re also helping reach your overall business goals, whether that’s boosting your overall sales or improving awareness of your brand.

Setting marketing objectives is, on one hand, a great way to motivate yourself and your team and work towards achieving better results for your business.

On the other hand, objectives and KPI-setting is about much more than just saying “I want to achieve that and that” – it’s how you can develop a plan or strategy that will help support your vision and help you reach them.

But how do you set goals the right way?

In this blog post, discover how to set better marketing goals, in order to maximize your results and grow your business.

Why you need set marketing goals

Without clear goals, you’re basically directionless – and working to achieve vanity metrics that don’t necessarily help you achieve your overall business objectives.

For example, without social media objectives, you’re basically working to get more likes and engagement – but does that necessarily translate to success?

Accelerate your ROI from digital marketing. We have dedicated digital marketing strategies to help you to set and achieve your digital marketing objectives.

Our RACE Growth System gives marketers and business owners, like you, everything you need to plan for success. It follows a three-step process of setting objectives via Opportunity, Strategy, and Action to drive the results you need. Download your free copy to get started today.

Create your 90-day plan with the RACE Growth System

Download your free RACE Growth System guide today and unlock our three-step plan of Opportunity, Strategy and Action to grow your business.

Download guide

The basics of setting digital marketing objectives: how to set SMART goals

When it comes to setting objectives, there are various criteria you can use. We recommend strategic marketing planning through SMART goals work best because they cover every important aspect of a successful marketing goal:

Specific: There are two ways to interpret this and both are very useful. For one thing, you need to be specific with your goals and ensure that it’s a very clear objective; and for another, you also need to be very specific about what this goal means and what it encompasses.

Measurable: In other words, what KPIs will help you understand whether you’ve reached your goals or not. This is very important as you want to be able to understand whether your efforts actually paid off and how they translate to revenue. Also, it’s worth noting that with some digital marketing strategies, it can be difficult to quantify your results and understand how they translate into revenue (particularly with social media marketing).

Achievable: When setting goals, it’s a great idea to aim high – but not so high that they’re unrealistic. When you’re setting your digital marketing objectives, ask yourself whether they can actually be achieved or whether you’re simply setting yourself up for failure.

Relevant: Or, how do your digital marketing goals help you reach your business goals? As I mentioned earlier, this is very important because, at the end of the day, you’re developing all of these marketing strategies to help grow your business. In fact, you should start with your business objectives first and think of what digital marketing objectives will help you reach for the former.

Time-bound: Giving your objectives a clear deadline tells you when you need to measure your results and benchmark these results against past (and future) campaigns. This is another highly important practice as it helps you understand how to optimize your future strategies – and objectives – in order to achieve better results with each new campaign.

In short:

The reasons why SMART goals work so well with digital marketing objectives are that:

They give you a clear direction

They ensure that your goals are relevant to your vision

They put great importance on measuring your results, which is very important in digital marketing

Every time you set new marketing goals, make sure you also go through this list of goal-setting criteria – it will ultimately help you set better, more achievable goals.

Improve your digital marketing objectives with The Ten Measures Design Tests

You can add to your tests of choosing the right marketing objectives using these 10-measure design tests developed by performance management specialist Professor Andy Neely.

Ask these questions for your KPIs as you develop them:

These tests show there are additional filters on top of SMART that are useful to choose the best measure. I particularly like the “So-what test, another way of explaining relevance and Gaming – a common issue with target setting that isn’t considered by SMART!

Plus, check out our top 18 digital marketing techniques to ensure you are covering the key areas of digital marketing that are relevant today.

What kinds of digital marketing goals can you set?

Now that we’ve gone over the theoretical side of setting goals, what do digital marketing goals actually look like in practice?

There is a plethora of goals you can set up, but here are some of the most popular:

Improve your conversion rate: Another popular digital marketing objective is to convert more readers/viewers/followers into customers.

Increase your sales: Which is, after all, the ultimate goal for many different businesses.

Increase traffic: For example, organic traffic, traffic from social media, and so on.

Moreover, in the age of digital disruption don’t forget about brand-building and full-funnel techniques.

When you set your marketing goals, though, it’s not just about saying “I want more sales” – you have to be a lot more specific than that: how much do you want your sales to increase? Which channels will help you? What is your deadline?

That’s why we’ve created our strategic marketing goal setting training for marketers and managers to plan, manage and achieve their goals. Our popular RACE Practical Digital Strategy Learning Path includes modules like ‘set digital marketing goals and objectives’, to help you translate your vision into goals, objectives and KPIs.

When setting your marketing goals, it’s crucial to define each stage of the customer journey. For example, your objectives for new website visitors will be different from email subscribers. The RACE Practical Digital Strategy Learning Path will take you through setting marketing goals at each stage of reach, act, convert and engage.

Use our RACE Growth System to develop your marketing strategy. Within RACE you’ll find a plethora of solutions for large and small businesses, including marketing strategy and planning, digital channel specialist resources, and industry trends and innovations.

Marketing tactics

Whenever you put together a new marketing strategy or even when you use a digital marketing tactic, your overall strategic goals should always be at the back of your mind.

In fact, your strategies should essentially be action plans for your objectives – the tactics and strategies you need to leverage in order to ultimately achieve each goal.

When you create a digital marketing strategy, always start with your goals.

And once you have clear, specific goals, think of what tactics you can use to help you achieve your goals.

For example, let’s say you wanted to increase your organic traffic by 15% in 60 days.

In that case, you’d use tactics like:

Research keywords and create content for keywords you want to rank

Update and optimize your content to help boost your rankings

Start a link-building campaign to generate more backlinks

And any other tactics that can actually help you achieve your goal.


As you can see, setting marketing goals is imperative to the success of your business. Without goals, you’re essentially throwing stuff at the wall and hoping that something will stick – but in such a competitive landscape, using this method is not likely to get you far.

On the other hand, when you have clear goals for yourself, you’re motivating yourself and giving yourself and the entire marketing team a clear direction that they need to follow and an action plan – or strategy – that fully supports that.

We’ve got marketing training to support you in taking those next steps to a planned approach to marketing.

Create your 90-day plan with the RACE Growth System

Download your free RACE Growth System guide today and unlock our three-step plan of Opportunity, Strategy and Action to grow your business.

Download guide

Power Bi Measure Total Is Incorrect: How To Fix It

You might encounter issues with measures in Power BI table visualizations with a total row. The usual complaint is that the “Total” row is “incorrect” for the measure. Usually, the total row is correct for the measure, it’s just not what most people expect. In this tutorial, I’m going to take us through this common problem with Power BI measure total and how to solve it. You can watch the full video of this tutorial at the bottom of this blog.

Power BI measure total has been a problem for as long as Power BI has been around. So to fix this problem, the first thing I did was I went into DAX Studio and created a dump filters measure. If you’ve never seen a dump filters measure, it looks a lot like the one below.

All you have to do to get it into Power BI is take it from this VAR MAX filters and copy all of that code and then just paste it into a new measure, which is what I did. That’s where this dump filters measure came from.

I also built a tooltip page where I created a card visualization and put the dump filters measure in that card visualization. This allows us to view the filters within the table visualizations or the matrix visualizations.

If I hover over this 290, I can see that it has a filter on it of table category equals category one. This one is category two, where we have a Subcategory equal to blue, green, and red. This all comes from a very simple data query where I have six rows.

The last step is to create a measure that’s going to exhibit the measure total’s problem. And if you’re looking closely, you can already see that the total is not correct.

As far as DAX is concerned, it’s correct, but any normal human looking at this would think this is completely wrong. In fact, I have to believe that this is probably a leading cause of why people think it’s a huge barrier to the adoption of Power BI.

There’s the simple fact that measure totals are wrong out of the gate. The table visualizations and matrix visualizations don’t actually exhibit correct behavior.

I have this table here with my measure totals. All I’ve done is a quick SUMX and subtracted 10. This is a surrogate for any reasonably complicated measure. If you can exhibit the measure totals problem with something as simple as this, it’s going to crop up in a lot of your measures.

So, it’s obviously taking the filter context of the Category = Category 1.

It doesn’t have this additional filter context of this category without external influence. If I hover over here, you can see that it’s blank. There’s nothing filtering this right now. These rows have a filter on the category, but it is not taking that into account at all.

So how do we fix it? Let’s take a look at the measure I created.

I called this one Measure Totals Category, and it says, if ISINSCOPE table category, then just return my measure that is correct on a line-by-line item.

Otherwise, I’m going to SUMMARIZE that table by category, and then I’m going to calculate my measure for each line. Then I’m going to sum up the values.

Basically, I’m recreating this table visualization, then I’m summing up the resulting values from that measure on those rows, and that fixes it.

Now, this is all well and good. It’s a fairly easy fix, but it’s really damaging to self-service visualization, in my opinion.

If you have a reasonably complex measure and you’re promoting self-service visualization back to your end users, then it’s a good bet that you have no idea how they’re going to use that measure, and how they’re going to be working with it.

For example, you can see the problem here. I have the Measure Totals categories correct, but it’s incorrect down here. It even returns the wrong line-by-line items.

The reason for this is that categories are never in scope. So according to our measure, the ISINSCOPE table category returns the measure totals.

Well, that’s never the case. It’s always doing a summarization by category and then by value, which is completely wrong.

So now, we have to create another measure, Measure Total Subcategory, which if ISINSCOPE subcategory, returns the line item on that row. Otherwise, you sum up by subcategory in total the values, which gives us the correct answer.

This is why it’s damaging to self-service visualization because you have no idea how the end users are going to be using it, and what they will put into a table. You’ll need to write a measure for every possible combination, which is crazy.

I hope you’ve learned something from this tutorial. If you have encountered problems like this, share with us how you’ve dealt with them.

All the best!


How To Measure Execution Time In R (5 Methods + Examples)

Measuring the execution time is common in programming to get a better idea of how a program or a part of the code is performing.

This is a comprehensive guide to measuring the elapsed time in R. In this guide, there are some built-in mechanisms and examples of popular libraries’ ways of computing the execution times.

Here’s a short overview of the approaches you will learn in this guide:



Tictoc library

Microbenchmark Library

Rbenchmark Library

Let’s take a closer look at these methods and see some examples.

Method 1: Use Sys.time()

An easy way to measure the execution time of a piece of code is by using the built-in Sys.time() function.

To measure the execution time with the Sys.time() function:

Call Sys.time() before the part of code you want to measure and store the time instance into a variable.

Run the piece of code whose execution time you want to measure.

Call Sys.time() again and store the time instance to a variable.

Subtract the last Sys.time() call from the first Sys.time() call. This returns a description of the time difference between these two instances of time.

Here’s an example code where I measure the time of a function called example_func:

example_func <- function() { Sys.sleep(5) } start <- Sys.time() example_func() end <- Sys.time() print(end - start)


Time difference of 5.000719 secs Method 2: Use system.time()

Another way to measure the execution time in R is by using the system.time() function. Unlike the Sys.time() function, the system.time() function takes an expression, evaluates it, and measures the elapsed time.

Here’s an example of measuring how long it takes to run an example function:

example_func <- function() { Sys.sleep(5) } system.time({example_func()})


user system elapsed 0.001 0.000 5.018

But what do these values mean? To put it short, the elapsed column reveals the time it took to run your code.

Here’s a more specific breakdown of the returned values:

user. This is the user CPU time spent by the current R session.

system. This is the system CPU time that the operating system spent on behalf of the current process.

elapsed. The execution time of the actual code you are measuring.

Method 3: Use tictoc Library

Thus far, you’ve seen examples of measuring the execution time with built-in mechanisms. Sometimes using an external library to measure elapsed time can be handy as it makes code readable and intuitive.

One example of such a library is the tictoc library.

Notice that you need to install the tictoc library first before you can use it.

The idea of the tictoc function is to make measuring the elapsed time easy and readable:

Call tic() to start the clock.

Run some code.

Call toc() to stop the clock.

Calling toc() shows the elapsed time in the console.

Here’s an example of measuring the execution time of a sample function in R:

library(tictoc) example_func <- function() { Sys.sleep(3.5) } tic() example_func() toc()


3.525 sec elapsed Method 4: Use microbenchmark Library

Another library you might want to consider to measure the execution time of your program is the microbenchmark library.

To then use this library to measure the elapsed time, you need to run code inside the microbenchmark() function call.

This function not only returns the time it took to execute your code but also some useful details, such as minimum time, maximum time, median elapsed time, and such based on the numerous runs the function performs.

For example:

library(microbenchmark) example_func <- function() { Sys.sleep(0.1) } microbenchmark(example_func())


Unit: milliseconds expr min lq mean median uq max neval example_func() 100.1813 100.4506 101.3933 100.5973 100.8105 142.5669 100

Let’s take a quick look at the returned values:

expr is the code expression you want to find the execution time for.

min is the minimum amount of time it took to run the code out of the numerous tries.

lq is the lower quartile, that is, 25% of the runs took at most this amount of time to run.

mean is the average execution time.

median is the median time it took to run the code.

uq is the upper quartile, that is, 75% of the runs took at most this amount of time to run.

max is the maximum amount of time that the code ran.

neval is the number of evaluations which is 100 by default.

To change the number of times the code is timed, specify the optional times parameter in the microbenchmark() function call.

For example, let’s only do 10 runs:

library(microbenchmark) example_func <- function() { Sys.sleep(0.1) } microbenchmark(example_func(), times=10)


Unit: milliseconds expr min lq mean median uq max neval example_func() 100.3563 100.6037 103.1201 100.6237 100.7375 125.16 10

Using the microbenchmark library offers a great replacement for the system.time() function thanks to the level of detail it gives you about the code execution.

Method 5: Use rbenchmark Library

rbenchmark is another useful timing library for measuring execution times. rbenchmark is a great alternative to the system.time() function is the rbenchmark library.

Once again, you need to install this library before using it!

Very similar to the previous examples, the rbenchmark library comes with a built-in timing function, benchmark(). This function takes the piece of code you want to time as an argument. It then returns a bunch of data related to the runs it performed.

Notice that this method returns the total amount of time elapsed. By default, the function runs your code 100 times. So for example, if your code takes 1 second to run, this method returns an elapsed time of 100 seconds because it runs the code 100 times.

For example, let’s measure the execution time of an example function.

library(rbenchmark) example_func <- function() { Sys.sleep(0.2) } benchmark(example_func())


test replications elapsed relative chúng tôi sys.self 1 example_func() 100 20.183 1 0.1 0

As another example, let’s change the number of runs to 10 from the 100 default:

library(rbenchmark) example_func <- function() { Sys.sleep(0.2) } benchmark(example_func(), replications=10)


test replications elapsed relative chúng tôi sys.self 1 example_func() 10 2.036 1 0.02 0 Summary

Today you learned how to measure the execution time of your program in R.

A simple and native way to measure elapsed time is by using the system.time() function or the Sys.time() function.

If you want to make the code more clean, you use the tictoc library. It makes computing the execution time easy by using the tic() and toc() function calls.

To get more details about the elapsed time, you can use a library like rbenchmark or microbenchmark.

Thanks for reading. Happy coding!

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