Discover and read the best of Twitter Threads about #rstats
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Need to extract days, months, years or more from time data?
Don't compute them all manually with {lubridate}. That's way too tedious.
The {timetk} package has a nice function that does all the heavy lifting for you.
LEFT: {lubridate} workflow
RIGHT: {timetk} workflow
#rstats
Don't compute them all manually with {lubridate}. That's way too tedious.
The {timetk} package has a nice function that does all the heavy lifting for you.
LEFT: {lubridate} workflow
RIGHT: {timetk} workflow
#rstats
BONUS: Maybe you don't want use all of the stuff that {timetk} computes for you.
Here's a simple function that extracts only the parts you want.
All of the code can be found on GitHub at gist.github.com/AlbertRapp/2c9…
Here's a simple function that extracts only the parts you want.
All of the code can be found on GitHub at gist.github.com/AlbertRapp/2c9…
Also, shoutout to @EatsleepfitJeff for teaching me about this function from {timetk} ☺️
Everybody loves colors but only few know how to use them well.
With the right guidelines, using colors becomes super easy.
Let me show you how to implement these guidelines with ggplot 🧵
#rstats
With the right guidelines, using colors becomes super easy.
Let me show you how to implement these guidelines with ggplot 🧵
#rstats
Anyone can create a stacked bar chart with ggplot.
But that can end up in a colorful & messy plot.
Let's implement a couple of guidelines from this datawrapper blog post to level up our color game blog.datawrapper.de/10-ways-to-use…
But that can end up in a colorful & messy plot.
Let's implement a couple of guidelines from this datawrapper blog post to level up our color game blog.datawrapper.de/10-ways-to-use…
The key is to reduce the amount of colors and leverage the `alpha` aesthetic as well.
Sometimes people ask me if I can do one-on-one R tutoring.
Sure I can. But then my hourly rate applies. And there are many amazing *free* resources. Want to try them first?
Here are a few that I recommend. #rstats
Sure I can. But then my hourly rate applies. And there are many amazing *free* resources. Want to try them first?
Here are a few that I recommend. #rstats
1 // Yet Again: R + Data Science
Find it at yards.albert-rapp.de
I'll start with one of my own bc I assume that you like my style (otherwise why ask me?)
Beware though: YARDS is a graduate-level course that I taught for math students w/ a bit of programming experience.
Find it at yards.albert-rapp.de
I'll start with one of my own bc I assume that you like my style (otherwise why ask me?)
Beware though: YARDS is a graduate-level course that I taught for math students w/ a bit of programming experience.
2 // R for Data Science
To me this book is like the R bible. It introduced me to the so-called tidyverse and taught me much of what I know.
This one starts out slow and is really beginner-friendly
r4ds.had.co.nz
To me this book is like the R bible. It introduced me to the so-called tidyverse and taught me much of what I know.
This one starts out slow and is really beginner-friendly
r4ds.had.co.nz
The #rstats datawizard package (from the @easystats4u ecosystem) has two very useful functions to deal with duplicates.
* data_duplicated: Extract all duplicates including the first, unlike duplicated() or dplyr::distinct()
* data_unique: by default selects the "best" duplicate
* data_duplicated: Extract all duplicates including the first, unlike duplicated() or dplyr::distinct()
* data_unique: by default selects the "best" duplicate
data_duplicated() also contains an additional column reporting the number of missing values for that row, to help in the decision-making when selecting which duplicates to keep.
data_unique() can keep either the first, last, or "best" duplicate. The "best" duplicate (default) will pick the row with the smallest number of missing values. In case of ties, it picks the first one, as it is the one most likely to be valid and authentic, given practice effects
I've been experimenting with #chatgpt for #datascience for 16 weeks.
And I now have a process I'm happy with.
Here are the details. 🧵
#datascience #rstats
And I now have a process I'm happy with.
Here are the details. 🧵
#datascience #rstats
Using ChatGPT for data science has been a MASSIVE learning curve.
I began using it for complex workflows.
And I FAILED miserably.
I began using it for complex workflows.
And I FAILED miserably.
Case in point- My first try was asking it to build me a machine learning model + a shiny app for scoring customers.
FAIL.
My ask was just too complex.
FAIL.
My ask was just too complex.
{modelsummary} looks like a pretty slick package for reporting. Definitely keeping that one at the back of my mind 🤔 #rstats
And it looks like it is dangerous. The kind of dangerous I enjoy 🤣
If you liked this post, you may enjoy my 3-minute newsletter too.
Every week, my newsletter shares insights on
- R & dataviz,
- Shiny and web dev
Reading time: 3 minutes or less
You can join at
alberts-newsletter.beehiiv.com/subscribe
Every week, my newsletter shares insights on
- R & dataviz,
- Shiny and web dev
Reading time: 3 minutes or less
You can join at
alberts-newsletter.beehiiv.com/subscribe
This is coming from a person that has created two R packages that combine for 1.5 Million downloads.
Has trained elite data scientists at Apple, Walmart, Google.
And has built a career teaching students how to become data scientists.
Why did it take so long?
Has trained elite data scientists at Apple, Walmart, Google.
And has built a career teaching students how to become data scientists.
Why did it take so long?
👉 Too many resources.
I thought I had to learn everything:
Deep learning.
Machine learning.
Algorithms.
The toughest part was figuring out which tools to learn and which were “red herrings” (a waste of time).
I thought I had to learn everything:
Deep learning.
Machine learning.
Algorithms.
The toughest part was figuring out which tools to learn and which were “red herrings” (a waste of time).
There are over 2,000 AI tools that have hit the market over the last 365 days.
So I condensed them into the best.
Here are the TOP 15 AI TOOLS for Data Scientists. 🧵
#datascience #rstats #python #career #ai
So I condensed them into the best.
Here are the TOP 15 AI TOOLS for Data Scientists. 🧵
#datascience #rstats #python #career #ai
It's hard not to get excited about #AI. The potential is insane. It's also scary.
And the worst thing you can do for your career is ignore AI.
I mean, there are literally 2,000 new tools that have hit the market in 365 days. So where do you start?
I want to help.
And the worst thing you can do for your career is ignore AI.
I mean, there are literally 2,000 new tools that have hit the market in 365 days. So where do you start?
I want to help.
Here are the 15 AI tools that, as a data scientist, MUST be on your radar (I'm road-testing ALL of these).
CODE:
1. ChatGPT: OpenAI's AI Chatbot openai.com/blog/chatgpt
CODE:
1. ChatGPT: OpenAI's AI Chatbot openai.com/blog/chatgpt
Data cleaning is tedious.
But it's much easier with the {janitor} package. Especially if you work with Excel files.
Here are 5 underrated features from {janitor}. #rstats
But it's much easier with the {janitor} package. Especially if you work with Excel files.
Here are 5 underrated features from {janitor}. #rstats
1 // Create clean names
This is absolutely the best function. It transforms column names such that they are easier to use for programming.
Left: Bad for programming
Right: Good for programming
This is absolutely the best function. It transforms column names such that they are easier to use for programming.
Left: Bad for programming
Right: Good for programming
2 // Remove empty or constant cells from Excel files
Excel files can be messy to read in R. Lots of weird column names and empty cells.
{janitor} takes care of that for us.
Excel files can be messy to read in R. Lots of weird column names and empty cells.
{janitor} takes care of that for us.
3 battle-tested skills that every data scientist should have.
(and how to apply them to a job interview)🧵
#datascience #skills #rstats #python
(and how to apply them to a job interview)🧵
#datascience #skills #rstats #python
People don’t realize this but I was a data science consultant and corporate trainer...
That was long before I was a “teacher” and a “6-figure data science mentor”.
That’s where I learned these skills through battle-testing.
And my clients were my test subjects. 🧪🧑🔬
That was long before I was a “teacher” and a “6-figure data science mentor”.
That’s where I learned these skills through battle-testing.
And my clients were my test subjects. 🧪🧑🔬
1. Focus on results
Outcomes are what moves mountains. Not analysis.
Yes- data analysis is incredibly important.
But, what’s more important is what you and your company do with the analysis.
Outcomes are what moves mountains. Not analysis.
Yes- data analysis is incredibly important.
But, what’s more important is what you and your company do with the analysis.
“Why python vs R?”
“What’s inside your new python course?”
“What will it do for me (if I’m an #R user)?”
I’ve been getting a ton of questions so I figured Twitter can help me explain.
#python #rstats #datascience
“What’s inside your new python course?”
“What will it do for me (if I’m an #R user)?”
I’ve been getting a ton of questions so I figured Twitter can help me explain.
#python #rstats #datascience
1. R vs Python.
My guess is 90% of my followers use R.
So why am I promoting python?
Well I just wrote a full article on why R users should learn **some** Python…
Even if python is harder.
business-science.io/code-tools/202…
My guess is 90% of my followers use R.
So why am I promoting python?
Well I just wrote a full article on why R users should learn **some** Python…
Even if python is harder.
business-science.io/code-tools/202…
2. What’s inside my new python course?
I just put together a 5 minute video that shows one powerful use case for python.
And it’s the course project.
I just put together a 5 minute video that shows one powerful use case for python.
And it’s the course project.
ChatGPT just made a Data Science Web App for me in under 15 minutes.
Here's the story. 🧵
#datascience #rstats
Here's the story. 🧵
#datascience #rstats
1. Machine Learning
ChatGPT created the basic code for an XGBoost Machine Learning model
The model is used for scoring the customer spend
ChatGPT created the basic code for an XGBoost Machine Learning model
The model is used for scoring the customer spend
2. Shiny App
ChatGPT produced the code for a basic shiny app that packages the model...
...And the app can make new spend predictions based on tweaking inputs for a new customer.
ChatGPT produced the code for a basic shiny app that packages the model...
...And the app can make new spend predictions based on tweaking inputs for a new customer.
If you don't know me, I'm Matt.
I'm an R user.
Nice to meet you...
I'm an R user.
Nice to meet you...
And, I'm super pumped for today...
GOOD NEWS: My Python Course Launch is happening today at 2PM EST
BAD NEWS: It's officially over-registered. 4,872 registered. But ONLY 1000 get seats.
Here's what you need to do...
GOOD NEWS: My Python Course Launch is happening today at 2PM EST
BAD NEWS: It's officially over-registered. 4,872 registered. But ONLY 1000 get seats.
Here's what you need to do...
I just revealed the data science project that helped my small business generate $3,500,000 in revenue in the last 2 years.
Here are the juicy details...
#datascience #python #rstats #career
Here are the juicy details...
#datascience #python #rstats #career
It's true. My company did almost $2,000,000 last year with an email list size of under 100,000.
And a big part of it was the Marketing Analytics that I'm revealing in my new Python course.
What did we do?
And a big part of it was the Marketing Analytics that I'm revealing in my new Python course.
What did we do?
We used machine learning to score leads.
We then created nature vs sales targeting segments.
Then we did this...
We then created nature vs sales targeting segments.
Then we did this...
The basics of R to Advance for Beginners.
Bookmark this Tweet and Follow me
COMMENT PDF - and I will send you the compiled PDF of more than 10 Cheat Sheets
If your inbox is disabled please DM me.
#DataAnalytics #DataScience #rstats #data #BigData
Bookmark this Tweet and Follow me
COMMENT PDF - and I will send you the compiled PDF of more than 10 Cheat Sheets
If your inbox is disabled please DM me.
#DataAnalytics #DataScience #rstats #data #BigData
1/20
2/20
The basics of R for Beginners.
Bookmark this Tweet and Follow for More
COMMENT PDF - and I will send you the compiled PDF of more than 10 Cheat Sheets
#DataAnalytics #DataScience #rstats #data #BigData
Bookmark this Tweet and Follow for More
COMMENT PDF - and I will send you the compiled PDF of more than 10 Cheat Sheets
#DataAnalytics #DataScience #rstats #data #BigData
If you have not received any DM from me that means your Inbox is disabled.
Follow me so that I can Inbox you.
Follow me so that I can Inbox you.
Don't forget to:
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✅Like the first tweet in the thread
✅ Retweet this post to share with the world.
Have an amazing day 🙏
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@dr_abdulali125
🔥
✅Like the first tweet in the thread
✅ Retweet this post to share with the world.
Have an amazing day 🙏
Ever heard of logistic regression? Or Poisson regression? Both are generalized linear models (GLMs).
They're versatile statistical models. And by now, they've probably been reframed as super hot #MachineLearning.
Brush up on their math with this thread. #rstats
They're versatile statistical models. And by now, they've probably been reframed as super hot #MachineLearning.
Brush up on their math with this thread. #rstats
Let's start with logistic regression. Assume you want to classify a penguin as male or female based on its
* weight,
* species and
* bill length
Better yet, let's make this specific. Here's a dataviz for this exact scenario. It is based on the {palmerpenguins} data set.
* weight,
* species and
* bill length
Better yet, let's make this specific. Here's a dataviz for this exact scenario. It is based on the {palmerpenguins} data set.
As you can see, the male and female penguins form clusters that do not overlap too much.
However, regular linear regression (LR) won't help us to distinguish them. Think about it. Its output is something numerical. Here, we want to find classes.
However, regular linear regression (LR) won't help us to distinguish them. Think about it. Its output is something numerical. Here, we want to find classes.
Over the past 3 years, I've been writing Python code daily.
And this coming Thursday, I'm excited to share what I've learned. 🧵
#datascience #career #python #rstats
And this coming Thursday, I'm excited to share what I've learned. 🧵
#datascience #career #python #rstats
Most of you know me as an #R guy. And I 1000% love R. ❤️
But I've also grown over the past 10+ years that I've been practicing data science as a data scientist, as a consultant, and as an educator.
But I've also grown over the past 10+ years that I've been practicing data science as a data scientist, as a consultant, and as an educator.
What I've come to realize is that no one language is perfect.
Each has its strengths. And weaknesses.
And more often than not we get caught up in debating things that **don't** really matter...
Like R vs Python.
Each has its strengths. And weaknesses.
And more often than not we get caught up in debating things that **don't** really matter...
Like R vs Python.
There are more than 10,685,000 people learning to work with data.
Yet most data science jobs now have over 1000+ applicants.
And only 5 actually make it to an interview.
Learn these 4 skills to stand out (free to steal & use to advance your career).
#datascience #rstats
Yet most data science jobs now have over 1000+ applicants.
And only 5 actually make it to an interview.
Learn these 4 skills to stand out (free to steal & use to advance your career).
#datascience #rstats
1. Time series analysis
Up to 90% of companies need better forecasting.
And less than 5% of data scientists are competent in time series analysis.
Up to 90% of companies need better forecasting.
And less than 5% of data scientists are competent in time series analysis.
Key Point:
Becoming good at time series is a massive advantage that automatically places you in the 95th percentile of applicants.
Try This:
Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Becoming good at time series is a massive advantage that automatically places you in the 95th percentile of applicants.
Try This:
Demonstrate an application of time series analysis in the interview, and set yourself apart from the crowd.
Becoming a 6-figure data scientist (with #R) in today's economy is different than it was in 2022.
It requires a transformation...
#datascience #career #rstats
It requires a transformation...
#datascience #career #rstats
And those that successfully make the transformation:
1. Get hired 75% faster (3-6 months vs 12+ months)
2. Earn 17% more ($145K vs $117K)
3. Get promoted 2X faster (1-2 years vs 2-4 years)
1. Get hired 75% faster (3-6 months vs 12+ months)
2. Earn 17% more ($145K vs $117K)
3. Get promoted 2X faster (1-2 years vs 2-4 years)
Want to learn how?
I have a LIVE Masterclass today where I will reveal the exact steps to take.
Strict limit: 500 Seats (and we are over-capacity)
I have a LIVE Masterclass today where I will reveal the exact steps to take.
Strict limit: 500 Seats (and we are over-capacity)
There are 100,000 data scientists on the market. How can you possibly get a job?
This is one way… 🧵
#datascience #rstats
This is one way… 🧵
#datascience #rstats
Imagine this.
You walk into the Central Bank of Canada, the backbone of Canada’s financial system.
You’re applying for a data science position.
You walk into the Central Bank of Canada, the backbone of Canada’s financial system.
You’re applying for a data science position.
On paper the odds are stacked against you.
You use R.
The whole team uses Python.
And you are a complete unknown.
You use R.
The whole team uses Python.
And you are a complete unknown.
87% of data science projects fail.
One reason is the lack of production.
Here's an easy solution. 🧵
#datascience #python #R #rstats #career
One reason is the lack of production.
Here's an easy solution. 🧵
#datascience #python #R #rstats #career
This is called an #API (Application Programming Interface).
An API allows the user to make requests using a tool they are comfortable with.
The API runs a program that the data scientist has created on the server.
And the API returns a result.
The API runs a program that the data scientist has created on the server.
And the API returns a result.
What stresses me out isn’t the same thing that stressed my parents out.
True story. 🧵
#datascience #rstats
True story. 🧵
#datascience #rstats
It was the day before R/Finance 2018.
I had a presentation for R/Finance where I was going to present a Shiny App that I was working on.
I updated my R software…
Big mistake.
I had a presentation for R/Finance where I was going to present a Shiny App that I was working on.
I updated my R software…
Big mistake.
My shiny app suddenly stopped working and I went into straight panic-mode. 😱
This was before I knew how to use GitHub & Docker like I do now.
So I was freaking out.
This was before I knew how to use GitHub & Docker like I do now.
So I was freaking out.
I continue to be impressed by the ease of doing machine learning with Pycaret.
This is especially great for #R people that want to learn #Python.
Let me explain...
#DataScience #Rstats
This is especially great for #R people that want to learn #Python.
Let me explain...
#DataScience #Rstats
As many of you know, my primary data science toolkit is #R. ❤️
I've been doing data science in production at @bizScienc and developing open-source R software for over 10 years.
- Modeltime (+4 ecosystem pkgs)
- Timetk
- Tidyquant
- CorrelationFunnel
I've been doing data science in production at @bizScienc and developing open-source R software for over 10 years.
- Modeltime (+4 ecosystem pkgs)
- Timetk
- Tidyquant
- CorrelationFunnel
But, I interact with team members and interface with clients...
and their language of choice is often Python.
It's refreshing that I can quickly switch to Python when needed, and not need to write 5000 lines of #ScikitLearn code to do basic machine learning.
and their language of choice is often Python.
It's refreshing that I can quickly switch to Python when needed, and not need to write 5000 lines of #ScikitLearn code to do basic machine learning.
