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Harold Thomas
Приєднався 27 кві 2023
A channel for video tutorials on software and apps. Microsoft Office all apps (specialize in Excel) Power BI, Sway, ChatGPT and whatever other rabbit hole I go down.
SQLPyR: Cumulative & Rolling Calculations for Data Analysis in SQL, Python & R
You will learn about two powerful data science techniques: cumulative and rolling calculations. These techniques can be used to understand trends and patterns in your data over time, and can be applied across a variety of data science tools.
Переглядів: 3
Відео
SQLPyR: Comparing Window Functions in SQL Python and R : Part 2 Value and Aggregate Functions
Переглядів 721 годину тому
A new series where I compare different functions in SQ Python Pandas and the R tidyverse. The first part of the series will be on window functions. This video will cover value and aggregate functions in all 3 languages. All the files are available in my GitHub repository: github.com/hthomas229/PurpleCrown @haroldspurplecrowntraining
SQLPyR: Comparing Window Functions in SQL Python and R : Part 1 Ranking
Переглядів 1914 днів тому
A new series where I compare different functions in SQ Python Pandas and the R tidyverse. The first part of the series will be on window functions. This video will cover ranking function in all 3 languages. All the files are available in my GitHub repository: github.com/hthomas229/PurpleCrown @haroldspurplecrowntraining
Data Cleaning in R Tidyr: UNITE & SEPARATE
Переглядів 14Місяць тому
Last of 4 videos on tidying data for analysis with Tidyr. Here I explain how to use UNITE and SEPARATE. Split and join columns. Code is available at github.com/hthomas229/PurpleCrown
Data Cleaning in R Tidyr: COMPLETE, FILL, REPLACE_NA, DROP_NA
Переглядів 37Місяць тому
3rd of 4 videos on tidying data for analysis with Tidyr. Here I explain how to use COMPLETE and FILL to fill out your data and DROP_NA and REPLACE_NA to handle NAs. Code is available at github.com/hthomas229/PurpleCrown
Data Cleaning in R Tidyr: Pivot_Longer, Pivot_Wider
Переглядів 30Місяць тому
2nd of 4 videos on tidying data for analysis with Tidyr. Here I explain how to pivot data wider and longer. Getting your data in and out of tidy format. Includes values_fn argument for pivoting with aggregation. Code is available at github.com/hthomas229/PurpleCrown
Data Cleaning in R Tidyr: Nest, Unnest & Hoist
Переглядів 56Місяць тому
First of 3 videos on tidying data for analysis with Tidyr. Here I explain how to nest data into lists, unnest data from lists, and hoist specific columns. Code is available at github.com/hthomas229/PurpleCrown
R Wars: 100 tidyverse Puzzles 86 - 91 GGPLOT2
Переглядів 11Місяць тому
R Wars: A series where I show the solutions to the 100 Star War tidyverse puzzles posted on my github repo: github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles.R github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles_with_solutions.R 100 problems from beginner to advanced levels. Give them a try. You might even have some fun. MAY THE FORCE BE WITH YOU
R Data Visualization & Analysis: Impute Missing Values With MICE
Переглядів 60Місяць тому
Use the MICE package to impute missing values. Advanced regression and classification techniques. You choose the method, or MICE will choose for you. Excellent data visualization. github.com/hthomas229/PurpleCrown
R Data Visualization & Analysis: Impute Missing Values With VIM
Переглядів 502 місяці тому
Use the VIM package to impute missing values. kNN, hotdeck, matchImpute, IRMI, regressionImp and rangerImpute. Excellent before and after imputation data visualization. github.com/hthomas229/PurpleCrown
R Wars: 100 tidyverse Puzzles 78 - 84 Working with Dates and ggplot
Переглядів 122 місяці тому
R Wars: A series where I show the solutions to the 100 Star War tidyverse puzzles posted on my github repo: github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles.R github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles_with_solutions.R 100 problems from beginner to advanced levels. Give them a try. You might even have some fun. MAY THE FORCE BE WITH YOU
R Data Visualization & Analysis: Impute Missing Values With Zoo
Переглядів 472 місяці тому
Using the ZOO R package na.* functions to impute missing values/NAs in a data frame. Starts with a short primer on why, when and how to impute. You can get the code at my github repository: github.com/hthomas229/PurpleCrown Additional Resources: www.bookdown.org/rwnahhas/RMPH/mi.html cran.r-project.org/web/packages/zoo/zoo.pdf (see the vignettes)
R skimr janitor ggally psych VIM : Data Visualization and Analysis
Переглядів 422 місяці тому
5 more excellent data visualization and summary statistical packages are explored. See how your data is distributed. View correlations. Check for missingness and imputed values and more , , ,
R Wars: 100 tidyverse Puzzles 69 -77 DISTINCT, FILL, MAP, & Forcats
Переглядів 272 місяці тому
R Wars: A series where I show the solutions to the 100 Star War tidyverse puzzles posted on my github repo: github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles.R github.com/hthomas229/PurpleCrown/blob/main/100starwarspuzzles_with_solutions.R 100 problems from beginner to advanced levels. Give them a try. You might even have some fun. MAY THE FORCE BE WITH YOU
R inspectdf : Data Visualization and Analysis
Переглядів 692 місяці тому
The R inspectdf package lets you visualize metadata about your data frame in both tabular form and really cool plots. Categorical and numerical distribution and imbalances, NAs and missingness, correlations, datatypes, and memory usage. And again really cool plots!
Advanced Dax: Maven Halloween Challenge
Переглядів 592 місяці тому
Advanced Dax: Maven Halloween Challenge
R Wars: 100 tidyverse Puzzles 63 - 68 RANK, DENSE_RANK, NTILE, PERCENT_RANK, CUMSUM
Переглядів 123 місяці тому
R Wars: 100 tidyverse Puzzles 63 - 68 RANK, DENSE_RANK, NTILE, PERCENT_RANK, CUMSUM
R VisDat: Get A Good Look at Your Data
Переглядів 403 місяці тому
R VisDat: Get A Good Look at Your Data
R Wars: 100 tidyverse Puzzles 58 - 62 Window Functions Lead Lag & RowNumber
Переглядів 53 місяці тому
R Wars: 100 tidyverse Puzzles 58 - 62 Window Functions Lead Lag & RowNumber
R Wars: 100 tidyverse Puzzles 55-56 Count, Proportion, Top N
Переглядів 63 місяці тому
R Wars: 100 tidyverse Puzzles 55-56 Count, Proportion, Top N
R Wars: 100 tidyverse Puzzles 51 - 54 group_by
Переглядів 13 місяці тому
R Wars: 100 tidyverse Puzzles 51 - 54 group_by
R Wars: 100 tidyverse Puzzles 47 - 50 count & Intro to ggplot
Переглядів 123 місяці тому
R Wars: 100 tidyverse Puzzles 47 - 50 count & Intro to ggplot
R Wars: 100 tidyverse Puzzles 41 - 46 Stringr & Calculated Columns
Переглядів 43 місяці тому
R Wars: 100 tidyverse Puzzles 41 - 46 Stringr & Calculated Columns
R Wars: 100 tidyverse Puzzles 34 - 40 Stringr, Unite & Separate
Переглядів 83 місяці тому
R Wars: 100 tidyverse Puzzles 34 - 40 Stringr, Unite & Separate
R Wars: 100 tidyverse Puzzles 27 - 33 Dplyr Continued
Переглядів 53 місяці тому
R Wars: 100 tidyverse Puzzles 27 - 33 Dplyr Continued
R Wars: 100 tidyverse Puzzles 13 - 26 Dplyr Select
Переглядів 83 місяці тому
R Wars: 100 tidyverse Puzzles 13 - 26 Dplyr Select
R Wars: 100 tidyverse Puzzles 1 - 12 Getting Started & Getting to Know Your Data
Переглядів 133 місяці тому
R Wars: 100 tidyverse Puzzles 1 - 12 Getting Started & Getting to Know Your Data
Data Cleaning in the R tidyverse: Cleaning Up
Переглядів 274 місяці тому
Data Cleaning in the R tidyverse: Cleaning Up
Data Cleaning in the R tidyverse: Getting Started
Переглядів 824 місяці тому
Data Cleaning in the R tidyverse: Getting Started
Data Cleaning: SQL v Python Pandas v Power BI Power Query: Find & Remove Duplicates
Переглядів 564 місяці тому
Data Cleaning: SQL v Python Pandas v Power BI Power Query: Find & Remove Duplicates
Hello, thanks for sharing the video about Treatas. I am curious to know why you include 'Calendar Lookup'[Month Name] at the end of the DAX measure, and likewise the same for Budgeted Cost measure: Budgeted Revenue = CALCULATE( SUM(adWorksBudget[Budgeted Revenue]), TREATAS( SUMMARIZE('Calendar Lookup', 'Calendar Lookup'[Year], 'Calendar Lookup'[Month Name]), AdWorksBudget[Year],AdWorksBudget[Month]), 'Calendar Lookup'[Month Name])
The formula applies an additional filter to the calculate for the specific Month Name from the Calendar Lookup table. This makes sure the calculation is in the context of the current Month Name. Hope this helps! Thanks for watching.
Hello Harold, hope you're doing well. Currently I'm solving this challenge but I'm facing a problem to make a relationship between the events table and offers table can you guide me with this please? I'm using powet bi
Here is my model with the relationships. Events is the transaction table. I then used offers and customers as lookups (offer_id to OfferID, customer_id to customer _id) in a star schema with the date table as a snowflake off of customers(Date to became_member_on). Hope this helps! Good luck with your challenge. Harold
Thanks so much! it works!
This has been updated, you can find visual level formatting in the properties pane of the viz.
+1
Thanks! Just what I was looking for
Glad I could help!
Good morning sir excellent teaching
It's really helpful to me thank you
Glad to hear that
My favourite: Power Query.
me too for ease -- i do like to play with Pandas for fun though
Thank You so much
You're most welcome
Forget about it..👍
Very helpful. Thank you.
Glad it was helpful! Thanks for the comment. schlemiel schlimazel
nice. please put excel file in the description
Very helpful video. Just what I was looking for. Fair play to you.
'Promo sm' ✌️
Good. Give the excel files
Nice job. Please put excel file in the description