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Adam Petrie
United States
เข้าร่วมเมื่อ 26 ก.ย. 2012
Senior Lecturer in Business Analytics at the University of Tennessee. Super-old video walkthroughs of activities from BAS 320 (regression) and BAS 474 (data mining) are posted here, but the focus is on delivering edited and produced video lectures covering content in BAS 471 and BZAN 533 (Statistical Methods). And I'm a drone hobbyist, so you'll see some drone footage too!
Data Wrangling 3 - dplyr for taking subsets (filter), sorting (arrange), and summarizing (group_by)
00:00 - Skippable Introduction
00:43 - Overview of what we do with a flat file
01:40 - Commands in this video: select, arrange, filter, group_by, summarize
02:32 - Reading in the 3 Telecom tables and joining (link at bottom)
03:38 - Merging to get a flat file
05:45 - select command for keeping columns
06:45 - arrange for sorting rows
09:14 - Using t.test to compare average monthly charges
11:57 - Overview of filter command
13:23 - Example 1 - Dependents=="Yes"
16:09 - Example 2 - tenure == 1
17:28 - Example 3 - not equal to !=
18:45 - Example 4 - %in% (one of the following)
20:54 - Example 5 - inequalities like less than, at least, greater than or equal to, etc
21:41 - Example 6 - getting rows where a value is inside a range
22:52 - Example 7 - multiple conditions in general (AND &)
24:24 - Example 8 - one OR another condition with |
27:13 - select function and its uses
28:01 - Optional challenge for you - complex subset and t.test
28:47 - Solution to challenge
35:19 - group_by and summarize
35:56 - Purpose of group_by
37:25 - Example: Average tenure for customers that are and are not senior citizens
38:03 - Example: Average tenure for each payment method
38:51 - Optional - ANOVA and connecting letters report for detecting differences
40:24 - Intro to comparing churn rates
41:28 - The mean(logical) condition trick to get churn rates (0-1) when entries are Yes/No
43:46 - Getting churn rates for each payment method
46:10 - Example - median fraction of charges that happened last month for each Contract type
48:21 - Summarizing combinations of groups
52:04 - Skippable Outro Video
52:59 - Outtakes
Files on Dropbox: www.dropbox.com/scl/fo/qgvma7gr7p6e5kwxdqvbz/AMWlM6HQDXJ-xnNRXwittJg?rlkey=eudt1siz96bsyrxto8k9zz1u3&st=yndwsyyv&dl=0
Reading in data from google sheets - get the dplyrscript.R file and you'll see the code in there. Unfortunately if I try pasting the code here it turns it into a URL that doesn't work!
00:43 - Overview of what we do with a flat file
01:40 - Commands in this video: select, arrange, filter, group_by, summarize
02:32 - Reading in the 3 Telecom tables and joining (link at bottom)
03:38 - Merging to get a flat file
05:45 - select command for keeping columns
06:45 - arrange for sorting rows
09:14 - Using t.test to compare average monthly charges
11:57 - Overview of filter command
13:23 - Example 1 - Dependents=="Yes"
16:09 - Example 2 - tenure == 1
17:28 - Example 3 - not equal to !=
18:45 - Example 4 - %in% (one of the following)
20:54 - Example 5 - inequalities like less than, at least, greater than or equal to, etc
21:41 - Example 6 - getting rows where a value is inside a range
22:52 - Example 7 - multiple conditions in general (AND &)
24:24 - Example 8 - one OR another condition with |
27:13 - select function and its uses
28:01 - Optional challenge for you - complex subset and t.test
28:47 - Solution to challenge
35:19 - group_by and summarize
35:56 - Purpose of group_by
37:25 - Example: Average tenure for customers that are and are not senior citizens
38:03 - Example: Average tenure for each payment method
38:51 - Optional - ANOVA and connecting letters report for detecting differences
40:24 - Intro to comparing churn rates
41:28 - The mean(logical) condition trick to get churn rates (0-1) when entries are Yes/No
43:46 - Getting churn rates for each payment method
46:10 - Example - median fraction of charges that happened last month for each Contract type
48:21 - Summarizing combinations of groups
52:04 - Skippable Outro Video
52:59 - Outtakes
Files on Dropbox: www.dropbox.com/scl/fo/qgvma7gr7p6e5kwxdqvbz/AMWlM6HQDXJ-xnNRXwittJg?rlkey=eudt1siz96bsyrxto8k9zz1u3&st=yndwsyyv&dl=0
Reading in data from google sheets - get the dplyrscript.R file and you'll see the code in there. Unfortunately if I try pasting the code here it turns it into a URL that doesn't work!
มุมมอง: 285
วีดีโอ
Data Wrangling 2 - using dplyr to bind, join, and merge data (bind_rows, inner_join, etc., mutate)
มุมมอง 3975 หลายเดือนก่อน
00:00 - Skippable Introduction 02:17 - Overview of combining data sources 05:29 - Overview of commands covered in video 06:20 - bind_rows to append tables 12:10 - Analytics on the Spotify data 14:30 - Towards joins - why we merge sources 17:33 - left_join 19:42 - by=join_by shortcuts 20:37 - Joining by composite keys (combination of columns) 23:16 - right_join 24:38 - inner_join 25:07 - full_jo...
Data Wrangling 1 - learning to use pipes in R
มุมมอง 4775 หลายเดือนก่อน
00:00 - Skippable Introduction 00:30 - Why learn about piping? 02:00 - History of the Pipe 03:30 - Getting Petrie ready with Pipes 06:53 - What is pipe doing 07:52 - Using pipes to make donuts 08:29 - Baking a cake with pipes 09:42 - Don't feel compelled to pipe EVERYTHING 11:37 - Example pipe - sequence of mathematical operations on a vector 16:36 - Example - piping to get smallest 4 values in...
Tribute to Cannon and Jackson
มุมมอง 2696 หลายเดือนก่อน
Two of the best Frenchies on the planet, unfortunately taken from us much too soon! 00:05 - Introducing Cannon! His first year. 07:45 - Who is it? It's Jackson! They were forever playmates. 11:14 - Would you like to go to doggy day care? 13:58 - Day to day living with Cannon and Jackson 18:07 - Mamaw Pokey's! 19:24 - Snow! 20:10 - Do you want to want to go for a ride? 20:34 - Cannon's crazy ant...
Gold Creek Pond and Mirror Lake in the Cascade Mountain Range in Washington State, July 6-8, 2024
มุมมอง 506 หลายเดือนก่อน
Gold Creek Pond and Mirror Lake in the Cascade Mountain Range in Washington State, July 6-8, 2024
Burgess Falls Tennessee, June 13, 2024
มุมมอง 317 หลายเดือนก่อน
Somehow I've lived 1.5 hours away from Burgess Falls for almost 17 years and had never heard of it. Now it's one of my favorite places!
Little River Canyon and Vulcan Park Alabama October 2024
มุมมอง 517 หลายเดือนก่อน
Little River Canyon and Vulcan Park Alabama October 2024
Ch 6B - Multiple Logistic Regression
มุมมอง 768ปีที่แล้ว
00:00 - Skippable Introduction 00:12 - Overview of Multiple Logistic Regression 01:12 - Hotel dataset 02:58 - Simple logistic regression review 04:14 - Categorical variable as sole predictor 06:16 - drop1 with test=Chisq 06:58 - Multiple Logistic Regression model 07:26 - Statistical significance meaning 10:42 - Interpretation of coefficients (signs) 12:33 - Linear regression: Categorical predic...
Ch 5B - Incorporating interactions with a categorical variable in a multiple regression model
มุมมอง 783ปีที่แล้ว
00:00 - Skippable Introduction 00:15 - Overview: categorical predictors 00:54 - Review : encoding categorical variables 02:00 - Indicator variables allow use to compare averages between levels after accounting for lurking variables (Harris Bank discrimination review) 02:47 - Adding categorical predictor (no interaction) lets each level have its own intercept (common slope) 03:10 - Separation be...
Ch4D - Multiple Linear Regression (Polynomial Models and Interaction Terms)
มุมมอง 1.2Kปีที่แล้ว
00:00 - Skippable Introduction 00:40 - Linear regression is good when relationships are linear 01:23 - Examples of nonlinearity: Sale Price vs Year Made for bulldozers and Fuel Efficiency vs Horsepower for cars 02:33 - Review: find_transformations for fixing nonlinearity 04:12 - Intro to polynomials (order of model) 04:55 - Choose order for bulldozer data 08:33 - Fitting a polynomial model with...
Ch4A - Multiple Linear Regression (Predictive and Descriptive Analytics)
มุมมอง 1.2Kปีที่แล้ว
00:00 - Skippable Introduction 00:18 - Simple linear regression is a good but incomplete tool 01:24 - Overview of multiple regression 03:16 - Predictive analytics with multiple regression 10:23 - Overview of descriptive analytics with multiple regression 11:03 - Review of simple linear regression 11:46 - Coefficients in simple linear regression can be misleading if there is more than 1 reason w...
Ch2B - Pearson vs Spearman Rank Correlations and the Permutation Test for Statistical Significance
มุมมอง 1.1Kปีที่แล้ว
00:00 - Skippable Intro 00:08 - Overview of analyzing associations between 2 numeric variables 01:59 - Always start with a scatterplot 02:21 - When to use Pearson's correlation 02:54 - When to use Spearman's rank correlation 03:36 - Examples where Pearson's correlation should be used 06:11 - Examples where Pearson's correlation doesn't work (use Spearman instead) 09:20 - Steps for performing th...
Working with Data in R: Data Frames
มุมมอง 2.1Kปีที่แล้ว
00:00 - Skippable Introduction 00:26 - Spreadsheet in Excel (tips.csv) 01:09 - Using data.frame() to make a "spreadsheet" 02:17 - Looking at and previewing contents of data frames 03:11 - Built-in dataframes in base R; data() command 03:45 - Loading data from other libraries; library() and data() command 04:30 - Using read.csv() to read in data files 05:14 - stringsAsFactors = TRUE vs FALSE whe...
Working with Data in R: Vectors
มุมมอง 2.3Kปีที่แล้ว
00:00 - Skippable Introduction 01:00 - Creating vectors with c() function 02:30 - Creating a factor / categorical variables 02:53 - Converting between vector types 05:52 - Put everything in quotes if text vector 06:53 - seq() for making regularly spaced sequences 08:41 - Integer sequence shortcut with : 09:18 - rep() for creating patterns 11:18 - Referring to positions in vector with [ ] 14:29 ...
Mystical Coasts of California and Oregon
มุมมอง 912 ปีที่แล้ว
Mystical Coasts of California and Oregon
Madonna Inn and Pacific Coast Highway July 16 2022
มุมมอง 192 ปีที่แล้ว
Madonna Inn and Pacific Coast Highway July 16 2022
Lake Michigan and Lake Superior, June 2022
มุมมอง 422 ปีที่แล้ว
Lake Michigan and Lake Superior, June 2022
Palm Springs and Lake Hemet December 2022
มุมมอง 722 ปีที่แล้ว
Palm Springs and Lake Hemet December 2022
👌👌👌👌
thank you for this great series of lectures , you are awesome 👌👌👌
Nice video. These days people will use |> instead of %>% but they pretty much doing the same thing.
man, this is the most comprehensive and easy tutorial I came across. I was almost giving up learning shiny haha thanks and congratz!
Do you have any videos showing how to do simple linear regression in R?
Beautiful! October 2024 looks amazing!
A glimpse into the future 😆 Yes it's fantastic!
Amazing shots! I am in love with Oregon's coasts as well as Mt. Hood region. Thor's Well was a very interesting sight as well (ba-dum-tss).
Best shiny applications tutorial !! Thank you so muchhh
Excellent quality 👌
Superhelpful man!
Man, love how you teach!
This is a great video that I keep returning to. Your students are lucky to have a course on this.
Awesome video!
How to get the initial probabilities?
The probabilities in the transition matrix are usually found via data or by educated guesses!
This looks awesome!
Awesome video! It's to the point and easy to follow with timestamps!
Awesome tutorial, thank u for sharing
What a great lesson, thank you so much!
💕🤗 Love this video!
Gorgeous!
Thank you very much for sharing this useful information!
Planning a trip up there in two weeks so I was curious what to expect. Wasn't sure if their would be any color or of I would be too late.
The best color is gone...but you'll see some pockets of some that do remain!
Wow, holy smokes. This is an exceptional video. I wish I attended this class because I would have learned so much. I stumbled onto this video after looking for some more clarity on interpreting the md.pattern output. I had no idea about this package and the tools to assess the associations around missingness (without a manual way). Thanks for sharing this video with the general public! This is a great help for someone that has learned MI outside the classroom. I appreciate your tone, pacing, and clarity :)
Thanks for sharing.
That was the most clearly explained analysis ive ever seen, thanks!
Stunning footage!
Thank you so much for the tutorial, Adam. Excellent Explanation!.. can you please help understand the topic crosstalk for graphs and filters in shiny? Thanks in advance.
wow so nicly and fun way you explained throughly i loved it, Thank you sir
Many thanks! Very well presented
I love you guy , thank you for your help and be sure it help me a lot and continue to do this
Is there an index structuRe for all your videos?
There's a brief description of what's being discussed in the list of timestamps (clicking Show More in the video description will show the full list). This also brings up a useful Chapters view.
@@AdamPetrie thanks Adam. Talking about all videos. Starting from probability videos to eventually learn modelling
Very well explained. Thank You! Is there a index or playlist I can follow to know topics of interest?
There's a brief description of what's being discussed in the list of timestamps (clicking Show More in the video description will show the full list). This also brings up a useful Chapters view.
can you share the excel file sir for educational purpose
helpful
Thank you for starting with the data format. So many videos and tutorials just assume you've got good clean data.
I have to thumb up this video! thank you!
Great video Dr. Petrie! Would you be alright STAT 201 using this footage in our videos?
Of course!
Thank you for sharing such a wonderful video!
wish we had a part 2!
THANK YOU! I'm wondering about the rescale function from package arm, which / per 2 standard deviations, what changes from the former method?
Excellent introduction to Shiny Apps. When I first heard about it, I thought it was going to be very complex but it is actually very straight-forward. Definitely a game-changer when it comes to visualising your data.
The cat and the lizard at the end of this video had me sitting at the edge of my chair.
Your Video is superb. Clear interpretation Thank you so much.
thanks for your videos , great lecture
nothing like the smokies
Soothing is the word that came to my mind after watching. Thanks for the treasure!
Hi, can you please share the codes or video of how to fit copula meethodology to data in R
Thank you so much for the tutorial Adam~! Just wondering is there any video teaching us how we can preprocess our messy data in shiny codes, do we just put all the codes in front of the UI?? I am very new to this. Thanks.
If this pre-processing only needs to happen once, I'd recommend doing it separately and saving your data into an .RData file, then using load() before the UI load it in. But having all the code that does the preprocessing before the UI is an option as well (but since that code will be run when loading up the app, if that preprocessing takes a very long time, it will take the app a very long time to load!).
@@AdamPetrie Thank you so much Adam for your response. I wonder when we have a dataset which is not tidy enough, let's say the date variables are not in proper format and Rstudio reads it as character. So if I don't preprocess it, will shiny automatically reads it properly? Sorry for the very basic question.
@@zehuizhao1531 I'd love it if shiny new how to process date information correctly when it's read in as characters. But unfortunately it does not. The good news is there's a very easy-to-use package called lubridate that can convert to a date object easy and can extract out the parts you want (day of week, hour of day, etc). I actually have a video for lubridate somewhere on my channel too, but googling it and some examples would be sufficient.
great job man! thanks for the lovely tutorial
Your content is fantastic. I have been trying to develop my R Studio skills for the past year. I wish I found your TH-cam channel months ago. Please keep posting more content as it will help many develop their skills.