4 Examples of Using R Functions for Exploratory Data Analysis (EDA)
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- Опубліковано 23 лип 2024
- In this video, I will show you four examples of Exploratory data analysis (EDA) using R.
EDA is a critical data analysis technique that can help you identify important insights in your data.
I will explain the most important functions, that will equip you to identify important patterns in your data and make better decisions based on it.
⏱ Time Stamps ⌚
0:00 - Intro
1:28 - mtcars
10:47 - gapminder
20:28 - movie_profits
22:38 - diamonds
27:12 - EDA outro
External Links:
www.r-bloggers.com/2019/08/ke...
www.youtube.com/@safe4democra...
github.com/tacookson/data-scr...
docs.google.com/spreadsheets/...
Movie profits: • Analyzing Horror Movie...
R-Gallery playlist: • R Graph Gallery Tutorials
Spend some quality time here and you will be able to accomplish 90% of what you need! The last 1:30 gives wonderful references too..... Thanks very much man!
My pleasure. Glad so many people found it helpful. Thanks for leaving a comment.
Excellent video - thank you
Thanks for the compliment :)
Incredible video!! I'm currently in the beginning stages of learning R, and this video really helped put EDA into perspective. Thank you for taking the time to make such an insightful video guide!!
Thank you for taking the time to leave a comment. That is one of the most rewarding things for me, getting positive feedback and knowing that my videos help other R users or beginners.
What an Incredibly stunning tutorial on EDA in R
Thanks for the kind words. I am glad you liked it. Feel free to share it with other R-users you know :)
Thank you for this video. One of the most intense and valuable sets of information on how to understand the data. The only thing I miss is some basic statistical tests in order to express the significance of differences between groups. Anyway - perfect job.
Thanks you for this comment :) I am working on a video about the most common stats tests you can do in R. It will come out in March.
Really Great Tutorial Thank You.
My pleasure. Thanks for leaving a comment.
Spectacular video. Thank you
Glad you liked it and left a comment. Helps the channel with engagement and promotion.
Thanks for the video.
add_count(wt = pop, name = "continent_pop") was really handy, I did not know that one. Thanks.
Also for data summary I sometimes use psych::describe(), it is not as versatile as summary() but for me describe() gives better descriptive statistics.
Thanks for the comment. I haven't used describe() much yet, but thanks for mentioning it. Yes, the "wt" function argument is quite helpful :)
Summary() is ok, but describe() from pysch and skim() from skimr appear to be slightly better summarizing as they output more details such disctint, count, missing values and a better view.
@@afonsoosorio2099 Thanks for the input. Maybe that should be another video, comparing these functions. Especially showing missing values is quite important so I will look into these. Really cool to learn from other R-users here in the comments or on twitter :)
Thanks a million sir for your value adding contents...
Thanks for taking the time to leave such a nice comment :)
Insightful video. Thank you for sharing
Great video. Thank you very much
Glad you liked it eyadha1.
Excellent summary!
I think need to use fct_reorder and fct_lump more.
Also I was about to write a (very slightly) mocking comment, but was wise enough to check beforehand and learned that "normally distributed" is not only a valid expression but actually the only correct form (unlike "normal distributed" which I used so far!) :)
Thanks for watching the video :)
Hello Professor Alex,
Could you please make the script of this class available?
Thank you for the explanation: Manoel - Maceió/Alagoas/Brasil