Powerful R Functions Every Data Analyst Should Know

In this video I show you how you can quickly calculate summary statistics in R for one or more categorical variables of the diamonds dataset.
The summarize() function can be used to calculate many different results for one continuous variable. group_by() allows you to replicate these results for different levels of a categorical variable. And with pivot_wider() you can calculate a result of interest for the intersections of the levels of two categorical variables. With the geom_tile() function I show you how to produce a visualization of these results.
⏱ Time Stamps ⌚
0:00 - Intro
1:20 - exploring the diamonds data set
3:02 - summarize() for summary statistics of price
5:23 - group_by() for 1 category
6:36 - pivot_wider() for 2 categories
8:30 - dealing with missing data
11:30 - heatmap to visualize the results
Link to heatmap video:
• How to Create Heatmaps...

[@floriansitte-kratzsch7355]

Aggregation and pivoting are super central concepts in analysis. Neat summary!

[@forrestoakley4882]

Your videos are always very useful. Thank you!

[@TheDataDigest]

Thanks you, that's the purpose. Let me know if you want some specific topics covered or if you find something that I could have explained better. Always try to get better :)

[@luisacuna1729]

Amazing video, thanks!

[@TheDataDigest]

Thank you. I am glad you liked it. I will do more videos into the direction of data manipulation, base and dplyr R-functions and data exploration in the near future.

[@luisacuna1729]

@@TheDataDigest i Will stay tuned

[@osoriomatucurane9511]

Awesome! That is a great trick piping into the table() and converting the output into a dataframe. I always wanted to use pivot_wider() to get proportions similar to base R prop.table() or crosstab. I got stuck, but now, where you summarize and get the mean I could instead get percentagem = n/sum(n)*100 and then proceed to pivot_wider() and get the proportion table. Am I right?
As always I find your tutorials informative and surely commendable.