After week 3, I now understand how to interpret graph/plots that are generated through R. As well as how to correctly identify any errors within my code. #mktgfiasco4000

During week 3, I learned how to code and generate different plots/graphs in R, how to set an x and y variable, and how to find any errors within my variable's/code.
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In the last class for the week we were finaly able to solve at least why I was having problems, turns out we needed to download some files beforehand to be able to make the stat sheat, with that solved the lesson was actually really interesting.

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This week was a bit more confusing as we were supposed to make a stat sheat showing info about almonds using R, but we ran into some complications in the first class that it became necessary to continue the lesson in the next class. It was still fun though.

Hello gendel5,

Great catch, price elasticity split makes sense given how different LA and NY are as markets. Also, nice work teaching yourself R data visualization !! #mktgfiasco4000

In week 3, I learned about the Hass avocado board's dataset and saw that avocado prices were from about $0.50 to $2.78 depending on city, type and season. I also saw that organic was consistently about $0.40 to $0.50 higher in comparison to conventional. #mktgfiasco4000

The biggest takeaway would be the LA and NY dominate avocado sales but for different reasons. LA leads conventional (proximity to growers) and NY leads in organic (health conscious citizens). #mktgfiasco4000

R is really proving to be a great visualizer for large datasets, making it easier to interpret and conclude results #mktgfiasco4000

So nice to try out different functions using R! Can't wait to see what else we come up with #mktgfiasco4000

Biggest "aha" moment this week was analyzing how price elasticity drives avocado sales volume in key cities like LA and NY. Learning R data visualization on my own is starting to click...👍🥑 #mktgfiasco4000

Week 3 in R: small details. Trimmed a space from a column name in the avocado data using the trimws() function. Also published my work to RPubs. Looking forward to further tweaking of the ggplot. #mktgfiasco4000

Ahah moment, week 3: I used aggregate() in R to compute the average avocado price per week and found that conventional avocados were at their lowest on Feb 2, 2020, during the Super Bowl. Guacamole season is here, and the data reflects it. #mktgfiasco4000

This week we worked on using R as a tool to organize data, creat visualizations, and understand information more effectively! #mktgfiasco4000

Using R to turn raw data into clear graphs and help identify patterns, trends, and differences has been such a useful tool in analyzing data. #mktgfiasco4000

Learning the different codes and things on R is very useful. #mktgfiasco4000

I found it cool to learn about histograms and the different codes on R. #mktgfiasco4000

In week one of class we learned the basics of R Markdown. I like how it combines everything into one like codes, charts, and writing. I will be using this to help me stay more organized. I am also looking forward to becoming more confident in my use of R Markdown! #mktgfiasco4000

This week I learned how marketing analytics can help businesses make more informed choices depending on the data given from things such as an ad. #mktgfiasco4000