There are different graphes that can illustrate le most important purchased items and the most important costumers.
With this highlight table we can view the frequency of the purchases of each item, for each costumer, and during certain period.
Top10 costumers - Top10 products
This graph is useful if we want to compare simultaneously each items consumption for the most important costumers.
Individual Histogram for Top 10 vs Top 10
This bar plot is intersting and make easy to identify the difference between costumers uptakes.
Stacked Histigram
This highlight table shows us that 2015 has more active business than 2014, at less for whole milk.
Costumers Behavior
Costumers Behavior
We use arules
algorithme to plot the top 10 purchased items. Surprisingly, the top 10 items are also the most correlated.
Items sold correlation
R
into TABLEAU: A simple running exampleI tried to run a simple scripts writen with R into TABLEAU. TABLEAU has four types of data that can handle with R: Booleen
, String
, Real
, Integer
. I tried to return from R a dataframe that collapse the transactions, and obtain all items (basket) for each transaction in one row. This function return to TABLEAU a new dataframe with 3 columns: factor (memberID), Date, and string (items list) data type. The question is: How can TABLEAU handle dataframe or tibble with R? It is very useful and important to handle multiple type of data in the same run or task!
R
script into TABLEAUIntegrate TABLEAU with R
Data Science for Marketing: R, Python, TABLEAU
TABLEAU 10: Mastering Calculations