11 de febrero de 2018
For the final project of Developing Data Products I selected a very simple project: visualization of the dataset of the dataset of the Abalone Dataset and running a simple lineal regression. The dataset comes from https://archive.ics.uci.edu/ml/datasets/abalone
The application is available in https://quevedin.shinyapps.io/DevDataProd/ , and the code in https://github.com/quevedin/DevDataProd
Abalone is a mollusc with high importance in acquiculture and gastronomy. Predicting the viability and optimizing the (slow) growth of specimens means the viability or the bankrrupcy of a company.
abalone <- read.csv(
"http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data",
header = FALSE)
names(abalone) <- c("sex", "length", "diameter", "height", "weight.whole",
"weight.shucked", "weight.viscera", "weight.shell", "rings")
summary(abalone)
## sex length diameter height ## F:1307 Min. :0.075 Min. :0.0550 Min. :0.0000 ## I:1342 1st Qu.:0.450 1st Qu.:0.3500 1st Qu.:0.1150 ## M:1528 Median :0.545 Median :0.4250 Median :0.1400 ## Mean :0.524 Mean :0.4079 Mean :0.1395 ## 3rd Qu.:0.615 3rd Qu.:0.4800 3rd Qu.:0.1650 ## Max. :0.815 Max. :0.6500 Max. :1.1300 ## weight.whole weight.shucked weight.viscera weight.shell ## Min. :0.0020 Min. :0.0010 Min. :0.0005 Min. :0.0015 ## 1st Qu.:0.4415 1st Qu.:0.1860 1st Qu.:0.0935 1st Qu.:0.1300 ## Median :0.7995 Median :0.3360 Median :0.1710 Median :0.2340 ## Mean :0.8287 Mean :0.3594 Mean :0.1806 Mean :0.2388 ## 3rd Qu.:1.1530 3rd Qu.:0.5020 3rd Qu.:0.2530 3rd Qu.:0.3290 ## Max. :2.8255 Max. :1.4880 Max. :0.7600 Max. :1.0050 ## rings ## Min. : 1.000 ## 1st Qu.: 8.000 ## Median : 9.000 ## Mean : 9.934 ## 3rd Qu.:11.000 ## Max. :29.000
I just included a very simple multiple choice selector. But With more time I could have extended to all the levels and ranges of all the variables (like a range selector for the weights or diameter, for instance)