• The benefits of this interactive data visualization app for the Iris datas set are numerous:

  1. The app provides two methods to view the Iris data set to assist with any data exploration goal.
  2. The pre-populated summary table provides a synopsis of the data set that can assist the viewer with their interpretation of the plots.
  3. The interactive scatter plots provides flexibility and control over the user interface and a number of plots and visual mapping so that it can be arranged in a way that best meets any viewers data exploration goals.

• This presentation focuses on a data visualization app for a widely known dataset Iris (Fisher, 1936).
• The data set quantifies the morphologic variation of three species of the Iris flower (setosa, virginica, versicolor).
• The data set 150 records, and each record includes four measures: the petal length and width, and the sepal length and width (centimeters).
• The app summarizes and plots and how Petal Length and Sepal Length are related across the three different species.
• The remaining two slide show a data summary and sample scatter plot produced by the app.

iris_sumry <- iris %>% group_by(Species) %>% summarise(“Mean sepal length” = mean(Sepal.Length), “Mean sepal width” = mean(Sepal.Width), “Mean petal length” = mean(Petal.Length), “Mean petal length” = mean(Petal.Width)) iris_sumry

## # A tibble: 3 × 4
##   Species    `Mean sepal length` `Mean sepal width` `Mean petal length`
##   <fct>                    <dbl>              <dbl>               <dbl>
## 1 setosa                    5.01               3.43               0.246
## 2 versicolor                5.94               2.77               1.33 
## 3 virginica                 6.59               2.97               2.03

ggplot( iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + geom_point()