Welcome to this R shiny application. This application may be used to explore and analyse the Lifelines dataset.
This shiny application is completely open source and licenced under AGPL. You can find all code related to this project on it's
GitHub page.Number of rows:
Number of columns:
Number of datapoints:
Number of NA values:
Here you may find definitions for all variables in the dataset. For more information, please visit the
Lifelines wiki.Here you may explore the dataset. Select variables to view using the field below.
The table will (should) update as you alter the data using this application.
Here you may select variables to plot against each other to gain new insights.
Scatterplots are great for exploring possible correlations!
Boxplots are great for exploring means, quartile distances, and outliers!
Please note that choosing a categorical variable like "GENDER" would be best for creating a good looking boxplot.
Bar plots are great for exploring distributions, and checking frequencies!
Missing values in a dataset (otherwise known as NA values) may lead to countless problems down the road. It is best to deal with them ASAP!
The data that you're working with may be in completely different ballparks when it comes to ranges. For example, an age variable will usually range between 0 and 100, while something like caloric intake in kcal may range in the thousands. To remedy this difference in range, one may opt to normalise the dataset.
Standard score normalisation: Converts the given values to a normal distribution with a mean of 0 and a standard deviation of 1.
Min-Max normalisation: Converts the given values to a range between 0 and 1.
Skewness of a given set of data can introduce a lot of problems when trying to work on a dataset. This issue can be resolved by transforming the data. Below you will find a selection of options for transforming this dataset.
Here you may select variables and examine their correlation. You may decide a certain variable is redundant, in which case you may opt to remove it from the dataset using the button below.
In the plot you find below the size of the circle indicates the strength of the correlation, while the shade of colour indicates whether the correlation is positive (more blue) or negative (more red).
Please note that the plot will display an error message until you select some variables to compare.