jeff1evesque / interview-deloitte Goto Github PK
View Code? Open in Web Editor NEWAnalyze fictitious 10K race data
Analyze fictitious 10K race data
This issue is similar to jeff1evesque/ist-687#31.
We will create main.R
initially to load, then format our provided datasets. This will later be extended to perform various exploratory analytics.
We need to implement nzchar(Sys.getenv("RSTUDIO_USER_IDENTITY"))
to determine if we are running code in RStudio. This way our implemented dirname(rstudioapi::getSourceEditorContext()$path)
doesn't break our current script.
We need to reduce the current verbose colname syntax.
We need to analyze the race results for each division.
We will split the hometown
column into city
, and state
columns.
We will add a basic README.md
, as well as remove the unnecessary docx file.
We will create a basic deloitte
library.
We need to analyze how much time separates Chris Doe against the 10% of the same division.
We need to adjust our gender coloring dichotomy, when two visualizations are side by side.
We need to adjust the color scale for the female map visualization.
We need to a visualization between the net and gun time.
We will create a dedicated package, to contain our current data munging logic.
We forgot to return only the top 10 percentile, in our chris_doe.R
.
We need to determine the mean, median, mode, and range, of the race result for all racers by gender.
We need to remove any runners with an age < 0.
We need to upload the data exercise .
We will add .gitignore
to ignore generated visualizations, as well as R generated files.
We will create a barchart to compare the descriptive statistics between gender.
We'll assume all logic is correct so far. Therefore, any empty, Unknown
, or NA
rows need to be removed.
We need to relocate the color
aesthetics property within our chris_doe.R
.
We need to fix a docstring typo in munger.R
.
We need to analyze the difference between gun and net time race results.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.