This case study analyzes ride patterns of Cyclistic bike share members and casual riders using the Divvy dataset for Q1 2019 and Q1 2020. The analysis explores: Differences in ride frequency, duration, and day-of-week usage between members and casual riders Average ride length trends and monthly patterns Behavioral insights to identify why casual riders might convert to annual memberships The report includes data cleaning, exploratory analysis, visualizations, and actionable insights for Cyclistic’s marketing and membership strategy. The goal is to demonstrate data analysis skills, visualization, and business insight using R Markdown.

Exploring and Understanding Data With R

Your task is to first choose—or create—any dataset that you find interesting: To receive full credit, you should: 1. Take the data, and create one or more code blocks. You should finish with a data frame that contains a subset of the columns in your selected dataset. If there is an obvious target (aka predictor or independent) variable, you should include this in your set of columns. You should include (or add if necessary) meaningful column names and replace (if necessary) any non-intuitive abbreviations used in the data that you selected. For example, if you had instead been tasked with working with the UCI mushroom dataset, you would include the target column for edible or poisonous, and transform “e” values to “edible.” Your deliverable is the R code to perform these transformation tasks. 2. Make sure that the original data file is accessible through your code—for example, stored in a GitHub repository or AWS S3 bucket and referenced in your code. If the code references data on your local machine, then your work is not reproducible! 3. Start your R Markdown (.Qmd or ..Rmd) document with a two to three sentence “Overview” or “Introduction” description of what the article that you chose is about, and include a link to the article. 4. Finish with a “Conclusions” or “Findings and Recommendations” text block that includes what you might do to extend, verify, or update the work from the selected article. 5. Each of your text blocks should minimally include at least one header, and additional non-header text. 6. You’re of course welcome—but not required--to include additional information, such as exploratory data analysis graphics (which we will cover later in the course). 7. Place your solution into a single R Markdown (.Rmd) file and publish your solution out to rpubs.com. 8. Post the .Rmd file in your GitHub repository, and provide the appropriate URLs to your GitHub repositor y and your rpubs.com file in your assignment link.

Análisis de principales indicadores de mercado vs Cooperativa Unimos

Welcome to this motivational presentation designed to inspire you on your path to achieving greatness. Whether you're feeling stuck or just need a boost, this guide will walk you through key steps to unlock your full potential. We'll back it up with visual charts and diagrams to make the concepts clearer and more engaging. Feel free to adapt this for your blog post-copy the text, embed the visuals where suggested, and share the inspiration! Motivation is the fuel that drives us toward our dreams. Without it, even the best plans fall flat. But with the right mindset, you can turn obstacles into opportunities and dreams into reality. Remember, every great achievement starts with a single step. Let's dive in and explore how to harness that inner drive.