🚀 Explore data types in R with simple functions like typeof(), class(), and is.*(). Dive into coding adventures, unlock data's power, and discover endless possibilities! Try it out today! 💻📊
Level up your data wrangling! Learn how to add index columns in R – both base & tidyverse Choose your weapon & customize! Ready to try? Create your own data frame & experiment! Share your creations & challenges!
Wrangling dates in R got you pulling your hair? ⏱️ Time travel to mastery with these 3 powerful tools:
Base R's seq.Date: Your daily/weekly/monthly hero.
lubridate's seq: Filter magic for specific weekdays. Analyze those Tuesdays!
timetk's tk_make_timeseries: Define complex sequences in a simple table. Easy time travel!
Drowning in daily data? Conquer weekly analysis with R's strftime() magic! Extract ISO week numbers & group your data like a pro. Ready to level up? Explore "U" for Sunday starts & packages for more grouping power. Challenge: calc weekly averages, peak sales, etc. Share your data wrangling wins in the comments!
R data mystery: is that column a date? ️♀️ Fear not! Unleash lubridate's date magic & healthyR.ts' time series power to unmask them! 🪄 Explore formats, validate time series, conquer your data! Ready? Test these tools, share your finds! Happy coding, clear dates await!
Master date manipulation in R with two simple methods: 1) Use ifelse() to create an indicator column, and 2) Utilize subsetting to filter data based on date range. Essential for various data tasks. Try it out and enhance your R skills!
Unleash Excel date power in R! Convert formats to proper dates effortlessly. With as.Date() & convertToDateTime(), transform data for smoother analysis. Dive into R, empower your data journey! Try it yourself & elevate your analysis game!
🚀 Dive into the world of data exploration with R! 📊 Uncover the earliest date lurking within your dataset using the power of R. With just a few lines of code, you can conquer this challenge and gain valuable insights into your data.
🚀 Mastering time manipulation in R is crucial for data professionals, and today we're tackling a common task: subtracting hours from time objects! 💡
In this engaging post, we explored two powerful methods: using base R functions and the popular lubridate package. With base R, we can perform basic arithmetic operations on time objects, while lubridate simplifies complex date-time calculations.
🚀 Dive into the world of statistical magic with TidyDensity's bootstrap_stat_plot()! 📊✨ Uncover the nuances of your data using R with this powerful function. 🤓
Example 1 explores central tendency, visualizing bootstrapped means, mins, maxes, and standard deviations. Example 2 customizes the plot for a cleaner look. 💡
Dive into the latest tidyAML release! 🌟 The spotlight is on the new .drop_na parameter, enhancing the functionality of fast_classification() and fast_regression() functions.
🌟 Join Pfizer's exclusive webinar covering their journey from SAS to R with Natalia Andriychuk from Pfizer. Discover how they're shaping the future with community-driven development.
🌟 Ready to unlock the power of time series analysis in R? 🤓 Check out this quick guide on converting a data frame into a time series using base R, and xts libraries.
🔍 Base R Brilliance: The trusty ts() function is your go-to!
🚀 XTS Magic: Explore the xts library! Employ the as.xts() function!
📈 Master Spline Regression in R! 🚀 Learn to capture non-linear data relationships effortlessly. Follow my simple guide: load 'splines,' generate data, fit the model, visualize results, and assess goodness of fit. Ready to elevate your analyses? Dive in and share your experience! 🤓
Dive into quantile regression in R! 🔍 My latest blog explores quantreg library, visualizing 'hours' vs. 'score' with a scatter plot. Using rq(), I ran models at three quantiles, unveiling nuances. 🎨 Visualized model performance by overlaying regression lines. Now, it's your turn! 🔥 Explore the hidden data gems and share your insights. Check out the guide [link] and let's keep the data conversations alive!✨ #DataScience#RProgramming#QuantileRegression#DataMagic
Compare traditional lm() with robust rlm() using a dataset. Blue vs. red residuals visually unveil how each model handles outliers. Dive in, experiment with your data, and empower your coding journey! 💻
🚀 Unleash the power of regression in R! 🔍 Follow this quick guide: load data, visualize with a scatter plot, fit a power regression model using nls, add visual flair, and embrace uncertainty with prediction intervals. Ready to code? Dive in! 💻✨
R's EnvStats library
1️⃣ dtri(x): Calculate the density at a given value.
2️⃣ ptri(q): Find the cumulative probability up to a specific value.
3️⃣ qtri(p): Determine a value for a given cumulative probability.
4️⃣ rtri(n): Generate a random set of numbers following this distribution.
Logistic regression is a crucial tool for predicting binary outcomes. In my latest blog post, I walk you through the process of plotting a logistic regression curve in R. It's an essential skill for data scientists, statisticians, and anyone interested in predictive modeling.