The Posit::conf 2023 (formerly known as RStudio conference) was held in Hyatt Regency in Chicago, Illinois, USA from 16 to 20 September, 2023. This was the first conference held since the name was changed from RStudio to Posit. It was a conference full of learning, making connections, and feeling inspired. As an attendee, I had the privilege of participating in workshops, attending various events, listening to great talks, meeting talented individuals, and experiencing the empowering atmosphere of R Ladies.

Time series data wrangling

In this post, you will learn how to build a website using Quarto.

Delete .Rprofile

Logistic regression is a widely used modelling approach, however little is known about the modelling processes and interpretation of model outputs. This post demonstrates how to build a logistic model with R and how to interpret the results.

You can use facet_zoom from the ggforce package to zoom out graphs instead of using your mouse (Contextual zoom).

With the ggplot2 package, you've got full control over the axes labels in charts. Here we are going to look at some of the most commonly needed formatting options in order to make your graph aesthetically pleasing

LOcal regrESSion (LOESS) or LOcally WEighted Scatter-plot Smoother (LOWESS) LOESS or LOWESS is a nonparametric technique to fit a smooth curve through points in a scatter plot. This approach uses locally estimated linear regression at its core.
The following code illustrates how to include a loess line using the ggplot2 package.
library(ggplot2) set.seed(1) x <- rnorm(60) y <- c(rnorm(40), 10, rnorm(19)) df <- data.frame(x=x, y=y) ## Without weights ggplot(data=df, aes(x=x, y=y)) + geom_point() + geom_smooth(method=loess, legend=FALSE) Furthermore your can plot loess smoothing weighting the observations as follows:

MEDIPI, a statistical machine learning algorithm for medicinal plant identification and a leaf image database for plant classification.

Let’s see how to format your model outputs with parameters package in R by Lüdecke et al. (2020).
Fit a simple linear regression model #install.packages("parameters") library(parameters) library(magrittr) library(gt) library(see) model <- lm(Volume ~ Height + Girth, data=trees) Reporting model parameter estimates model %>% model_parameters() Parameter | Coefficient | SE | 95% CI | t(28) | p -------------------------------------------------------------------- (Intercept) | -57.99 | 8.64 | [-75.68, -40.29] | -6.71 | < .

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