This is the latest in my series of screencasts demonstrating how to use the tidymodels packages, from just getting started to tuning more complex models. Today’s screencast walks through how to train and evalute a random forest model, with this week’...

A new rOpenSci package katex is now on CRAN. This package allows for converting latex math expressions to HTML and MathML for use in markdown documents or package documentation. The R package uses the katex javascript library, but the rendering is don...

Introduction Nonlinear regression model As a model setup, we consider noisy observations \(y_1,\ldots, y_n \in \mathbb{R}\) obtained from a standard nonlinear regression model of the form: \[ \begin{aligned} y_i &\ = \ f(\boldsymbol{x}_i, \boldsymbol{\theta}) + \epsilon_i, \quad i = 1,\ldots, n \end{aligned} \] where \(f: \mathbb{...

I have recently just pushed my fourth package to CRAN, I will do a separate post on this, but the FeatureTerminatoR package has been built to perform automated feature selection, utilising methods such as recursive partitioning, multicollinearity purging and other types will be built into the second version. Installing the ...

Summer. Sunny weather. Vitamin D. And if you are missing vitamin Rain because you are growing a garden or simply want an inner cooling, hit this useless function to see, if there is a rain anywhere to be seen. Besides…Read more ›

👉 Sign Up For More Blog Articles 👈 Updated July 2021 Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem. ...

Often (especially when working with large and/or rich datasets) our (gg)plots can feel cluttered with information. But they don’t have to be! Let’s look at the following plot: Generate some data

library(dplyr)

bfi <- psychTools::bfi %>% 
  mutate(
    O = across(starts_with("O")) %>% rowMeans(na.rm = TRUE),
    C = across(starts_with("C")) %>% rowMeans(na.rm = TRUE),
    E = across(starts_with("E")) %>% rowMeans(na.rm = TRUE),
    A = across(starts_with("A")) %>% rowMeans(na.rm = TRUE),
    N = across(starts_with("N")) %>% rowMeans(na.rm = TRUE)
  ) %>% 
  mutate(
    gender = factor(gender, labels = c("Man", "Woman")),
    education = factor(education, labels = c("HS", "finished HS", "some college", "college graduate", "graduate degree"))
  ) %>% 
  select(gender, education, age, O:N) %>% 
  tidyr::drop_na(education) %>% 
  # multiply the data set
  sample_n(size = 10000, replace = TRUE) %>% 
  # and add some noise
  mutate(across(O:N, \(x) x + rnorm(x, 0, sd(x))))

library(ggplot2)

theme_set(theme_bw())

base_plot <- ggplot(bfi, aes(age, O, color = education)) + 
  facet_wrap(facets = vars(gender)) + 
  coord_cartesian(ylim = c(1, 6)) + 
  scale_color_viridis_d()

base_plot + 
  geom_point(shape = 16, alpha = 0.1) + 
  geom_smooth(se = FALSE)

This is a busy plot. It’s hard to see what the each ...

This is a guest post from Marcin Dubel, a 2021 Shiny Contest Grand Prize winner and Software Engineer at Appsilon, a Full Service RStudio Partner. Finding The Right Tool For The Job With strong competition in the Business Intelligence market, choosing ...

Often (especially when working with large and/or rich datasets) our (gg)plots can feel cluttered with information. But they don’t have to be! Let’s look at the following plot: Generate some data

library(dplyr)

bfi <- psychTools::bfi %>% 
  mutate(
    O = across(starts_with("O")) %>% rowMeans(na.rm = TRUE),
    C = across(starts_with("C")) %>% rowMeans(na.rm = TRUE),
    E = across(starts_with("E")) %>% rowMeans(na.rm = TRUE),
    A = across(starts_with("A")) %>% rowMeans(na.rm = TRUE),
    N = across(starts_with("N")) %>% rowMeans(na.rm = TRUE)
  ) %>% 
  mutate(
    gender = factor(gender, labels = c("Man", "Woman")),
    education = factor(education, labels = c("HS", "finished HS", "some college", "college graduate", "graduate degree"))
  ) %>% 
  select(gender, education, age, O:N) %>% 
  tidyr::drop_na(education) %>% 
  # multiply the data set
  sample_n(size = 10000, replace = TRUE) %>% 
  # and add some noise
  mutate(across(O:N, \(x) x + rnorm(x, 0, sd(x))))

library(ggplot2)

theme_set(theme_bw())

base_plot <- ggplot(bfi, aes(age, O, color = education)) + 
  facet_wrap(facets = vars(gender)) + 
  coord_cartesian(ylim = c(1, 6)) + 
  scale_color_viridis_d()

base_plot + 
  geom_point(shape = 16, alpha = 0.1) + 
  geom_smooth(se = FALSE)

This is a busy plot. It’s hard to see what the each ...

Introduction In addition to gglot2 wrapper functions, simplevis also provides leaflet wrapper functions as a bonus. The way these functions have been designed is to follow the logic of the ggplot2 wrapper functions.

library(simplevis)
library(dplyr)
library(palmerpenguins)

sf objects The sf package makes it easy to work with vector data (e.g. points, ...

Covariance Matrix in R, Covariance is a measure of the degree to which two variables are linearly associated. We can measure how changes in... The post How to Create a Covariance Matrix in R appeared first on finnstats.

This latest update of GooglyPlusPlus2021 includes new controls which allow for granular analysis of teams and matches. This version includes a new ‘Date Range’ widget which will allow you to choose a specific interval between which you would like to analyze data. The Date Range widget has been added to 2 ...

I was reading the book “How Not to Be Wrong: The Power of Mathematical Thinking” by Jordan Ellenberg. The book introduces a paradox named after Daniel Ellsberg, a young analyst at RAND Corporation and famous for leaking the Pentagon papers ...

Sometimes you generate lots of ggplots of a similar kind, e.g. visualizations of different timeseries. Then you want to highlight some dates where something special had happened and you want to show that the value you are plotting changed at these date...

Heroku is a cloud platform-as-a-service (PaaS) to deploy apps without worrying about infrastructure. Yes, this includes Shiny apps!

Background In his post on hierarchical models, Michael Betancourt goes in-depth on the funnel pathologies that often plague hierarchical modeling. My goal here is to reproduce his analysis in PyMC3 and explore these problems and their solutions. A hierarchical model is one that simultaneously models data from individual distributions and ...