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 is good for folks who are newer to modeling or tidymodels; it focuses on how ...

What defines a production-ready Shiny application? And more importantly, how do we get there? Shiny is hands down one of the best dev tools available for quick production of Proof of Concepts (PoC). But here in lies a trap. People tend to mislabel Shiny as a poor production tool because ...

Like many people during the COVID19 crisis, I turned to the stock market as a new hobby. Like the ignorant investor that I am, I thought it wise to hop on the cloud computing bandwagon. Hence, I bought, among others, a small position in Rackspace Technologies. A long way down ...

You can read the original post in its original format on Rtask website by ThinkR here: {gitlabr} v2.0 is on CRAN! You regularly create projects on GitLab with a common skeleton, you want to retrieve the list of GitLab issues to create a report, you are looking for batch modifications ...

I’m part of a team that recently submitted the results of a randomized clinical trial for publication in a journal. The overall findings of the study were inconclusive, and we certainly didn’t try to hide that fact in our paper. Of course, the stor...

By Sergio Garcia Mora R4HR, formerly known as the Club de R para RRHH, is a Latin American based community whose mission is to spread the adoption of R in... The post A Latin American R community for HR appeared first on R Consortium.

ShinyProxy is a great way to deploy containerized Shiny apps to production and it is only 1 click away.

Custom Branding Many organizations want to align RStudio Connect with their branding strategy. Whether you use RStudio Connect to deliver R and Python content to external clients, or internal stakeholders, branding and clear, consistent presentation is...

Python Updates At RStudio we know that many data science teams leverage both R and Python in their work, so it’s important that we build products to support the best tools available in both languages. For an overview of all the ways our pro produ...

This post explains a combined usage of split(), lapply() and do.call() R functions, so called Split-Apply-Combine approach. These are frequently used for group based calculations such as weighted average or aggregation. It will be useful when compl...

Click to see R set-up code

# Libraries
if(!require("pacman")) {
  install.packages("pacman")
}
pacman::p_load(
  data.table,
  re2,
  scales,
  ggplot2,
  plotly, 
  DT,
  patchwork,
  survival,
  ggfortify,
  scales)

# Set knitr params
knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%'
)

NOTE: The read time for this post is overstated because of the formatting of the Plotly code. There are ~2,500 words, so read time should be ~10 minutes. Click to see R code generating plot

# Load function to plot dual y-axis plot
source("train_sec.R")

# Get data series from FRED
symbols <- c("CP", "GDP", "WASCUR")
start_date <- '1947-01-01'
end_date <- '2021-07-30'
quantmod::getSymbols(
  Symbols = symbols,
  src = "FRED",
  start_date = start_date,
  end_date = end_date
)

[1] "CP"     "GDP"    "WASCUR"

# Merge series and convert to dt
d <- as.data.table(merge(WASCUR/GDP, CP/GDP, join = "inner"))

# Build superimposed dual y-axis line plot
sec <- with(d, train_sec(CP, WASCUR))
p <- 
  ggplot(d, aes(index)) +
    geom_line(aes(y = CP),
              colour = "blue", 
              size = 1) +
    geom_line(aes(y = sec$fwd(WASCUR)),
              colour = "red", 
              size = 1) +
    scale_y_continuous(
      "Corporate Profits to GDP",
      labels = scales::percent,
      sec.axis = sec_axis(
        ~ sec$rev(.),
        name = "Compensation of Employees to GDP",
        labels = scales::percent)
    ) +
    scale_x_date(date_breaks = "10 years",
                 date_labels = "%Y") + 
    labs(title = "Labor vs Capital",
         x = "Year",
         caption = "Source: Lots of places") +
    theme_bw(base_size = 22)

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The tsoutliers() function in the forecast package for R is useful for identifying anomalies in a time series. However, it is not properly documented anywhere. This post is intended to fill that gap. The function began as an answer on CrossValidated and was later added to the forecast package because ...

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Dunnett’s test in R, After the ANOVA test has been completed the next step is to determine which group means are significantly different from one another. Different types of post hoc tests are available... The post How to Perform Dunnett’s Test in R appeared first on finnstats.

This is a guest post by Colin Gillespie from Jumping Rivers, a Full Service RStudio Partner. Earlier this month, Jack Walton and I delivered a webinar with RStudio on the benefits of putting R into production environments, and how to do it successfully...