We all hear about Maximum Likelihood Estimation (MLE) and we often see hints of it in our model output. As usual, doing things manually can give a better grasp on how to better understand how our models work. Here’s a very short example impl... [Read more...]

caret is a magical package for doing machine learning in R. Look at this code for running a regularized regression:

library(caret)

inTrain <- createDataPartition(y = mtcars$mpg,
                               p = 0.75,
                               list = FALSE)  

reg_mod <- train(
  mpg ~ .,
  data = mtcars[inTrain, ],
  method = "glmnet",
  tuneLength = 10,
  preProc = c("center", "scale"),
  trControl = trainControl(method = "cv", number = 10)
)

The two function calls in the expression above perform these operations: Create a training set containing a random sample of 75% of the initial sample Center and scale all predictors ... [Read more...]

I was wondering the realms of StackOver Flow answering some questions when I encoutered a question that looked to extract some parts of a string based on a regex. I thought I knew how to do this with the package stringr using, for example, str_sub but I found it ... [Read more...]

The new essurvey 1.0.3 is here! This release is mainly about downloading weight data from the European Social Survey (ESS), which has been on the works since 2017! As usual, you can install from CRAN or Github with:

# From CRAN
install.packages("essurvey")

# or development version from Github
devtools::install_github("ropensci/essurvey")

# and load
library(essurvey)
set_email("[email protected]")

Remember to set your registered email with set_email to download ESS data. ... [Read more...]

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I had to spend about 1 hour yesterday because R did something completely unpredictable (for my taste). It dropped an attribute without a warning.

df <- data.frame(x = rep(c(1, 2), 20))

attr(df$x, "label") <- "This is clearly a label"

df$x

##  [1] 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1
## [36] 2 1 2 1 2
## attr(,"label")
## [1] "This is clearly a label"

The label is clearly there. To my surprise, if I subset this data frame, R drops the attribute.

new_df <- df[df$x == 2, , drop = FALSE]

new_df$x

##  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

It doesn’t matter if ... [Read more...]