In manymodelr: Build and Tune Several Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(manymodelr)

agg_by_group

As can be guessed from the name, this function provides an easy way to manipulate grouped data. We can for instance find the number of observations in the yields data set. The formula takes the form x~y where y is the grouping variable(in this case normal). One can supply a formula as shown next.

# Load the yields dataset
data("yields")
head(agg_by_group(yields,.~normal,length))

head(agg_by_group(mtcars,cyl~hp+vs,sum))

rowdiff

This is useful when trying to find differences between rows. The direction argument specifies how the subtractions are made while the exclude argument is used to specify classes that should be removed before calculations are made. Using direction="reverse" performs a subtraction akin to x-(x-1) where x is the row number.

head(rowdiff(yields,exclude = "factor",direction = "reverse"))

na_replace

This allows the user to conveniently replace missing values. Current options are ffill which replaces with the next non-missing value, samples that samples the data and does replacement, value that allows one to fill NAs with a specific value. Other common mathematical methods like min, max,get_mode, sd, etc are no longer supported. They are now available with more flexibility in standalone mde

head(na_replace(airquality, how="value", value="Missing"),8)

na_replace_grouped

This provides a convenient way to replace values by group.

test_df <- data.frame(A=c(NA,1,2,3), B=c(1,5,6,NA),groups=c("A","A","B","B"))
# Replace NAs by group
# replace with the next non NA by group.
na_replace_grouped(df=test_df,group_by_cols = "groups",how="ffill")