R Has No Primitives

Some weeks ago Hadley tweeted this graphic about objects and names in R. Someone asked him to give a situation where this was important and he said:

I haven’t been able to figure that out. But you’ll make terrible predictions about performance unless you know

Penalised spline regression

Sometimes you don’t know the functional form of a regression relationship. In such an instance, the use of a penalised spline regression can help you model it without having a ridiculously wiggly smooth function.

Subsetting Dataframes by Column Name with Regular Expressions

Description

Selecting columns of a dataframe with regular expressions.

Code Snippet/Console Buffer Yank

Lets make a test set of data. Column names that follow some sort of system will make this example easier to understand.

> CN.df <- expand.grid(LETTERS, month.abb)
> 
> head(CN.df)
  Var1 Var2
1    A  Jan
2    B  Jan
3    C  Jan
4    D  Jan
5    E  Jan
6    F  Jan
> 
> tail(CN.df)
    Var1 Var2
307    U  Dec
308    V  Dec
309    W  Dec
310    X  Dec
311    Y  Dec
312    Z  Dec
> 
> CN.df$CN <- paste(CN.df$Var1, CN.df$Var2, sep = '_')

What the shist()?

More than cheap wordplay

I love hist(). It is both a go to plot for data exploration and a really simple way to dazzle users of Microsoft Excel. base::hist() is fast, both to type and in execution, but its downfall is you end up using it many times in a row while you fumble for the right bin width. All that fumbling can kill the magic.

Enter shist() the shifting-histogram… or something… it sounded cool. shist() is a histogram I built from Hadley’s ggvis that lets you interactively select the bin width while it updates the frequencies in real time. This means you only need to plot at most twice: One for shape, two for pretty.

Plotting reference maps from shapefiles using ggplot2

This is about plotting reference maps from shapefiles using ggplot2. But it’s not just about plotting reference maps per se; it’s about plotting the reference map over some sort of raster or other data layer, like you would in a GIS application.

I will show you the ggplot2 approach and how it avoids the problems inherent in other approaches.

You need these packages: rgdal, sp, ggplot2

library(rgdal)    # to read in the shapefile
library(sp)       # for Spatial* classes and coordinate projections`
library(ggplot2)  # for visuallising the data`

To do what I have done with my data you will also need: gstat, dplyr

library(gstat)     # to support geostatistical stuff
library(dplyr)     # for aggregation of data