Update: ggplot in python is not regularly being maintained, so best to work with the R version in RStudio. SUMMARY – ggplot (python) within conda This is a guide to setting up the ggplot package for use with python in a conda virtual environment. This description uses the ‘conda’ application. Conda is not Anaconda, but… Continue reading ggplot and python with conda
Category: Computing
The need for clear information for NSW Land Rights and Native Title Claims
A recent news report [1] highlighted the potential for conflict and administration work in NSW indigenous land areas. The work required is to determine the extent of overlap between native title claims and historical dispossession in NSW indigenous land areas, the latter being a possible basis for compensation. The Land Rights regime created by the… Continue reading The need for clear information for NSW Land Rights and Native Title Claims
R Studio and data import
In R, data importing can be done in several ways, including using the ‘readr’ function. R Studio provides an interface for these common functions. You can import as text (readr). R Studio will then allow you to preview the data, and to skip rows that are unnecessary, in order to ensure that the data appears… Continue reading R Studio and data import
R extensions and the tidyverse
The basic features of R are incredibly useful for someone coming from a non-R background, but experienced R users have sought faster and more effective data manipulation and improved plotting abilities, as well as multimedia options. Hadley Wickham’s contribution to the R universe includes his ‘ tidyverse‘ concept, and the associated set of packages. These… Continue reading R extensions and the tidyverse
R extensions and the data.table
One alternative to R’s native data frames is the data.table package first released to CRAN in 2006. data table extends base R’s data.frame. data.table was authored principally by Arun Srinivasan and Matt Dowle. One of the central features of the data.table was that it replaced the focus on data.frame rownames with the concept of a… Continue reading R extensions and the data.table
Python’s adoption of R’s ‘data frame’
Due to the influence of R on python, some of the terminology is similar and could become confusing, but this is avoided by understanding that many of the concepts originated with R and have since been incrementally picked up in the python universe or elsewhere. Here are some examples A ‘data frame’ was introduced to… Continue reading Python’s adoption of R’s ‘data frame’
Live Coding Overview
Live Coding is a topic raised when software developers talk about the tools they like to use, or might like to use in the future. The main idea behind live coding is to allow a software developer to illustrate the state of variables and other data in the code as coding is carried out.… Continue reading Live Coding Overview
R extensions and interactivity
Yihui Xie is a creative software engineer who works at RStudio. His R packages are innovative and useful. 1. shiny. Shiny enables you to build interactive web applications straight from R. 2. knitR and animation. Yihui Xie created a literate programming package for R, called knitR. knitR enables R code to be embedded in other… Continue reading R extensions and interactivity
R extensions: Grammar of Graphics, tidyverse and ggplot2
The Grammar of Graphics (the ‘gg’ in the ‘ggplot2’) Hadley Wickham is the author of several popular R packages and an employee of R Studio. In developing his graphical plotting package for R, called ggplot2, he has applied many of the ideas in Leland Wilkinson’s influential Grammar of Graphics concept and book. This 2010 article… Continue reading R extensions: Grammar of Graphics, tidyverse and ggplot2
Data science literacy
The data life cycle and software One of the requirements to work professionally with data is not only mathematical knowledge, but familiarity with data processing hardware and software. Software packages can be useful through many stages of the life cycle, from data acquisition and cleaning, through to formatting and eventually, data visualisation. Data-focussed scientists are… Continue reading Data science literacy