Category: R

Rob Grant shows how to connect to Google Sheets with R:

That was a quick overview of the most basic functions of the google sheets package.

This is a really useful package. A lot of my work involves reading data in Google Sheets either before or after using R.

Googlesheets means I won’t have to bother with read.csv() or write.csv() as much in the future, saving me time.

Click through for a good tutorial.

Blazej Moska demonstrates how to use Rcpp to parallelize R code:

One of the frustrating moments while working with data is when you need results urgently, but your dataset is large enough to make it impossible. This happens often when we need to use algorithm with high computational complexity. I will demonstrate it on the example I’ve been working with.

Suppose we have large dataset consisting of association rules. For some reasons we want to slim it down. Whenever two rules consequents are the same and one rule’s antecedent is a subset of second rule’s antecedent, we want to choose the smaller one (probability of obtaining smaller set is bigger than probability of obtaining bigger set).

Read the whole thing.

Jenny Bryan explains how building projects in R can reduce the likelihood that someone will come in and set your computer on fire:

I suggest organizing each data analysis into a project: a folder on your computer that holds all the files relevant to that particular piece of work. I’m not assuming this is an RStudio Project, though this is a nice implementation discussed below.

Any resident R script is written assuming that it will be run from a fresh R process with working directory set to the project directory. It creates everything it needs, in its own workspace or folder, and it touches nothing it did not create. For example, it does not install additional packages (another pet peeve of mine).

This convention guarantees that the project can be moved around on your computer or onto other computers and will still “just work”. I argue that this is the only practical convention that creates reliable, polite behavior across different computers or users and over time. This convention is neither new, nor unique to R.

I admit that I’m just now getting into using projects regularly for my one-off stuff.  This is very good advice.  H/T David Smith

Steph Locke shows us how to scrape a PDF, specifically, the PASS operating budget:

With tabulizer, if the data is relatively well formatted in a PDF you can use tabulizer::extract_tables(). This gives you a bunch of data.frames which you can process. Unfortunately, in the case of the PASS budget with 22 pages of tables, including tables that span multiple pages, we’re not so lucky!

We need to fall back to tabulizer::extract_text() and do a lot of wrangling to reconstruct the tables.

Steph shows her work, so click through to see the scripts.

John Mount announces rquery:

rquery is Win-Vector LLC‘s currently in development big data query tool for R.

rquery supplies set of operators inspired by Edgar F. Codd‘s relational algebra (updated to reflect lessons learned from working with R, SQL, and dplyr at big data scale in production).

As an example: rquery operators allow us to write our earlier “treatment and control” example as follows.

dQ <- d %.>%
  extend_se(.,
            if_else_block(
              testexpr =
                "rand()>=0.5",
              thenexprs = qae(
                a_1 := 'treatment',
                a_2 := 'control'),
              elseexprs = qae(
                a_1 := 'control',
                a_2 := 'treatment'))) %.>%
  select_columns(., c("rowNum", "a_1", "a_2"))

It’s an interesting idea.

Abdul Majed Raja has a dplyr tutorial:

dplyr is one of the most popular r-packages and also part of tidyverse that’s been developed by Hadley Wickham. The mere fact that dplyr package is very famous means, it’s one of the most frequently used. Being a data scientist is not always about creating sophisticated models but Data Analysis (Manipulation) and Data Visualization play a very important role in BAU of many us – in fact, a very important part before any modeling exercise since Feature Engineering and EDA are the most important differentiating factors of your model and someone else’s.
Hence, this post aims to bring out some well-known and not-so-well-known applications of dplyr so that any data analyst could leverage its potential using a much familiar – Titanic Dataset.

This covers the main pieces  of dplyr, including its pipeline.  dplyr is a key part of the tidyverse, and knowing it well makes R so much easier.  H/T R-Bloggers