Ian Cook | Bridging the Gap between SQL and R | RStudio (2020)
Ian Cook | January 31, 2020
Like it or not, SQL is the closest thing we have to a universal language for working with structured data. Celebrating its 50th birthday in 2020, SQL today integrates with thousands of applications and has millions of users worldwide. Data analysts using SQL represent a large audience of potential R users motivated to expand their data science skills. But learning R can be frustrating for SQL users. One major frustration is the inability to directly query R data frames with SQL SELECT statements. Eager to use R for tasks that are not possible with SQL (like data visualization and machine learning), these users are dismayed to find that they must first learn an unfamiliar syntax for data manipulation. The popularity of the sqldf package (which automatically exports an R data frame into an embedded database, then runs a SQL query on it) demonstrates this frustration. But now there is a way to directly query an R data frame without moving the data out of R. In this talk, I introduce tidyquery, a new R package that runs SQL queries directly on R data frames. tidyquery is powered by dplyr and by queryparser, a new pure-R, no-dependency SQL query parser
dplyr
rstudio
Ian Cook
Rstudio::conf(2020)
SQL
RStudio
Data Science
Machine Learning
Python
Stats
Tidyverse
Data Visualization
Data Viz
Ggplot
Technology
Coding
Connect
Server Pro
Shiny
Rmarkdown
Package Manager
CRAN
Interoperability
Serious Data Science
Dplyr
Forcats
Ggplot2
Tibble
Readr
Stringr
Tidyr
Purrr
Github
Data Wrangling
Tidy Data
Odbc
Rayshader
Plumber
Blogdown
Gt
Lazy Evaluation
Tidymodels
Statistics
Debugging
Programming Education
Rstats
Open Source
Oss
Reticulate