Imports: Rcpp (>= 0.12.12), ggplot2, sp, pracma, reshape2
Suggests: plyr, MBA, ggmap, plotrix, rworldmap, rgl, ABCanalysis,
choroplethr, dplyr, R6, parallelDist, knitr (>= 1.12),
rmarkdown (>= 0.9), vioplot, ggExtra, plotly, htmlwidgets,
diptest, moments, signal, DatabionicSwarm, ggrepel

diptest, moments, signal, DatabionicSwarm, ggrepel, MASS

Title: Data Visualisation Using an HTML Page and 'D3.js'
Description: Gives access to data visualisation methods that are relevant from the statistician's point of view. Using 'D3''s existing data visualisation tools to empower R language and environment. The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female).

'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', and 'SQLite'). Also includes support for
fetching data as 'ffdf' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
SqlRender (>= 1.6.0), methods, utils, DBI (>= 1.0.0), urltools

SqlRender (>= 1.6.3), methods, utils, DBI (>= 1.0.0), urltools

fetching data as 'Andromeda' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
Imports: DatabaseConnectorJars, rJava, SqlRender (>= 1.6.3), methods,
stringr, utils, DBI (>= 1.0.0), urltools

SystemRequirements: Java version 8 or higher (https://www.java.com/)
Imports: rJava, SqlRender (>= 1.6.3), methods, stringr, rlang, utils,
Suggests: aws.s3, R.utils, withr, testthat, DBItest, knitr, rmarkdown,

Imports: rJava, SqlRender (>= 1.7.0), methods, stringr, rlang, utils,

'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Spark', and 'SQLite'). Also includes support for
Imports: rJava, SqlRender (>= 1.7.0), methods, stringr, readr, rlang,
RSQLite, ssh, Andromeda (>= 0.5.0), dplyr
Packaged: 2021-11-18 08:49:38 UTC; admin_mschuemi

Imports: rJava, SqlRender (>= 1.8.3), methods, stringr, readr, rlang,
RSQLite, ssh, Andromeda (>= 0.6.0), dplyr