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SmartData Collective > Uncategorized > 10 R Packages Every Data Scientist Should Know About
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10 R Packages Every Data Scientist Should Know About

DavidMSmith
Last updated: 2013/02/19 at 4:08 PM
DavidMSmith
1 Min Read
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The yhat blog lists 10 R packages they wish they’d known about earlier. Drew Conway calls them “10 reasons to always start your analysis in R.” They’re all very useful R packages that every data scientist should be aware of. Here they are:

The yhat blog lists 10 R packages they wish they’d known about earlier. Drew Conway calls them “10 reasons to always start your analysis in R.” They’re all very useful R packages that every data scientist should be aware of. Here they are:

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  1. sqldf (for selecting from data frames using SQL)
  2. forecast (for easy forecasting of time series)
  3. plyr (data aggregation)
  4. stringr (string manipulation)
  5. Database connection packages RPostgreSQL, RMYSQL, RMongo, RODBC, RSQLite
  6. lubridate (time and date manipulation)
  7. ggplot2 (data visulization)
  8. qcc (statistical quality control and QC charts)
  9. reshape2 (data restructuring)
  10. randomForest (random forest predictive models)

You can find links to all of these packages and tips on how to use them at link below.

yhat blog: 10 R packages I wish I knew about earlier

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TAGGED: acc, data aggregation, data restructuring, data visualization, Database connection package, forecast, forecasting time series, ggplot2, lubridate, plyr, QC charts, r language, random forest predictive models, randomForest, reshape2, RMongo, RMYSQL, RODBC, RPostgreSQL, RSQLite, sql, sqldf, statistical quality control, stringer
DavidMSmith February 19, 2013
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