PivotalR is a package that enables users of R, the most popular open source statistical programming language and environment to interact with the Pivotal (Greenplum) Database as well as Pivotal HD/HAWQ for Big Data analytics. It does so by providing an interface to the operations on tables/views in the database. These operations are almost the same as those of data.frame. Thus the users of R do not need to learn SQL when they operate on the objects in the database. The latest code, along with a training video and a quick-start guide, are available at https://github.com/pivotalsoftware/PivotalR.
|License:||GPL (>= 2)|
|Depends:||methods, DBI, RPostgreSQL|
This package enables R users to easily develop, refine and deploy R scripts that leverage the parallelism and scalability of the database as well as in-database analytics libraries to operate on big data sets that would otherwise not fit in R memory - all this without having to learn SQL because the package provides an interface that they are familiar with.
The package also provides a wrapper for MADlib. MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine-learning algorithms for structured and unstructured data. The number of machine learning algorithms that MADlib covers is quickly increasing.
As an R front-end to the PostgreSQL-like databases, this package minimizes the amount of data transferred between the database and R. All the big data is stored in the database. The user enters their familiar R syntax, and the package translates it into SQL queries and sends the SQL query into database for parallel execution. The computation result, which is small (if it is as big as the original data, what is the point of big data analytics?), is returned to R to the user.
On the other hand, this package also gives the usual SQL users the access of utilizing the powerful analytics and graphics functionalities of R. Although the database itself has difficulty in plotting, the result can be analyzed and presented beautifully with R.
This current version of PivotalR provides the core R infrastructure and data frame functions as well as over 50 analytical functions in R that leverage in- database execution. These include
* Data Connectivity - db.connect, db.disconnect, db.Rquery
* Data Exploration - db.data.frame, subsets
* R language features - dim, names, min, max, nrow, ncol, summary etc
* Reorganization Functions - merge, by (group-by), samples
* Transformations - as.factor, null replacement
* Algorithms - linear regression and logistic regression wrappers for MADlib
This package is differernt from PL/R, which is another way of using R with PostgreSQL-like databases. PL/R enables the users to run R scripts from SQL. In the parallel Greenplum database, one can use PL/R to implement parallel algorithms.
However, PL/R still requires non-trivial knowledge of SQL to use it effectively. It is mostly limited to explicitly parallel jobs. And for the end user, it is still a SQL interface.
This package does not require any knowledge of SQL, and it works for both explicitly and implicitly parallel jobs by employing the open-source MADlib library. It is much more scalable. And for the end user, it is a pure R interface with the conventional R syntax.
Author: Predictive Analytics Team at Pivotal Inc. [email protected], with contributions from Data Scientist Team at Pivotal Inc.
Maintainer: Frank McQuillan, Pivotal Inc. [email protected]
 MADlib website, http://madlib.incubator.apache.org
 MADlib user docs, http://madlib.incubator.apache.org/docs/latest/
 MADlib Wiki page, https://cwiki.apache.org/confluence/display/MADLIB
 MADlib contribution guide, https://cwiki.apache.org/confluence/display/MADLIB/Contribution+Guidelines
 MADlib on GitHub, https://github.com/apache/incubator-madlib
madlib.lm Linear regression
madlib.glm Linear, logistic and multinomial logistic
madlib.summary summary of a table in the database.
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## Not run: ## get the help for the package help("PivotalR-package") ## get help for a function help(madlib.lm) ## create multiple connections to different databases db.connect(port = 5433) # connection 1, use default values for the parameters db.connect(dbname = "test", user = "qianh1", password = "", host = "remote.machine.com", madlib = "madlib07", port = 5432) # connection 2 db.list() # list the info for all the connections ## list all tables/views that has "ornst" in the name db.objects("ornst") ## list all tables/views db.objects(conn.id = 1) ## create a table and the R object pointing to the table ## using the example data that comes with this package delete("abalone", conn.id = cid) x <- as.db.data.frame(abalone, "abalone") ## OR if the table already exists, you can create the wrapper directly ## x <- db.data.frame("abalone") dim(x) # dimension of the data table names(x) # column names of the data table madlib.summary(x) # look at a summary for each column lk(x, 20) # look at a sample of the data ## look at a sample sorted by id column lookat(sort(x, decreasing = FALSE, x$id), 20) lookat(sort(x, FALSE, NULL), 20) # look at a sample ordered randomly ## linear regression Examples -------- ## fit one different model to each group of data with the same sex fit1 <- madlib.lm(rings ~ . - id | sex, data = x) fit1 # view the result lookat(mean((x$rings - predict(fit1, x))^2)) # mean square error ## plot the predicted values v.s. the true values ap <- x$rings # true values ap$pred <- predict(fit1, x) # add a column which is the predicted values ## If the data set is very big, you do not want to load all the ## data points into R and plot. We can just plot a random sample. random.sample <- lk(sort(ap, FALSE, "random"), 1000) # sort randomly plot(random.sample) # plot a random sample ## fit a single model to all data treating sex as a categorical variable --------- y <- x # make a copy, y is now a db.data.frame object y$sex <- as.factor(y$sex) # y becomes a db.Rquery object now fit2 <- madlib.lm(rings ~ . - id, data = y) fit2 # view the result lookat(mean((y$rings - predict(fit2, y))^2)) # mean square error ## logistic regression Examples -------- ## fit one different model to each group of data with the same sex fit3 <- madlib.glm(rings < 10 ~ . - id | sex, data = x, family = "binomial") fit3 # view the result ## the percentage of correct prediction lookat(mean((x$rings < 10) == predict(fit3, x))) ## fit a single model to all data treating sex as a categorical variable ---------- y <- x # make a copy, y is now a db.data.frame object y$sex <- as.factor(y$sex) # y becomes a db.Rquery object now fit4 <- madlib.glm(rings < 10 ~ . - id, data = y, family = "binomial") fit4 # view the result ## the percentage of correct prediction lookat(mean((y$rings < 10) == predict(fit4, y))) ## Group by Examples -------- ## mean value of each column except the "id" column lk(by(x[,-1], x$sex, mean)) ## standard deviation of each column except the "id" column lookat(by(x[,-1], x$sex, sd)) ## Merge Examples -------- ## create two objects with different rows and columns key(x) <- "id" y <- x[1:300, 1:6] z <- x[201:400, c(1,2,4,5)] ## get 100 rows m <- merge(y, z, by = c("id", "sex")) lookat(m, 20) ## operator Examples -------- y <- x$length + x$height + 2.3 z <- x$length * x$height / 3 lk(y < z, 20) ## ------------------------------------------------------------------------ ## Deal with NULL values delete("null_data") x <- as.db.data.frame(null.data, "null_data") ## OR if the table already exists, you can create the wrapper directly ## x <- db.data.frame("null_data") dim(x) names(x) ## ERROR, because of NULL values fit <- madlib.lm(sf_mrtg_pct_assets ~ ., data = x) ## remove NULL values y <- x # make a copy for (i in 1:10) y <- y[!is.na(y[i]),] dim(y) fit <- madlib.lm(sf_mrtg_pct_assets ~ ., data = y) fit ## Or we can replace all NULL values x[is.na(x)] <- 45 ## End(Not run)
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