![]() ![]() The example pushes the iris data set to the database and gets the ore.frame object iris_of. Sepal.Length Sepal.Width Petal.Length Petal.Width Species RID R> ore.exec("alter table IRIS_TABLE add constraint IRIS_TABLE R> ore.create(iris, table = 'IRIS_TABLE') R> iris$RID ore.drop(table = 'IRIS_TABLE') Iris$RID # Add a column to the iris data set to use as row identifiers. # Add a column to the iris data set to use as row identifiers. It then selects 3 rows by row name and displays the result. ![]() It displays the first six rows of iris_selrows. The example next selects 51 rows from IRIS_TABLE by row number and creates the ordered ore.frame object iris_selrows with them. It then invokes the ore.sync function to synchronize the IRIS_TABLE ore.frame object with the table and displays the first three rows of the proxy ore.frame object. The example invokes the ore.exec function to execute a SQL statement that makes the RID column the primary key of the database table. It then creates a database table, with the corresponding proxy ore.frame object IRIS_TABLE, from the iris ame. It invokes the ore.drop function to delete the database table IRIS_TABLE, if it exists. ![]() This example first adds a column to the iris ame object for use in creating an ordered ore.frame object. OML4R provides you with the ability to perform many data preparation operations on time series data, such as filtering, ordering, and transforming the data. In analyzing large data sets, a typical operation is to randomly partition the data set into subsets. Sampling is an important capability for statistical analytics. In preparing data for analysis, a typical step is to transform data by reformatting it or deriving new columns and adding them to the data set. Summarize data with the aggregate function. You can join data from ore.frame objects that represent database tables by using the merge function. You can use integer or character vectors to index an ordered ore.frame object. ![]() quantile() was hard to use previously because it returns multiple values.Oracle Machine Learning for R provides functions that enable you to use R to prepare database data for analysis.Ī typical step in preparing data for analysis is selecting or filtering values of interest from a larger data set. To demonstrate this new flexibility in a more useful situation, let’s take a look at quantile(). This is a big change to summarise() but it should have minimal impact on existing code because it broadens the interface: all existing code will continue to work, and a number of inputs that would have previously errored now work. To put this another way, before dplyr 1.0.0, each summary had to be a single value (one row, one column), but now we’ve lifted that restriction so each summary can generate a rectangle of arbitrary size. (This isn’t very useful when used directly, but as you’ll see shortly, it’s really useful inside of functions.) Df %>% group_by ( grp ) %>% summarise ( tibble ( min = min ( x ), mean = mean ( x ))) #> `summarise()` ungrouping output (override with `.groups` argument) #> # A tibble: 2 x 3 #> grp min mean #> * #> 1 1 -2.69 -0.843 #> 2 2 -2.73 -0.434 ![]()
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