.. _R_Tutorial: R Tutorial ========== This tutorial provides a sample workflow for new users of H\ :sub:`2`\ O's R API. Readers will learn the basic syntax of H\ :sub:`2`\ O, including importing and parsing files, specifying a model, and obtaining model output. New H\ :sub:`2`\ O users should refer to the `quick start guide `_ for additional instructions on how to run H\ :sub:`2`\ O. The following tutorial assumes that H\ :sub:`2`\ O is installed in R. Getting Started """"""""""""""" R uses an REST API to send functions to H\ :sub:`2`\ O, so a reference object in R to the H\ :sub:`2`\ O instance is required. You can start H\ :sub:`2`\ O outside of R and connect to it. You can also launch directly from R, but if you close the R session, the H\ :sub:`2`\ O instance is closed as well. The client object is used to direct R to datasets and models located in H\ :sub:`2`\ O. **Launch From R** By default, if the argument **max_mem_size** is not specified when running **h2o.init()**, the heap size of the H\ :sub:`2`\ O running on 32-bit Java is 1g. On 64-bit Java, the heap size is 1/4 of the total memory available on the machine. For a 32-bit version, the function runs a check and suggests an upgrade. :: > library(h2o) > localH2O <- h2o.init(ip = 'localhost', port = 54321, max_mem_size = '4g') Successfully connected to http://localhost:54321 R is connected to H2O cluster: H2O cluster uptime: 11 minutes 35 seconds H2O cluster version: 2.7.0.1497 H2O cluster name: H2O_started_from_R H2O cluster total nodes: 1 H2O cluster total memory: 3.56 GB H2O cluster total cores: 8 H2O cluster allowed cores: 8 H2O cluster healthy: TRUE **Launch From Command Line** Follow one of the `deployment tutorials `_ to launch an instance from the command line: * on your desktop * on ec2 instances * on Hadoop servers After launching the H\ :sub:`2`\ O cluster, initialize the connection by taking one node in the cluster and run **h2o.init** with the node's IP Address and port in the parentheses. Note that the IP Address must be on your local machine. For the following example, change **192.168.1.161** to your local host. :: > library(h2o) > localH2O <- h2o.init(ip = '192.168.1.161', port =54321) .. WARNING:: If the version of the current H\ :sub:`2`\ O instance is not the same as the package version loaded in R, a "version mismatch" warning message displays. To fix this issue, update the R package or launch an H\ :sub:`2`\ O instance using the jar file from the installed package. :: Error in h2o.init(): Version mismatch! H2O is running version # but R package is version # **Cluster Info** To check the status and health of the H\ :sub:`2`\ O cluster, use **h2o.clusterInfo()** to display an easy-to-read summary of information about the cluster. :: > library(h2o) > localH2O = h2o.init(ip = 'localhost', port = 54321) > h2o.clusterInfo(localH2O) R is connected to H2O cluster: H2O cluster uptime: 43 minutes 43 seconds H2O cluster version: 2.7.0.1497 H2O cluster name: H2O_started_from_R H2O cluster total nodes: 1 H2O cluster total memory: 3.56 GB H2O cluster total cores: 8 H2O cluster allowed cores: 8 H2O cluster healthy: TRUE Importing Data """""""""""""" **Import File** The H\ :sub:`2`\ O package consolidates all of the various supported import functions. Although **h2o.importFolder** and **h2o.importHDFS** will still work, these functions are deprecated and should be updated to **h2o.importFile**. :: ## To import small iris data file from H\ :sub:`2`\ O's package > irisPath = system.file("extdata", "iris.csv", package="h2o") > iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex") |=================================================| 100% ## To import an entire folder of files as one data object > pathToFolder = "/Users/Amy/0xdata/data/airlines/" > airlines.hex = h2o.importFile(localH2O, path = pathToFolder, key = "airlines.hex") |=================================================| 100% ## To import from HDFS > pathToData = "hdfs://mr-0xd6.0xdata.loc/datasets/airlines_all.csv" > airlines.hex = h2o.importFile(localH2O, path = pathToData, key = "airlines.hex") |=================================================| 100% **Upload File** To upload a file from your local disk, **importFile** is recommended. However, you can still run **upload file**. :: > irisPath = system.file("extdata", "iris.csv", package="h2o") > iris.hex = h2o.uploadFile(localH2O, path = irisPath, key = "iris.hex") |====================================================| 100% Data Manipulation and Description """"""""""""""""""""""""""""""""" **Any Factor** Determine if any column in a data set is a factor. :: > irisPath = system.file("extdata", "iris_wheader.csv", package="h2o") > iris.hex = h2o.importFile(localH2O, path = irisPath) |===================================================| 100% > h2o.anyFactor(iris.hex) [1] TRUE **As Data Frame** Convert an H\ :sub:`2`\ O parsed data object into an R data frame that can be manipulated using R calls. While this can be very useful, be careful with **as.data.frame** when converting H\ :sub:`2`\ O Parsed Data objects. Data sets that are easily and quickly handled by H\ :sub:`2`\ O are often too large to be treated equivalently well in R. :: > prosPath <- system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |===================================================| 100% > prostate.data.frame<- as.data.frame(prostate.hex) > summary(prostate.data.frame) ID CAPSULE AGE RACE Min. : 1.00 Min. :0.0000 Min. :43.00 Min. :0.000 1st Qu.: 95.75 1st Qu.:0.0000 1st Qu.:62.00 1st Qu.:1.000 .... > head(prostate.data.frame) ID CAPSULE AGE RACE DPROS DCAPS PSA VOL GLEASON 1 1 0 65 1 2 1 1.4 0.0 6 2 2 0 72 1 3 2 6.7 0.0 7 .... **As Factor** Convert an integer into a non-ordered factor (also called an enum or categorical). :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |===================================================| 100% > prostate.hex[,4] = as.factor(prostate.hex[,4]) > summary(prostate.hex) ID CAPSULE AGE RACE DPROS Min. : 1.00 Min. :0.0000 Min. :43.00 1 :341 Min. :1.000 1st Qu.: 95.75 1st Qu.:0.0000 1st Qu.:62.00 2 : 36 1st Qu.:1.000 .... **As H2O** Pass a data frame from inside the R environment to the H\ :sub:`2`\ O instance. :: > data(iris) > summary(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 .... > iris.r <- iris > iris.h2o <- as.h2o(localH2O, iris.r, key="iris.h2o") |===================================================| 100% > class(iris.h2o) [1] "H2OParsedData" attr(,"package") [1] "h2o" **Assign H2O** Create a hex key on the server running H\ :sub:`2`\ O for data sets manipulated in R. For instance, in the example below, the prostate data set was uploaded to the H\ :sub:`2`\ O instance and manipulated to remove outliers. To save the new data set on the H\ :sub:`2`\ O server so that it can be subsequently be analyzed with H\ :sub:`2`\ O without overwriting the original data set, use **h2o.assign**. :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |===================================================| 100% > prostate.qs = quantile(prostate.hex$PSA) > PSA.outliers = prostate.hex[prostate.hex$PSA <= prostate.qs[2] | prostate.hex$PSA >= prostate.qs[10],] > PSA.outliers = h2o.assign(PSA.outliers, "PSA.outliers") > nrow(prostate.hex) [1] 380 > nrow(PSA.outliers) [1] 380 **Colnames** Obtain a list of the column names in a data set. :: > irisPath = system.file("extdata", "iris.csv", package="h2o") > iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex") |===================================================| 100% > colnames(iris.hex) [1] "C1" "C2" "C3" "C4" "C5" **Extremes** Obtain the maximum and minimum values in real-valued columns. :: > ausPath = system.file("extdata", "australia.csv", package="h2o") > australia.hex = h2o.importFile(localH2O, path = ausPath, key = "australia.hex") |===================================================| 100% > min(australia.hex) [1] 0 > min(c(-1, 0.5, 0.2), FALSE, australia.hex[,1:4]) [1] -1 **Quantile** Request quantiles for an H\ :sub:`2`\ O parsed data set. To request a quantile for a single numeric column, use the column name (for example, **$AGE**). When you request for a full parsed data set, **quantile()** returns a matrix that displays quantile information for all numeric columns in the data set. :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |===================================================| 100% > quantile(prostate.hex$AGE) **Summary** Generate an R-like summary for each of the columns in a data set. For continuous real functions, this produces a summary that includes information on quartiles, min, max, and mean. For factors, this produces information about counts of elements within each factor level. For information on the Summary algorithm, see :ref:`SUMmath`. :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |===================================================| 100% > summary(prostate.hex) ID CAPSULE AGE RACE Min. : 1.00 Min. :0.0000 Min. :43.00 Min. :0.000 1st Qu.: 95.75 1st Qu.:0.0000 1st Qu.:62.00 1st Qu.:1.000 .... > summary(prostate.hex$GLEASON) GLEASON Min. :0.000 1st Qu.:6.000 Median :6.000 Mean :6.384 3rd Qu.:7.000 Max. :9.000 > summary(prostate.hex[,4:6]) RACE DPROS DCAPS Min. :0.000 Min. :1.000 Min. :1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 Median :1.000 Median :2.000 Median :1.000 Mean :1.087 Mean :2.271 Mean :1.108 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:1.000 Max. :2.000 Max. :4.000 Max. :2.000 **H2O Table** Summarize information in data. Because H\ :sub:`2`\ O handles such large data sets, it is possible to generate tables that are larger than R's capacity. To minimize this risk and enable uninterrupted work, **h2o.table** is called inside of a call for **head()** or **tail()**. Within **head()** and **tail()**, specify the number of rows in the table to return. :: > head(h2o.table(prostate.hex[,3])) row.names Count 1 43 1 2 47 1 3 50 2 4 51 3 5 52 2 6 53 4 > head(h2o.table(prostate.hex[,c(3,4)])) row.names X0 X1 X2 1 43 1 0 0 2 47 0 1 0 3 50 0 2 0 4 51 0 3 0 5 52 0 2 0 6 53 0 3 1 **Generate Random Uniformly Distributed Numbers** **h2o.runif()** appends a column of random numbers to an H\ :sub:`2`\ O data frame and facilitates creating testing/training data splits for analysis and validation in H\ :sub:`2`\ O. :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "prostate.hex") |===================================================| 100% > s = h2o.runif(prostate.hex) > summary(s) rnd Min. :0.001434 1st Qu.:0.241275 Median :0.496995 Mean :0.489468 3rd Qu.:0.740592 Max. :0.994894 > prostate.train = prostate.hex[s <= 0.8,] > prostate.train = h2o.assign(prostate.train, "prostate.train") > prostate.test = prostate.hex[s > 0.8,] > prostate.test = h2o.assign(prostate.test, "prostate.test") > nrow(prostate.train) + nrow(prostate.test) [1] 380 **Split Frame** Generate two subsets from an existing H2O data set, according to user-specified ratios that can be used as testing/training sets. This is the preferred method of splitting a data frame because it's faster and more stable than running **runif** across entire the data set. However, **runif** can be used for customized frame splitting. :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "prostate.hex") |===================================================| 100% > prostate.split = h2o.splitFrame(data = prostate.hex , ratios = 0.75) > prostate.train = prostate.split[1] > prostate.test = prostate.split[2] > summary(prostate.train) Length Class Mode [1,] 9 H2OParsedData S4 > summary(prostate.test) Length Class Mode [1,] 9 H2OParsedData S4 Running Models """""""""""""" **GBM** Generate Gradient Boosted Models (GBM), which are used to develop forward-learning ensembles. For information on the GBM algorithm, see :ref:`GBMmath`. :: > ausPath = system.file("extdata", "australia.csv", package="h2o") > australia.hex = h2o.importFile(localH2O, path = ausPath) |===================================================| 100% > independent <- c("premax", "salmax","minairtemp", "maxairtemp", "maxsst", "maxsoilmoist", "Max_czcs") > dependent <- "runoffnew" > h2o.gbm(y = dependent, x = independent, data = australia.hex, > n.trees = 10, interaction.depth = 3, n.minobsinnode = 2, shrinkage = 0.2, distribution= "gaussian") |======================================================| 100% IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: australia1.hex GBM Model Key: GBM_a3ae2edf5dfadbd9ba5dc2e9560c405d Mean-squared Error by tree: [1] 230760.11 166957.80 124904.30 94031.17 72367.01 57180.17 47092.85 [8] 39168.05 34456.00 31095.86 28397.10 *Run multinomial classification GBM on abalone data* To generate a classification model that uses labels, use a **multinomial** distribution. :: > h2o.gbm(y = dependent, x = independent, data = australia.hex, n.trees = 15, interaction.depth = 5, n.minobsinnode = 2, shrinkage = 0.01, distribution= "multinomial") IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: australia1.hex GBM Model Key: GBM_8e4591a9b413407b983d73fbd9eb44cf Confusion matrix: Reported on australia1.hex Predicted Actual 0 3 6 7 14 16 17 19 20 25 38 43 61 75 82 107 138 150 167 191 200 0 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .... Totals 120 1 1 2 1 2 2 2 2 31 1 1 1 6 1 1 1 6 1 1 1 Predicted Actual 210 245 300 343 396 400 462 480 514 533 545 600 750 764 840 933 960 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .... Totals 1 1 20 1 1 1 1 1 1 1 1 8 1 1 1 1 1 Predicted Actual 1154 1200 2000 2400 Error 0 0 0 0 0 0.000 3 0 0 0 0 0.000 6 0 0 0 0 0.000 7 0 0 0 0 0.000 .... Mean-squared Error by tree: [1] 0.9529478 0.9337646 0.9157476 0.8985756 0.8818316 0.8654845 0.8497011 [8] 0.8341974 0.8187867 0.8036760 0.7887764 0.7741757 0.7594546 0.7452223 [15] 0.7309634 0.7168317 **GLM** Generate Generalized Linear Models, which are used to develop linear models for exponential distributions. Regularization can be applied. For information on the GLM algorithm, see :ref:`GLMmath`. :: > prostate.hex = h2o.importFile(localH2O, path = "https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv", key = "prostate.hex") |===================================================| 100% > h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), data = prostate.hex, family = "binomial", nfolds = 10, alpha = 0.5) |=====================================================================| 100% IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: prostate.hex GLM2 Model Key: GLMModel__a2fdb4e3fdd92e0325141cdbd1bd43e1 Coefficients: AGE RACE DCAPS PSA Intercept -0.01104 -0.63136 1.31888 0.04713 -1.10896 Normalized Coefficients: AGE RACE DCAPS PSA Intercept -0.07208 -0.19495 0.40972 0.94253 -0.33707 Degrees of Freedom: 379 Total (i.e. Null); 375 Residual Null Deviance: 514.9 Residual Deviance: 461.3 AIC: 471.3 Deviance Explained: 0.10404 AUC: 0.68875 Best Threshold: 0.328 Confusion Matrix: Predicted Actual false true Error false 127 100 0.441 true 51 102 0.333 Totals 178 202 0.397 Cross-Validation Models: Nonzeros AUC Deviance Explained Model 1 4 0.6532738 0.048419803 Model 2 4 0.6316527 -0.006414532 Model 3 4 0.7100840 0.087779178 Model 4 4 0.8268698 0.243020554 Model 5 4 0.6354167 0.153190735 Model 6 4 0.6888889 0.041892118 Model 7 4 0.7366071 0.164717509 Model 8 4 0.6711310 0.004897310 Model 9 4 0.7803571 0.200384622 Model 10 4 0.7435897 0.114548543 :: > myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL")) > h2o.glm(y = "VOL", x = myX, data = prostate.hex, family = "gaussian", nfolds = 5, alpha = 0.1) |=========================================================| 100% IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: prostate.hex GLM2 Model Key: GLMModel__b8339af00fbe8951ba0871611c9e42eb Coefficients: CAPSULE AGE RACE PSA GLEASON Intercept -4.29014 0.29787 4.35557 0.04946 -0.51274 -4.35359 Normalized Coefficients: CAPSULE AGE RACE PSA GLEASON Intercept -2.10678 1.94424 1.34488 0.98908 -0.55989 15.81292 Degrees of Freedom: 379 Total (i.e. Null); 374 Residual Null Deviance: 126623.9 Residual Deviance: 127402 AIC: 11059.1 Deviance Explained: -0.00615 Cross-Validation Models: Nonzeros AIC Deviance Explained Model 1 5 685.6101 -0.02827868 Model 2 5 660.3719 -0.15397511 Model 3 5 658.0768 0.05826293 Model 4 5 665.8665 0.05117173 Model 5 5 683.6276 0.01333543 **K-Means** Generate a K-means model, which is a clustering algorithm that allows users to characterize data. This algorithm does not rely on a dependent variable. For information on the K-Means algorithm, see :ref:`KMmath` :: > prosPath = system.file("extdata", "prostate.csv", package="h2o") > prostate.hex = h2o.importFile(localH2O, path = prosPath) |=========================================================| 100% > prostate.km = h2o.kmeans(data = prostate.hex, centers = 10, cols = c("AGE", "RACE", "VOL", "GLEASON")) |=========================================================| 100% print(prostate.km) IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: prostate6.hex K-Means Model Key: KMeans2_99fea55be4a22f741df74532d7844bb4 K-means clustering with 10 clusters of sizes 41, 27, 59, 17, 21, 47, 26, 61, 47, 34 Cluster means: AGE RACE VOL GLEASON 1 69.73171 1.024390 37.99756098 6.512195 2 54.48148 1.111111 0.32222222 6.518519 3 62.59322 1.067797 0.19322034 5.966102 ..... **Principal Components Analysis** Map a set of variables onto a subspace using linear transformations. Principle Components Analysis (PCA) is the first step in Principal Components Regression. For more information on PCA, see :ref:`PCAmath`. :: > ausPath = system.file("extdata", "australia.csv", package="h2o") > australia.hex = h2o.importFile(localH2O, path = ausPath) |=========================================================| 100% > australia.pca = h2o.prcomp(data = australia.hex, standardize = TRUE) |=========================================================| 100% > print(australia.pca) IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: australia2.hex PCA Model Key: PCA_90d7162c6d4855392ba1272c2f314bec Standard deviations: 1.750703 1.512142 1.031181 0.8283127 0.6083786 0.5481364 0.4181621 0.2314953 .... summary(australia.pca) Importance of components: .... **Principal Components Regression** Map a set of variables to a new set of linearly independent variables. The new set of variables are linearly independent linear combinations of the original variables and exist in a subspace of lower dimension. This transformation is then prepended to a regression model, often improving results. For more information on PCA, see :ref:`PCAmath`. :: > prostate.hex = h2o.importFile(localH2O, path = "https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv", key = "prostate.hex") |=========================================================| 100% > h2o.pcr(x = c("AGE","RACE","PSA","DCAPS"), y = "CAPSULE", data = prostate.hex, family = "binomial", nfolds = 10, alpha = 0.5, ncomp = 3) |==========================================================| 100% IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: PCAPredict_80069467adfe441c92282ac766f9de7e GLM2 Model Key: GLMModel__a1454a5b8a212d1069376356543a4887 Coefficients: PC0 PC1 PC2 Intercept 3.76219 1.26824 -1.35455 -0.36271 .... Obtaining Predictions """"""""""""""""""""" **Predict** Apply an H\ :sub:`2`\ O model to a holdout set to obtain predictions based on model results. In the examples below, models are generated first, and then the predictions for that model are displayed. :: > prostate.hex = h2o.importFile(localH2O, path = "https://raw.github.com/0xdata/h2o/master/smalldata/logreg/prostate.csv", key = "prostate.hex") |==========================================================| 100% > prostate.glm = h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), data = prostate.hex, family = "binomial", nfolds = 10, alpha = 0.5) |==========================================================| 100% > prostate.fit = h2o.predict(object = prostate.glm, newdata = prostate.hex) > (prostate.fit) IP Address: 127.0.0.1 Port : 54321 Parsed Data Key: GLM2Predict_8b6890653fa743be9eb3ab1668c5a6e9 predict X0 X1 1 0 0.7452267 0.2547732 2 1 0.3969807 0.6030193 3 1 0.4120950 0.5879050 4 1 0.3726134 0.6273866 5 1 0.6465137 0.3534863 6 1 0.4331880 0.5668120 Other Useful Functions """""""""""""""""""""" **Get Frame** For users that alternate between using the web interface and the R API, or for multiple users accessing the same H\ :sub:`2`\ O, this function gives the user the option to create a reference object for a data frame sitting in H\ :sub:`2`\ O (assuming there's a **prostate.hex** in the KV store). :: > prostate.hex = h2o.getFrame(h2o = localH2O, key = "prostate.hex") **Get Model** For users that alternate between using the web interface and the R API, this function gives the user the option to create a reference object for a data frame sitting in H\ :sub:`2`\ O (assuming there's a **GLMModel__ba724fe4f6d6d5b8b6370f776df94e47** model in the KV store). :: > glm.model = h2o.getModel(h2o = localH2O, key = "GLMModel__ba724fe4f6d6d5b8b6370f776df94e47") > glm.model **List all H2O Objects** Generate a list of all H\ :sub:`2`\ O objects generated during a work session, along with each object's byte size. :: > prostate.hex = h2o.importFile(localH2O, path = prosPath, key = "prostate.hex") |==========================================================| 100% > prostate.split = h2o.splitFrame(prostate.hex , ratio = 0.8) > prostate.train = prostate.split[[1]] > prostate.train = h2o.assign(prostate.train, "prostate.train") > h2o.ls(localH2O) Key Bytesize 1 GBM_8e4591a9b413407b983d73fbd9eb44cf 40617 2 GBM_a3ae2edf5dfadbd9ba5dc2e9560c405d 1516 **Remove an H2O object from the server where H2O is running** To remove an H\ :sub:`2`\ O object on the server associated with an object in the R environment, we recommend also removing the object from the R environment. :: > h2o.ls(localH2O) Key Bytesize 1 Last.value.39 448 2 Last.value.42 73 3 prostate.hex 4874 4 prostate.train 4028 5 prostate_part0.hex 4028 6 prostate_part1.hex 1432 > h2o.rm(object= localH2O, keys= "prostate.train") > h2o.ls(localH2O) Key Bytesize 1 Last.value.39 448 2 Last.value.42 73 3 prostate.hex 4874 4 prostate_part0.hex 4028 5 prostate_part1.hex 1432