A C D E F G H I J L M N P R S T U misc
augment.ml_model_aft_survival_regression | Tidying methods for Spark ML Survival Regression |
augment.ml_model_als | Tidying methods for Spark ML ALS |
augment.ml_model_bisecting_kmeans | Tidying methods for Spark ML unsupervised models |
augment.ml_model_decision_tree_classification | Tidying methods for Spark ML tree models |
augment.ml_model_decision_tree_regression | Tidying methods for Spark ML tree models |
augment.ml_model_gaussian_mixture | Tidying methods for Spark ML unsupervised models |
augment.ml_model_gbt_classification | Tidying methods for Spark ML tree models |
augment.ml_model_gbt_regression | Tidying methods for Spark ML tree models |
augment.ml_model_generalized_linear_regression | Tidying methods for Spark ML linear models |
augment.ml_model_isotonic_regression | Tidying methods for Spark ML Isotonic Regression |
augment.ml_model_kmeans | Tidying methods for Spark ML unsupervised models |
augment.ml_model_lda | Tidying methods for Spark ML LDA models |
augment.ml_model_linear_regression | Tidying methods for Spark ML linear models |
augment.ml_model_linear_svc | Tidying methods for Spark ML linear svc |
augment.ml_model_logistic_regression | Tidying methods for Spark ML Logistic Regression |
augment.ml_model_multilayer_perceptron_classification | Tidying methods for Spark ML MLP |
augment.ml_model_naive_bayes | Tidying methods for Spark ML Naive Bayes |
augment.ml_model_pca | Tidying methods for Spark ML Principal Component Analysis |
augment.ml_model_random_forest_classification | Tidying methods for Spark ML tree models |
augment.ml_model_random_forest_regression | Tidying methods for Spark ML tree models |
augment._ml_model_decision_tree_classification | Tidying methods for Spark ML tree models |
augment._ml_model_decision_tree_regression | Tidying methods for Spark ML tree models |
augment._ml_model_gbt_classification | Tidying methods for Spark ML tree models |
augment._ml_model_gbt_regression | Tidying methods for Spark ML tree models |
augment._ml_model_linear_regression | Tidying methods for Spark ML linear models |
augment._ml_model_logistic_regression | Tidying methods for Spark ML Logistic Regression |
augment._ml_model_random_forest_classification | Tidying methods for Spark ML tree models |
augment._ml_model_random_forest_regression | Tidying methods for Spark ML tree models |
checkpoint_directory | Set/Get Spark checkpoint directory |
collect_from_rds | Collect Spark data serialized in RDS format into R |
compile_package_jars | Compile Scala sources into a Java Archive (jar) |
connection_config | Read configuration values for a connection |
copy_to.spark_connection | Copy an R Data Frame to Spark |
distinct | Distinct |
download_scalac | Downloads default Scala Compilers |
dplyr_hof | dplyr wrappers for Apache Spark higher order functions |
ensure | Enforce Specific Structure for R Objects |
fill | Fill |
filter | Filter |
find_scalac | Discover the Scala Compiler |
ft_binarizer | Feature Transformation - Binarizer (Transformer) |
ft_bucketed_random_projection_lsh | Feature Transformation - LSH (Estimator) |
ft_bucketizer | Feature Transformation - Bucketizer (Transformer) |
ft_chisq_selector | Feature Transformation - ChiSqSelector (Estimator) |
ft_count_vectorizer | Feature Transformation - CountVectorizer (Estimator) |
ft_dct | Feature Transformation - Discrete Cosine Transform (DCT) (Transformer) |
ft_discrete_cosine_transform | Feature Transformation - Discrete Cosine Transform (DCT) (Transformer) |
ft_dplyr_transformer | Feature Transformation - SQLTransformer |
ft_elementwise_product | Feature Transformation - ElementwiseProduct (Transformer) |
ft_feature_hasher | Feature Transformation - FeatureHasher (Transformer) |
ft_hashing_tf | Feature Transformation - HashingTF (Transformer) |
ft_idf | Feature Transformation - IDF (Estimator) |
ft_imputer | Feature Transformation - Imputer (Estimator) |
ft_index_to_string | Feature Transformation - IndexToString (Transformer) |
ft_interaction | Feature Transformation - Interaction (Transformer) |
ft_lsh | Feature Transformation - LSH (Estimator) |
ft_lsh_utils | Utility functions for LSH models |
ft_max_abs_scaler | Feature Transformation - MaxAbsScaler (Estimator) |
ft_minhash_lsh | Feature Transformation - LSH (Estimator) |
ft_min_max_scaler | Feature Transformation - MinMaxScaler (Estimator) |
ft_ngram | Feature Transformation - NGram (Transformer) |
ft_normalizer | Feature Transformation - Normalizer (Transformer) |
ft_one_hot_encoder | Feature Transformation - OneHotEncoder (Transformer) |
ft_one_hot_encoder_estimator | Feature Transformation - OneHotEncoderEstimator (Estimator) |
ft_pca | Feature Transformation - PCA (Estimator) |
ft_polynomial_expansion | Feature Transformation - PolynomialExpansion (Transformer) |
ft_quantile_discretizer | Feature Transformation - QuantileDiscretizer (Estimator) |
ft_regex_tokenizer | Feature Transformation - RegexTokenizer (Transformer) |
ft_robust_scaler | Feature Transformation - RobustScaler (Estimator) |
ft_r_formula | Feature Transformation - RFormula (Estimator) |
ft_sql_transformer | Feature Transformation - SQLTransformer |
ft_standard_scaler | Feature Transformation - StandardScaler (Estimator) |
ft_stop_words_remover | Feature Transformation - StopWordsRemover (Transformer) |
ft_string_indexer | Feature Transformation - StringIndexer (Estimator) |
ft_string_indexer_model | Feature Transformation - StringIndexer (Estimator) |
ft_tokenizer | Feature Transformation - Tokenizer (Transformer) |
ft_vector_assembler | Feature Transformation - VectorAssembler (Transformer) |
ft_vector_indexer | Feature Transformation - VectorIndexer (Estimator) |
ft_vector_slicer | Feature Transformation - VectorSlicer (Transformer) |
ft_word2vec | Feature Transformation - Word2Vec (Estimator) |
full_join | Full join |
full_join.tbl_spark | Join Spark tbls. |
generic_call_interface | Generic Call Interface |
get_spark_sql_catalog_implementation | Retrieve the Spark connection's SQL catalog implementation property |
glance.ml_model_aft_survival_regression | Tidying methods for Spark ML Survival Regression |
glance.ml_model_als | Tidying methods for Spark ML ALS |
glance.ml_model_bisecting_kmeans | Tidying methods for Spark ML unsupervised models |
glance.ml_model_decision_tree_classification | Tidying methods for Spark ML tree models |
glance.ml_model_decision_tree_regression | Tidying methods for Spark ML tree models |
glance.ml_model_gaussian_mixture | Tidying methods for Spark ML unsupervised models |
glance.ml_model_gbt_classification | Tidying methods for Spark ML tree models |
glance.ml_model_gbt_regression | Tidying methods for Spark ML tree models |
glance.ml_model_generalized_linear_regression | Tidying methods for Spark ML linear models |
glance.ml_model_isotonic_regression | Tidying methods for Spark ML Isotonic Regression |
glance.ml_model_kmeans | Tidying methods for Spark ML unsupervised models |
glance.ml_model_lda | Tidying methods for Spark ML LDA models |
glance.ml_model_linear_regression | Tidying methods for Spark ML linear models |
glance.ml_model_linear_svc | Tidying methods for Spark ML linear svc |
glance.ml_model_logistic_regression | Tidying methods for Spark ML Logistic Regression |
glance.ml_model_multilayer_perceptron_classification | Tidying methods for Spark ML MLP |
glance.ml_model_naive_bayes | Tidying methods for Spark ML Naive Bayes |
glance.ml_model_pca | Tidying methods for Spark ML Principal Component Analysis |
glance.ml_model_random_forest_classification | Tidying methods for Spark ML tree models |
glance.ml_model_random_forest_regression | Tidying methods for Spark ML tree models |
hive_context | Access the Spark API |
hive_context_config | Runtime configuration interface for Hive |
hof_aggregate | Apply Aggregate Function to Array Column |
hof_array_sort | Sorts array using a custom comparator |
hof_exists | Determine Whether Some Element Exists in an Array Column |
hof_filter | Filter Array Column |
hof_forall | Checks whether all elements in an array satisfy a predicate |
hof_map_filter | Filters a map |
hof_map_zip_with | Merges two maps into one |
hof_transform | Transform Array Column |
hof_transform_keys | Transforms keys of a map |
hof_transform_values | Transforms values of a map |
hof_zip_with | Combines 2 Array Columns |
inner_join | Inner join |
inner_join.tbl_spark | Join Spark tbls. |
invoke | Invoke a Method on a JVM Object |
invoke_new | Invoke a Method on a JVM Object |
invoke_static | Invoke a Method on a JVM Object |
is_ml_estimator | Spark ML - Transform, fit, and predict methods (ml_ interface) |
is_ml_transformer | Spark ML - Transform, fit, and predict methods (ml_ interface) |
jarray | Instantiate a Java array with a specific element type. |
java_context | Access the Spark API |
jfloat | Instantiate a Java float type. |
jfloat_array | Instantiate an Array[Float]. |
join.tbl_spark | Join Spark tbls. |
j_invoke | Invoke a Java function. |
j_invoke_new | Invoke a Java function. |
j_invoke_static | Invoke a Java function. |
left_join | Left join |
left_join.tbl_spark | Join Spark tbls. |
list_sparklyr_jars | list all sparklyr-*.jar files that have been built |
livy_config | Create a Spark Configuration for Livy |
livy_service_start | Start Livy |
livy_service_stop | Start Livy |
ml-params | Spark ML - ML Params |
ml-persistence | Spark ML - Model Persistence |
ml-transform-methods | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml-tuning | Spark ML - Tuning |
ml_aft_survival_regression | Spark ML - Survival Regression |
ml_als | Spark ML - ALS |
ml_als_tidiers | Tidying methods for Spark ML ALS |
ml_approx_nearest_neighbors | Utility functions for LSH models |
ml_approx_similarity_join | Utility functions for LSH models |
ml_association_rules | Frequent Pattern Mining - FPGrowth |
ml_binary_classification_eval | Spark ML - Evaluators |
ml_binary_classification_evaluator | Spark ML - Evaluators |
ml_bisecting_kmeans | Spark ML - Bisecting K-Means Clustering |
ml_chisquare_test | Chi-square hypothesis testing for categorical data. |
ml_classification_eval | Spark ML - Evaluators |
ml_clustering_evaluator | Spark ML - Clustering Evaluator |
ml_compute_cost | Spark ML - K-Means Clustering |
ml_compute_silhouette_measure | Spark ML - K-Means Clustering |
ml_corr | Compute correlation matrix |
ml_cross_validator | Spark ML - Tuning |
ml_decision_tree | Spark ML - Decision Trees |
ml_decision_tree_classifier | Spark ML - Decision Trees |
ml_decision_tree_regressor | Spark ML - Decision Trees |
ml_default_stop_words | Default stop words |
ml_describe_topics | Spark ML - Latent Dirichlet Allocation |
ml_evaluate | Evaluate the Model on a Validation Set |
ml_evaluate.ml_evaluator | Evaluate the Model on a Validation Set |
ml_evaluate.ml_generalized_linear_regression_model | Evaluate the Model on a Validation Set |
ml_evaluate.ml_linear_regression_model | Evaluate the Model on a Validation Set |
ml_evaluate.ml_logistic_regression_model | Evaluate the Model on a Validation Set |
ml_evaluate.ml_model_classification | Evaluate the Model on a Validation Set |
ml_evaluate.ml_model_clustering | Evaluate the Model on a Validation Set |
ml_evaluate.ml_model_generalized_linear_regression | Evaluate the Model on a Validation Set |
ml_evaluate.ml_model_linear_regression | Evaluate the Model on a Validation Set |
ml_evaluate.ml_model_logistic_regression | Evaluate the Model on a Validation Set |
ml_evaluator | Spark ML - Evaluators |
ml_feature_importances | Spark ML - Feature Importance for Tree Models |
ml_find_synonyms | Feature Transformation - Word2Vec (Estimator) |
ml_fit | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_fit.default | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_fit_and_transform | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_fpgrowth | Frequent Pattern Mining - FPGrowth |
ml_freq_itemsets | Frequent Pattern Mining - FPGrowth |
ml_freq_seq_patterns | Frequent Pattern Mining - PrefixSpan |
ml_gaussian_mixture | Spark ML - Gaussian Mixture clustering. |
ml_gbt_classifier | Spark ML - Gradient Boosted Trees |
ml_gbt_regressor | Spark ML - Gradient Boosted Trees |
ml_generalized_linear_regression | Spark ML - Generalized Linear Regression |
ml_glm_tidiers | Tidying methods for Spark ML linear models |
ml_gradient_boosted_trees | Spark ML - Gradient Boosted Trees |
ml_isotonic_regression | Spark ML - Isotonic Regression |
ml_isotonic_regression_tidiers | Tidying methods for Spark ML Isotonic Regression |
ml_is_set | Spark ML - ML Params |
ml_kmeans | Spark ML - K-Means Clustering |
ml_kmeans_cluster_eval | Evaluate a K-mean clustering |
ml_labels | Feature Transformation - StringIndexer (Estimator) |
ml_lda | Spark ML - Latent Dirichlet Allocation |
ml_lda_tidiers | Tidying methods for Spark ML LDA models |
ml_linear_regression | Spark ML - Linear Regression |
ml_linear_svc | Spark ML - LinearSVC |
ml_linear_svc_tidiers | Tidying methods for Spark ML linear svc |
ml_load | Spark ML - Model Persistence |
ml_logistic_regression | Spark ML - Logistic Regression |
ml_logistic_regression_tidiers | Tidying methods for Spark ML Logistic Regression |
ml_log_likelihood | Spark ML - Latent Dirichlet Allocation |
ml_log_perplexity | Spark ML - Latent Dirichlet Allocation |
ml_metrics_binary | Extracts metrics from a fitted table |
ml_metrics_multiclass | Extracts metrics from a fitted table |
ml_metrics_regression | Extracts metrics from a fitted table |
ml_model_data | Extracts data associated with a Spark ML model |
ml_multiclass_classification_evaluator | Spark ML - Evaluators |
ml_multilayer_perceptron | Spark ML - Multilayer Perceptron |
ml_multilayer_perceptron_classifier | Spark ML - Multilayer Perceptron |
ml_multilayer_perceptron_tidiers | Tidying methods for Spark ML MLP |
ml_naive_bayes | Spark ML - Naive-Bayes |
ml_naive_bayes_tidiers | Tidying methods for Spark ML Naive Bayes |
ml_one_vs_rest | Spark ML - OneVsRest |
ml_param | Spark ML - ML Params |
ml_params | Spark ML - ML Params |
ml_param_map | Spark ML - ML Params |
ml_pca | Feature Transformation - PCA (Estimator) |
ml_pca_tidiers | Tidying methods for Spark ML Principal Component Analysis |
ml_pipeline | Spark ML - Pipelines |
ml_power_iteration | Spark ML - Power Iteration Clustering |
ml_predict | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_predict.ml_model_classification | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_prefixspan | Frequent Pattern Mining - PrefixSpan |
ml_random_forest | Spark ML - Random Forest |
ml_random_forest_classifier | Spark ML - Random Forest |
ml_random_forest_regressor | Spark ML - Random Forest |
ml_recommend | Spark ML - ALS |
ml_regression_evaluator | Spark ML - Evaluators |
ml_save | Spark ML - Model Persistence |
ml_save.ml_model | Spark ML - Model Persistence |
ml_stage | Spark ML - Pipeline stage extraction |
ml_stages | Spark ML - Pipeline stage extraction |
ml_sub_models | Spark ML - Tuning |
ml_summary | Spark ML - Extraction of summary metrics |
ml_survival_regression | Spark ML - Survival Regression |
ml_survival_regression_tidiers | Tidying methods for Spark ML Survival Regression |
ml_topics_matrix | Spark ML - Latent Dirichlet Allocation |
ml_train_validation_split | Spark ML - Tuning |
ml_transform | Spark ML - Transform, fit, and predict methods (ml_ interface) |
ml_tree_feature_importance | Spark ML - Feature Importance for Tree Models |
ml_tree_tidiers | Tidying methods for Spark ML tree models |
ml_uid | Spark ML - UID |
ml_unsupervised_tidiers | Tidying methods for Spark ML unsupervised models |
ml_validation_metrics | Spark ML - Tuning |
ml_vocabulary | Feature Transformation - CountVectorizer (Estimator) |
mutate | Mutate |
na.replace | Replace Missing Values in Objects |
nest | Nest |
pivot_longer | Pivot longer |
pivot_wider | Pivot wider |
random_string | Random string generation |
reactiveSpark | Reactive spark reader |
registerDoSpark | Register a Parallel Backend |
registered_extensions | Register a Package that Implements a Spark Extension |
register_extension | Register a Package that Implements a Spark Extension |
replace_na | Replace NA |
right_join | Right join |
right_join.tbl_spark | Join Spark tbls. |
sdf-saveload | Save / Load a Spark DataFrame |
sdf-transform-methods | Spark ML - Transform, fit, and predict methods (sdf_ interface) |
sdf_along | Create DataFrame for along Object |
sdf_bind | Bind multiple Spark DataFrames by row and column |
sdf_bind_cols | Bind multiple Spark DataFrames by row and column |
sdf_bind_rows | Bind multiple Spark DataFrames by row and column |
sdf_broadcast | Broadcast hint |
sdf_checkpoint | Checkpoint a Spark DataFrame |
sdf_coalesce | Coalesces a Spark DataFrame |
sdf_collect | Collect a Spark DataFrame into R. |
sdf_copy_to | Copy an Object into Spark |
sdf_crosstab | Cross Tabulation |
sdf_debug_string | Debug Info for Spark DataFrame |
sdf_describe | Compute summary statistics for columns of a data frame |
sdf_dim | Support for Dimension Operations |
sdf_distinct | Invoke distinct on a Spark DataFrame |
sdf_drop_duplicates | Remove duplicates from a Spark DataFrame |
sdf_expand_grid | Create a Spark dataframe containing all combinations of inputs |
sdf_fit | Spark ML - Transform, fit, and predict methods (sdf_ interface) |
sdf_fit_and_transform | Spark ML - Transform, fit, and predict methods (sdf_ interface) |
sdf_from_avro | Convert column(s) from avro format |
sdf_import | Copy an Object into Spark |
sdf_is_streaming | Spark DataFrame is Streaming |
sdf_last_index | Returns the last index of a Spark DataFrame |
sdf_len | Create DataFrame for Length |
sdf_load_parquet | Save / Load a Spark DataFrame |
sdf_load_table | Save / Load a Spark DataFrame |
sdf_ncol | Support for Dimension Operations |
sdf_nrow | Support for Dimension Operations |
sdf_num_partitions | Gets number of partitions of a Spark DataFrame |
sdf_partition | Partition a Spark Dataframe |
sdf_partition_sizes | Compute the number of records within each partition of a Spark DataFrame |
sdf_persist | Persist a Spark DataFrame |
sdf_pivot | Pivot a Spark DataFrame |
sdf_predict | Spark ML - Transform, fit, and predict methods (sdf_ interface) |
sdf_project | Project features onto principal components |
sdf_quantile | Compute (Approximate) Quantiles with a Spark DataFrame |
sdf_random_split | Partition a Spark Dataframe |
sdf_rbeta | Generate random samples from a Beta distribution |
sdf_rbinom | Generate random samples from a binomial distribution |
sdf_rcauchy | Generate random samples from a Cauchy distribution |
sdf_rchisq | Generate random samples from a chi-squared distribution |
sdf_read_column | Read a Column from a Spark DataFrame |
sdf_register | Register a Spark DataFrame |
sdf_repartition | Repartition a Spark DataFrame |
sdf_residuals | Model Residuals |
sdf_residuals.ml_model_generalized_linear_regression | Model Residuals |
sdf_residuals.ml_model_linear_regression | Model Residuals |
sdf_rexp | Generate random samples from an exponential distribution |
sdf_rgamma | Generate random samples from a Gamma distribution |
sdf_rgeom | Generate random samples from a geometric distribution |
sdf_rhyper | Generate random samples from a hypergeometric distribution |
sdf_rlnorm | Generate random samples from a log normal distribution |
sdf_rnorm | Generate random samples from the standard normal distribution |
sdf_rpois | Generate random samples from a Poisson distribution |
sdf_rt | Generate random samples from a t-distribution |
sdf_runif | Generate random samples from the uniform distribution U(0, 1). |
sdf_rweibull | Generate random samples from a Weibull distribution. |
sdf_sample | Randomly Sample Rows from a Spark DataFrame |
sdf_save_parquet | Save / Load a Spark DataFrame |
sdf_save_table | Save / Load a Spark DataFrame |
sdf_schema | Read the Schema of a Spark DataFrame |
sdf_separate_column | Separate a Vector Column into Scalar Columns |
sdf_seq | Create DataFrame for Range |
sdf_sort | Sort a Spark DataFrame |
sdf_sql | Spark DataFrame from SQL |
sdf_to_avro | Convert column(s) to avro format |
sdf_transform | Spark ML - Transform, fit, and predict methods (sdf_ interface) |
sdf_unnest_longer | Unnest longer |
sdf_unnest_wider | Unnest wider |
sdf_weighted_sample | Perform Weighted Random Sampling on a Spark DataFrame |
sdf_with_sequential_id | Add a Sequential ID Column to a Spark DataFrame |
sdf_with_unique_id | Add a Unique ID Column to a Spark DataFrame |
select | Select |
separate | Separate |
spark-api | Access the Spark API |
spark-connections | Manage Spark Connections |
sparklyr_get_backend_port | Return the port number of a 'sparklyr' backend. |
spark_adaptive_query_execution | Retrieves or sets status of Spark AQE |
spark_advisory_shuffle_partition_size | Retrieves or sets advisory size of the shuffle partition |
spark_apply | Apply an R Function in Spark |
spark_apply_bundle | Create Bundle for Spark Apply |
spark_apply_log | Log Writer for Spark Apply |
spark_auto_broadcast_join_threshold | Retrieves or sets the auto broadcast join threshold |
spark_available_versions | Download and install various versions of Spark |
spark_coalesce_initial_num_partitions | Retrieves or sets initial number of shuffle partitions before coalescing |
spark_coalesce_min_num_partitions | Retrieves or sets the minimum number of shuffle partitions after coalescing |
spark_coalesce_shuffle_partitions | Retrieves or sets whether coalescing contiguous shuffle partitions is enabled |
spark_compilation_spec | Define a Spark Compilation Specification |
spark_config | Read Spark Configuration |
spark_config_kubernetes | Kubernetes Configuration |
spark_config_settings | Retrieve Available Settings |
spark_connect | Manage Spark Connections |
spark_connection | Retrieve the Spark Connection Associated with an R Object |
spark_connection-class | spark_connection class |
spark_connection_find | Find Spark Connection |
spark_connection_is_open | Manage Spark Connections |
spark_connect_method | Function that negotiates the connection with the Spark back-end |
spark_context | Access the Spark API |
spark_context_config | Runtime configuration interface for the Spark Context. |
spark_dataframe | Retrieve a Spark DataFrame |
spark_default_compilation_spec | Default Compilation Specification for Spark Extensions |
spark_dependency | Define a Spark dependency |
spark_dependency_fallback | Fallback to Spark Dependency |
spark_disconnect | Manage Spark Connections |
spark_disconnect_all | Manage Spark Connections |
spark_extension | Create Spark Extension |
spark_get_checkpoint_dir | Set/Get Spark checkpoint directory |
spark_home_set | Set the SPARK_HOME environment variable |
spark_ide_columns | Set of functions to provide integration with the RStudio IDE |
spark_ide_connection_actions | Set of functions to provide integration with the RStudio IDE |
spark_ide_connection_closed | Set of functions to provide integration with the RStudio IDE |
spark_ide_connection_open | Set of functions to provide integration with the RStudio IDE |
spark_ide_connection_updated | Set of functions to provide integration with the RStudio IDE |
spark_ide_objects | Set of functions to provide integration with the RStudio IDE |
spark_ide_preview | Set of functions to provide integration with the RStudio IDE |
spark_insert_table | Inserts a Spark DataFrame into a Spark table |
spark_install | Download and install various versions of Spark |
spark_installed_versions | Download and install various versions of Spark |
spark_install_dir | Download and install various versions of Spark |
spark_install_tar | Download and install various versions of Spark |
spark_integ_test_skip | It lets the package know if it should test a particular functionality or not |
spark_jobj | Retrieve a Spark JVM Object Reference |
spark_jobj-class | spark_jobj class |
spark_last_error | Surfaces the last error from Spark captured by internal 'spark_error' function |
spark_load_table | Reads from a Spark Table into a Spark DataFrame. |
spark_log | View Entries in the Spark Log |
spark_read | Read file(s) into a Spark DataFrame using a custom reader |
spark_read_avro | Read Apache Avro data into a Spark DataFrame. |
spark_read_binary | Read binary data into a Spark DataFrame. |
spark_read_csv | Read a CSV file into a Spark DataFrame |
spark_read_delta | Read from Delta Lake into a Spark DataFrame. |
spark_read_image | Read image data into a Spark DataFrame. |
spark_read_jdbc | Read from JDBC connection into a Spark DataFrame. |
spark_read_json | Read a JSON file into a Spark DataFrame |
spark_read_libsvm | Read libsvm file into a Spark DataFrame. |
spark_read_orc | Read a ORC file into a Spark DataFrame |
spark_read_parquet | Read a Parquet file into a Spark DataFrame |
spark_read_source | Read from a generic source into a Spark DataFrame. |
spark_read_table | Reads from a Spark Table into a Spark DataFrame. |
spark_read_text | Read a Text file into a Spark DataFrame |
spark_save_table | Saves a Spark DataFrame as a Spark table |
spark_session | Access the Spark API |
spark_session_config | Runtime configuration interface for the Spark Session |
spark_set_checkpoint_dir | Set/Get Spark checkpoint directory |
spark_statistical_routines | Generate random samples from some distribution |
spark_submit | Manage Spark Connections |
spark_table_name | Generate a Table Name from Expression |
spark_uninstall | Download and install various versions of Spark |
spark_version | Get the Spark Version Associated with a Spark Connection |
spark_version_from_home | Get the Spark Version Associated with a Spark Installation |
spark_web | Open the Spark web interface |
spark_write | Write Spark DataFrame to file using a custom writer |
spark_write_avro | Serialize a Spark DataFrame into Apache Avro format |
spark_write_csv | Write a Spark DataFrame to a CSV |
spark_write_delta | Writes a Spark DataFrame into Delta Lake |
spark_write_jdbc | Writes a Spark DataFrame into a JDBC table |
spark_write_json | Write a Spark DataFrame to a JSON file |
spark_write_orc | Write a Spark DataFrame to a ORC file |
spark_write_parquet | Write a Spark DataFrame to a Parquet file |
spark_write_rds | Write Spark DataFrame to RDS files |
spark_write_source | Writes a Spark DataFrame into a generic source |
spark_write_table | Writes a Spark DataFrame into a Spark table |
spark_write_text | Write a Spark DataFrame to a Text file |
src_databases | Show database list |
stream_find | Find Stream |
stream_generate_test | Generate Test Stream |
stream_id | Spark Stream's Identifier |
stream_lag | Apply lag function to columns of a Spark Streaming DataFrame |
stream_name | Spark Stream's Name |
stream_read_cloudfiles | Read files created by the stream |
stream_read_csv | Read files created by the stream |
stream_read_delta | Read files created by the stream |
stream_read_json | Read files created by the stream |
stream_read_kafka | Read files created by the stream |
stream_read_orc | Read files created by the stream |
stream_read_parquet | Read files created by the stream |
stream_read_socket | Read files created by the stream |
stream_read_table | Read files created by the stream |
stream_read_text | Read files created by the stream |
stream_render | Render Stream |
stream_stats | Stream Statistics |
stream_stop | Stops a Spark Stream |
stream_trigger_continuous | Spark Stream Continuous Trigger |
stream_trigger_interval | Spark Stream Interval Trigger |
stream_view | View Stream |
stream_watermark | Watermark Stream |
stream_write_console | Write files to the stream |
stream_write_csv | Write files to the stream |
stream_write_delta | Write files to the stream |
stream_write_json | Write files to the stream |
stream_write_kafka | Write files to the stream |
stream_write_memory | Write Memory Stream |
stream_write_orc | Write files to the stream |
stream_write_parquet | Write files to the stream |
stream_write_table | Write Stream to Table |
stream_write_text | Write files to the stream |
tbl_cache | Cache a Spark Table |
tbl_change_db | Use specific database |
tbl_uncache | Uncache a Spark Table |
tidy.ml_model_aft_survival_regression | Tidying methods for Spark ML Survival Regression |
tidy.ml_model_als | Tidying methods for Spark ML ALS |
tidy.ml_model_bisecting_kmeans | Tidying methods for Spark ML unsupervised models |
tidy.ml_model_decision_tree_classification | Tidying methods for Spark ML tree models |
tidy.ml_model_decision_tree_regression | Tidying methods for Spark ML tree models |
tidy.ml_model_gaussian_mixture | Tidying methods for Spark ML unsupervised models |
tidy.ml_model_gbt_classification | Tidying methods for Spark ML tree models |
tidy.ml_model_gbt_regression | Tidying methods for Spark ML tree models |
tidy.ml_model_generalized_linear_regression | Tidying methods for Spark ML linear models |
tidy.ml_model_isotonic_regression | Tidying methods for Spark ML Isotonic Regression |
tidy.ml_model_kmeans | Tidying methods for Spark ML unsupervised models |
tidy.ml_model_lda | Tidying methods for Spark ML LDA models |
tidy.ml_model_linear_regression | Tidying methods for Spark ML linear models |
tidy.ml_model_linear_svc | Tidying methods for Spark ML linear svc |
tidy.ml_model_logistic_regression | Tidying methods for Spark ML Logistic Regression |
tidy.ml_model_multilayer_perceptron_classification | Tidying methods for Spark ML MLP |
tidy.ml_model_naive_bayes | Tidying methods for Spark ML Naive Bayes |
tidy.ml_model_pca | Tidying methods for Spark ML Principal Component Analysis |
tidy.ml_model_random_forest_classification | Tidying methods for Spark ML tree models |
tidy.ml_model_random_forest_regression | Tidying methods for Spark ML tree models |
transform_sdf | transform a subset of column(s) in a Spark Dataframe |
unite | Unite |
unnest | Unnest |
%->% | Infix operator for composing a lambda expression |
[.tbl_spark | Subsetting operator for Spark dataframe |