https://cran.r-project.org/src/base/R-3/
1.2 环境变量配置:
1.3 测试安装:
https://cran.r-project.org/bin/windows/Rtools/
2.1 配置环境变量
2.2 测试:
https://www.rstudio.com/products/rstudio/download/ 直接下一步即可安装
4.1环境变量配置:
4.2测试:
5.1 URL: http://spark.apache.org/downloads.html
5.2解压到本地磁盘的对应目录
注意:如果发现了提示 WARN NativeCodeLader:Unable to load native-hadoop library for your platform.....using
builtin-java classes where applicable 需要安装本地的hadoop库
http://hadoop.apache.org/releases.html
10.1 如果测试时候出现以下提示,需要修改log4j文件INFO为WARN,位于\spark\conf下
10.2 修改conf中的log4j文件:
10.3 重新运行SparkR
在Spark2.0中增加了RSparkSql进行Sql查询
dataframe为数据框操作
data-manipulation为数据转化
ml为机器学习
11.1 使用crtl+ALT+鼠標左鍵 打开控制台在此文件夹下
11.2 执行spark-submit xxx.R文件即可
12.1 将spark安装目录下的R/lib中的SparkR文件拷贝到..\R-3.3.2\library中,注意是将整个Spark文件夹,而非里面每一个文件。
源文件夹:
目的文件夹:
12.2 在RStudio中打开SparkR文件并运行代码dataframe.R文件,采用Ctrl+Enter一行行执行即可
SparkR语言的dataframe.R源代码如下
# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # library(SparkR) # Initialize SparkContext and SQLContext sc <- sparkR.init(appName="SparkR-DataFrame-example") sqlContext <- sparkRSQL.init(sc) # Create a simple local data.frame localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18)) # Convert local data frame to a SparkR DataFrame df <- createDataFrame(sqlContext, localDF) # Print its schema printSchema(df) # root # |-- name: string (nullable = true) # |-- age: double (nullable = true) # Create a DataFrame from a JSON file path <- file.path(Sys.getenv("SPARK_HOME"), "examples/src/main/resources/people.json") peopleDF <- read.json(sqlContext, path) printSchema(peopleDF) # Register this DataFrame as a table. registerTempTable(peopleDF, "people") # SQL statements can be run by using the sql methods provided by sqlContext teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19") # Call collect to get a local data.frame teenagersLocalDF <- collect(teenagers) # Print the teenagers in our dataset print(teenagersLocalDF) # Stop the SparkContext now sparkR.stop()
END~
原文:http://www.cnblogs.com/jackchen-Net/p/6648755.html