spark1.4.0的sparkR的思路：用spark从大数据集中抽取小数据（sparkR的DataFrame），然后到R里分析（DataFrame）。
这两个DataFrame是不同的，前者是分布式的，集群上的DF，R里的那些包都不能用；后者是单机版的DF，包里的函数都能用。sparkR的开发计划，个人觉得是将目前包里的函数，迁移到sparkR的DataFrame里，这样就打开一片天地。> a<- sql(hiveContext, "SELECT count(*) FROM anjuke_scores where restaurant>=10");
> a<- sql(hiveContext, "SELECT * FROM anjuke_scores limit 5")> aDataFrame[city:string, housingname:string, ori_traffic_score:int, ori_traffic_score_normal:double, metro_station:double, metro_station_normal:double,...
> first(a) #显示Formal Data Frame的第一行> head(a) ; #列出a的前6行> columns(a) # 列出全部的列[1] "city" "housingname" "ori_traffic_score" "ori_traffic_score_normal"[5] "metro_station" "metro_station_normal" "bus_station" "bus_station_normal" ...
> showDF(a)
> b<-filter(a, a$ori_comfort>8); # 行筛选， ori_comfort_normal:double
> print(a); #打印列名及类型 DataFrame[city:string, housingname:string, ori_traffic_score:int, ......> printSchema(a); # 打印列名的树形框架概要 root |-- city: string (nullable = true) |-- housingname: string (nullable = true) |-- ori_traffic_score: integer (nullable = true) |-- ori_traffic_score_normal: double (nullable = true) |-- metro_station: double (nullable = true)> take(a,10) ; # 提取Formal class DataFrame的前面num行，成为R中普通的 data frame ， take(x, num)city housingname ori_traffic_score ori_traffic_score_normal metro_station metro_station_normal1 \t\x9a \xddrw\xb8 NA 0 NA 02 \t\x9a \xe4\xf04\u03a2\021~ NA 0 NA 03 \t\x9a \xf6\xe3w\xb8 NA 0 NA 04 \t\x9a \x8e=\xb0w\xb8 NA 0 NA 05 \t\x9a \t\x9a\xe4\xf04\xce\xe4\xf0~ NA 0 NA 06 \t\x9a q4\xfdE NA 0 NA 07 \t\x9a \xe4\xf04\xce NA 0 NA 08 \t\x9a )\xfdVT NA 0 NA 09 \t\x9a q\177V NA 0 NA 010 \t\x9a \xe4\xf04\xceW\xb8 NA 0 NA 0
> b<-take(a,10) > dim(b)[1] 10 41
> aa <- withColumn(a, "ori_comfort_aa", a$ori_comfort * 5) #用现有的列生成新的列, 新增一列，ori_comfort_aa，结果还是Formal data frame结构> printSchema(aa)root |-- city: string (nullable = true)......... |-- comfort_normal: double (nullable = true) |-- ori_comfort_aa: double (nullable = true)> aa <- mutate(a, newCol1 = a$commerce_normal * 5, newCol2 = a$bank_normal * 2) ； #与withColumn类似> printSchema(aa)root|-- city: string (nullable = true)。。。。。。。。。。。。。。。。。。|-- comfort_normal: double (nullable = true)|-- newCol1: double (nullable = true)|-- newCol2: double (nullable = true)a1<-arrange(a,asc(a$level_tow)); # 按列排序， asc升序，desc降序a1<-orderBy(a,asc(a$level_tow)); # 按列排序count(a) ; # 统计 Formal Data Frame有多少行数据
> dtypes(a); #以list的形式列出Formal Data Frame的全部列名及类型[[1]][1] "city" "string"[[2]][1] "housingname" "string"
> a<-withColumnRenamed(a,"comfort_normal","AA"); # 更改列名 > printSchema(a)root |-- city: string (nullable = true) |-- housingname: string (nullable = true).......... |-- AA: double (nullable = true)创建sparkR的数据框的函数
createDataFrame
> df<-createDataFrame(sqlContext,a.df); # a.df是R中的数据框， df是sparkR的数据框，注意：使用sparkR的数据库，需要sqlContext> str(a.df)'data.frame': 5 obs. of 41 variables:> str(df)Formal class 'DataFrame' [package "SparkR"] with 2 slots..@ env:<environment: 0x4fce350> ..@ sdf:Class 'jobj' <environment: 0x4fc70b0> > destDF <- select(SFO_DF, "dest", "cancelled"); #选择列> showDF(destDF); #显示sparkR的DF+----+---------+|dest|cancelled|+----+---------+| SFO| 0|................> registerTempTable(SFO_DF, "flightsTable"); #要对sparkDF使用SQL语句，首先需要将DF注册成一个table> wa <- sql(sqlContext, "SELECT dest, cancelled FROM flightsTable"); #在sqlContext下使用SQL语句> showDF(wa); #查询的结果还是sparkDF+----+---------+|dest|cancelled|+----+---------+| SFO| 0|................> local_df <- collect(wa); #将sparkDF转换成R中的DF> str(local_df)'data.frame': 2818 obs. of 2 variables:$ dest : chr "SFO" "SFO" "SFO" "SFO" ...$ cancelled: int 0 0 0 0 0 0 0 0 0 0 ...> wa<-flights_df[1:1000,]; #wa是R中的DF> flightsDF<-createDataFrame(sqlContext,wa) ; #flightsDF是sparkR的DF> library(magrittr); #管道函数的包对sparkRDF适用> groupBy(flightsDF, flightsDF$date) %>%+ summarize(avg(flightsDF$dep_delay), avg(flightsDF$arr_delay)) -> dailyDelayDF; #注意，语法和dplyr中的有所不同，结果还是sparkRDF> str(dailyDelayDF)Formal class 'DataFrame' [package "SparkR"] with 2 slots..@ env:<environment: 0x4cd3118> ..@ sdf:Class 'jobj' <environment: 0x4cd6968> > showDF(dailyDelayDF)+----------+--------------------+--------------------+| date| AVG(dep_delay)| AVG(arr_delay)|+----------+--------------------+--------------------+|2011-01-01| 5.2| 5.8||2011-01-02| 1.8333333333333333| -2.0|................在39机器上跑的collect将sparkDF转化成DFCollects all the elements of a Spark DataFrame and coerces them into an R data.frame.collect(x, stringsAsFactors = FALSE)，x：A SparkSQL DataFrame> dist_df<- sql(hiveContext, "SELECT * FROM anjuke_scores where restaurant<=1");> local_df <- dist_df %>% groupBy(dist_df$city) %>% summarize(count = n(dist_df$housingname)) %>% collect> local_dfcity count1 \t\x9a 52 8\xde 73 \xf0\xde 2....................take也可将sparkDF转化成DFTake the first NUM rows of a DataFrame and return a the results as a data.frametake(x, num)> local_df <- dist_df %>% groupBy(dist_df$city) %>% summarize(count = n(dist_df$housingname))> a<-take(local_df,100)[Stage 16:=========================================> (154 + 1) / 199] > View(a)> acity count1 \t\x9a 52 8\xde 73 \xf0\xde 2....................不通的函数：
> describe(a)Error in x[present, drop = FALSE] : object of type 'S4' is not subsettable
> jfkDF <- filter(flightsDF, flightsDF$dest == "DFW")Error in filter(flightsDF, flightsDF$dest == "DFW") : no method for coercing this S4 class to a vector