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Diffstat (limited to 'docs/sparkr.md')
| -rw-r--r-- | docs/sparkr.md | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/docs/sparkr.md b/docs/sparkr.md index a0b4f93776..760534ae14 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -141,7 +141,7 @@ head(people) # SparkR automatically infers the schema from the JSON file printSchema(people) # root -# |-- age: integer (nullable = true) +# |-- age: long (nullable = true) # |-- name: string (nullable = true) {% endhighlight %} @@ -195,7 +195,7 @@ df <- createDataFrame(sqlContext, faithful) # Get basic information about the DataFrame df -## DataFrame[eruptions:double, waiting:double] +## SparkDataFrame[eruptions:double, waiting:double] # Select only the "eruptions" column head(select(df, df$eruptions)) @@ -228,14 +228,13 @@ SparkR data frames support a number of commonly used functions to aggregate data # We use the `n` operator to count the number of times each waiting time appears head(summarize(groupBy(df, df$waiting), count = n(df$waiting))) ## waiting count -##1 81 13 -##2 60 6 -##3 68 1 +##1 70 4 +##2 67 1 +##3 69 2 # We can also sort the output from the aggregation to get the most common waiting times waiting_counts <- summarize(groupBy(df, df$waiting), count = n(df$waiting)) head(arrange(waiting_counts, desc(waiting_counts$count))) - ## waiting count ##1 78 15 ##2 83 14 |