| describe {SparkR} | R Documentation |
Computes statistics for numeric columns. If no columns are given, this function computes statistics for all numerical columns.
Returns the summary of a model produced by glm(), similarly to R's summary().
## S4 method for signature 'DataFrame,character' describe(x, col, ...) ## S4 method for signature 'DataFrame,ANY' describe(x) ## S4 method for signature 'DataFrame' summary(object, ...) describe(x, col, ...) summary(object, ...) ## S4 method for signature 'PipelineModel' summary(object, ...)
x |
A DataFrame to be computed. |
col |
A string of name |
... |
Additional expressions |
object |
A fitted MLlib model |
A DataFrame
a list with 'devianceResiduals' and 'coefficients' components for gaussian family
or a list with 'coefficients' component for binomial family.
For gaussian family: the 'devianceResiduals' gives the min/max deviance residuals
of the estimation, the 'coefficients' gives the estimated coefficients and their
estimated standard errors, t values and p-values. (It only available when model
fitted by normal solver.)
For binomial family: the 'coefficients' gives the estimated coefficients.
See summary.glm for more information.
Other DataFrame functions: $,
$<-, select,
select,
select,DataFrame,Column-method,
select,DataFrame,list-method,
selectExpr; DataFrame-class,
dataFrame, groupedData;
[, [, [[,
subset; agg,
agg,
count,GroupedData-method,
summarize, summarize;
arrange, arrange,
arrange, orderBy,
orderBy; as.data.frame,
as.data.frame,DataFrame-method;
attach,
attach,DataFrame-method;
cache; collect;
colnames, colnames,
colnames<-, colnames<-,
columns, names,
names<-; coltypes,
coltypes, coltypes<-,
coltypes<-; columns,
dtypes, printSchema,
schema, schema;
count, nrow;
dim; distinct,
unique; dropna,
dropna, fillna,
fillna, na.omit,
na.omit; dtypes;
except, except;
explain, explain;
filter, filter,
where, where;
first, first;
groupBy, groupBy,
group_by, group_by;
head; insertInto,
insertInto; intersect,
intersect; isLocal,
isLocal; join;
limit, limit;
merge, merge;
mutate, mutate,
transform; ncol;
persist; printSchema;
rbind, rbind,
unionAll, unionAll;
registerTempTable,
registerTempTable; rename,
rename, withColumnRenamed,
withColumnRenamed;
repartition; sample,
sample, sample_frac,
sample_frac;
saveAsParquetFile,
saveAsParquetFile,
write.parquet, write.parquet;
saveAsTable, saveAsTable;
saveDF, saveDF,
write.df, write.df;
selectExpr; showDF,
showDF; show,
show,
show,GroupedData-method;
take; transform,
withColumn, withColumn;
unpersist; write.json,
write.json
## Not run:
##D sc <- sparkR.init()
##D sqlContext <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- read.json(sqlContext, path)
##D describe(df)
##D describe(df, "col1")
##D describe(df, "col1", "col2")
## End(Not run)
## Not run:
##D model <- glm(y ~ x, trainingData)
##D summary(model)
## End(Not run)