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<table width="100%" summary="page for dropna,DataFrame-method {SparkR}"><tr><td>dropna,DataFrame-method {SparkR}</td><td align="right">R Documentation</td></tr></table>
<h2>dropna</h2>
<h3>Description</h3>
<p>Returns a new DataFrame omitting rows with null values.
</p>
<p>Replace null values.
</p>
<h3>Usage</h3>
<pre>
## S4 method for signature 'DataFrame'
dropna(x, how = c("any", "all"), minNonNulls = NULL,
cols = NULL)
## S4 method for signature 'DataFrame'
fillna(x, value, cols = NULL)
dropna(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)
na.omit(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)
fillna(x, value, cols = NULL)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
<p>A SparkSQL DataFrame.</p>
</td></tr>
<tr valign="top"><td><code>how</code></td>
<td>
<p>"any" or "all".
if "any", drop a row if it contains any nulls.
if "all", drop a row only if all its values are null.
if minNonNulls is specified, how is ignored.</p>
</td></tr>
<tr valign="top"><td><code>minNonNulls</code></td>
<td>
<p>If specified, drop rows that have less than
minNonNulls non-null values.
This overwrites the how parameter.</p>
</td></tr>
<tr valign="top"><td><code>cols</code></td>
<td>
<p>Optional list of column names to consider.</p>
</td></tr>
<tr valign="top"><td><code>value</code></td>
<td>
<p>Value to replace null values with.
Should be an integer, numeric, character or named list.
If the value is a named list, then cols is ignored and
value must be a mapping from column name (character) to
replacement value. The replacement value must be an
integer, numeric or character.</p>
</td></tr>
<tr valign="top"><td><code>x</code></td>
<td>
<p>A SparkSQL DataFrame.</p>
</td></tr>
<tr valign="top"><td><code>cols</code></td>
<td>
<p>optional list of column names to consider.
Columns specified in cols that do not have matching data
type are ignored. For example, if value is a character, and
subset contains a non-character column, then the non-character
column is simply ignored.</p>
</td></tr>
</table>
<h3>Value</h3>
<p>A DataFrame
</p>
<p>A DataFrame
</p>
<h3>Examples</h3>
<pre><code class="r">## Not run:
##D sc <- sparkR.init()
##D sqlCtx <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- jsonFile(sqlCtx, path)
##D dropna(df)
## End(Not run)
## Not run:
##D sc <- sparkR.init()
##D sqlCtx <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- jsonFile(sqlCtx, path)
##D fillna(df, 1)
##D fillna(df, list("age" = 20, "name" = "unknown"))
## End(Not run)
</code></pre>
<hr><div align="center">[Package <em>SparkR</em> version 1.4.0 <a href="00Index.html">Index</a>]</div>
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