spread_

data

Strings giving names of key and value cols.

key_col, value_col

If set, missing values will be replaced with this value. Note
that there are two types of missingness in the input: explicit missing
values (i.e. <code>NA</code>), and implicit missings, rows that simply aren&#39;t
present. Both types of missing value will be replaced by <code>fill</code>.

fill

If <code>TRUE</code>, <code><a rd-options="" href="/link/type.convert?package=tidyr&version=0.6.2" data-mini-rdoc="tidyr::type.convert">type.convert</a></code> with <code>asis =
TRUE</code> will be run on each of the new columns. This is useful if the value
column was a mix of variables that was coerced to a string. If the class of
the value column was factor or date, note that will not be true of the new
columns that are produced, which are coerced to character before type
conversion.

convert

If <code>FALSE</code>, will keep factor levels that don&#39;t appear in the
data, filling in missing combinations with <code>fill</code>.

drop

If <code>NULL</code>, the column names will be taken from the values of
<code>key</code> variable. If non-<code>NULL</code>, the column names will be given
by &quot;&lt;key_name&gt;&lt;sep&gt;&lt;key_value&gt;&quot;.

internal

An evolution of 'reshape2'. It's designed specifically for data
tidying (not general reshaping or aggregating) and works well with
'dplyr' data pipelines.

Hadley Wickham

tidyr

Easily Tidy Data with 'spread()' and 'gather()' Functions

RStudio 

spread_ function

If <code>TRUE</code>, <code><a rd-options='' href='type.convert'>type.convert</a></code> with <code>asis =
TRUE</code> will be run on each of the new columns. This is useful if the value
column was a mix of variables that was coerced to a string. If the class of
the value column was factor or date, note that will not be true of the new
columns that are produced, which are coerced to character before type
conversion.

spread_: Standard-evaluation version of

Description

Usage

Arguments