file_merge

A string with the path of the folder in which files to be
merged are searched. Search is recursive (i.e., can search also in
subdirectories). <code>folder_path</code> must be provided. Default is
<code>NULL</code>.

folder_path

Logical. If <code>TRUE</code>, the function takes the first line
of the first file found as the header of the merged table. Default is
<code>TRUE</code>.

has_header

String vector with names for columns of the merged table.
Default is <code>c()</code>. If used, <code>new_header</code> should be the same length
as the number of columns in the merged table.

new_header

A string with the name of the files to be searched
and then merged. File extension should NOT be included here (see
<code>raw_file_extension</code>). <code>raw_file_name</code> must be provided. Default
is <code>NULL</code>.

raw_file_name

A string with the format of the files (i.e.,
<code>csv</code> or <code>txt</code>) to be merged. <code>raw_file_extension</code> must be
provided. Default is <code>NULL</code>.

raw_file_extension

A string with the name of the file of the merged table the
function creates in case <code>save_table</code> is <code>TRUE</code>. Extension of the
the file can be txt or csv and should be included. Default is
<code>"dataset.txt"</code>.

file_name

Logical. If <code>TRUE</code>, saves the merged table. Default is
<code>TRUE</code>.

save_table

A string with the path of the folder in which the
merged table is saved in case <code>save_table</code> is <code>TRUE</code>. Default is the
path provided in <code>folder_path</code>.

dir_save_table

Logical. If <code>TRUE</code>, prints messages about the
progress of the function. Default is <code>TRUE</code>.

notification


Vertically concatenates files containing data tables in a long
  format into a single large dataset. In order for the function to work, all
  files you wish to merge should be in the same format (either txt or csv).
  This function is very useful for concatenating raw data files
  of individual subjects in an experiment (in which each line corresponds to
  a single observation in the experiment) to one raw data file that includes
  all subjects.


Prepares data for statistical analysis (e.g., analysis of variance
;ANOVA) by enabling the user to easily and quickly merge (using the
file_merge() function) raw data files into one merged table and then
aggregate the merged table (using the prep() function) into a finalized
table while keeping track and summarizing every step of the preparation.
The finalized table contains several possibilities for dependent measures of
the dependent variable. Most suitable when measuring variables in an
interval or ratio scale (e.g., reaction-times) and/or discrete values such
as accuracy. Main functions included are file_merge() and prep(). The
file_merge() function vertically merges individual data files (in a long
format) in which each line is a single observation to one single dataset.
The prep() function aggregates the single dataset according to any
combination of grouping variables (i.e., between-subjects and
within-subjects independent variables, respectively), and returns a data
frame with a number of dependent measures for further analysis for each cell
according to the combination of provided grouping variables. Dependent
measures for each cell include among others means before and after rejecting
all values according to a flexible standard deviation criteria, number of
rejected values according to the flexible standard deviation criteria,
proportions of rejected values according to the flexible standard deviation
criteria, number of values before rejection, means after rejecting values
according to procedures described in Van Selst & Jolicoeur (1994; suitable
when measuring reaction-times), standard deviations, medians, means according
to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
frame prep() returns can also be exported as a txt file to be used for
statistical analysis in other statistical programs.

file_merge: Vertically Merge Files in a Directory into a Single Large Dataset

Description

Usage

Arguments

Value