Merge pull request #63 from sindribaldur/spell-check

fix typos in documentation

R/create_dataset.R

@@ -1,7 +1,7 @@
 #' @rdname create_dataset
 #' @title Create or update a dataset
 #' @description Create or update dataset within a Dataverse
-#' @details \code{create_dataset} creates a Dataverse dataset. In Dataverse, a \dQuote{dataset} is the lowest-level structure in which to organize files. For example, a Dataverse dataset might contain the files used to reproduce a published article, including data, analysis code, and related materials. Datasets can be organized into \dQuote{Dataverse} objects, which can be further nested within other Dataverses. For someone creating an archive, this would be the first step to producing said archive (after creating a Dataverse, if one does not already exist). Once files and metadata have been added, the dataset can be publised (i.e., made public) using \code{\link{publish_dataset}}.
+#' @details \code{create_dataset} creates a Dataverse dataset. In Dataverse, a \dQuote{dataset} is the lowest-level structure in which to organize files. For example, a Dataverse dataset might contain the files used to reproduce a published article, including data, analysis code, and related materials. Datasets can be organized into \dQuote{Dataverse} objects, which can be further nested within other Dataverses. For someone creating an archive, this would be the first step to producing said archive (after creating a Dataverse, if one does not already exist). Once files and metadata have been added, the dataset can be published (i.e., made public) using \code{\link{publish_dataset}}.
 #'
 #' \code{update_dataset} updates a Dataverse dataset that has already been created using \code{\link{create_dataset}}. This creates a draft version of the dataset or modifies the current draft if one is already in-progress. It does not assign a new version number to the dataset nor does it make it publicly visible (which can be done with \code{\link{publish_dataset}}).
 #'

README.Rmd

@@ -164,7 +164,7 @@ The results are paginated using `per_page` argument. To retrieve subsequent page
 
 ### Data Archiving
 
-Dataverse provides two - basically unrelated - workflows for managing (adding, documenting, and publishing) datasets. The first is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
+Dataverse provides two - basically unrelated - workflows for managing (adding, documenting, and publishing) datasets. The first is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have to first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
 
 ``` r
 # retrieve your service document

README.md

@@ -256,13 +256,14 @@ subsequent pages, specify `start`.
 
 ### Data Archiving
 
-Dataverse provides two - basically unrelated - workflows for managing
-(adding, documenting, and publishing) datasets. The first is built on
-[SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a
-new dataset listing, you will have first initialize a dataset entry with
-some metadata, add one or more files to the dataset, and then publish
+Dataverse provides two - basically unrelated - workflows for managing 
+(adding, documenting, and publishing) datasets. The first is built on 
+[SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a 
+new dataset listing, you will have to first initialize a dataset entry with 
+some metadata, add one or more files to the dataset, and then publish 
 it. This looks something like the following:
 
+
 ``` r
 # retrieve your service document
 d <- service_document()

vignettes/A-introduction.Rmd

@@ -39,7 +39,7 @@ library("dataverse")
 
 ## Quick Start
 
-Dataverse has some terminology that is worth quickly reviewing before showing how to work with Dataverse in R. Dataverse is an application that can be installed in many places. As a result, **dataverse** can work with any instllation but you need to specify which installation you want to work with. This can be set by default with an environment variable, `DATAVERSE_SERVER`:
+Dataverse has some terminology that is worth quickly reviewing before showing how to work with Dataverse in R. Dataverse is an application that can be installed in many places. As a result, **dataverse** can work with any installation but you need to specify which installation you want to work with. This can be set by default with an environment variable, `DATAVERSE_SERVER`:
 
 
 ```r

vignettes/A-introduction.Rmd2

@@ -39,7 +39,7 @@ library("dataverse")
 
 ## Quick Start
 
-Dataverse has some terminology that is worth quickly reviewing before showing how to work with Dataverse in R. Dataverse is an application that can be installed in many places. As a result, **dataverse** can work with any instllation but you need to specify which installation you want to work with. This can be set by default with an environment variable, `DATAVERSE_SERVER`:
+Dataverse has some terminology that is worth quickly reviewing before showing how to work with Dataverse in R. Dataverse is an application that can be installed in many places. As a result, **dataverse** can work with any installation but you need to specify which installation you want to work with. This can be set by default with an environment variable, `DATAVERSE_SERVER`:
 
 ```{r}
 library("dataverse")

vignettes/C-retrieval.Rmd

@@ -249,7 +249,7 @@ dataset$files[c("label", "UNF")]
 
 ## Reusing Files and Reproducing Analysis
 
-To reproduce the analysis, we can simply run the code file either as a `system()` call or directly in R using `source()` (note this particular file begins with an `rm()` call so you may want to run it in a [new enviroment](https://stat.ethz.ch/R-manual/R-devel/library/base/html/environment.html)):
+To reproduce the analysis, we can simply run the code file either as a `system()` call or directly in R using `source()` (note this particular file begins with an `rm()` call so you may want to run it in a [new environment](https://stat.ethz.ch/R-manual/R-devel/library/base/html/environment.html)):
 
 ```R
 # Option 1
@@ -259,7 +259,7 @@ system("Rscript chapter03.R")
 source("chapter03.R", local=new.env())
 ```
 
-Any well-produced set of analysis reproduction files, like this one, should run without error once the data and code are in-hand.  Troubleshooting anlaysis files is beyond the scope of this vignette, but common sources are
+Any well-produced set of analysis reproduction files, like this one, should run without error once the data and code are in-hand.  Troubleshooting analysis files is beyond the scope of this vignette, but common sources are
 
 1. The working directory is not set the same as the author intended.  This could affect code files not finding the relative position of datasets or of other code files.
 1. Your local machine hasn't downloaded or installed all the necessary datasets and packages.

vignettes/D-archiving.Rmd

@@ -25,7 +25,7 @@ Sys.setenv("DATAVERSE_SERVER" = "dataverse.harvard.edu")
 
 ## SWORD-based Data Archiving
 
-The main data archiving (or "deposit") workflow for Dataverse is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
+The main data archiving (or "deposit") workflow for Dataverse is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have to first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
 
 ```R
 # retrieve your service document

vignettes/D-archiving.Rmd2

@@ -24,7 +24,7 @@ Sys.setenv("DATAVERSE_SERVER" = "dataverse.harvard.edu")
 
 ## SWORD-based Data Archiving
 
-The main data archiving (or "deposit") workflow for Dataverse is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
+The main data archiving (or "deposit") workflow for Dataverse is built on [SWORD v2.0](http://swordapp.org/sword-v2/). This means that to create a new dataset listing, you will have to first initialize a dataset entry with some metadata, add one or more files to the dataset, and then publish it. This looks something like the following:
 
 ```R
 # retrieve your service document