Merge pull request #183 from jpquast/Add-predict_alphafold_domain

Update janitor link

vignettes/data_analysis_dose_response_workflow.Rmd

@@ -54,7 +54,7 @@ For this vignette we use a subset of proteins from an experiment of HeLa cell ly
 
 We included 39 random proteins and FKBP12 in this sample data set. The proteins were sampled using the seed 123. 
 
-If you want to read your data into R, we suggest using the `read_protti()` function. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](http://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis.
+If you want to read your data into R, we suggest using the `read_protti()` function. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](https://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis.
 To ensure that your data input has the right format you can check out our [input preparation vignette](https://jpquast.github.io/protti/articles/input_preparation_workflow.html).
 
 ```{r use_read_protti, eval=FALSE}

vignettes/data_analysis_single_dose_treatment_workflow.Rmd

@@ -82,7 +82,7 @@ library(dplyr)
 ```
 
 After having loaded the required packages we will load our data set into the R environment.
-In order to do this for your data set you can use the function `read_protti()`. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](http://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case to make them more R-friendly. This will make it easier to remember them and to use them in your data analysis.
+In order to do this for your data set you can use the function `read_protti()`. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](https://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case to make them more R-friendly. This will make it easier to remember them and to use them in your data analysis.
 
 ```{r eval=FALSE}
 # To read in your own data you can use read_protti()

vignettes/input_preparation_workflow.Rmd

@@ -76,7 +76,7 @@ Spectronaut reports already contain data in the tidy data format. Therefore noth
 * FG.Charge (precursor charge state, relevant for quality control)
 * PG.Quantity (protein group quantity, required for protein-centric analyses if you prefer not to calculate them with `calculate_protein_abundance()`)
 
-Please make sure that the report is a .csv file. You can use the `read_protti()` function in order to load the spectronaut report into R. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](http://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis. For the Spectronaut columns `R.FileName` will change for example into `r_file_name`. 
+Please make sure that the report is a .csv file. You can use the `read_protti()` function in order to load the spectronaut report into R. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](https://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis. For the Spectronaut columns `R.FileName` will change for example into `r_file_name`. 
 
 ```{r Spectronaut, eval=FALSE}
 # To read in your own data you can use read_protti()

vignettes/protein_structure_workflow.Rmd

@@ -88,7 +88,7 @@ The example data is included in **protti** and you can easily use it by calling
 utils::data("ptsi_pgk")
 ```
 
-If you want to read your own data into R, we suggest using the `read_protti()` function. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](http://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis. Usually you would perform the following analysis after the analyses described in either the [vignette for single dose treatments](https://jpquast.github.io/protti/articles/data_analysis_single_dose_treatment_workflow.html) or the [vignette for dose response data](https://jpquast.github.io/protti/articles/data_analysis_dose_response_workflow.html).
+If you want to read your own data into R, we suggest using the `read_protti()` function. This function is a wrapper around the fast `fread()` function from the [`data.table`](https://rdatatable.gitlab.io/data.table/) package and the `clean_names()` function from the [`janitor`](https://sfirke.github.io/janitor/) package. This will allow you to not only load your data into R very fast, but also to clean up the column names into lower snake case. This will make it easier to remember them and to use them in your data analysis. Usually you would perform the following analysis after the analyses described in either the [vignette for single dose treatments](https://jpquast.github.io/protti/articles/data_analysis_single_dose_treatment_workflow.html) or the [vignette for dose response data](https://jpquast.github.io/protti/articles/data_analysis_dose_response_workflow.html).
 
 ```{r use_read_protti, eval=FALSE}
 # Load data