README.md

In UW-GAC/contactparser: Parse the TOPMed contact table

contactparser

The contactparser package was designed to parse and reformat the Contacts table on the TOPMed website.

Installation

You can install the released version of contactparser from github with:

# install.packages("devtools") 
devtools::install_github("UW-GAC/contactparser")

Example

This package has one primary function to read, parse, and reformat the contacts table. It starts with either a csv file or an html file, parses it, and reshapes the table so that it can easily be filtered by TOPMed study, project, or contact type (PI, Co-PI, Contact, etc.).

First, export the table to a csv file with the “export” button. Then call:

library(contactparser)
csv_file <- system.file(package = "contactparser", "testdata", "example.csv")
parsed <- parse_contact_table(csv_file)
parsed
#> # A tibble: 7 x 5
#>   record_type   study_short_name project  contact_type   email             
#>   <chr>         <chr>            <chr>    <chr>          <chr>             
#> 1 Parent Study  Study1           Project1 pi             nia.vonrueden@yah…
#> 2 Parent Study  Study1           Project1 co_pi          fnitzsche@gmail.c…
#> 3 Parent Study  Study1           Project1 contact        koelpin.kathaleen…
#> 4 Parent Study  Study1           Project1 contact        ilona.cartwright@…
#> 5 Parent Study  Study1           Project1 dataset_conta… sdenesik@hotmail.…
#> 6 TOPMed Proje… Study2           Project2 pi             karren.schmeler@g…
#> 7 TOPMed Proje… Study2           Project2 contact        karren.schmeler@g…

You can also save the webpage as an html file and parse the contact table from the html:

library(contactparser)
html_file <- system.file(package = "contactparser", "testdata", "example.html")
parsed <- parse_contact_table(html_file)
parsed
#> # A tibble: 7 x 5
#>   record_type   study_short_name project  contact_type   email             
#>   <chr>         <chr>            <chr>    <chr>          <chr>             
#> 1 Parent Study  Study1           Project1 pi             nia.vonrueden@yah…
#> 2 Parent Study  Study1           Project1 co_pi          fnitzsche@gmail.c…
#> 3 Parent Study  Study1           Project1 contact        koelpin.kathaleen…
#> 4 Parent Study  Study1           Project1 contact        ilona.cartwright@…
#> 5 Parent Study  Study1           Project1 dataset_conta… sdenesik@hotmail.…
#> 6 TOPMed Proje… Study2           Project2 pi             karren.schmeler@g…
#> 7 TOPMed Proje… Study2           Project2 contact        karren.schmeler@g…

Once you’ve parsed the file, you can easily filter to specific types of contacts using dplyr functions:

library(dplyr, quietly = TRUE)

# Just PIs:
pis <- parsed %>%
  filter(contact_type %in% c("pi", "co_pi"))
pis
#> # A tibble: 3 x 5
#>   record_type    study_short_name project contact_type email               
#>   <chr>          <chr>            <chr>   <chr>        <chr>               
#> 1 Parent Study   Study1           Projec… pi           nia.vonrueden@yahoo…
#> 2 Parent Study   Study1           Projec… co_pi        [e-mail] 
#> 3 TOPMed Project Study2           Projec… pi           karren.schmeler@gro…

There are two functions you can use to create a string of emails that you can cut and paste into your email client. In both cases, the list is sorted by default, and emails for duplicate records only appear once.

You can use get_emails to returns a string that you can use to email PIs:

email_string <- get_emails(pis)
email_string
#> [1] "[e-mail], [e-mail], [e-mail]"

Alternately, you can use print_emails to print the same string as a message:

print_emails(pis)
#> [e-mail], [e-mail], [e-mail]

UW-GAC/contactparser documentation built on Feb. 5, 2020, 4:59 a.m.