We send a fair amount of email in STAT 545. For example, it's how we inform students of their marks on weekly homework and peer review. We use R for essentially all aspects of the course and this is no exception.
In this repo I describe our workflow, with Lord of the Rings characters playing the role of our students.
Must haves:
- A Google account with an associated Gmail email address.
- The
gmailr
R package by Jim Hester, which wraps the Gmail API (development on GitHub).
Nice to haves:
- A project in Google Developers Console to manage your use of the Gmail API. Optional but recommended once your use
gmailr
is more than casual, as mine is. - The
readr
,dplyr
andpurrr
packages for data wrangling and iteration. "Past me" usedplyr
instead ofpurrr
and you could certainly do all of this with base R if you prefer. addresses.csv
a file containing email addresses, identified by a key. In our case, student names.marks.csv
a file containing the variable bits of the email you plan to send, including the same identifying key as above. In our case, the homework marks.- The script
send-email-with-r.R
that- joins email addresses to marks
- creates valid email objects from your stuff
- provides your Gmail credentials
- sends email
YES, be my guest! If you can get that working quickly, I salute you -- you clearly don't need this tutorial. For everyone else, I have found this Gmail + gmailr
approach less exasperating.
Verify that your Google account is mail-capable by visiting https://mail.google.com/mail/ while logged in. If it's not, you might as well stop reading now. Or accept the offer at that link to enable Gmail or create a new mail-capable account.
Install the gmailr
package from CRAN or the development version from GitHub (pick ONE):
install.packages("gmailr")
## OR ...
devtools::install_github("jimhester/gmailr")
If your use of gmailr
is more than casual, you should get your own client id. Don't worry if you don't know what that means. Just do it.
I have found this works better -- or only works at all -- if I use Google Chrome versus, say, Safari. YMMV.
-
Pick a name for your project. I chose "gmailr-tutorial" for this write-up. Let's call this
PROJ-NAME
from now on. -
Create a new project at https://console.developers.google.com/project.
-
Overview screen > Google Apps APIs > Gmail API.
- Enable!
-
Click "Go to Credentials" or navigate directly to Credentials.
-
You want a "client ID".
-
Yes, you will have to "Configure consent screen".
- The email should be pre-filled, since presumably you are signed in with the correct Google account. Enter
PROJ-NAME
as the Product name and leave everything else blank. Click Save.
- The email should be pre-filled, since presumably you are signed in with the correct Google account. Enter
-
Back to ... Create client ID. Select application type "Other". Enter
PROJ-NAME
again here for the name. Click "Create". -
OAuth client pop up will show you your client ID and secret. You don't need to copy them -- there are better ways to get this info. Dismiss with "OK".
-
Click on the download icon at the far right of your project's OAuth 2.0 client ID listing to get a JSON file with your ID and secret. You'll get a file named something like this:
client_secret_<LOTS OF DIGITS AND LETTERS>.apps.googleusercontent.com.json
I rename this after the project, e.g.,
PROJ-NAME.json
and move into the directory where the bulk emailing project lives. -
Optional if you are using Git, add a line like this to your
.gitignore
filePROJ-NAME.json
-
You can add members to specific projects from Google Developers Console, allowing them to also download JSON credentials for the same project. I do that for my TAs, for example.
See dryrun.R
for clean code.
Load gmailr
. PSA: it masks several functions from base
and utils
, including message()
.
suppressPackageStartupMessages(library(gmailr))
If you chose to get your own client id, tell gmailr
about that JSON file now. Otherwise, skip this and gmailr
will use its own built-in client id.
use_secret_file("gmailr-tutorial.json")
You can force the auth process explicitly via gmail_auth()
or simply go about your business and it will happen when needed. Let's send a test email.
test_email <- mime(
To = "PUT_A_VALID_EMAIL_ADDRESS_THAT_YOU_CAN_CHECK_HERE",
From = "PUT_THE_GMAIL_ADDRESS_ASSOCIATED_WITH_YOUR_GOOGLE_ACCOUNT_HERE",
Subject = "this is just a gmailr test",
body = "Can you hear me now?")
send_message(test_email)
#> Id: 155324357156abf4
#> To:
#> From:
#> Date:
#> Subject:
#>
You may be presented with this question
Use a local file to cache OAuth access credentials between R sessions?
1: Yes
2: No
Selection:
No matter what, the first time, you should get kicked into a browser to authenticate yourself and authorize the application. If you say "No", this will happen every time and is appropriate for interactive execution of your bulk emailing R code. If you say "Yes", your credentials will be cached in a file named .httr-oauth
so the browser dance won't happen in the future. Choose this if you plan to execute your bulk emailing code non-interactively.
Optional if you opt for OAuth credential caching and you're using Git, add this to your .gitignore
file. Note: it appears that gmailr
does this for you, but make sure it happens!:
.httr-oauth
Did your email get through? Do not proceed until the answer is YES.
If this doesn't work, running it inside a loop with typo-ridden email addresses is unlikely work any better.
The hard parts are over! See send-email-with-r.R
for clean code to compose and send email. Here's the guided tour.
The file addresses.csv
holds names and email addresses. The file marks.csv
holds names and homework marks. In this case, the LoTR characters receive marks based on the number of words they spoke in the Fellowship of the Ring. Read those in and join.
suppressPackageStartupMessages(library(gmailr))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(purrr))
library(readr)
addresses <- read_csv("addresses.csv")
marks <- read_csv("marks.csv")
(my_dat <- left_join(marks, addresses))
#> Joining by: "name"
#> Source: local data frame [34 x 3]
#>
#> name mark email
#> <chr> <int> <chr>
#> 1 Gandalf 100 gandalf@maiar.example.org
#> 2 Bilbo 97 bilbo@shire.example.org
#> 3 Galadriel 94 galadriel@valinor.example.org
#> 4 Frodo 91 frodo@shire.example.org
#> 5 Boromir 88 boromir@gondor.example.org
#> 6 Aragorn 85 aragorn@gondor.example.org
#> 7 Elrond 79 elrond@valinor.example.org
#> 8 Sam 79 sam@shire.example.org
#> 9 Saruman 76 saruman@maiar.example.org
#> 10 Gimli 74 gimli@erebor.example.org
#> .. ... ... ...
Next we create a data frame where each variable is a key piece of the email, e.g. the "To" field or the body.
this_hw <- "The Fellowship Of The Ring"
email_sender <- 'Peter Jackson <peter@tolkien.example.org>' # your Gmail address
optional_bcc <- 'Anonymous <anon@palantir.example.org>' # for me, TA address
body <- "Hi, %s.
Your mark for %s is %s.
Thanks for participating in this film!
"
edat <- my_dat %>%
mutate(
To = sprintf('%s <%s>', name, email),
Bcc = optional_bcc,
From = email_sender,
Subject = sprintf('Mark for %s', this_hw),
body = sprintf(body, name, this_hw, mark)) %>%
select(To, Bcc, From, Subject, body)
write_csv(edat, "composed-emails.csv")
We write this data frame to .csv
for an easy-to-read record of the composed emails. You will never regret saving such small, easy-to-read things.
We use the gmailr::mime()
function to convert each row of this data frame into a MIME-formatted message object. We use purrr::pmap()
to generate the list of mime objects, one per row of the input data frame. I present commented-out plyr
code, for historical interest. If you want base R, you're on your own.
emails <- edat %>%
pmap(mime)
str(emails, max.level = 2, list.len = 2)
#> List of 34
#> $ :List of 4
#> ..$ parts : list()
#> ..$ header:List of 6
#> .. .. [list output truncated]
#> .. [list output truncated]
#> ..- attr(*, "class")= chr "mime"
#> $ :List of 4
#> ..$ parts : list()
#> ..$ header:List of 6
#> .. .. [list output truncated]
#> .. [list output truncated]
#> ..- attr(*, "class")= chr "mime"
#> [list output truncated]
## plyr code to do similar: Option A
# emails <- plyr::dlply(edat, ~ To, function(x) mime(
# To = x$To,
# Bcc = x$Bcc,
# From = x$From,
# Subject = x$Subject,
# body = x$body))
## plyr code to do similar: Option B
# emails <- plyr::alply(edat, 1, function(x) mime(
# To = x$To,
# Bcc = x$Bcc,
# From = x$From,
# Subject = x$Subject,
# body = x$body))
If you chose to get your own client id, tell gmailr
about that JSON file now. Otherwise, skip this and gmailr
will use its own built-in client id.
use_secret_file("gmailr-tutorial.json")
If you've cached your OAuth credentials, sending mail should "just work", though you might see something about refreshing your token. Otherwise, you can expect to do the OAuth browser dance when executing the next chunk.
Send your emails!
- I choose to create a "safe" version of
send_message()
withpurrr::safely()
, so no single failure can derail my bulk emailing effort in the middle. purrr::map()
iterates over all the MIME objects stored inemails.
- Always retain the return value, for inspection and writing to file.
safe_send_message <- safely(send_message)
sent_mail <- emails %>%
map(safe_send_message)
saveRDS(sent_mail,
paste(gsub("\\s+", "_", this_hw), "sent-emails.rds", sep = "_"))
How does this safely()
thing work? In a hidden chunk, I've used code like the above to attempt to send 3 emails. The one in the middle intentionally has a nonsensical "To" field and fails. Here's how I inspect the result and access the error.
errors <- sent_mail %>%
transpose() %>%
.$error %>%
map_lgl(Negate(is.null))
sent_mail[errors]
#> [[1]]
#> [[1]]$result
#> NULL
#>
#> [[1]]$error
#> <condition in gmailr_POST(c("messages", "send"), user_id, class = "gmail_message", query = list(uploadType = type), body = jsonlite::toJSON(auto_unbox = TRUE, null = "null", c(threadId = thread_id, list(raw = base64url_encode(mail)))), add_headers(`Content-Type` = "application/json")): Gmail API error: 400
#> Invalid to header
#> >
The end.
devtools::session_info()
#> Session info --------------------------------------------------------------
#> setting value
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#> system x86_64, darwin13.4.0
#> ui X11
#> language (EN)
#> collate en_CA.UTF-8
#> tz America/Los_Angeles
#> date 2016-06-08
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