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library(tidyverse)
library(leaflet)

lang_dialects <- read_csv(
  # CSV file of languages with region, latitude and longitude
  file = "https://git.io/vFeOj"
)

abs_2016 <- read_csv(
  # CSBV file of ABS 2016 census data with language names and number of speakers

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RStudio on Nectar for teaching R

Set up and configure your computer and Nectar for SSH access

1. Generate a public-private key pair

Perform Step 1 in this guide: https://kb.dynamicsoflanguage.edu.au/contribute/setup/

Add your computer's public key to the Nectar system

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library(shiny)
library(dplyr)
library(stringr)
library(zoo)
library(purrr)
library(tidyr)

options(shiny.maxRequestSize=30*1024^2) 

reversalize <- function(wrl_txt_path) {

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# You may need to run
npm link moo
npm link nearley
# npm link [package-name-of-a-global-install]

# for the first you run things to get the packages linked to
# the directory in which you are running the scripts

# Compile nearley grammar to javascript code
nearleyc grammar.ne > grammar.js

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<%
    my_table = [
        { "me" : "voiture", "eng": "car" },
        { "me": "rouge", "eng": "red" }
    ]
%>

<table>
    <% my_table.map(row => {%>
        <tr>

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\me jakanypa (N): (H)
\dm flora: watiya: \edm
\lat Petalostylis cassioides \elat
\me jakutakuta (N): (La,Y)
\dm flora: miyi: \edm
\lat Cassytha filiformis \elat
\me janjinki (N): (Y)
\dm flora: watiya: \edm
\lat Acacia inaequilatera \elat
\me janjinngi (N): (Y)

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# How to export TextGrid and Formant data from Praat: http://recordit.co/ybhGxsf99a
# Make sure to setwd() to where annotations.csv and formants.csv are located

library(dplyr)
library(readr)

left_join(
    read_csv("annotations.csv") %>% mutate(join_col = "temp"),
    read_csv("formants.csv") %>% mutate(join_col = "temp")
    ) %>%

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# Use install.packages() on dplyr and tidyr
library(dplyr)
library(tidyr)

# mock up data frame (or use read_xlsx function from readxl package to read in an excel sheet)
wrl_wide <-
    data.frame(
        Warlpiri = c("wumparlpa", "wartarurru"),
        `scientificName 1` = c("ecucalyptus leucophloia", "acacia validinervia"),
        `scientificName 2` = c("eucalyptus pruinosa", "acacia jennerae")

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library(base64enc)

token2text <- function(token_obj, txt_file) {
    rds_path <- tempfile()
    saveRDS(object = token_obj,
            file   = rds_path)
    
    # return base64 string
    writeLines(base64encode(rds_path), txt_file)
}

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library(phonpack)
library(base64enc)
library(googlesheets)

# Get from BitBucket pipeline environment
BATCH_NO <- Sys.getenv("BITBUCKET_REPO_SLUG")
AUTH_STR <- Sys.getenv("GOOGLE_AUTH")

# Convert env variable to txt file, then to RDS file, then supply
# to gs_auth() as RDS file