The {correltools} 📦 aims to provide utility functions for useful plots, analyses and tools in the CorrelAid context.
remotes::install_github("correlaid/correltools")correltools makes available a CorrelAid branded HTML document theme
for RMarkdown, derived from html_document.
To use it, add corretools::html_yeti as your output format.
---
title: "A CorrelAid themed report"
output:
correltools::html_yeti:
toc: true
toc_float: true
---
Alternatively, you can directly create a CorrelAid document by creating it from the template:
- File -> New File -> RMarkdown
- in the popup, select “From Template” and then select the “Simple CorrelAid HTML Report”
library(correltools)
#first get the data from the website and using geocoding
chapters <- get_correlaidx_data()## geocoding - this can take a couple of seconds
## No results found for "You want to know more about CorrelAid? Sign up for our Newsletter!".
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
correlaidx_map(chapters)## Assuming "lon" and "lat" are longitude and latitude, respectively
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Build a German version:
chapters_de <- get_correlaidx_data(lang = 'de')## geocoding - this can take a couple of seconds
## No results found for "Du willst mehr über CorrelAid erfahren? Dann abonniere unseren Newsletter!".
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
correlaidx_map(chapters_de, lang = 'de')## Assuming "lon" and "lat" are longitude and latitude, respectively
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
library(ggplot2)
library(correltools)
theme_set(theme_correlaid())The theme uses the Roboto fonts and has a generally minimal look:
simple_plot <- ggplot(penguins, aes(x = species, fill = island))+
geom_bar()
simple_plot
simple_plot+
scale_fill_correlaid_d()+
add_correlaid_logo() # this needs to be the last line, otherwise might cause problems
simple_plot+
scale_fill_correlaid_d(option = "grey")+
add_correlaid_logo()
a somewhat more fancy plot with titles and subtitles:
ggplot(data = penguins,
aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point(aes(color = species,
shape = species),
size = 3,
alpha = 0.8) +
scale_color_correlaid_d()+
labs(title = "Penguin size, Palmer Station LTER",
subtitle = "Flipper length and body mass for Adelie, Chinstrap, and Gentoo Penguins",
x = "Flipper length (mm)",
y = "Body mass (g)",
color = "Penguin species",
shape = "Penguin species")+
add_correlaid_logo()## Warning: Removed 2 rows containing missing values (geom_point).
continuous scale:
p <- ggplot(data = penguins,
aes(x = bill_length_mm,
y = bill_depth_mm)) +
geom_point(aes(color = body_mass_g),
size = 3,
alpha = 0.8) +
theme_correlaid(base_size = 12)+ # smaller font size
labs(title = "Penguin bills, Palmer Station LTER",
subtitle = "Bill length, bill width and body mass",
x = "Bill length (mm)",
y = "Bill length (mm)",
color = "Body mass (g)")
p+
scale_color_correlaid_c(option = 'gradient_x')## Warning: Removed 2 rows containing missing values (geom_point).
we can also have a binned color scale:
p+
scale_color_correlaid_b(option = 'gradient_x')## Warning: Removed 2 rows containing missing values (geom_point).
You can also manually construct color palettes that you can use in your ggplots:
pal <- correlaid_pal(option = 'qualitative') # default qualitative
scales::show_col(pal(4))
pal_cax <- correlaid_pal(option = 'gradient_x') # correlaidx
scales::show_col(pal_cax(9))
pal_ca <- correlaid_pal(option = 'gradient', direction = -1) # correlaid, reversed order
scales::show_col(pal_ca(9))
correltools provides functions to make administrative tasks involving
the CorrelCloud - our Nextcloud instance - easier.
In order to do so, you will need to make your username and password
available as CORRELCLOUD_USR respectively CORRELCLOUD_PWD
environment variables (e.g. use usethis::edit_r_environ() to edit your
environment variables).
First, create a connection:
con <- new_correlcloud_con()Notably, you can:
create a new user (admin-only):
new_correlcloud_user(con, "Leo", "Muster", "[email protected]")The default username will be {first name}{first letter of last name},
e.g. “LeoM”. If you want to override this behaviour, you can specify the
user_id argument:
new_correlcloud_user(con, "Leo", "Muster", "[email protected]", user_id = "LeoMu")By default, users will not be added to any user groups, so they won’t
have access to any data/files. If you want to give them access, you can
specify a character vector to the groups argument.
new_correlcloud_user(con, "Leo", "Muster", "[email protected]", groups = c("User", "2021-01-TES"), user_id = "LeoMu")create a new group:
new_correlcloud_group(con, "2022-01-TES")add a user to a group:
add_correlcloud_user_to_group(con, "LeoM", "2022-01-TES")Finally, you can list users and groups (admin rights required):
get_correlcloud_groups(con)
get_correlcloud_users(con)Some mini-projects exist as
issues. For each
issue there is a “get started” RMarkdown in playground with code how
to get example data that you can work with during development. If you
want to pick up an issue, just comment under it and you’ll be assigned!
:)
Depending on your skill levels, the contributing workflows could be as follows:
Prerequisites: you know how to work in R on your laptop, you know how to work with R Markdown
- Download the repository as a zip (by clicking on the green “code”
button and “download as zip”). Unzip the directory and double-click
on the file
correltools.Rprojto open the project in RStudio. - work in the RMarkdown for your issue in the
playgroundfolder - work until you have something that you think is cool.
- If you know how to write a R function, try to put your code into a function. If you don’t know about functions yet, you can read more about them here.
Prerequisites: you know about pull-commit-push and (optionally) branching
- clone the repository (or fork it and work on your own copy and later make a pull request)
- make a branch for your issue (e.g. issue1-ggplot-theme). If you
don’t know about branching, you can also work on the
mainbranch. Ask Frie to add you as a contributor to the GitHub repo. - work in the RMarkdown for your issue in the
playgroundfolder - work until you have something that you think is cool. Commit whenever you feel you have made some progress.
- write a function that generates the output in your r markdown. (or multiple functions if necessary)
- write @frie a message on Slack for guidance and/or read into how you can possibly add your function to the R package by reading https://r-pkgs.org/intro.html
Prerequisites: you know how to develop R packages (or you are confident you can quickly learn about it by reading R packages). You know how to fork and/or how to work on Git branches (or you want to learn about it!).
- clone the repository (or fork it and work on your own copy and later make a pull request). If you choose the former, ask Frie to add you as a contributor to the GitHub repo.
- make a branch for your issue (e.g. issue1-ggplot-theme).
- work in the RMarkdown for your issue in the
playgroundfolder or directly work inRon your function. :) - work until you have something that you think is cool. Commit whenever you feel you have made some progress.
- Make a PR if you’re ready :)
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