Annotate lines instead of a legend #183
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@maurolepore - I started this PR to have my work online but it is a draft and I am still going to do quite a bit of refactoring to simplify how the plot is created before I consider it ready for review. |
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It looks like we need to
https://github.com/2DegreesInvesting/r2dii.plot.static/pull/183/checks?check_run_id=2693143049 |
Yes, definitely. I'm just fooling around for now. Once I decide on the packages I will add them to imports, etc. |
cjyetman
reviewed
May 31, 2021
| nudge_y = 0.01 * value_span, | ||
| hjust = 0, | ||
| segment.size = 0.3, | ||
| xlim = c(min(data$year), last_year + 5) |
There was a problem hiding this comment.
Is this meant to min(.data$year) here, versus min(data$year)?
Maybe it's best to use a different argument name than data since it's so easily confused with .data? Maybe it doesn't make a difference? 🤷🏻
here's an example of a case where it might matter...
library(dplyr)
data <- tibble::tribble(
~name, ~gender, ~grade,
"Bill", "m", 65L,
"Bob", "m", 73L,
"Mary", "f", 98L,
"Melissa", "f", 87L
)
data %>%
filter(gender == "f") %>%
mutate(mean(.data$grade))
#> # A tibble: 2 x 4
#> name gender grade `mean(.data$grade)`
#> <chr> <chr> <int> <dbl>
#> 1 Mary f 98 92.5
#> 2 Melissa f 87 92.5
data %>%
filter(gender == "f") %>%
mutate(mean(data$grade))
#> # A tibble: 2 x 4
#> name gender grade `mean(data$grade)`
#> <chr> <chr> <int> <dbl>
#> 1 Mary f 98 80.8
#> 2 Melissa f 87 80.8There was a problem hiding this comment.
in this case I actually meant min(data) not min(.data)
maurolepore
reviewed
May 31, 2021
| @@ -40,7 +40,8 @@ | |||
| #' | |||
| #' p <- plot_trajectory(data, | |||
| #' scenario_specs_good_to_bad = scenario_specs, | |||
| #' main_line_metric = main_line_metric | |||
| #' main_line_metric = main_line_metric, | |||
| #' additional_line_metrics = additional_line_metrics | |||
| #' ) | |||
There was a problem hiding this comment.
I added additional_line_metrics because they were defined but not used.
jdhoffa
added a commit
that referenced
this pull request
Sep 21, 2022
* Export example_data (#127) * In plot_timeline() remove labels (#128) * Remove dependency on ggpubr (#152) * Clarify and simplify messages and tests (#154) * In plot_timeline(), order legend to match lines (#158) * New versions of plot_timeline() (#156) * Enforce atomic sector and warn selection (#148) * Show how to change colours (#162) * Update readme (#164) * In prepare_for_timeline() sector_filter is now obligatory (#163) * New versions of prepare_for_timeline*() (#165) * update readme (#167) * Add workflow to render all rmarkdown documents (#174) * Update readme and move error (#171) * theme_2dii() now includes common arguments (#168) * Fix gh action to render readme (#181) * Update wordslist (#182) * Rename package (#184) * Move process_input_data() into prepare_*() functions (#176) * Revert "Move process_input_data() into prepare_*() functions (#176)" * Rename datasets (#186) * prepare_for_ -> prep_ (#188) * Fix names of data (#191) * Polish file names and prune unused files (#192) * Move process_input_data() inside prep_*() (#194) * Use colours as data, and name them consistently (#189) * Annotate lines instead of a legend (#183) * Remove '.static' from package name (#195) * Show theme set (#190) * Style (#196) * Structure website's reference section (#197) * Colour datasets are now internal (#199) * Add trajectory version B (#201) * Fix timeline (#204) * Remove filtering ability from prep functions (#206) * Refactor for consistency and polish documentation (#207) * Add main_line argument trajectory (#210) * Fix error message (#214) * Remove needless exapmle-chunk (#217) * Prune thick API (#222) * Fix annotations overlap (#221) * Rename thick API with suffix "Y" (#224) * prep_timeline() again gains 'sector_filter' (#225) * prep_timelineY() again starts at common start year (#227) * plot_techmix*() now enforce a single scenario (#229) * plot_trajectoryX() looses arg 'value' for consistency with X API (#231) * New articles for X and Y API (#232) * Filter year to make plots of X and Y API identical (#236) * Add example Y techmix (#238) * Align timeline examples (#239) * show similarities pros and cons (#240) * Align X and Y articles for techmix and trajectory (#241) * dry high level docs (#246) * DRY again and polish text (#247) * Dry-run of CRAN release (#251) * Polish DESCRIPTION Title and Description (#252) * Redocument (#253) * New abort_if_multiple() hints users how to filter (#254) * GH workflows now work with branch 'develop' (#257) * Trigger CI on PR too (#258) * Make the X API the main API (#256) * Fix website reference (#272) * Rename to plot_emission_intensity() (#273) * 268 In examples use base r (#274) * Add authors and roles (#248) * Rename plot timeline and add missing test (#279) * Remove main_line (#280) * plot_trajectory() loses the argumet 'normalize' (#281) * Don't beatifull lengend labels (#284) * Style (#286) * All plots again beautify legend labels by default (#293) * Update DESCRIPTION's Title and Description (#296) * Rename timeline to emission intensity, eveverywhere (#295) * Filter data to startyear (#290) * Remove dead code and increase test coverage (#297) * Handle messages so they don't pollute test output (#298) * Rename to emission intensity (#299) * Refactor (#300) * 294 Remove extrapolate (#301) * In internal code use dplyr::filter() not subset() (#302) * 271 New article of entire r2dii workflow (#303) * New integration test for sda (#304) * Polish documentation (#308) * Rebuild readme (#309) * Improve messages by showing the start year (#306) * Refactor (#310) * Simplify get_area_borders code (#311) * Fix maintainer's email (#312) * Prepare for initial release 0.0.1 (#314) * Spellcheck (#316) * Update readme (#317) * Increment version number (#318) Co-authored-by: Mauro Lepore <maurolepore@gmail.com> Co-authored-by: Monika Furdyna <monika.furdyna@gmail.com> Co-authored-by: Mauro Lepore <mauro@2degrees-investing.org> Co-authored-by: GitHub Actions <actions@github.com> Co-authored-by: Alex Axthelm <Alex@2degrees-investing.org>
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Closes #177
This PR replaces the complicated legend creation with annotations. In this way we are also able to retain the ggplot object properties and (for example) add custom labels to the plot. See example of a resulting plot below.