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Not urgent but would be good to have solved this sometime

Quoting @amnmalik: "using mip:: scenTool() on a historical MIF file, and while accessing the "Tables" tab, get this error "

entities <- c('AFR', 'AAA', 'AFR', 'UNKNOWN_ELEMENT2')
plotstyle(entities, plot = TRUE)

gives
Error: StatBin requires a continuous x variable: the x variable is discrete. Perhaps you want stat="count"?

Differentiating entities by unit ("x (y)") in plotstyle() does not work.

@johannah-pik

> plotstyle(c('Trucks', 'Trucks (<3.5t)', 'Trucks (3.5t-16)', 'Trucks (>16)'))
   Trucks    Trucks    Trucks    Trucks 
"#ff6a6a" "#ff6a6a" "#ff6a6a" "#ff6a6a" 

You might want to replace the parentheses with brackets (Trucks [<3.5t]) to get that working.

Due to code-quirks, these however work

> plotstyle(c('(0−3.5t)', '(7.5t)', '(40t)', '(26t)', '(18t)'))
 (0−3.5t)    (7.5t)     (40t)     (26t)     (18t) 
"#B0EBEE" "#2893A9" "#172f56" "#25467c" "#325fa4" 

But maybe you want to be consistent and change it there, too.

@dklein-pik: The origin of these got wiped by the move from svn to git

> plotstyle(c('Forcing|CO2 (W/m2)', 'Forcing|CH4 (W/m2)', 'Forcing|N2O (W/m2)',
+             'Forcing|F-Gases (W/m2)', 'Forcing|Aerosol (W/m2)',
+             'Forcing|Other (W/m2)', 'Emi|GHGtot (Mt CO2-equiv/yr)'))
    Forcing|CO2     Forcing|CH4     Forcing|N2O Forcing|F-Gases Forcing|Aerosol   Forcing|Other 
      "#331a00"       "#ffe7a0"       "#44ba97"       "#ff9977"       "#6e7700"       "#08262f" 
     Emi|GHGtot 
          "red" 

but plotstyle() seems to be mostly your thing.

Should I clean those up in ./inst/extdata/plotstyle.csv? I'm working on an improvement to plotstyle() and would prefer not to carry over legacy edge cases, but issue a warning for entities with units instead.

#66 works

devtools::install_github('pik-piam/mip', 'c64c7ed')
library(mip)
library(tidyverse)

d <- tribble(
  ~model,   ~scenario,      ~region,   ~variable,   ~unit,   ~period,   ~value,
  'foo',    'foo',          'foo',     'foo',       'foo',   1,         1,
  'foo',    'foo',          'foo',     'foo',       'foo',   2,         2,
  'bar',    'historical',   'foo',     'foo',       'foo',   1,         1,
  'bar',    'historical',   'foo',     'foo',       'foo',   2,         4)

showLinePlots(d, 'foo', mainReg = 'foo')

#68 does not

devtools::install_github('pik-piam/mip', 'b8a98a7')
library(mip)
library(tidyverse)

d <- tribble(
  ~model,   ~scenario,      ~region,   ~variable,   ~unit,   ~period,   ~value,
  'foo',    'foo',          'foo',     'foo',       'foo',   1,         1,
  'foo',    'foo',          'foo',     'foo',       'foo',   2,         2,
  'bar',    'historical',   'foo',     'foo',       'foo',   1,         1,
  'bar',    'historical',   'foo',     'foo',       'foo',   2,         4)

showLinePlots(d, 'foo', mainReg = 'foo')

When called through output.R -> compareScenarios.R with non-default order of scenarios (e.g. 10,8 instead of 8,10) mipBarYearData() does not produce any plots, but throws an error

## Error: Insufficient values in manual scale. 2 needed but only 1 provided.

Plots using showAreaAndBarPlots which calls mipArea and mipBarYearData can have strange y-axes labels:

The variables involved are:

Agricultural Demand|Crops|Energy|1st generation
Agricultural Demand|Crops|Energy|2nd generation

On the left page, showAreaAndBarPlots just uses longestCommonPrefix and the generation part is dropped completely.

On the right page, this code in mipArea turns it into:

varnames <- c("Agricultural Demand|Crops|Energy|1st generation", "Agricultural Demand|Crops|Energy|2nd generation")
mip:::shorten_legend(varnames, identical_only = TRUE)
[1] "1st" "2nd"
attr(,"front")
[1] "Agricultural Demand|Crops|Energy|"
attr(,"back")
[1] " generation"

which leads to an y-axis label

Agricultural Demand|Crops|Energy generation

which reads as if it was "Energy generation" which is very confusing. The legend just states 1st and 2nd. Why is the back attribute just pasted to the front, that seems weird to me.

In any case, I would be pleased if that could be harmonized, either by leaving the back part attached to the legend label, or by writing something like Agricultural Demand|Crops|Energy|… generation into the ylabel.

I can implement that, but I have no overview on how these functions are used and why it was implemented as such. Asking @LaviniaBaumstark, @dklein-pik, @tscheypidi…

mipArea() uses plotstyle(as.character(unique(x[[dimToStack]]))), mipBarYearData() uses plotstyle(levels(x$variable)) to obtain colors for plotted objects. This may lead to inconsistent assignment of colors, in particular when x$variable and x[[dimToStack]] are factors with levels not in plotstyle.csv: Compare plotstyle(levels(factor(3:1))) and plotstyle(as.character(unique(factor(3:1)))).

Works:

library(mip)

mipLineHistorical(x = mip_example_data[,,'REMIND', pmatch = TRUE],
                  color.dim = 'variable') +
  theme(legend.position = 'none')

Does not work:

mipLineHistorical(x = mip_example_data[,,'REMIND', pmatch = TRUE],
                  linetype.dim = 'variable') +
  theme(legend.position = 'none')

Is this

> plotstyle('e', verbosity = 1, out = 'all')
      legend   color marker linestyle
e emicapregi #A6CEE3     NA   

Extend plotstyle() for partial matching of entities via regular expression to allow for browsing of what's there and what isn't.

• not urgent at all, just some suggestions
• somehow an explanation what the ribbons mean would be awesome. No idea how this should best be done.
• harmonize background to a white one, similar to all other plots from this library. Adding this to line 91 did not seem to work, maybe I missed something.

The legends for the emissions figure from the compareScenarios pdf don't fit.

therefore, it does not show up anymore in the legend title

To reproduce:

q <- dplyr::filter(quitte::quitte_example_dataAR6, model == "GCAM")
q$identifier <- mip::identifierModelScen(q)
message(attr(q$identifier, "deletedinfo"))
q <- droplevels(q)
message(attr(q$identifier, "deletedinfo"))

That is for me a rather unexpected behavior and I would like to know how I can attach some piece of information here to a quitte object such that it is still available after going through droplevels in mip plotting scripts.

It seems to me that replacing droplevels(q) by droplevels(q, except = intersect(names(q), "identifier")) everywhere in the relevant mip functions should do the job. Any objections?

• The all new functions with a scales argument have "free_y" as a default.

— pik-piam/remind2/#182

Maybe it is worthwhile to extend y-axes to zero by default, or at least give the option to do so (using ggplot2::expand_limits(). It makes judging relative changes easier. @robertpietzcker might have feelings on this topic, too.

Dear colleagues, I have a strange bug. Using piamInterfaces::plotIntercomparison which calls mip::showLinePlots, I get the following document, which has nice and colorful plots, except for the Nationally Determined Contributions (NDCs) scenario. Anyone an idea, why?

On line 246, the validationpdf function tried to add a tex_caption to the figure.

swfigure(sw, print, p, tex_caption = preptitle(getNames(m$x)[s]), 
        sw_option = "width=10,height=2")

The function preptitle was called to generate the title from the origin name of the figure.

preptitle <- function(x) {
    x <- sub("\\.([^\\.]*)$", " (\\1)", x)
    x <- sub(".", " ", x, fixed = TRUE)
    x <- sub(" historical", "", x, fixed = TRUE)
    x <- gsub(".", " | ", x, fixed = TRUE)
    return(x)
  }

However, if there are "%" characters in the name of the figure, the pdf would not be generated successfully due to the grammar error in the generated tex file.

For example:
preptitle will generate a caption like "MAgPIE default | Agricultural Research Intensity (% of Total GDP)".
Then swfigure will add this line to the tex file: \caption{MAgPIE default | Agricultural Research Intensity (% of Total GDP)}.
It is here that the problem arises! The "%" in the caption should be preceded by a "", otherwise "% of Total GDP..." will be recognized as a latex command, resulting in an error when compiling to PDF.
This latex command is correct instead: \caption{MAgPIE default | Agricultural Research Intensity (\% of Total GDP)}

The possible solution:
In preptitle function, replace the "%" in the figure name with "percent" (or other string).
Also, I think there will be more elegant and general solutions.

The updated function may looks like:

preptitle <- function(x) {
    x <- sub("\\.([^\\.]*)$", " (\\1)", x)
    x <- sub(".", " ", x, fixed = TRUE)
    x <- sub(" historical", "", x, fixed = TRUE)
    x <- gsub(".", " | ", x, fixed = TRUE)
    x <- gsub("%", "percent", x, fixed = TRUE)
    return(x)
  }

Thank you for providing such a good data analysis tool as mip. Hope this bug can be resolved.

@pfuehrlich-pik

Check again whether this is fixed with #63 once this is merged.

I find the colours plotstyle() returns for PE carriers … odd.

mip::plotstyle(c('PE|Coal', 
                 'PE|Oil', 
                 'PE|Gas', 
                 'PE|Nuclear', 
                 'PE|Hydro', 
                 'PE|Biomass', 
                 'PE|Geothermal', 
                 'PE|Solar', 
                 'PE|Wind'),
               plot = TRUE)

Wouldn't it be worthwhile to change that to something more easily associated with the energy carriers in question? I think we were using different colours before.

Depending on the use case, different color palettes are desired for the REMIND regions.
The new region colors from this PR are optimized for distinguishability (e.g. for the REMIND validation).
For publications, grouped colors for like-wise regions could be better.

Here is a proposal from Nico for a grouped color palette, that could be implemented as an alternative choice:

OECD regions
plotstyle.add("EUR", "EU", rgb(0.0, 0.0, 0.99, 1), marker = 23, replace = TRUE )
plotstyle.add("NEU", "Other Europe", rgb(0.0, 0.0, 0.65, 1), marker = 9, replace = TRUE )
plotstyle.add("USA", "USA", rgb(0.5, 0.5, 0.99, 1), marker = 10, replace = TRUE )
plotstyle.add("JPN", "Japan", rgb(0.0, 0.5, 0.99, 1), marker = 13, replace = TRUE )
plotstyle.add("CAZ", "Can, Aus, NZ", rgb(0.3, 0.3, 0.8, 1), marker = 17, replace = TRUE )

Regions with developing and Emerging markets
plotstyle.add("CHA", "China", rgb(0.3, 0.99, 0.3, 1), marker = 19, replace = TRUE )
plotstyle.add("IND", "India", rgb(0.3, 0.65, 0.3, 1), marker = 8, replace = TRUE )
plotstyle.add("OAS", "Other Asia", rgb(0.6, 0.99, 0.0, 1), marker = 11, replace = TRUE )
plotstyle.add("SSA", "Sub-Sahra Africa", rgb(0.5, 0.0, 0.99, 1), marker = 15, replace = TRUE )
plotstyle.add("MEA", "Middle East & N. Africa", rgb(0.7, 0.2, 0.99, 1), marker = 17, replace = TRUE )
plotstyle.add("LAM", "Latin America", rgb(0.9, 0.6, 0.5, 1), marker = 12, replace = TRUE