plot_trajectory should correctly label corporate_economy as "Corporate Economy" and projected as "Portfolio"

[jdhoffa]

IMPORTANT: The reprex above does not actually highlight the issue.
Due to a bug in r2dii.plot.static, the labels for "Corporate Economy" and "Projected" are swapped, causing terrible confusion.

The "Corporate Economy" in this case starts at 1 and ends at 1.55 in both plots, as expected.

Originally posted by @jdhoffa in https://github.com/2DegreesInvesting/r2dii.analysis/issues/269#issuecomment-848619268

[jdhoffa]

See reprex below:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(r2dii.plot.static)

out1_trajectory <- tibble::tribble(
  ~year, ~metric_type,             ~metric, ~value,     ~technology,
  2020L,  "portfolio",         "projected",      1, "renewablescap",
  2020L,   "scenario",              "b2ds",      1, "renewablescap",
  2020L,   "scenario",               "nps",      1, "renewablescap",
  2020L,   "scenario",               "sds",      1, "renewablescap",
  2025L,  "portfolio",         "projected",    1.1, "renewablescap",
  2025L,   "scenario",              "b2ds",    1.3, "renewablescap",
  2025L,   "scenario",               "nps",    1.1, "renewablescap",
  2025L,   "scenario",               "sds",    1.2, "renewablescap",
  2020L,  "benchmark", "corporate_economy",      1, "renewablescap",
  2025L,  "benchmark", "corporate_economy",   1.55, "renewablescap"
)

scenario_specs <- tibble(
  scenario = c("b2ds", "sds", "nps", "worse"),
  color = c("#9CAB7C", "#FFFFCC", "#FDE291", "#E07B73"),
  label = c("B2DS", "SDS", "NPS", "worse")
)

main_line_metric <- tibble(
  metric = "projected",
  label = "Portfolio"
)

additional_line_metrics <- tibble(
  metric = "corporate_economy",
  label = "Corporate Economy"
)

# notice here that the final value for "projected" is 1.1, and 
# "corporate_economy" is 1.55
out1_trajectory %>% 
  filter(year == 2025) %>% 
  filter(metric %in% c("projected", "corporate_economy"))
#> # A tibble: 2 x 5
#>    year metric_type metric            value technology   
#>   <int> <chr>       <chr>             <dbl> <chr>        
#> 1  2025 portfolio   projected          1.1  renewablescap
#> 2  2025 benchmark   corporate_economy  1.55 renewablescap


# however in this plot, we can see that the portfolio seems to end at 1.55, and 
# corporate_economy at 1.1
plot_trajectory(
  out1_trajectory,
  scenario_specs_good_to_bad = scenario_specs,
  main_line_metric = main_line_metric,
  additional_line_metrics = additional_line_metrics
)

^{Created on 2021-05-26 by the reprex package (v2.0.0)}

[MonikaFu]

@jdhoffa - with the new version of the plot_trajectory function (with annotations) this issue should be solved. Can you confirm?

[jdhoffa]

It seems like it is solved!!
For your reference, the following reprex was generated using the latest version of the package:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(r2dii.plot)

out1_trajectory <- tibble::tribble(
  ~year, ~metric_type,             ~metric, ~value,     ~technology,
  2020L,  "portfolio",         "projected",      1, "renewablescap",
  2020L,   "scenario",              "b2ds",      1, "renewablescap",
  2020L,   "scenario",               "nps",      1, "renewablescap",
  2020L,   "scenario",               "sds",      1, "renewablescap",
  2025L,  "portfolio",         "projected",    1.1, "renewablescap",
  2025L,   "scenario",              "b2ds",    1.3, "renewablescap",
  2025L,   "scenario",               "nps",    1.1, "renewablescap",
  2025L,   "scenario",               "sds",    1.2, "renewablescap",
  2020L,  "benchmark", "corporate_economy",      1, "renewablescap",
  2025L,  "benchmark", "corporate_economy",   1.55, "renewablescap"
)

scenario_specs <- tibble(
  scenario = c("b2ds", "sds", "nps"),
  label = c("B2DS", "SDS", "NPS")
)

main_line_metric <- tibble(
  metric = "projected",
  label = "Portfolio"
)

additional_line_metrics <- tibble(
  metric = "corporate_economy",
  label = "Corporate Economy"
)

# notice here that the final value for "projected" is 1.1, and 
# "corporate_economy" is 1.55
out1_trajectory %>% 
  filter(year == 2025) %>% 
  filter(metric %in% c("projected", "corporate_economy"))
#> # A tibble: 2 x 5
#>    year metric_type metric            value technology   
#>   <int> <chr>       <chr>             <dbl> <chr>        
#> 1  2025 portfolio   projected          1.1  renewablescap
#> 2  2025 benchmark   corporate_economy  1.55 renewablescap

# however in this plot, we can see that the portfolio seems to end at 1.55, and 
# corporate_economy at 1.1
plot_trajectory(
  out1_trajectory,
  scenario_specs_good_to_bad = scenario_specs,
  main_line_metric = main_line_metric,
  additional_line_metrics = additional_line_metrics
)

^{Created on 2021-06-02 by the reprex package (v2.0.0)}

[MonikaFu]

@jdhoffa great! Although I see another problem of annotations overlap here since portfolio overlaps with scenario:/ I'll put it in a new issue.