diff --git a/README.Rmd b/README.Rmd
index 088a9744..1f0582a3 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -12,30 +12,6 @@ knitr::opts_chunk$set(
fig.align = "left",
out.width = "100%"
)
-
-#' Pull the title of a documented object
-#' This function helps keep README and help files in sync.
-#' @param name String. The name of a documented object.
-#' @return String.
-#' @examples
-#' pull_title("plot_trajectory")
-pull_title <- function(name, db = enframe_documentation("r2dii.plot")) {
- is_name <- db$name == name
- out <- db$title[is_name]
- tolower(out)
-}
-
-enframe_documentation <- function(package) {
- db <- utils::hsearch_db(package, lib.loc = locate_package(package))[["Base"]]
- names(db) <- tolower(names(db))
- db
-}
-
-locate_package <- function(pkg) {
- locate <- function(path) any(grepl(pkg, list.files(path)))
- has_pkg <- unlist(lapply(.libPaths(), locate))
- .libPaths()[has_pkg][[1]]
-}
```
# r2dii.plot
@@ -48,197 +24,9 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
[![CRAN status](https://www.r-pkg.org/badges/version/r2dii.plot)](https://CRAN.R-project.org/package=r2dii.plot)
-The goal of r2dii.plot is to provide users with plotting and data
-processing functions that will allow the users to create standard 2DII plots
-using `PACTA_analysis` or banks' output data as input. The plots are in the form
-of ggplot objects.
-
-## Installation
-
-You can install the development version of r2dii.plot from
-[GitHub](https://github.com/2DegreesInvesting/r2dii.plot) with:
-
-```r
-# install.packages("devtools")
-devtools::install_github("2DegreesInvesting/r2dii.plot")
-```
-
-[How to minimize installation errors?](https://gist.github.com/maurolepore/a0187be9d40aee95a43f20a85f4caed6#installation)
-
-## Example
-
-```{r}
-library(dplyr, warn.conflicts = FALSE)
-library(ggplot2, warn.conflicts = FALSE)
-library(r2dii.plot)
-```
-
-* `market_share` `r pull_title("market_share")`.
-
-```{r}
-market_share
-```
-
-* `prep_trajectoryY()`: `r pull_title("prep_trajectory")`.
-
-```{r}
-data_trajectory <- prep_trajectoryY(
- market_share,
- sector_filter = "power",
- technology_filter = "oilcap",
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- end_year_filter = 2025,
- normalize = TRUE
-)
+```{r echo=FALSE}
+intro <- system.file("intro.Rmd", package = "r2dii.plot")
```
-* `plot_trajectoryY()` is an alternative to `plot_trajectoryB()`.
-
-```{r}
-# `plot_trajectoryY()` takes more arguments
-
-scenario_specs <- tibble(
- scenario = c("sds", "sps", "cps"),
- label = c("SDS", "STEPS", "CPS")
-)
-
-main_line_metric <- tibble(
- metric = "projected",
- label = "Portfolio"
-)
-
-additional_line_metrics <- tibble(
- metric = "corporate_economy",
- label = "Corporate Economy"
-)
-
-plot_trajectoryY(
- data_trajectory,
- scenario_specs_good_to_bad = scenario_specs,
- main_line_metric = main_line_metric,
- additional_line_metrics = additional_line_metrics
-)
-
-# more elaborate annotations, title and labels
-
-data_trajectory <- prep_trajectoryY(
- market_share,
- sector_filter = "power",
- technology_filter = "renewablescap",
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- value = "production",
- end_year_filter = 2025,
- normalize = TRUE
-)
-
-scenario_specs <- tibble(
- scenario = c("sds", "sps", "cps"),
- label = c(
- "Sustainable Development Scenario",
- "Stated Policies Scenario",
- "Current Policies Scenario")
-)
-
-plot <- plot_trajectoryY(data_trajectory,
- scenario_specs_good_to_bad = scenario_specs,
- main_line_metric = main_line_metric,
- additional_line_metrics = additional_line_metrics
-)
-
-plot +
- ggplot2::theme(
- plot.margin = ggplot2::unit(c(0.5, 7, 0.5, 0.5), "cm")
- ) +
- ggplot2::labs(
- title = "Production trajectory of Renewables Capacity technology\n in the Power sector",
- subtitle = "The coloured areas indicate trajectories in reference to a scenario.\n The red area indicates trajectories below any sustainble scenario.",
- x = "Year",
- y = "Production rate (normalized to 2020)"
- )
-```
-
-* `prep_techmixY()` `r pull_title("prep_techmix")`.
-* `plot_techmixY()` `r pull_title("plot_techmix")`.
-
-```{r}
-# Default colours, all data, added title
-sector <- "power"
-
-data <- prep_techmixY(
- market_share,
- sector_filter = sector,
- years_filter = c(2020, 2025),
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- scenario_filter = "sds",
- value = "technology_share"
-)
-
-plot <- plot_techmixY(data)
-plot +
- ggplot2::labs(title = "Technology mix for the Power sector")
-
-# Custom colours, all data, no title
-power_colors_custom <- tibble(
- technology = c("coalcap", "oilcap", "gascap", "nuclearcap", "hydrocap", "renewablescap"),
- label = c("Coal Capacity", "Oil Capacity", "Gas Capacity", "Nuclear Capacity", "Hydro Capacity", "Renewables Capacity"),
- hex = palette.colors(n = length(technology), palette = "ggplot2")
-)
-
-plot <- plot_techmixY(data,
- tech_colours = power_colors_custom
-)
-plot
-
-# Default colours, selected data and labels (metric_type parameters), added title
-
-sector <- "automotive"
-
-data <- prep_techmixY(market_share,
- sector_filter = sector,
- years_filter = c(2020, 2025), region_filter = "global",
- scenario_source_filter = "demo_2020",
- scenario_filter = "sds", value = "technology_share"
-)
-
-metric_type_order = c(
- "portfolio_2020", "benchmark_2020", "portfolio_2025",
- "benchmark_2025", "scenario_2025"
-)
-metric_type_labels = c(
- "Portfolio 2020", "Benchmark 2020", "Portfolio 2025",
- "Benchmark 2025", "Target SDS 2025"
- )
-
-plot <- plot_techmixY(data,
- metric_type_order = metric_type_order,
- metric_type_labels = metric_type_labels
-)
-plot +
- ggplot2::labs(title = "Technology mix for the Automotive sector")
-```
-
-* `prep_timelineY()` `r pull_title("prep_timeline")`.
-* `plot_timelineY()` `r pull_title("plot_timelineA")`.
-
-```{r}
-data <- prep_timelineY(sda, sector_filter = "cement", extrapolate = TRUE)
-
-# Plot and customize with ggplot2
-plot_timelineY(data) +
- labs(
- title = "Emission intensity trend for Cement.",
- x = "Year",
- y = "Tons of CO2 per ton",
- caption = "Dashed line is an extrapolation of the last value in the dataset."
- )
-```
-
-* `timeline_specs()` `r pull_title("timeline_specs")`.
-
-```{r}
-# You may use it as a template to create your custom specs
-timeline_specs(data)
+```{r child=intro}
```
diff --git a/README.md b/README.md
index ed289390..c56a3da1 100644
--- a/README.md
+++ b/README.md
@@ -14,10 +14,8 @@ coverage](https://codecov.io/gh/2DegreesInvesting/r2dii.plot/branch/master/graph
status](https://www.r-pkg.org/badges/version/r2dii.plot)](https://CRAN.R-project.org/package=r2dii.plot)
-The goal of r2dii.plot is to provide users with plotting and data
-processing functions that will allow the users to create standard 2DII
-plots using `PACTA_analysis` or banks’ output data as input. The plots
-are in the form of ggplot objects.
+The goal of r2dii.plot is to help you plot 2DII data in an informative,
+beautiful, and easy way.
## Installation
@@ -29,21 +27,48 @@ You can install the development version of r2dii.plot from
devtools::install_github("2DegreesInvesting/r2dii.plot")
```
-[How to minimize installation
-errors?](https://gist.github.com/maurolepore/a0187be9d40aee95a43f20a85f4caed6#installation)
-
## Example
+The r2dii.plot package is designed to work smoothly with other “r2dii”
+packages –
+[r2dii.data](https://2degreesinvesting.github.io/r2dii.data/),
+[r2dii.match](https://2degreesinvesting.github.io/r2dii.match/), and
+[r2dii.analysis](https://2degreesinvesting.github.io/r2dii.analysis/).
+It also plays well with the popular packages
+[dplyr](https://www.tidyverse.org/) and
+[ggplot2](https://ggplot2.tidyverse.org/), which help you customize your
+plots.
+
``` r
library(dplyr, warn.conflicts = FALSE)
library(ggplot2, warn.conflicts = FALSE)
library(r2dii.plot)
```
-- `market_share` dataset imitating the output of
- ‘r2dii.analysis::target\_market\_share()’.
+Your data typically comes from the output of two functions in the
+r2dii.analysis package:
+[`target_sda()`](https://2degreesinvesting.github.io/r2dii.analysis/reference/target_sda.html)
+and
+[`target_market_share()`](https://2degreesinvesting.github.io/r2dii.analysis/reference/target_market_share.html).
+Here you’ll use two example datasets that come with r2dii.plot.
``` r
+sda
+#> # A tibble: 208 x 4
+#> sector year emission_factor_metric emission_factor_value
+#>
+#> 1 automotive 2002 projected 0.228
+#> 2 automotive 2003 projected 0.226
+#> 3 automotive 2004 projected 0.224
+#> 4 automotive 2005 projected 0.222
+#> 5 automotive 2006 projected 0.220
+#> 6 automotive 2007 projected 0.218
+#> 7 automotive 2008 projected 0.216
+#> 8 automotive 2009 projected 0.214
+#> 9 automotive 2010 projected 0.212
+#> 10 automotive 2011 projected 0.210
+#> # … with 198 more rows
+
market_share
#> # A tibble: 1,170 x 8
#> sector technology year region scenario_source metric production
@@ -61,192 +86,114 @@ market_share
#> # … with 1,160 more rows, and 1 more variable: technology_share
```
-- `prep_trajectoryY()`: .
+r2dii.plot currently supports three kinds of plots: `plot_timeline*()`,
+`plot_techmix*()`, and `plot_trajectory*()`. Each plot has specific
+requirements about the main input – passed to the first argument `data`.
+To meet those requirements we currently provide two experimental sets of
+functions ([API](https://en.wikipedia.org/wiki/API)s) – “X” and “Y”.
+Both APIs can help you get the same basic plots, which you can further
+customization with ggplot2. Their difference difference is not in what
+you can do but in how you can do it:
+
+- With the “X” API you meet the `data` requirements mainly with
+ `dplyr::filter()`, and with “internal magic” based on the known
+ structure of r2dii data. This API should be best for users who
+ already use dplyr or want to learn it. It allows for slightly less
+ customization than “Y” but with the advantage of a much simpler
+ interface.
+
+- With the “Y” API you could meet the `data` requirements with dplyr
+ but you can also use dedicated “preparation” functions
+ (`prep_*Y()`). There are explicit arguments to both the preparation
+ and plotting functions which allow for a customization inside the
+ plotting function of what appears in the plot and how (colours,
+ labels). This API should be best for users who do not use dplyr or
+ care about it.
+
+Users and developers may have different preferences. The tables below
+compare the X and Y APIs across a number of criteria relevant to them.
+
+| Criteria | Thin API “X” | Thin API, “Y” |
+|:----------------------------------------|:---------------------------------|:----------------------------------------------|
+| Required knowledge of dplyr and ggplot2 | More | Less |
+| Customization possible | Limitless with dplyr and ggplot2 | Limitless with dplyr, ggplot2, and r2dii.plot |
+| Integration with other R workflows | More | Less |
+
+The X and Y APIs compared from a user’s perspective.
+
+| Criteria | Thin API “X” | Thin API, “Y” |
+|:-------------------|:-------------|:--------------|
+| Maintenance burden | Less | More |
+| Easy to extend | More | Less |
+
+The X and Y APIs compared from a developer’s perspective.
+
+To make the comparison concrete consider this small example of a
+trajectory plot (the other plot types you can find in the detailed [“X”
+API](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-X.html)
+and [“Y”
+API](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-Y.html))
+articles. Notice the resulting plot is almost the same (except for the
+labels) but the toolkit is different.
+
+- “X” API
``` r
-data_trajectory <- prep_trajectoryY(
- market_share,
- sector_filter = "power",
- technology_filter = "oilcap",
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- end_year_filter = 2025,
- normalize = TRUE
+data <- market_share
+
+prep <- filter(
+ data,
+ sector == "power",
+ technology == "renewablescap",
+ region == "global",
+ scenario_source == "demo_2020",
+ year <= 2025
)
+
+plot_trajectoryX(prep) +
+ labs (title = "Trajectory plot with the thin 'X' API")
```
-- `plot_trajectoryY()` is an alternative to `plot_trajectoryB()`.
+
+
+- “Y” API
``` r
-# `plot_trajectoryY()` takes more arguments
+data <- market_share
-scenario_specs <- tibble(
+prep <- prep_trajectoryY(
+ data,
+ sector_filter = "power",
+ technology_filter = "renewablescap",
+ region_filter = "global",
+ scenario_source_filter = "demo_2020",
+ value = "production"
+)
+
+scenario_specs <- dplyr::tibble(
scenario = c("sds", "sps", "cps"),
label = c("SDS", "STEPS", "CPS")
)
-main_line_metric <- tibble(
- metric = "projected",
- label = "Portfolio"
-)
+main_line_metric <- dplyr::tibble(metric = "projected", label = "Portfolio")
-additional_line_metrics <- tibble(
+additional_line_metrics <- dplyr::tibble(
metric = "corporate_economy",
label = "Corporate Economy"
)
plot_trajectoryY(
- data_trajectory,
- scenario_specs_good_to_bad = scenario_specs,
- main_line_metric = main_line_metric,
- additional_line_metrics = additional_line_metrics
-)
-```
-
-
-
-``` r
-# more elaborate annotations, title and labels
-
-data_trajectory <- prep_trajectoryY(
- market_share,
- sector_filter = "power",
- technology_filter = "renewablescap",
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- value = "production",
- end_year_filter = 2025,
- normalize = TRUE
-)
-
-scenario_specs <- tibble(
- scenario = c("sds", "sps", "cps"),
- label = c(
- "Sustainable Development Scenario",
- "Stated Policies Scenario",
- "Current Policies Scenario")
-)
-
-plot <- plot_trajectoryY(data_trajectory,
+ prep,
scenario_specs_good_to_bad = scenario_specs,
main_line_metric = main_line_metric,
additional_line_metrics = additional_line_metrics
-)
-
-plot +
- ggplot2::theme(
- plot.margin = ggplot2::unit(c(0.5, 7, 0.5, 0.5), "cm")
- ) +
- ggplot2::labs(
- title = "Production trajectory of Renewables Capacity technology\n in the Power sector",
- subtitle = "The coloured areas indicate trajectories in reference to a scenario.\n The red area indicates trajectories below any sustainble scenario.",
- x = "Year",
- y = "Production rate (normalized to 2020)"
- )
-```
-
-
-
-- `prep_techmixY()` .
-- `plot_techmixY()` .
-
-``` r
-# Default colours, all data, added title
-sector <- "power"
-
-data <- prep_techmixY(
- market_share,
- sector_filter = sector,
- years_filter = c(2020, 2025),
- region_filter = "global",
- scenario_source_filter = "demo_2020",
- scenario_filter = "sds",
- value = "technology_share"
-)
-
-plot <- plot_techmixY(data)
-plot +
- ggplot2::labs(title = "Technology mix for the Power sector")
-```
-
-
-
-``` r
-# Custom colours, all data, no title
-power_colors_custom <- tibble(
- technology = c("coalcap", "oilcap", "gascap", "nuclearcap", "hydrocap", "renewablescap"),
- label = c("Coal Capacity", "Oil Capacity", "Gas Capacity", "Nuclear Capacity", "Hydro Capacity", "Renewables Capacity"),
- hex = palette.colors(n = length(technology), palette = "ggplot2")
-)
-
-plot <- plot_techmixY(data,
- tech_colours = power_colors_custom
-)
-plot
-```
-
-
-
-``` r
-# Default colours, selected data and labels (metric_type parameters), added title
-
-sector <- "automotive"
-
-data <- prep_techmixY(market_share,
- sector_filter = sector,
- years_filter = c(2020, 2025), region_filter = "global",
- scenario_source_filter = "demo_2020",
- scenario_filter = "sds", value = "technology_share"
-)
-
-metric_type_order = c(
- "portfolio_2020", "benchmark_2020", "portfolio_2025",
- "benchmark_2025", "scenario_2025"
-)
-metric_type_labels = c(
- "Portfolio 2020", "Benchmark 2020", "Portfolio 2025",
- "Benchmark 2025", "Target SDS 2025"
- )
-
-plot <- plot_techmixY(data,
- metric_type_order = metric_type_order,
- metric_type_labels = metric_type_labels
-)
-plot +
- ggplot2::labs(title = "Technology mix for the Automotive sector")
-```
-
-
-
-- `prep_timelineY()` .
-- `plot_timelineY()` .
-
-``` r
-data <- prep_timelineY(sda, sector_filter = "cement", extrapolate = TRUE)
-
-# Plot and customize with ggplot2
-plot_timelineY(data) +
- labs(
- title = "Emission intensity trend for Cement.",
- x = "Year",
- y = "Tons of CO2 per ton",
- caption = "Dashed line is an extrapolation of the last value in the dataset."
- )
+) +
+ labs (title = "Trajectory plot with the thick 'Y' API")
```
-- `timeline_specs()` creates the default specs data frame for
- ‘plot\_timeliney()’.
-
-``` r
-# You may use it as a template to create your custom specs
-timeline_specs(data)
-#> # A tibble: 4 x 3
-#> line_name label hex
-#>
-#> 1 projected Projected #1b324f
-#> 2 corporate_economy Corporate Economy #00c082
-#> 3 target_demo Target Demo #ff9623
-#> 4 adjusted_scenario_demo Adjusted Scenario Demo #d0d7e1
-```
+For full examples see the dedicated articles [r2dii.plot
+X](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-X.html)
+and [r2dii.plot
+Y](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-Y.html).
diff --git a/inst/intro.Rmd b/inst/intro.Rmd
new file mode 100644
index 00000000..567ec5f1
--- /dev/null
+++ b/inst/intro.Rmd
@@ -0,0 +1,150 @@
+The goal of r2dii.plot is to help you plot 2DII data in an informative,
+beautiful, and easy way.
+
+## Installation
+
+You can install the development version of r2dii.plot from
+[GitHub](https://github.com/2DegreesInvesting/r2dii.plot) with:
+
+```r
+# install.packages("devtools")
+devtools::install_github("2DegreesInvesting/r2dii.plot")
+```
+
+## Example
+
+The r2dii.plot package is designed to work smoothly with other "r2dii" packages
+-- [r2dii.data](https://2degreesinvesting.github.io/r2dii.data/),
+[r2dii.match](https://2degreesinvesting.github.io/r2dii.match/), and
+[r2dii.analysis](https://2degreesinvesting.github.io/r2dii.analysis/). It also
+plays well with the popular packages [dplyr](https://www.tidyverse.org/) and
+[ggplot2](https://ggplot2.tidyverse.org/), which help you customize your plots.
+
+```{r setup}
+library(dplyr, warn.conflicts = FALSE)
+library(ggplot2, warn.conflicts = FALSE)
+library(r2dii.plot)
+```
+
+Your data typically comes from the output of two functions in the r2dii.analysis
+package:
+[`target_sda()`](https://2degreesinvesting.github.io/r2dii.analysis/reference/target_sda.html)
+and
+[`target_market_share()`](https://2degreesinvesting.github.io/r2dii.analysis/reference/target_market_share.html).
+Here you'll use two example datasets that come with r2dii.plot.
+
+```{r}
+sda
+
+market_share
+```
+
+r2dii.plot currently supports three kinds of plots: `plot_timeline*()`,
+`plot_techmix*()`, and `plot_trajectory*()`. Each plot has specific requirements
+about the main input -- passed to the first argument `data`. To meet those
+requirements we currently provide two experimental sets of functions
+([API](https://en.wikipedia.org/wiki/API)s) -- "X" and "Y". Both APIs can help
+you get the same basic plots, which you can further customization with ggplot2.
+Their difference difference is not in what you can do but in how you can do it:
+
+* With the "X" API you meet the `data` requirements mainly with
+`dplyr::filter()`, and with "internal magic" based on the known structure of
+r2dii data. This API should be best for users who already use dplyr or want to
+learn it. It allows for slightly less customization than "Y" but with the
+advantage of a much simpler interface.
+
+* With the "Y" API you could meet the `data` requirements with dplyr but you can
+also use dedicated "preparation" functions (`prep_*Y()`). There are explicit
+arguments to both the preparation and plotting functions which allow for a
+customization inside the plotting function of what appears in the plot and how
+(colours, labels). This API should be best for users who do not use dplyr or
+care about it.
+
+Users and developers may have different preferences. The tables below compare
+the X and Y APIs across a number of criteria relevant to them.
+
+```{r echo=FALSE}
+users <- tibble::tribble(
+ ~Criteria, ~`Thin API "X"`, ~`Thin API, "Y"`,
+ "Required knowledge of dplyr and ggplot2", "More", "Less",
+ "Customization possible", "Limitless with dplyr and ggplot2", "Limitless with dplyr, ggplot2, and r2dii.plot",
+ "Integration with other R workflows", "More", "Less",
+)
+caption <- "The X and Y APIs compared from a user's perspective."
+knitr::kable(users, caption = caption)
+
+devs <- tibble::tribble(
+ ~Criteria, ~`Thin API "X"`, ~`Thin API, "Y"`,
+ "Maintenance burden", "Less", "More",
+ "Easy to extend", "More", "Less",
+
+)
+caption <- "The X and Y APIs compared from a developer's perspective."
+knitr::kable(devs, caption = caption)
+```
+
+To make the comparison concrete consider this small example of a trajectory plot
+(the other plot types you can find in the detailed ["X"
+API](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-X.html)
+and ["Y"
+API](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-Y.html))
+articles. Notice the resulting plot is almost the same (except for the labels)
+but the toolkit is different.
+
+* "X" API
+
+```{r}
+data <- market_share
+
+prep <- filter(
+ data,
+ sector == "power",
+ technology == "renewablescap",
+ region == "global",
+ scenario_source == "demo_2020",
+ year <= 2025
+)
+
+plot_trajectoryX(prep) +
+ labs (title = "Trajectory plot with the thin 'X' API")
+```
+
+* "Y" API
+
+```{r}
+data <- market_share
+
+prep <- prep_trajectoryY(
+ data,
+ sector_filter = "power",
+ technology_filter = "renewablescap",
+ region_filter = "global",
+ scenario_source_filter = "demo_2020",
+ value = "production"
+)
+
+scenario_specs <- dplyr::tibble(
+ scenario = c("sds", "sps", "cps"),
+ label = c("SDS", "STEPS", "CPS")
+)
+
+main_line_metric <- dplyr::tibble(metric = "projected", label = "Portfolio")
+
+additional_line_metrics <- dplyr::tibble(
+ metric = "corporate_economy",
+ label = "Corporate Economy"
+)
+
+plot_trajectoryY(
+ prep,
+ scenario_specs_good_to_bad = scenario_specs,
+ main_line_metric = main_line_metric,
+ additional_line_metrics = additional_line_metrics
+) +
+ labs (title = "Trajectory plot with the thick 'Y' API")
+```
+
+For full examples see the dedicated articles [r2dii.plot
+X](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-X.html)
+and [r2dii.plot
+Y](https://2degreesinvesting.github.io/r2dii.plot/articles/articles/r2dii-plot-Y.html).
diff --git a/man/figures/README-unnamed-chunk-6-1.png b/man/figures/README-unnamed-chunk-6-1.png
index dbb95e92..8ecb0221 100644
Binary files a/man/figures/README-unnamed-chunk-6-1.png and b/man/figures/README-unnamed-chunk-6-1.png differ
diff --git a/man/figures/README-unnamed-chunk-7-1.png b/man/figures/README-unnamed-chunk-7-1.png
index e3dbb994..4b04d680 100644
Binary files a/man/figures/README-unnamed-chunk-7-1.png and b/man/figures/README-unnamed-chunk-7-1.png differ