How to use [WIP]

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How to use [WIP]

The goal of the profiplots package is to unify graphics across analyses made by Profinit team. In this document we’re introducing basic ways to do so. Take a look on the plot gallery page, too.

Set/Unset

To ease the adaptation, there is a pair of functions to set up the most general option globaly. This affects both baseR and ggplot2 graphics.

profiplots::set_theme() – to set the profinit-look globaly. There are two params:
- pal_name – color palette to be used by default (ggplot: for continuous variables only).
- pal_name_discrete – color palette to be used for discrete mapping by default (ggplot only).
- You can list available color palettes via profiplots::profinit_pal.pals().
profiplots::unset_theme() – revert the settings.

ggplot2

Code

plot_gg <- ggplot(sample_df, aes(x=x, y=y, fill=x)) + 
  stat_identity(geom="bar") + 
  theme_profinit()
plot_gg + labs(title = "ggplot - Default")

Code


# turn on the settings
profiplots::set_theme("blue-red")  
plot_gg + labs(title = "ggplot - Profinit")

Code


# turn off the settings
profiplots::unset_theme()
plot_gg + labs(title = "ggplot - Default again")

base R

Code

barplot(sample_df$x, col=sample_df$category, main = "Base R - Default", border = NA)

Code


# turn on the settings
profiplots::set_theme("blue-red", "blue-red")  
barplot(sample_df$x, col=sample_df$category, main = "Base R - Profinit", border = NA)

Code


# turn off the settings
profiplots::unset_theme()
barplot(sample_df$x, col=sample_df$category, main = "Base R - Default again", border = NA)

Using color scales

We’re going to use barplot here. For examples of other plot types see the plot gallery page. For demonstration purposes, we’re going to tweak fill palette in ggplto2. Of course this can be applied to the color versions as well (scale_color_profinit_c, scale_color_profinit_d).

ggplot2

Monochromatic color scale

Use either reds, reds-dark, blues, blues-dark or greys:

Code

plot_gg + 
  scale_fill_profinit_c(palette = "reds") +                             # HERE WE CHANGE THE PALETTE
  labs(title = "Example - monochromatic fill", fill = "Variable")

Code


plot_gg + 
  scale_fill_profinit_c(palette = "blues-dark") +                       # HERE WE CHANGE THE PALETTE
  labs(title = "Example - monochromatic fill", fill = "Variable")

Gradient color scale

Use prespecified blue-red if possible. To create another gradient, you may use scale_color_gradientand profinit_cols.

Code

plot_gg + 
  scale_fill_profinit_c(palette = "blue-red") +                         # HERE WE CHANGE THE PALETTE
  labs(title = "Example - gradient fill", fill = "Variable")

Code


plot_gg + 
  scale_fill_gradient(low = profinit_cols("red"), high = profinit_cols("yellow")) +  # HERE WE CHANGE THE PALETTE
  labs(title = "Example - gradient fill custom (NOT RECOMMEADED)", fill = "Variable")

Diverging color scale

Use prespecified blue-white-red palette. To create a new one, you can use scale_color_gradient2 and profinit_cols:

Code

plot_gg + 
  scale_fill_profinit_c(palette = "blue-white-red", reverse = TRUE) +                         # HERE WE CHANGE THE PALETTE
  labs(title = "Example - diverging fill", fill = "Variable")

Code


plot_gg + 
  scale_fill_gradient2(low = profinit_cols("red"), mid = "white", high = profinit_cols("pink"), midpoint = 4)                         # HERE WE CHANGE THE PALETTE

Code

  labs(title = "Example - diverging fill (NOT RECOMMANDED)", fill = "Variable")
#> $fill
#> [1] "Variable"
#> 
#> $title
#> [1] "Example - diverging fill (NOT RECOMMANDED)"
#> 
#> attr(,"class")
#> [1] "labels"

Discrete color palette

There are two discrete palettes, discrete (6 colors) and discrete-full (all colors specified in the Visual identity guidelines document).
- Do not use it this way, read the Fundamentals of data vizualization chapter on common use of color pitfalls.
- To deal with too many levels – you can extrapolate (set exact to FALSE)

Code

plot_gg + 
  aes(fill = as.character(x)) + 
  scale_fill_profinit_d(palette = "discrete") +                                # HERE WE CHANGE THE PALETTE
  labs(title = "Example - discrete fill (exact)", fill = "Variable")

Code


plot_gg + 
  aes(fill = as.character(x)) + 
  scale_fill_profinit(palette = "discrete", exact = FALSE) +                   # HERE WE CHANGE THE PALETTE
  labs(title = "Example - discrete fill (interpolated)", fill = "Variable")

Code


plot_gg + 
  aes(fill = as.character(x)) + 
  scale_fill_profinit_d(palette = "discrete-full") +                   # HERE WE CHANGE THE PALETTE
  labs(title = "Example - discrete fill (full, exact)", fill = "Variable")

base R

Monochromatic color scale

Use either reds, reds-dark, blues, blues-dark or greys:

Code

barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  las = "1",                                  # rotate y-axis labels
  lwd.ticks = 1,
  bty = "7",
  col = profinit_pal("blue-red")(8), 
  main = "Base R - monochromatic fill"
)

Code


barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
   las = "1",                                  # rotate y-axis labels
  lwd.ticks = 1,
  bty = "]",
  col = profinit_pal("blues-dark")(8), 
  main = "Base R - monochromatic fill, another palette"
)

Gradient color scale

Use prespecified blue-red if possible. To create another gradient, you may use scale_color_gradientand profinit_cols.

Code

barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = profinit_pal("blue-red")(8),
  main = "Base R - gradient fill"
)

Code


# This way you can define your own palette based on Profinit colors
red_yellow_pal <- grDevices::colorRampPalette(c(profinit_cols("red"), profinit_cols("yellow")))
barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = red_yellow_pal(8), 
  main = "Example - custom gradient fill (NOT RECOMMEADED)"
)

Diverging color scale

Use prespecified blue-white-red palette. To create a new one, you can use scale_color_gradient2 and profinit_cols:

Code

barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = profinit_pal("blue-white-red")(8),
  main = "Base R - diverging fill"
)

Code



# Create your own diverging palette based on Profinit colors
red_white_pink_pal <- grDevices::colorRampPalette(c(profinit_cols("red"), "white", profinit_cols("pink")))
barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = red_white_pink_pal(8),
  main = "Base R - custom diverging fill (NOT RECOMMANDED)"
)

Discrete color palette

There are two discrete palettes, discrete (6 colors) and discrete-full (all colors specified in the Visual identity guidelines document).
- Do not use it this way, read the Fundamentals of data visualisation chapter on common use of color pitfals.
- To deal with too many levels – you can extrapolate (set exact to FALSE)

Code

barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = profinit_pal("discrete")(8),
  main = "Base R - discrete fill (exact)"
)

Code


barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = profinit_pal("discrete", exact = FALSE)(8),
  main = "Base R - discrete fill (interpolated, NOT RECOMMANDED)"
)

Code



barplot(
  height = sample_df$x, 
  names.arg = sample_df$category, 
  border = NA, 
  col = profinit_pal("discrete-full")(8),
  main = "Base R - discrete fill (full, exact)"
)

Session info

Code

sessionInfo()
#> R version 4.2.0 (2022-04-22 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19045)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=C                           
#> [2] LC_CTYPE=English_United Kingdom.utf8   
#> [3] LC_MONETARY=English_United Kingdom.utf8
#> [4] LC_NUMERIC=C                           
#> [5] LC_TIME=English_United Kingdom.utf8    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_3.4.4    profiplots_0.2.3
#> 
#> loaded via a namespace (and not attached):
#>  [1] highr_0.10        bslib_0.5.1       compiler_4.2.0    pillar_1.9.0     
#>  [5] jquerylib_0.1.4   tools_4.2.0       digest_0.6.29     jsonlite_1.8.0   
#>  [9] evaluate_0.23     lifecycle_1.0.3   tibble_3.2.1      gtable_0.3.4     
#> [13] pkgconfig_2.0.3   rlang_1.1.1       cli_3.4.1         rstudioapi_0.14  
#> [17] yaml_2.3.5        xfun_0.41         fastmap_1.1.0     withr_2.5.2      
#> [21] dplyr_1.1.2       knitr_1.45        generics_0.1.3    vctrs_0.6.3      
#> [25] sass_0.4.7        grid_4.2.0        tidyselect_1.2.0  glue_1.6.2       
#> [29] R6_2.5.1          fansi_1.0.3       rmarkdown_2.25    farver_2.1.1     
#> [33] magrittr_2.0.3    scales_1.2.1      htmltools_0.5.6.1 colorspace_2.0-3 
#> [37] labeling_0.4.3    utf8_1.2.2        munsell_0.5.0     cachem_1.0.6

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.