Visibly is a handful of functions I use for color palettes, themes, etc. in R. Inside you will find:

- some ready-made palettes, e.g. based on R blue and Stan red
- a function to quickly and easily create palettes with using
`colortools::complementary`

`colortools::adjacent`

etc. - clean, web-friendly themes for ggplot2 and plotly
- a function to interact with colorgorical
- coefficient plots for fixed and random effects, plotting of GAM results.

Install the development version directly from GitHub:

# install.packages("devtools") devtools::install_github("m-clark/visibly")

Visibly is currently in its early stages, so more may be added soon. For some additional palettes for those fond of another time, you might be interested in NineteenEightyR.

Create a palette from a single starting point. This requires the colortools package to create equally spaced colors.

```
library(visibly)
create_palette('papayawhip')
$papayawhip
[1] "papayawhip"
$complementary
[1] "#FFEFD5" "#D5E5FF"
$analogous
[1] "#FFEFD5" "#FAFFD5" "#FFDAD5"
$split_complentary
[1] "#FFEFD5" "#D5FAFF" "#DAD5FF"
$triadic
[1] "#FFEFD5" "#D5FFEF" "#EFD5FF"
$square
[1] "#FFEFD5" "#D5FFDA" "#D5E5FF" "#FFD5FA"
$tetradic
[1] "#FFEFD5" "#E5FFD5" "#D5E5FF" "#EFD5FF"
```

Plot it to get a feel for things.

create_palette('#ff5500', plot = T)

```
$`#ff5500`
[1] "#ff5500"
$complementary
[1] "#FF5500" "#00AAFF"
$analogous
[1] "#FF5500" "#FFD500" "#FF002B"
$split_complentary
[1] "#FF5500" "#00FFD4" "#002BFF"
$triadic
[1] "#FF5500" "#00FF55" "#5500FF"
$square
[1] "#FF5500" "#2AFF00" "#00AAFF" "#D500FF"
$tetradic
[1] "#FF5500" "#AAFF00" "#00AAFF" "#5500FF"
```

One of the built-in palettes is based on R’s blue. Others are based on Stan’s red, plotly’s base colors, and the red-blue palette from RColorBrewer.

A clean theme for plotly.

library(plotly) mtcars %>% plot_ly(x=~wt, y=~mpg, color=~cyl) %>% add_markers(marker=list(size=15)) %>% theme_plotly()

Visualize a correlation matrix via factor analysis.

Plot some model coefficients. Requires the scico package.

fit_lm = lm(mpg ~ ., mtcars) plot_coefficients(fit_lm)

Plot GAM results

```
library(mgcv)
d = gamSim()
Gu & Wahba 4 term additive model
gam_model = gam(y ~ x0 + s(x1) + s(x2, bs='gp') + s(x3, bs='ps'), data=d)
plot_gam(gam_model, main_var = x2)
```

plot_gam_check(gam_model)

See the intro for more.