‘ggasym’ (pronounced “gg-awesome”) plots a symmetric matrix with three different fill aesthetics for the top-left and bottom-right triangles and along the diagonal. It operates within the Grammar of Graphics paradigm implemented in ‘ggplot2’.

Checkout the documentation and vignettes at the pkgdown website https://jhrcook.github.io/ggasym/

For information on using ‘ggplot2’, start here.

‘ggasym’ is available on CRAN. Use the following command to install.

```
install.packages("ggasym")
```

You can download and install the latest development version from the GitHub repo.

```
devtools::install_github("jhrcook/ggasym")
```

And load the package with the standard `library`

function.

```
library(ggasym)
```

Here is a basic example. `tib`

is a “tibble” (i.e.. fancy “data.frame”)
of comparisons between groups “A” through “E”. There are two values to
be plotted, `val_1`

and `val_2`

, that hold data on the comparison
between `g1`

and `g2`

. `tib`

is first passed to `asymmetrise()`

to fill
in all the missing combinations between `g1`

and `g2`

such that the
symmetric matrix can be built. All added values take the value `NA`

. The
modified data table is finally passed to `ggplot()`

and `geom_asymmat()`

is added on. Here, `asymmetrise()`

added the rows where `g1`

and `g2`

are equal, thus will fill the diagonal. I set these values to `val_3`

.

```
tib <- tibble(g1 = c("A", "A", "A", "A", "B", "B", "B", "C", "C", "D"),
g2 = c("B", "C", "D", "E", "C", "D", "E", "D", "E", "E"),
val_1 = seq(1, 10, 1),
val_2 = rnorm(10, mean = 0, sd = 3))
tib <- asymmetrise(tib, g1, g2)
tib$val_3 <- runif(nrow(tib))
ggplot(tib, aes(x = g1, y = g2)) +
geom_asymmat(aes(fill_tl = val_1, fill_br = val_2, fill_diag = val_3)) +
scale_fill_tl_gradient(low = "lightpink", high = "tomato") +
scale_fill_br_gradient(low = "lightblue1", high = "dodgerblue") +
scale_fill_diag_gradient(low = "yellow", high = "orange3")
```

The new aesthetics `fill_tl`

, `fill_br`

, and `fill_diag`

behave just
like the normal `fill`

, except that they correspond to the top-left
(“tl”) and bottom-right (“br”) triangles of the matrix,
respectively. This package also includes analogous functions for scaling
the fill colors such as `scale_fill_tl_gradient2()`

and
`scale_fill_br_gradientn()`

that operate just as expected when using
‘ggplot2’.

```
ggplot(tib, aes(x = g1, y = g2)) +
geom_asymmat(aes(fill_tl = val_1, fill_br = val_2, fill_diag = val_3)) +
scale_fill_tl_gradient(low = "lightpink", high = "tomato") +
scale_fill_br_gradient2(low = "orange", mid = "white", high = "dodgerblue") +
scale_fill_diag_gradientn(colors = rainbow(25))
```

Of note, with three colorbars, it may be useful to control their
position and other properties. This can be done just like normal in
‘ggplot2’ by passing the correct values to the `guide`

parameter in
`scale_fill_*_gradient()`

(original
documentation).
Below are a few of the options where I put the bars horizontal, adjust
the ordering, and put the title above each.

```
ggplot(tib, aes(x = g1, y = g2)) +
geom_asymmat(aes(fill_tl = val_1, fill_br = val_2, fill_diag = val_3)) +
scale_fill_tl_gradient(low = "lightpink", high = "tomato",
guide = guide_colourbar(direction = "horizontal",
order = 1,
title.position = "top")) +
scale_fill_br_gradient2(low = "orange", mid = "white", high = "dodgerblue",
guide = guide_colourbar(direction = "horizontal",
order = 3,
title.position = "top")) +
scale_fill_diag_gradientn(colors = rainbow(25),
guide = guide_colourbar(direction = "horizontal",
order = 2,
title.position = "top"))
```

Since the new geom is a normal ‘ggplot2’ object, it can be introduced
into a standard ‘ggplot2’ workflow. Note that the labels can be adjusted
like normal using the `labs`

function and using the `fill_tl`

,
`fill_br`

, and `fill_diag`

arguments.

```
ggplot(tib, aes(x = g1, y = g2)) +
geom_asymmat(aes(fill_tl = log(val_1),
fill_br = val_2,
fill_diag = val_3)) +
scale_fill_tl_gradient(low = "lightpink", high = "tomato",
guide = guide_colourbar(direction = "horizontal",
order = 1,
title.position = "top")) +
scale_fill_br_gradient(low = "lightblue1", high = "dodgerblue",
guide = guide_colourbar(direction = "horizontal",
order = 3,
title.position = "top")) +
scale_fill_diag_gradient(low = "grey80", high = "grey20",
guide = guide_colourbar(direction = "horizontal",
order = 2,
title.position = "top")) +
labs(fill_tl = "top-left fill",
fill_br = "bottom-right fill",
fill_diag = "diagonal fill",
title = "Example of ggasym") +
theme_bw() +
theme(axis.title = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_rect(fill = "grey70"),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0))
```

If you have multiple categories, faceting works as expected. The only
difference is in the preparation of the data table: you must
`group_by()`

the value(s) you will facet by before passing to
`asymmetrise()`

. This is shown below.

```
tib <- tibble(g1 = rep(c("A", "A", "B"), 2),
g2 = rep(c("B", "C", "C"), 2),
val_1 = seq(1, 6),
val_2 = rnorm(6),
grps = c(1, 1, 1, 2, 2, 2))
tib
#> # A tibble: 6 x 5
#> g1 g2 val_1 val_2 grps
#> <chr> <chr> <int> <dbl> <dbl>
#> 1 A B 1 0.0746 1
#> 2 A C 2 -1.99 1
#> 3 B C 3 0.620 1
#> 4 A B 4 -0.0561 2
#> 5 A C 5 -0.156 2
#> 6 B C 6 -1.47 2
```

Grouping first by `grps`

, the tibble is asymmetrized while retaining the
`grps`

assignments. I then added values to the diagonal.

```
tib <- tib %>% group_by(grps) %>% asymmetrise(g1, g2) %>% ungroup()
tib <- tib %>% mutate(val_3 = ifelse(g1 == g2, runif(nrow(tib)), NA))
tib
#> # A tibble: 18 x 6
#> grps g1 g2 val_1 val_2 val_3
#> <dbl> <chr> <chr> <int> <dbl> <dbl>
#> 1 1 A B 1 0.0746 NA
#> 2 1 A C 2 -1.99 NA
#> 3 1 B C 3 0.620 NA
#> 4 1 B A 1 0.0746 NA
#> 5 1 C A 2 -1.99 NA
#> 6 1 C B 3 0.620 NA
#> 7 1 A A NA NA 0.459
#> 8 1 B B NA NA 0.332
#> 9 1 C C NA NA 0.651
#> 10 2 A B 4 -0.0561 NA
#> 11 2 A C 5 -0.156 NA
#> 12 2 B C 6 -1.47 NA
#> 13 2 B A 4 -0.0561 NA
#> 14 2 C A 5 -0.156 NA
#> 15 2 C B 6 -1.47 NA
#> 16 2 A A NA NA 0.839
#> 17 2 B B NA NA 0.347
#> 18 2 C C NA NA 0.334
```

```
ggplot(tib, aes(x = g1, y = g2)) +
geom_asymmat(aes(fill_tl = log(val_1),
fill_br = val_2,
fill_diag = val_3)) +
scale_fill_tl_gradient(low = "lightpink", high = "tomato",
guide = guide_colourbar(direction = "horizontal",
order = 1,
title.position = "top")) +
scale_fill_br_gradient(low = "lightblue1", high = "dodgerblue",
guide = guide_colourbar(direction = "horizontal",
order = 3,
title.position = "top")) +
scale_fill_diag_gradient(low = "grey80", high = "grey20",
guide = guide_colourbar(direction = "horizontal",
order = 2,
title.position = "top")) +
labs(fill_tl = "top-left fill",
fill_br = "bottom-right fill",
fill_diag = "diagonal fill",
title = "Example of faceting with ggasym") +
theme_bw() +
theme(axis.title = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.background = element_rect(fill = "grey70"),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
facet_grid(. ~ grps)
```

I created a wrapper for handling the results of a statistical test to
produce a tibble ready to be plotted with ggasym. Here is a brief
example - a more detailed example is shown in the vignette “Statistical
Test
Plotting”.
Here I test if the median sale price of houses in Austin, Texas is
different between any of the years (for more information on the data
source: `?ggplot2::txhousing`

).

```
tib <- ggplot2::txhousing %>%
filter(city == "Austin") %>%
mutate(year = as.character(year))
aov_res <- aov(median ~ year, data = tib)
broom::tidy(aov_res)
#> # A tibble: 2 x 6
#> term df sumsq meansq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 year 15 169514394144. 11300959610. 213. 4.29e-102
#> 2 Residuals 171 9077385000. 53084123. NA NA
```

Before plotting, the results of the Tukey post-hoc test are passed to
`asymmetrise_stats()`

that prepares the data for `geom_asymmat()`

. The
resulting tibble is then plotted and styled in ‘ggplot2’.

```
asymmat_tib <- asymmetrise_stats(TukeyHSD(aov_res))
ggplot(asymmat_tib, aes(x = x, y = y)) +
geom_asymmat(aes(fill_tl = estimate,
fill_br = -log10(adj.p.value + 0.0000001))) +
scale_fill_tl_gradient2(low = "dodgerblue", high = "tomato") +
scale_fill_br_distiller(type = "seq", palette = "Greens", direction = 1) +
labs(title = "Median House Prices in Austin",
fill_tl = "diff. in\nmean values",
fill_br = "-log10( adj. p-value )") +
theme(panel.background = element_rect(fill = "grey75"),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0))
```

I would like to thank the team behind ‘ggplot2’ for creating a flexible and powerful package for the R community.

If you see any mistakes (including small typos) *please* open an
issue and leave a quick
statement. Do not worry about appearing annoying.

jhrcook/ggasym documentation built on April 8, 2020, 10:13 a.m.

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