Sam Albers

Ilaria Prosdocimi

Sam Zipper

### Importance of Hydrology

Given that liquid water is [essential to life on
Earth](https://www.amnh.org/explore/science-topics/water-and-life-on-earth),
water research cuts across numerous disciplines including hydrology,
meteorology, geography, climate science, engineering, ecology, and more.
Numerous R packages have emerged from this diversity of approaches, and
we recently gathered many of them into a new rOpenSci [task
view](https://github.com/ropensci/Hydrology) which we broadly titled
'Hydrology' and published to
[CRAN](https://CRAN.R-project.org/view=Hydrology). Our intent is to be
exhaustive and compile R packages to access, model, and summarise
information related to the movement of water across the Earth's
landscape. We hope to contribute to a [nascent community of hydrological
R users](https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-50/)
and develop an infrastructure of packages that provide a comprehensive
toolkit for water practitioners who use R as their preferred
computational analysis tool. Making sense of water data is critical to
understanding the response of landscapes to a changing climate.
Consolidating water-related packages will promote their usage and
discovery and ultimately facilitate reproducible workflows for water
research. Since this is a new task view, it serves our purpose to
evaluate the current *State of Hydrology* in R and look at the
interdependency of hydrology packages relative to some better known
collections of packages in the general R ecosystem.

### State of Hydrology in R

We will conduct a high-level assessment of the state of hydrology in R
by visualizing network connectivity among package dependencies -- which
packages use code from another package to accomplish a task. For
context, we will evaluate our Hydrology task view against one of the
most well-known collection of packages in R, the
[tidyverse](https://www.tidyverse.org/), as well as the venerable
[Environmetrics task
view](https://CRAN.R-project.org/view=Environmetrics) which includes
many packages for environmental analysis. To do that we need to write
some R code and load our required packages:

![](https://media.giphy.com/media/xT8qBmSnYDXS21ZvHO/giphy.gif)

::: {.highlight}
``` {.chroma tabindex="0"}
library(tidyverse)library(tidygraph)library(ggraph)library(ctv)library(tools)library(cranlogs)
```
:::

We will use the handy `CRAN_package_db` function from the `tools`
package (part of base R) which conveniently grabs information from the
DESCRIPTION file of every package on CRAN and turns it into a dataframe.
Here we are only after a few columns so we will extract those right
away:

::: {.highlight}
``` {.chroma tabindex="0"}
all_cran_packages <- CRAN_package_db()[,c("Package", "Imports")] 
```
:::

We are also going to parse some of the info we receive from CRAN to make
it a little easier to work with:

::: {.highlight}
``` {.chroma tabindex="0"}
tidied_cran_imports <- as_tibble(all_cran_packages) %>%   separate_rows(Imports, sep = ",") %>%   mutate(Imports = str_replace(Imports,"\\s*\\([^\\)]+\\)","")) %>%  mutate(Imports = str_trim(Imports, side = "both"))
```
:::

#### Package Connectivity

First, let's take a look at the tidyverse. We can take the rare step of
actually using a function from the tidyverse package (appropriately
named `tidyverse_packages`), which unsurprisingly identifies those
packages officially belonging to the tidyverse. We filter for those
packages and their imports, convert to `tbl_graph` and plot using
`ggraph` to ultimately visualize package connectivity:

::: {.highlight}
``` {.chroma tabindex="0"}
tidyverse_tbl <- tidied_cran_imports %>%   filter(Package %in% tidyverse_packages()) %>%  filter(Imports %in% tidyverse_packages()) %>%  as_tbl_graph() ggraph(tidyverse_tbl, layout = 'nicely') +   geom_edge_link(colour = "grey50") +   geom_node_text(aes(label = name), colour = "#F0F921FF", size = 6) 
```
:::

![](/img/blog-images/2019-04-02-hydrology-task-view/tidyverse.png)

As you might expect, we see many intersecting lines traversing in all
directions; many packages in the tidyverse import other packages in the
tidyverse. This is expected as tidyverse development is coordinated.

Next, let's have a look at the Environmetrics task view where we might
not expect to see the same degree of connectivity. The `ctv` package,
the infrastructure behind CRAN task views, (sort of) provides us with a
function to access a vector of package names for a given task view,
which we can use to similarly create a network plot:

::: {.highlight}
``` {.chroma tabindex="0"}
env_packages <- ctv:::.get_pkgs_from_ctv_or_repos(views = "Environmetrics") %>%   unlist(use.names = FALSE)env_tbl <- tidied_cran_imports %>%  filter(Package %in% env_packages) %>%  filter(Imports %in% env_packages) %>%  as_tbl_graph()ggraph(env_tbl, layout = 'nicely') +   geom_edge_link(colour = "grey50") +   geom_node_text(aes(label = name), colour = "#FDB32FFF", size = 6) 
```
:::

![](/img/blog-images/2019-04-02-hydrology-task-view/environmetrics.png)

While there are clearly fewer connections than in the tidyverse package
ecosystem, there is still quite a large amount of connectivity in
Environmetrics task view In particular the `mgcv`, `MASS`, `vegan` and
`zoo` packages all provide critical infrastructure for those developing
packages in this field. This presumably can at least partially be
attributed to the age (and therefore the maturity) of the Environmetrics
task view.

Finally, we can evaluate the new Hydrology task view by a similar means:

::: {.highlight}
``` {.chroma tabindex="0"}
hyd_packages <- ctv:::.get_pkgs_from_ctv_or_repos(views = "Hydrology") %>%   unlist(use.names = FALSE)hyd_tbl <- tidied_cran_imports %>%   filter(Package %in% hyd_packages) %>%   filter(Imports %in% hyd_packages) %>%   as_tbl_graph() ggraph(hyd_tbl, layout = 'nicely') +   geom_edge_link(colour = "grey50") +   geom_node_text(aes(label = name), colour = "#ED7953FF", size = 6)
```
:::

![](/img/blog-images/2019-04-02-hydrology-task-view/hydrology.png)

Clearly some connectivity among water-related packages is already
happening, which serves as a reminder that CRAN task views aren't the
only place people discover packages to use. Some tools form a hub of
connected packages because they share a common package maintainer (see
`hydroGOF`) while others like `CityWaterBalance` draw upon a diverse
suite of `Hydrology` packages to study urban water systems. Many
rOpenSci packages have been included in the task view
([`bomrang`](https://github.com/ropensci/bomrang),
[`clifro`](https://github.com/ropensci/clifro),
[`dbhydroR`](https://github.com/ropensci/dbhydroR),
[`GSODR`](https://github.com/ropensci/GSODR),
[`hddtools`](https://github.com/ropensci/hddtools),
[`hydroscoper`](https://github.com/ropensci/hydroscoper),
[`MODIStsp`](https://github.com/ropensci/MODIStsp),
[`rnoaa`](https://github.com/ropensci/rnoaa),
[`smapr`](https://github.com/ropensci/smapr),
[`tidyhydat`](https://github.com/ropensci/tidyhydat),
[`weathercan`](https://github.com/ropensci/weathercan)). `rnoaa` in
particular is a key piece of infrastructure that underpins many analyses
in hydrological research. The plot below summarises the number of
downloads from the RStudio CRAN mirror over the past year via the
`cranlogs` package.

::: {.highlight}
``` {.chroma tabindex="0"}
pkgcount <- cran_downloads(packages = hyd_packages,                            from = Sys.Date()-1*365,                            to = Sys.Date()) %>%  group_by(package) %>%  summarise(downloads = sum(count)) %>%  filter(downloads >= 5000)pkgcount %>%  ggplot(aes(x = reorder(package, downloads), y = downloads)) +  geom_col(aes(fill = downloads)) +  geom_text(aes(y = 1000, label = package), size = 5.5, colour = "white",             hjust = 0, nudge_y = -500, angle = 90) +  scale_fill_viridis_c(name = "Downloads", option = "D", end = 0.75) 
```
:::

It is illustrative that the top four packages feature two modelling
packages and two data packages. These are the basic tenets of
hydrological research and indicate how those in the hydrological space
are using R.

![](/img/blog-images/2019-04-02-hydrology-task-view/countDownloads-1.png)

### Future of R for Hydrology

Hydrology, like many other sciences, is becoming more and more data
driven and programmatic ways to access, manipulate and model data in a
reproducible workflow are becoming a necessity. Water related data
though has its own specific challenges, which are similar but different
from those of other data sources. It can be expected that with a growing
uptake of R in the hydrological community more packages will be
available, with more interconnectivity between them. At the moment the
landscape of R for hydrologists is quite fragmented, but in time it
might be that some *core* packages to manipulate data will emerge and
several computational approaches to hydrological models will be
centralised in a unique package. Regardless of how the landscape of
hydrology packages will evolve, R and open source tools are becoming
increasingly relevant in the hydrological community, and we are happy to
have made a small contribution to make it easier for everybody to know
what can already be done with water data in R.

If the Hydrology CRAN task view can connect developers and hydrologists,
we hope it can leverage the efforts of others in the hydrology space. A
topic in R can live in many places without detracting from others.
Twitter, Stack Overflow and GitHub are all obviously great places to
strengthen the community of water practitioners using R. Our task view
hopefully will join those spaces as a resource to enable shared efforts
towards water management. You can contribute to the task view by opening
an issue or pull request at our [project
repository](https://github.com/ropensci/Hydrology).


Importance of Hydrology   Given that liquid water is essential to life on Earth, water research cuts across numerous disciplines including hydrology, meteorology, geography, climate science, engineering, ecology, and more. Numerous R packages have emerged from this diversity of approaches, and we recently gathered many of them into a new rOpenSci task view which we broadly titled ‘Hydrology’ and published to CRAN.

Messaggi di Rogue Scholar

Getting your toes wet in R: Hydrology, meteorology, and more