---
title: "simplevis"
author: "David Hodge"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{simplevis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, warning = FALSE, message = FALSE}
library(simplevis)
library(dplyr)
```

### Making simplevis ggplot graphs

`simplevis` provides the following types of `ggplot` graph:

* horizontal bar (e.g. `gglot_hbar`)
* vertical bar (e.g. `gglot_vbar`)
* line plot (e.g. `gglot_line`)
* scatter plot (e.g. `gglot_scatter`)
* boxplot (e.g. `gglot_box`)

For each graph type 4 functions are available.

1. A `ggplot` not coloured or faceted (e.g. `gglot_hbar`)
```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
plot_data <- ggplot2::diamonds %>%
  mutate(cut = stringr::str_to_sentence(cut)) %>%
  group_by(cut) %>%
  summarise(average_price = mean(price)) %>%
  ungroup() %>% 
  mutate(average_price_thousands = round(average_price / 1000, 1)) %>%
  mutate(cut = factor(cut, levels = c("Fair", "Good", "Very good", "Premium", "Ideal"))) 

plot <- ggplot_hbar(data = plot_data, 
                    x_var = average_price_thousands, 
                    y_var = cut,
                    title = "Average diamond price by cut", 
                    x_title = "Average price ($US thousands)", 
                    y_title = "Cut")

plot 
```

2. A `ggplot` coloured, but not faceted (e.g. `gglot_hbar_col`)
```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
plot_data <- ggplot2::diamonds %>%
  mutate(cut = stringr::str_to_sentence(cut)) %>%
  group_by(cut, clarity) %>%
  summarise(average_price = mean(price)) %>%
  mutate(average_price_thousands = round(average_price / 1000, 1)) %>%
  ungroup()

plot <- ggplot_hbar_col(data = plot_data, 
                        x_var = average_price_thousands, 
                        y_var = cut, 
                        col_var = clarity, 
                        legend_ncol = 4,
                        title = "Average diamond price by cut and clarity", 
                        x_title = "Average price ($US thousands)", 
                        y_title = "Cut")

plot
```

3. A `ggplot` facetted, but not coloured (e.g. `gglot_hbar_facet`)
```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
plot_data <- ggplot2::diamonds %>%
  mutate(cut = stringr::str_to_sentence(cut)) %>%
  group_by(cut, clarity) %>%
  summarise(average_price = mean(price)) %>%
  mutate(average_price_thousands = round(average_price / 1000, 1)) %>%
  ungroup()

plot <- ggplot_hbar_facet(data = plot_data, 
                          x_var = average_price_thousands, 
                          y_var = cut, 
                          facet_var = clarity,
                          title = "Average diamond price by cut and clarity", 
                          x_title = "Average price ($US thousands)", 
                          y_title = "Cut")

plot
```

4. A `ggplot` coloured and facetted (e.g. `gglot_hbar_col_facet`)
```{r, message = FALSE, warning = FALSE, fig.height = 5, fig.width = 7}
plot_data <- ggplot2::diamonds %>%
  mutate(cut = stringr::str_to_sentence(cut)) %>%
  group_by(cut, clarity, color) %>%
  summarise(average_price = mean(price)) %>%
  mutate(average_price_thousands = round(average_price / 1000, 1)) %>%
  ungroup()

plot <- ggplot_hbar_col_facet(data = plot_data, 
                              x_var = average_price_thousands, 
                              y_var = color, 
                              col_var = clarity, 
                              facet_var = cut,
                              legend_ncol = 4, 
                              title = "Average diamond price by colour, clarity and cut", 
                              x_title = "Average price ($US thousands)", 
                              y_title = "Colour")

plot
```

These `ggplot` graphs have been designed that users can convert them easily to html interactive objects by wrapping them in `plotly::gglotly` with the `tooltip = "text"` specification. This results in the tooltip converting the applicable variable name to sentence case and replacing underscores with spaces. If users wish to specify a subset of variables, then they need to provide these as a vector instead. Note these variables will turn up in the tooltip as they are specified in the data (e.g. snakecase), unless converted prior in the plot data.


```{r, echo = TRUE, eval = FALSE, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
plot_data <- storms %>%
  group_by(year) %>%
  summarise(average_wind = round(mean(wind), 2)) %>%
  ungroup()

plot <- ggplot_vbar(data = plot_data, 
                    x_var = year, 
                    y_var = average_wind, 
                    title = "Average wind speed of Atlantic storms, 1975\u20132015",
                    x_title = "Year",
                    y_title = "Average maximum sustained wind speed (knots)")

plotly::ggplotly(plot, tooltip = "text") %>% 
  plotly_remove_buttons() 

plotly::ggplotly(plot, tooltip = c("average_wind")) %>% 
  plotly_remove_buttons()
```

### Making simplevis ggplot maps

`simplevis` provides the following types of `ggplot` map:

* simple feature (`sf`) maps
* spatial-temporal array (i.e. `stars`) maps 

Simple feature (`sf`) maps are maps of points, lines or polygons.  

The following functions are available:

* `ggplot_sf`
* `ggplot_sf_col`
* `ggplot_sf_facet`
* `ggplot_sf_col_facet`

These functions work in the same way as the `ggplot` graph functions, but with the following key differences:

* Data must be provided in the format of an `sf` object. 
* Data must be of `POINT`/`MULTIPOINT`, `LINESTRING`/`MULTILINESTRING`, or `POLYGON`/`MULTIPOLYGON` geometry types
* Data can have any coordinate reference system (CRS), but it must be defined
* No `x_var` and `y_var` variables are required
* There is a coastline specification, which allows for a further `sf` object as a coastline or administrative boundaries to be added to the map. A New Zealand coastline (`nz`) and New Zealand coastline with regional boundaries (`nz_region`) has been provided with the package. 
* They do _not_ support conversion to interactive objects through `plotly::gglotly`.

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
map_data <- example_sf_nz_river_wq %>%
  dplyr::filter(period == "1998-2017", indicator == "Nitrate-nitrogen") 

ggplot_sf(data = map_data, 
          coastline = nz, 
          size = 0.25,
          title = "Monitored river nitrate-nitrogen trend sites, 2008\u201317",
          wrap_title = 40)
```

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
map_data <- example_sf_nz_river_wq %>%
  filter(period == "1998-2017", indicator == "Nitrate-nitrogen") 

pal <- c("#4575B4", "#D3D3D3", "#D73027")

ggplot_sf_col(data = map_data, 
              col_var = trend_category, 
              coastline = nz, 
              size = 0.25, 
              pal = pal, 
              title = "Monitored river nitrate-nitrogen trends, 2008\u201317",
              wrap_title = 40)
```

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
map_data <- example_sf_nz_river_wq %>%
 filter(period == "1998-2017", indicator == "Nitrate-nitrogen") 

ggplot_sf_facet(data = map_data, 
                facet_var = trend_category, 
                coastline = nz, 
                size = 0.25,
                title = "Monitored river nitrate-nitrogen trends, 2008\u201317")
```

```{r, message = FALSE, warning = FALSE, fig.height = 5, fig.width = 7}
map_data <- example_sf_nz_river_wq %>%
 filter(period == "1998-2017", indicator %in% c("Nitrate-nitrogen", "Dissolved reactive phosphorus")) 

pal <- c("#4575B4", "#D3D3D3", "#D73027")

ggplot_sf_col_facet(data = map_data, 
                    col_var = trend_category, 
                    facet_var = indicator,
                    coastline = nz, 
                    size = 0.25, 
                    pal = pal,
                    title = "Monitored river nitrate-nitrogen trends, 2008\u201317")
```

`simplevis` provides `ggplot` maps made for spatial temporal arrays (`stars`). 

The following functions are available:

* `ggplot_sf_col`
* `ggplot_sf_col_facet`

These functions work in the same way as the `ggplot` `sf` map functions, but with the following key differences:

* No non-colouring functions have been made, as it is assumed that this functionality is not required
* They do _not_ support conversion to interactive objects through `plotly::gglotly`.
* Data must be provided in the format of a `stars` object. For, `ggplot_sf_col`, the `stars` object must have 2 dimensions x and y, and only 1 attribute layer. Required input. For, `ggplot_sf_col_facet`,  the stars object must have 2 dimensions, x and y, and multiple named attribute layers with the usual convention of lower case and underscores. Use `select`, `slice`, `c` and `split`to get the `stars` object into the appropriate format.

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
ggplot_stars_col(data = example_stars_nz_no3n, 
                 coastline = nz,
                 col_method = "quantile", quantile_cuts = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1),
                 title = "River modelled median nitrate-nitrogen concentrations, 2013\u201317",
                 wrap_title = 40, 
                 legend_digits = 1)
```

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
map_data1 <- example_stars_nz_no3n %>%
  rlang::set_names("NO3N")

map_data2 <- example_stars_nz_drp %>%
  rlang::set_names("DRP")

map_data <- c(map_data1, map_data2)

ggplot_stars_col_facet(data = map_data, 
                       coastline = nz,
                       col_method = "quantile", quantile_cuts = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1),
                       title = "River modelled nutrient concentrations, 2013\u201317")
```

### Making simplevis leaflet maps

`simplevis` provides the following types of `leaflet` map:

* simple feature (`sf`) maps
* spatial-temporal array (i.e. `stars`) maps 

These work in the same way as the `ggplot` map functions, but with no coastline arguments.

Outputs are hidden to keep the size of the vignette manageable. 

```{r, echo = TRUE, results = 'hide', message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
leaflet_sf(data = example_sf_nz_livestock)
```

```{r, echo = TRUE, results = 'hide', message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
map_data <- example_sf_nz_livestock %>%
  mutate(dairydens = round(dairydens, 2))

leaflet_sf_col(data = map_data, 
               col_var = dairydens, 
               col_method = "bin", 
               bin_cuts = c(0, 10, 50, 100, 150, 200, Inf), 
               legend_digits = 0,
               title = "Dairy density in count per km\u00b2, 2017")
```

```{r, echo = TRUE, results = 'hide', fig.height = 4, fig.width = 7}
leaflet_stars_col(data = example_stars_nz_no3n,
  col_method = "quantile", quantile_cuts = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1),
   title = "River modelled median nitrate-nitrogen concentrations in g/m\u00b3, 2013\u201317")
```

### Working with quoted variable inputs

`simplevis` can also work with quoted variable inputs. The user must place each quoted variable within a `simplevis` function within a `!!sym` function, as per the example below. This can be helpful, particularly when working in shiny apps.

```{r, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7}
plot_data <- ggplot2::diamonds %>%
  mutate_at(vars("cut"), ~stringr::str_to_sentence(.)) %>%
  group_by_at(vars("cut")) %>%
  summarise_at(vars("price"), ~mean(.)) %>%
  ungroup() %>% 
  mutate_at(vars("price"), ~round(. / 1000, 2)) %>%
  mutate_at(vars("cut"), ~factor(., levels = c("Fair", "Good", "Very good", "Premium", "Ideal"))) 

x_var <- "price"
y_var <- "cut"

plot <- ggplot_hbar(data = plot_data, 
                    x_var = !!sym(x_var), 
                    y_var = !!sym(y_var),
                    title = "Average diamond price by cut", 
                    x_title = "Average price ($US thousands)", 
                    y_title = "Cut")

plot 
```

### Making `shiny` apps with `simplevis`

`simplevis` provides two template `shiny` apps called `template1` and `template2`. Users can access these functions by using the `run_template` functions for the applicable app, and then clicking on the `download_code` button to access a zip file of the code. 

```{r, eval=F}
run_template("template1") # a graph and table
run_template("template2") # a leaflet map, as well as graph and table

```

For a simple app, the basic method to create an app is:

* run `run_template("template1")` or  `run_template("template2")` and download the code to use as a template
* In `make_app_vis.R`, draft your visualisations with dummy character inputs
* In `data` subfolder, add your data
* In `global.R`, read your data in, and create any vectors required
* In `ui.R`, add a app title
* In `ui.R`. add `radioButtons` and other widgets
* In `server.R`, add code within reactive plot_data
* In `server.R`, add code within reactive plot (Note: ensure you have isMobile = input$isMobile within any `simplevis` `ggplot` functions, and that `simplevis` `leaflet` functions have the argument `shiny = TRUE`) 
* In `server.R`, check the table is referring to your right dataset
* In `server.R`, check download code is referring to your dataset. If there are many files, use the download zip code and add a file called download.zip to your data subfolder
* In `ui.R`, check that the heights of graphs for desktop and mobile are appropriate. (Note: you will need to publish the app and check on your phone before confident that the height for the graph on a mobile device is appropriate) 
* In `www/About.Rmd`, update as necessary
* If using google tag-manager, obtain a tag and replace `GTM-XXXXXXX` with it in the `www/js/tag-manager-js` file. 

### Mobile compliance - `ggplotly` for desktop and `ggplot` for mobile

The template apps have a function with javascript in it that provides a `TRUE` or `FALSE` values if the user is on a mobile device. This can value can be referred to in the server code as input$isMobile. This is using the method developed by <a href="https://g3rv4.com/2017/08/shiny-detect-mobile-browsers" target="_blank">Gervasio Marchand</a>. 

The `simplevis` `ggplot` functions have a `isMobile` specification with values of `TRUE` or `FALSE` for whether the user is on a mobile device or not. When `isMobile` equals `TRUE`,  titles, legend elements, and facets are wrapped accordingly for the smaller screen size. Therefore, when `simplevis` `ggplot` functions are used within shiny, the app developer must specify `isMobile = input$isMobile` within the `simplevis` `ggplot` function.

The safest option to ensuring that a graph diplays well on a mobile device is to render it as a `ggplot` object. Subsequently, the current recommended approach used is to render a `ggplotly` interactive object for desktop users and a `ggplot` object for mobile users. This is the approach taken by the template apps through the use of conditional panels.

### Iframing

Iframing apps can provide a great experience for users.

Template apps are build to be compatible with one of two approaches to iframing:

* if your website has ability for height responsive iframes, then follow the instructions described in the method developed by <a href="https://www.cultureofinsight.com/post/responsive-iframes-for-shiny-apps" target="_blank">Paul Campbell</a>. 
* if your website does not support responsive iframes, then templates apps have css that supports a iframe of 750px for desktop users. The app developer can then code the iframe height to be bigger if a mobile user is detected. If this is not possible, a hyperlink to the app url can be provided to mobile users. 
