GitHub - irJERAD/AppleHealthDashboard: Using the Export function from Apple's iOS health app to explore the dataset in a fun dashboard way I hope will become a story

Leaflet is a JavaScript library for creating dynamic maps that support panning and zooming along with various annotations.

library(leaflet)
leaflet() %>%
  addTiles() %>%
  addMarkers(lng=-122.4125580, lat=37.7781400, popup="Current Residence of Jerad")

https://github.com/irJERAD/AppleHealthDashboard

Here you can see a Pin of my home in San Francisco!
Interactive panning/zooming
Compose maps using arbitrary combinations of map tiles, markers, polygons, lines, popups, and GeoJSON.
Create maps right from the R console or RStudio
Embed maps in knitr/R Markdown documents and Shiny apps
Easily render Spatial objects from the sp package, or data frames with latitude/longitude columns
Use map bounds and mouse events to drive Shiny logic

ggplot2 - while well known and used - has a very diverse set of plot types; while old can be boring the selection is indispensable to data exploration and discovery.

df %>%
  filter(type == 'HKQuantityTypeIdentifierStepCount') %>%
  group_by(date,dayofweek,hour) %>% 
  summarize(steps=sum(value)) %>% 
  group_by(hour,dayofweek) %>% 
  summarize(steps=sum(steps)) %>% 
  arrange(desc(steps)) %>%
  #print table steps by date by month by year
  ggplot(aes(x=dayofweek, y=hour, fill=steps)) + 
  geom_tile() + 
  scale_fill_continuous(labels = scales::comma, low = 'white', high = 'red') +
  theme_bw() + 
  theme(panel.grid.major = element_blank())

Here I used the ggplot2 libraries geometric tile plotting ability to create a heat map of my walking data

Week Days around 6pm were the busiest time
- This makes sense since that is when I am walking home from work
This Library was written by one of my favorite data scientists

More ggplot this time box plot - should make this a lattice

df %>%
  filter(type == 'HKQuantityTypeIdentifierStepCount') %>%
  group_by(dayofweek,date,year) %>%
  summarize(steps=sum(value)) %>%
  #print table steps by date by month by year
  ggplot(aes(x=dayofweek, y=steps)) + 
  geom_boxplot(aes(fill=(year))) +
  # uses nice default blue-ish palette
  scale_fill_brewer() +
  # sets background theme to my rediculous standards
  # Note: still not completely satisfied but productivity beckons me
  theme(panel.grid.major.x = element_blank(), panel.grid.minor =
          element_blank(), panel.grid.major.y = element_line(color = "gray",
                                                             size = 0.2),
        panel.background = element_rect(color = "black", fill = "#fbfbfb")) + scale_fill_brewer()

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

With 2017 only just starting (I am writing this at 10am on Jan 5th and only started last evening). A boxplot does really help much.

I considered filtering out this new year. However I am determined to impliment an automation that will continue to take my exported data and update this page - mostly because this is a static site and I love getting as much as possible from as little as necessary

Plotly provides bindings to the plotly.js library and allows you to easily translate your ggplot2 graphics into an interactive web-based version.

# plotly with ggplot2 for interactive graphics
h <- heart %>%
  # text is the tool tip
  ggplot(aes(x = date, y = value,
             # create custome hover tooltip variables
             text = (paste("BPM:", value,
                           "<br>Source:", sourceName,
                           "<br>Date", date)))) +
  # Color and implied key
  geom_point(aes(colour=sourceName)) +
  scale_color_manual(name = "Source of<br>Collected Data", values = wes_palette(n=5, name = "Darjeeling")) +
  labs(title = "My Heart Rate From April 13th, 2015<br> through current data Export Jan 5th 2017",
       x = "2015 --- Date --- 2017", y = "Beats per min") +
  theme(plot.title = element_text(size = 10,
                                  color = "#FF0000",
                                  face = "bold"),
        legend.text = element_text(size = 6,
                                   color = "#00c8ff",
                                   face = "italic"),
        legend.title = element_text(size = 13,
                                    color = "#00A08A",
                                    face = "italic"),
        axis.title.x = element_text(size = 12,
                                    color = "#ffffff"),  
        axis.title.y = element_text(size = 12,
                                    color = "#ffffff"),
        plot.background = element_rect(fill = "#5BBCD6"))

## TODO nticks on x axis $xaxis$ntick needs to be a number greater than 0
# sets the number of ticks on the x axis

## TODO tick angle $colorbar$tickangle -90 is vertical
# Create plotly object
p <- plotly_build(h)
# center legend
# replace plotly objects annotations settings
p$x$layout$annotations[[1]]$y = 0.4
p$x$layout$legend$y = 0
# Customize the hoveron tooltip content
# need to go through each variable in legend source
for(i in 1:length(p$x$data)){
  p$x$data[[i]]$text <- paste("Beats Per Min:", heart$value,
                              "<br>Source:", heart$sourceName,
                              "<br>Date", heart$date)
}
p

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
MyHealthStoryDashboard_cache/html		MyHealthStoryDashboard_cache/html
MyHealthStoryDashboard_files/figure-html		MyHealthStoryDashboard_files/figure-html
Pics		Pics
AppleHealthDashboardScrips.R		AppleHealthDashboardScrips.R
MyHealthStoryDashboard.Rmd		MyHealthStoryDashboard.Rmd
MyHealthStoryDashboard.html		MyHealthStoryDashboard.html
README.MD		README.MD
export.zip		export.zip
heartBySource.jpeg		heartBySource.jpeg
heatGGplotlyFinal.html		heatGGplotlyFinal.html

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Leaflet is a JavaScript library for creating dynamic maps that support panning and zooming along with various annotations.

ggplot2 - while well known and used - has a very diverse set of plot types; while old can be boring the selection is indispensable to data exploration and discovery.

More ggplot this time box plot - should make this a lattice

Plotly provides bindings to the plotly.js library and allows you to easily translate your ggplot2 graphics into an interactive web-based version.

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Leaflet is a JavaScript library for creating dynamic maps that support panning and zooming along with various annotations.

ggplot2 - while well known and used - has a very diverse set of plot types; while old can be boring the selection is indispensable to data exploration and discovery.

More ggplot this time box plot - should make this a lattice

Plotly provides bindings to the plotly.js library and allows you to easily translate your ggplot2 graphics into an interactive web-based version.

About

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