README.Rmd

---
title: "MacLeish"
output:
  github_document:
    html_preview: true
---

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The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation. More information can be found at (http://www.smith.edu/ceeds/macleish.php)

![MacLeish Field Station](inst/extdata/macleish_600px.jpg)

This R package allows you to download and process weather data using the [ETL](http://www.github.com/beanumber/etl) framework from the MacLeish Field Station. It also contains shapefiles for contextualizing spatial information. 

## To install

```{r, eval=FALSE}
install.packages("macleish")
```

## Use 2015 weather data

Weather data from 2015 is available immediately from both the `Whately` and `Orchard` weather stations. 

```{r, message=FALSE}
library(macleish)
glimpse(whately_2015)
glimpse(orchard_2015)
```

## Live weather data

Weather readings are logged every 10 minutes. Current and historical (dating back to 1/3/2012 for `whately` and 6/27/2014 for `orchard`) meteorological readings are available through the [ETL](http://www.github.com/beanumber/etl) framework. Please see the documentation for that package for more information about how this works. 

```{r, message=FALSE, eval=FALSE}
macleish <- etl("macleish")
macleish %>%
  etl_update()
```

```{r, eval=FALSE}
whately <- macleish %>%
  tbl("whately")
whately %>%
  mutate(the_year = strftime('%Y', when)) %>%
  group_by(the_year) %>%
  summarize(N = n(), begin = min(when), end = max(when), avg_temp = mean(temperature))
orchard <- macleish %>%
  tbl("orchard")
orchard %>%
  mutate(the_year = strftime('%Y', when)) %>%
  group_by(the_year) %>%
  summarize(N = n(), begin = min(when), end = max(when), avg_temp = mean(temperature))
```


```{r, eval=FALSE, fig.width = 7, fig.height = 5, message=FALSE}
daily <- whately %>%
  mutate(the_date = date(when)) %>%
  group_by(the_date) %>%
  summarize(N = n(), avgTemp = mean(temperature)) %>%
  collect()
library(ggplot2)
ggplot(data = daily, aes(x = as.Date(the_date), y = avgTemp)) +
  geom_point() + geom_smooth()
```

## Maps

Spatial data is available through the `macleish_layers` data object. 

```{r}
class(macleish_layers)
names(macleish_layers)
```


```{r, eval=FALSE, fig.width=7, fig.height=7, message=FALSE}
library(leaflet)
leaflet() %>%
  addTiles() %>%
  addPolygons(data = macleish_layers[["boundary"]], 
              weight = 1, fillOpacity = 0.1) %>%
  addPolygons(data = macleish_layers[["buildings"]], 
              weight = 1, popup = ~ name) %>%
  addPolylines(data = macleish_layers[["trails"]], 
               weight = 1, color = "brown",
               popup = ~ name) %>%
  addPolylines(data = macleish_layers[["streams"]], 
               weight = 2) %>%
  addMarkers(data = filter(macleish_layers[["landmarks"]], grepl("Met", Label)), 
             popup = ~Label)
```