Slides.Rmd

---
title: "Advanced Raster"
author: "Jan Verbesselt"
date: "13 Jan 2016"
output: slidy_presentation
---

# Excercise RasterVector

All ok? Any questions?

# Examples

```{r, eval=FALSE}
NDVI <- list.files(path='data/', pattern = glob2rx('*.grd'), full.names=TRUE)
NDVIstack <- stack(NDVI)
NDVI
```

```{r, eval=FALSE}
unzip('data/NDVI_data.zip', exdir=ifolder)  # unzip NDVI data
NDVIlist <- list.files(path=ifolder,pattern = '+.grd$', full.names=TRUE) # list NDVI raster
NDVI_12 <- stack(NDVIlist) # NDVI rasters
```

# More

```{r, eval=FALSE}
# downloading and preparing MODIS NDVI data
download.file("https://github.com/GeoScripting-WUR/VectorRaster/raw/gh-pages/data/MODIS.zip", "data/MODIS.zip", method="wget")
unzip("data/MODIS.zip", exdir="data/")
modisPath <- list.files(path="data/", pattern = glob2rx('MO*.grd'), full.names = TRUE)
NLmodis <- brick(modisPath)
```

# Solution (Thanks to Loïc Dutrieux)

---

Use of extract.

```{r, eval=FALSE}
?extract
## S4 method for signature 'Raster,SpatialPolygons'
extract(x, y, fun=NULL, na.rm=FALSE, weights=FALSE, cellnumbers=FALSE,
     small=TRUE, df=FALSE, layer, nl, factors=FALSE, sp=FALSE, ...)
```

`sp = TRUE` will return the initial SPDF, with the extracted values appended to the `@data` slot.

* No need to run for each feature independently
* Also you can extract all layers of the raster brick at once
* Watch out for NAs

```{r}
a <- c(1,2,3,4,5,NA)
a
mean(a)
mean(a, na.rm = TRUE)
```

---

# Subset to identify the greenest city

```{r, eval=FALSE}
# Find out the greenest city in January
maxJanuarySPDF <- green[green$January == max(green$January),]
maxAugustSPDF <- green[green$August == max(green$August),]

# Or using subset() on the dataframe only
greenDf <- green@data
# The subset= argument selects rows; the select= argument selects columns
subset(greenDf, subset = January == max(greenDf$January), select = c(NAME_2, January))
subset(greenDf, subset = August == max(greenDf$August), select = c(NAME_2, August))
```

Subset():

* Only works on dataframes (spdf@data)
* `subset =` selects row
* `select =` selects columns


# More

Provide some feedback to the other teams! 

There are lot's of different solutions for the same step

# Tip of the day

[R-SIG-GEO](https://stat.ethz.ch/mailman/listinfo/r-sig-geo)

[And here for an overview](http://r-sig-geo.2731867.n2.nabble.com/)

Also check out: 

  *  GitHub Education
  *  Microsoft Azure
  *  DreamSpark
    
# Tutorial of today

Any questions?

# Todays lesson

[Advanced Raster](https://geoscripting-wur.github.io/AdvancedRasterAnalysis/)

Deadline Friday morning 1030u!
Finalise all the excercises!