R for geographic map: Session 2

# R for geographic map: Session 2
## Manipulate a raster data in R

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## DAAD climapAfrica - Climate change research in Africa

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## Keep in touch

<center><img src="https://pbs.twimg.com/profile_images/1230804251950977024/Ymb__9tM_400x400.jpg" height="200x" /></center>
<a href="mailto:yedomon@jbnu.ac.kr">&nbsp; <center>Mail: yedomon@jbnu.ac.kr<center></a> 
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<a href="http://twitter.com/AngeBovys27">&nbsp; <center>Twitter: @AngeBovys27<center></a> 
<a href="http://github.com/Yedomon">&nbsp; <center>github: @Yedomon<center></a>

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## Contents

- **Definition of a raster data**

- **Utility of a raster data**

- **Hands on session**

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# What is a raster data?

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## What is a raster data?

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## Resolution matters

![](https://datacarpentry.org/organization-geospatial/fig/dc-spatial-raster/raster_resolution.png)

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## You say multi-band raster data?

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# Why store data as a raster?

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## Why store data as a raster?

**The advantages of storing your data as a raster are as follows:**

- A simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table

- A powerful format for advanced spatial and statistical analysis

- The ability to represent continuous surfaces and perform surface analysis

- The ability to uniformly store points, lines, polygons, and surfaces

- The ability to perform fast overlays with complex datasets

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## Why store data as a raster?

- There can be spatial inaccuracies due to the limits imposed by the raster dataset cell dimensions 
 
 
- Raster datasets are potentially very large. Resolution increases as the size of the cell decreases

- However, normally cost also increases in both disk space and processing speeds. For a given area, changing cells to one-half the current size requires as much as four times the storage space, depending on the type of data and storage techniques used.

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## Hands on session

### Packages loading

```r
library(ggplot2)
library(raster)
library(tidyr)
library(rnaturalearth)
library(rgeos)
library(cowplot)
```

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### Data acquisition

```r
climate = getData('worldclim', var = 'bio', res = 2.5)
```

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### See the data

```
  
plot(climate)

```

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### Crop the data to the African continent

```r
climate = crop(climate, extent(-20, 60, -40, 40))
```

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### Check the data

```

plot(climate)

```

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### Get the raster 12 relative to the rainfall

```r
raster_rainfall = climate$bio12
```

### Get countries borders shapefiles from the package rnaturalearth

```r
africa = rnaturalearth::ne_countries(continent = 'africa', returnclass = 'sf')
```

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### Plot it

```

plot(africa)

```

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### Raster data conversion into dataframe

```r
rasdf = as.data.frame(raster_rainfall, xy = TRUE)%>%drop_na()
```

### Check it

```r
head(rasdf)
```

```
##           x        y bio12
## 1 -8.937500 39.97917   838
## 2 -8.895833 39.97917   856
## 3 -8.854167 39.97917   863
## 4 -8.812500 39.97917   882
## 5 -8.770833 39.97917   888
## 6 -8.729167 39.97917   872
```

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### Plotting

```r
m = ggplot() +
  geom_tile(aes(x=x, y=y, fill=bio12), data = rasdf) +
  geom_sf(fill = 'transparent', data = africa) +
  scale_fill_viridis_c(name= 'mm/yr', direction = -1 ) +
  labs(x= 'Longitute' , y = 'Latitude',
       title = "Africa's climate map",
       subtitle = "Annual precipitation",
       caption = 'Source: WordClim, 2020') +
  cowplot::theme_cowplot() +
  theme(panel.grid.major = element_line(color = "black",
                                         linetype = 'dashed',
                                         size = .5),
         panel.grid.minor = element_blank(),
         panel.ontop = TRUE,
         panel.background = element_rect(fill = NA, color = 'black'))
```

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### Should get this

```

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# Thanks!