Geospatial Analysis with R

class: center, middle, inverse, title-slide

.title[
# Geospatial Analysis with R
]
.subtitle[
## Class 12
]

---

```r
library(leaflet)
library(sf)
library(dplyr)
districts <- read_sf(
  system.file("extdata/districts.geojson", package = "geospaar")
)
bb <- unname(st_bbox(districts))
xy <- st_centroid(districts) %>% st_coordinates() %>% 
  bind_cols(name = districts$distName, .)
slist <- list("color" = "white")
label_opts <- labelOptions(noHide = TRUE, style = slist, direction = 'center',
                           textOnly = TRUE, textsize = "5px")

m <- leaflet() %>% 
  addProviderTiles("Esri.WorldImagery") %>% 
  fitBounds(bb[1], bb[2], bb[3], bb[4]) %>% 
  addPolygons(data = districts, fill = FALSE, color = "white", 
              group = "Districts", weight = 1) %>% 
  addLabelOnlyMarkers(xy$X, xy$Y, label = xy$name, group = "Names",
                      labelOptions = label_opts) %>%
  addLayersControl(overlayGroups = c("Districts", "Names"),
                   options = layersControlOptions(collapsed = FALSE,
                                                  autoZIndex = FALSE))
```

---

---
## Homework review

- Use `lapply` to make three `data.frame`s captured in a list `l`, each composed of one randomly sampled column `v1` (selecting from integers 1:10, with length = 20), and the second being `v2` composed of lowercase letters, randomly selected using `sample`, also of length 20.  
- The iterator in the `lapply` should be 10, 20, 30, which become the random seeds for the sampling (in the body of the `lapply`)
- After making `l`, use a `for` loop to iterate through each element of `l`, writing each out to a folder `external/data/` in your project. 
- Change the name of each as part of the iteration, so that `l[[1]]` is written out as `external/data/dataset1.csv`, etc. Hint: you can use `paste0` to make each file path and name.  
- After writing these out, use another `lapply` to read back in the three datasets into a new list `l2`.  Bonus: Use `dir` to programmatically read in the file paths from your `external/data` folder.

---

## Working with data

### `do.call`

```r
csv_files <- list.files(here::here("external/data/"), pattern = ".csv", 
                        full.names = TRUE)
l <- lapply(csv_files, readr::read_csv)

do.call(rbind, l)
```

---
### Manipulating and analyzing data

- reshape
- mutate
- select
- filter
- split-apply-combine