Class 11 slides

docs/class11.Rmd

@@ -0,0 +1,196 @@
+---
+title: "Geospatial Analysis with R"
+subtitle: Class 11
+output:
+  xaringan::moon_reader:
+    lib_dir: libs
+    css: ["default", "lucy", "middlebury-fonts", "themes/class4plus.css"]
+    nature:
+      highlightStyle: github
+      highlightLines: true
+      countIncrementalSlides: false
+---
+
+```{r, out.width = "100%", echo=FALSE, fig.align='center', message=FALSE, warning=FALSE}
+library(dplyr)
+library(ggplot2)
+library(sf)
+knitr::include_graphics("figures/tanzam_rainfall.png")
+```
+
+---
+
+
+```{r, eval=FALSE}
+library(dplyr)
+library(ggplot2)
+library(sf)
+tz <- st_read(here::here("external/data/tanzania.geojson"))
+sagcot <- st_read(here::here("external/data/sagcotcl.geojson"))
+zambia <- st_read(
+  system.file("extdata/districts.geojson", package = "geospaar")
+) %>% st_union()
+prec <- geodata::worldclim_global(var = "prec", res = 2.5, 
+                                  path = "external/data/")
+precsum <- terra::app(prec, sum)
+tz_sagcot <- terra::vect(st_union(sagcot, zambia))
+prec_tzzam <- terra::mask(terra::crop(precsum, tz_sagcot), tz_sagcot)
+
+prec_stars <- stars::st_as_stars(prec_tzzam)
+p <- ggplot() + 
+  geom_sf(data = tz) +  
+  stars::geom_stars(data = prec_stars) +
+  scale_fill_viridis_c(name = "Rainfall (mm)", na.value = "transparent") + 
+  geom_sf(data = zambia, fill = "transparent") +
+  geom_sf(data = sagcot, fill = "transparent") +
+  labs(x = NULL, y = NULL) +
+  theme_linedraw()
+ggsave(p, filename = "docs/figures/tanzam_rainfall.png", height = 4, 
+       width = 7, units = "in", dpi = 300)  
+```
+
+---
+## Reading and writing data
+
+### File paths
+
+Let's read in a csv a few different ways.
+
+Full path - clear for you, bad for code sharing.
+
+```{r}
+data_tib <- read.csv(
+  "/Users/lestes/Dropbox/teaching/geog246346/geospaar/inst/extdata/cdf_corn.csv"
+)
+str(data_tib)
+```
+
+---
+```{r}
+data_tib <- readr::read_csv(
+  "/Users/lestes/Dropbox/teaching/geog246346/geospaar/inst/extdata/cdf_corn.csv"
+)
+str(data_tib)
+```
+
+---
+## Working directory `"."`
+
+- Working directory. Use `getwd()` (from console)  
+- Usually set to project folder.  
+
+```{r}
+getwd() ## if in an RMD, this will show the folder of the RMD
+```
+
+Use `.` to start a file path from the working directory
+
+```{r, eval = F}
+list.files(".") ## 
+```
+
+```{r, eval = F}
+data_tib <- readr::read_csv("./inst/extdata/cdf_corn.csv")
+```
+
+- Use ".." to go up one folder level
+```{r, eval = F}
+list.files(".") ## files in working directory
+list.files("..") ## files in folder one level up
+```
+---
+## User directory `"~"`
+
+- Set by environment variable
+- Use command below to see value
+
+```{r}
+path.expand("~")
+```
+
+
+```{r, eval = F}
+data_tib <- readr::read_csv(
+  "~/Dropbox/teaching/geog246346/geospaar/inst/extdata/cdf_corn.csv"
+)
+```
+
+---
+## Writing files
+
+- Use `write.csv` or `readr::write_csv` to write
+
+```{r, eval = FALSE}
+readr::write_csv(data_tib, file = "temp.csv") ## by default writes to wd()
+```
+
+---
+## Saving/loading files
+- If you want to save an R object, like a `data.frame`, `tibble` etc.
+- Use save, and `.rda` extension
+
+```{r, eval = FALSE}
+save(data_tib, file = "temp.rda") ## by default writes to wd()
+```
+
+```{r, eval = FALSE}
+data_tib <- NULL
+load(file = "temp.rda") ## loads file back to environment
+```
+
+---
+## Dates with `lubridate`
+
+- The main function you want to use is `as_date`, which can convert a character to date format. 
+
+```{r, message = FALSE}
+library(lubridate)
+date1 <- as_date("2022-03-01") ## date in standard YYYY-MM-DD format
+print(date1)
+```
+---
+## Dates with `lubridate`
+
+- More challenging with unclear date formats.
+
+```{r}
+date2 <- as_date("3/1/22") ## is month or date first?
+date2
+```
+
+Include format as shown below. See [formats in this link](https://epirhandbook.com/en/working-with-dates.html)
+```{r}
+date2 <- as_date("3/1/22", format = "%m/%d/%y" )
+date2
+```
+
+We can also write dates in desired format
+```{r}
+date2_char <- as.character(date2, format = "%A %B %d, %Y")
+date2_char
+```
+---
+## Date formats
+
+```{r, out.width = "100%", echo=FALSE, fig.align='center'}
+knitr::include_graphics("figures/class10_date_formats.png")
+```
+---
+How can we read in this date?
+```{r}
+date3 <- as_date("Apr 3, 1999", format = "...")
+date3
+```
+---
+
+## Exercises
+
+- 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.  
+
+
+
+