R Notebook

library(terra)

## Warning: package 'terra' was built under R version 4.3.3

## terra 1.7.71

library(tidyterra)

## Warning: package 'tidyterra' was built under R version 4.3.3

## 
## Attaching package: 'tidyterra'

## The following object is masked from 'package:stats':
## 
##     filter

library(sp)

## Warning: package 'sp' was built under R version 4.3.2

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.3.2

library(urbnmapr)
library(sf)

## Warning: package 'sf' was built under R version 4.3.2

## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.3.3

library(dplyr)   

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:terra':
## 
##     intersect, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(spData) 

## Warning: package 'spData' was built under R version 4.3.3

## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`

usa_rast <- rast("NE1_50M_SR_W.tif")
usa_rast <- terra::project(usa_rast, "EPSG:32116", method = "bilinear")

## 
|---------|---------|---------|---------|
=========================================
                                          

plot(usa_rast)

states <- get_urbn_map("states", sf = TRUE)
states  %>% 
  st_transform("EPSG:32116")

## old-style crs object detected; please recreate object with a recent sf::st_crs()

## Warning in CPL_crs_from_input(x): GDAL Message 1: CRS EPSG:2163 is deprecated.
## Its non-deprecated replacement EPSG:9311 will be used instead. To use the
## original CRS, set the OSR_USE_NON_DEPRECATED configuration option to NO.

plot (states)

## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()

crs_rast <- crs(usa_rast) 
crs_states <- st_crs(states)

## old-style crs object detected; please recreate object with a recent sf::st_crs()

US_map <- crop(usa_rast, states) 
plot(US_map)

question 2 leaflet

counties <- get_urbn_map("counties", sf = TRUE)
counties <- counties %>% 
  filter(state_abbv == "NY") %>% #No duplicate counties from other states
  st_transform("EPSG:32116")

## old-style crs object detected; please recreate object with a recent sf::st_crs()

soybeans <- st_read("N-7.xls")

## Multiple layers are present in data source C:\Users\jules\Downloads\Spring 24\DIDA 370\Assignment 3\N-7.xls, reading layer `Planted'.
## Use `st_layers' to list all layer names and their type in a data source.
## Set the `layer' argument in `st_read' to read a particular layer.

## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, :
## automatically selected the first layer in a data source containing more than
## one.

## Reading layer `Planted' from data source 
##   `C:\Users\jules\Downloads\Spring 24\DIDA 370\Assignment 3\N-7.xls' 
##   using driver `XLS'

## Warning: no simple feature geometries present: returning a data.frame or tbl_df

soybeans_years <- soybeans %>% 
  select (Field1, Field2, Field21)

colnames(soybeans_years) <- c("county_name","sb_2019","sb_2000")

#Only include counties in data 
soybeans_years = soybeans_years[8:64,]

#misc values set to 0
soybeans_years[soybeans_years == "a"] <- 0
soybeans_years[soybeans_years == "b"] <- 0

make county names identical

soybeans_years$county_name <- gsub(" ", "", soybeans_years$county_name)


counties$county_name <- gsub(" County", "", counties$county_name)
counties$county_name <- gsub(" ", "", counties$county_name)

merge data

county_and_sb_data <- counties %>%
  left_join(soybeans_years, by = c("county_name" = "county_name"))

plot(county_and_sb_data)

all_county_data.sf = st_transform(county_and_sb_data, "EPSG:4326")

all_county_data.sf$sb_2000 <- as.numeric(all_county_data.sf$sb_2000)
all_county_data.sf$sb_2019 <- as.numeric(all_county_data.sf$sb_2019)

pal_fun <- colorBin("YlOrRd", NULL, n = 5)
pal_fun2 <- colorBin("Blues", NULL, n = 5)

leaflet() %>%
  addPolygons(data = all_county_data.sf, 
              stroke = FALSE, 
              fillColor = ~pal_fun(sb_2019), 
              fillOpacity = 0.8, 
              weight = 1,
              popup = ~paste("County:", all_county_data.sf$county_name, "<br>", "2019 Data:", all_county_data.sf$sb_2019),
              group = "Soybean Production in 2019") %>% 
  addPolygons(data = all_county_data.sf, 
              stroke = FALSE, 
              fillColor = ~pal_fun2(sb_2000), 
              fillOpacity = 1, 
              smoothFactor = 0.5, 
              popup = ~paste("County:", all_county_data.sf$county_name, "<br>", "2000 Data:", all_county_data.sf$sb_2000),
              group = "Soybean Production in 2000") %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addLayersControl(overlayGroups = c("Soybean Production in 2019", "Soybean Production in 2000"),
                   options = layersControlOptions(collapsed = FALSE))

Citations: https://epirhandbook.com/en/cleaning-data-and-core-functions.html Leaflet practice in class https://www.geeksforgeeks.org/data-cleaning-in-r/ I took MATH 455 and have had to clean data in R in that class