The goal of Lab3package is to thin the large shape files and convert it into a data frame containing Longitude, Latitude, group, order and NAME_1 or HASC_1 so that you can make a polygon figure colored by region(NAME_1 or HASC_1) using this data frame.
You can install the development version of Lab3package from GitHub with:
# install.packages("devtools")
devtools::install_github("JinjiPang/Lab3package")This is a basic example to show how to use functions in this package to create geometric data frame and some implementations.
library(Lab3package)
#> Registered S3 method overwritten by 'geojsonlint':
#> method from
#> print.location dplyr
data("Yemen")First, you can download a shape file of a country of your choice. Then,
you can either read the file using sf::read_sf()first or just use the
file path to try the following codes:
This function will return a data frame with four basic variables, which are long, lat, order, and group.
library(tidyverse)
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.5 v purrr 0.3.4
#> v tibble 3.1.6 v dplyr 1.0.7
#> v tidyr 1.1.4 v stringr 1.4.0
#> v readr 2.1.1 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
df1 <- team_2(Yemen, 0.1)
df1 %>%
ggplot(aes(x = long, y = lat, group = group)) + geom_polygon()
This function will return a data frame with five basic variables, which are long, lat, order, group, and HASC_1.
library(tidyverse)
df2 <- team_5(Yemen, 0.1)
df2 %>%
ggplot(aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = HASC_1))
This function will return a data frame with 15 variables, basically it keeps all the original variables and add three more variables long, lat, group, order and NAME_1.
library(tidyverse)
df3 <- team_6(Yemen, 0.1)
df3 %>%
ggplot(aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = NAME_1))
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