Read and Write Shapefiles

Authors

Andree Valle Campos

Laure Vancauwenberghe

Kene David Nwosu

Published

December 6, 2024

1 Introduction

• We built Thematic maps using {ggplot2}.

Figure 1. Thematic maps: (A) Choropleth map, (B) Dot map, (C) Density map with city roads as background, (D) Basemap below a Dot map.

• But, how can we use external data from other GIS software?

• What is the standard file format to store and share data?

Figure 2. sf package illustration by Allison Horst.

• Today we are going to read and write Shapefiles, and

• Dive into the components of a sf object!

2 Learning objectives

Read Spatial data from Shapefiles using the read_sf() function from the {sf} package.
Identify the components of sf objects.
Identify the components of Shapefiles.
Write Spatial data in Shapefiles using write_sf().

3 Prerequisites

This lesson requires the following packages:

Code

if(!require('pacman')) install.packages('pacman')

pacman::p_load(rnaturalearth,
               ggplot2,
               cholera,
               here,
               sf, 
               rgeoboundaries)

pacman::p_load_gh("afrimapr/afrilearndata")

4 Shapefiles

• The most common data format for storing Spatial data.

4.1 How to read Shapefiles?

• Using read_sf() from {sf}.

• From local files with a .shp extension,

• To a ready-to-use sf object.

• Let’s read the sle_adm3.shp file:

Identify the file path up to the .shp filename:

"data/boundaries/sle_adm3.shp"

Then, paste that path into read_sf() within here():

Code

shape_file <- read_sf(here("data/boundaries/sle_adm3.shp"))

• The output is an sf object and can be plotted using geom_sf():

Code

shape_file %>% class()

# Plot with geom_sf
# Your code here
shape_file %>% 
  ggplot() +
  geom_sf()

Read the shapefile called sle_hf.shp inside the data/healthsites/ folder. Use the read_sf() function:

Code

q1 <- read_sf(here("data/healthsites/sle_hf.shp"))
q1

• Wait! Shapefiles do not come alone!

• They came with a list of sub-component files.

• Let’s check at the files in the data/boundaries/ folder:

# A tibble: 4 × 1
  value       
  <chr>       
1 sle_adm3.dbf
2 sle_adm3.prj
3 sle_adm3.shp
4 sle_adm3.shx

• How are these files related with the sf object?

• Let’s now look under the hood to understand sf objects better.

5 Understanding `sf` objects

• "sf" stands for Simple Features, a set of widely-used standards for storing geospatial information in databases.

• Now, what do sf objects look like and how do we work with them?

• We’ll look at a slice of the countries object:

Code

countries <- ne_countries(returnclass = "sf")

• sf is a special kind of data frame

• We can manipulate it with {tidyverse} functions like dplyr::select().

• Let’s select three columns:

Code

countries %>% 
  select(name,    # country name
         pop_est) # estimated population

Simple feature collection with 177 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
First 10 features:
                       name   pop_est                       geometry
1                      Fiji    889953 MULTIPOLYGON (((180 -16.067...
2                  Tanzania  58005463 MULTIPOLYGON (((33.90371 -0...
3                 W. Sahara    603253 MULTIPOLYGON (((-8.66559 27...
4                    Canada  37589262 MULTIPOLYGON (((-122.84 49,...
5  United States of America 328239523 MULTIPOLYGON (((-122.84 49,...
6                Kazakhstan  18513930 MULTIPOLYGON (((87.35997 49...
7                Uzbekistan  33580650 MULTIPOLYGON (((55.96819 41...
8          Papua New Guinea   8776109 MULTIPOLYGON (((141.0002 -2...
9                 Indonesia 270625568 MULTIPOLYGON (((141.0002 -2...
10                Argentina  44938712 MULTIPOLYGON (((-68.63401 -...

• What do we see?

• 5-line header and a data frame.

5.1 The `sf` header

• The header provides context about the rest of the object.

• Let’s go through the most relevant sections:

5.2 Features and Fields

• Tells you the number of features and fields in the sf object:

👉Simple feature collection with 177 features and 2 fields👈
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
Geodetic CRS: +proj=longlat +datum=WGS84

• Features are the row of the data frame.

• In our countries dataset, each country is a feature.

• Fields are the Attributes of each Feature.

• Equivalent to columns, not counting the “geometry” column.

Figure 3. Features and Fields of an `sf` object

The spData::nz dataset contains mapping information for the regions of New Zealand. How many features and fields does the dataset have?

Code

# Delete the wrong lines and run the correct line
q_nz_features_fields <- "C. 5 features and 4 fields"

5.3 Geometry

• Gives you the type of geometry in the sf object:

Simple feature collection with 177 features and 2 fields
👉Geometry type: MULTIPOLYGON👈
Dimension: XY
Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
Geodetic CRS: +proj=longlat +datum=WGS84

• Geometry is a synonym for “shape”.

• Three main geometry types: points, lines and polygons.

• Each has its respective “multi” version: multipoints, multilines and multipolygons.

Fig: Geometry types and example maps for each. Points, lines (or linestrings) and polygons are the most common geometries you will encounter.

The ne_download() function from {rnaturalearth} can be used to obtain a map of major world roads, using the code below:

Code

roads <- 
  ne_download(scale = 10, 
              category = "physical",
              type = "geographic_lines", 
              returnclass = "sf")

◘ What type of geometry is used to represent the rivers?

Code

# Delete the wrong lines and run the correct line:
q_rivers_geom_type <- "MULTILINESTRING"

• Each individual sf object can only contain one geometry type

• All points, all lines or all polygons.

• You will not find a mixture of geometries in a single sf object.

• It is related with the geometry column of the sf dataframe

• The geometry column is the most special property of the sf data frame.

• It holds the core geospatial data (points, linestrings or polygons).

👉Geometry type: MULTIPOLYGON👈

First 10 features:
                                  👇👇👇👇👇👇👇👇👇👇👇👇👇👇👇👇
                    name  pop_est                       geometry
0            Afghanistan 28400000 MULTIPOLYGON (((61.21082 35...
1                 Angola 12799293 MULTIPOLYGON (((16.32653 -5...
2                Albania  3639453 MULTIPOLYGON (((20.59025 41...
3   United Arab Emirates  4798491 MULTIPOLYGON (((51.57952 24...
4              Argentina 40913584 MULTIPOLYGON (((-65.5 -55.2...
5                Armenia  2967004 MULTIPOLYGON (((43.58275 41...
6             Antarctica     3802 MULTIPOLYGON (((-59.57209 -...
7 Fr. S. Antarctic Lands      140 MULTIPOLYGON (((68.935 -48....
8              Australia 21262641 MULTIPOLYGON (((145.398 -40...
9                Austria  8210281 MULTIPOLYGON (((16.97967 48...

• Some noteworthy points about this column:

• The geometry column can’t be dropped,

• geom_sf() automatically recognizes the geometry column.

5.4 Geodetic CRS

• Tells us the Coordinate Reference System (CRS) used.

Simple feature collection with 177 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
👉Geodetic CRS: +proj=longlat +datum=WGS84👈

• CRS relate the spatial elements of the data with the surface of Earth.

Figure 4. CRS components include (A) the Coordinate System (Longitude/Latitude) and (B) the Map **Projection** (e.g., Conical or Cylindrical)

• CRS are a key component of geographic objects.

• We will cover them in detail later!

6 Delving into Shapefiles

• They are a collection of files,

• At least three files: .shp, .shx, and .dbf.

• Related with components of a sf object.

Figure 5. Shapefile is a collection of at least three files.

• Let’s see the component files of a Shapefile called sle_adm3.shp.

• All of them are located in the same data/boundaries/ folder:

# A tibble: 4 × 1
  value       
  <chr>       
1 sle_adm3.dbf
2 sle_adm3.prj
3 sle_adm3.shp
4 sle_adm3.shx

• What is inside each file?

.shp: contains the Geometry data,
.dbf: stores the Attributes (Fields) for each shape.
.shx: is a positional index that links each Geometry with its Attributes,
.prj: plain text file describing the CRS, including the Map Projection,

• These files can be compressed into a ZIP folder and shared!

All of these sub-component files must be present in a given directory (folder) for the shapefile to be readable.

Which of the following options of component files of Shapefiles:

"shp"
"shx"
"dbf"

contains the Geometry data?

stores the Attributes for each shape?

6.1 How to write Shapefiles?

• Let’s write the countries object to an countries.shp file:

Define the file path up to the .shp filename:

"data/newshapefile/countries.shp"

Paste that path in write_sf() within here():

Code

countries %>% write_sf(here("data/newshapefile/countries.shp"))

• Now, all the components of a sf object are in four new files of one Shapefile:

# A tibble: 5 × 1
  value        
  <chr>        
1 countries.dbf
2 countries.prj
3 countries.shp
4 countries.shx
5 ignore.md

7 Wrap up

• We read and write Shapefiles using the {sf} package,

• Identified the components of an sf object, and

• Their relation with the files within a Shapefile.

Figure 6. sf package illustration by Allison Horst.

• Now we need to dive into CRS’s.

• Learn how to manage their zoom and transform them!

• Follow along with the lessons to find how to train these skills!

Contributors

The following team members contributed to this lesson:

ANDREE VALLE CAMPOS
R Developer and Instructor, the GRAPH Network
Motivated by reproducible science and education

LAURE VANCAUWENBERGHE
Data analyst, the GRAPH Network
A firm believer in science for good, striving to ally programming, health and education

KENE DAVID NWOSU
Data analyst, the GRAPH Network
Passionate about education

References

Some material in this lesson was adapted from the following sources:

Seimon, Dilinie. Administrative Boundaries. (2021). Retrieved 15 April 2022, from https://rspatialdata.github.io/admin_boundaries.html
Varsha Ujjinni Vijay Kumar. Malaria. (2021). Retrieved 15 April 2022, from https://rspatialdata.github.io/malaria.html
Batra, Neale, et al. The Epidemiologist R Handbook. Chapter 28: GIS Basics. (2021). Retrieved 01 April 2022, from https://epirhandbook.com/en/gis-basics.html
Lovelace, R., Nowosad, J., & Muenchow, J. Geocomputation with R. Chapter 2: Geographic data in R. (2019). Retrieved 01 April 2022, from https://geocompr.robinlovelace.net/spatial-class.html
Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapter 2: Spatial data and R packages for mapping. (2019). Retrieved 01 April 2022, from https://www.paulamoraga.com/book-geospatial/sec-spatialdataandCRS.html

This work is licensed under the Creative Commons Attribution Share Alike license.

Answer Key

Practice Q1

Code

q1 <- sf::read_sf(here("data/healthsites/sle_hf.shp"))
q1

Simple feature collection with 44 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -13.26473 ymin: 8.358015 xmax: -13.0821 ymax: 8.490478
Geodetic CRS:  WGS 84
# A tibble: 44 × 12
    osm_id source addrfull building healthcare operatorty addrcity name  amenity
     <dbl> <chr>  <chr>    <chr>    <chr>      <chr>      <chr>    <chr> <chr>  
 1  3.20e9 UNMEE… <NA>     <NA>     <NA>       <NA>       <NA>     Chin… hospit…
 2  3.20e9 UNMEE… <NA>     <NA>     <NA>       <NA>       <NA>     Lakk… hospit…
 3  3.21e9 https… <NA>     <NA>     <NA>       <NA>       <NA>     Prin… hospit…
 4  3.34e9 MSF-CH <NA>     <NA>     <NA>       <NA>       <NA>     COMM… clinic 
 5  3.34e9 MSF-CH <NA>     <NA>     <NA>       <NA>       <NA>     Den … clinic 
 6  3.34e9 <NA>   <NA>     <NA>     <NA>       <NA>       <NA>     MABE… clinic 
 7  3.34e9 MSF-CH <NA>     <NA>     <NA>       <NA>       <NA>     MAYE… clinic 
 8  3.34e9 MSF-CH <NA>     <NA>     <NA>       <NA>       <NA>     HEAL… clinic 
 9  3.34e9 <NA>   <NA>     <NA>     <NA>       <NA>       <NA>     Dent… dentist
10  3.34e9 MSF-CH <NA>     <NA>     <NA>       <NA>       <NA>     GINE… clinic 
# ℹ 34 more rows
# ℹ 3 more variables: healthca_1 <chr>, capacitype <chr>, geometry <POINT [°]>

Practice Q2

The spData::nz dataset contains mapping information for the regions of New Zealand. How many features and fields does the dataset have?

Code

# The correct line is:
q_nz_features_fields <- "A. 16 features and 6 fields"

Practice Q3

The ne_download() function from {rnaturalearth} can be used to obtain a map of major world roads, using the code below:

Code

roads <- 
  ne_download(scale = 10, 
              category = "physical",
              type = "geographic_lines", 
              returnclass = "sf")

What type of geometry is used to represent the rivers?

Code

# The correct line is:
q_rivers_geom_type <- "MULTILINESTRING"

Practice Q4 & Q5

Which of the following options of component files of Shapefiles: a. "shp" b. "shx" c. "dbf"

contains the Geometry data?

Code

# The correct line is:
q4 <- "shp"

stores the Attributes for each shape?

Code

# The correct line is:
q5 <- "dbf"

--- title: 'Read and Write Shapefiles' author: - name: 'Andree Valle Campos' - name: 'Laure Vancauwenberghe' - name: 'Kene David Nwosu' date: '2024-12-6' format: html: code-fold: true code-tools: true number-sections: true toc: true css: global/style/style.css --- ```{r, include = FALSE, warning = FALSE, message = FALSE} # Load packages if(!require(pacman)) install.packages("pacman") pacman::p_load(tidyverse, knitr, here) # Source functions source(here("global/functions/misc_functions.R")) # knitr settings knitr::opts_chunk$set(warning = F, message = F, class.source = "tgc-code-block", error = T) ``` ```{r,echo=FALSE} ggplot2::theme_set(new = theme_bw()) ``` ------------------------------------------------------------------------ # Introduction • We built **Thematic maps** using `{ggplot2}`. ![Figure 1. Thematic maps: (A) Choropleth map, (B) Dot map, (C) Density map with city roads as background, (D) Basemap below a Dot map.](images/intro_thematic_map_06.png) • But, how can we use **external data** from *other* GIS software? • What is the *standard file format* to **store** and **share** data? ![Figure 2. sf package illustration by Allison Horst.](images/sf-allison_horst.jpg) • Today we are going to read and write **Shapefiles**, and • Dive into the components of a **`sf` object**! ------------------------------------------------------------------------ # Learning objectives 1. Read Spatial data from **Shapefiles** using the `read_sf()` function from the `{sf}` package. 2. Identify the components of **`sf` objects**. 3. Identify the components of **Shapefiles**. 4. Write Spatial data in **Shapefiles** using `write_sf()`. ------------------------------------------------------------------------ # Prerequisites This lesson requires the following packages: ```{r,eval=TRUE,echo=TRUE,message=FALSE} if(!require('pacman')) install.packages('pacman') pacman::p_load(rnaturalearth, ggplot2, cholera, here, sf, rgeoboundaries) pacman::p_load_gh("afrimapr/afrilearndata") ``` ------------------------------------------------------------------------ # Shapefiles • The most common data format for *storing* Spatial data. ## How to read Shapefiles? • Using `read_sf()` from `{sf}`. • From **local files** with a `.shp` extension, • To a ready-to-use **`sf` object**. • Let's read the `sle_adm3.shp` file: 1. Identify the **file path** up to the `.shp` filename: ``` r "data/boundaries/sle_adm3.shp" ``` 2. Then, paste that *path* into `read_sf()` within `here()`: ```{r} shape_file <- read_sf(here("data/boundaries/sle_adm3.shp")) ``` • The output is an `sf` object and can be plotted using `geom_sf()`: ```{r,eval=FALSE} shape_file %>% class() # Plot with geom_sf # Your code here shape_file %>% ggplot() + geom_sf() ``` ::: practice Read the shapefile called `sle_hf.shp` inside the `data/healthsites/` folder. Use the `read_sf()` function: ```{r,eval = FALSE} q1 <- read_sf(here("data/healthsites/sle_hf.shp")) q1 ``` ::: • Wait! *Shapefiles* do not come alone! • They came with a list of sub-component files. • Let's check at the files in the `data/boundaries/` folder: ```{r,eval=TRUE,echo=FALSE} list.files(path = here("data/boundaries"), full.names = FALSE) %>% tibble::enframe(name = NULL) ``` • How are these files *related* with the **`sf` object**? • Let's now look under the hood to understand `sf` objects better. ------------------------------------------------------------------------ # Understanding `sf` objects • `"sf"` stands for [Simple Features](https://en.wikipedia.org/wiki/Simple_Features), a set of widely-used *standards for storing geospatial information in databases*. • Now, what do `sf` objects look like and how do we work with them? • We'll look at a slice of the `countries` object: ```{r} countries <- ne_countries(returnclass = "sf") ``` • `sf` is a special kind of **data frame** • We can manipulate it with `{tidyverse}` functions like `dplyr::select()`. • Let's select three columns: ```{r} countries %>% select(name, # country name pop_est) # estimated population ``` • What do we see? • 5-line **header** and a **data frame**. ## The `sf` header • The *header* provides context about the rest of the object. • Let's go through *the most relevant sections*: ## Features and Fields • Tells you the number of **features** and **fields** in the `sf` object: ``` 👉Simple feature collection with 177 features and 2 fields👈 Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513 Geodetic CRS: +proj=longlat +datum=WGS84 ``` • **Features** are the *row* of the data frame. • In our `countries` dataset, each country is a feature. • **Fields** are the **Attributes** of each Feature. • Equivalent to *columns*, not counting the "geometry" column. ![Figure 3. Features and Fields of an \`sf\` object](images/features_and_fields.png){width="624"} ::: practice The `spData::nz` dataset contains mapping information for the regions of New Zealand. How many features and fields does the dataset have? ```{r eval = FALSE} # Delete the wrong lines and run the correct line q_nz_features_fields <- "C. 5 features and 4 fields" ``` ::: ## Geometry • Gives you the *type of geometry* in the `sf` object: ``` Simple feature collection with 177 features and 2 fields 👉Geometry type: MULTIPOLYGON👈 Dimension: XY Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513 Geodetic CRS: +proj=longlat +datum=WGS84 ``` • **Geometry** is a synonym for *"shape"*. • Three main geometry types: points, lines and polygons. • Each has its respective *"multi"* version: multipoints, multilines and multipolygons. ![Fig: Geometry types and example maps for each. Points, lines (or linestrings) and polygons are the most common geometries you will encounter.](images/geometry_types.png) ::: practice The `ne_download()` function from {rnaturalearth} can be used to obtain a map of major world roads, using the code below: ```{r eval = F, message = F} roads <- ne_download(scale = 10, category = "physical", type = "geographic_lines", returnclass = "sf") ``` ◘ What type of geometry is used to represent the rivers? ```{r eval = FALSE} # Delete the wrong lines and run the correct line: q_rivers_geom_type <- "MULTILINESTRING" ``` ::: ::: side-note • Each **individual `sf` object** can only contain *one geometry type* • All points, all lines or all polygons. • You will not find a mixture of geometries in a single sf object. ::: ::: key-point • **It is related with the `geometry` column of the `sf` dataframe** • The `geometry` column is the most special property of the `sf` data frame. • It holds the *core* geospatial data (points, linestrings or polygons). ``` r 👉Geometry type: MULTIPOLYGON👈 First 10 features: 👇👇👇👇👇👇👇👇👇👇👇👇👇👇👇👇 name pop_est geometry 0 Afghanistan 28400000 MULTIPOLYGON (((61.21082 35... 1 Angola 12799293 MULTIPOLYGON (((16.32653 -5... 2 Albania 3639453 MULTIPOLYGON (((20.59025 41... 3 United Arab Emirates 4798491 MULTIPOLYGON (((51.57952 24... 4 Argentina 40913584 MULTIPOLYGON (((-65.5 -55.2... 5 Armenia 2967004 MULTIPOLYGON (((43.58275 41... 6 Antarctica 3802 MULTIPOLYGON (((-59.57209 -... 7 Fr. S. Antarctic Lands 140 MULTIPOLYGON (((68.935 -48.... 8 Australia 21262641 MULTIPOLYGON (((145.398 -40... 9 Austria 8210281 MULTIPOLYGON (((16.97967 48... ``` • Some noteworthy points about this column: • The `geometry` column can't be dropped, • `geom_sf()` automatically recognizes the geometry column. ::: ## Geodetic CRS • Tells us the Coordinate Reference System (CRS) used. ``` Simple feature collection with 177 features and 2 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513 👉Geodetic CRS: +proj=longlat +datum=WGS84👈 ``` • **CRS** relate the spatial elements of the data with the *surface of Earth*. ![Figure 4. CRS components include (A) the Coordinate System (Longitude/Latitude) and (B) the Map **Projection** (e.g., Conical or Cylindrical)](images/crs-coordsys_projections.JPG) • CRS are a *key component* of geographic objects. • **We will cover them in detail later!** ------------------------------------------------------------------------ # Delving into Shapefiles • They are a *collection* of files, • At least three files: `.shp`, `.shx`, and `.dbf`. • Related with components of a **`sf` object**. ![Figure 5. Shapefile is a collection of at least three files.](images/shapefile_model.png) • Let's see the component files of a *Shapefile* called `sle_adm3.shp`. • All of them are located in **the same** `data/boundaries/` folder: ```{r,eval=TRUE,echo=FALSE} list.files(path = here("data/boundaries"), full.names = FALSE) %>% tibble::enframe(name = NULL) ``` • What is inside each file? - **`.shp`**: contains the **Geometry** data, - `.dbf`: stores the **Attributes (Fields)** for each shape. - `.shx`: is a *positional index* that **links** each Geometry with its Attributes, - `.prj`: plain text file describing the **CRS**, including the Map **Projection**, • These *files* can be compressed into a ZIP folder and shared! ::: watch-out All of these *sub-component files* must be present in a given directory (folder) for the shapefile to be readable. ::: ::: practice Which of the following options of *component files of Shapefiles*: a. `"shp"` b. `"shx"` c. `"dbf"` contains the *Geometry* data? ```{r,include=FALSE} # unlock your answer: # q4 <- "shp" # q4 <- "shx" # q4 <- "dbf" ``` stores the *Attributes* for each shape? ```{r,include=FALSE} # unlock your answer: # q5 <- "shp" # q5 <- "shx" # q5 <- "dbf" ``` ::: ## How to write Shapefiles? • Let's write the `countries` object to an `countries.shp` file: 1. Define the **file path** up to the `.shp` filename: ``` r "data/newshapefile/countries.shp" ``` 2. Paste that *path* in `write_sf()` within `here()`: ```{r,warning=FALSE} countries %>% write_sf(here("data/newshapefile/countries.shp")) ``` • Now, *all the components* of a **`sf` object** are in *four new files* of one **Shapefile**: ```{r,eval=TRUE,echo=FALSE} list.files(path = here("data/newshapefile"), full.names = FALSE) %>% tibble::enframe(name = NULL) ``` ------------------------------------------------------------------------ # Wrap up • We **read** and **write** *Shapefiles* using the *`{sf}`* package, • Identified the **components** of an *`sf` object*, and • Their relation with the files within a *Shapefile*. ![Figure 6. sf package illustration by Allison Horst.](images/sf-allison_horst.jpg) • Now we need to dive into **CRS**'s. • Learn how to manage their zoom and transform them! • Follow along with the lessons to find how to train these skills! ------------------------------------------------------------------------ # Contributors {.unlisted .unnumbered} The following team members contributed to this lesson: `r tgc_contributors_list(ids = c("avallecam", "lolovanco", "kendavidn"))` ------------------------------------------------------------------------ # References {.unlisted .unnumbered} Some material in this lesson was adapted from the following sources: - *Seimon, Dilinie. Administrative Boundaries.* (2021). Retrieved 15 April 2022, from <https://rspatialdata.github.io/admin_boundaries.html> - *Varsha Ujjinni Vijay Kumar. Malaria.* (2021). Retrieved 15 April 2022, from <https://rspatialdata.github.io/malaria.html> - *Batra, Neale, et al. The Epidemiologist R Handbook. Chapter 28: GIS Basics*. (2021). Retrieved 01 April 2022, from <https://epirhandbook.com/en/gis-basics.html> - *Lovelace, R., Nowosad, J., & Muenchow, J. Geocomputation with R. Chapter 2: Geographic data in R*. (2019). Retrieved 01 April 2022, from <https://geocompr.robinlovelace.net/spatial-class.html> - *Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapter 2: Spatial data and R packages for mapping*. (2019). Retrieved 01 April 2022, from <https://www.paulamoraga.com/book-geospatial/sec-spatialdataandCRS.html> `r tgc_license()` ------------------------------------------------------------------------ ## Answer Key {.unnumbered} ### Practice Q1 {.unlisted .unnumbered} ```{r} q1 <- sf::read_sf(here("data/healthsites/sle_hf.shp")) q1 ``` ### Practice Q2 {.unlisted .unnumbered} The `spData::nz` dataset contains mapping information for the regions of New Zealand. How many features and fields does the dataset have? ```{r eval = FALSE} # The correct line is: q_nz_features_fields <- "A. 16 features and 6 fields" ``` ### Practice Q3 {.unlisted .unnumbered} The `ne_download()` function from {rnaturalearth} can be used to obtain a map of major world roads, using the code below: ```{r eval = F, message = F} roads <- ne_download(scale = 10, category = "physical", type = "geographic_lines", returnclass = "sf") ``` What type of geometry is used to represent the rivers? ```{r eval = FALSE} # The correct line is: q_rivers_geom_type <- "MULTILINESTRING" ``` ### Practice Q4 & Q5 {.unlisted .unnumbered} Which of the following options of *component files of Shapefiles*: a. `"shp"` b. `"shx"` c. `"dbf"` contains the *Geometry* data? ```{r,include = T} # The correct line is: q4 <- "shp" ``` stores the *Attributes* for each shape? ```{r,include = T} # The correct line is: q5 <- "dbf" ```

1 Introduction

2 Learning objectives

3 Prerequisites

4 Shapefiles

4.1 How to read Shapefiles?

5 Understanding sf objects

5.1 The sf header

5.2 Features and Fields

5.3 Geometry

5.4 Geodetic CRS

6 Delving into Shapefiles

6.1 How to write Shapefiles?

7 Wrap up

Contributors

References

Answer Key

Practice Q1

Practice Q2

Practice Q3

Practice Q4 & Q5

5 Understanding `sf` objects

5.1 The `sf` header