Week 3: From Base R to sf

PPOL 6805: GIS for Spatial Data Science

Christy Hsu
th1010@georgetown.edu

2026-09-11

Intro to Coding Workshop!

Connecting Positron to Jupyterhub

Outline for Today

  • Base R syntax and coding style
  • R data types and data structures
    • Working with Vectors and Data Frames

Base R syntax and coding style

Variables

<-: initialize a variable and assign a value

# variable_name <- variable_value
x <- 1984
y <- "World Geodetic System"
  • R variable naming

R Data Types class()

  • “numeric”
    • double
    • integer
  • “character”
  • “logical”
print(class(x))
[1] "numeric"
print(class(y))
[1] "character"
z <- class(x) == class(y)
print(class(z))
[1] "logical"
  • Operators and Logical Operators
  • Type Casting

R Data Structures

  • Vector
  • Matrix
  • Array
  • Factor
  • List
  • Data Frame

Starting with Vectors

Ways to create a vector

  • c()
  • seq(from, to, by)
  • :
v1 <- c(
    "Bryce Canyon", "Canyonlands", "Arches", "Zion", "Capitol Reef"
    )
v2 <- 1:5
v2
[1] 1 2 3 4 5
v3 <- seq(21.9, 25.3, 0.1)
is.vector(v2)
[1] TRUE

Subsetting a Vector

  • Indexing
# v1 <- c(
    # "Bryce Canyon", "Canyonlands", "Arches", "Zion", "Capitol Reef"
    # )
v1[1]
[1] "Bryce Canyon"
v1[-1]
[1] "Canyonlands"  "Arches"       "Zion"         "Capitol Reef"
  • Subsetting using Logical Vectors
v4 <- 1:12
bool_v4 <- v4 > 8
v4[bool_v4]
[1]  9 10 11 12
  • Access by name
names(v4) <- month.abb
v4["Jan"]
Jan 
  1

Data Frame objects

  • Thinking of data.frame as a collection of column vectors

Ways to create a df

mth_df <- data.frame(
    mth = 1:12,
    mth_str = month.abb
)
mth_df
mth mth_str
1 Jan
2 Feb
3 Mar
4 Apr
5 May
6 Jun
7 Jul
8 Aug
9 Sep
10 Oct
11 Nov
12 Dec 
mth_df |> class()
[1] "data.frame"
  • import external tabular dataset

data.frame attributes

utah_df <- data.frame(
    national_park = v1,
    lat = c(
        37.640621053549125, 38.478777627059635, 38.6167568289248,
        37.200271934321734, 38.291603924096385
        ),
    lon = c(
        -112.16957627116382, -109.8251716515892, -109.61982474559946, 
        -112.98700616100083, -111.2619347149233
        )
)
head(utah_df, 2)
national_park lat lon
Bryce Canyon 37.64062 -112.1696
Canyonlands 38.47878 -109.8252 
# nrow() and ncol()
dim(utah_df)
[1] 5 3
colnames(utah_df)
[1] "national_park" "lat"           "lon"

Subsetting data.frame

print(utah_df[, 1])
[1] "Bryce Canyon" "Canyonlands"  "Arches"       "Zion"         "Capitol Reef"
print(utah_df[1, ])
  national_park      lat       lon
1  Bryce Canyon 37.64062 -112.1696
print(utah_df$national_park)
[1] "Bryce Canyon" "Canyonlands"  "Arches"       "Zion"         "Capitol Reef"
utah_df[["national_park"]]
[1] "Bryce Canyon" "Canyonlands"  "Arches"       "Zion"         "Capitol Reef"

R List

  • A vector that allows to have elements of different data type
  • Nested
summary(utah_df)
 national_park           lat             lon        
 Length:5           Min.   :37.20   Min.   :-113.0  
 Class :character   1st Qu.:37.64   1st Qu.:-112.2  
 Mode  :character   Median :38.29   Median :-111.3  
                    Mean   :38.05   Mean   :-111.2  
                    3rd Qu.:38.48   3rd Qu.:-109.8  
                    Max.   :38.62   Max.   :-109.6  
class(utah_df[1])
[1] "data.frame"

Getting to sf and a map

library(sf)
library(mapview)
utah_sf <- st_as_sf(
    utah_df,
    coords = c('lon', 'lat'),
    crs = 4326
)
utah_sf |> mapview()
utah_sf
national_park geometry
Bryce Canyon POINT (-112.1696 37.64062)
Canyonlands POINT (-109.8252 38.47878)
Arches POINT (-109.6198 38.61676)
Zion POINT (-112.987 37.20027)
Capitol Reef POINT (-111.2619 38.2916)
  • prelude to factor class: How were the points colored? Why are we having a legend on the map?
    • How to avoid presenting this map