R for GIS
BayGeo, Spring 2024

Working with Attribute Tables

GIS Attribute Tables

Each layer has its own attribute table.

Common Uses of the Attribute Table

1. Symbology

2. Querying Features

3. Analysis

Exploring the Attribute Table

Before you start working with the attribute table, it helps to understand the data.

First place to check - metadata files that come with the data.

Simple Feature Data Frames

sf objects are
data frames!

You can use any function from dplyr or base R that works on data frames on sf data frames.

Most dplyr functions will return a sf object

Just the attribute table please!

To ‘drop’ the geometry column of a sf object, use st_drop_geometry() (returns a data frame)

To get the values for a single column, use the $ operator or dplyr::pull().

Exploring a Data Frame

Useful R functions to explore data frames:

View() – popular
names() summary() class() unique() table() hist()

Attribute Queries

query = select features

attribute query = select features by their attribute values

Go-to functions for attribute queries:

dplyr::filter()

dplyr::slice()

Sample Attribute Queries

## Fires from the 1930s
fires_80s <- yose_fires_pt |> filter(DECADE==1980)

## Fires between 1996 and 2005
fires_9605 <- yose_fires_pt |> filter(YEAR >= 1996 & YEAR <= 2005)

## Top 10 largest fires
fires_top10 <- yose_fires_pt |> slice_max(order_by = ACRES, n = 10)

Join an Attribute Table to Another Data Frame

To join an attribute table to another data frame based on a common column, you can use:

dplyr::left_join()
dplyr::right_join()
dplyr::inner_join()

Why would you do this?

So you can use the additional columns for:

Symbology
Querying
Analysis

Group & Summarize with sf Objects: Double-Duty

A common method in data analysis involves grouping rows in a table followed by computing summaries for each group.

This is accomplished in dplyr with group_by() and summarise():

library(palmerpenguins)
library(dplyr)
library(tidyr)

penguins |> 
  tidyr::drop_na() |> 
  group_by(species, sex) |> 
  summarize(avg_bill_len = mean(bill_length_mm),
            avg_flipper_len = mean(flipper_length_mm))

## `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.

## # A tibble: 6 × 4
## # Groups:   species [3]
##   species   sex    avg_bill_len avg_flipper_len
##   <fct>     <fct>         <dbl>           <dbl>
## 1 Adelie    female         37.3            188.
## 2 Adelie    male           40.4            192.
## 3 Chinstrap female         46.6            192.
## 4 Chinstrap male           51.1            200.
## 5 Gentoo    female         45.6            213.
## 6 Gentoo    male           49.5            222.

Group and Summarize a sf Data Frame

If you use group_by() and summarize() on a sf Data Frame, you’ll get back another SF data frame with:

the attribute values summarized for each group of features (as usual)
the union (aka dissolve) of each group of features as the geometry

Example:

We illustrate this with the attribute table from watersheds layer.

yose_watersheds_sf <- sf::st_read("./data/yose_watersheds.gpkg", layer="calw221")

tm_shape(yose_watersheds_sf) + tm_borders()

## Reading layer `calw221' from data source `D:\Workshops\R-Spatial\rspatial_mod\outputs\rspatial_bgs24\exercises\data\yose_watersheds.gpkg' using driver `GPKG'
## Simple feature collection with 127 features and 12 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 1383.82 ymin: -61442.93 xmax: 81596.71 ymax: 26405.66
## Projected CRS: NAD83 / California Albers

The watersheds attribute table has a column “HU” and “HUNAME” which are the id number and name of larger watersheds. To find the number of smaller watersheds in each larger watershed, we can run:

watersheds_df <- yose_watersheds_sf |> st_drop_geometry()

hu_df <- watersheds_df |> 
  group_by(HU) |> 
  summarise(HUNAME = first(HUNAME), 
            NUM_WATERSHEDS = n(),
            AVG_ACRES = mean(ACRES))

glimpse(hu_df)

## Rows: 6
## Columns: 4
## $ HU             <int> 1, 30, 31, 36, 37, 40
## $ HUNAME         <chr> "MONO", "EAST WALKER RIVER", "WEST WALKER RIVER", "TUOLUMNE RIVER", "MERCED RIVER", "SAN JOAQUIN RIVER"
## $ NUM_WATERSHEDS <int> 7, 5, 2, 60, 47, 6
## $ AVG_ACRES      <dbl> 9311.916, 9385.448, 6426.835, 8128.559, 8399.112, 8911.178

The exact same dplyr functions can be used with sf dataframes. The result is another sf object where the groups of features have been unioned together.

hu_sf <- yose_watersheds_sf |> 
  group_by(HU) |> 
  summarise(HUNAME = first(HUNAME), 
            NUM_WATERSHEDS = n(),
            AVG_ACRES = mean(ACRES))

hu_sf

## Plot
tm_shape(hu_sf) + 
  tm_polygons(col="HUNAME", title="HU Watershed") + 
tm_shape(yose_watersheds_sf) +
  tm_borders(col="gray60", lwd=0.8) +
tm_layout(legend.outside = T)

## Simple feature collection with 6 features and 4 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 1383.82 ymin: -61442.93 xmax: 81596.71 ymax: 26405.66
## Projected CRS: NAD83 / California Albers
## # A tibble: 6 × 5
##      HU HUNAME            NUM_WATERSHEDS AVG_ACRES                                                                                         geom
##   <int> <chr>                      <int>     <dbl>                                                                                <POLYGON [m]>
## 1     1 MONO                           7     9312. ((70613.55 -24551.79, 70576.82 -24620.61, 70555.84 -24713.47, 70550.74 -24775.76, 70553.5...
## 2    30 EAST WALKER RIVER              5     9385. ((66826.29 17825.42, 66842.88 17853.58, 66861.79 17868.39, 66915.55 17879.66, 66963.11 17...
## 3    31 WEST WALKER RIVER              2     6427. ((41216.82 21006.26, 41275.41 20974.09, 41329.18 20937.78, 41342.79 20896.13, 41363.94 20...
## 4    36 TUOLUMNE RIVER                60     8129. ((23146.17 -25588.36, 23112.63 -25612.56, 23054.77 -25681.3, 23031.97 -25713.11, 23006.08...
## 5    37 MERCED RIVER                  47     8399. ((29670.04 -58750.01, 29640.86 -58738.67, 29586.24 -58761.95, 29552.98 -58821.12, 29523.3...
## 6    40 SAN JOAQUIN RIVER              6     8911. ((55704.49 -53548.52, 55754.08 -53648.64, 55792.94 -53706.26, 55849 -53780.87, 55956.66 -...