qm_summarize: Summarize Clusters

In qualmap: Opinionated Approach for Digitizing Semi-Structured Qualitative GIS Data

Summarize Clusters

Description

This function creates a column that contains a single observation for each unique value in the key variable. For each feature, a count corresponding to the number of times that feature is identified in a cluster for the give category is also provided.

Usage

qm_summarize(ref, key, clusters, category, count, geometry = TRUE, use.na = FALSE)

Arguments

ref	An sf object that serves as a master list of features
key	Name of geographic id variable in the ref object to match input values to
clusters	A tibble created by qm_combine with two or more clusters worth of data
category	Value of the CAT variable to be analyzed
count	How should clusters be summarized: by counting each time a feature is included in a cluster ("clusters") or by counting the number of respondents ("respondents") who associated a feature with the given category.
geometry	A logical scalar that returns the full geometry and attributes of ref when TRUE (default). If FALSE, only the key and count of features is returned after validation.
use.na	A logical scalar that returns NA values in the count variable if a feature is not included in any clusters when TRUE. If FALSE (default), a 0 value is returned in the count variable for each feature that is not included in any clusters. This parameter only impacts output if the geometry argument is TRUE.

Value

A tibble or a sf object (if geometry = TRUE) that contains a count of the number of clusters a given feature is included in. The tibble option (when geometry = FALSE) will only return valid features. The sf option (default; when geometry = TRUE) will return all features with either zeros (when use.na = FALSE) or NA values (when use.na = TRUE) for features not included in any clusters.

See Also

qm_combine

Examples

# load and format reference data
stl <- stLouis
stl <- dplyr::mutate(stl, TRACTCE = as.numeric(TRACTCE))

# create clusters
cluster1 <- qm_define(118600, 119101, 119300)
cluster2 <- qm_define(119300, 121200, 121100)

# create cluster objects
cluster_obj1 <- qm_create(ref = stl, key = TRACTCE, value = cluster1,
    rid = 1, cid = 1, category = "positive")
cluster_obj2 <- qm_create(ref = stl, key = TRACTCE, value = cluster2,
    rid = 1, cid = 2, category = "positive")

# combine cluster objects
clusters <- qm_combine(cluster_obj1, cluster_obj2)

# summarize cluster objects
positive1 <- qm_summarize(ref = stl, key = TRACTCE, clusters = clusters, category = "positive",
    count = "clusters")
class(positive1)
mean(positive1$positive)

# summarize cluster objects with NA's instead of 0's
positive2 <- qm_summarize(ref = stl, key = TRACTCE, clusters = clusters, category = "positive",
    count = "clusters", use.na = TRUE)
class(positive2)
mean(positive2$positive, na.rm = TRUE)

# return tibble of valid features only
positive3 <- qm_summarize(ref = stl, key = TRACTCE, clusters = clusters, category = "positive",
    count = "clusters", geometry = FALSE)
class(positive3)
mean(positive3$positive)

# count respondents instead of clusters
positive4 <- qm_summarize(ref = stl, key = TRACTCE, clusters = clusters, category = "positive",
    count = "respondents")
mean(positive4$positive)

qualmap documentation built on May 31, 2022, 9:05 a.m.