- Notifications
You must be signed in to change notification settings - Fork0
Unofficial R Metopio Health Atlas Wrapper
License
Unknown, MIT licenses found
Licenses found
ryanzomorrodi/healthatlas
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
Explore and ImportMetopio Powered Health Atlases.
Metopio helps many public health departmentsbuild curated data platforms. These data platforms are a convenient wayfor community members and researchers to explore and download publichealth data. With the same spirit in mind, this package aims to makeinterfacing with these data portals within R morepainless andreproducible.
Note: This is an unofficial R wrapper for Metopio Health Atlases.This package is in no way affiliated with the Metopio or any of thepublic health departments Metopio serves.
Examples of Metopio health atlases include:
- Chicago Health Atlas:https://chicagohealthatlas.org/
- Cook County Health Atlas:https://cookcountyhealthatlas.org/
- University of Illinois Cancer Center Data Hub:https://uicc.metop.io/
- Idaho Oregon Community Health Atlas:https://idahooregoncommunityhealthatlas.org/
- Northern Kentucky Atlas:https://atlas.northernkentuckyusa.com/
- Allen County Health Atlas:https://allencountyhealthatlas.org/
- Kane County Health Atlas:https://kanehealthatlas.org/
- Contra Costa Health Atlas:https://atlas.cchealth.org/
You can install healthatlas from CRAN.
install.packages("healthatlas")Or, you can install the development version of healthatlas fromR-universe with:
install.packages("healthatlas",repos= c("https://ryanzomorrodi.r-universe.dev","https://cloud.r-project.org"))
library(healthatlas)Set your health atlas. For this example, we are going to use the ChicagoHealth Atlas, and can do so, by providing the Chicago Health Atlas URLtoha_set().
ha_set("chicagohealthatlas.org")We can list all the topics (aka indicators) present within ChicagoHealth Atlas usingha_topics(). The most important column here is thetopic_key which can be used to identify the topic within subsequentfunctions.
ha_topics(progress=FALSE)#> # A tibble: 411 × 7#> topic_name topic_key topic_description topic_units topic_keywords#> <chr> <chr> <chr> <chr> <list>#> 1 9th grade education r… EDA Residents 25 or … % of resid… <chr [2]>#> 2 ACA marketplace enrol… ENR Number of plan s… plan selec… <chr [5]>#> 3 Accidents mortality VRAC Number of people… count of d… <chr [2]>#> 4 Accidents mortality r… VRACR Age-adjusted rat… per 100,00… <chr [2]>#> 5 Active business licen… CHANVYI Count of active … licenses p… <chr [1]>#> 6 Adult asthma HCSATH Number of adults… count of a… <chr [2]>#> 7 Adult asthma rate HCSATHP Percent of adult… % of adults <chr [2]>#> 8 Adult binge drinking HCSBD Number of adults… count of a… <chr [1]>#> 9 Adult binge drinking … HCSBDP Percent of adult… % of adults <chr [1]>#> 10 Adult diabetes HCSDIA Number of adults… count of a… <chr [1]>#> # ℹ 401 more rows#> # ℹ 2 more variables: topic_datasets <list>, topic_subcategories <list>
Then, we can explore what populations, time periods, and geographicscales that data is available for usingha_coverage(). Again, the mostimportant columns here are the key columns which can be used to specifythe data desired.
ha_coverage("EDA",progress=FALSE)#> # A tibble: 156 × 7#> topic_key population_key population_name population_grouping period_key#> <chr> <chr> <chr> <chr> <chr>#> 1 EDA "" Full population "" 2011-2015#> 2 EDA "" Full population "" 2007-2011#> 3 EDA "F" Female "Sex" 2016-2020#> 4 EDA "F" Female "Sex" 2015-2019#> 5 EDA "F" Female "Sex" 2014-2018#> 6 EDA "" Full population "" 2009-2013#> 7 EDA "" Full population "" 2018-2022#> 8 EDA "" Full population "" 2017-2021#> 9 EDA "" Full population "" 2006-2010#> 10 EDA "" Full population "" 2015-2019#> # ℹ 146 more rows#> # ℹ 2 more variables: layer_key <chr>, layer_name <chr>
Now, we can import our data usingha_data() specifying the keys weidentified above.
data<- ha_data(topic_key="EDA",population_key="",period_key="2018-2022",layer_key="neighborhood")data#> # A tibble: 77 × 7#> geoid topic_key population_key period_key layer_key value standardError#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>#> 1 1714000-35 EDA "" 2018-2022 neighborh… 96.1 4.64#> 2 1714000-36 EDA "" 2018-2022 neighborh… 98.5 4.64#> 3 1714000-37 EDA "" 2018-2022 neighborh… 96.0 6.93#> 4 1714000-38 EDA "" 2018-2022 neighborh… 97.0 4.69#> 5 1714000-39 EDA "" 2018-2022 neighborh… 98.4 7.34#> 6 1714000-4 EDA "" 2018-2022 neighborh… 96.1 3.18#> 7 1714000-40 EDA "" 2018-2022 neighborh… 97.5 6.86#> 8 1714000-41 EDA "" 2018-2022 neighborh… 99.0 5.27#> 9 1714000-42 EDA "" 2018-2022 neighborh… 96.3 3.63#> 10 1714000-1 EDA "" 2018-2022 neighborh… 93.9 2.72#> # ℹ 67 more rows
Let’s create a map. But first, we will need to download the CommunityAreas geographic layer. We can do that withha_layer().
layer<- ha_layer(layer_key="neighborhood")layer#> Simple feature collection with 77 features and 6 fields#> Geometry type: MULTIPOLYGON#> Dimension: XY#> Bounding box: xmin: -87.94011 ymin: 41.64454 xmax: -87.52419 ymax: 42.02305#> Geodetic CRS: WGS 84#> First 10 features:#> geoid layer_key name population state#> 1 1714000-1 neighborhood Rogers Park (Chicago, IL) 55454 IL#> 2 1714000-10 neighborhood Norwood Park (Chicago, IL) 41069 IL#> 3 1714000-11 neighborhood Jefferson Park (Chicago, IL) 26201 IL#> 4 1714000-12 neighborhood Forest Glen (Chicago, IL) 19579 IL#> 5 1714000-13 neighborhood North Park (Chicago, IL) 17522 IL#> 6 1714000-14 neighborhood Albany Park (Chicago, IL) 48549 IL#> 7 1714000-15 neighborhood Portage Park (Chicago, IL) 63038 IL#> 8 1714000-16 neighborhood Irving Park (Chicago, IL) 51911 IL#> 9 1714000-17 neighborhood Dunning (Chicago, IL) 43120 IL#> 10 1714000-18 neighborhood Montclare (Chicago, IL) 14412 IL#> notes geometry#> 1 Far North Side MULTIPOLYGON (((-87.65456 4...#> 2 Far North Side MULTIPOLYGON (((-87.78002 4...#> 3 Far North Side MULTIPOLYGON (((-87.75264 4...#> 4 Far North Side MULTIPOLYGON (((-87.72642 4...#> 5 Far North Side MULTIPOLYGON (((-87.7069 41...#> 6 Far North Side MULTIPOLYGON (((-87.70404 4...#> 7 Northwest Side MULTIPOLYGON (((-87.75264 4...#> 8 Northwest Side MULTIPOLYGON (((-87.69475 4...#> 9 Northwest Side MULTIPOLYGON (((-87.77621 4...#> 10 Northwest Side MULTIPOLYGON (((-87.78942 4...
Now we can make our map!
library(dplyr)library(ggplot2)map_data<-layer|> left_join(data,"geoid") ggplot(map_data)+ geom_sf(aes(fill=value),alpha=0.7)+ scale_fill_distiller(palette="GnBu",direction=1)+ labs(title="9th Grade Education Rate",fill="" )+ theme_minimal()
About
Unofficial R Metopio Health Atlas Wrapper
Resources
License
Unknown, MIT licenses found
Licenses found
Uh oh!
There was an error while loading.Please reload this page.
Stars
Watchers
Forks
Uh oh!
There was an error while loading.Please reload this page.