States

Posted on July 31, 2023 by Louise E. Sinks in R bloggers | 0 Comments

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Today’s TidyTuesday takes some data about US states from Wikipedia.

Last week I saw this post on Mastodon and was really intrigued by the package mentioned, GWalkr. I thought this package looked very cool, so I put it on my to try list.

Embedding Social Media Posts in Quarto

As a side note, I used the Quarto social embeds extension to show the Mastodon toot. I use RStudio to compose my quarto documents, so to install it for this project, I run the code given at the terminal (not console) window in RStudio. I did see that the toot overflowed the text below it, so you may need to adjust the spacing with some hard returns. The specific code to render the toot is found here.

While it worked(ish) I discovered that it overflowed the following text, even with lots of space and hard returns entered after.

I also found that the actual link to the toot kept disappearing and defaulting back to the server name only (e.g. https://fosstodon.org/) which then refused the connection.

To uninstall a quarto extension, use quarto remove extension from the terminal and select the one you want from the list or use quarto remove extension - NAME_OF_EXTENSION.

Something also broke the formatting of this post. Headers, spacing, and justification are all different. Removing the extension did not restore this, so I will dig more later into this. (I suppose it could be the vis config for GWalkR, but removing that also did not fix the problem.)

Installing GWalkR

The GWalkR package isn’t available on CRAN yet, so you’ll need to install it via the devtools package using the following code: devtools::install_url("https://kanaries-app.s3.ap-northeast-1.amazonaws.com/oss/gwalkr/GWalkR_latest.tar.gz")

library(tidyverse)
library(GWalkR)

Loading the tidytuesday data.

tuesdata <- tidytuesdayR::tt_load(2023, week = 31)

states <- tuesdata$states
state_name_etymology <- tuesdata$state_name_etymology

GWalkR is a interactive EDA tool styled similarly to Tableau. It is a lighter weight version of Graphics Walker and is designed to work in R. To use it, you just call it on the dataframe.

I’ve created 3 simple visualizations that I will walk you through. You can interact with any of them and change the data as you wish. This is designed to be an exploratory interactive tool. It isn’t the best option to create static, polished figures.

Another side note, if you are doing this in quarto, you may wish to use the #| column: page option to give the gwalkr panel more room.

gwalkr(states)

Making your Viz “Stick”

This part is only necessary if you are publishing or sharing your visualizations. When you run gwalkr you first get the empty explorer/builder window. Once you have created the visualizations, click on the export configuration button (like <>) and copy the text into a code block. This initializes GWalkr to the way you created it. Without this, you end up with an empty viewer every time you run the code. I’m including the entire configuration here, since this is a semi-tutorial format, but you’d probably want to hide this in your markdown/ quarto document. This code chunk also works find when I am rendering my Quarto document, but fails when I am executing code normally.

visConfig <- '[{"visId":"gw_Gtm3","name":"Bar Chart of State Pop.","encodings":{"dimensions":[{"dragId":"gw_jx5q","fid":"c3RhdGU=","name":"state","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_3nGt","fid":"cG9zdGFsX2FiYnJldmlhdGlvbg==","name":"postal_abbreviation","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_FNnC","fid":"Y2FwaXRhbF9jaXR5","name":"capital_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_-YgP","fid":"bGFyZ2VzdF9jaXR5","name":"largest_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_QC9G","fid":"YWRtaXNzaW9u","name":"admission","semanticType":"temporal","analyticType":"dimension"},{"dragId":"gw_hm9z","fid":"ZGVtb255bQ==","name":"demonym","semanticType":"nominal","analyticType":"dimension"}],"measures":[{"dragId":"gw_FnuC","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_hRWB","fid":"dG90YWxfYXJlYV9taTI=","name":"total_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_cUQK","fid":"dG90YWxfYXJlYV9rbTI=","name":"total_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_QH84","fid":"bGFuZF9hcmVhX21pMg==","name":"land_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_tzCn","fid":"bGFuZF9hcmVhX2ttMg==","name":"land_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_1ggm","fid":"d2F0ZXJfYXJlYV9taTI=","name":"water_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_0lnU","fid":"d2F0ZXJfYXJlYV9rbTI=","name":"water_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_eiwY","fid":"bl9yZXByZXNlbnRhdGl2ZXM=","name":"n_representatives","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_count_fid","fid":"gw_count_fid","name":"Row count","analyticType":"measure","semanticType":"quantitative","aggName":"sum","computed":true,"expression":{"op":"one","params":[],"as":"gw_count_fid"}}],"rows":[{"dragId":"gw_Tlb_","fid":"c3RhdGU=","name":"state","semanticType":"nominal","analyticType":"dimension"}],"columns":[{"dragId":"gw_tHvs","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"color":[],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[],"filters":[],"text":[]},"config":{"defaultAggregated":true,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","zeroScale":true,"size":{"mode":"auto","width":320,"height":200},"format":{}}},{"visId":"gw_uHPW","name":"Area vs. Pop (Agg)","encodings":{"dimensions":[{"dragId":"gw_fHm7","fid":"c3RhdGU=","name":"state","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_GIbq","fid":"cG9zdGFsX2FiYnJldmlhdGlvbg==","name":"postal_abbreviation","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_j9gr","fid":"Y2FwaXRhbF9jaXR5","name":"capital_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_UlJ3","fid":"bGFyZ2VzdF9jaXR5","name":"largest_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_7t-q","fid":"YWRtaXNzaW9u","name":"admission","semanticType":"temporal","analyticType":"dimension"},{"dragId":"gw__0zS","fid":"ZGVtb255bQ==","name":"demonym","semanticType":"nominal","analyticType":"dimension"}],"measures":[{"dragId":"gw_5ToL","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_hIf6","fid":"dG90YWxfYXJlYV9taTI=","name":"total_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_MxWn","fid":"dG90YWxfYXJlYV9rbTI=","name":"total_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_YbvT","fid":"bGFuZF9hcmVhX21pMg==","name":"land_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_nnL-","fid":"bGFuZF9hcmVhX2ttMg==","name":"land_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_zJz3","fid":"d2F0ZXJfYXJlYV9taTI=","name":"water_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_67yP","fid":"d2F0ZXJfYXJlYV9rbTI=","name":"water_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_ta24","fid":"bl9yZXByZXNlbnRhdGl2ZXM=","name":"n_representatives","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_count_fid","fid":"gw_count_fid","name":"Row count","analyticType":"measure","semanticType":"quantitative","aggName":"sum","computed":true,"expression":{"op":"one","params":[],"as":"gw_count_fid"}}],"rows":[{"dragId":"gw_YKoG","fid":"dG90YWxfYXJlYV9taTI=","name":"total_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"columns":[{"dragId":"gw_3HaQ","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"color":[],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[],"filters":[],"text":[]},"config":{"defaultAggregated":true,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","zeroScale":true,"size":{"mode":"auto","width":320,"height":200},"format":{}}},{"visId":"gw_RuCs","name":"Area vs. Pop (dis-agg)","encodings":{"dimensions":[{"dragId":"gw_f-aN","fid":"c3RhdGU=","name":"state","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_T0HW","fid":"cG9zdGFsX2FiYnJldmlhdGlvbg==","name":"postal_abbreviation","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_Uga-","fid":"Y2FwaXRhbF9jaXR5","name":"capital_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_CZOt","fid":"bGFyZ2VzdF9jaXR5","name":"largest_city","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_NhIi","fid":"YWRtaXNzaW9u","name":"admission","semanticType":"temporal","analyticType":"dimension"},{"dragId":"gw_kD6Y","fid":"ZGVtb255bQ==","name":"demonym","semanticType":"nominal","analyticType":"dimension"}],"measures":[{"dragId":"gw_80B1","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_oncj","fid":"dG90YWxfYXJlYV9taTI=","name":"total_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_p-Ch","fid":"dG90YWxfYXJlYV9rbTI=","name":"total_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_MtXt","fid":"bGFuZF9hcmVhX21pMg==","name":"land_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_fGM4","fid":"bGFuZF9hcmVhX2ttMg==","name":"land_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_s6UR","fid":"d2F0ZXJfYXJlYV9taTI=","name":"water_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_D1e8","fid":"d2F0ZXJfYXJlYV9rbTI=","name":"water_area_km2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_4hWE","fid":"bl9yZXByZXNlbnRhdGl2ZXM=","name":"n_representatives","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_count_fid","fid":"gw_count_fid","name":"Row count","analyticType":"measure","semanticType":"quantitative","aggName":"sum","computed":true,"expression":{"op":"one","params":[],"as":"gw_count_fid"}}],"rows":[{"dragId":"gw_g_M-","fid":"dG90YWxfYXJlYV9taTI=","name":"total_area_mi2","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"columns":[{"dragId":"gw_A9lF","fid":"cG9wdWxhdGlvbl8yMDIw","name":"population_2020","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"color":[],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[{"dragId":"gw_Fv7k","fid":"c3RhdGU=","name":"state","semanticType":"nominal","analyticType":"dimension"}],"filters":[],"text":[]},"config":{"defaultAggregated":true,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","zeroScale":true,"size":{"mode":"auto","width":320,"height":200},"format":{}}}]'
gwalkr(data=states, visConfig=visConfig)

Bar Chart of State Population

GWalkr produces a Tableau style interface. To graph something, just drag variables to X-axis and Y-axis. I did population (x) and state (y). The default setting is to autogenerate the best possible visualization, so you end up with the bar chart. You can transpose the axes using the circular arrows button at the top of the screen- probably not the best choice for this dataset though.

It doesn’t look like you can join data in GWalkR yet, so I’m going to do that now in R.

states_joined <- states %>% left_join(state_name_etymology)
Joining with `by = join_by(state)`

Now explore by starting gwalkr the same way.

gwalkr(states_joined)
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