Merge pull request #63 from UrbanInstitute/amy

add interactive plot

.Rhistory

@@ -1,38 +1,3 @@
-limits = c(0.04, 0.1),
-labels = scales::percent) +
-scale_y_continuous(expand = expansion(mult = c(0, 0.002)),
-breaks = c(0, 0.01, 0.02, 0.03, 0.04, 0.05),
-limits = c(0, 0.05),
-labels = scales::percent) +
-labs(x = "Seasonally-adjusted unemployment rate",
-y = "Seasonally-adjusted vacancy rate") +
-scatter_grid()
-# Chunk 22: slope-plot
-# https://www.bls.gov/lau/
-library(ggrepel)
-unemployment <- tibble(
-time = c("October 2009", "October 2009", "October 2009", "August 2017", "August 2017", "August 2017"),
-rate = c(7.4, 7.1, 10.0, 3.9, 3.8, 6.4),
-state = c("Maryland", "Virginia", "Washington, D.C.", "Maryland", "Virginia", "Washington, D.C.")
-)
-label <- tibble(label = c("October 2009", "August 2017"))
-october <- filter(unemployment, time == "October 2009")
-august <- filter(unemployment, time == "August 2017")
-unemployment %>%
-mutate(time = factor(time, levels = c("October 2009", "August 2017")),
-state = factor(state, levels = c("Washington, D.C.", "Maryland", "Virginia"))) %>%
-ggplot() +
-geom_line(aes(time, rate, group = state, color = state), show.legend = FALSE) +
-geom_point(aes(x = time, y = rate, color = state)) +
-labs(subtitle = "Unemployment Rate") +
-theme(axis.ticks.x = element_blank(),
-axis.title.x = element_blank(),
-axis.ticks.y = element_blank(),
-axis.title.y = element_blank(),
-axis.text.y = element_blank(),
-panel.grid.major.y = element_blank(),
-panel.grid.minor.y = element_blank(),
-panel.grid.major.x = element_blank(),
 axis.line = element_blank()) +
 geom_text_repel(data = october, mapping = aes(x = time, y = rate, label = as.character(rate)), nudge_x = -0.06) +
 geom_text_repel(data = august, mapping = aes(x = time, y = rate, label = as.character(rate)), nudge_x = 0.06)
@@ -510,3 +475,38 @@ palette_urbn_main[1:4]
 palette_urbn_spacegray[1:5]
 # Chunk 59: System Info and Package Versioning
 sessionInfo()
+renv::status()
+# load ggsankey package
+remotes::install_github("davidsjoberg/ggsankey")
+# create a dummy dataset of housing status
+df <- data_frame(entry_status = c(rep("Housed", 7), rep("Unhoused", 15), rep("Staying w/ Family", 8)),
+exit_status = c(rep("Housed", 15), rep("Unhoused", 2), rep("Staying w/ Family", 13))) %>%
+# transform the data frame into the proper format for the sankey plot
+make_long(entry_status, exit_status) %>%
+# recode the labels to be cleaner in the plot
+mutate(x = recode(x, entry_status = "Prior Housing Status", exit_status = "Exit Housing Status"),
+next_x = recode(next_x, entry_status = "Prior Housing Status", exit_status = "Exit Housing Status"))
+library(tidyverse)
+# load ggsankey package
+remotes::install_github("davidsjoberg/ggsankey")
+library(ggsankey)
+# create a dummy dataset of housing status
+df <- data_frame(entry_status = c(rep("Housed", 7), rep("Unhoused", 15), rep("Staying w/ Family", 8)),
+exit_status = c(rep("Housed", 15), rep("Unhoused", 2), rep("Staying w/ Family", 13))) %>%
+# transform the data frame into the proper format for the sankey plot
+make_long(entry_status, exit_status) %>%
+# recode the labels to be cleaner in the plot
+mutate(x = recode(x, entry_status = "Prior Housing Status", exit_status = "Exit Housing Status"),
+next_x = recode(next_x, entry_status = "Prior Housing Status", exit_status = "Exit Housing Status"))
+# create sankey plot
+ggplot(df, aes(x = x,
+next_x = next_x,
+node = node,
+next_node = next_node,
+fill = factor(node),
+label = node)) +
+geom_sankey(flow.alpha = 0.5, node.color = 1, show.legend = FALSE) +
+# add labels to plot and style
+geom_sankey_label(size = 3.5, color = 1, fill = "white") +
+theme_sankey(base_size = 16)+
+labs(x = NULL)

.Rproj.user/4C8EE3A7/pcs/source-pane.pper

@@ -1,3 +1,3 @@
 {
-    "activeTab": 5
+    "activeTab": 0
 }

.Rproj.user/4C8EE3A7/pcs/windowlayoutstate.pper

@@ -1,13 +1,13 @@
 {
     "left": {
-        "splitterpos": 157,
+        "splitterpos": 246,
         "topwindowstate": "NORMAL",
         "panelheight": 682,
         "windowheight": 720
     },
     "right": {
-        "splitterpos": 426,
-        "topwindowstate": "NORMAL",
+        "splitterpos": 424,
+        "topwindowstate": "MINIMIZE",
         "panelheight": 682,
         "windowheight": 720
     }

.Rproj.user/4C8EE3A7/pcs/workbench-pane.pper

@@ -1,5 +1,5 @@
 {
     "TabSet1": 0,
-    "TabSet2": 1,
+    "TabSet2": 4,
     "TabZoom": {}
 }

.Rproj.user/4C8EE3A7/sources/prop/53D28D73

@@ -5,7 +5,7 @@
     "source_window_id": "",
     "Source": "Source",
     "cursorPosition": "1101,0",
-    "scrollLine": "0",
+    "scrollLine": "299",
     "docOutlineVisible": "1",
     "rmdVisualCollapsedChunks": "",
     "rmdVisualModeLocation": "33850:26770.400390625",

.Rproj.user/4C8EE3A7/sources/prop/91139DE3

@@ -1,13 +1,13 @@
 {
-    "rmdVisualMode": "true",
+    "rmdVisualMode": "false",
     "rmdVisualWrapConfigured": "true",
     "tempName": "Untitled1",
     "source_window_id": "",
     "Source": "Source",
-    "cursorPosition": "884,0",
-    "scrollLine": "0",
+    "cursorPosition": "1354,15",
+    "scrollLine": "1352",
     "docOutlineVisible": "1",
     "rmdVisualCollapsedChunks": "",
-    "rmdVisualModeLocation": "33796:16858.400390625",
+    "rmdVisualModeLocation": "50667:25980",
     "chunk_output_type": "console"
 }

.quarto/crossref/index.qmd/index.html.json

@@ -1 +1 @@
-{"entries":[],"headings":["r-users-group","sign-up-for-list-serv","section","section-1","contact-info","r-lunch-labs"]}
+{"headings":["r-users-group","sign-up-for-list-serv","section","section-1","contact-info","r-lunch-labs"],"entries":[]}

.quarto/crossref/optimization.qmd/optimization.html.json

@@ -1 +1 @@
-{"headings":["introduction","update-your-installation","profiling-benchmarking","parallel-computing","learn-lapplypurrrmap","motivating-example","a-somewhat-more-complex-example","additional-packages","the-parallel-package","the-doparallel-package","machine-learning---caret","big-data","object-size","each-of-these-data-types-occupies-a-different-amount-of-space-in-memory","cloud-computing","common-pitfalls","for-loops-and-vector-allocation","vectorized-functions"],"entries":[]}
+{"entries":[],"headings":["introduction","update-your-installation","profiling-benchmarking","parallel-computing","learn-lapplypurrrmap","motivating-example","a-somewhat-more-complex-example","additional-packages","the-parallel-package","the-doparallel-package","machine-learning---caret","big-data","object-size","each-of-these-data-types-occupies-a-different-amount-of-space-in-memory","cloud-computing","common-pitfalls","for-loops-and-vector-allocation","vectorized-functions"]}

.quarto/idx/index.qmd.json

@@ -1 +1 @@
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+{"title":"R Users Group","markdown":{"yaml":{"output":{"html_document":{"includes":{"in_header":"analytics.html"},"output_dir":"."}}},"headingText":"R Users Group","containsRefs":false,"markdown":"\n\n<link rel=\"stylesheet\" href=\"//fonts.googleapis.com/css?family=Lato\" />\n\n<div id=\"header\">\n<img src=\"graphics-guide/www/images/urban-institute-logo.png\" width=\"350\">\n</div>\n\n\nThis website contains resources for using R at the Urban Institute for analysis, visualization, mapping, and more. Click on the links above to get started learning about R!\n\n*The Urban Institute R Users Group is committed to exposing researchers to the joy and power of R; developing beginner, intermediate, and advanced R skills; encouraging and supporting novel applications of R to public policy research; and building a diverse and mutually supportive community of R Users.*\n\n<!-- ![](images/heartyr.gif) -->\n\n<img src=\"images/heartyr.gif\" alt=\"gif of the R logo with hearts pourint out of it\" width=\"200\" height=\"200\"/>\n\ngif credits: [Allison Horst](https://twitter.com/allison_horst)\n\n## Sign up for List Serv!\n\nPlease fill out the following form to receive email updates about upcoming RUG events and trainings. We promise not to spam your inbox:\n\n###\n\n###\n\n<center>\n<iframe width=\"600\" height=\"330\" frameborder=\"0\" src=\"https://app.smartsheet.com/b/form/b47eba5ed1d74f8ab06f017311c339b9\"></iframe>\n</center>\n\n<center>\n\nFill out [this Smartsheet form](https://app.smartsheet.com/b/form/0e9d04ced47b489b8d14971ae6c2fb15) to unsubscribe from the RUG List Serv.\n\n</center>\n\n## Contact Info\n\nPlease don't hesitate to contact Aaron Williams ([email protected]) or Amy Rogin ([email protected]) with any thoughts or questions about R at the Urban Institute. \n\n## R Lunch Labs\n\nThe Urban Institute R Users Group hosts weekly lunch labs. R Lunch Labs are hands-on trainings for R users of all skill levels and soon-to-be R users. Each meeting begins with a 5-10 minute quick tip. Afterwards, attendees break into small groups and work on a range of topics including introduction to R, data management and plotting, mapping, and machine learning. 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Click on the links above to get started learning about R!\n\n*The Urban Institute R Users Group is committed to exposing researchers to the joy and power of R; developing beginner, intermediate, and advanced R skills; encouraging and supporting novel applications of R to public policy research; and building a diverse and mutually supportive community of R Users.*\n\n<!-- ![](images/heartyr.gif) -->\n\n<img src=\"images/heartyr.gif\" alt=\"gif of the R logo with hearts pourint out of it\" width=\"200\" height=\"200\"/>\n\ngif credits: [Allison Horst](https://twitter.com/allison_horst)\n\n## Sign up for List Serv!\n\nPlease fill out the following form to receive email updates about upcoming RUG events and trainings. We promise not to spam your inbox:\n\n###\n\n###\n\n<center>\n<iframe width=\"600\" height=\"330\" frameborder=\"0\" src=\"https://app.smartsheet.com/b/form/b47eba5ed1d74f8ab06f017311c339b9\"></iframe>\n</center>\n\n<center>\n\nFill out [this Smartsheet form](https://app.smartsheet.com/b/form/0e9d04ced47b489b8d14971ae6c2fb15) to unsubscribe from the RUG List Serv.\n\n</center>\n\n## Contact Info\n\nPlease don't hesitate to contact Aaron Williams ([email protected]) or Amy Rogin ([email protected]) with any thoughts or questions about R at the Urban Institute. \n\n## R Lunch Labs\n\nThe Urban Institute R Users Group hosts weekly lunch labs. R Lunch Labs are hands-on trainings for R users of all skill levels and soon-to-be R users. Each meeting begins with a 5-10 minute quick tip. Afterwards, attendees break into small groups and work on a range of topics including introduction to R, data management and plotting, mapping, and machine learning. Most users bring laptops, but there are a few extras for users without laptops. \n\nWe have currently paused R Lunch Labs, but they will be back soon! 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