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hrbrmstr/ahealth.md

Last active June 30, 2019 12:09
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  1. hrbrmstr revised this gist Apr 11, 2016. 1 changed file with 2 additions and 0 deletions.
    2 changes: 2 additions & 0 deletions geom_dumbbell.r
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    library(dplyr)
    library(tidyr)
    library(scales)
    library(ggplot2)
    library(ggalt) # devtools::install_github("hrbrmstr/ggalt")

    health <- read.csv("zhealth.csv", stringsAsFactors=FALSE,
    header=FALSE, col.names=c("pct", "area_id"))
  2. hrbrmstr revised this gist Apr 11, 2016. 1 changed file with 35 additions and 0 deletions.
    35 changes: 35 additions & 0 deletions geom_dumbbell.r
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    library(dplyr)
    library(tidyr)
    library(scales)

    health <- read.csv("zhealth.csv", stringsAsFactors=FALSE,
    header=FALSE, col.names=c("pct", "area_id"))

    areas <- read.csv("zarea_trans.csv", stringsAsFactors=FALSE, header=TRUE)

    health %>%
    mutate(area_id=trunc(area_id)) %>%
    arrange(area_id, pct) %>%
    mutate(year=rep(c("2014", "2013"), 26),
    pct=pct/100) %>%
    left_join(areas, "area_id") %>%
    mutate(area_name=factor(area_name, levels=unique(area_name))) -> health

    setNames(bind_cols(filter(health, year==2014), filter(health, year==2013))[,c(4,1,5)],
    c("area_name", "pct_2014", "pct_2013")) -> health


    gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=area_name, group=area_name))
    gg <- gg + geom_dumbbell(color="#a3c4dc", size=0.75, point.colour.l="#0e668b")
    gg <- gg + scale_x_continuous(label=percent)
    gg <- gg + labs(x=NULL, y=NULL)
    gg <- gg + theme_bw()
    gg <- gg + theme(plot.background=element_rect(fill="#f7f7f7"))
    gg <- gg + theme(panel.background=element_rect(fill="#f7f7f7"))
    gg <- gg + theme(panel.grid.minor=element_blank())
    gg <- gg + theme(panel.grid.major.y=element_blank())
    gg <- gg + theme(panel.grid.major.x=element_line())
    gg <- gg + theme(axis.ticks=element_blank())
    gg <- gg + theme(legend.position="top")
    gg <- gg + theme(panel.border=element_blank())
    gg
  3. hrbrmstr revised this gist Oct 24, 2015. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion ahealth.md
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    @@ -5,6 +5,6 @@ This hit `#rstats` today:

    So, I figured it was worth a cpl mins to reproduce.

    While the US gov did give the data behind the chart it was _all_ the data and a pain to work with so I used [# WebPlotDigitizer](http://arohatgi.info/WebPlotDigitizer/app/) to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.
    While the US gov did give the data behind the chart it was _all_ the data and a pain to work with so I used [WebPlotDigitizer](http://arohatgi.info/WebPlotDigitizer/app/) to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.

    It is possible to make the top "dumbbell" legend in ggplot2 (but not by using a guide) and color the "All Metro Areas" text but that's an exercise left to the reader (totally doable and not much code, but not the point of the example).
  4. hrbrmstr revised this gist Oct 23, 2015. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion ahealth.md
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    @@ -7,4 +7,4 @@ So, I figured it was worth a cpl mins to reproduce.

    While the US gov did give the data behind the chart it was _all_ the data and a pain to work with so I used [# WebPlotDigitizer](http://arohatgi.info/WebPlotDigitizer/app/) to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.

    It is possible to make the top "dumbell" legend in ggplot2 (but not by using a guide) and color the "All Metro Areas" text but that's an exercise left to the reader (totally doable and not much code, but not the point of the example).
    It is possible to make the top "dumbbell" legend in ggplot2 (but not by using a guide) and color the "All Metro Areas" text but that's an exercise left to the reader (totally doable and not much code, but not the point of the example).
  5. hrbrmstr revised this gist Oct 23, 2015. 2 changed files with 9 additions and 3 deletions.
    10 changes: 9 additions & 1 deletion ahealth.md
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    This hit `#rstats` today:

    <blockquote class="twitter-tweet" lang="en"><p lang="en" dir="ltr">Has anyone made a dumbbell dot plot in <a href="https://twitter.com/hashtag/rstats?src=hash">#rstats</a>, or better yet exported to <a href="https://twitter.com/plotlygraphs">@plotlygraphs</a> using the API? <a href="https://t.co/rWUSpH1rRl">https://t.co/rWUSpH1rRl</a></p>&mdash; Ken Davis (@ken_mke) <a href="https://twitter.com/ken_mke/status/657539344929071104">October 23, 2015</a></blockquote>
    <script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>
    <script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>

    So, I figured it was worth a cpl mins to reproduce.

    While the US gov did give the data behind the chart it was _all_ the data and a pain to work with so I used [# WebPlotDigitizer](http://arohatgi.info/WebPlotDigitizer/app/) to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.

    It is possible to make the top "dumbell" legend in ggplot2 (but not by using a guide) and color the "All Metro Areas" text but that's an exercise left to the reader (totally doable and not much code, but not the point of the example).
    2 changes: 0 additions & 2 deletions health.R
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    @@ -1,5 +1,3 @@
    # WebPlotDigitizer: http://arohatgi.info/WebPlotDigitizer/app/

    library(dplyr)
    library(ggplot2)
    library(scales)
  6. hrbrmstr revised this gist Oct 23, 2015. 4 changed files with 2 additions and 2 deletions.
    0 Health.md → ahealth.md
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    File renamed without changes.
    4 changes: 2 additions & 2 deletions Health.R → health.R
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    @@ -4,10 +4,10 @@ library(dplyr)
    library(ggplot2)
    library(scales)

    health <- read.csv("health.csv", stringsAsFactors=FALSE,
    health <- read.csv("zhealth.csv", stringsAsFactors=FALSE,
    header=FALSE, col.names=c("pct", "area_id"))

    areas <- read.csv("area_trans.csv", stringsAsFactors=FALSE, header=TRUE)
    areas <- read.csv("zarea_trans.csv", stringsAsFactors=FALSE, header=TRUE)

    health %>%
    mutate(area_id=trunc(area_id)) %>%
    0 area_trans.csv → zarea_trans.csv
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    File renamed without changes.
    0 health.csv → zhealth.csv
    View file
    File renamed without changes.
  7. hrbrmstr revised this gist Oct 23, 2015. 2 changed files with 2 additions and 0 deletions.
    0 health.R → Health.R
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    File renamed without changes.
    2 changes: 2 additions & 0 deletions Health.md
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    <blockquote class="twitter-tweet" lang="en"><p lang="en" dir="ltr">Has anyone made a dumbbell dot plot in <a href="https://twitter.com/hashtag/rstats?src=hash">#rstats</a>, or better yet exported to <a href="https://twitter.com/plotlygraphs">@plotlygraphs</a> using the API? <a href="https://t.co/rWUSpH1rRl">https://t.co/rWUSpH1rRl</a></p>&mdash; Ken Davis (@ken_mke) <a href="https://twitter.com/ken_mke/status/657539344929071104">October 23, 2015</a></blockquote>
    <script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>
  8. hrbrmstr created this gist Oct 23, 2015.
    27 changes: 27 additions & 0 deletions area_trans.csv
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    area_id,area_name
    0,Houston
    1,Miami
    2,Dallas
    3,"San Antonio"
    4,Atlanta
    5,"Los Angeles"
    7,Tampa
    8,"Riverside, Calif."
    9,Phoenix
    10,Charlotte
    11,"San Diego"
    13,"All Metro Areas"
    15,Chicago
    16,"New York"
    17,Denver
    18,"Washington, D.C."
    20,Portland
    21,"St. Louis"
    22,Detroit
    23,Philadelphia
    24,Seattle
    26,"San Francisco"
    27,Baltimore
    28,Pittsburgh
    29,Minneapolis
    30,Boston
    40 changes: 40 additions & 0 deletions health.R
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    # WebPlotDigitizer: http://arohatgi.info/WebPlotDigitizer/app/

    library(dplyr)
    library(ggplot2)
    library(scales)

    health <- read.csv("health.csv", stringsAsFactors=FALSE,
    header=FALSE, col.names=c("pct", "area_id"))

    areas <- read.csv("area_trans.csv", stringsAsFactors=FALSE, header=TRUE)

    health %>%
    mutate(area_id=trunc(area_id)) %>%
    arrange(area_id, pct) %>%
    mutate(year=rep(c("2014", "2013"), 26),
    pct=pct/100) %>%
    left_join(areas, "area_id") %>%
    mutate(area_name=factor(area_name, levels=unique(area_name))) %>%
    mutate(color=rep(c("#0e668b", "#a3c4dc"), 26),
    line_col="#a3c4dc") -> health

    health[health$area_name=="All Metro Areas",]$color <- c("#bc1f31", "#e5b9a5")
    health[health$area_name=="All Metro Areas",]$line_col <- "#e5b9a5"

    gg <- ggplot(health)
    gg <- gg + geom_path(aes(x=pct, y=area_name, group=area_id, color=line_col), size=0.75)
    gg <- gg + geom_point(aes(x=pct, y=area_name, color=color), size=2.25)
    gg <- gg + scale_color_identity()
    gg <- gg + scale_x_continuous(label=percent)
    gg <- gg + labs(x=NULL, y=NULL)
    gg <- gg + theme_bw()
    gg <- gg + theme(plot.background=element_rect(fill="#f7f7f7"))
    gg <- gg + theme(panel.background=element_rect(fill="#f7f7f7"))
    gg <- gg + theme(panel.grid.minor=element_blank())
    gg <- gg + theme(panel.grid.major.y=element_blank())
    gg <- gg + theme(panel.grid.major.x=element_line())
    gg <- gg + theme(axis.ticks=element_blank())
    gg <- gg + theme(legend.position="top")
    gg <- gg + theme(panel.border=element_blank())
    gg
    52 changes: 52 additions & 0 deletions health.csv
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    22.209907307969008, 0.19674900165782105
    19.036743162789726, 0.25618033761169556
    18.86689466056366, 1.159849440615588
    24.347402225025796, 1.3381434484772043
    17.766163759396925, 2.2095945114639974
    20.81765006412328, 2.3003054979199007
    15.171516750252842, 3.251832466191914
    18.783221595470707, 3.3453586211930073
    14.875767654756068, 4.079492018475847
    18.24130165052289, 4.32253490287669
    19.752108769771347, 5.234649511516132
    14.151330949128862, 5.281881783774203
    16.551887726907797, 7.1783669937127925
    14.312421149214359, 7.24249027724197
    14.080326142489234, 8.145846583740841
    18.99920758218728, 8.245941465347371
    12.856509816596983, 9.270350019289118
    16.902532609035646, 9.290681792115443
    12.563575889645392, 10.248777486992886
    15.489161601101042, 10.263478922728844
    11.834916431200408, 11.225015379161505
    15.943185728138129, 11.24565994849285
    14.050141279754765, 13.195946157294934
    11.06371664807265, 13.256315882763872
    10.537280130123342, 15.06271569925659
    13.71325944384781, 15.154052278722538
    9.998175353720711, 16.190659896360096
    12.176803011187687, 16.20160777403581
    13.75126421920779, 17.189419136890177
    9.955635029037943, 17.245722507793847
    9.226975570592955, 18.221960399962466
    11.095778289837238, 18.30672825282299
    8.950932654912467, 20.104995360185175
    13.247349049619952, 20.201962276741494
    8.721652816732526, 21.159119582103873
    10.215568924709881, 21.166626698224363
    11.105318583240361, 22.15099729952351
    8.741358996548808, 22.21449499004265
    8.323932060599105, 23.192296864736363
    9.880094673075519, 23.200116777361874
    8.216330062872094, 24.096278764245277
    12.13785984631265, 24.11598494406156
    7.694116297740564, 26.12883045386773
    10.86868802719244, 26.14478307562377
    8.52161945177199, 27.112888258661858
    6.2821528740785535, 27.177011542191035
    6.175958460624134, 28.15637739940986
    7.420888550605261, 28.162633329510268
    5.693469851630191, 29.983098562178736
    8.061652191139517, 29.14575274478933
    3.725510640294443, 30.254616355086593
    4.16123617178784, 30.256805930621738
    1 change: 1 addition & 0 deletions output.md
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    ![](http://rud.is/dl/health.png)