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Joseph Damiba
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adding more posts to docs repo
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‎r/2015-07-30-axes.Rmd

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‎r/2015-07-30-bubble-maps.Rmd

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---
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name: Bubble Maps
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permalink: r/bubble-maps/
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description: How to make a bubble chart and map in R.
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layout: base
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thumbnail: thumbnail/bubble-map.jpg
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language: r
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page_type: example_index
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display_as: maps
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order: 5
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output:
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html_document:
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keep_md: true
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---
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```{r, echo = FALSE, message=FALSE}
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knitr::opts_chunk$set(message = FALSE, warning=FALSE)
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```
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### New to Plotly?
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Plotly's R library is free and open source!<br>
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[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
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You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
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We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
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### Version Check
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Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
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Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
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```{r}
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library(plotly)
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packageVersion('plotly')
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```
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#### United States Bubble Map
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```{r}
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library(plotly)
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df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')
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df$q <- with(df, cut(pop, quantile(pop)))
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levels(df$q) <- paste(c("1st", "2nd", "3rd", "4th", "5th"), "Quantile")
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df$q <- as.ordered(df$q)
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g <- list(
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scope = 'usa',
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projection = list(type = 'albers usa'),
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showland = TRUE,
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landcolor = toRGB("gray85"),
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subunitwidth = 1,
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countrywidth = 1,
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subunitcolor = toRGB("white"),
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countrycolor = toRGB("white")
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)
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p <- plot_geo(df, locationmode = 'USA-states', sizes = c(1, 250)) %>%
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add_markers(
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x = ~lon, y = ~lat, size = ~pop, color = ~q, hoverinfo = "text",
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text = ~paste(df$name, "<br />", df$pop/1e6, " million")
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) %>%
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layout(title = '2014 US city populations<br>(Click legend to toggle)', geo = g)
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p
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```

‎r/2015-07-30-choropleth.Rmd

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---
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name: Choropleth Maps
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permalink: r/choropleth-maps/
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description: How to make a choropleth map in R. A choropleth map shades geographic regions by value.
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layout: base
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thumbnail: thumbnail/choropleth.jpg
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language: r
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page_type: example_index
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display_as: maps
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order: 0
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output:
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html_document:
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keep_md: true
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---
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```{r, echo = FALSE, message=FALSE}
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knitr::opts_chunk$set(message = FALSE, warning=FALSE)
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```
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### New to Plotly?
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Plotly's R library is free and open source!<br>
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[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
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You can set up Plotly to work in [online](https://plot.ly/r/getting-started/#hosting-graphs-in-your-online-plotly-account) or [offline](https://plot.ly/r/offline/) mode.<br>
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We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!
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### Version Check
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Version 4 of Plotly's R package is now [available](https://plot.ly/r/getting-started/#installation)!<br>
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Check out [this post](http://moderndata.plot.ly/upgrading-to-plotly-4-0-and-above/) for more information on breaking changes and new features available in this version.
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```{r}
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library(plotly)
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packageVersion('plotly')
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```
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# Choropleth Maps in R
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```{r}
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library(plotly)
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df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
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df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
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"Fruits", total.fruits, "Veggies", total.veggies,
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"<br>", "Wheat", wheat, "Corn", corn))
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# give state boundaries a white border
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l <- list(color = toRGB("white"), width = 2)
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# specify some map projection/options
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g <- list(
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scope = 'usa',
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projection = list(type = 'albers usa'),
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showlakes = TRUE,
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lakecolor = toRGB('white')
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)
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p <- plot_geo(df, locationmode = 'USA-states') %>%
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add_trace(
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z = ~total.exports, text = ~hover, locations = ~code,
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color = ~total.exports, colors = 'Purples'
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) %>%
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colorbar(title = "Millions USD") %>%
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layout(
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title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
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geo = g
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)
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p
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```
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### World Choropleth Map
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```{r}
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df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
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# light grey boundaries
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l <- list(color = toRGB("grey"), width = 0.5)
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# specify map projection/options
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g <- list(
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showframe = FALSE,
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showcoastlines = FALSE,
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projection = list(type = 'Mercator')
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)
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p <- plot_geo(df) %>%
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add_trace(
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z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
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text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
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) %>%
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colorbar(title = 'GDP Billions US$', tickprefix = '$') %>%
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layout(
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title = '2014 Global GDP<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
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geo = g
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)
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p
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```
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### Choropleth Inset Map
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```{r}
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df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')
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# restrict from June to September
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df <- subset(df, Month %in% 6:9)
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# ordered factor variable with month abbreviations
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df$abbrev <- ordered(month.abb[df$Month], levels = month.abb[6:9])
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# September totals
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df9 <- subset(df, Month == 9)
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# common plot options
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g <- list(
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scope = 'africa',
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showframe = F,
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showland = T,
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landcolor = toRGB("grey90")
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)
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g1 <- c(
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g,
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resolution = 50,
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showcoastlines = T,
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countrycolor = toRGB("white"),
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coastlinecolor = toRGB("white"),
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projection = list(type = 'Mercator'),
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list(lonaxis = list(range = c(-15, -5))),
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list(lataxis = list(range = c(0, 12))),
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list(domain = list(x = c(0, 1), y = c(0, 1)))
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)
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g2 <- c(
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g,
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showcountries = F,
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bgcolor = toRGB("white", alpha = 0),
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list(domain = list(x = c(0, .6), y = c(0, .6)))
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)
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p <- df %>%
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plot_geo(
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locationmode = 'country names', sizes = c(1, 600), color = I("black")
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) %>%
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add_markers(
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y = ~Lat, x = ~Lon, locations = ~Country,
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size = ~Value, color = ~abbrev, text = ~paste(Value, "cases")
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) %>%
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add_text(
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x = 21.0936, y = 7.1881, text = 'Africa', showlegend = F, geo = "geo2"
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) %>%
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add_trace(
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data = df9, z = ~Month, locations = ~Country,
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showscale = F, geo = "geo2"
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) %>%
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layout(
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title = 'Ebola cases reported by month in West Africa 2014<br> Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">HDX</a>',
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geo = g1, geo2 = g2
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)
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p
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```

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