![\"forwp\"](\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/forwp.png?resize=510%2C351&ssl=1\")
<\/p>\nD Kelly O\u2019Day did a [great post](https:\/\/chartsgraphs.wordpress.com\/2015\/01\/16\/nasa-gisss-annual-global-temperature-anomaly-trends\/) on charting NASA\u2019s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. While there’s absolutely nothing wrong with base R operations, I thought a modern take on the chart using `dplyr`, `magrittr` & `tidyr` for data manipulation and `ggplot2` for formatting would be helpful for the scores of new folk learning R this year (our little language is becoming [all the rage](http:\/\/redmonk.com\/sogrady\/2015\/01\/14\/language-rankings-1-15\/), it seems). I also really enjoy working with weather data.<\/p>\n
Before further exposition, here’s the result:<\/p>\n
<\/p>\n
I made liberal use of the “piping” idiom encouraged `magrittr`, `dplyr` and other new R packages, including the forward assignment operator `->` (which may put some folks off a bit). That also meant using `magrittr`’s aliases for `[` and `[[`, which are more readable in pipes.<\/p>\n
I don’t use `library(tidyr)` since `tidyr`’s `extract` conflicts with `magrittr`’s, but you’ll see a `tidyr::gather` in the code for wide-to-long data shaping.<\/p>\n
I chose to use the monthly temperature anomaly data as a base layer in the chart as a contrast to the monthly- and annual-anomaly means. I also marked the hottest annual- and annual-mean anomalies and framed the decades with vertical markers. <\/p>\n
There are no hardcoded years or decades anywhere in the `ggplot2` code, so this should be quite reusable as the data source gets updated.<\/p>\n
As I come back to the chart, I think there may be a bit too much “chart junk” on it, but you can tweak it to your own aesthetic preferences (if you do, drop a note in the comments with a link to your creation).<\/p>\n
The code is below and in [this gist](https:\/\/gist.github.com\/hrbrmstr\/07ba10fb4c3fe9c9f3a0).<\/p>\n
library(httr)\r\nlibrary(magrittr)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\n\r\n# data retrieval ----------------------------------------------------------\r\n\r\n# the user agent string was necessary for me; YMMV\r\n\r\npg <- GET(\"http:\/\/data.giss.nasa.gov\/gistemp\/tabledata_v3\/GLB.Ts+dSST.txt\",\r\n user_agent(\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit\/537.75.14 (KHTML, like Gecko) Version\/7.0.3 Safari\/7046A194A\"))\r\n\r\n# extract monthly data ----------------------------------------------------\r\n\r\ncontent(pg, as=\"text\") %>%\r\n strsplit(\"\\n\") %>%\r\n extract2(1) %>%\r\n grep(\"^[[:digit:]]\", ., value=TRUE) -> lines\r\n\r\n# extract column names ----------------------------------------------------\r\n\r\ncontent(pg, as=\"text\") %>%\r\n strsplit(\"\\n\") %>%\r\n extract2(1) %>%\r\n extract(8) %>%\r\n strsplit(\"\\ +\") %>%\r\n extract2(1) -> lines_colnames\r\n\r\n# make data frame ---------------------------------------------------------\r\n\r\ndata <- read.table(text=lines, stringsAsFactors=FALSE)\r\ncolnames(data) <- lines_colnames\r\n\r\n# transform data frame ----------------------------------------------------\r\n\r\ndata %>%\r\n tidyr::gather(month, value, Jan, Feb, Mar, Apr, May, Jun,\r\n Jul, Aug, Sep, Oct, Nov, Dec) %>% # wide to long\r\n mutate(value=value\/100) %>% # convert to degree Celcius change\r\n select(year=Year, month, value) %>% # only need these fields\r\n mutate(date=as.Date(sprintf(\"%d-%d-%d\", year, month, 1)), # make proper dates\r\n decade=year %\/% 10, # calc decade\r\n start=decade*10, end=decade*10+9) %>% # calc decade start\/end\r\n group_by(decade) %>%\r\n mutate(decade_mean=mean(value)) %>% # calc decade mean\r\n group_by(year) %>%\r\n mutate(annum_mean=mean(value)) %>% # calc annual mean\r\n ungroup -> data\r\n\r\n# start plot --------------------------------------------------------------\r\n\r\ngg <- ggplot()\r\n\r\n# decade vertical markers -------------------------------------------------\r\n\r\ngg <- gg + geom_vline(data=data %>% select(end),\r\n aes(xintercept=as.numeric(as.Date(sprintf(\"%d-12-31\", end)))),\r\n size=0.5, color=\"#4575b4\", linetype=\"dotted\", alpha=0.5)\r\n\r\n# monthly data ------------------------------------------------------------\r\n\r\ngg <- gg + geom_line(data=data, aes(x=date, y=value, color=\"monthly anomaly\"),\r\n size=0.35, alpha=0.25)\r\ngg <- gg + geom_point(data=data, aes(x=date, y=value, color\"monthly anomaly\"),\r\n size=0.75, alpha=0.5)\r\n\r\n# decade mean -------------------------------------------------------------\r\n\r\ngg <- gg + geom_segment(data=data %>% distinct(decade, decade_mean, start, end),\r\n aes(x=as.Date(sprintf(\"%d-01-01\", start)),\r\n xend=as.Date(sprintf(\"%d-12-31\", end)),\r\n y=decade_mean, yend=decade_mean,\r\n color=\"decade mean anomaly\"),\r\n linetype=\"dashed\")\r\n\r\n# annual data -------------------------------------------------------------\r\n\r\ngg <- gg + geom_line(data=data %>% distinct(year, annum_mean),\r\n aes(x=as.Date(sprintf(\"%d-06-15\", year)), y=annum_mean,\r\n color=\"annual mean anomaly\"),\r\n size=0.5)\r\ngg <- gg + geom_point(data=data %>% distinct(year, annum_mean),\r\n aes(x=as.Date(sprintf(\"%d-06-15\", year)), y=annum_mean,\r\n color=\"annual mean anomaly\"),\r\n size=2)\r\n\r\n# additional annotations --------------------------------------------------\r\n\r\n# max annual mean anomaly horizontal marker\/text\r\n\r\ngg <- gg + geom_hline(yintercept=max(data$annum_mean), alpha=0.9,\r\n color=\"#d73027\", linetype=\"dashed\", size=0.25)\r\n\r\ngg <- gg + annotate(\"text\",\r\n x=as.Date(sprintf(\"%d-12-31\", mean(range(data$year)))),\r\n y=max(data$annum_mean),\r\n color=\"#d73027\", alpha=0.9,\r\n hjust=0.25, vjust=-1, size=3,\r\n label=sprintf(\"Max annual mean anomaly %2.1f\u00baC\", max(data$annum_mean)))\r\n\r\ngg <- gg + geom_hline(yintercept=max(data$value), alpha=0.9,\r\n color=\"#7f7f7f\", linetype=\"dashed\", size=0.25)\r\n\r\n# max annual anomaly horizontal marker\/text\r\n\r\ngg <- gg + annotate(\"text\",\r\n x=as.Date(sprintf(\"%d-12-31\", mean(range(data$year)))),\r\n y=max(data$value),\r\n color=\"#7f7f7f\", alpha=0.9,\r\n hjust=0.25, vjust=-1, size=3,\r\n label=sprintf(\"Max annual anomaly %2.1f\u00baC\", max(data$value)))\r\n\r\ngg <- gg + annotate(\"text\",\r\n x=as.Date(sprintf(\"%d-12-31\", range(data$year)[2])),\r\n y=min(data$value), size=3, hjust=1,\r\n label=\"Data: http:\/\/data.giss.nasa.gov\/gistemp\/tabledata_v3\/GLB.Ts+dSST.txt\")\r\n\r\n# set colors --------------------------------------------------------------\r\n\r\ngg <- gg + scale_color_manual(name=\"\", values=c(\"#d73027\", \"#4575b4\", \"#7f7f7f\"))\r\n\r\n# set x axis limits -------------------------------------------------------\r\n\r\ngg <- gg + scale_x_date(expand=c(0, 1),\r\n limits=c(as.Date(sprintf(\"%d-01-01\", range(data$year)[1])),\r\n as.Date(sprintf(\"%d-12-31\", range(data$year)[2]))))\r\n\r\n# add labels --------------------------------------------------------------\r\n\r\ngg <- gg + labs(x=NULL, y=\"GLOBAL Temp Anomalies in 1.0\u00baC\",\r\n title=sprintf(\"GISS Land and Sea Temperature Annual Anomaly Trend (%d to %d)\\n\",\r\n range(data$year)[1], range(data$year)[2]))\r\n\r\n# theme\/legend tweaks -----------------------------------------------------\r\n\r\ngg <- gg + theme_bw()\r\ngg <- gg + theme(panel.grid=element_blank())\r\ngg <- gg + theme(panel.border=element_blank())\r\ngg <- gg + theme(legend.position=c(0.9, 0.2))\r\ngg <- gg + theme(legend.key=element_blank())\r\ngg <- gg + theme(legend.background=element_blank())\r\ngg\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"D Kelly O\u2019Day did a [great post](https:\/\/chartsgraphs.wordpress.com\/2015\/01\/16\/nasa-gisss-annual-global-temperature-anomaly-trends\/) on charting NASA\u2019s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. While there’s absolutely nothing wrong with base R operations, I thought a modern take on the chart using `dplyr`, `magrittr` & `tidyr` for data manipulation […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[24,678,673,674,91,680],"tags":[810],"class_list":["post-3236","post","type-post","status-publish","format-standard","hentry","category-charts-graphs","category-data-visualization","category-datavis-2","category-dataviz","category-r","category-weather","tag-post"],"yoast_head":"\n
NASA GISS\u2019s Annual Global Temperature Anomaly Trends (dplyr\/ggplot version) - rud.is<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rud.is\/b\/2015\/01\/18\/nasa-gisss-annual-global-temperature-anomaly-trends-dplyrggplot-version\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NASA GISS\u2019s Annual Global Temperature Anomaly Trends (dplyr\/ggplot version) - rud.is\" \/>\n<meta property=\"og:description\" content=\"D Kelly O\u2019Day did a [great post](https:\/\/chartsgraphs.wordpress.com\/2015\/01\/16\/nasa-gisss-annual-global-temperature-anomaly-trends\/) on charting NASA\u2019s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. 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