diff --git a/DESCRIPTION b/DESCRIPTION
index 8d2d771698217be947e1377ef72d1c81418517fd..c312d2cc63c06fc5809814ae5dcb1566eb8b308e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -19,6 +19,7 @@ Suggests:
dplyr,
ggplot2,
testthat
-RoxygenNote: 6.1.0
+RoxygenNote: 6.1.1
Imports:
tibble
+Encoding: UTF-8
diff --git a/README.Rmd b/README.Rmd
index aba87b3edaacdec12398ac4dfa1e1d51ed8200fd..fcf64263c366769eb56661c7e6b0c00be13d3cc7 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -171,7 +171,7 @@ Visualization of the temporal trends in life expectancy, by country, is particul
<blockquote class="twitter-tweet" lang="en"><p>Data cleaning code cannot be clean. It's a sort of sin eater.</p>— Stat Fact (@StatFact) <a href="https://twitter.com/StatFact/status/492753200190341120">July 25, 2014</a></blockquote>
-The [`data-raw`](data-raw) directory contains the Excel spreadsheets downloaded from [Gapminder](http://www.gapminder.org) in 2008 and 2009 and all the scripts necessary to create everything in this package, in raw and "compiled notebook" form.
+The [`data-raw`](data-raw/) directory contains the Excel spreadsheets downloaded from [Gapminder](http://www.gapminder.org) in 2008 and 2009 and all the scripts necessary to create everything in this package, in raw and "compiled notebook" form.
### Plain text delimited files
diff --git a/README.md b/README.md
index d25f59fa17d57aca90f4b8883ce8e1da93698f1d..b2d3f26dbedeafef990aba3af3b14f2da621b059 100644
--- a/README.md
+++ b/README.md
@@ -10,18 +10,25 @@
- [Plain text delimited files](#plain-text-delimited-files)
- [License](#license)
- [Citation](#citation)
+
+[](https://doi.org/10.5281/zenodo.594018)
+[](http://cran.r-project.org/package=gapminder)
+
-[](https://doi.org/10.5281/zenodo.594018) [](http://cran.r-project.org/package=gapminder) 
-
-gapminder
-=========
+# gapminder
-Excerpt from the [Gapminder](http://www.gapminder.org/data/) data. The main object in this package is the `gapminder` data frame or "tibble". There are other goodies, such as the data in tab delimited form, a larger unfiltered dataset, premade color schemes for the countries and continents, and ISO 3166-1 country codes.
+Excerpt from the [Gapminder](http://www.gapminder.org/data/) data. The
+main object in this package is the `gapminder` data frame or “tibble”.
+There are other goodies, such as the data in tab delimited form, a
+larger unfiltered dataset, premade color schemes for the countries and
+continents, and ISO 3166-1 country codes.
-The `gapminder` data frames include six variables, ([Gapminder.org documentation page](http://www.gapminder.org/data/documentation/)):
+The `gapminder` data frames include six variables, ([Gapminder.org
+documentation page](http://www.gapminder.org/data/documentation/)):
| variable | meaning |
-|:----------|:-------------------------|
+| :-------- | :----------------------- |
| country | |
| continent | |
| year | |
@@ -29,14 +36,25 @@ The `gapminder` data frames include six variables, ([Gapminder.org documentation
| pop | total population |
| gdpPercap | per-capita GDP |
-Per-capita GDP (Gross domestic product) is given in units of [international dollars](http://en.wikipedia.org/wiki/Geary%E2%80%93Khamis_dollar), "a hypothetical unit of currency that has the same purchasing power parity that the U.S. dollar had in the United States at a given point in time" -- 2005, in this case.
+Per-capita GDP (Gross domestic product) is given in units of
+[international
+dollars](http://en.wikipedia.org/wiki/Geary%E2%80%93Khamis_dollar), “a
+hypothetical unit of currency that has the same purchasing power parity
+that the U.S. dollar had in the United States at a given point in time”
+– 2005, in this case.
Package contains two main data frames or tibbles:
-- `gapminder`: 12 rows for each country (1952, 1955, ..., 2007). It's a subset of ...
-- `gapminder_unfiltered`: more lightly filtered and therefore about twice as many rows.
+ - `gapminder`: 12 rows for each country (1952, 1955, …, 2007). It’s a
+ subset of …
+ - `gapminder_unfiltered`: more lightly filtered and therefore about
+ twice as many rows.
-**Note: this package exists for the purpose of teaching and making code examples. It is an excerpt of data found in specific spreadsheets on Gapminder.org circa 2010. It is not a definitive source of socioeconomic data and I don't update it. Use other data sources if it's important to have the current best estimate of these statistics.**
+**Note: this package exists for the purpose of teaching and making code
+examples. It is an excerpt of data found in specific spreadsheets on
+Gapminder.org circa 2010. It is not a definitive source of socioeconomic
+data and I don’t update it. Use other data sources if it’s important to
+have the current best estimate of these statistics.**
### Install and test drive
@@ -72,12 +90,12 @@ gapminder %>%
summarise(lifeExp = median(lifeExp))
#> # A tibble: 5 x 2
#> continent lifeExp
-#> <fctr> <dbl>
-#> 1 Africa 52.9265
-#> 2 Americas 72.8990
-#> 3 Asia 72.3960
-#> 4 Europe 78.6085
-#> 5 Oceania 80.7195
+#> <fct> <dbl>
+#> 1 Africa 52.9
+#> 2 Americas 72.9
+#> 3 Asia 72.4
+#> 4 Europe 78.6
+#> 5 Oceania 80.7
library("ggplot2")
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
@@ -89,7 +107,9 @@ ggplot(gapminder, aes(x = continent, y = lifeExp)) +
### Color schemes for countries and continents
-`country_colors` and `continent_colors` are provided as character vectors where elements are hex colors and the names are countries or continents.
+`country_colors` and `continent_colors` are provided as character
+vectors where elements are hex colors and the names are countries or
+continents.
``` r
head(country_colors, 4)
@@ -104,8 +124,10 @@ head(continent_colors)
The country scheme is available in this repo as
-- [PNG](data-raw/gapminder-color-scheme-ggplot2.png) or [PDF](data-raw/gapminder-color-scheme-base.pdf)
-- [`continent-colors.tsv`](inst/extdata/continent-colors.tsv) and [`country-colors.tsv`](inst/extdata/country-colors.tsv)
+ - [PNG](data-raw/gapminder-color-scheme-ggplot2.png) or
+ [PDF](data-raw/gapminder-color-scheme-base.pdf)
+ - [`continent-colors.tsv`](inst/extdata/continent-colors.tsv) and
+ [`country-colors.tsv`](inst/extdata/country-colors.tsv)
### How to use color scheme in `ggplot2`
@@ -149,7 +171,9 @@ plot(lifeExp ~ gdpPercap, gap_with_colors,
### ISO 3166-1 country codes
-The `country_codes` data frame provides ISO 3166-1 country codes for all the countries in the `gapminder` and `gapminder_unfiltered` data frames. This can be used to practice joining or merging.
+The `country_codes` data frame provides ISO 3166-1 country codes for all
+the countries in the `gapminder` and `gapminder_unfiltered` data frames.
+This can be used to practice joining or merging.
``` r
library(dplyr)
@@ -162,58 +186,92 @@ gapminder %>%
#> into character vector
#> # A tibble: 3 x 4
#> country continent iso_alpha iso_num
-#> <chr> <fctr> <chr> <int>
-#> 1 Kenya Africa KEN 404
-#> 2 Peru Americas PER 604
-#> 3 Syria Asia SYR 760
+#> <chr> <fct> <chr> <dbl>
+#> 1 Kenya Africa KEN 404
+#> 2 Peru Americas PER 604
+#> 3 Syria Asia SYR 760
```
### What is `gapminder` good for?
-I have used this excerpt in [STAT 545](http://stat545-ubc.github.io) since 2008 and, more recently, in [R-flavored Software Carpentry Workshops](http://jennybc.github.io/2014-05-12-ubc/) and a [`ggplot2` tutorial](https://github.com/jennybc/ggplot2-tutorial). `gapminder` is very useful for teaching novices data wrangling and visualization in R.
+I have used this excerpt in [STAT 545](http://stat545-ubc.github.io)
+since 2008 and, more recently, in [R-flavored Software Carpentry
+Workshops](http://jennybc.github.io/2014-05-12-ubc/) and a [`ggplot2`
+tutorial](https://github.com/jennybc/ggplot2-tutorial). `gapminder` is
+very useful for teaching novices data wrangling and visualization in R.
Description:
-- 1704 observations; fills a size niche between `iris` (150 rows) and the likes of `diamonds` (54K rows)
-- 6 variables
- - `country` a factor with 142 levels
- - `continent`, a factor with 5 levels
- - `year`: going from 1952 to 2007 in increments of 5 years
- - `pop`: population
- - `gdpPercap`: GDP per capita
- - `lifeExp`: life expectancy
-
-There are 12 rows for each country in `gapminder`, i.e. complete data for 1952, 1955, ..., 2007.
-
-The two factors provide opportunities to demonstrate factor handling, in aggregation and visualization, for factors with very few and very many levels.
-
-The four quantitative variables are generally quite correlated with each other and these trends have interesting relationships to `country` and `continent`, so you will find that simple plots and aggregations tell a reasonable story and are not completely boring.
-
-Visualization of the temporal trends in life expectancy, by country, is particularly rewarding, since there are several countries with sharp drops due to political upheaval. This then motivates more systematic investigations via data aggregation to proactively identify all countries whose data exhibits certain properties.
+ - 1704 observations; fills a size niche between `iris` (150 rows) and
+ the likes of `diamonds` (54K rows)
+ - 6 variables
+ - `country` a factor with 142 levels
+ - `continent`, a factor with 5 levels
+ - `year`: going from 1952 to 2007 in increments of 5 years
+ - `pop`: population
+ - `gdpPercap`: GDP per capita
+ - `lifeExp`: life expectancy
+
+There are 12 rows for each country in `gapminder`, i.e. complete data
+for 1952, 1955, …, 2007.
+
+The two factors provide opportunities to demonstrate factor handling, in
+aggregation and visualization, for factors with very few and very many
+levels.
+
+The four quantitative variables are generally quite correlated with each
+other and these trends have interesting relationships to `country` and
+`continent`, so you will find that simple plots and aggregations tell a
+reasonable story and are not completely boring.
+
+Visualization of the temporal trends in life expectancy, by country, is
+particularly rewarding, since there are several countries with sharp
+drops due to political upheaval. This then motivates more systematic
+investigations via data aggregation to proactively identify all
+countries whose data exhibits certain properties.
### How this sausage was made
<blockquote class="twitter-tweet" lang="en">
+
<p>
+
Data cleaning code cannot be clean. It's a sort of sin eater.
+
</p>
-— Stat Fact (@StatFact) <a href="https://twitter.com/StatFact/status/492753200190341120">July 25, 2014</a>
+
+— Stat Fact (@StatFact)
+<a href="https://twitter.com/StatFact/status/492753200190341120">July
+25, 2014</a>
+
</blockquote>
-The [`data-raw`](data-raw) directory contains the Excel spreadsheets downloaded from [Gapminder](http://www.gapminder.org) in 2008 and 2009 and all the scripts necessary to create everything in this package, in raw and "compiled notebook" form.
+
+The [`data-raw`](data-raw/) directory contains the Excel spreadsheets
+downloaded from [Gapminder](http://www.gapminder.org) in 2008 and 2009
+and all the scripts necessary to create everything in this package, in
+raw and “compiled notebook” form.
### Plain text delimited files
-If you want to practice importing from file, various tab delimited files are included:
+If you want to practice importing from file, various tab delimited files
+are included:
-- [`gapminder.tsv`](inst/extdata/gapminder.tsv): the same dataset available via `library("gapminder"); gapminder`
-- [`gapminder-unfiltered.tsv`](inst/extdata/gapminder-unfiltered.tsv): the larger dataset available via `library("gapminder"); gapminder_unfiltered`.
-- [`continent-colors.tsv`](inst/extdata/continent-colors.tsv) and [`country-colors.tsv`](inst/extdata/country-colors.tsv): color schemes
+ - [`gapminder.tsv`](inst/extdata/gapminder.tsv): the same dataset
+ available via `library("gapminder"); gapminder`
+ - [`gapminder-unfiltered.tsv`](inst/extdata/gapminder-unfiltered.tsv):
+ the larger dataset available via `library("gapminder");
+ gapminder_unfiltered`.
+ - [`continent-colors.tsv`](inst/extdata/continent-colors.tsv) and
+ [`country-colors.tsv`](inst/extdata/country-colors.tsv): color
+ schemes
Here in the source, these delimited files can be found:
-- in the [`inst/extdata/`](inst/extdata/) sub-directory
+ - in the [`inst/extdata/`](inst/extdata/) sub-directory
-Once you've installed the `gapminder` package they can be found locally and used like so:
+Once you’ve installed the `gapminder` package they can be found locally
+and used like
+so:
``` r
gap_tsv <- system.file("extdata", "gapminder.tsv", package = "gapminder")
@@ -255,15 +313,17 @@ gap_bigger_tsv %>% # Bhutan IS here though! :)
#> 8 Bhutan Asia 2007 65.625 2327849 4744.6400
```
-License
--------
+## License
-Gapminder's data is released under the Creative Commons Attribution 3.0 Unported license. See their [terms of use](https://docs.google.com/document/pub?id=1POd-pBMc5vDXAmxrpGjPLaCSDSWuxX6FLQgq5DhlUhM).
+Gapminder’s data is released under the Creative Commons Attribution 3.0
+Unported license. See their [terms of
+use](https://docs.google.com/document/pub?id=1POd-pBMc5vDXAmxrpGjPLaCSDSWuxX6FLQgq5DhlUhM).
-Citation
---------
+## Citation
-Run this command to get info on how to cite this package. If you've installed gapminder from CRAN, the year will be populated and populated correctly (unlike below).
+Run this command to get info on how to cite this package. If you’ve
+installed gapminder from CRAN, the year will be populated and populated
+correctly (unlike below).
``` r
citation("gapminder")
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