The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

A simple look into tidygapminder

This package aims to make really easy to tidy data retrieved from Gapminder. A the beginning is:

library(tidygapminder)

When you have loaded the package you are now in possession of two super powers (functions): tidy_indice and tidy_bunch.

tidy_indice

tidy_indice function tidy as explain above tidy a data sheet downloaded on Gapminder. This data sheet can be either in csv or xlsx as indicated on the gapminder site.

tidy_indice take as argument the path to the file and return the data as a tidy data frame.

filepath <- system.file("extdata", "life_expectancy_years.csv", package = "tidygapminder")

# From .............................
df <- data.table::fread(filepath)

head(df)
#>             V1     V2     V3     V4     V5     V6     V7     V8     V9    V10
#> 1:     country 1800.0 1801.0 1802.0 1803.0 1804.0 1805.0 1806.0 1807.0 1808.0
#> 2: Afghanistan   28.2   28.2   28.2   28.2   28.2   28.2   28.1   28.1   28.1
#> 3:     Albania   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:     Algeria   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8
#> 5:     Andorra     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:      Angola   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V11    V12    V13    V14    V15    V16    V17    V18    V19    V20    V21
#> 1: 1809.0 1810.0 1811.0 1812.0 1813.0 1814.0 1815.0 1816.0 1817.0 1818.0 1819.0
#> 2:   28.1   28.1   28.1   28.1   28.1   28.1   28.1   28.1   28.0   28.0   28.0
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V22    V23    V24    V25    V26    V27    V28    V29    V30    V31    V32
#> 1: 1820.0 1821.0 1822.0 1823.0 1824.0 1825.0 1826.0 1827.0 1828.0 1829.0 1830.0
#> 2:   28.0   28.0   28.0   28.0   28.0   27.9   27.9   27.9   27.9   27.9   27.9
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V33    V34    V35    V36    V37    V38    V39    V40    V41    V42    V43
#> 1: 1831.0 1832.0 1833.0 1834.0 1835.0 1836.0 1837.0 1838.0 1839.0 1840.0 1841.0
#> 2:   27.9   27.9   27.9   27.9   27.9   27.8   27.8   27.8   27.8   27.8   27.8
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V44    V45    V46    V47    V48    V49    V50    V51    V52    V53    V54
#> 1: 1842.0 1843.0 1844.0 1845.0 1846.0 1847.0 1848.0 1849.0 1850.0 1851.0 1852.0
#> 2:   27.8   27.8   27.8   27.8   27.7   27.7   27.7   27.7   27.7   27.7   27.7
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   28.8   28.8   28.8   28.8   20.0   15.0   22.0   28.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V55    V56    V57    V58    V59    V60    V61    V62    V63    V64    V65
#> 1: 1853.0 1854.0 1855.0 1856.0 1857.0 1858.0 1859.0 1860.0 1861.0 1862.0 1863.0
#> 2:   27.7   27.7   27.6   27.6   27.6   27.6   27.6   27.6   27.6   27.6   27.6
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8   28.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0
#>       V66    V67    V68    V69    V70    V71    V72    V73    V74    V75    V76
#> 1: 1864.0 1865.0 1866.0 1867.0 1868.0 1869.0 1870.0 1871.0 1872.0 1873.0 1874.0
#> 2:   27.6   27.5   27.5   27.5   27.5   27.5   27.5   27.6   27.6   27.7   27.7
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   28.8   28.8   28.8   21.0   11.0   15.0   22.0   28.9   28.9   28.9   29.0
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.1   27.1   27.2   27.3
#>       V77    V78    V79    V80    V81    V82    V83    V84    V85    V86    V87
#> 1: 1875.0 1876.0 1877.0 1878.0 1879.0 1880.0 1881.0 1882.0 1883.0 1884.0 1885.0
#> 2:   27.8   27.8   27.9   28.0   28.0   28.1   28.1   28.2   28.2   28.3   28.4
#> 3:   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4   35.4
#> 4:   29.0   29.1   29.1   29.1   29.2   29.2   29.3   29.3   29.4   29.4   29.4
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   27.4   27.5   27.6   27.7   27.8   27.8   27.9   28.0   28.1   28.2   28.3
#>       V88    V89    V90    V91    V92    V93    V94    V95    V96    V97    V98
#> 1: 1886.0 1887.0 1888.0 1889.0 1890.0 1891.0 1892.0 1893.0 1894.0 1895.0 1896.0
#> 2:   28.4   28.5   28.5   28.6   28.6   28.7   28.8   28.8   28.9   28.9   29.0
#> 3:   35.4   35.4   35.4   35.4   35.5   35.5   35.5   35.5   35.5   35.5   35.5
#> 4:   29.5   29.5   29.6   29.6   29.6   29.7   29.7   29.8   29.8   29.8   29.9
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   28.3   28.4   28.5   28.6   28.7   28.8   28.9   28.9   29.0   29.1   29.2
#>       V99   V100   V101   V102   V103   V104   V105   V106   V107   V108   V109
#> 1: 1897.0 1898.0 1899.0 1900.0 1901.0 1902.0 1903.0 1904.0 1905.0 1906.0 1907.0
#> 2:   29.1   29.1   29.2   29.2   29.3   29.3   29.4   29.4   29.5   29.6   29.6
#> 3:   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
#> 4:   29.9   30.0   30.0   30.1   30.2   30.3   31.3   25.3   28.0   29.5   29.4
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   29.3   29.4   29.4   29.5   29.6   29.7   29.8   29.9   30.0   30.1   30.1
#>      V110   V111   V112   V113   V114   V115   V116   V117   V118   V119
#> 1: 1908.0 1909.0 1910.0 1911.0 1912.0 1913.0 1914.0 1915.0 1916.0 1917.0
#> 2:   29.7   29.7   29.8   29.8   29.9   29.9   30.0   30.1   30.1   30.2
#> 3:   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
#> 4:   29.3   30.9   32.5   32.3   33.7   31.5   31.0   30.5   30.1   30.2
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   30.2   30.3   30.4   30.5   30.6   30.6   30.7   30.8   30.9   31.0
#>       V120   V121   V122   V123   V124   V125   V126   V127   V128   V129
#> 1: 1918.00 1919.0 1920.0 1921.0 1922.0 1923.0 1924.0 1925.0 1926.0 1927.0
#> 2:    7.89   30.3   30.3   30.4   30.4   30.5   30.6   30.6   30.7   30.7
#> 3:   19.50   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
#> 4:   23.60   30.3   29.4   29.5   29.2   31.8   33.3   34.1   33.4   28.6
#> 5:      NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   12.00   31.2   31.2   31.3   31.4   31.5   31.6   31.7   31.8   31.8
#>      V130   V131   V132   V133   V134   V135   V136   V137   V138   V139   V140
#> 1: 1928.0 1929.0 1930.0 1931.0 1932.0 1933.0 1934.0 1935.0 1936.0 1937.0 1938.0
#> 2:   30.8   30.8   30.9   30.9   31.0   31.1   31.1   31.2   31.2   31.3   31.3
#> 3:   35.5   35.5   36.4   37.3   38.2   39.1   40.0   40.9   41.8   42.8   43.6
#> 4:   32.2   32.5   33.8   31.7   33.1   34.3   33.7   35.6   36.8   34.9   34.3
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   31.9   32.0   32.1   32.2   32.3   32.4   32.4   32.5   32.6   32.7   32.8
#>      V141   V142   V143   V144   V145   V146   V147   V148   V149   V150   V151
#> 1: 1939.0 1940.0 1941.0 1942.0 1943.0 1944.0 1945.0 1946.0 1947.0 1948.0 1949.0
#> 2:   31.4   31.4   31.5   31.6   31.6   31.7   31.7   31.8   31.8   31.9   31.9
#> 3:   43.2   42.2   41.7   40.2   37.2   34.2   47.2   50.3   51.8   52.7   53.6
#> 4:   36.6   37.1   35.3   34.7   30.0   35.5   33.2   35.4   38.8   42.0   44.4
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   32.9   33.0   33.3   33.7   34.0   34.4   34.8   35.1   35.5   35.9   36.2
#>      V152   V153   V154   V155   V156   V157   V158   V159   V160   V161   V162
#> 1: 1950.0 1951.0 1952.0 1953.0 1954.0 1955.0 1956.0 1957.0 1958.0 1959.0 1960.0
#> 2:   32.0   32.4   33.0   33.7   34.4   35.1   35.8   36.5   37.2   37.9   38.6
#> 3:   54.5   54.7   55.2   55.8   56.5   57.3   58.3   59.3   60.4   61.6   62.7
#> 4:   46.9   47.1   47.6   48.1   48.6   49.2   49.7   50.3   50.9   51.4   52.0
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
#> 6:   36.6   36.9   37.5   38.1   38.7   39.3   39.9   40.5   41.2   41.8   42.4
#>      V163   V164   V165   V166   V167   V168   V169   V170   V171   V172   V173
#> 1: 1961.0 1962.0 1963.0 1964.0 1965.0 1966.0 1967.0 1968.0 1969.0 1970.0 1971.0
#> 2:   39.4   40.1   40.8   41.5   42.2   42.9   43.7   44.4   45.1   45.8   45.9
#> 3:   63.7   64.6   65.3   65.9   66.3   66.5   66.7   66.9   67.1   67.4   68.0
#> 4:   52.6   53.2   53.8   54.3   54.9   55.4   56.0   56.5   57.0   57.5   57.8
#> 5:     NA     NA     NA     NA     NA     NA     NA     NA     NA   76.0   76.3
#> 6:   43.0   43.6   44.3   44.9   45.5   46.2   46.8   47.4   48.1   48.7   49.0
#>      V174   V175   V176   V177   V178   V179   V180   V181   V182   V183   V184
#> 1: 1972.0 1973.0 1974.0 1975.0 1976.0 1977.0 1978.0 1979.0 1980.0 1981.0 1982.0
#> 2:   45.9   46.0   46.1   46.3   46.5   46.6   45.0   43.6   43.3   44.1   43.8
#> 3:   68.6   69.2   69.8   70.3   70.8   71.3   71.7   72.0   72.3   72.4   72.5
#> 4:   58.2   58.5   59.1   59.5   60.0   60.6   61.2   61.9   62.1   63.4   64.4
#> 5:   76.6   76.9   77.2   77.4   77.7   78.0   78.3   78.6   78.7   78.8   78.8
#> 6:   49.2   49.4   49.6   49.5   49.5   49.6   49.7   49.8   49.9   50.0   50.0
#>      V185   V186   V187   V188   V189   V190   V191   V192   V193   V194   V195
#> 1: 1983.0 1984.0 1985.0 1986.0 1987.0 1988.0 1989.0 1990.0 1991.0 1992.0 1993.0
#> 2:   42.0   39.8   41.6   42.6   44.7   47.0   50.8   51.6   51.3   51.4   51.4
#> 3:   72.6   72.8   73.0   73.2   73.2   73.4   73.7   73.9   73.9   73.9   73.9
#> 4:   65.7   66.9   68.0   68.7   69.4   70.0   70.5   71.0   71.4   71.7   72.0
#> 5:   78.8   79.0   79.1   79.2   79.3   79.3   79.4   79.5   79.5   79.6   79.8
#> 6:   50.1   50.2   50.3   50.2   50.0   49.8   50.2   50.2   50.8   51.0   49.7
#>      V196   V197   V198   V199   V200   V201   V202   V203   V204   V205   V206
#> 1: 1994.0 1995.0 1996.0 1997.0 1998.0 1999.0 2000.0 2001.0 2002.0 2003.0 2004.0
#> 2:   50.7   51.1   51.4   51.1   50.1   51.5   51.6   51.7   52.4   53.0   53.5
#> 3:   74.0   74.1   74.3   72.5   74.3   74.4   74.4   74.5   74.5   74.6   74.7
#> 4:   72.1   72.3   72.8   73.0   73.1   73.5   73.9   74.1   74.4   74.5   75.1
#> 5:   80.0   80.3   80.6   81.0   81.3   81.5   81.8   82.0   82.3   82.4   82.3
#> 6:   51.1   52.0   52.3   52.7   52.8   52.9   53.4   53.6   54.5   55.1   55.7
#>      V207   V208   V209   V210   V211   V212   V213   V214   V215   V216   V217
#> 1: 2005.0 2006.0 2007.0 2008.0 2009.0 2010.0 2011.0 2012.0 2013.0 2014.0 2015.0
#> 2:   53.9   54.1   54.6   55.2   55.7   56.2   56.7   57.2   57.7   57.8   57.9
#> 3:   74.9   75.2   75.4   75.6   75.9   76.3   76.7   77.0   77.2   77.4   77.6
#> 4:   75.4   75.6   75.9   76.1   76.3   76.5   76.7   76.8   77.0   77.1   77.3
#> 5:   82.5   82.5   82.7   82.7   82.7   82.7   82.6   82.6   82.6   82.6   82.5
#> 6:   56.5   57.0   57.8   58.6   59.3   60.1   60.9   61.7   62.5   63.3   64.0
#>      V218   V219   V220
#> 1: 2016.0 2017.0 2018.0
#> 2:   58.0   58.4   58.7
#> 3:   77.7   77.9   78.0
#> 4:   77.4   77.6   77.9
#> 5:   82.5     NA     NA
#> 6:   64.7   64.9   65.2

# To................................

ti_df <- tidy_indice(filepath)

head(ti_df)
#> # A tibble: 6 x 3
#>   country      year life_expectancy_years
#>   <chr>       <dbl>                 <dbl>
#> 1 Afghanistan  1800                  28.2
#> 2 Afghanistan  1801                  28.2
#> 3 Afghanistan  1802                  28.2
#> 4 Afghanistan  1803                  28.2
#> 5 Afghanistan  1804                  28.2
#> 6 Afghanistan  1805                  28.2

tidy_bunch

tidy_bunch makes use of tidy_indice to tidy a whole set of data sheets and have the options to merge all data frames into one big data frame with merge set to TRUE:

dir_path <- system.file("extdata", package = "tidygapminder")

# From ................................
list.files(dir_path)
#> [1] "agriculture_land.xlsx"     "life_expectancy_years.csv"

# To ..................................
td_dp <- tidy_bunch(dir_path, merge = TRUE)

head(td_dp)
#>       country year Agricultural land (% of land area) life_expectancy_years
#> 1 Afghanistan 1800                                 NA                  28.2
#> 2 Afghanistan 1801                                 NA                  28.2
#> 3 Afghanistan 1802                                 NA                  28.2
#> 4 Afghanistan 1803                                 NA                  28.2
#> 5 Afghanistan 1804                                 NA                  28.2
#> 6 Afghanistan 1805                                 NA                  28.2

Enjoy!!!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.