Description
Easily Tidy Gapminder Datasets.
Description
A toolset that allows you to easily import and tidy data sheets retrieved from Gapminder data web tools. It will therefore contribute to reduce the time used in data cleaning of Gapminder indicator data sheets as they are very messy.
README.md
tidygapminder
tidygapminder is designed to make easy to tidy data retrieved from Gapminder. Learn more in vignette("tidygapminder").
Installation
You can install the released version of tidygapminder from CRAN with:
install.packages("tidygapminder")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("ebedthan/tidygapminder")
Example
This is a basic example which shows you how to solve a common problem:
library(tidygapminder)
# From ----------------------------------
df <- readxl::read_xlsx(system.file("extdata", "agriculture_land.xlsx", package = "tidygapminder"))
df
#> # A tibble: 213 x 53
#> `Agricultural l… `1960` `1961` `1962` `1963` `1964` `1965`
#> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan NA 57.8 57.9 58.0 58.1 58.1
#> 2 Albania NA 45.0 45.0 45 44.9 45.1
#> 3 Algeria NA 19.1 18.9 18.7 18.5 18.5
#> 4 American Samoa NA 20 20 20 20 20
#> 5 Andorra NA 55.3 55.3 55.3 55.3 55.3
#> 6 Angola NA 45.9 45.9 45.9 45.9 45.9
#> 7 Antigua and Bar… NA 22.7 20.5 22.7 20.5 25
#> 8 Argentina NA 50.4 49.9 49.3 48.7 48.2
#> 9 Armenia NA NA NA NA NA NA
#> 10 Aruba NA 11.1 11.1 11.1 11.1 11.1
#> # … with 203 more rows, and 46 more variables: `1966` <dbl>,
#> # `1967` <dbl>, `1968` <dbl>, `1969` <dbl>, `1970` <dbl>,
#> # `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
#> # `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
#> # `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>,
#> # `1983` <dbl>, `1984` <dbl>, `1985` <dbl>, `1986` <dbl>,
#> # `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>,
#> # `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>,
#> # `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
#> # `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>,
#> # `2003` <dbl>, `2004` <dbl>, `2005` <dbl>, `2006` <dbl>,
#> # `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <lgl>,
#> # `2011` <lgl>
# To ------------------------------------
file <- system.file("extdata", "agriculture_land.xlsx", package = "tidygapminder")
tidy_indice(file)
#> # A tibble: 11,076 x 3
#> country year `Agricultural land (% of land area)`
#> <chr> <dbl> <dbl>
#> 1 Afghanistan 1960 NA
#> 2 Afghanistan 1961 57.8
#> 3 Afghanistan 1962 57.9
#> 4 Afghanistan 1963 58.0
#> 5 Afghanistan 1964 58.1
#> 6 Afghanistan 1965 58.1
#> 7 Afghanistan 1966 58.1
#> 8 Afghanistan 1967 58.2
#> 9 Afghanistan 1968 58.2
#> 10 Afghanistan 1969 58.3
#> # … with 11,066 more rows
Or more powerful:
# From ----------------------------------------
path <- system.file("extdata", package = "tidygapminder")
list.files(path)
#> [1] "agriculture_land.xlsx" "life_expectancy_years.csv"
df
#> # A tibble: 213 x 53
#> `Agricultural l… `1960` `1961` `1962` `1963` `1964` `1965`
#> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan NA 57.8 57.9 58.0 58.1 58.1
#> 2 Albania NA 45.0 45.0 45 44.9 45.1
#> 3 Algeria NA 19.1 18.9 18.7 18.5 18.5
#> 4 American Samoa NA 20 20 20 20 20
#> 5 Andorra NA 55.3 55.3 55.3 55.3 55.3
#> 6 Angola NA 45.9 45.9 45.9 45.9 45.9
#> 7 Antigua and Bar… NA 22.7 20.5 22.7 20.5 25
#> 8 Argentina NA 50.4 49.9 49.3 48.7 48.2
#> 9 Armenia NA NA NA NA NA NA
#> 10 Aruba NA 11.1 11.1 11.1 11.1 11.1
#> # … with 203 more rows, and 46 more variables: `1966` <dbl>,
#> # `1967` <dbl>, `1968` <dbl>, `1969` <dbl>, `1970` <dbl>,
#> # `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
#> # `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
#> # `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>,
#> # `1983` <dbl>, `1984` <dbl>, `1985` <dbl>, `1986` <dbl>,
#> # `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>,
#> # `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>,
#> # `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
#> # `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>,
#> # `2003` <dbl>, `2004` <dbl>, `2005` <dbl>, `2006` <dbl>,
#> # `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <lgl>,
#> # `2011` <lgl>
df_ <- data.table::fread(system.file("extdata", "life_expectancy_years.csv", package = "tidygapminder"))
df_
#> V1 V2 V3 V4 V5 V6 V7
#> 1: country 1800.0 1801.0 1802.0 1803.0 1804.0 1805.0
#> 2: Afghanistan 28.2 28.2 28.2 28.2 28.2 28.2
#> 3: Albania 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
#> V8 V9 V10 V11 V12 V13 V14 V15
#> 1: 1806.0 1807.0 1808.0 1809.0 1810.0 1811.0 1812.0 1813.0
#> 2: 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1
#> 3: 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
#> V16 V17 V18 V19 V20 V21 V22 V23
#> 1: 1814.0 1815.0 1816.0 1817.0 1818.0 1819.0 1820.0 1821.0
#> 2: 28.1 28.1 28.1 28.0 28.0 28.0 28.0 28.0
#> 3: 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
#> V24 V25 V26 V27 V28 V29 V30 V31
#> 1: 1822.0 1823.0 1824.0 1825.0 1826.0 1827.0 1828.0 1829.0
#> 2: 28.0 28.0 28.0 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
#> 4: 28.8 28.8 28.8 28.8 28.8 28.8 28.8 28.8
#> V32 V33 V34 V35 V36 V37 V38 V39
#> 1: 1830.0 1831.0 1832.0 1833.0 1834.0 1835.0 1836.0 1837.0
#> 2: 27.9 27.9 27.9 27.9 27.9 27.9 27.8 27.8
#> 3: 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
#> V40 V41 V42 V43 V44 V45 V46 V47
#> 1: 1838.0 1839.0 1840.0 1841.0 1842.0 1843.0 1844.0 1845.0
#> 2: 27.8 27.8 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
#> 4: 28.8 28.8 28.8 28.8 28.8 28.8 28.8 28.8
#> V48 V49 V50 V51 V52 V53 V54 V55
#> 1: 1846.0 1847.0 1848.0 1849.0 1850.0 1851.0 1852.0 1853.0
#> 2: 27.7 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
#> 4: 28.8 28.8 28.8 20.0 15.0 22.0 28.8 28.8
#> V56 V57 V58 V59 V60 V61 V62 V63
#> 1: 1854.0 1855.0 1856.0 1857.0 1858.0 1859.0 1860.0 1861.0
#> 2: 27.7 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
#> 4: 28.8 28.8 28.8 28.8 28.8 28.8 28.8 28.8
#> V64 V65 V66 V67 V68 V69 V70 V71
#> 1: 1862.0 1863.0 1864.0 1865.0 1866.0 1867.0 1868.0 1869.0
#> 2: 27.6 27.6 27.6 27.5 27.5 27.5 27.5 27.5
#> 3: 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 21.0 11.0 15.0
#> V72 V73 V74 V75 V76 V77 V78 V79
#> 1: 1870.0 1871.0 1872.0 1873.0 1874.0 1875.0 1876.0 1877.0
#> 2: 27.5 27.6 27.6 27.7 27.7 27.8 27.8 27.9
#> 3: 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4
#> 4: 22.0 28.9 28.9 28.9 29.0 29.0 29.1 29.1
#> V80 V81 V82 V83 V84 V85 V86 V87
#> 1: 1878.0 1879.0 1880.0 1881.0 1882.0 1883.0 1884.0 1885.0
#> 2: 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
#> 4: 29.1 29.2 29.2 29.3 29.3 29.4 29.4 29.4
#> V88 V89 V90 V91 V92 V93 V94 V95
#> 1: 1886.0 1887.0 1888.0 1889.0 1890.0 1891.0 1892.0 1893.0
#> 2: 28.4 28.5 28.5 28.6 28.6 28.7 28.8 28.8
#> 3: 35.4 35.4 35.4 35.4 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
#> V96 V97 V98 V99 V100 V101 V102 V103
#> 1: 1894.0 1895.0 1896.0 1897.0 1898.0 1899.0 1900.0 1901.0
#> 2: 28.9 28.9 29.0 29.1 29.1 29.2 29.2 29.3
#> 3: 35.5 35.5 35.5 35.5 35.5 35.5 35.5 35.5
#> 4: 29.8 29.8 29.9 29.9 30.0 30.0 30.1 30.2
#> V104 V105 V106 V107 V108 V109 V110 V111
#> 1: 1902.0 1903.0 1904.0 1905.0 1906.0 1907.0 1908.0 1909.0
#> 2: 29.3 29.4 29.4 29.5 29.6 29.6 29.7 29.7
#> 3: 35.5 35.5 35.5 35.5 35.5 35.5 35.5 35.5
#> 4: 30.3 31.3 25.3 28.0 29.5 29.4 29.3 30.9
#> V112 V113 V114 V115 V116 V117 V118 V119
#> 1: 1910.0 1911.0 1912.0 1913.0 1914.0 1915.0 1916.0 1917.0
#> 2: 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
#> 4: 32.5 32.3 33.7 31.5 31.0 30.5 30.1 30.2
#> V120 V121 V122 V123 V124 V125 V126 V127
#> 1: 1918.00 1919.0 1920.0 1921.0 1922.0 1923.0 1924.0 1925.0
#> 2: 7.89 30.3 30.3 30.4 30.4 30.5 30.6 30.6
#> 3: 19.50 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
#> V128 V129 V130 V131 V132 V133 V134 V135
#> 1: 1926.0 1927.0 1928.0 1929.0 1930.0 1931.0 1932.0 1933.0
#> 2: 30.7 30.7 30.8 30.8 30.9 30.9 31.0 31.1
#> 3: 35.5 35.5 35.5 35.5 36.4 37.3 38.2 39.1
#> 4: 33.4 28.6 32.2 32.5 33.8 31.7 33.1 34.3
#> V136 V137 V138 V139 V140 V141 V142 V143
#> 1: 1934.0 1935.0 1936.0 1937.0 1938.0 1939.0 1940.0 1941.0
#> 2: 31.1 31.2 31.2 31.3 31.3 31.4 31.4 31.5
#> 3: 40.0 40.9 41.8 42.8 43.6 43.2 42.2 41.7
#> 4: 33.7 35.6 36.8 34.9 34.3 36.6 37.1 35.3
#> V144 V145 V146 V147 V148 V149 V150 V151
#> 1: 1942.0 1943.0 1944.0 1945.0 1946.0 1947.0 1948.0 1949.0
#> 2: 31.6 31.6 31.7 31.7 31.8 31.8 31.9 31.9
#> 3: 40.2 37.2 34.2 47.2 50.3 51.8 52.7 53.6
#> 4: 34.7 30.0 35.5 33.2 35.4 38.8 42.0 44.4
#> V152 V153 V154 V155 V156 V157 V158 V159
#> 1: 1950.0 1951.0 1952.0 1953.0 1954.0 1955.0 1956.0 1957.0
#> 2: 32.0 32.4 33.0 33.7 34.4 35.1 35.8 36.5
#> 3: 54.5 54.7 55.2 55.8 56.5 57.3 58.3 59.3
#> 4: 46.9 47.1 47.6 48.1 48.6 49.2 49.7 50.3
#> V160 V161 V162 V163 V164 V165 V166 V167
#> 1: 1958.0 1959.0 1960.0 1961.0 1962.0 1963.0 1964.0 1965.0
#> 2: 37.2 37.9 38.6 39.4 40.1 40.8 41.5 42.2
#> 3: 60.4 61.6 62.7 63.7 64.6 65.3 65.9 66.3
#> 4: 50.9 51.4 52.0 52.6 53.2 53.8 54.3 54.9
#> V168 V169 V170 V171 V172 V173 V174 V175
#> 1: 1966.0 1967.0 1968.0 1969.0 1970.0 1971.0 1972.0 1973.0
#> 2: 42.9 43.7 44.4 45.1 45.8 45.9 45.9 46.0
#> 3: 66.5 66.7 66.9 67.1 67.4 68.0 68.6 69.2
#> 4: 55.4 56.0 56.5 57.0 57.5 57.8 58.2 58.5
#> V176 V177 V178 V179 V180 V181 V182 V183
#> 1: 1974.0 1975.0 1976.0 1977.0 1978.0 1979.0 1980.0 1981.0
#> 2: 46.1 46.3 46.5 46.6 45.0 43.6 43.3 44.1
#> 3: 69.8 70.3 70.8 71.3 71.7 72.0 72.3 72.4
#> 4: 59.1 59.5 60.0 60.6 61.2 61.9 62.1 63.4
#> V184 V185 V186 V187 V188 V189 V190 V191
#> 1: 1982.0 1983.0 1984.0 1985.0 1986.0 1987.0 1988.0 1989.0
#> 2: 43.8 42.0 39.8 41.6 42.6 44.7 47.0 50.8
#> 3: 72.5 72.6 72.8 73.0 73.2 73.2 73.4 73.7
#> 4: 64.4 65.7 66.9 68.0 68.7 69.4 70.0 70.5
#> V192 V193 V194 V195 V196 V197 V198 V199
#> 1: 1990.0 1991.0 1992.0 1993.0 1994.0 1995.0 1996.0 1997.0
#> 2: 51.6 51.3 51.4 51.4 50.7 51.1 51.4 51.1
#> 3: 73.9 73.9 73.9 73.9 74.0 74.1 74.3 72.5
#> 4: 71.0 71.4 71.7 72.0 72.1 72.3 72.8 73.0
#> V200 V201 V202 V203 V204 V205 V206 V207
#> 1: 1998.0 1999.0 2000.0 2001.0 2002.0 2003.0 2004.0 2005.0
#> 2: 50.1 51.5 51.6 51.7 52.4 53.0 53.5 53.9
#> 3: 74.3 74.4 74.4 74.5 74.5 74.6 74.7 74.9
#> 4: 73.1 73.5 73.9 74.1 74.4 74.5 75.1 75.4
#> V208 V209 V210 V211 V212 V213 V214 V215
#> 1: 2006.0 2007.0 2008.0 2009.0 2010.0 2011.0 2012.0 2013.0
#> 2: 54.1 54.6 55.2 55.7 56.2 56.7 57.2 57.7
#> 3: 75.2 75.4 75.6 75.9 76.3 76.7 77.0 77.2
#> 4: 75.6 75.9 76.1 76.3 76.5 76.7 76.8 77.0
#> V216 V217 V218 V219 V220
#> 1: 2014.0 2015.0 2016.0 2017.0 2018.0
#> 2: 57.8 57.9 58.0 58.4 58.7
#> 3: 77.4 77.6 77.7 77.9 78.0
#> 4: 77.1 77.3 77.4 77.6 77.9
#> [ reached getOption("max.print") -- omitted 7 rows ]
# To ------------------------------------------
tidy_bunch(dirpath = path, merge = TRUE)
#> We take in only csv or xlsx files
#> country year Agricultural land (% of land area)
#> 1 Afghanistan 1800 NA
#> 2 Afghanistan 1801 NA
#> 3 Afghanistan 1802 NA
#> 4 Afghanistan 1803 NA
#> 5 Afghanistan 1804 NA
#> 6 Afghanistan 1805 NA
#> 7 Afghanistan 1806 NA
#> 8 Afghanistan 1807 NA
#> 9 Afghanistan 1808 NA
#> 10 Afghanistan 1809 NA
#> 11 Afghanistan 1810 NA
#> 12 Afghanistan 1811 NA
#> 13 Afghanistan 1812 NA
#> 14 Afghanistan 1813 NA
#> 15 Afghanistan 1814 NA
#> 16 Afghanistan 1815 NA
#> 17 Afghanistan 1816 NA
#> 18 Afghanistan 1817 NA
#> 19 Afghanistan 1818 NA
#> 20 Afghanistan 1819 NA
#> 21 Afghanistan 1820 NA
#> 22 Afghanistan 1821 NA
#> 23 Afghanistan 1822 NA
#> 24 Afghanistan 1823 NA
#> 25 Afghanistan 1824 NA
#> 26 Afghanistan 1825 NA
#> 27 Afghanistan 1826 NA
#> 28 Afghanistan 1827 NA
#> 29 Afghanistan 1828 NA
#> 30 Afghanistan 1829 NA
#> 31 Afghanistan 1830 NA
#> 32 Afghanistan 1831 NA
#> 33 Afghanistan 1832 NA
#> 34 Afghanistan 1833 NA
#> 35 Afghanistan 1834 NA
#> 36 Afghanistan 1835 NA
#> 37 Afghanistan 1836 NA
#> 38 Afghanistan 1837 NA
#> 39 Afghanistan 1838 NA
#> 40 Afghanistan 1839 NA
#> 41 Afghanistan 1840 NA
#> 42 Afghanistan 1841 NA
#> 43 Afghanistan 1842 NA
#> 44 Afghanistan 1843 NA
#> 45 Afghanistan 1844 NA
#> 46 Afghanistan 1845 NA
#> 47 Afghanistan 1846 NA
#> 48 Afghanistan 1847 NA
#> 49 Afghanistan 1848 NA
#> 50 Afghanistan 1849 NA
#> 51 Afghanistan 1850 NA
#> 52 Afghanistan 1851 NA
#> 53 Afghanistan 1852 NA
#> 54 Afghanistan 1853 NA
#> 55 Afghanistan 1854 NA
#> 56 Afghanistan 1855 NA
#> 57 Afghanistan 1856 NA
#> 58 Afghanistan 1857 NA
#> 59 Afghanistan 1858 NA
#> 60 Afghanistan 1859 NA
#> 61 Afghanistan 1860 NA
#> 62 Afghanistan 1861 NA
#> 63 Afghanistan 1862 NA
#> 64 Afghanistan 1863 NA
#> 65 Afghanistan 1864 NA
#> 66 Afghanistan 1865 NA
#> 67 Afghanistan 1866 NA
#> 68 Afghanistan 1867 NA
#> 69 Afghanistan 1868 NA
#> 70 Afghanistan 1869 NA
#> 71 Afghanistan 1870 NA
#> 72 Afghanistan 1871 NA
#> 73 Afghanistan 1872 NA
#> 74 Afghanistan 1873 NA
#> 75 Afghanistan 1874 NA
#> 76 Afghanistan 1875 NA
#> 77 Afghanistan 1876 NA
#> 78 Afghanistan 1877 NA
#> 79 Afghanistan 1878 NA
#> 80 Afghanistan 1879 NA
#> 81 Afghanistan 1880 NA
#> 82 Afghanistan 1881 NA
#> 83 Afghanistan 1882 NA
#> 84 Afghanistan 1883 NA
#> 85 Afghanistan 1884 NA
#> 86 Afghanistan 1885 NA
#> 87 Afghanistan 1886 NA
#> 88 Afghanistan 1887 NA
#> 89 Afghanistan 1888 NA
#> 90 Afghanistan 1889 NA
#> 91 Afghanistan 1890 NA
#> 92 Afghanistan 1891 NA
#> 93 Afghanistan 1892 NA
#> 94 Afghanistan 1893 NA
#> 95 Afghanistan 1894 NA
#> 96 Afghanistan 1895 NA
#> 97 Afghanistan 1896 NA
#> 98 Afghanistan 1897 NA
#> 99 Afghanistan 1898 NA
#> 100 Afghanistan 1899 NA
#> 101 Afghanistan 1900 NA
#> 102 Afghanistan 1901 NA
#> 103 Afghanistan 1902 NA
#> 104 Afghanistan 1903 NA
#> 105 Afghanistan 1904 NA
#> 106 Afghanistan 1905 NA
#> 107 Afghanistan 1906 NA
#> 108 Afghanistan 1907 NA
#> 109 Afghanistan 1908 NA
#> 110 Afghanistan 1909 NA
#> 111 Afghanistan 1910 NA
#> 112 Afghanistan 1911 NA
#> 113 Afghanistan 1912 NA
#> 114 Afghanistan 1913 NA
#> 115 Afghanistan 1914 NA
#> 116 Afghanistan 1915 NA
#> 117 Afghanistan 1916 NA
#> 118 Afghanistan 1917 NA
#> 119 Afghanistan 1918 NA
#> 120 Afghanistan 1919 NA
#> 121 Afghanistan 1920 NA
#> 122 Afghanistan 1921 NA
#> 123 Afghanistan 1922 NA
#> 124 Afghanistan 1923 NA
#> 125 Afghanistan 1924 NA
#> 126 Afghanistan 1925 NA
#> 127 Afghanistan 1926 NA
#> 128 Afghanistan 1927 NA
#> 129 Afghanistan 1928 NA
#> 130 Afghanistan 1929 NA
#> 131 Afghanistan 1930 NA
#> 132 Afghanistan 1931 NA
#> 133 Afghanistan 1932 NA
#> 134 Afghanistan 1933 NA
#> 135 Afghanistan 1934 NA
#> 136 Afghanistan 1935 NA
#> 137 Afghanistan 1936 NA
#> 138 Afghanistan 1937 NA
#> 139 Afghanistan 1938 NA
#> 140 Afghanistan 1939 NA
#> 141 Afghanistan 1940 NA
#> 142 Afghanistan 1941 NA
#> 143 Afghanistan 1942 NA
#> 144 Afghanistan 1943 NA
#> 145 Afghanistan 1944 NA
#> 146 Afghanistan 1945 NA
#> 147 Afghanistan 1946 NA
#> 148 Afghanistan 1947 NA
#> 149 Afghanistan 1948 NA
#> 150 Afghanistan 1949 NA
#> 151 Afghanistan 1950 NA
#> 152 Afghanistan 1951 NA
#> 153 Afghanistan 1952 NA
#> 154 Afghanistan 1953 NA
#> 155 Afghanistan 1954 NA
#> 156 Afghanistan 1955 NA
#> 157 Afghanistan 1956 NA
#> 158 Afghanistan 1957 NA
#> 159 Afghanistan 1958 NA
#> 160 Afghanistan 1959 NA
#> 161 Afghanistan 1960 NA
#> 162 Afghanistan 1961 57.80170
#> 163 Afghanistan 1962 57.89369
#> 164 Afghanistan 1963 57.97035
#> 165 Afghanistan 1964 58.06694
#> 166 Afghanistan 1965 58.07001
#> 167 Afghanistan 1966 58.12827
#> 168 Afghanistan 1967 58.22946
#> 169 Afghanistan 1968 58.23099
#> 170 Afghanistan 1969 58.25552
#> 171 Afghanistan 1970 58.27086
#> 172 Afghanistan 1971 58.31685
#> 173 Afghanistan 1972 58.33218
#> 174 Afghanistan 1973 58.33525
#> 175 Afghanistan 1974 58.33525
#> 176 Afghanistan 1975 58.33525
#> 177 Afghanistan 1976 58.33525
#> 178 Afghanistan 1977 58.33832
#> 179 Afghanistan 1978 58.33832
#> 180 Afghanistan 1979 58.33678
#> 181 Afghanistan 1980 58.33678
#> 182 Afghanistan 1981 58.34292
#> 183 Afghanistan 1982 58.34445
#> 184 Afghanistan 1983 58.34445
#> 185 Afghanistan 1984 58.34445
#> 186 Afghanistan 1985 58.34445
#> 187 Afghanistan 1986 58.34445
#> 188 Afghanistan 1987 58.33065
#> 189 Afghanistan 1988 58.32298
#> 190 Afghanistan 1989 58.32298
#> 191 Afghanistan 1990 58.32298
#> 192 Afghanistan 1991 58.30765
#> 193 Afghanistan 1992 58.30765
#> 194 Afghanistan 1993 58.16046
#> 195 Afghanistan 1994 57.97495
#> 196 Afghanistan 1995 57.88296
#> 197 Afghanistan 1996 57.88142
#> 198 Afghanistan 1997 57.93968
#> 199 Afghanistan 1998 58.05774
#> 200 Afghanistan 1999 57.88296
#> 201 Afghanistan 2000 57.88296
#> 202 Afghanistan 2001 57.88296
#> 203 Afghanistan 2002 57.88296
#> 204 Afghanistan 2003 58.12367
#> 205 Afghanistan 2004 58.12520
#> 206 Afghanistan 2005 58.12367
#> 207 Afghanistan 2006 58.12367
#> 208 Afghanistan 2007 58.12367
#> 209 Afghanistan 2008 58.12367
#> 210 Afghanistan 2009 58.12367
#> 211 Afghanistan 2010 NA
#> 212 Afghanistan 2011 NA
#> 213 Afghanistan 2012 NA
#> 214 Afghanistan 2013 NA
#> 215 Afghanistan 2014 NA
#> 216 Afghanistan 2015 NA
#> 217 Afghanistan 2016 NA
#> 218 Afghanistan 2017 NA
#> 219 Afghanistan 2018 NA
#> 220 Albania 1800 NA
#> 221 Albania 1801 NA
#> 222 Albania 1802 NA
#> 223 Albania 1803 NA
#> 224 Albania 1804 NA
#> 225 Albania 1805 NA
#> 226 Albania 1806 NA
#> 227 Albania 1807 NA
#> 228 Albania 1808 NA
#> 229 Albania 1809 NA
#> 230 Albania 1810 NA
#> 231 Albania 1811 NA
#> 232 Albania 1812 NA
#> 233 Albania 1813 NA
#> 234 Albania 1814 NA
#> 235 Albania 1815 NA
#> 236 Albania 1816 NA
#> 237 Albania 1817 NA
#> 238 Albania 1818 NA
#> 239 Albania 1819 NA
#> 240 Albania 1820 NA
#> 241 Albania 1821 NA
#> 242 Albania 1822 NA
#> 243 Albania 1823 NA
#> 244 Albania 1824 NA
#> 245 Albania 1825 NA
#> 246 Albania 1826 NA
#> 247 Albania 1827 NA
#> 248 Albania 1828 NA
#> 249 Albania 1829 NA
#> 250 Albania 1830 NA
#> life_expectancy_years
#> 1 28.20
#> 2 28.20
#> 3 28.20
#> 4 28.20
#> 5 28.20
#> 6 28.20
#> 7 28.10
#> 8 28.10
#> 9 28.10
#> 10 28.10
#> 11 28.10
#> 12 28.10
#> 13 28.10
#> 14 28.10
#> 15 28.10
#> 16 28.10
#> 17 28.10
#> 18 28.00
#> 19 28.00
#> 20 28.00
#> 21 28.00
#> 22 28.00
#> 23 28.00
#> 24 28.00
#> 25 28.00
#> 26 27.90
#> 27 27.90
#> 28 27.90
#> 29 27.90
#> 30 27.90
#> 31 27.90
#> 32 27.90
#> 33 27.90
#> 34 27.90
#> 35 27.90
#> 36 27.90
#> 37 27.80
#> 38 27.80
#> 39 27.80
#> 40 27.80
#> 41 27.80
#> 42 27.80
#> 43 27.80
#> 44 27.80
#> 45 27.80
#> 46 27.80
#> 47 27.70
#> 48 27.70
#> 49 27.70
#> 50 27.70
#> 51 27.70
#> 52 27.70
#> 53 27.70
#> 54 27.70
#> 55 27.70
#> 56 27.60
#> 57 27.60
#> 58 27.60
#> 59 27.60
#> 60 27.60
#> 61 27.60
#> 62 27.60
#> 63 27.60
#> 64 27.60
#> 65 27.60
#> 66 27.50
#> 67 27.50
#> 68 27.50
#> 69 27.50
#> 70 27.50
#> 71 27.50
#> 72 27.60
#> 73 27.60
#> 74 27.70
#> 75 27.70
#> 76 27.80
#> 77 27.80
#> 78 27.90
#> 79 28.00
#> 80 28.00
#> 81 28.10
#> 82 28.10
#> 83 28.20
#> 84 28.20
#> 85 28.30
#> 86 28.40
#> 87 28.40
#> 88 28.50
#> 89 28.50
#> 90 28.60
#> 91 28.60
#> 92 28.70
#> 93 28.80
#> 94 28.80
#> 95 28.90
#> 96 28.90
#> 97 29.00
#> 98 29.10
#> 99 29.10
#> 100 29.20
#> 101 29.20
#> 102 29.30
#> 103 29.30
#> 104 29.40
#> 105 29.40
#> 106 29.50
#> 107 29.60
#> 108 29.60
#> 109 29.70
#> 110 29.70
#> 111 29.80
#> 112 29.80
#> 113 29.90
#> 114 29.90
#> 115 30.00
#> 116 30.10
#> 117 30.10
#> 118 30.20
#> 119 7.89
#> 120 30.30
#> 121 30.30
#> 122 30.40
#> 123 30.40
#> 124 30.50
#> 125 30.60
#> 126 30.60
#> 127 30.70
#> 128 30.70
#> 129 30.80
#> 130 30.80
#> 131 30.90
#> 132 30.90
#> 133 31.00
#> 134 31.10
#> 135 31.10
#> 136 31.20
#> 137 31.20
#> 138 31.30
#> 139 31.30
#> 140 31.40
#> 141 31.40
#> 142 31.50
#> 143 31.60
#> 144 31.60
#> 145 31.70
#> 146 31.70
#> 147 31.80
#> 148 31.80
#> 149 31.90
#> 150 31.90
#> 151 32.00
#> 152 32.40
#> 153 33.00
#> 154 33.70
#> 155 34.40
#> 156 35.10
#> 157 35.80
#> 158 36.50
#> 159 37.20
#> 160 37.90
#> 161 38.60
#> 162 39.40
#> 163 40.10
#> 164 40.80
#> 165 41.50
#> 166 42.20
#> 167 42.90
#> 168 43.70
#> 169 44.40
#> 170 45.10
#> 171 45.80
#> 172 45.90
#> 173 45.90
#> 174 46.00
#> 175 46.10
#> 176 46.30
#> 177 46.50
#> 178 46.60
#> 179 45.00
#> 180 43.60
#> 181 43.30
#> 182 44.10
#> 183 43.80
#> 184 42.00
#> 185 39.80
#> 186 41.60
#> 187 42.60
#> 188 44.70
#> 189 47.00
#> 190 50.80
#> 191 51.60
#> 192 51.30
#> 193 51.40
#> 194 51.40
#> 195 50.70
#> 196 51.10
#> 197 51.40
#> 198 51.10
#> 199 50.10
#> 200 51.50
#> 201 51.60
#> 202 51.70
#> 203 52.40
#> 204 53.00
#> 205 53.50
#> 206 53.90
#> 207 54.10
#> 208 54.60
#> 209 55.20
#> 210 55.70
#> 211 56.20
#> 212 56.70
#> 213 57.20
#> 214 57.70
#> 215 57.80
#> 216 57.90
#> 217 58.00
#> 218 58.40
#> 219 58.70
#> 220 35.40
#> 221 35.40
#> 222 35.40
#> 223 35.40
#> 224 35.40
#> 225 35.40
#> 226 35.40
#> 227 35.40
#> 228 35.40
#> 229 35.40
#> 230 35.40
#> 231 35.40
#> 232 35.40
#> 233 35.40
#> 234 35.40
#> 235 35.40
#> 236 35.40
#> 237 35.40
#> 238 35.40
#> 239 35.40
#> 240 35.40
#> 241 35.40
#> 242 35.40
#> 243 35.40
#> 244 35.40
#> 245 35.40
#> 246 35.40
#> 247 35.40
#> 248 35.40
#> 249 35.40
#> 250 35.40
#> [ reached 'max' / getOption("max.print") -- omitted 42679 rows ]
Enjoy 😄 !!!