tidygapminder is designed to make easy to tidy data retrieved from Gapminder. Learn more in vignette("tidygapminder").
You can install the released version of tidygapminder from CRAN with:
And the development version from GitHub with:
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 rowsOr 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 😄 !!!