Διαγράμματα
Κώστας Κούδας
2025-08-25
1 Διάγραμμα διασποράς
Στην ενότητα αυτή θα μελετήσουμε τη συσχέτιση ανάμεσα στο προσδόκιμο ζωής και στα χρήματα που επενδύονται για την υγεία. Τα δεδομένα αντλήθηκαν από από τη σελίδα Our World in Data και αποθηκεύτηκαν σε αρχείο ονόματι ProsdokimoVSperith.csv, αφού πρώτα διαγράψαμε τις μεταβλητές της προηγούμενης ενότητας:
| Entity | Code | Year | Total.population..Gapminder..HYDE…UN. | Continent | Health.Expenditure.and.Financing..per.capita…OECDstat..2017.. | Life.expectancy.at.birth..total..years. |
|---|---|---|---|---|---|---|
| Abkhazia | OWID_ABK | 2015 | NA | Asia | NA | NA |
| Afghanistan | AFG | 1800 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1801 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1802 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1803 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1804 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1805 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1806 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1807 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1808 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1809 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1810 | 3280000 | NA | NA | NA |
| Afghanistan | AFG | 1811 | 3280779 | NA | NA | NA |
| Afghanistan | AFG | 1812 | 3282342 | NA | NA | NA |
| Afghanistan | AFG | 1813 | 3284692 | NA | NA | NA |
| Afghanistan | AFG | 1814 | 3287834 | NA | NA | NA |
| Afghanistan | AFG | 1815 | 3291770 | NA | NA | NA |
| Afghanistan | AFG | 1816 | 3296506 | NA | NA | NA |
| Afghanistan | AFG | 1817 | 3302044 | NA | NA | NA |
| Afghanistan | AFG | 1818 | 3308390 | NA | NA | NA |
| Afghanistan | AFG | 1819 | 3315547 | NA | NA | NA |
| Afghanistan | AFG | 1820 | 3323519 | NA | NA | NA |
| Afghanistan | AFG | 1821 | 3332311 | NA | NA | NA |
| Afghanistan | AFG | 1822 | 3341926 | NA | NA | NA |
| Afghanistan | AFG | 1823 | 3352368 | NA | NA | NA |
| Afghanistan | AFG | 1824 | 3363642 | NA | NA | NA |
| Afghanistan | AFG | 1825 | 3375751 | NA | NA | NA |
| Afghanistan | AFG | 1826 | 3388701 | NA | NA | NA |
| Afghanistan | AFG | 1827 | 3402494 | NA | NA | NA |
| Afghanistan | AFG | 1828 | 3417136 | NA | NA | NA |
| Afghanistan | AFG | 1829 | 3432630 | NA | NA | NA |
| Afghanistan | AFG | 1830 | 3448982 | NA | NA | NA |
| Afghanistan | AFG | 1831 | 3466194 | NA | NA | NA |
| Afghanistan | AFG | 1832 | 3483492 | NA | NA | NA |
| Afghanistan | AFG | 1833 | 3500877 | NA | NA | NA |
| Afghanistan | AFG | 1834 | 3518348 | NA | NA | NA |
| Afghanistan | AFG | 1835 | 3535906 | NA | NA | NA |
| Afghanistan | AFG | 1836 | 3553552 | NA | NA | NA |
| Afghanistan | AFG | 1837 | 3571286 | NA | NA | NA |
| Afghanistan | AFG | 1838 | 3589109 | NA | NA | NA |
| Afghanistan | AFG | 1839 | 3607021 | NA | NA | NA |
| Afghanistan | AFG | 1840 | 3625022 | NA | NA | NA |
| Afghanistan | AFG | 1841 | 3643112 | NA | NA | NA |
| Afghanistan | AFG | 1842 | 3661294 | NA | NA | NA |
| Afghanistan | AFG | 1843 | 3679565 | NA | NA | NA |
| Afghanistan | AFG | 1844 | 3697928 | NA | NA | NA |
| Afghanistan | AFG | 1845 | 3716383 | NA | NA | NA |
| Afghanistan | AFG | 1846 | 3734930 | NA | NA | NA |
| Afghanistan | AFG | 1847 | 3753569 | NA | NA | NA |
| Afghanistan | AFG | 1848 | 3772301 | NA | NA | NA |
| Afghanistan | AFG | 1849 | 3791127 | NA | NA | NA |
| Afghanistan | AFG | 1850 | 3810047 | NA | NA | NA |
| Afghanistan | AFG | 1851 | 3826140 | NA | NA | NA |
| Afghanistan | AFG | 1852 | 3842299 | NA | NA | NA |
| Afghanistan | AFG | 1853 | 3858524 | NA | NA | NA |
| Afghanistan | AFG | 1854 | 3874815 | NA | NA | NA |
| Afghanistan | AFG | 1855 | 3891173 | NA | NA | NA |
| Afghanistan | AFG | 1856 | 3907598 | NA | NA | NA |
| Afghanistan | AFG | 1857 | 3924089 | NA | NA | NA |
| Afghanistan | AFG | 1858 | 3940648 | NA | NA | NA |
| Afghanistan | AFG | 1859 | 3957274 | NA | NA | NA |
| Afghanistan | AFG | 1860 | 3973968 | NA | NA | NA |
| Afghanistan | AFG | 1861 | 3991172 | NA | NA | NA |
| Afghanistan | AFG | 1862 | 4008889 | NA | NA | NA |
| Afghanistan | AFG | 1863 | 4027125 | NA | NA | NA |
| Afghanistan | AFG | 1864 | 4045883 | NA | NA | NA |
| Afghanistan | AFG | 1865 | 4065170 | NA | NA | NA |
| Afghanistan | AFG | 1866 | 4084989 | NA | NA | NA |
| Afghanistan | AFG | 1867 | 4105346 | NA | NA | NA |
| Afghanistan | AFG | 1868 | 4126245 | NA | NA | NA |
| Afghanistan | AFG | 1869 | 4147692 | NA | NA | NA |
| Afghanistan | AFG | 1870 | 4169690 | NA | NA | NA |
| Afghanistan | AFG | 1871 | 4192245 | NA | NA | NA |
| Afghanistan | AFG | 1872 | 4215362 | NA | NA | NA |
| Afghanistan | AFG | 1873 | 4236341 | NA | NA | NA |
| Afghanistan | AFG | 1874 | 4260730 | NA | NA | NA |
| Afghanistan | AFG | 1875 | 4285744 | NA | NA | NA |
| Afghanistan | AFG | 1876 | 4311388 | NA | NA | NA |
| Afghanistan | AFG | 1877 | 4336107 | NA | NA | NA |
| Afghanistan | AFG | 1878 | 4363439 | NA | NA | NA |
| Afghanistan | AFG | 1879 | 4391478 | NA | NA | NA |
| Afghanistan | AFG | 1880 | 4419695 | NA | NA | NA |
| Afghanistan | AFG | 1881 | 4447479 | NA | NA | NA |
| Afghanistan | AFG | 1882 | 4476050 | NA | NA | NA |
| Afghanistan | AFG | 1883 | 4504802 | NA | NA | NA |
| Afghanistan | AFG | 1884 | 4533190 | NA | NA | NA |
| Afghanistan | AFG | 1885 | 4562304 | NA | NA | NA |
| Afghanistan | AFG | 1886 | 4591602 | NA | NA | NA |
| Afghanistan | AFG | 1887 | 4620608 | NA | NA | NA |
| Afghanistan | AFG | 1888 | 4650274 | NA | NA | NA |
| Afghanistan | AFG | 1889 | 4680128 | NA | NA | NA |
| Afghanistan | AFG | 1890 | 4710171 | NA | NA | NA |
| Afghanistan | AFG | 1891 | 4740404 | NA | NA | NA |
| Afghanistan | AFG | 1892 | 4770828 | NA | NA | NA |
| Afghanistan | AFG | 1893 | 4801444 | NA | NA | NA |
| Afghanistan | AFG | 1894 | 4832253 | NA | NA | NA |
| Afghanistan | AFG | 1895 | 4863258 | NA | NA | NA |
| Afghanistan | AFG | 1896 | 4894458 | NA | NA | NA |
| Afghanistan | AFG | 1897 | 4925855 | NA | NA | NA |
| Afghanistan | AFG | 1898 | 4957450 | NA | NA | NA |
Παραπάνω παρουσιάζουμε μόνο τις 100 πρώτες γραμμές του αρχείου ProsdokimoVSperith.csv, διότι συνολικά ήταν 49626 και ο πίνακας αργούσε πολύ να απεικονιστεί.
Πριν μπούμε στο ζουμί του πράγματος, πάμε να σουλουπώσουμε λίγο τα δεδομένα μας.
Καταρχάς, για κάποιον παράδοξο λόγο σε κάποια σημεία γράφει την
ήπειρο στην οποία ανήκει η τάδε χώρα, αλλά σε κάποια άλλα όχι. Μπορούμε
να αντικαταστήσουμε τα NA με την ένδειξη που έχει στο κάθε
φορά αρχικό σημείο σημείο γράφοντας:
if(!require(dplyr)){
install.packages("dplyr")
library(dplyr)
}
if(!require(tidyr)){
install.packages("tidyr")
library(tidyr)
}
ProsdokimoVSperith2 <- ProsdokimoVSperith %>% group_by(Code) %>% fill(Continent)%>% fill(Continent, .direction = "up")| Entity | Code | Year | Total.population..Gapminder..HYDE…UN. | Continent | Health.Expenditure.and.Financing..per.capita…OECDstat..2017.. | Life.expectancy.at.birth..total..years. |
|---|---|---|---|---|---|---|
| Abkhazia | OWID_ABK | 2015 | NA | Asia | NA | NA |
| Afghanistan | AFG | 1800 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1801 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1802 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1803 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1804 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1805 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1806 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1807 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1808 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1809 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1810 | 3280000 | Asia | NA | NA |
| Afghanistan | AFG | 1811 | 3280779 | Asia | NA | NA |
| Afghanistan | AFG | 1812 | 3282342 | Asia | NA | NA |
| Afghanistan | AFG | 1813 | 3284692 | Asia | NA | NA |
| Afghanistan | AFG | 1814 | 3287834 | Asia | NA | NA |
| Afghanistan | AFG | 1815 | 3291770 | Asia | NA | NA |
| Afghanistan | AFG | 1816 | 3296506 | Asia | NA | NA |
| Afghanistan | AFG | 1817 | 3302044 | Asia | NA | NA |
| Afghanistan | AFG | 1818 | 3308390 | Asia | NA | NA |
| Afghanistan | AFG | 1819 | 3315547 | Asia | NA | NA |
| Afghanistan | AFG | 1820 | 3323519 | Asia | NA | NA |
| Afghanistan | AFG | 1821 | 3332311 | Asia | NA | NA |
| Afghanistan | AFG | 1822 | 3341926 | Asia | NA | NA |
| Afghanistan | AFG | 1823 | 3352368 | Asia | NA | NA |
| Afghanistan | AFG | 1824 | 3363642 | Asia | NA | NA |
| Afghanistan | AFG | 1825 | 3375751 | Asia | NA | NA |
| Afghanistan | AFG | 1826 | 3388701 | Asia | NA | NA |
| Afghanistan | AFG | 1827 | 3402494 | Asia | NA | NA |
| Afghanistan | AFG | 1828 | 3417136 | Asia | NA | NA |
| Afghanistan | AFG | 1829 | 3432630 | Asia | NA | NA |
| Afghanistan | AFG | 1830 | 3448982 | Asia | NA | NA |
| Afghanistan | AFG | 1831 | 3466194 | Asia | NA | NA |
| Afghanistan | AFG | 1832 | 3483492 | Asia | NA | NA |
| Afghanistan | AFG | 1833 | 3500877 | Asia | NA | NA |
| Afghanistan | AFG | 1834 | 3518348 | Asia | NA | NA |
| Afghanistan | AFG | 1835 | 3535906 | Asia | NA | NA |
| Afghanistan | AFG | 1836 | 3553552 | Asia | NA | NA |
| Afghanistan | AFG | 1837 | 3571286 | Asia | NA | NA |
| Afghanistan | AFG | 1838 | 3589109 | Asia | NA | NA |
| Afghanistan | AFG | 1839 | 3607021 | Asia | NA | NA |
| Afghanistan | AFG | 1840 | 3625022 | Asia | NA | NA |
| Afghanistan | AFG | 1841 | 3643112 | Asia | NA | NA |
| Afghanistan | AFG | 1842 | 3661294 | Asia | NA | NA |
| Afghanistan | AFG | 1843 | 3679565 | Asia | NA | NA |
| Afghanistan | AFG | 1844 | 3697928 | Asia | NA | NA |
| Afghanistan | AFG | 1845 | 3716383 | Asia | NA | NA |
| Afghanistan | AFG | 1846 | 3734930 | Asia | NA | NA |
| Afghanistan | AFG | 1847 | 3753569 | Asia | NA | NA |
| Afghanistan | AFG | 1848 | 3772301 | Asia | NA | NA |
| Afghanistan | AFG | 1849 | 3791127 | Asia | NA | NA |
| Afghanistan | AFG | 1850 | 3810047 | Asia | NA | NA |
| Afghanistan | AFG | 1851 | 3826140 | Asia | NA | NA |
| Afghanistan | AFG | 1852 | 3842299 | Asia | NA | NA |
| Afghanistan | AFG | 1853 | 3858524 | Asia | NA | NA |
| Afghanistan | AFG | 1854 | 3874815 | Asia | NA | NA |
| Afghanistan | AFG | 1855 | 3891173 | Asia | NA | NA |
| Afghanistan | AFG | 1856 | 3907598 | Asia | NA | NA |
| Afghanistan | AFG | 1857 | 3924089 | Asia | NA | NA |
| Afghanistan | AFG | 1858 | 3940648 | Asia | NA | NA |
| Afghanistan | AFG | 1859 | 3957274 | Asia | NA | NA |
| Afghanistan | AFG | 1860 | 3973968 | Asia | NA | NA |
| Afghanistan | AFG | 1861 | 3991172 | Asia | NA | NA |
| Afghanistan | AFG | 1862 | 4008889 | Asia | NA | NA |
| Afghanistan | AFG | 1863 | 4027125 | Asia | NA | NA |
| Afghanistan | AFG | 1864 | 4045883 | Asia | NA | NA |
| Afghanistan | AFG | 1865 | 4065170 | Asia | NA | NA |
| Afghanistan | AFG | 1866 | 4084989 | Asia | NA | NA |
| Afghanistan | AFG | 1867 | 4105346 | Asia | NA | NA |
| Afghanistan | AFG | 1868 | 4126245 | Asia | NA | NA |
| Afghanistan | AFG | 1869 | 4147692 | Asia | NA | NA |
| Afghanistan | AFG | 1870 | 4169690 | Asia | NA | NA |
| Afghanistan | AFG | 1871 | 4192245 | Asia | NA | NA |
| Afghanistan | AFG | 1872 | 4215362 | Asia | NA | NA |
| Afghanistan | AFG | 1873 | 4236341 | Asia | NA | NA |
| Afghanistan | AFG | 1874 | 4260730 | Asia | NA | NA |
| Afghanistan | AFG | 1875 | 4285744 | Asia | NA | NA |
| Afghanistan | AFG | 1876 | 4311388 | Asia | NA | NA |
| Afghanistan | AFG | 1877 | 4336107 | Asia | NA | NA |
| Afghanistan | AFG | 1878 | 4363439 | Asia | NA | NA |
| Afghanistan | AFG | 1879 | 4391478 | Asia | NA | NA |
| Afghanistan | AFG | 1880 | 4419695 | Asia | NA | NA |
| Afghanistan | AFG | 1881 | 4447479 | Asia | NA | NA |
| Afghanistan | AFG | 1882 | 4476050 | Asia | NA | NA |
| Afghanistan | AFG | 1883 | 4504802 | Asia | NA | NA |
| Afghanistan | AFG | 1884 | 4533190 | Asia | NA | NA |
| Afghanistan | AFG | 1885 | 4562304 | Asia | NA | NA |
| Afghanistan | AFG | 1886 | 4591602 | Asia | NA | NA |
| Afghanistan | AFG | 1887 | 4620608 | Asia | NA | NA |
| Afghanistan | AFG | 1888 | 4650274 | Asia | NA | NA |
| Afghanistan | AFG | 1889 | 4680128 | Asia | NA | NA |
| Afghanistan | AFG | 1890 | 4710171 | Asia | NA | NA |
| Afghanistan | AFG | 1891 | 4740404 | Asia | NA | NA |
| Afghanistan | AFG | 1892 | 4770828 | Asia | NA | NA |
| Afghanistan | AFG | 1893 | 4801444 | Asia | NA | NA |
| Afghanistan | AFG | 1894 | 4832253 | Asia | NA | NA |
| Afghanistan | AFG | 1895 | 4863258 | Asia | NA | NA |
| Afghanistan | AFG | 1896 | 4894458 | Asia | NA | NA |
| Afghanistan | AFG | 1897 | 4925855 | Asia | NA | NA |
| Afghanistan | AFG | 1898 | 4957450 | Asia | NA | NA |
Ένα πρόβλημα που αντιμετωπίζουμε επίσης είναι τα πολλά
ΝΑ στις στήλες
Health Expenditure and Financing (per capita) (OECDstat (2017))
και Life expectancy at birth, total (years). Αυτά τα
εξαλείφουμε ως ακολούθως:
ProsdokimoVSperith2 <- ProsdokimoVSperith2[!(is.na(ProsdokimoVSperith$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..)), ]| Entity | Code | Year | Total.population..Gapminder..HYDE…UN. | Continent | Health.Expenditure.and.Financing..per.capita…OECDstat..2017.. | Life.expectancy.at.birth..total..years. |
|---|---|---|---|---|---|---|
| Australia | AUS | 1970 | 12793000 | Oceania | .. | 71.01854 |
| Australia | AUS | 1971 | 13033000 | Oceania | 982.3137 | 71.06829 |
| Australia | AUS | 1972 | 13244000 | Oceania | 989.9003 | 71.45756 |
| Australia | AUS | 1973 | 13432000 | Oceania | 1007.0003 | 71.84683 |
| Australia | AUS | 1974 | 13606000 | Oceania | 1138.423 | 72.23610 |
| Australia | AUS | 1975 | 13773000 | Oceania | 1299.7468 | 72.62537 |
| Australia | AUS | 1976 | 13936000 | Oceania | 1337.0718 | 73.01463 |
| Australia | AUS | 1977 | 14093000 | Oceania | 1403.1552 | 73.34439 |
| Australia | AUS | 1978 | 14249000 | Oceania | 1410.1761 | 73.67415 |
| Australia | AUS | 1979 | 14413000 | Oceania | 1410.7119 | 74.00390 |
| Australia | AUS | 1980 | 14588000 | Oceania | 1440.4453 | 74.33366 |
| Australia | AUS | 1981 | 14777000 | Oceania | 1467.8877 | 74.66341 |
| Australia | AUS | 1982 | 14979000 | Oceania | 1475.0446 | 74.90488 |
| Australia | AUS | 1983 | 15195000 | Oceania | 1508.2358 | 75.14634 |
| Australia | AUS | 1984 | 15423000 | Oceania | 1561.121 | 75.38780 |
| Australia | AUS | 1985 | 15664000 | Oceania | 1617.1644 | 75.62927 |
| Australia | AUS | 1986 | 15918000 | Oceania | 1688.0412 | 75.87073 |
| Australia | AUS | 1987 | 16183000 | Oceania | 1712.2838 | 76.15171 |
| Australia | AUS | 1988 | 16452000 | Oceania | 1736.7152 | 76.43268 |
| Australia | AUS | 1989 | 16714000 | Oceania | 1783.3481 | 76.71366 |
| Australia | AUS | 1990 | 16961000 | Oceania | 1853.1596 | 76.99463 |
| Australia | AUS | 1991 | 17189000 | Oceania | 1920.2033 | 77.27561 |
| Australia | AUS | 1992 | 17402000 | Oceania | 1994.4415 | 77.37805 |
| Australia | AUS | 1993 | 17603000 | Oceania | 2060.3732 | 77.87805 |
| Australia | AUS | 1994 | 17799000 | Oceania | 2129.3472 | 77.87805 |
| Australia | AUS | 1995 | 17993000 | Oceania | 2202.176 | 77.82927 |
| Australia | AUS | 1996 | 18189000 | Oceania | 2307.517 | 78.07805 |
| Australia | AUS | 1997 | 18387000 | Oceania | 2392.0429 | 78.48049 |
| Australia | AUS | 1998 | 18587000 | Oceania | 2543.6197 | 78.63171 |
| Australia | AUS | 1999 | 18788000 | Oceania | 2646.7086 | 78.93171 |
| Australia | AUS | 2000 | 18991000 | Oceania | 2767.6725 | 79.23415 |
| Australia | AUS | 2001 | 19195000 | Oceania | 2872.3738 | 79.63415 |
| Australia | AUS | 2002 | 19401000 | Oceania | 2999.5093 | 79.93659 |
| Australia | AUS | 2003 | 19624000 | Oceania | 3090.6573 | 80.23902 |
| Australia | AUS | 2004 | 19880000 | Oceania | 3240.3186 | 80.49024 |
| Australia | AUS | 2005 | 20179000 | Oceania | 3242.9106 | 80.84146 |
| Australia | AUS | 2006 | 20526000 | Oceania | 3323.2565 | 81.04146 |
| Australia | AUS | 2007 | 20916000 | Oceania | 3415.193 | 81.29268 |
| Australia | AUS | 2008 | 21332000 | Oceania | 3494.4375 | 81.39512 |
| Australia | AUS | 2009 | 21751000 | Oceania | 3629.7041 | 81.54390 |
| Australia | AUS | 2010 | 22155000 | Oceania | 3607.3407 | 81.69512 |
| Australia | AUS | 2011 | 22538000 | Oceania | 3738.1767 | 81.89512 |
| Australia | AUS | 2012 | 22904000 | Oceania | 3831.3635 | 82.04634 |
| Australia | AUS | 2013 | 23255000 | Oceania | 3901.7343 | 82.14878 |
| Australia | AUS | 2014 | 23596000 | Oceania | 4009.8881 | 82.30000 |
| Australia | AUS | 2015 | 23932000 | Oceania | 4164.2213 | 82.40000 |
| Austria | AUT | 1970 | 7516000 | Europe | 849.863 | 69.91463 |
| Austria | AUT | 1971 | 7550000 | Europe | 876.7198 | 70.11463 |
| Austria | AUT | 1972 | 7581000 | Europe | 924.9049 | 70.46341 |
| Austria | AUT | 1973 | 7607000 | Europe | 983.3833 | 71.01463 |
| Austria | AUT | 1974 | 7626000 | Europe | 1068.3942 | 71.01220 |
| Austria | AUT | 1975 | 7638000 | Europe | 1365.5542 | 71.11463 |
| Austria | AUT | 1976 | 7641000 | Europe | 1481.9085 | 71.56585 |
| Austria | AUT | 1977 | 7637000 | Europe | 1543.0276 | 71.91463 |
| Austria | AUT | 1978 | 7628000 | Europe | 1602.6562 | 72.01220 |
| Austria | AUT | 1979 | 7618000 | Europe | 1685.3273 | 72.31220 |
| Austria | AUT | 1980 | 7610000 | Europe | 1738.9518 | 72.46341 |
| Austria | AUT | 1981 | 7605000 | Europe | 1485.8219 | 72.81220 |
| Austria | AUT | 1982 | 7602000 | Europe | 1508.3548 | 72.96098 |
| Austria | AUT | 1983 | 7603000 | Europe | 1512.4136 | 73.01220 |
| Austria | AUT | 1984 | 7607000 | Europe | 1525.3671 | 73.61220 |
| Austria | AUT | 1985 | 7615000 | Europe | 1597.0039 | 73.81463 |
| Austria | AUT | 1986 | 7625000 | Europe | 1661.8794 | 74.31707 |
| Austria | AUT | 1987 | 7639000 | Europe | 1763.1259 | 74.76829 |
| Austria | AUT | 1988 | 7659000 | Europe | 1816.3715 | 75.21707 |
| Austria | AUT | 1989 | 7687000 | Europe | 1920.5468 | 75.26585 |
| Austria | AUT | 1990 | 7724000 | Europe | 2347.0307 | 75.56829 |
| Austria | AUT | 1991 | 7773000 | Europe | 2423.6339 | 75.61707 |
| Austria | AUT | 1992 | 7831000 | Europe | 2540.0944 | 75.81707 |
| Austria | AUT | 1993 | 7892000 | Europe | 2696.6659 | 76.06829 |
| Austria | AUT | 1994 | 7947000 | Europe | 2875.3106 | 76.41951 |
| Austria | AUT | 1995 | 7990000 | Europe | 2925.7651 | 76.66829 |
| Austria | AUT | 1996 | 8018000 | Europe | 2978.1928 | 76.87073 |
| Austria | AUT | 1997 | 8033000 | Europe | 3074.4388 | 77.31951 |
| Austria | AUT | 1998 | 8041000 | Europe | 3240.746 | 77.67073 |
| Austria | AUT | 1999 | 8051000 | Europe | 3404.3271 | 77.87561 |
| Austria | AUT | 2000 | 8069000 | Europe | 3472.7366 | 78.12683 |
| Austria | AUT | 2001 | 8098000 | Europe | 3531.3159 | 78.57561 |
| Austria | AUT | 2002 | 8134000 | Europe | 3620.3297 | 78.67805 |
| Austria | AUT | 2003 | 8176000 | Europe | 3695.6138 | 78.63171 |
| Austria | AUT | 2004 | 8217000 | Europe | 3797.4718 | 79.18049 |
| Austria | AUT | 2005 | 8254000 | Europe | 3826.3191 | 79.33171 |
| Austria | AUT | 2006 | 8285000 | Europe | 3893.9178 | 79.88049 |
| Austria | AUT | 2007 | 8314000 | Europe | 4020.3699 | 80.18049 |
| Austria | AUT | 2008 | 8342000 | Europe | 4136.9982 | 80.43171 |
| Austria | AUT | 2009 | 8373000 | Europe | 4178.429 | 80.33171 |
| Austria | AUT | 2010 | 8410000 | Europe | 4236.3164 | 80.58049 |
| Austria | AUT | 2011 | 8454000 | Europe | 4253.5175 | 80.98293 |
| Austria | AUT | 2012 | 8502000 | Europe | 4362.1556 | 80.93659 |
| Austria | AUT | 2013 | 8556000 | Europe | 4352.5783 | 81.13659 |
| Austria | AUT | 2014 | 8615000 | Europe | 4390.1259 | 81.49024 |
| Austria | AUT | 2015 | 8679000 | Europe | 4451.1826 | 81.19024 |
| Belgium | BEL | 1970 | 9632000 | Europe | 683.3738 | 70.97195 |
| Belgium | BEL | 1971 | 9663000 | Europe | 723.5906 | 71.06049 |
| Belgium | BEL | 1972 | 9693000 | Europe | 788.4374 | 71.40512 |
| Belgium | BEL | 1973 | 9721000 | Europe | 891.1623 | 71.63537 |
| Belgium | BEL | 1974 | 9747000 | Europe | 939.3939 | 71.98585 |
| Belgium | BEL | 1975 | 9772000 | Europe | 1154.0117 | 71.97122 |
| Belgium | BEL | 1976 | 9795000 | Europe | 1262.6339 | 72.11976 |
| Belgium | BEL | 1977 | 9817000 | Europe | 1372.7239 | 72.77390 |
ProsdokimoVSperith2 <- ProsdokimoVSperith2[!(is.na(ProsdokimoVSperith2$Life.expectancy.at.birth..total..years.)), ]| Entity | Code | Year | Total.population..Gapminder..HYDE…UN. | Continent | Health.Expenditure.and.Financing..per.capita…OECDstat..2017.. | Life.expectancy.at.birth..total..years. |
|---|---|---|---|---|---|---|
| Australia | AUS | 1970 | 12793000 | Oceania | .. | 71.01854 |
| Australia | AUS | 1971 | 13033000 | Oceania | 982.3137 | 71.06829 |
| Australia | AUS | 1972 | 13244000 | Oceania | 989.9003 | 71.45756 |
| Australia | AUS | 1973 | 13432000 | Oceania | 1007.0003 | 71.84683 |
| Australia | AUS | 1974 | 13606000 | Oceania | 1138.423 | 72.23610 |
| Australia | AUS | 1975 | 13773000 | Oceania | 1299.7468 | 72.62537 |
| Australia | AUS | 1976 | 13936000 | Oceania | 1337.0718 | 73.01463 |
| Australia | AUS | 1977 | 14093000 | Oceania | 1403.1552 | 73.34439 |
| Australia | AUS | 1978 | 14249000 | Oceania | 1410.1761 | 73.67415 |
| Australia | AUS | 1979 | 14413000 | Oceania | 1410.7119 | 74.00390 |
| Australia | AUS | 1980 | 14588000 | Oceania | 1440.4453 | 74.33366 |
| Australia | AUS | 1981 | 14777000 | Oceania | 1467.8877 | 74.66341 |
| Australia | AUS | 1982 | 14979000 | Oceania | 1475.0446 | 74.90488 |
| Australia | AUS | 1983 | 15195000 | Oceania | 1508.2358 | 75.14634 |
| Australia | AUS | 1984 | 15423000 | Oceania | 1561.121 | 75.38780 |
| Australia | AUS | 1985 | 15664000 | Oceania | 1617.1644 | 75.62927 |
| Australia | AUS | 1986 | 15918000 | Oceania | 1688.0412 | 75.87073 |
| Australia | AUS | 1987 | 16183000 | Oceania | 1712.2838 | 76.15171 |
| Australia | AUS | 1988 | 16452000 | Oceania | 1736.7152 | 76.43268 |
| Australia | AUS | 1989 | 16714000 | Oceania | 1783.3481 | 76.71366 |
| Australia | AUS | 1990 | 16961000 | Oceania | 1853.1596 | 76.99463 |
| Australia | AUS | 1991 | 17189000 | Oceania | 1920.2033 | 77.27561 |
| Australia | AUS | 1992 | 17402000 | Oceania | 1994.4415 | 77.37805 |
| Australia | AUS | 1993 | 17603000 | Oceania | 2060.3732 | 77.87805 |
| Australia | AUS | 1994 | 17799000 | Oceania | 2129.3472 | 77.87805 |
| Australia | AUS | 1995 | 17993000 | Oceania | 2202.176 | 77.82927 |
| Australia | AUS | 1996 | 18189000 | Oceania | 2307.517 | 78.07805 |
| Australia | AUS | 1997 | 18387000 | Oceania | 2392.0429 | 78.48049 |
| Australia | AUS | 1998 | 18587000 | Oceania | 2543.6197 | 78.63171 |
| Australia | AUS | 1999 | 18788000 | Oceania | 2646.7086 | 78.93171 |
| Australia | AUS | 2000 | 18991000 | Oceania | 2767.6725 | 79.23415 |
| Australia | AUS | 2001 | 19195000 | Oceania | 2872.3738 | 79.63415 |
| Australia | AUS | 2002 | 19401000 | Oceania | 2999.5093 | 79.93659 |
| Australia | AUS | 2003 | 19624000 | Oceania | 3090.6573 | 80.23902 |
| Australia | AUS | 2004 | 19880000 | Oceania | 3240.3186 | 80.49024 |
| Australia | AUS | 2005 | 20179000 | Oceania | 3242.9106 | 80.84146 |
| Australia | AUS | 2006 | 20526000 | Oceania | 3323.2565 | 81.04146 |
| Australia | AUS | 2007 | 20916000 | Oceania | 3415.193 | 81.29268 |
| Australia | AUS | 2008 | 21332000 | Oceania | 3494.4375 | 81.39512 |
| Australia | AUS | 2009 | 21751000 | Oceania | 3629.7041 | 81.54390 |
| Australia | AUS | 2010 | 22155000 | Oceania | 3607.3407 | 81.69512 |
| Australia | AUS | 2011 | 22538000 | Oceania | 3738.1767 | 81.89512 |
| Australia | AUS | 2012 | 22904000 | Oceania | 3831.3635 | 82.04634 |
| Australia | AUS | 2013 | 23255000 | Oceania | 3901.7343 | 82.14878 |
| Australia | AUS | 2014 | 23596000 | Oceania | 4009.8881 | 82.30000 |
| Australia | AUS | 2015 | 23932000 | Oceania | 4164.2213 | 82.40000 |
| Austria | AUT | 1970 | 7516000 | Europe | 849.863 | 69.91463 |
| Austria | AUT | 1971 | 7550000 | Europe | 876.7198 | 70.11463 |
| Austria | AUT | 1972 | 7581000 | Europe | 924.9049 | 70.46341 |
| Austria | AUT | 1973 | 7607000 | Europe | 983.3833 | 71.01463 |
| Austria | AUT | 1974 | 7626000 | Europe | 1068.3942 | 71.01220 |
| Austria | AUT | 1975 | 7638000 | Europe | 1365.5542 | 71.11463 |
| Austria | AUT | 1976 | 7641000 | Europe | 1481.9085 | 71.56585 |
| Austria | AUT | 1977 | 7637000 | Europe | 1543.0276 | 71.91463 |
| Austria | AUT | 1978 | 7628000 | Europe | 1602.6562 | 72.01220 |
| Austria | AUT | 1979 | 7618000 | Europe | 1685.3273 | 72.31220 |
| Austria | AUT | 1980 | 7610000 | Europe | 1738.9518 | 72.46341 |
| Austria | AUT | 1981 | 7605000 | Europe | 1485.8219 | 72.81220 |
| Austria | AUT | 1982 | 7602000 | Europe | 1508.3548 | 72.96098 |
| Austria | AUT | 1983 | 7603000 | Europe | 1512.4136 | 73.01220 |
| Austria | AUT | 1984 | 7607000 | Europe | 1525.3671 | 73.61220 |
| Austria | AUT | 1985 | 7615000 | Europe | 1597.0039 | 73.81463 |
| Austria | AUT | 1986 | 7625000 | Europe | 1661.8794 | 74.31707 |
| Austria | AUT | 1987 | 7639000 | Europe | 1763.1259 | 74.76829 |
| Austria | AUT | 1988 | 7659000 | Europe | 1816.3715 | 75.21707 |
| Austria | AUT | 1989 | 7687000 | Europe | 1920.5468 | 75.26585 |
| Austria | AUT | 1990 | 7724000 | Europe | 2347.0307 | 75.56829 |
| Austria | AUT | 1991 | 7773000 | Europe | 2423.6339 | 75.61707 |
| Austria | AUT | 1992 | 7831000 | Europe | 2540.0944 | 75.81707 |
| Austria | AUT | 1993 | 7892000 | Europe | 2696.6659 | 76.06829 |
| Austria | AUT | 1994 | 7947000 | Europe | 2875.3106 | 76.41951 |
| Austria | AUT | 1995 | 7990000 | Europe | 2925.7651 | 76.66829 |
| Austria | AUT | 1996 | 8018000 | Europe | 2978.1928 | 76.87073 |
| Austria | AUT | 1997 | 8033000 | Europe | 3074.4388 | 77.31951 |
| Austria | AUT | 1998 | 8041000 | Europe | 3240.746 | 77.67073 |
| Austria | AUT | 1999 | 8051000 | Europe | 3404.3271 | 77.87561 |
| Austria | AUT | 2000 | 8069000 | Europe | 3472.7366 | 78.12683 |
| Austria | AUT | 2001 | 8098000 | Europe | 3531.3159 | 78.57561 |
| Austria | AUT | 2002 | 8134000 | Europe | 3620.3297 | 78.67805 |
| Austria | AUT | 2003 | 8176000 | Europe | 3695.6138 | 78.63171 |
| Austria | AUT | 2004 | 8217000 | Europe | 3797.4718 | 79.18049 |
| Austria | AUT | 2005 | 8254000 | Europe | 3826.3191 | 79.33171 |
| Austria | AUT | 2006 | 8285000 | Europe | 3893.9178 | 79.88049 |
| Austria | AUT | 2007 | 8314000 | Europe | 4020.3699 | 80.18049 |
| Austria | AUT | 2008 | 8342000 | Europe | 4136.9982 | 80.43171 |
| Austria | AUT | 2009 | 8373000 | Europe | 4178.429 | 80.33171 |
| Austria | AUT | 2010 | 8410000 | Europe | 4236.3164 | 80.58049 |
| Austria | AUT | 2011 | 8454000 | Europe | 4253.5175 | 80.98293 |
| Austria | AUT | 2012 | 8502000 | Europe | 4362.1556 | 80.93659 |
| Austria | AUT | 2013 | 8556000 | Europe | 4352.5783 | 81.13659 |
| Austria | AUT | 2014 | 8615000 | Europe | 4390.1259 | 81.49024 |
| Austria | AUT | 2015 | 8679000 | Europe | 4451.1826 | 81.19024 |
| Belgium | BEL | 1970 | 9632000 | Europe | 683.3738 | 70.97195 |
| Belgium | BEL | 1971 | 9663000 | Europe | 723.5906 | 71.06049 |
| Belgium | BEL | 1972 | 9693000 | Europe | 788.4374 | 71.40512 |
| Belgium | BEL | 1973 | 9721000 | Europe | 891.1623 | 71.63537 |
| Belgium | BEL | 1974 | 9747000 | Europe | 939.3939 | 71.98585 |
| Belgium | BEL | 1975 | 9772000 | Europe | 1154.0117 | 71.97122 |
| Belgium | BEL | 1976 | 9795000 | Europe | 1262.6339 | 72.11976 |
| Belgium | BEL | 1977 | 9817000 | Europe | 1372.7239 | 72.77390 |
Παρατηρούμε όμως ότι στη στήλη
Health Expenditure and Financing (per capita) (OECDstat (2017))
έχουμε κάποιους μη αριθμητικούς χαρακτήρες (τα «..»). Θα
απορρίψουμε και τις γραμμές του πίνακα που έχουν τέτοια. Αρχικά όμως θα
δώσουμε ένα μικρότερο όνομα στη στήλη, για λόγους δική μας
διευκόλυνσης:
Και ακολούθως έχουμε:
n <- length(XrimataYgeia)
delete_items <- rep(TRUE,n)
for (i in 1:n) {
if (XrimataYgeia[i] == "..") {
delete_items[i] <- FALSE
}
}
ProsdokimoVSperith2 <- ProsdokimoVSperith2[delete_items, ]| Entity | Code | Year | Total.population..Gapminder..HYDE…UN. | Continent | Health.Expenditure.and.Financing..per.capita…OECDstat..2017.. | Life.expectancy.at.birth..total..years. |
|---|---|---|---|---|---|---|
| Australia | AUS | 1971 | 13033000 | Oceania | 982.3137 | 71.06829 |
| Australia | AUS | 1972 | 13244000 | Oceania | 989.9003 | 71.45756 |
| Australia | AUS | 1973 | 13432000 | Oceania | 1007.0003 | 71.84683 |
| Australia | AUS | 1974 | 13606000 | Oceania | 1138.423 | 72.23610 |
| Australia | AUS | 1975 | 13773000 | Oceania | 1299.7468 | 72.62537 |
| Australia | AUS | 1976 | 13936000 | Oceania | 1337.0718 | 73.01463 |
| Australia | AUS | 1977 | 14093000 | Oceania | 1403.1552 | 73.34439 |
| Australia | AUS | 1978 | 14249000 | Oceania | 1410.1761 | 73.67415 |
| Australia | AUS | 1979 | 14413000 | Oceania | 1410.7119 | 74.00390 |
| Australia | AUS | 1980 | 14588000 | Oceania | 1440.4453 | 74.33366 |
| Australia | AUS | 1981 | 14777000 | Oceania | 1467.8877 | 74.66341 |
| Australia | AUS | 1982 | 14979000 | Oceania | 1475.0446 | 74.90488 |
| Australia | AUS | 1983 | 15195000 | Oceania | 1508.2358 | 75.14634 |
| Australia | AUS | 1984 | 15423000 | Oceania | 1561.121 | 75.38780 |
| Australia | AUS | 1985 | 15664000 | Oceania | 1617.1644 | 75.62927 |
| Australia | AUS | 1986 | 15918000 | Oceania | 1688.0412 | 75.87073 |
| Australia | AUS | 1987 | 16183000 | Oceania | 1712.2838 | 76.15171 |
| Australia | AUS | 1988 | 16452000 | Oceania | 1736.7152 | 76.43268 |
| Australia | AUS | 1989 | 16714000 | Oceania | 1783.3481 | 76.71366 |
| Australia | AUS | 1990 | 16961000 | Oceania | 1853.1596 | 76.99463 |
| Australia | AUS | 1991 | 17189000 | Oceania | 1920.2033 | 77.27561 |
| Australia | AUS | 1992 | 17402000 | Oceania | 1994.4415 | 77.37805 |
| Australia | AUS | 1993 | 17603000 | Oceania | 2060.3732 | 77.87805 |
| Australia | AUS | 1994 | 17799000 | Oceania | 2129.3472 | 77.87805 |
| Australia | AUS | 1995 | 17993000 | Oceania | 2202.176 | 77.82927 |
| Australia | AUS | 1996 | 18189000 | Oceania | 2307.517 | 78.07805 |
| Australia | AUS | 1997 | 18387000 | Oceania | 2392.0429 | 78.48049 |
| Australia | AUS | 1998 | 18587000 | Oceania | 2543.6197 | 78.63171 |
| Australia | AUS | 1999 | 18788000 | Oceania | 2646.7086 | 78.93171 |
| Australia | AUS | 2000 | 18991000 | Oceania | 2767.6725 | 79.23415 |
| Australia | AUS | 2001 | 19195000 | Oceania | 2872.3738 | 79.63415 |
| Australia | AUS | 2002 | 19401000 | Oceania | 2999.5093 | 79.93659 |
| Australia | AUS | 2003 | 19624000 | Oceania | 3090.6573 | 80.23902 |
| Australia | AUS | 2004 | 19880000 | Oceania | 3240.3186 | 80.49024 |
| Australia | AUS | 2005 | 20179000 | Oceania | 3242.9106 | 80.84146 |
| Australia | AUS | 2006 | 20526000 | Oceania | 3323.2565 | 81.04146 |
| Australia | AUS | 2007 | 20916000 | Oceania | 3415.193 | 81.29268 |
| Australia | AUS | 2008 | 21332000 | Oceania | 3494.4375 | 81.39512 |
| Australia | AUS | 2009 | 21751000 | Oceania | 3629.7041 | 81.54390 |
| Australia | AUS | 2010 | 22155000 | Oceania | 3607.3407 | 81.69512 |
| Australia | AUS | 2011 | 22538000 | Oceania | 3738.1767 | 81.89512 |
| Australia | AUS | 2012 | 22904000 | Oceania | 3831.3635 | 82.04634 |
| Australia | AUS | 2013 | 23255000 | Oceania | 3901.7343 | 82.14878 |
| Australia | AUS | 2014 | 23596000 | Oceania | 4009.8881 | 82.30000 |
| Australia | AUS | 2015 | 23932000 | Oceania | 4164.2213 | 82.40000 |
| Austria | AUT | 1970 | 7516000 | Europe | 849.863 | 69.91463 |
| Austria | AUT | 1971 | 7550000 | Europe | 876.7198 | 70.11463 |
| Austria | AUT | 1972 | 7581000 | Europe | 924.9049 | 70.46341 |
| Austria | AUT | 1973 | 7607000 | Europe | 983.3833 | 71.01463 |
| Austria | AUT | 1974 | 7626000 | Europe | 1068.3942 | 71.01220 |
| Austria | AUT | 1975 | 7638000 | Europe | 1365.5542 | 71.11463 |
| Austria | AUT | 1976 | 7641000 | Europe | 1481.9085 | 71.56585 |
| Austria | AUT | 1977 | 7637000 | Europe | 1543.0276 | 71.91463 |
| Austria | AUT | 1978 | 7628000 | Europe | 1602.6562 | 72.01220 |
| Austria | AUT | 1979 | 7618000 | Europe | 1685.3273 | 72.31220 |
| Austria | AUT | 1980 | 7610000 | Europe | 1738.9518 | 72.46341 |
| Austria | AUT | 1981 | 7605000 | Europe | 1485.8219 | 72.81220 |
| Austria | AUT | 1982 | 7602000 | Europe | 1508.3548 | 72.96098 |
| Austria | AUT | 1983 | 7603000 | Europe | 1512.4136 | 73.01220 |
| Austria | AUT | 1984 | 7607000 | Europe | 1525.3671 | 73.61220 |
| Austria | AUT | 1985 | 7615000 | Europe | 1597.0039 | 73.81463 |
| Austria | AUT | 1986 | 7625000 | Europe | 1661.8794 | 74.31707 |
| Austria | AUT | 1987 | 7639000 | Europe | 1763.1259 | 74.76829 |
| Austria | AUT | 1988 | 7659000 | Europe | 1816.3715 | 75.21707 |
| Austria | AUT | 1989 | 7687000 | Europe | 1920.5468 | 75.26585 |
| Austria | AUT | 1990 | 7724000 | Europe | 2347.0307 | 75.56829 |
| Austria | AUT | 1991 | 7773000 | Europe | 2423.6339 | 75.61707 |
| Austria | AUT | 1992 | 7831000 | Europe | 2540.0944 | 75.81707 |
| Austria | AUT | 1993 | 7892000 | Europe | 2696.6659 | 76.06829 |
| Austria | AUT | 1994 | 7947000 | Europe | 2875.3106 | 76.41951 |
| Austria | AUT | 1995 | 7990000 | Europe | 2925.7651 | 76.66829 |
| Austria | AUT | 1996 | 8018000 | Europe | 2978.1928 | 76.87073 |
| Austria | AUT | 1997 | 8033000 | Europe | 3074.4388 | 77.31951 |
| Austria | AUT | 1998 | 8041000 | Europe | 3240.746 | 77.67073 |
| Austria | AUT | 1999 | 8051000 | Europe | 3404.3271 | 77.87561 |
| Austria | AUT | 2000 | 8069000 | Europe | 3472.7366 | 78.12683 |
| Austria | AUT | 2001 | 8098000 | Europe | 3531.3159 | 78.57561 |
| Austria | AUT | 2002 | 8134000 | Europe | 3620.3297 | 78.67805 |
| Austria | AUT | 2003 | 8176000 | Europe | 3695.6138 | 78.63171 |
| Austria | AUT | 2004 | 8217000 | Europe | 3797.4718 | 79.18049 |
| Austria | AUT | 2005 | 8254000 | Europe | 3826.3191 | 79.33171 |
| Austria | AUT | 2006 | 8285000 | Europe | 3893.9178 | 79.88049 |
| Austria | AUT | 2007 | 8314000 | Europe | 4020.3699 | 80.18049 |
| Austria | AUT | 2008 | 8342000 | Europe | 4136.9982 | 80.43171 |
| Austria | AUT | 2009 | 8373000 | Europe | 4178.429 | 80.33171 |
| Austria | AUT | 2010 | 8410000 | Europe | 4236.3164 | 80.58049 |
| Austria | AUT | 2011 | 8454000 | Europe | 4253.5175 | 80.98293 |
| Austria | AUT | 2012 | 8502000 | Europe | 4362.1556 | 80.93659 |
| Austria | AUT | 2013 | 8556000 | Europe | 4352.5783 | 81.13659 |
| Austria | AUT | 2014 | 8615000 | Europe | 4390.1259 | 81.49024 |
| Austria | AUT | 2015 | 8679000 | Europe | 4451.1826 | 81.19024 |
| Belgium | BEL | 1970 | 9632000 | Europe | 683.3738 | 70.97195 |
| Belgium | BEL | 1971 | 9663000 | Europe | 723.5906 | 71.06049 |
| Belgium | BEL | 1972 | 9693000 | Europe | 788.4374 | 71.40512 |
| Belgium | BEL | 1973 | 9721000 | Europe | 891.1623 | 71.63537 |
| Belgium | BEL | 1974 | 9747000 | Europe | 939.3939 | 71.98585 |
| Belgium | BEL | 1975 | 9772000 | Europe | 1154.0117 | 71.97122 |
| Belgium | BEL | 1976 | 9795000 | Europe | 1262.6339 | 72.11976 |
| Belgium | BEL | 1977 | 9817000 | Europe | 1372.7239 | 72.77390 |
| Belgium | BEL | 1978 | 9837000 | Europe | 1453.5338 | 72.69805 |
Βλέπουμε όμως πως ενώ η στήλη αυτή έχει μόνο αριθμούς, αυτοί λογίζονται ως χαρακτήρες. Οπότε θα κάνουμε μία ακόμα επέμβαση:
ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017.. <- as.numeric(ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..)Έτσι, ξαναορίζουμε κάποιες συντομογραφίες:
Prosdokimo <- ProsdokimoVSperith2$Life.expectancy.at.birth..total..years.
XrimataYgeia <- ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..Πάμε τώρα στο ζουμί της υπόθεσης! Ένας τρόπος, λοιπόν, να παραστήσουμε με γραφικό τρόπο τη σχέση μεταξύ του προσδόκιμου ζωής και των δαπανούμενων χρημάτων για την υγεία είναι το διάγραμμα διασποράς. Δηλαδή η μία μεταβλητή, τα χρήματα που επενδύονται στην υγεία, αναπαρίσταται σ’ έναν οριζόντιο άξονα, η άλλη μεταβλητή, το προσδόκιμο ζωής, αναπαρίσταται σ’ έναν κατακόρυφο και μια πλειάδα από σημεία δηλώνει τις μεταξύ τους αντιστοιχίες.
Μια εύκολη κατασκευή διαγράμματος διασποράς στην R γίνεται μέσω της
συνάρτησης plot(), δηλαδή γράφοντας:
Για περισσότερες λειτουργίες θα χρησιμοποιήσουμε άλλη μέθοδο, χρησιμοποιώντας το πακέτο ggplot2, το οποίο επικαλούμαστε γράφοντας:
Ακολούθως, για να κάνουμε τη ζωή μας ευκολότερη, ορίζουμε:
κι έτσι το διάγραμμα διασποράς προκύπτει γράφοντας:
Επειδή έχουμε πολλές αλληλοεπικαλύψεις σημείων, για να φαίνεται καλύτερα η πυκνότητα αυτών, θα ήταν προτιμότερο να είναι ημιδιαφανή. Έτσι γράφουμε:
Έτσι ανακαλύπτουμε ευκολότερα τις διαφοροποιήσεις στην συσσώρευση σημείων σε διάφορες περιοχές.
Η επιλογή alpha = 1/5 μάς παρέχει την δυνατότητα να
μειώσουμε την αδιαφάνεια των σημείων μειώνοντας το κλάσμα, δηλαδή
αυξάνοντας τον παρονομαστή αυξάνουμε και την διαφάνεια. Έτσι,
γράφοντας:
έχουμε ακόμα πιο διαφανείς κουκκίδες, πράγμα που βολεύει σε διαγράμματα με πάρα πολλά σημεία.
Χρησιμοποιώντας το πακέτο ggplot2, έχουμε την δυνατότητα
να χρωματίσουμε διαφορετικά τα σημεία, ανάλογα με μια παράμετρο. Π.χ.
εδώ θέλουμε τα σημεία να είναι χρωματισμένα βάσει της ηπείρου στην οποία
ανήκουν. Με άλλα λόγια θέλουμε να κάνουμε έξι διαγράμματα διασποράς
μαζί, ένα για κάθε ήπειρο. Γράφοντας:
προκύπτει κατά το μάλλον ή ήττον αυτό που θέλαμε.
Επειδή όμως οι αλληλοεπικαλύψεις κάνουν πολλές επιλογές να χάνονται κάτω από άλλες, θα είναι χρήσιμη η διαφανοποίησή τους, όπως δείξαμε πριν. Έτσι γράφουμε:
Ποσοτικοποίηση της πυκνότητας των σημείων μπορούμε να πετύχουμε μέσω
της stat_density_2d(). Γράφοντας δηλαδή:
έχουμε έναν χρωματισμό της περιοχής, ανάλογα με την πυκνότητα των
σημείων εκεί. Οι επιλογή geom = "raster" προσδιορίζει το
γεωμετρικό στιλ του γραφήματος, η
aes(fill = after_stat(density)) δηλώνει ότι ο χρωματισμός
θα γίνει βάσει της πυκνότητας σημείων και η contour = FALSE
ότι δεν θέλουμε περιγράμματα.
Ο χρωματισμός μπορεί να γίνει επίσης μέσω της προσθήκης της
scale_fill_viridis_c(), ώστε να γίνει ευκρινέστερη η
ανάγνωση του γραφήματος σε ασπρόμαυρη εκτύπωση. Έτσι γράφουμε:
ProsdXrhmata + stat_density_2d(geom = "raster", aes(fill = after_stat(density)), contour = FALSE) + scale_fill_viridis_c()
Όλος ο κώδικας που γράψαμε έως τώρα είναι ο κάτωθι:
rm(list = ls())
if(!require(dplyr)){
install.packages("dplyr")
library(dplyr)
}
if(!require(tidyr)){
install.packages("tidyr")
library(tidyr)
}
ProsdokimoVSperith2 <- ProsdokimoVSperith %>% group_by(Code) %>% fill(Continent)%>% fill(Continent, .direction = "up")
ProsdokimoVSperith2 <- ProsdokimoVSperith2[!(is.na(ProsdokimoVSperith$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..)), ]
ProsdokimoVSperith2 <- ProsdokimoVSperith2[!(is.na(ProsdokimoVSperith2$Life.expectancy.at.birth..total..years.)), ]
XrimataYgeia <- ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..
n <- length(XrimataYgeia)
delete_items <- rep(TRUE,n)
for (i in 1:n) {
if (XrimataYgeia[i] == "..") {
delete_items[i] <- FALSE
}
}
ProsdokimoVSperith2 <- ProsdokimoVSperith2[delete_items, ]
ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017.. <- as.numeric(ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..)
Prosdokimo <- ProsdokimoVSperith2$Life.expectancy.at.birth..total..years.
XrimataYgeia <- ProsdokimoVSperith2$Health.Expenditure.and.Financing..per.capita...OECDstat..2017..
plot(Prosdokimo ~ XrimataYgeia)
if(!require(ggplot2)){
install.packages("ggplot2")
library(ggplot2)
}
ProsdXrhmata <- ggplot(ProsdokimoVSperith2, aes(x = XrimataYgeia, y = Prosdokimo))
ProsdXrhmata + geom_point()
ProsdXrhmata + geom_point(alpha = 1/5)
ProsdXrhmata + geom_point(alpha = 1/20)
ProsdXrhmata + geom_point(aes(colour = Continent))
ProsdXrhmata + geom_point(alpha = 1/5, aes(colour = Continent))
ProsdXrhmata + stat_density_2d(geom = "raster", aes(fill = after_stat(density)), contour = FALSE)
ProsdXrhmata + stat_density_2d(geom = "raster", aes(fill = after_stat(density)), contour = FALSE) + scale_fill_viridis_c()2 Χρονοδιάγραμμα
Στην παρούσα υποενότητα θα μελετήσουμε τους θανάτους αστυνομικών εν
ώρα υπηρεσίας στις ΗΠΑ. Τα δεδομένα αντλήθηκαν από την σελίδα του Dan Wang,
συμπληρώθηκαν από τα στοιχεία τη σελίδας του FBI και αποθηκεύτηκαν σ’ ένα
έγγραφο ονόματι ThanatoiAstyn.xlsx. Για να εισαχθεί στην έπρεπε να
γίνουν κάποιες αλλαγές. Λίγο πριν επιλέξουμε Import γράψαμε
στη θέση NA: το σύμβολο ?, ώστε να λογιστούν
τα ερωτηματικά ως NA. Επίσης έγιναν και κάποιες αλλαγές
στους χαρακτηρισμούς των μεταβλητών. Φυσικά, πρώτα έγιναν οι διαγραφές
των παλαιότερων:
Το χρονοδιάγραμμα είναι λίγο πολύ ένα διάγραμμα διασποράς που συσχετίζει μία μεταβλητή με τον χρόνο. Εν προκειμένω θέλουμε μέσω αυτού να οπτικοποιήσουμε την σχέση των θανάτων αστυνομικών σε σύγκριση με το έτος που αυτοί συνέβησαν.
Για να το σχεδιάσουμε στην R θα χρειαστούμε τη συνάρτηση
plot(), όπου πρέπει να της αλλάξουμε στιλ. Αρχικά, βέβαια,
για λόγους ευκολίας γράφουμε:
Ακολούθως συμπληρώνουμε στην ήδη γνωστή (βλ. Διάγραμμα
διασποράς) συνάρτηση plot() το type = "l"
ως ακολούθως:
Στην περίπτωση που θέλουμε να φαίνονται τα σημεία, μπορούμε να γράψουμε:
Εναλλακτικά μπορούμε να χρησιμοποιήσουμε το πακέτο ggplot2, οπότε γράφουμε:
if(!require(ggplot2)){
install.packages("ggplot2")
library(ggplot2)
}
PolDeathGr <- ggplot(ThanatoiAstyn, aes(x=Year, y=`Number of officers feloniously killed`))
PolDeathGr + geom_line()
2.1 Παλινδρόμηση γύρω από τη μέση τιμή
Με τον όρο παλινδρόμηση γύρω από τη μέση τιμή εννοούμε ότι αν μια μεταβλητή παίρνει κάποια στιγμή μια ακραία τιμή, τότε πιθανότατα η επόμενή της να είναι εγγύτερα στην μέση τιμή.
Στο παράδειγμά μας, για να υπολογίσουμε την μέση τιμή των πεσόντων αστυνομικών γράφουμε:
## [1] 71.15517οπότε βρίσκουμε ότι κατά μέσο όρο γίνονται 71.1551724 θάνατοι ετησίως.
Παρατηρώντας το χρονοδιάγραμμα της προηγούμενης ενότητας ή και εκτελώντας τις παρακάτω εντολές διαπιστώνουμε ότι ακραίες τιμές στους θανάτους έχουμε στα έτη 1971 έως 1975 και στο έτος 2013. Σε κάθε μία από αυτές τις περιπτώσεις υπάρχει μια τάση φυγής των τιμών προς την μεριά της μέσης τιμής.
| Year | Number of officers feloniously killed | Number of officers (non-civilian) | Felonious killings per 100,000 officers | Five year averages of felonious fatalities | Source (Uniform Crime Report for that year) |
|---|---|---|---|---|---|
| 1971 | 126 | NA | NA | 125.2 | http://www.archive.org/stream/uniformcrimerepo1971unit/uniformcrimerepo1971unit_djvu.txt |
| 1972 | 112 | NA | NA | 125.2 | http://www.archive.org/stream/uniformcrimerepo1972unit/uniformcrimerepo1972unit_djvu.txt |
| 1973 | 127 | NA | NA | 125.2 | http://www.archive.org/stream/uniformcrimerepo1973unit/uniformcrimerepo1973unit_djvu.txt |
| 1974 | 132 | NA | NA | 125.2 | http://www.archive.org/stream/uniformcrimerepo1974unit/uniformcrimerepo1974unit_djvu.txt |
| 1975 | 129 | NA | NA | 125.2 | http://www.archive.org/stream/uniformcrimerepo1975unit/uniformcrimerepo1975unit_djvu.txt |
| NA | NA | NA | NA | NA | NA |
| Year | Number of officers feloniously killed | Number of officers (non-civilian) | Felonious killings per 100,000 officers | Five year averages of felonious fatalities | Source (Uniform Crime Report for that year) |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| 2013 | 27 | 533.895 | 5.06 | NA | https://www.fbi.gov/about-us/cjis/ucr/leoka/2012 |
Συνολικά γράψαμε τον παρακάτω κώδικα:
rm(list = ls())
etos <- ThanatoiAstyn$Year
thanatoi <- ThanatoiAstyn$`Number of officers feloniously killed`
plot(etos, thanatoi, type = "l")
plot(etos, thanatoi, type = "b")
if(!require(ggplot2)){
install.packages("ggplot2")
library(ggplot2)
}
PolDeathGr <- ggplot(ThanatoiAstyn, aes(x=Year, y=`Number of officers feloniously killed`))
PolDeathGr + geom_line()
mean(thanatoi, na.rm=TRUE)
maxPolDeath <- ThanatoiAstyn[thanatoi>110, ]
minPolDeath <- ThanatoiAstyn[thanatoi<30, ]