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尝试应用过滤器并选择时我是否正确？

OFB 发表于 Dev
2
OFB

我试图观察这些年来由于凯恩斯在人均 GDP 中发生的变化。这是我写的代码： 我得到的错误：

ggplot(., aes(y = year, x = new_gdp, color = as.factor(Keynes))) 中的错误：找不到函数“ggplot”

这是我在插入 rlang::last_trace() 时看到的

Backtrace:
     x
  1. +-... %>% ...
  2. +-dplyr::filter(...)
  3. +-dplyr:::filter.data.frame(...)
  4. | \-dplyr:::filter_rows(.data, ..., caller_env = caller_env())
  5. |   \-dplyr:::filter_eval(dots, mask = mask, error_call = error_call)
  6. |     +-base::withCallingHandlers(...)
  7. |     \-mask$eval_all_filter(dots, env_filter)
  8. +-dplyr:::dplyr_internal_error(...)
  9. | \-rlang::abort(class = c(class, "dplyr:::internal_error"), dplyr_error_data = data)
 10. |   \-rlang:::signal_abort(cnd, .file)
 11. |     \-base::signalCondition(cnd)
 12. \-dplyr `<fn>`(`<dpl:::__>`)
 13.   \-rlang::abort(bullets, call = error_call, parent = skip_internal_condition(e))

USGDPpresidents %>%
  select(year,Keynes,realGDPperCapita) %>%
  filter(new_year<-year, na.rm = TRUE,
         keynes, na.rm = TRUE,
         new_gdp<-realGDPperCapita, na.rm = TRUE) %>%
  ggplot(aes(y=year,x=new_gdp,color= as.factor(Keynes)))+
  geom_point()+
  scale_color_manual(values = c("#270181", "coral"))

dput(USGDPpresidents)

structure(list(Year = c(1610, 1620, 1630, 1640, 1650, 1660, 1670, 
1680, 1690, 1700, 1710, 1720, 1730, 1740, 1750, 1760, 1770, 1774, 
1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 
1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 
1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 
1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 
1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 
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1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 
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1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 
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1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 
2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 
2017, 2018), CPI = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
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17.3, 17.6, 18, 19.54, 22.34, 24.08, 23.85, 24.08, 25.98, 26.55, 
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240.01, 245.12, 251.11), GDPdeflator = c(NA, NA, NA, NA, NA, 
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.14, 4.25, 4.33, 
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    1339.22, 1318.22, 668.14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 52575.07, 64031.19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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    110153.23, 68018.11, 0, 0, 0, 0, 9089.22, 17646.63, 17694.98, 
    10056.16, 0, 0, 0, 0, 0, 0, 0, 123.29, 123.29, 123.29, 157.66, 
    6644.42, 7347.66, 7359.05, 7388.86, 7360.31, 7431.86, 7501.82, 
    7557.57, 669.91, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), battleDeathsPMP = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 293.558051626222, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, 29.4858742682616, 28.6190711462451, 
    27.8427612655801, 26.948127471505, 26.1571460826372, 14.9550734166119, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 58.0394719644491, 
    104.582432947756, 101.762523191095, 13.2944150216659, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 323.096539162113, 320.195359031782, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2034.58482073568, 
    2734.60427498556, 2676.24453238909, 2623.56440539912, 693.373031663919, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39.5380434782609, 
    15.9831802441405, 17.5995479275633, 16.9908744070942, 8.44005406566199, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 508.394124586613, 
    612.4456241033, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 56.4019280070764, 814.565178703841, 
    803.371825155954, 795.922093687002, 486.093633868847, 0, 
    0, 0, 0, 59.9220748397985, 114.375352427619, 112.739911056743, 
    63.0223419922915, 0, 0, 0, 0, 0, 0, 0, 0.670995199791011, 
    0.660753523768691, 0.651294241944004, 0.821458158570707, 
    34.1884361477152, 37.3738421863794, 37.0262940750282, 36.8071932053102, 
    36.3048989819272, 36.237243343134, 36.1199275850779, 36.001457670395, 
    3.16086232359311, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Keynes = c(0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), unemployment = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.02, 0.02, 
    0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 
    0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.04, 0.02, 0.02, 
    0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 
    0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.07, 0.02, 0.045, 0.045, 
    0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 
    0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.07, 0.045, 
    0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 0.045, 
    0.045, 0.1, 0.1, 0.1, 0.1, 0.13, 0.13, 0.13, 0.1, 0.1, 0.1, 
    0.04, 0.04, 0.04, 0.04, 0.04, 0.075, 0.04, 0.04, 0.04, 0.04, 
    0.0397, 0.0477, 0.0372, 0.0809, 0.1233, 0.1111, 0.1196, 0.1243, 
    0.1162, 0.0866, 0.05, 0.0459, 0.043, 0.0435, 0.0508, 0.0462, 
    0.0329, 0.0357, 0.0617, 0.0513, 0.0586, 0.0627, 0.0525, 0.0493, 
    0.0663, 0.0718, 0.0563, 0.0523, 0.0338, 0.0295, 0.0516, 0.0873, 
    0.0693, 0.048, 0.058, 0.0492, 0.0402, 0.0457, 0.0502, 0.0461, 
    0.0894, 0.13, 0.188, 0.198, 0.213, 0.195, 0.166, 0.141, 0.178, 
    0.16, 0.146, 0.099, 0.047, 0.019, 0.012, 0.019, 0.039, 0.039, 
    0.038, 0.059, 0.053, 0.033, 0.03, 0.029, 0.055, 0.044, 0.041, 
    0.043, 0.068, 0.055, 0.055, 0.067, 0.055, 0.057, 0.052, 0.045, 
    0.038, 0.038, 0.036, 0.035, 0.049, 0.059, 0.056, 0.049, 0.056, 
    0.085, 0.077, 0.071, 0.061, 0.058, 0.071, 0.076, 0.097, 0.096, 
    0.075, 0.072, 0.07, 0.062, 0.055, 0.053, 0.056, 0.068, 0.075, 
    0.069, 0.061, 0.056, 0.054, 0.049, 0.045, 0.042, 0.04, 0.047, 
    0.058, 0.06, 0.055, 0.051, 0.046, 0.046, 0.058, 0.093, 0.096, 
    0.089, 0.081, 0.074, 0.062, 0.053, 4.875, 4.35, 3.89166666666667
    ), unempSource = structure(c(NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L
    ), .Label = c("Lebergott", "Romer", "Coen", "BLS"), class = c("ordered", 
    "factor"))), row.names = c("1610", "1620", "1630", "1640", 
"1650", "1660", "1670", "1680", "1690", "1700", "1710", "1720", 
"1730", "1740", "1750", "1760", "1770", "1774", "1775", "1776", 
"1777", "1778", "1779", "1780", "1781", "1782", "1783", "1784", 
"1785", "1786", "1787", "1788", "1789", "1790", "1791", "1792", 
"1793", "1794", "1795", "1796", "1797", "1798", "1799", "1800", 
"1801", "1802", "1803", "1804", "1805", "1806", "1807", "1808", 
"1809", "1810", "1811", "1812", "1813", "1814", "1815", "1816", 
"1817", "1818", "1819", "1820", "1821", "1822", "1823", "1824", 
"1825", "1826", "1827", "1828", "1829", "1830", "1831", "1832", 
"1833", "1834", "1835", "1836", "1837", "1838", "1839", "1840", 
"1841", "1842", "1843", "1844", "1845", "1846", "1847", "1848", 
"1849", "1850", "1851", "1852", "1853", "1854", "1855", "1856", 
"1857", "1858", "1859", "1860", "1861", "1862", "1863", "1864", 
"1865", "1866", "1867", "1868", "1869", "1870", "1871", "1872", 
"1873", "1874", "1875", "1876", "1877", "1878", "1879", "1880", 
"1881", "1882", "1883", "1884", "1885", "1886", "1887", "1888", 
"1889", "1890", "1891", "1892", "1893", "1894", "1895", "1896", 
"1897", "1898", "1899", "1900", "1901", "1902", "1903", "1904", 
"1905", "1906", "1907", "1908", "1909", "1910", "1911", "1912", 
"1913", "1914", "1915", "1916", "1917", "1918", "1919", "1920", 
"1921", "1922", "1923", "1924", "1925", "1926", "1927", "1928", 
"1929", "1930", "1931", "1932", "1933", "1934", "1935", "1936", 
"1937", "1938", "1939", "1940", "1941", "1942", "1943", "1944", 
"1945", "1946", "1947", "1948", "1949", "1950", "1951", "1952", 
"1953", "1954", "1955", "1956", "1957", "1958", "1959", "1960", 
"1961", "1962", "1963", "1964", "1965", "1966", "1967", "1968", 
"1969", "1970", "1971", "1972", "1973", "1974", "1975", "1976", 
"1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", 
"1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", 
"1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", 
"2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", 
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", 
"2017", "2018"), class = "data.frame")
>
昆腾

也许你想要这样的东西：

library(tidyverse)
USGDPpresidents %>%
  select(Year, Keynes, realGDPperCapita) %>%
  filter(!is.na(Year), !is.na(Keynes), !is.na(realGDPperCapita)) %>%
  ggplot(aes(y=Year,x=realGDPperCapita,color= as.factor(Keynes)))+
  geom_point()+
  scale_color_manual("Keynes", values = c("#270181", "coral"))

输出：

在此处输入图像描述

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