腳本無法運行（terms.formula（formula，data = data）中的錯誤：'data'參數的類型錯誤

Question

我已經多次運行了該腳本，並且一直工作到今天早上，突然產生了錯誤消息：

（有關term.formula（formula，data = data）的錯誤：'data'參數的類型錯誤。

我沒有做任何更改，我需要找出為什么它突然似乎不起作用的原因。 以前對類似問題的答案沒有幫助。

我的資料：

DPUT(harvest2)
structure(list(Year = c(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), Count = c(750, 757, 592, 
693, 667, 757, 719, 670, 733, 796, 923, 921, 944, 1010, 825, 
762, 825, 844, 809, 830, 768, 823, 749, 675, 700, 637, 708, 697, 
754, 694, 636, 717, 786, 731, 769, 732, 710, 610.5, 593, 529, 
664, 788, 731, 644, 653, 656), SexRat = c(1.91812865497076, 2.34567901234568, 
1.69178082191781, 1.46766169154229, 1.30396475770925,     
1.4364406779661, 1.32098765432099, 1.48584905660377, 1.5906976744186, 
1.91414141414141, 1.48905109489051, 1.61382113821138, 1.52380952380952, 
1.87777777777778, 1.75438596491228, 1.6695652173913, 1.81566820276498, 
1.79295154185022, 1.85024154589372, 1.75446428571429, 1.83163265306122, 
1.92857142857143, 1.76635514018692, 1.5, 2.26190476190476,     1.76704545454545,
2.38125, 1.80924855491329, 2.33333333333333, 1.81182795698925, 
2.20446096654275, 2.02790697674419, 2.1140350877193, 2.05, 2.20183486238532, 
1.90983606557377, 2.02262443438914, 1.75116279069767, 1.86842105263158, 
1.87951807228916, 2.08542713567839, 2.01724137931034, 1.95833333333333, 
1.81165919282511, 2.12135922330097, 1.97260273972603)), class = "data.frame", 
row.names = c(NA, -46L))

我的劇本：

# Function for the equation

lm_eqn = function(df){
  m = lm(y ~ poly(x, 3), df) #3rd degree polynomial
  eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
                   list(a = format(coef(m)[1], digits = 2),
                        b = format(coef(m)[2], digits = 2),
                        r2 = format(summary(m)$r.squared, digits = 4)))

  as.character(as.expression(eq))
}

# Make the plot

library(ggplot2)
ggplot(harvest2, aes(x = Year, y = Count)) +  
  scale_y_continuous(minor_breaks = seq(500, 1100, by = 50), 
                     breaks = seq(500, 1100, by = 100),
                     limits = c(500, 1100), expand = c(0, 0)) +  
  scale_x_continuous(minor_breaks = seq(1970, 2018, by = 1), 
                     breaks = seq(1970, 2018, by = 5), limits = c(1970, 2018)) +
  geom_point(stat = 'identity', size=2) +
  stat_smooth(method = "lm", se = TRUE, fill = NA, size = 1.3,
              formula = y ~ poly(x, 3, raw = TRUE), col = "red") +
  annotate("text", x = 1975, y = 1075, label = lm_eqn(df), 
           hjust = 0, size = 3.5, parse = TRUE) +
  xlab(" ") + 
  ylab("Count") +
  theme_light() +
  ggtitle(" ")

任何幫助，不勝感激。

Answer 1

如何使用stat_poly_eq從ggpmisc包？ 見這個 ，如果你想公式和R2分成兩行。

library(ggplot2)
library(ggpmisc)

# define formula
formula1 <- y ~ poly(x, 3, raw = TRUE)

ggplot(harvest2, aes(x = Year, y = Count)) +
  scale_y_continuous(
    minor_breaks = seq(500, 1100, by = 50), breaks = seq(500, 1100, by = 100),
    limits = c(500, 1100), expand = c(0, 0)) +
  scale_x_continuous(
    minor_breaks = seq(1970, 2018, by = 1), breaks = seq(1970, 2018, by = 5),
    limits = c(1970, 2018)) +
  geom_point(stat = "identity", size = 2) +
  stat_smooth(
    method = "lm", se = TRUE, fill = NA, size = 1.3,
    formula = formula1, col = "red") +
  # show the equation and R2
  stat_poly_eq(aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~")),
    label.x.npc = "left", label.y.npc = "top",
    formula = formula1, parse = TRUE, size = 5) +
  xlab(" ") + ylab("Count") +
  theme_light() +
  ggtitle(" ")

^{由reprex軟件包 （v0.2.1.9000）創建於2019-02-12}

Answer 2

@Tung的答案解決了這個問題，Tung的注釋暗示了原始代碼中的錯誤。 我在下面對此進行擴展。

最可能的問題是在以下語句中使用df ：

annotate("text", x = 1975, y = 1075, label = lm_eqn(df), hjust = 0, size = 3.5, parse = TRUE)

df是參數的名稱，而不是所需的參數，這應該是方程式ggplot繪制的曲線與傳遞給ggplot數據相同：

annotate("text", x = 1975, y = 1075, label = lm_eqn(df = harvest2), hjust = 0, size = 3.5, parse = TRUE)

誤差和可能的錯誤方程系數/ R ^ 2值將根據搜索路徑中是否存在名為df的變量以及其中存儲了什么對象而有所不同。

@Tung的代碼通過僅定義一次模型公式並將其存儲在變量中以及僅將數據作為參數傳遞一次，從而確保方程和繪制的曲線之間沒有不匹配。