圆形图中的标记点R

Question

有没有办法显示与圆上的点对应的数字？我读了文本（xy）函数，但它是散点图，而不是。脚本如下，附带的图像显示结果。我想确定情节中的要点。任何提供的帮助表示赞赏！感谢。

library (circular)
df<- read.csv("Direction.csv", header = TRUE)
df1 <- df [ which(df$Month==1 & df$Day>0 & df$Day <32) ,]
df2 <- df1[c(-1,-2,-3)]
df3<- lapply(df2, function(df2) circular(df2, units='degrees', template='geographics'))
dens<- lapply(df3, density.circular, bw =5)
par(mfrow=c(5,4), oma=c(2,1.3,2,2), mar=c(1.5,2,2,1), tcl=-0.2, mgp=c(0,1,0))
titles <- c("1000mb", "925mb", "850mb", "700mb", "600mb", "500mb", "400mb", "300mb", 
            "250mb", "200mb", "150mb", "100mb","70mb", "50mb", "30mb", "20mb", "10mb")
for(i in 1:17){
  plot(mean(df3[[1]]), main = titles[1],) 
  print(mean(df3[[1]]))
  print(var(df3[[1]]))
  print(summary(df3[[1]]))
}
dput(df3[1])
structure(list(X1000mb = structure(c(86L, 130L, 75L, 59L, 56L, 
69L, 139L, 358L, 98L, 175L, 322L, 17L, 336L, 46L, 137L, 1L, 2L, 
102L, 225L, 121L, 179L, 291L, 325L, 317L, 321L, 349L, 28L, 38L, 
36L, 117L, 144L, 73L, 121L, 135L, 131L, 127L, 139L, 167L, 298L, 
213L, 37L, 33L, 71L, 120L, 156L, 14L, 51L, 92L, 168L, 332L, 24L, 
71L, 128L, 98L, 104L, 86L, 155L, 5L, 281L, 342L, 356L, 346L, 
210L, 186L, 199L, 133L, 191L, 282L, 139L, 168L, 158L, 154L, 117L, 
149L, 162L, 157L, 192L, 175L, 197L, 171L, 184L, 305L, 70L, 169L, 
207L, 8L, 72L, 134L, 160L, 135L, 154L, 149L, 161L, 182L, 259L, 
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332L, 349L, 16L, 28L, 42L, 352L, 94L, 149L, 153L, 183L, 183L, 
196L, 170L, 164L, 212L, 169L, 180L, 206L, 81L, 135L, 145L, 148L, 
172L, 174L, 160L, 188L, 193L, 197L, 247L, 68L, 181L, 177L, 219L, 
204L, 86L, 333L, 354L, 132L, 0L, 35L, 27L, 38L, 77L, 123L, 174L, 
172L, 191L, 312L, 307L, 29L, 161L, 62L, 104L, 240L, 300L, 292L, 
194L, 202L, 274L, 349L, 26L, 198L, 294L, 185L, 178L, 324L, 28L, 
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45L, 238L, 333L, 342L, 111L, 233L, 183L, 193L, 212L, 188L, 164L, 
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53L, 126L, 179L, 161L, 163L, 168L, 280L, 336L, 89L, 175L, 253L, 
357L, 250L, 36L, 62L, 103L, 1L, 5L, 55L, 97L, 114L, 143L, 156L, 
156L, 178L, 183L, 191L, 285L, 4L, 16L, 69L, 340L, 63L, 131L, 
128L, 137L, 137L, 253L, 213L, 165L, 166L, 166L, 171L, 193L, 186L, 
180L, 194L, 255L, 294L, 60L, 175L, 123L, 136L, 147L, 144L, 146L, 
135L, 157L, 228L, 177L, 165L, 168L, 176L, 182L, 352L, 23L, 260L, 
298L, 283L, 152L, 151L, 180L, 170L, 2L, 60L, 121L, 110L, 153L, 
174L, 204L, 312L, 153L, 250L, 223L, 244L, 345L, 225L, 233L, 289L, 
212L, 190L, 285L, 226L, 136L, 111L, 179L, 200L, 274L, 2L, 351L, 
10L, 12L, 13L, 340L, 336L, 331L, 258L, 36L, 95L, 117L, 149L, 
151L, 155L, 135L, 187L, 191L, 195L, 15L, 103L, 161L, 194L, 186L, 
167L, 90L, 174L, 205L, 173L, 208L, 197L, 217L, 246L, 151L, 161L, 
119L, 128L, 159L, 232L, 198L, 227L, 175L, 213L, 220L, 226L, 171L, 
244L, 203L, 167L, 185L, 156L, 182L, 157L, 154L, 144L, 146L, 174L, 
196L, 141L, 348L, 22L, 63L, 125L, 163L, 32L, 331L, 19L, 72L, 
85L, 186L, 297L, 353L, 32L, 242L, 240L, 191L, 200L, 192L, 208L, 
256L, 193L, 243L, 3L, 18L, 293L, 357L, 233L, 169L, 160L, 189L, 
310L, 305L, 288L, 201L, 334L, 56L, 274L, 269L, 303L, 237L, 224L, 
230L, 170L, 192L, 135L, 194L, 132L, 122L, 149L, 171L, 199L, 217L, 
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325L, 352L, 5L, 72L, 136L, 123L, 149L, 154L, 132L, 155L, 233L, 
187L, 168L, 9L, 41L, 262L, 4L, 40L, 154L, 157L, 233L, 97L, 162L, 
171L, 171L, 181L, 355L, 35L, 103L, 214L, 355L, 335L, 345L, 13L, 
331L, 347L, 323L, 294L, 234L, 295L, 190L, 151L, 182L, 231L, 268L, 
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108L, 166L, 164L, 182L, 160L, 174L, 101L, 27L, 62L, 110L, 76L, 
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162L, 139L, 158L, 150L, 164L, 198L, 14L, 141L, 156L, 288L, 355L, 
36L, 73L, 208L, 215L, 323L, 135L, 188L, 289L, 232L, 227L, 317L, 
222L, 192L, 76L, 40L, 172L, 157L, 142L, 216L, 223L, 163L, 237L, 
344L, 30L, 126L, 143L, 162L, 162L, 104L, 103L, 123L, 110L, 140L, 
146L, 149L, 139L, 161L, 194L, 187L, 283L, 13L, 16L, 185L, 177L, 
200L, 155L, 152L, 169L, 238L, 282L, 161L, 185L, 224L, 198L, 159L, 
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162L, 262L, 245L, 206L, 225L, 193L, 331L, 34L, 133L, 216L, 277L, 
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356L, 34L, 19L, 346L, 359L, 25L, 45L, 97L, 151L, 67L, 100L, 23L, 
66L, 9L, 223L, 121L, 164L, 175L, 174L, 217L, 227L, 241L, 184L, 
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289L, 288L, 94L, 97L, 132L, 123L, 141L, 141L, 282L, 220L, 281L, 
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140L, 176L, 207L, 149L, 155L, 162L, 152L, 164L, 178L, 201L, 214L, 
169L, 175L, 180L, 168L, 183L, 163L, 186L, 257L, 223L, 166L, 157L, 
133L, 24L, 115L, 162L, 173L, 245L, 147L, 105L, 81L, 75L, 75L, 
47L, 27L, 15L, 347L, 21L, 116L, 160L, 178L, 193L, 51L, 232L, 
295L, 358L, 311L, 16L, 17L, 7L, 47L, 345L, 4L, 36L, 118L, 209L, 
173L, 231L, 8L, 90L, 156L, 237L, 163L, 343L, 350L, 354L, 36L, 
62L, 45L, 43L, 95L, 113L, 164L, 317L, 315L, 168L, 188L, 190L, 
168L, 227L, 185L, 142L, 249L, 200L, 228L, 7L, 50L, 95L, 265L, 
10L, 75L, 63L, 151L, 124L, 146L, 35L, 303L, 331L, 218L, 303L, 
312L, 341L, 33L, 36L, 9L, 74L, 85L, 105L, 99L, 101L, 91L, 130L, 
152L, 14L, 211L, 271L, 319L, 315L, 309L, 358L, 31L), circularp = structure(list(
    type = "angles", units = "degrees", template = "geographics", 
    modulo = "asis", zero = 1.5707963267949, rotation = "clock"), .Names = c("type", 
"units", "template", "modulo", "zero", "rotation")), class = c("circular", 
"integer"))), .Names = "X1000mb")

Answer 1

并不重要的是它并非严格意义上的“散点图”。 。现在您已经设置了一个子图数组，您可以再次循环它们，但这次使用text()将数据放在每个子图中的所需位置。粗略地说，

for (i in 1:17 )  text(x_loc[i],y_loc[i], some_text_vector[i])

您预先加载＆＃34;文本字符串和位置。