使用 purrr::map 时获得不同的 ggplot

Posted 2023-02-16

【中文标题】使用 purrr::map 时获得不同的 ggplot

【英文标题】：Getting different ggplot when using purrr::map

【发布时间】：2021-12-30 13:43:40

【问题描述】：

我正在尝试使用map() 生成ggplot 对象列表，之后我将与ggpubr::ggarrange 组合。

股票ggplot 电话工作正常：

# library(tidyverse)
ggplot(Tile,aes(x = Site, y = Volume))+
geom_point(aes(color = Event))+
geom_line(aes(x = Site, y = Volume, group = Event))+
ggtitle(paste("Tile", 'Volume'))

输出（所需图）：

现在我使用 map() 和稍微修改但总体相同的 ggplot 调用（应该是）：

x<-map(names(Tile)[-(1:2)], ~ 
      ggplot(Tile,aes(x = Site, y = .x))+
      geom_point(aes(color = Event))+
      geom_line(aes(x = Site, y = .x, group = Event))+
      ggtitle(paste("Tile", as.character(.x)))
)

给出：

x[[5]]

我错过了什么？谢谢。

数据：

Tile<-structure(list(Event = c("10/17/2019", "10/17/2019", "10/23/2019", 
"10/23/2019", "10/27/2019", "10/27/2019", "10/31/2019", "10/31/2019", 
"11/24/2019", "11/24/2019", "11/28/2019", "11/28/2019", "12/10/2019", 
"12/10/2019", "12/15/2019", "12/15/2019", "12/28/2019", "12/28/2019", 
"12/30/2019", "12/30/2019", "1/3/2020", "1/3/2020", "1/12/2020", 
"1/12/2020", "1/26/2020", "1/26/2020", "3/3/2020", "3/3/2020", 
"3/8/2020", "3/8/2020", "3/13/2020", "3/13/2020", "5/12/2020", 
"5/12/2020", "8/5/2020", "8/5/2020", "9/30/2020", "9/30/2020", 
"12/1/2020", "12/1/2020", "12/25/2020", "12/25/2020", "1/17/2021", 
"1/17/2021", "3/11/2021", "3/11/2021", "4/16/2021", "4/16/2021", 
"4/22/2021", "4/22/2021", "4/30/2021", "4/30/2021", "5/6/2021", 
"5/6/2021", "7/2/2021", "7/2/2021", "7/3/2021", "7/3/2021", "7/9/2021", 
"7/9/2021", "7/13/2021", "7/13/2021", "7/14/2021", "7/14/2021", 
"7/18/2021", "7/18/2021", "7/19/2021", "7/19/2021", "7/21/2021", 
"7/21/2021", "7/31/2021", "7/31/2021", "8/2/2021", "8/2/2021", 
"8/20/2021", "8/20/2021", "9/9/2021", "9/9/2021", "9/24/2021", 
"9/24/2021", "10/17/2021", "10/17/2021", "10/22/2021", "10/22/2021", 
"10/25/2021", "10/25/2021", "10/27/2021", "10/27/2021", "11/1/2021", 
"11/1/2021"), Site = structure(c(3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 
1L, 3L, 1L), .Label = c("DCNSUB", "DCNSUR", "DCSSUB", "DCSSUR"
), class = "factor"), TP.conc = c(1550, 2770, NA, NA, 1650, NA, 
1810, NA, 666, 468, 1190, 1120, 574, 538, 487, 580, NA, 238, 
610, 378, 398, 306, 744, 766, 447, 468, 504, 413, 384, 377, 714, 
542, 927.2, 1000.1, 265.4, 285.1, 1527, 1764.5, NA, 460.9, NA, 
469.8, NA, 172.8, 454, 432.8, 524.4, 476.4, 300, 303.6, 588.7, 
598.1, 852.5, 797.6, 144.4, 122.1, 170.6, 110.1, 301.8, 328.8, 
363.3, 498.5, 283.7, 104.9, 314.1, 327.6, 436.6, 262.1, 398.1, 
358, 312, 251, 598, 831, 348, 456, 345, 240, 648, 949, 852, 1260, 
643, 549, 712, 999, 982, 1100, 1240, 1555), TP.load = c(180.4, 
NA, NA, NA, 67.6, NA, 201.5, NA, NA, NA, 53.3, 131.7, 12.1, 38.1, 
7, 21.6, NA, NA, 21.2, 44.4, 6.1, 10.9, 79.7, 189.9, 10.5, 27.9, 
84.2, 178.7, 13.6, 46.3, 14.4, 26.2, 11.4, 35, 4.2, 10.1, 4.6, 
18.9, NA, 58.3, NA, 140.9, NA, 7.6, 181.8, 238.2, 72.5, 97.7, 
18.5, 30.6, 121.4, 177.7, 114.1, 166.3, 22.8, 21.9, 15.1, 9.2, 
25.8, 29.4, 7.6, 12.3, 3, 1.7, 58.5, 71, 23.3, 15.7, 32.7, 39.7, 
3.1, 3.4, 67.2, 126, 49.1, 79.1, 8.6, 5.8, 8.7, 15.3, 38.62755, 
62.40857143, NA, NA, NA, NA, NA, NA, NA, NA), SRP.conc = c(NA, 
NA, NA, NA, 403, NA, NA, NA, NA, NA, NA, NA, 245, 234, 238, 197, 
NA, 118, NA, NA, NA, NA, NA, 270, 121, 135, NA, NA, NA, NA, NA, 
NA, 596.7, 635.6, 48, 85.9, 514.8, 572.7, NA, 161.5, NA, 163.3, 
NA, 46.4, 96.9, 127, 83.1, 92.3, 53.5, 60.7, 111.7, 133.7, 132.2, 
164.1, 50.1, 49.1, 54, 42.5, 122.5, 131.9, 104.2, 194.5, 84.6, 
34.8, 90.2, 106.6, NA, NA, 129.9, 118.2, 62.2, 84.7, 105, 152, 
92.6, 66, 45.9, 50.5, 66.2, 167, 264, 412, 203, 175, 352, 560, 
503, 625, 621, 836), SRP.load = c(NA, NA, NA, NA, 16.5, NA, NA, 
NA, NA, NA, NA, NA, 5.2, 16.6, 3.4, 7.3, NA, NA, NA, NA, NA, 
NA, NA, 66.9, 2.8, 8, NA, NA, NA, NA, NA, NA, 7.3, 22.2, 0.8, 
3.1, 1.6, 6.1, NA, 20.4, NA, 49, NA, 2, 38.8, 69.9, 11.5, 18.9, 
3.3, 6.1, 23, 39.7, 17.7, 34.2, 7.9, 8.8, 4.8, 3.6, 10.5, 11.8, 
2.2, 4.8, 0.9, 0.6, 16.8, 23.1, NA, NA, 10.7, 13.1, 0.6, 1.2, 
11.8, 23, 13.1, 11.4, 1.1, 1.2, 0.9, 2.7, 12, 20.4, NA, NA, NA, 
NA, NA, NA, NA, NA), Volume = c(11.64, NA, 1.87, 4.5, 4.1, 9.69, 
11.13, 34, NA, NA, 4.48, 11.76, 2.1, 7.08, 1.45, 3.73, NA, NA, 
3.47, 11.74, 1.52, 3.56, 10.71, 24.79, 2.34, 5.96, 16.71, 43.28, 
3.54, 12.29, 2.02, 4.84, 1.22, 3.5, 1.59, 3.56, 0.3, 1.07, NA, 
12.66, NA, 29.99, NA, 4.37, 40.04, 55.03, 13.82, 20.51, 6.18, 
10.07, 20.62, 29.72, 13.38, 20.85, 15.76, 17.96, 8.82, 8.36, 
8.56, 8.94, 2.1, 2.46, 1.07, 1.58, 18.64, 21.67, 5.33, 6, 8.22, 
11.09, 0.99, 1.36, 11.23, 15.16, 14.11, 17.34, 2.48, 2.4, 1.34, 
1.61, 4.53, 4.95, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(1L, 
4L, 5L, 8L, 9L, 12L, 13L, 16L, 17L, 20L, 21L, 24L, 25L, 28L, 
29L, 32L, 33L, 36L, 37L, 40L, 41L, 44L, 45L, 48L, 49L, 52L, 53L, 
56L, 57L, 60L, 61L, 64L, 65L, 68L, 69L, 72L, 73L, 76L, 77L, 80L, 
81L, 84L, 85L, 88L, 89L, 92L, 93L, 96L, 97L, 100L, 101L, 104L, 
105L, 108L, 109L, 112L, 113L, 116L, 117L, 120L, 121L, 124L, 125L, 
128L, 129L, 132L, 133L, 136L, 137L, 140L, 141L, 144L, 145L, 148L, 
149L, 152L, 153L, 156L, 157L, 160L, 161L, 164L, 165L, 168L, 169L, 
172L, 173L, 176L, 177L, 180L), class = "data.frame")

【参考方案1】：

.x 是单独的列名，因为aes 只能采用符号或不带引号的列名，使用.data 子集数据列或另一个选项是转换为symbol 并评估（@ 987654327@)。但是，最好使用.data

library(purrr)
library(ggplot2)
x <- map(names(Tile)[-(1:2)], ~ 
      ggplot(Tile,aes(x = Site, y = .data[[.x]]))+
      geom_point(aes(color = Event))+
      geom_line(aes(x = Site, y = .data[[.x]], group = Event))+
      ggtitle(paste("Surface", .x))
)

-输出

x[[5]]