我有一些ggplot蜘蛛圖的復雜代碼。有人告訴我，人們希望將其視為傳奇的一部分，而不是在標題中包含資訊。但是，很多專案都不是特定規模的一部分。具體來說，人們希望將灰色虛線視為表示交叉的水平灰色虛線，綠色三角形表示抗 PD-1 劑量，以及中間帶有 x 的框（形狀編號 7）表示疾病進展。
是否可以將這些元素直接繪制到繪圖上？我最初的傾向是直接在圖上使用geom_rect和geom_text繪制，但我想我會看看是否可以先將它添加到圖例中。
這是一個示例圖，洗掉了一些識別資訊：
下面是對應的代碼：
pallete <- c("#202960", "#8CC63E", "#797e9f", "#628a2b", "#5B9BD5", "#8f94af", "#bebebe", "#a5a9bf", "#46631f")
example <- ggplot()
#scatter plot of target lesion
geom_point(data = target_data,
#if lymph node flag present, plot short axis instead of long diam
aes(x = mos_dur, y = if_else(lymphnode_flag == 1, short_axis, long_diam),
group = les_id, colour = les_id,
shape = trt_flag), na.rm = TRUE)
#connect the lines, grouping by target lesion
geom_line(data = target_data,
aes(x = mos_dur, y = if_else(lymphnode_flag == 1, short_axis, long_diam),
group = les_id, colour = les_id), na.rm = TRUE)
theme_classic()
labs(title = paste("Investigator Spider Plots: Subject 111-002"),
subtitle = "Lesion Size over Time",
caption = paste0("* Lesion size displayed as shortest axis for lymph nodes and longest diameter for all other target lesions.\n1. Gray dashed line indicates crossover from monotherapy to combination.\n2. Confirmed progressive disease per investigator RECIST evaluation of overall response.\n3. Measurements from TL form."),
color = "Lesion ID",
shape = "Injected with Study Drug")
geom_hline(aes(yintercept = 0), color = "black")
geom_vline(data = cross_data, aes(xintercept = mos_dur), color = "gray", linetype = "dashed")
geom_point(data = prog_data, aes(x = mos_dur, y = -8), shape = 7)
geom_point(data = pd1_data,
aes(x = mos_dur, y = -3), color = pallete[2], shape = 17)
scale_x_continuous("Months from Cycle 1 Day 1", breaks = c(-1,0,1,2,3,4,5))
scale_y_continuous("Lesion Size in mm*", breaks = c(-8, -3, seq(0,70,5)),
labels = c("Confirmed Progressive Disease", "Anti-PD-1 Treatment",
as.character(seq(0,70,5))))
scale_color_manual(values = pallete[1:5]) #, breaks = c("T01", "T02", "T03", "T04"))
scale_shape_manual(values = c(16,1), breaks = c(1,0), labels = c("Yes", "No"))
guides(color = guide_legend(order = 1), shape = guide_legend(order = 2))
coord_cartesian(xlim = c(-1,5), ylim = c(-8,35))
theme(plot.caption = element_text(size = 8))
這是人們想要看到的快速而骯臟的模型（請原諒它是在 MS Paint 中完成的）：
資料集的正則運算式：
target_data <- structure(list(subjid = c("111-002", "111-002", "111-002", "111-002",
"111-002", "111-002", "111-002", "111-002", "111-002", "111-002",
"111-002", "111-002", "111-002", "111-002", "111-002"), day1_dat = structure(c(1621468800,
1621468800, 1621468800, 1621468800, 1621468800, 1621468800, 1621468800,
1621468800, 1621468800, 1621468800, 1621468800, 1621468800, 1621468800,
1621468800, 1621468800), tzone = "UTC", class = c("POSIXct",
"POSIXt")), visit = c("Screening", "Screening", "Screening",
"Screening", "Screening", "Treatment Visit Cycle", "Treatment Visit Cycle",
"Treatment Visit Cycle", "Treatment Visit Cycle", "Treatment Visit Cycle",
"Treatment Visit Cycle", "Screening", "Treatment Visit Cycle",
"Treatment Visit Cycle", "Treatment Visit Cycle"), visdat = structure(c(18747,
18747, 18747, 18747, 18747, 18824, 18824, 18824, 18824, 18824,
18852, 18852, 18852, 18852, 18852), class = "Date"), mos_dur = c(-0.666666666666667,
-0.666666666666667, -0.666666666666667, -0.666666666666667, -0.666666666666667,
1.86666666666667, 1.86666666666667, 1.86666666666667, 1.86666666666667,
1.86666666666667, 2.7741935483871, 2.7741935483871, 2.7741935483871,
2.7741935483871, 2.7741935483871), cycle = c(NA, NA, NA, NA,
NA, 3, 3, 3, 3, 3, 4, NA, 4, 4, 4), les_flag = c("Target", "Target",
"Target", "Target", "Target", "Target", "Target", "Target", "Target",
"Target", "Target", "Target", "Target", "Target", "Target"),
les_id = c("T01", "T02", "T04", "T05", "T03", "T02", "T03",
"T04", "T01", "T05", "T03", "T04", "T02", "T05", "T01"),
les_site = c("LYMPH NODE", "SKIN", "LUNG", "SKIN", "SKIN",
"SKIN", "SKIN", "LUNG", "LYMPH NODE", "SKIN", "SKIN", "LUNG",
"SKIN", "SKIN", "LYMPH NODE"), lymphnode_flag = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L), .Label = c("0",
"1"), class = "factor"), other_spec = c("", "", "", "", "",
"", "", "", "", "", "", "", "", "", ""), les_details = c("right axilla",
"left back", "left upper lobe", "right thigh", "right back",
"left back", "right back", "left upper lobe", "right axilla",
"right thigh", "right back", "left upper lobe", "left back",
"right thigh", "right axilla"), long_diam = c(21, 19, 11,
15, 21, 30, 25, 11, 30, 18, 30, 11, 31, 24, 31), short_axis = c(18,
11, 11, 7, 11, 20, 18, 10, 27, 11, 19, 10, 26, 12, 25), les_stat = c("Present, Measured",
"Present, Measured", "Present, Measured", "Present, Measured",
"Present, Measured", "Present, Measured", "Present, Measured",
"Present, Measured", "Present, Measured", "Present, Measured",
"Present, Measured", "Present, Measured", "Present, Measured",
"Present, Measured", "Present, Measured"), prog_flag = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0",
"1"), class = c("ordered", "factor")), trt_flag = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("0",
"1"), class = "factor")), row.names = c(NA, -15L), groups = structure(list(
subjid = c("111-002", "111-002"), visit = c("Screening",
"Treatment Visit Cycle"), .rows = structure(list(c(1L, 2L,
3L, 4L, 5L, 12L), c(6L, 7L, 8L, 9L, 10L, 11L, 13L, 14L, 15L
)), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr",
"list"))), row.names = c(NA, -2L), class = c("tbl_df", "tbl",
"data.frame"), .drop = TRUE), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"))
prog_data <- structure(list(subjid = structure("111-002", label = "Subject name or identifier", format.sas = "$"),
mos_dur = 2.7741935483871), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame"))
pd1_data <- structure(list(subjid = structure(c("111-002", "111-002"), label = "Subject name or identifier", format.sas = "$"),
day1_dat = structure(c(18767, 18767), label = "Visit Date", format.sas = "DATETIME", class = "Date"),
admin_dat = structure(c(18877, 18905), label = "Infusion Date", format.sas = "DATETIME", class = "Date"),
mos_dur = c(3.58064516129032, 4.5), visit = structure(c("Crossover Treatment Visit Cycle 5",
"Crossover Treatment Visit Cycle 6"), label = "Folder instance name", format.sas = "$"),
trt = c("Anti-PD-1", "Anti-PD-1"), admin_flag = structure(c("Yes",
"Yes"), label = "Was drug administered at this visit", format.sas = "$"),
interuption_flag = structure(c("No", "No"), label = "Was infusion interrupted at this visit", format.sas = "$"),
intended_flag = structure(c(NA_character_, NA_character_), label = "Was intended volume administered?", format.sas = "$"),
dose = structure(c(480, 480), label = "Actual Dose Administered"),
les_id = c(NA_character_, NA_character_), les_msmt = structure(c(NA_real_,
NA_real_), label = "Lesion Measurement")), row.names = c(NA,
-2L), class = c("tbl_df", "tbl", "data.frame"))
cross_data <- structure(list(subjid = structure("111-002", label = "Subject name or identifier", format.sas = "$"),
crossover_dt = structure(1630972800, tzone = "UTC", class = c("POSIXct",
"POSIXt")), day1_dat = structure(1621468800, label = "Visit Date", tzone = "UTC", format.sas = "DATETIME", class = c("POSIXct",
"POSIXt")), mos_dur = 3.58064516129032), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame"))
uj5u.com熱心網友回復：
我能夠做一些嚴肅的“技巧”，但我得到了想要的輸出。
下面的解決方案代碼和輸出影像。
example <- ggplot()
#scatter plot of target lesion
geom_point(data = target_data,
#if lymph node flag present, plot short axis instead of long diam
aes(x = mos_dur, y = if_else(lymphnode_flag == 1, short_axis, long_diam),
group = les_id, colour = les_id,
shape = if_else(trt_flag == 1, "Injected", "Not Injected")), na.rm = TRUE)
#connect the lines, grouping by target lesion
geom_line(data = target_data,
aes(x = mos_dur, y = if_else(lymphnode_flag == 1, short_axis, long_diam),
group = les_id, colour = les_id), na.rm = TRUE)
theme_classic()
labs(title = paste("Investigator Spider Plots: Subject 110-001"),
subtitle = "Lesion Size over Time",
caption = paste0("* Lesion size displayed as shortest axis for lymph nodes and longest diameter for all other target lesions.\nPD = Progressive disease\nMeasurements from TL form."),
color = "Lesion ID",
shape = "Injected with Study Drug")
geom_hline(aes(yintercept = 0), color = "black")
#manually entered crossover - Used date for treatment cycle 9 as placeholder
geom_vline(data = cross_data, aes(xintercept = 5.0322581, linetype = "Crossover"), color = "gray")
geom_point(data = prog_data, aes(x = mos_dur, y = -12, alpha = "PD"), shape = 7)
geom_point(data = pd1_data,
aes(x = mos_dur, y = -5, alpha = "PD-1"), color = palette[2], shape = 17)
scale_x_continuous("Months from Cycle 1 Day 1", breaks = c(seq(-1,6,1)))
scale_y_continuous("Lesion Size in mm*", breaks = c(seq(0,70,5)))
scale_color_manual(values = palette[1:5]) #, breaks = c("T01", "T02", "T03", "T04"))
scale_shape_manual("Treatment with Study Drug", values = c(16,1), breaks = c("Injected", "Not Injected"))
scale_alpha_manual("", values = c(1,1), breaks = c("PD-1", "PD"), labels = c("Anti-PD-1 Treatment", "Confirmed PD"))
scale_linetype_manual("", values = c("dashed"), labels = "Crossover")
guides(color = guide_legend(order = 1),
shape = guide_legend(order = 2),
alpha = guide_legend(order = 3, override.aes = list(
shape = c(17, 7),
color = c(palette[2], "black")
)),
linetype = guide_legend(order = 4))
coord_cartesian(xlim = c(-1,6), ylim = c(-12,70))
theme(plot.caption = element_text(size = 8))
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