Facet Wrap 在一个因素上分页的功能？
Function for Facet Wrap Paginate over a Factor?
我有以下 table:
> tor.responsive
Transcript Gene.Name Genotype tpi Replicate.1 Replicate.2 Replicate.3
1 TRINITY_DN103903_c0_g1_i4 MsLHCB1.1 S 0 49.056366 144.0096979 159.051697
2 TRINITY_DN12862_c0_g1_i3 MsLHCA6 S 0 26.847656 57.1883135 62.923004
3 TRINITY_DN12910_c0_g1_i1 MsPLGG1-A S 0 4.856194 9.7402183 9.175898
4 TRINITY_DN13552_c0_g1_i2 MsRPI3-B S 0 26.221691 73.8716146 76.343180
5 TRINITY_DN231_c0_g1_i3 MsRPI1-B S 0 46.041232 35.1452605 77.562641
6 TRINITY_DN38889_c0_g1_i5 MsPGK1 S 0 1.610827 0.8531732 1.914664
7 TRINITY_DN524_c0_g1_i1 MsCFBP1-A S 0 72.722639 218.5629027 261.697935
8 TRINITY_DN5413_c1_g1_i2 MsPSBTN S 0 99.296010 950.4469250 598.875903
9 TRINITY_DN6017_c0_g1_i3 MsTKL-1 S 0 45.140098 12.2627406 18.338120
10 TRINITY_DN610_c0_g1_i8 MsRCA-A S 0 12.593589 33.6608395 47.774347
11 TRINITY_DN63166_c0_g1_i1 MsCRD1-B S 0 117.637784 279.4160944 307.335882
我想 facet_wrap_paginate 为 Gene.Name 制作的每个地块在列表中都有单独的地块。有没有办法让它成为一个功能？我正在使用以下代码创建绘图：
tor.resp.fpkm = ggplot() +
geom_bar(data = tor.responsive, aes(x = tpi, y = FPKM.Mean, fill = Genotype), stat = "identity", position = 'dodge') +
scale_fill_manual(values = c("red","blue"), guide = guide_legend(title = "Genotype")) +
scale_y_continuous(limit = c(NA,NA)) +
facet_wrap_paginate(~Gene.Name, nrow = 1, ncol = 1) +
theme_prism() +
theme_bw() +
labs(x = "Time Post-Infestation (tpi)", y = "Mean FPKM", title = "") +
theme(axis.ticks.y = element_line(color = "black")) + guides(y = guide_prism_minor()) +
theme(plot.title = element_text(face = "bold.italic", size = 16, family = "sans", hjust = .5)) +
theme(axis.title.x = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 12)) +
theme(axis.title.y = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 12)) +
theme(legend.position = "none") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.border = element_rect(colour = "black", fill = NA)) +
theme(plot.margin = unit(c(-1.25,0.5,0.25,0.5), "cm"))
P.S - 我这样计算 FPKM.Mean：
tor.responsive$FPKM.Mean = rowMeans(tor.responsive[5:7])
数据
structure(list(Transcript = c("TRINITY_DN103903_c0_g1_i4", "TRINITY_DN12862_c0_g1_i3",
"TRINITY_DN12910_c0_g1_i1", "TRINITY_DN13552_c0_g1_i2", "TRINITY_DN231_c0_g1_i3",
"TRINITY_DN38889_c0_g1_i5", "TRINITY_DN524_c0_g1_i1", "TRINITY_DN5413_c1_g1_i2",
"TRINITY_DN6017_c0_g1_i3", "TRINITY_DN610_c0_g1_i8", "TRINITY_DN63166_c0_g1_i1"
), Gene.Name = c("MsLHCB1.1", "MsLHCA6", "MsPLGG1-A", "MsRPI3-B",
"MsRPI1-B", "MsPGK1", "MsCFBP1-A", "MsPSBTN", "MsTKL-1", "MsRCA-A",
"MsCRD1-B"), Genotype = c("S", "S", "S", "S", "S", "S", "S",
"S", "S", "S", "S"), tpi = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Replicate.1 = c(49.05636627, 26.84765632, 4.856193803,
26.22169147, 46.04123195, 1.610826506, 72.72263912, 99.29601001,
45.14009822, 12.59358919, 117.6377843), Replicate.2 = c(144.0096979,
57.18831345, 9.740218281, 73.87161455, 35.14526047, 0.853173159,
218.5629027, 950.446925, 12.26274063, 33.66083948, 279.4160944
), Replicate.3 = c(159.0516969, 62.92300417, 9.175897983, 76.3431804,
77.56264079, 1.914663635, 261.6979354, 598.8759031, 18.33811992,
47.77434747, 307.3358824)), class = "data.frame", row.names = c(NA,
-11L))
如果您只想要一个只有一个方面的图列表，则不必使用 facet_wrap_paginate。直接生成图。工作流程如下：首先，为单个图定义一个函数；然后，nest 您的数据框基于您的组变量（在本例中为 Gene.Name）；最后，lapply 或 map 每个子数据帧的绘图函数。这是代码。请注意，我还删除了您的 ggplot 代码中一些不必要的部分。
library(ggplot2)
library(ggprism)
library(tidyr)
library(dplyr)
plotf <- function(data, title, extrema) {
extrema[[1L]] <- min(extrema[[1L]], 0)
ggplot(data) +
geom_col(aes(x = tpi, y = FPKM.Mean, fill = Genotype), position = "dodge") +
scale_fill_manual(values = c("S" = "red", "???" = "blue")) + # You should specify colors for each value for clarity. I also noticed that you have two colors defined so there is an unknown one `???` left for you to fill in.
scale_y_continuous(limits = extrema, expand = c(0, 0)) + # If you want to remove the white space left for axes limits, you should set expand = c(0, 0). It is almost certainly not recommended to set any margins to negative values.
labs(x = "Time Post-Infestation (tpi)", y = "Mean FPKM", title = title) +
guides(y = guide_prism_minor()) +
theme_bw() +
theme(
plot.title = element_text(face = "bold.italic", size = 16, family = "sans", hjust = .5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
axis.ticks.y = element_line(color = "black"),
panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill = NA),
plot.margin = unit(c(0,0.5,0.25,0.5), "cm"),
legend.position = "none"
)
}
tor.responsive$FPKM.Mean <- rowMeans(tor.responsive[5:7])
extrema <- range(tor.responsive$FPKM.Mean)
result <-
tor.responsive %>%
mutate(Genotype = factor(Genotype, c("S", "???"))) %>% # Don't forget to change this `???` too.
nest(data = -Gene.Name) %>%
mutate(plots = mapply(
plotf, data, Gene.Name,
MoreArgs = list(extrema = extrema), SIMPLIFY = FALSE
))
输出
> result
# A tibble: 11 x 3
Gene.Name data plots
<chr> <list> <list>
1 MsLHCB1.1 <tibble [1 x 7]> <gg>
2 MsLHCA6 <tibble [1 x 7]> <gg>
3 MsPLGG1-A <tibble [1 x 7]> <gg>
4 MsRPI3-B <tibble [1 x 7]> <gg>
5 MsRPI1-B <tibble [1 x 7]> <gg>
6 MsPGK1 <tibble [1 x 7]> <gg>
7 MsCFBP1-A <tibble [1 x 7]> <gg>
8 MsPSBTN <tibble [1 x 7]> <gg>
9 MsTKL-1 <tibble [1 x 7]> <gg>
10 MsRCA-A <tibble [1 x 7]> <gg>
11 MsCRD1-B <tibble [1 x 7]> <gg>
然后您可以通过在 R 控制台中键入 result$plots[[7L]] 来访问，例如，绘图 7。情节应该是这样的
我有以下 table:
> tor.responsive
Transcript Gene.Name Genotype tpi Replicate.1 Replicate.2 Replicate.3
1 TRINITY_DN103903_c0_g1_i4 MsLHCB1.1 S 0 49.056366 144.0096979 159.051697
2 TRINITY_DN12862_c0_g1_i3 MsLHCA6 S 0 26.847656 57.1883135 62.923004
3 TRINITY_DN12910_c0_g1_i1 MsPLGG1-A S 0 4.856194 9.7402183 9.175898
4 TRINITY_DN13552_c0_g1_i2 MsRPI3-B S 0 26.221691 73.8716146 76.343180
5 TRINITY_DN231_c0_g1_i3 MsRPI1-B S 0 46.041232 35.1452605 77.562641
6 TRINITY_DN38889_c0_g1_i5 MsPGK1 S 0 1.610827 0.8531732 1.914664
7 TRINITY_DN524_c0_g1_i1 MsCFBP1-A S 0 72.722639 218.5629027 261.697935
8 TRINITY_DN5413_c1_g1_i2 MsPSBTN S 0 99.296010 950.4469250 598.875903
9 TRINITY_DN6017_c0_g1_i3 MsTKL-1 S 0 45.140098 12.2627406 18.338120
10 TRINITY_DN610_c0_g1_i8 MsRCA-A S 0 12.593589 33.6608395 47.774347
11 TRINITY_DN63166_c0_g1_i1 MsCRD1-B S 0 117.637784 279.4160944 307.335882
我想 facet_wrap_paginate 为 Gene.Name 制作的每个地块在列表中都有单独的地块。有没有办法让它成为一个功能？我正在使用以下代码创建绘图：
tor.resp.fpkm = ggplot() +
geom_bar(data = tor.responsive, aes(x = tpi, y = FPKM.Mean, fill = Genotype), stat = "identity", position = 'dodge') +
scale_fill_manual(values = c("red","blue"), guide = guide_legend(title = "Genotype")) +
scale_y_continuous(limit = c(NA,NA)) +
facet_wrap_paginate(~Gene.Name, nrow = 1, ncol = 1) +
theme_prism() +
theme_bw() +
labs(x = "Time Post-Infestation (tpi)", y = "Mean FPKM", title = "") +
theme(axis.ticks.y = element_line(color = "black")) + guides(y = guide_prism_minor()) +
theme(plot.title = element_text(face = "bold.italic", size = 16, family = "sans", hjust = .5)) +
theme(axis.title.x = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 12)) +
theme(axis.title.y = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 12)) +
theme(legend.position = "none") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.border = element_rect(colour = "black", fill = NA)) +
theme(plot.margin = unit(c(-1.25,0.5,0.25,0.5), "cm"))
P.S - 我这样计算 FPKM.Mean：
tor.responsive$FPKM.Mean = rowMeans(tor.responsive[5:7])
数据
structure(list(Transcript = c("TRINITY_DN103903_c0_g1_i4", "TRINITY_DN12862_c0_g1_i3",
"TRINITY_DN12910_c0_g1_i1", "TRINITY_DN13552_c0_g1_i2", "TRINITY_DN231_c0_g1_i3",
"TRINITY_DN38889_c0_g1_i5", "TRINITY_DN524_c0_g1_i1", "TRINITY_DN5413_c1_g1_i2",
"TRINITY_DN6017_c0_g1_i3", "TRINITY_DN610_c0_g1_i8", "TRINITY_DN63166_c0_g1_i1"
), Gene.Name = c("MsLHCB1.1", "MsLHCA6", "MsPLGG1-A", "MsRPI3-B",
"MsRPI1-B", "MsPGK1", "MsCFBP1-A", "MsPSBTN", "MsTKL-1", "MsRCA-A",
"MsCRD1-B"), Genotype = c("S", "S", "S", "S", "S", "S", "S",
"S", "S", "S", "S"), tpi = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Replicate.1 = c(49.05636627, 26.84765632, 4.856193803,
26.22169147, 46.04123195, 1.610826506, 72.72263912, 99.29601001,
45.14009822, 12.59358919, 117.6377843), Replicate.2 = c(144.0096979,
57.18831345, 9.740218281, 73.87161455, 35.14526047, 0.853173159,
218.5629027, 950.446925, 12.26274063, 33.66083948, 279.4160944
), Replicate.3 = c(159.0516969, 62.92300417, 9.175897983, 76.3431804,
77.56264079, 1.914663635, 261.6979354, 598.8759031, 18.33811992,
47.77434747, 307.3358824)), class = "data.frame", row.names = c(NA,
-11L))
如果您只想要一个只有一个方面的图列表，则不必使用 facet_wrap_paginate。直接生成图。工作流程如下：首先，为单个图定义一个函数；然后，nest 您的数据框基于您的组变量（在本例中为 Gene.Name）；最后，lapply 或 map 每个子数据帧的绘图函数。这是代码。请注意，我还删除了您的 ggplot 代码中一些不必要的部分。
library(ggplot2)
library(ggprism)
library(tidyr)
library(dplyr)
plotf <- function(data, title, extrema) {
extrema[[1L]] <- min(extrema[[1L]], 0)
ggplot(data) +
geom_col(aes(x = tpi, y = FPKM.Mean, fill = Genotype), position = "dodge") +
scale_fill_manual(values = c("S" = "red", "???" = "blue")) + # You should specify colors for each value for clarity. I also noticed that you have two colors defined so there is an unknown one `???` left for you to fill in.
scale_y_continuous(limits = extrema, expand = c(0, 0)) + # If you want to remove the white space left for axes limits, you should set expand = c(0, 0). It is almost certainly not recommended to set any margins to negative values.
labs(x = "Time Post-Infestation (tpi)", y = "Mean FPKM", title = title) +
guides(y = guide_prism_minor()) +
theme_bw() +
theme(
plot.title = element_text(face = "bold.italic", size = 16, family = "sans", hjust = .5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
axis.ticks.y = element_line(color = "black"),
panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill = NA),
plot.margin = unit(c(0,0.5,0.25,0.5), "cm"),
legend.position = "none"
)
}
tor.responsive$FPKM.Mean <- rowMeans(tor.responsive[5:7])
extrema <- range(tor.responsive$FPKM.Mean)
result <-
tor.responsive %>%
mutate(Genotype = factor(Genotype, c("S", "???"))) %>% # Don't forget to change this `???` too.
nest(data = -Gene.Name) %>%
mutate(plots = mapply(
plotf, data, Gene.Name,
MoreArgs = list(extrema = extrema), SIMPLIFY = FALSE
))
输出
> result
# A tibble: 11 x 3
Gene.Name data plots
<chr> <list> <list>
1 MsLHCB1.1 <tibble [1 x 7]> <gg>
2 MsLHCA6 <tibble [1 x 7]> <gg>
3 MsPLGG1-A <tibble [1 x 7]> <gg>
4 MsRPI3-B <tibble [1 x 7]> <gg>
5 MsRPI1-B <tibble [1 x 7]> <gg>
6 MsPGK1 <tibble [1 x 7]> <gg>
7 MsCFBP1-A <tibble [1 x 7]> <gg>
8 MsPSBTN <tibble [1 x 7]> <gg>
9 MsTKL-1 <tibble [1 x 7]> <gg>
10 MsRCA-A <tibble [1 x 7]> <gg>
11 MsCRD1-B <tibble [1 x 7]> <gg>
然后您可以通过在 R 控制台中键入 result$plots[[7L]] 来访问，例如，绘图 7。情节应该是这样的