如何在ggplot方面内按值排序数据 [英] How to order data by value within ggplot facets
本文介绍了如何在ggplot方面内按值排序数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有以下数据框:
library(tidyverse)
tdat <- structure(list(term = c("Hepatic Fibrosis / Hepatic Stellate Cell Activation",
"Cellular Effects of Sildenafil (Viagra)", "Epithelial Adherens Junction Signaling",
"STAT3 Pathway", "Nitric Oxide Signaling in the Cardiovascular System",
"LXR/RXR Activation", "NF-κB Signaling", "PTEN Signaling", "Gap Junction Signaling",
"G-Protein Coupled Receptor Signaling", "Role of Osteoblasts, Osteoclasts and Chondrocytes in Rheumatoid Arthritis",
"Osteoarthritis Pathway", "VDR/RXR Activation", "Axonal Guidance Signaling",
"Basal Cell Carcinoma Signaling", "Putrescine Degradation III",
"Tryptophan Degradation X (Mammalian, via Tryptamine)", "Factors Promoting Cardiogenesis in Vertebrates",
"Dopamine Degradation", "Complement System", "Role of BRCA1 in DNA Damage Response",
"Granzyme B Signaling", "GADD45 Signaling", "ATM Signaling",
"Hereditary Breast Cancer Signaling", "Aryl Hydrocarbon Receptor Signaling",
"Role of Oct4 in Mammalian Embryonic Stem Cell Pluripotency",
"Factors Promoting Cardiogenesis in Vertebrates", "Sumoylation Pathway",
"Hepatic Fibrosis / Hepatic Stellate Cell Activation", "GP6 Signaling Pathway",
"Hepatic Fibrosis / Hepatic Stellate Cell Activation", "Intrinsic Prothrombin Activation Pathway",
"Atherosclerosis Signaling", "Gap Junction Signaling", "LXR/RXR Activation",
"FXR/RXR Activation", "HIF1α Signaling", "Bladder Cancer Signaling",
"Ephrin A Signaling"), tissue = c("tissue-A", "tissue-A", "tissue-A",
"tissue-A", "tissue-A", "tissue-A", "tissue-A", "tissue-A", "tissue-A", "tissue-A",
"tissue-B", "tissue-B", "tissue-B", "tissue-B", "tissue-B", "tissue-B",
"tissue-B", "tissue-B", "tissue-B", "tissue-B", "tissue-C", "tissue-C",
"tissue-C", "tissue-C", "tissue-C", "tissue-C", "tissue-C", "tissue-C", "tissue-C",
"tissue-C", "tissue-D", "tissue-D", "tissue-D", "tissue-D", "tissue-D",
"tissue-D", "tissue-D", "tissue-D", "tissue-D", "tissue-D"), score = c(2.85,
2.81, 2.53, 2.28, 2.19, 2.18, 2.13, 2.01, 1.97, 1.94, 6.01, 5.78,
4.29, 2.85, 2.75, 2.67, 2.56, 2.32, 2.22, 2.11, 5.61, 2.91, 2.6,
2.55, 2.23, 1.86, 1.56, 1.4, 1.34, 1.31, 6.26, 5.87, 4.47, 3.94,
3.2, 3.17, 3.07, 2.97, 2.71, 2.61)), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -40L), .Names = c("term", "tissue",
"score"))
tdat
#> # A tibble: 40 x 3
#> term tissue score
#> <chr> <chr> <dbl>
#> 1 Hepatic Fibrosis / Hepatic Stellate Cell Activation tissue-A 2.85
#> 2 Cellular Effects of Sildenafil (Viagra) tissue-A 2.81
#> 3 Epithelial Adherens Junction Signaling tissue-A 2.53
#> 4 STAT3 Pathway tissue-A 2.28
#> 5 Nitric Oxide Signaling in the Cardiovascular System tissue-A 2.19
#> 6 LXR/RXR Activation tissue-A 2.18
#> 7 NF-κB Signaling tissue-A 2.13
#> 8 PTEN Signaling tissue-A 2.01
#> 9 Gap Junction Signaling tissue-A 1.97
#> 10 G-Protein Coupled Receptor Signaling tissue-A 1.94
#> # ... with 30 more rows
我要做的是制作一个像图一样的小图，该图按组织进行分组，并根据每组的分数递减.
What I want to do is to make a barplot like a plot grouped by tissue and ordered descendingly according to the score in each group.
我尝试过:
term_order <- tdat$term[order(tdat$tissue, tdat$score)]
tdat$term <- factor(tdat$term, levels = unique(term_order))
tdat$tissue <- factor(tdat$tissue, levels = c("tissue-C", "tissue-A", "tissue-D", "tissue-B"), ordered = TRUE)
tp <- ggplot(tdat, aes(x = score, y = term)) +
geom_segment(aes(yend = term), xend = 0, colour = "grey50") +
geom_point(size = 3, aes(colour = tissue)) +
theme_bw() +
scale_colour_brewer(palette = "Dark2") +
theme(panel.grid.major.y = element_blank()) +
facet_grid(tissue ~ ., scales = "free_y", space = 'free_y')
tp
但是我得到的是这个情节:
But what I get is this plot:
请注意，在组织D中，该术语未相应排序. 怎么办呢?
Notice that in tissue-D the term is not sorted accordingly. What's the way to go about it?
推荐答案
我们可以使用
(1) reorder_within() 函数来重新排序term在tissue方面内.
library(tidyverse)
library(forcats)
tdat <- tdat %>%
mutate(term = factor(term),
tissue = factor(tissue, levels = c("tissue-C", "tissue-A", "tissue-D", "tissue-B"),
ordered = TRUE))
reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
new_x <- paste(x, within, sep = sep)
stats::reorder(new_x, by, FUN = fun)
}
scale_x_reordered <- function(..., sep = "___") {
reg <- paste0(sep, ".+$")
ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}
ggplot(tdat, aes(reorder_within(term, score, tissue), score)) +
geom_segment(aes(xend = reorder_within(term, score, tissue), yend = 0),
colour = "grey50") +
geom_point(size = 3, aes(colour = tissue)) +
scale_x_reordered() +
facet_grid(tissue ~ ., scales = "free", space = "free") +
coord_flip() +
scale_colour_brewer(palette = "Dark2") +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
theme(legend.position = "bottom")
或者(2)类似的想法
### https://trinkerrstuff.wordpress.com/2016/12/23/ordering-categories-within-ggplot2-facets/
tdat %>%
mutate(term = reorder(term, score)) %>%
group_by(tissue, term) %>%
arrange(desc(score)) %>%
ungroup() %>%
mutate(term = factor(paste(term, tissue, sep = "__"),
levels = rev(paste(term, tissue, sep = "__")))) %>%
ggplot(aes(term, score)) +
geom_segment(aes(xend = term, yend = 0),
colour = "grey50") +
geom_point(size = 3, aes(colour = tissue)) +
facet_grid(tissue ~., scales = "free", space = 'free') +
scale_x_discrete(labels = function(x) gsub("__.+$", "", x)) +
coord_flip() +
scale_colour_brewer(palette = "Dark2") +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
theme(legend.position = "bottom",
axis.ticks.y = element_blank())
或者(3)对整个数据框进行排序，还对每个构面组内的类别(tissue)进行排序！
Or (3) orders the entire data frame, and also orders the categories (tissue) within each facet group!
### https://drsimonj.svbtle.com/ordering-categories-within-ggplot2-facets
#
tdat2 <- tdat %>%
# 1. Remove grouping
ungroup() %>%
# 2. Arrange by
# i. facet group (tissue)
# ii. value (score)
arrange(tissue, score) %>%
# 3. Add order column of row numbers
mutate(order = row_number())
tdat2
#> # A tibble: 40 x 4
#> term tissue score order
#> <fct> <ord> <dbl> <int>
#> 1 Hepatic Fibrosis / Hepatic Stellate Cell Activation tissue~ 1.31 1
#> 2 Sumoylation Pathway tissue~ 1.34 2
#> 3 Factors Promoting Cardiogenesis in Vertebrates tissue~ 1.4 3
#> 4 Role of Oct4 in Mammalian Embryonic Stem Cell Plur~ tissue~ 1.56 4
#> 5 Aryl Hydrocarbon Receptor Signaling tissue~ 1.86 5
#> 6 Hereditary Breast Cancer Signaling tissue~ 2.23 6
#> 7 ATM Signaling tissue~ 2.55 7
#> 8 GADD45 Signaling tissue~ 2.6 8
#> 9 Granzyme B Signaling tissue~ 2.91 9
#> 10 Role of BRCA1 in DNA Damage Response tissue~ 5.61 10
#> # ... with 30 more rows
ggplot(tdat2, aes(order, score)) +
geom_segment(aes(xend = order, yend = 0),
colour = "grey50") +
geom_point(size = 3, aes(colour = tissue)) +
facet_grid(tissue ~ ., scales = "free", space = "free") +
coord_flip() +
scale_colour_brewer(palette = "Dark2") +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
theme(legend.position = "bottom")
# To finish we need to replace the numeric values on each x-axis
# with the appropriate labels
ggplot(tdat2, aes(order, score)) +
geom_segment(aes(xend = order, yend = 0),
colour = "grey50") +
geom_point(size = 3, aes(colour = tissue)) +
scale_x_continuous(
breaks = tdat2$order,
labels = tdat2$term) +
# scale_y_continuous(expand = c(0, 0)) +
facet_grid(tissue ~ ., scales = "free", space = "free") +
coord_flip() +
scale_colour_brewer(palette = "Dark2") +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
theme(legend.position = "bottom",
axis.ticks.y = element_blank())
这篇关于如何在ggplot方面内按值排序数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
