医学和生信笔记
，专注R语言在临床医学中的使用，R语言数据分析和可视化。主要分享R语言做医学统计学、meta分析、网络药理学、临床预测模型、机器学习、生物信息学等。
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前面讲了组合图形，今天继续组合！
前面的组合图形都是关于交集的，或者关于总体的一些统计汇总可视化，今天说的
metadata
是关于每个集合的属性。
还是以
movies
数据集为例。
自带数据集
movies
，是一个电影类型数据，共有3883行（3881部电影），21列。第1列是电影名字，还有上映时间（ReleaseDate）、评分（AvgRatings）、观看数（Watches）。其余列是由0-1矩阵表示的电影类型。
movies <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"),
header = T, sep = ";")
str(movies)
## 'data.frame': 3883 obs. of 21 variables:
## $ Name : chr "Toy Story (1995)" "Jumanji (1995)" "Grumpier Old Men (1995)" "Waiting to Exhale (1995)" ...
## $ ReleaseDate: int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
## $ Action : int 0 0 0 0 0 1 0 0 1 1 ...
## $ Adventure : int 0 1 0 0 0 0 0 1 0 1 ...
## $ Children : int 1 1 0 0 0 0 0 1 0 0 ...
## $ Comedy : int 1 0 1 1 1 0 1 0 0 0 ...
## $ Crime : int 0 0 0 0 0 1 0 0 0 0 ...
## $ Documentary: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Drama : int 0 0 0 1 0 0 0 0 0 0 ...
## $ Fantasy : int 0 1 0 0 0 0 0 0 0 0 ...
## $ Noir : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Horror : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Musical : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Mystery : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Romance : int 0 0 1 0 0 0 1 0 0 0 ...
## $ SciFi : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Thriller : int 0 0 0 0 0 1 0 0 0 1 ...
## $ War : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Western : int 0 0 0 0 0 0 0 0 0 0 ...
## $ AvgRating : num 4.15 3.2 3.02 2.73 3.01 3.88 3.41 3.01 2.66 3.54 ...
## $ Watches : int 2077 701 478 170 296 940 458 68 102 888 ...
这个数据一共21列，其中4列是属性，剩余17列是电影类型，例如动作片、剧情片、恐怖片、惊悚片等。
下面我们新建一个数据集，这个数据集是每种类型电影的烂番茄评分！
metadata <- data.frame(
sets = names(movies[3:19]),
RottenTomato = round(runif(17, min = 0, max = 90))
)
upset(movies,
set.metadata = list(
data = metadata,
plots = list(list(type = "hist", column = "RottenTomato", assign = 20))
)
)
OK，非常简单就显示了每种类型电影的烂番茄评分，以条形图的形式展示在最左侧。
当然也可以是其他类型的图形，比如热图。
# 再新建一个数据画热图用
metadata$Cities <- sample(c("Boston", "NYC", "LA"), 17, replace = T)
metadata$accepted <- round(runif(17, min = 0, max = 1))
str(metadata)
## 'data.frame': 17 obs. of 4 variables:
## $ sets : chr "Action" "Adventure" "Children" "Comedy" ...
## $ RottenTomato: num 84 79 66 43 13 26 74 54 70 25 ...
## $ Cities : chr "Boston" "Boston" "NYC" "Boston" ...
## $ accepted : num 0 0 0 1 0 0 1 1 0 0 ...
upset(movies,
set.metadata = list(
data = metadata,
plots = list(
list(type = "heat", column = "Cities", assign = 10, colors = c(Boston = "green", NYC = "navy", LA = "purple")),
list(type = "heat", column = "RottenTomato",
assign = 10),
list(type = "bool",
column = "accepted", assign = 5, colors = c("#FF3333", "#006400"))
)
)
)
还可以是文字，甚至把下方的矩阵换一个颜色：
upset(movies,
set.metadata = list(
data = metadata,
plots = list(
list(type = "text", column = "Cities", assign = 10, colors = c(Boston = "tomato", NYC = "grey70", LA = "red")),
list(type = "matrix_rows", column = "Cities", colors = c(Boston = "green", NYC = "navy", LA = "purple"),
alpha = 0.5)
)))
OK，以上就是
upset plot
的全部内容！一共分了4篇推文介绍，这个包真是666！
最后，给大家奉上完整的
upset plot
：
upset(movies, main.bar.color = "skyblue",
set.metadata = list(
data = metadata,
plots = list(
list(type = "hist", column = "RottenTomato", assign = 20),
list(type = "bool", column = "accepted", assign = 5, colors = c("#FF3333", "#006400")),
list(type = "text", column = "Cities", assign = 5, colors = c(Boston = "green", NYC = "navy", LA = "purple")),
list(type = "matrix_rows", column = "Cities", colors = c(Boston = "green", NYC = "navy", LA = "purple"), alpha = 0.5)
)
),
queries = list(
list(query = intersects, params = list("Drama"), color = "red", active = F),
list(query = intersects, params = list("Action", "Drama"), active = T),
list(query = intersects, params = list("Drama", "Comedy", "Action"), color = "orange", active = T)
),
attribute.plots = list(
gridrows = 45,
plots = list(
list(plot = scatter_plot, x = "ReleaseDate", y = "AvgRating", queries = T),
list(plot = scatter_plot, x = "AvgRating", y = "Watches", queries = F)
),
ncols = 2),
query.legend = "bottom")
最后，希望对大家有帮助哦！