为sankey图构造二进制数据 [英] structuring binary data for sankey plot
问题描述
我很难弄清楚如何为成功(1)或失败(0)的机会多的数据绘制sankey图.您可以使用以下代码生成示例:
#示例图书馆(networkD3)图书馆(tidyverse)图书馆(tidyr)种子(900)n = 1000example.data< -data.frame("A" = rep(1，n)，"B" = sample(c(0,1)，n，replace = T)，"C" = rep(NA，n)，"D" = rep(NA，n)，"E" = rep(NA，n)，"F" = rep(NA，n)，"G" = rep(NA，n))为(1:n中的i){example.data $ C [i] <-ifelse(example.data $ B [i] == 1，样本(c(0,1)，1，prob = c(0.3,0.7)，replace = F)，样本(c(0,1)，1，prob = c(0.55,0.45)，replace = F))example.data $ D [i]< -ifelse(example.data $ C [i] == 1，样本(c(0,1)，1，prob = c(0.95,0.05)，replace = F)，样本(c(0,1)，1，prob = c(0.65,0.35)，replace = F))example.data $ E [i]< -ifelse(example.data $ C [i] == 0& example.data $ D [i] == 0，样本(c(0,1)，1，prob = c(.9，.1)，replace = F)，ifelse(example.data $ C [i] == 0& example.data $ D [i] == 1，样本(c(0,1)，1，prob = c(.3，.7)，replace = F)，ifelse(example.data $ C [i] == 1& example.data $ D [i] == 0，样本(c(0,1)，1，prob = c(.9，.1)，replace = F)，样本(c(0,1)，1，prob = c(.1，.9)，replace = F))))example.data $ F [i]< -ifelse(example.data $ E == 1，样本(c(1,0)，1，prob = c(.85，.15)，replace = F)，样本(c(1,0)，1，prob = c(.01，.99)，replace = F))example.data $ G [i]< -sample(c(1,0)，1，prob = c(.78，.22)，replace = F)}example.data.1< -example.data％>％collect()％&％;％mutate(ORDER = c(rep(0，n)，rep(1，n)，rep(2，n)，rep(3，n)，rep(4，n)，rep(5，n)，rep(6，n)))％>％dplyr :: select("Event" =键，成功" =值，ORDER)％&％;％group_by(ORDER)％&％summarise("YES" = sum(成功== 1)，否" =总和(成功== 0)) 对我来说，最棘手的部分是如何生成链接数据而无需手动指定源目标和值.
我使用了
I am having trouble figuring out how to make a sankey plot for data where there are multiple opportunities of success (1) or failure (0). You can generate my sample with the following code:
# example
library(networkD3)
library(tidyverse)
library(tidyr)
set.seed(900)
n=1000
example.data<-data.frame("A" = rep(1,n),
"B" = sample(c(0,1),n,replace = T),
"C" = rep(NA,n),
"D" = rep(NA,n),
"E" = rep(NA,n),
"F" = rep(NA,n),
"G" = rep(NA,n))
for (i in 1:n){
example.data$C[i]<- ifelse(example.data$B[i]==1,
sample(c(0,1),1,prob = c(0.3,0.7),replace = F),
sample(c(0,1),1,prob = c(0.55,0.45),replace = F))
example.data$D[i]<-ifelse(example.data$C[i]==1,
sample(c(0,1),1,prob = c(0.95,0.05),replace = F),
sample(c(0,1),1,prob = c(0.65,0.35),replace = F))
example.data$E[i]<-ifelse(example.data$C[i]==0 & example.data$D[i]==0,
sample(c(0,1),1,prob = c(.9,.1),replace = F),
ifelse(example.data$C[i]==0 & example.data$D[i]==1,
sample(c(0,1),1,prob = c(.3,.7),replace = F),
ifelse(example.data$C[i]==1 & example.data$D[i]==0,
sample(c(0,1),1,prob = c(.9,.1),replace = F),
sample(c(0,1),1,prob = c(.1,.9),replace = F))))
example.data$F[i]<-ifelse(example.data$E==1,
sample(c(1,0),1,prob=c(.85,.15),replace = F),
sample(c(1,0),1,prob = c(.01,.99),replace = F))
example.data$G[i]<-sample(c(1,0),1,prob = c(.78,.22),replace = F)
}
example.data.1<-example.data%>%
gather()%>%
mutate(ORDER = c(rep(0,n),rep(1,n),rep(2,n),rep(3,n),rep(4,n),rep(5,n),rep(6,n)))%>%
dplyr::select("Event" = key,
"Success" = value,
ORDER)%>%
group_by(ORDER)%>%
summarise("YES" = sum(Success==1),
"NO" = sum(Success==0))
The tricky part for me is how I can generate the links data without having to manually specify the source targets and values.
I used the sankey example from this website, and proceeded to muscle my own example data in the least elegant way possible:
links<-data.frame("source" = sort(rep(seq(0,10,1),2)),
"target" = c(1,2,3,4,3,4,5,6,5,6,7,8,7,8,9,10,9,10,11,12,11,12),
"value" = c(sum(example.data$A==1 &example.data$B==1), #1
sum(example.data$A==1 & example.data$B==0),#2
sum(example.data$B==1 & example.data$C==1),#3
sum(example.data$B==1 & example.data$C==0),#4
sum(example.data$B==0 & example.data$C==1),#5
sum(example.data$B==0 & example.data$C==0),#6
sum(example.data$C==1 & example.data$D==1),#7
sum(example.data$C==1 & example.data$D==0),#8
sum(example.data$C==0 & example.data$D==1),#9
sum(example.data$C==0 & example.data$D==0),#10
sum(example.data$D==1 & example.data$E==1),#11
sum(example.data$D==1 & example.data$E==0),#12
sum(example.data$D==0 & example.data$E==1),#13
sum(example.data$D==0 & example.data$E==0),#14
sum(example.data$E==1 & example.data$F==1),#15
sum(example.data$E==1 & example.data$F==0),#16
sum(example.data$E==0 & example.data$F==1),#17
sum(example.data$E==0 & example.data$F==0),#18
sum(example.data$F==1 & example.data$G==1),#19
sum(example.data$F==1 & example.data$G==0),#20
sum(example.data$F==0 & example.data$G==1),#21
sum(example.data$F==0 & example.data$G==0)))#22
nodes<-data.frame("name" = names(example.data))
example.list<-list(nodes,links)
names(example.list)<-c("nodes","links")
My problem is this. 1) trying to use this data in the sankeyNetwork function does not actually produce a plot at all, and 2) Obviously this method will be prone to a lot of error especially if there are more than 2 targets per node.
I found an example on stack where the person used the match call in a dplyr::mutate function that looked promising for what I'm trying to accomplish, but the data had a slightly different structure and I did't really know how to get the match call to work with my own data.
The output I'm going for is a sankey plot that shows the number of observations moving between each of the events/outcomes [A:F]. So imagine each of the columns represent an event either successful or not successful. The sakey plot would illustrate a summary of total successes and failures of each event. So all 1000 observations starting at A with 493 going to a node of B = 1, and the remaining 507 going to the node indicating B = 0. Of the 493 in B = 1, 345 go to the node indicating C = 1, and 148 go to the node C = 0. Of the 507 in B = 0 263 go to C = 1 and 244 go to C = 0, and so on for the rest of the event A through F. I hope I've made this clear enough. Any help on this would be greatly appreciated.
The sankey plot does not work because you refer to nodes in you target and source columns that don't exist in your nodes data frame.
to demonstrate...
sort(unique(c(links$source, links$target)))
# [1] 0 1 2 3 4 5 6 7 8 9 10 11 12
nrow(nodes)
# [1] 7
To reshape your original data into the proper format...
The reason your original data is difficult to work with is because important information that you want to use is implicitly encoded in the shape of your data, but not explicitly included in the data. Each data point in a given row have an implicit relationship that they were chosen by the same entity, but that information does not exist explicitly in your data. Likewise, each column implicitly represents one of a sequential chain of actions. A good test for this situation is to ask yourself if you reshaped the data, or sorted it by a column, or reordered the columns, would you still have the same information? If you swapped column B for column D, would you still have all the same information? Ignoring the fact that one could implicitly assume the intended order of your columns because they are named in alphabetical order, the answer is no... so that's where you need to start, by encoding that information into your data.
Add the row number as a variable/column, then gather all the columns into long format, and add the column number...
events <-
example.data %>%
as_tibble() %>%
mutate(row = row_number()) %>%
gather(column, choice, -row) %>%
mutate(column_num = match(column, names(example.data))) %>%
arrange(row, column_num) %>%
select(row, column_num, everything())
events
# # A tibble: 7,000 x 4
# row column_num column choice
# <int> <int> <chr> <dbl>
# 1 1 1 A 1
# 2 1 2 B 1
# 3 1 3 C 1
# 4 1 4 D 0
# 5 1 5 E 1
# 6 1 6 F 1
# 7 1 7 G 0
# 8 2 1 A 1
# 9 2 2 B 0
# 10 2 3 C 1
# # ... with 6,990 more rows
Now the data represents one event/choice per row, with all the critical info you need. In your desired output, each "node" is defined by the column and a choice made at that stage... so A_1, B_0, B_1, C_0, C_1, etc. For each event in your reshaped data, you want to know at which node that choice/event took place ("target"), and which node was it coming from ("source"). The target node is the column name and the choice of that event. The source node is the column name and choice of the event that preceded it (-1 column_num) within the same row (person/entity/observation).
links <-
events %>%
mutate(target = paste0(column, "_", choice)) %>%
group_by(row) %>%
mutate(source = lag(target)) %>%
filter(!is.na(source) & !is.na(target))
links
# # A tibble: 6,000 x 6
# # Groups: row [1,000]
# row column_num column choice target source
# <int> <int> <chr> <dbl> <chr> <chr>
# 1 1 2 B 1 B_1 A_1
# 2 1 3 C 1 C_1 B_1
# 3 1 4 D 0 D_0 C_1
# 4 1 5 E 1 E_1 D_0
# 5 1 6 F 1 F_1 E_1
# 6 1 7 G 0 G_0 F_1
# 7 2 2 B 0 B_0 A_1
# 8 2 3 C 1 C_1 B_0
# 9 2 4 D 0 D_0 C_1
# 10 2 5 E 1 E_1 D_0
# # ... with 5,990 more rows
Now you want to summarize that data. You want to count the number of each unique link/path.
links <-
links %>%
select(source, target) %>%
group_by(source, target) %>%
summarise(value = n()) %>%
ungroup()
links
# # A tibble: 22 x 3
# source target value
# <chr> <chr> <int>
# 1 A_1 B_0 507
# 2 A_1 B_1 493
# 3 B_0 C_0 244
# 4 B_0 C_1 263
# 5 B_1 C_0 148
# 6 B_1 C_1 345
# 7 C_0 D_0 267
# 8 C_0 D_1 125
# 9 C_1 D_0 579
# 10 C_1 D_1 29
# # ... with 12 more rows
With that, you just need to put it in the format that sankeyNetwork requires... a nodes data frame with one row for each unique node, and a links data frame where the source and target columns are numeric and refer to the index (0-based) of the nodes in the nodes data frame (the row nunber they appear on - 1).
nodes <- data.frame(name = unique(c(links$source, links$target)))
links$source <- match(links$source, nodes$name) - 1
links$target <- match(links$target, nodes$name) - 1
sankeyNetwork(Links = links, Nodes = nodes, Source = "source",
Target = "target", Value = "value", NodeID = "name")
这篇关于为sankey图构造二进制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！
