说明

这里提供RStudio的两种出图方法，分别用于制作可交互的Sankey图和总揽全局的Chord图。

话不多说，上干货。

方法

事先准备

需先根据每个用户使用App各功能和按钮的时间戳顺序获取表格。

sql如下：

SELECT 
user_pseudo_id,
event_timestamp,
event_name
FROM `YourAppChartID.events_*`
WHERE
_TABLE_SUFFIX BETWEEN '20210801' AND '20210824'
AND app_info. version = "1.0.11"
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name like '%001ToPlay%'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name = 'app_remove'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name = 'first_open'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
GROUP BY 
user_pseudo_id,
event_timestamp,
event_name
ORDER BY
user_pseudo_id,
event_timestamp

主体部分其实不多，只是做了一些数据筛选，以确保同一时间用户仅触发了一个数据点，避免分析旅程时的混乱。以下部分都是在根据命名等信息作筛选，可根据自身情况删减。

_TABLE_SUFFIX BETWEEN '20210801' AND '20210824'
AND app_info. version = "1.0.11"
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name like '%001ToPlay%'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name = 'app_remove'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
AND user_pseudo_id in (
SELECT user_pseudo_id
FROM `YourAppChartID.events_*`
WHERE event_name = 'first_open'
AND _TABLE_SUFFIX BETWEEN '20210801' AND '20210824')
GROUP BY

制作Sankey图

准备好两张分别代表节点和连线信息的表格，在Rstuido内输入：

library(networkD3)

links <- read.table('D:\\Rdata\\sankey0501Archive_links.csv', header=T, sep=',')
nodes <- read.table('D:\\Rdata\\sankey0501Archive_nodes.csv', header=T, sep=',') 

setcolor <- 'd3.scaleOrdinal().domain(["B&E","Play","Expect","Ad","Revive","Exit","Download"]).range(["#9ADBC5","#A0DDE0","#DFDD6C","#FE8D6F","#FDC453","#F886A8","#FE8D6F"])'

sankeyNetwork(Links = links, Nodes = nodes, Source = 'source',
             Target = 'target', Value = 'value', NodeID = 'name',
             colourScale = setcolor, LinkGroup = 'linkgroup', NodeGroup = 'nodegroup',
             sinksRight = FALSE,
            #fontFamily = 'futura', fontSize = 10, nodeWidth = 20,
            height = 1080, width =1920 
            )

这里需要替换为事先准备好的表格的位置：

1 2	`links <- read.table('D:\\Rdata\\sankey0501Archive_links.csv', header=T, sep=',') nodes <- read.table('D:\\Rdata\\sankey0501Archive_nodes.csv', header=T, sep=','`

这里是在定义数据分类和对应颜色：

1	`setcolor <- 'd3.scaleOrdinal().domain(["B&E","Play","Expect","Ad","Revive","Exit","Download"]).range(["#9ADBC5","#A0DDE0","#DFDD6C","#FE8D6F","#FDC453","#F886A8","#FE8D6F"])'`

看看效果：

最左侧为版本玩家总数，最右侧为最终卸载数，中间为各阶段玩家流失具体情况。

制作Chord图

这里只需要准备一张表格即可，数据分类需要根据条目命名来区分，故准备表格时需要在取名字前缀上下功夫：

library(chorddiag)

mat <- read.table('D:\\Rdata\\chord0405ExpertMdx_links.csv', header=T, sep=',') 

chorddiag(mat, type = "bipartite", showTicks = F, groupnameFontsize = 14, groupnamePadding = 10, margin = 90)