Transform sequence of data into JSON for D3.js visualization - json

Question

I have a data that shows a series of actions (column Actions ) performed by several users (column Id). The order of the data frame is important - it is the order the actions were performed in. For each id, the first action performed is start. Consecutive identical actions are possible (for example, the sequence start -> D -> D -> D is valid ). This is some code to generate data:
set.seed(10)
i <- 0
all_id <- NULL
all_vals <- NULL
while (i < 5) {
i <- i + 1
print(i)
size <- sample(3:5, size = 1)
tmp_id <- rep(i, times = size + 1)
tmp_vals <- c("start",sample(LETTERS, size = size) )
all_id <- c(all_id, tmp_id)
all_vals <- c(all_vals, tmp_vals)
}
df <- data.frame(Id = all_id,
Action = all_vals)
Goal - transform this data in a JSON nested on multiple levels that will be used in a D3.js visualization (like this). I would like to see a counter for how many times each child appears for their respective parent (an maybe even a percentage out of the total appearances of the parent) - but I hope I can do that myself.
Expected output below - this is generic, not from the data I generated above, and real data will have quite a lot of nested values ( count and percentage are optional at this point in time):
{
"action": "start",
"parent": "null",
"count": "10",
"percentage": "100",
"children": [
{
"action": "H",
"parent": "start",
"count": "6",
"percentage": "60",
"children": [
{
"action": "D",
"parent": "H",
"count": "5",
"percentage": "83.3"
},
{
"action": "B",
"parent": "H",
"count": "3",
"percentage": "50"
}
]
},
{
"action": "R",
"parent": "start",
"count": "4",
"percentage": "40"
}
]
}
I know I am supposed to post something I've tried, but I really don't have anything remotely worth of being shown.

Answer 1

I have just started writing some R -> d3.js converters in https://github.com/timelyportfolio/d3r that should work well in these type situations. I will work up an example later today with your data.
The internal hierarchy builder in https://github.com/timelyportfolio/sunburstR also might work well here.
I'll add to the answer as I explore both of these paths.
example 1
set.seed(10)
i <- 0
all_id <- NULL
all_vals <- NULL
while (i < 5) {
i <- i + 1
print(i)
size <- sample(3:5, size = 1)
tmp_id <- rep(i, times = size + 1)
tmp_vals <- c("start",sample(LETTERS, size = size) )
all_id <- c(all_id, tmp_id)
all_vals <- c(all_vals, tmp_vals)
}
df <- data.frame(Id = all_id,
Action = all_vals)
# not sure I completely understand what this is
# supposed to become but here is a first try
# find position of start
start_pos <- which(df$Action=="start")
# get the sequences
# surely there is a better way but do this for now
sequences <- paste(
start_pos+1,
c(start_pos[-1],nrow(df))-1,
sep=":"
)
paths <- lapply(
sequences,
function(x){
data.frame(
t(as.character(df[eval(parse(text=x)),]$Action)),
stringsAsFactors=FALSE
)
}
)
paths_df <- dplyr::bind_rows(paths)
# use d3r
# devtools::install_github("timelyportfolio/d3r")
library(d3r)
d3_nest(paths_df) # if want list, then json=FALSE
# visualize with listviewer
# devtools::install_github("timelyportfolio/listviewer")
listviewer::jsonedit(d3_nest(paths_df))