Exports profiling analysis results in JSON format for consumption by AI agents, automated tools, or external applications.
Usage
pv_to_json(
x,
file = NULL,
pretty = TRUE,
include = c("summary", "self_time", "total_time", "hot_lines", "memory", "gc_pressure",
"suggestions", "recursive"),
system_info = FALSE
)Arguments
- x
A profvis object.
- file
Optional file path to write JSON to. If
NULL, returns the JSON string.- pretty
If
TRUE, formats JSON with indentation for readability.- include
Character vector specifying which analyses to include. Options: "summary", "self_time", "total_time", "hot_lines", "memory", "callers", "gc_pressure", "suggestions", "recursive". Default includes all.
- system_info
If
TRUE, includes R version and platform info in metadata. Useful for reproducibility.
Value
If file is NULL, returns a JSON string. Otherwise writes to file
and returns the file path invisibly.
Examples
p <- pv_example()
json <- pv_to_json(p)
cat(json)
#> {
#> "metadata": {
#> "total_time_ms": 70,
#> "total_samples": 14,
#> "interval_ms": 5,
#> "has_source_refs": true,
#> "exported_at": "2026-03-16T20:21:27+0000"
#> },
#> "summary": {
#> "total_time_ms": 70,
#> "unique_functions": 6,
#> "max_depth": 3
#> },
#> "self_time": [
#> {
#> "label": "rnorm",
#> "samples": 6,
#> "time_ms": 30,
#> "pct": 42.9
#> },
#> {
#> "label": "x[i] <- rnorm(1)",
#> "samples": 4,
#> "time_ms": 20,
#> "pct": 28.6
#> },
#> {
#> "label": "generate_data",
#> "samples": 3,
#> "time_ms": 15,
#> "pct": 21.4
#> },
#> {
#> "label": "result[i] <- sqrt(abs(x[i])) * 2",
#> "samples": 1,
#> "time_ms": 5,
#> "pct": 7.1
#> }
#> ],
#> "total_time": [
#> {
#> "label": "process_data",
#> "samples": 14,
#> "time_ms": 70,
#> "pct": 100
#> },
#> {
#> "label": "generate_data",
#> "samples": 13,
#> "time_ms": 65,
#> "pct": 92.9
#> },
#> {
#> "label": "rnorm",
#> "samples": 6,
#> "time_ms": 30,
#> "pct": 42.9
#> },
#> {
#> "label": "x[i] <- rnorm(1)",
#> "samples": 4,
#> "time_ms": 20,
#> "pct": 28.6
#> },
#> {
#> "label": "result[i] <- sqrt(abs(x[i])) * 2",
#> "samples": 1,
#> "time_ms": 5,
#> "pct": 7.1
#> },
#> {
#> "label": "transform_data",
#> "samples": 1,
#> "time_ms": 5,
#> "pct": 7.1
#> }
#> ],
#> "hot_lines": [
#> {
#> "location": "example_code.R:13",
#> "samples": 10,
#> "label": "rnorm",
#> "filename": "example_code.R",
#> "linenum": 13,
#> "time_ms": 50,
#> "pct": 71.4
#> },
#> {
#> "location": "example_code.R:5",
#> "samples": 3,
#> "label": "generate_data",
#> "filename": "example_code.R",
#> "linenum": 5,
#> "time_ms": 15,
#> "pct": 21.4
#> },
#> {
#> "location": "example_code.R:21",
#> "samples": 1,
#> "label": "result[i] <- sqrt(abs(x[i])) * 2",
#> "filename": "example_code.R",
#> "linenum": 21,
#> "time_ms": 5,
#> "pct": 7.1
#> }
#> ],
#> "memory": {
#> "by_function": [
#> {
#> "label": "rnorm",
#> "mem_mb": 1.37526702880859
#> },
#> {
#> "label": "x[i] <- rnorm(1)",
#> "mem_mb": 1.25504302978516
#> },
#> {
#> "label": "generate_data",
#> "mem_mb": 0.563003540039062
#> },
#> {
#> "label": "result[i] <- sqrt(abs(x[i])) * 2",
#> "mem_mb": 0.356483459472656
#> }
#> ],
#> "by_line": [
#> {
#> "location": "example_code.R:13",
#> "mem_mb": 2.63031005859375,
#> "label": "rnorm",
#> "filename": "example_code.R",
#> "linenum": 13
#> },
#> {
#> "location": "example_code.R:5",
#> "mem_mb": 0.563003540039062,
#> "label": "generate_data",
#> "filename": "example_code.R",
#> "linenum": 5
#> },
#> {
#> "location": "example_code.R:21",
#> "mem_mb": 0.356483459472656,
#> "label": "result[i] <- sqrt(abs(x[i])) * 2",
#> "filename": "example_code.R",
#> "linenum": 21
#> }
#> ]
#> },
#> "gc_pressure": [],
#> "suggestions": [
#> {
#> "priority": 1,
#> "category": "hot line",
#> "location": "example_code.R:13",
#> "action": "Optimize hot line (71.4%)",
#> "pattern": "rnorm",
#> "replacement": null,
#> "potential_impact": "50 ms (71.4%)"
#> },
#> {
#> "priority": 1,
#> "category": "hot line",
#> "location": "example_code.R:5",
#> "action": "Optimize hot line (21.4%)",
#> "pattern": "generate_data",
#> "replacement": null,
#> "potential_impact": "15 ms (21.4%)"
#> },
#> {
#> "priority": 1,
#> "category": "hot line",
#> "location": "example_code.R:21",
#> "action": "Optimize hot line (7.1%)",
#> "pattern": "result[i] <- sqrt(abs(x[i])) * 2",
#> "replacement": null,
#> "potential_impact": "5 ms (7.1%)"
#> },
#> {
#> "priority": 2,
#> "category": "hot function",
#> "location": "rnorm",
#> "action": "Profile in isolation (42.9% self-time)",
#> "pattern": "rnorm",
#> "replacement": null,
#> "potential_impact": "30 ms (42.9%)"
#> }
#> ],
#> "recursive": []
#> }
# Include only specific analyses
json <- pv_to_json(p, include = c("self_time", "hot_lines"))
# Include system info for reproducibility
json <- pv_to_json(p, system_info = TRUE)