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R profvis package for interactive profiling. Use for visualizing R code profiling data.
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Based on SOC occupation classification
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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| name | profvis |
| description | R profvis package for interactive profiling. Use for visualizing R code profiling data. |
Interactive visualizations for profiling R code.
library(profvis)
# Profile code block
profvis({
# Code to profile
data <- read.csv("large_file.csv")
result <- lm(y ~ x, data = data)
summary(result)
})
# Save to file
p <- profvis({
# Code
})
# Save HTML
htmlwidgets::saveWidget(p, "profile.html")
# Profile a function
my_function <- function(n) {
x <- rnorm(n)
y <- x^2
mean(y)
}
profvis({
my_function(1e6)
})
# Adjust sampling interval (ms)
profvis({
# Code
}, interval = 0.01) # 10ms intervals
# Profile memory
profvis({
x <- 1:1e7
y <- x^2
rm(x)
gc()
})
# View as flame graph
p <- profvis({
# Code
})
# Interactive viewer shows:
# - Flame graph (call stack over time)
# - Data tab (detailed timing)
# - Source code highlighting
# Profile Shiny app
profvis({
runApp("myapp", display.mode = "normal")
}, interval = 0.01)
profvis({
# Profiled code
pause(FALSE) # Stop profiling
# Not profiled
pause(TRUE) # Resume profiling
# Profiled again
})
p <- profvis({
# Code
})
# Print summary
print(p)
# 1. Use small interval for short code
profvis({ fast_code() }, interval = 0.005)
# 2. Run multiple times for stable results
profvis({
for (i in 1:10) {
my_function()
}
})
# 3. Profile realistic workloads