rstudio | RStudio is an integrated development environment for R

by rstudio Java Version: Current License: Non-SPDX

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kandi X-RAY | rstudio Summary

rstudio is a Java library typically used in Editor applications. rstudio has high support. However rstudio has 11906 bugs, it has 1 vulnerabilities, it build file is not available and it has a Non-SPDX License. You can download it from GitHub.

RStudio is an integrated development environment (IDE) for R

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Quality

Security

License

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kandi-support Support

rstudio has a highly active ecosystem.
It has 3927 star(s) with 965 fork(s). There are 249 watchers for this library.
It had no major release in the last 12 months.
There are 1188 open issues and 5254 have been closed. On average issues are closed in 105 days. There are 10 open pull requests and 0 closed requests.
It has a negative sentiment in the developer community.
The latest version of rstudio is current.

rstudio Support

Best in #Java

Average in #Java

rstudio Support

Best in #Java

Average in #Java

quality kandi Quality

rstudio has 11906 bugs (0 blocker, 3 critical, 5585 major, 6318 minor) and 27695 code smells.

rstudio Quality

Best in #Java

Average in #Java

rstudio Quality

Best in #Java

Average in #Java

security Security

rstudio has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
rstudio code analysis shows 1 unresolved vulnerabilities (1 blocker, 0 critical, 0 major, 0 minor).
There are 11 security hotspots that need review.

rstudio Security

Best in #Java

Average in #Java

rstudio Security

Best in #Java

Average in #Java

license License

rstudio has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

rstudio License

Best in #Java

Average in #Java

rstudio License

Best in #Java

Average in #Java

build Reuse

rstudio releases are not available. You will need to build from source code and install.
rstudio has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.

rstudio Reuse

Best in #Java

Average in #Java

rstudio Reuse

Best in #Java

Average in #Java

Top functions reviewed by kandi - BETA

kandi has reviewed rstudio and discovered the below as its top functions. This is intended to give you an instant insight into rstudio implemented functionality, and help decide if they suit your requirements.

Sets a JSONArray value to a JSONArray object .
Called when a chunk was received .
Synchronize preferences from a layer .
Create a new project .
Called when a line widget is being displayed .
Gets the autocompletion context .
Parse a multi - line expression .
Initialize the workbench .
return null if invalid
Collects keyboard shortcuts .

rstudio Key Features

Customizable workbench with all of the tools required to work with R in one place (console, source, plots, workspace, help, history, etc.).

Syntax highlighting editor with code completion.

Execute code directly from the source editor (line, selection, or file).

Full support for authoring Sweave and TeX documents.

Runs on Windows, Mac, and Linux, and has a community-maintained [FreeBSD port](https://www.freshports.org/devel/RStudio/).

Can also be run as a server, enabling multiple users to access the RStudio IDE using a web browser.

COPYING - RStudio license (AGPLv3)

NOTICE - Additional open source software included with RStudio

SOURCE - How to obtain the source code for RStudio

INSTALL - How to build and install RStudio from source

VSCODE.md - How to get started with development using Visual Studio Code

rstudio Examples and Code Snippets

See all related Code Snippets

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RStudio is licensed under the AGPLv3, the terms of which are included in
the file COPYING. You can find our source code repository on GitHub at [https://github.com/rstudio/rstudio](https://github.com/rstudio/rstudio).

Documentation

Convert multiple lines to single line using RStudio text editor

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dput(c(
"097",
"085",
"041",
"055"
))
#> c("097", "085", "041", "055")

Is it possible to break 1 line of code into multiple in atom IDE, just like in Rstudio

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julia> println("Hello \
       world")
Hello world


julia> 1 + 
       2 + 
       3 * 
       4
15

julia> DEPOT_PATH |>
       first |> 
       readdir
16-element Vector{String}:
 "artifacts"
 "bin"
  ⋮

julia> 1,
       2,
       3
(1, 2, 3)

-----------------------

julia> println("Hello \
       world")
Hello world


julia> 1 + 
       2 + 
       3 * 
       4
15

julia> DEPOT_PATH |>
       first |> 
       readdir
16-element Vector{String}:
 "artifacts"
 "bin"
  ⋮

julia> 1,
       2,
       3
(1, 2, 3)

-----------------------

julia> println("""This
       is
       a multi-line \
       text""")
This
is
a multi-line text

How can I make a Shiny app W3C compliant?

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# Reprex adapted from https://shiny.rstudio.com/gallery/tabsets.html
library(shiny)
library(htmltools)

# Define UI for random distribution app ----
ui <- fluidPage(
  
  # App title ----
  titlePanel("Tabsets"),
  
  # Sidebar layout with input and output definitions ----
  sidebarLayout(
    
    # Sidebar panel for inputs ----
    {querySidebarPanel <- tagQuery(sidebarPanel(
      # Input: Select the random distribution type ----
      radioButtons("dist", "Distribution type:",
                   c("Normal" = "norm",
                     "Uniform" = "unif",
                     "Log-normal" = "lnorm",
                     "Exponential" = "exp")),
      
      # br() element to introduce extra vertical spacing ----
      br(),
      
      # Input: Slider for the number of observations to generate ----
      sliderInput("n",
                  "Number of observations:",
                  value = 500,
                  min = 1,
                  max = 1000)
    ))
    querySidebarPanel$find(".well")$removeAttrs("role")$addAttrs("role" = "none")$allTags()},
    
    # Main panel for displaying outputs ----
    mainPanel(
      
      # Output: Tabset w/ plot, summary, and table ----
      tabsetPanel(type = "tabs",
                  tabPanel("Plot", plotOutput("plot")),
                  tabPanel("Summary", verbatimTextOutput("summary")),
                  tabPanel("Table", tableOutput("table"))
      )
      
    )
  )
)

# Define server logic for random distribution app ----
server <- function(input, output) {
  
  # Reactive expression to generate the requested distribution ----
  d <- reactive({
    dist <- switch(input$dist,
                   norm = rnorm,
                   unif = runif,
                   lnorm = rlnorm,
                   exp = rexp,
                   rnorm)
    
    dist(input$n)
  })
  
  # Generate a plot of the data ----
  output$plot <- renderPlot({
    dist <- input$dist
    n <- input$n
    
    hist(d(),
         main = paste("r", dist, "(", n, ")", sep = ""),
         col = "#75AADB", border = "white")
  })
}

# Create Shiny app ----
shinyApp(ui, server)

Fast method of getting all the descendants of a parent

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library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

-----------------------

library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

-----------------------

library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

-----------------------

library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

-----------------------

library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

-----------------------

all_nodes <- unique(c(parent_id, child_id)  # all nodes
uid <- match(all_nodes, all_nodes)
pid <- match(parent_id, all_nodes)
cid <- match(child_id, all_nodes)

edge_list <- unname(split(cid, factor(pid, levels = uid)))
edge_lengths <- lengths(edge_list)

while (length(pid)) {
    pid <- rep(pid, edge_lengths[cid])
    cid <- unlist(edge_list[cid])
}

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE  # update nodes that we will now visit
        to_visit
    }
}

> visit = visitor(1:10)
> visit(1:3, 2:4)
[1] TRUE TRUE TRUE
> visit(2:4, 3:5)
[1] FALSE FALSE  TRUE

visitor <- function(uid, n_max = 3000) {
    n <- length(uid)
    if (n <= n_max) {
        ## over-allocate, to support `key = pid * n + cid`
        visited <- logical((n + 1L) * n) # FALSE on construction
    } else {
        stop("length(uid) greater than n_max = ", n_max)
    }
    function(pid, cid) {
        key <- pid * n + cid
        to_visit <- !(duplicated(key) | visited[key])
        visited[key[to_visit]] <<- TRUE
        to_visit
    }
}

ancestor_descendant <- function(df) {
    ## encode parent and child to unique integer values
    ids <- unique(c(df$parent_id, df$child_id))
    uid <- match(ids, ids)
    pid <- match(df$parent_id, ids)
    cid <- match(df$child_id, ids)
    n <- length(uid)

    ## edge list of parent-offspring relationships, based on unique
    ## integer values; list is ordered by id, all ids are present, ids
    ## without children have zero-length elements. Use `unname()` so
    ## that edge_list is always indexed by integer
    edge_list <- unname(split(cid, factor(pid, levels = uid), drop = FALSE))
    edge_lengths <- lengths(edge_list)

    visit <- visitor(uid)
    keep <- visit(uid, uid) # all TRUE
    aid = did = list(uid) # results -- all uid's are there own ancestor / descendant
    i = 1L
   
    while (length(pid)) {
        ## only add new edges
        keep <- visit(pid, cid)
        ## record current generation ancestors and descendants
        pid <- pid[keep]
        cid <- cid[keep]
        i <- i + 1L
        aid[[i]] <- pid
        did[[i]] <- cid

        ## calculate next generation pid and cid.
        pid <- rep(pid, edge_lengths[cid])
        cid <- unlist(edge_list[cid])
    }
    ## decode results to original ids and clean up return value
    df <- data.frame(
        ancestor_id = ids[unlist(aid)],
        descendant_id = ids[unlist(did)]
    )
    df <- df[order(df$ancestor_id, df$descendant_id),]
    rownames(df) <- NULL
    df
}

## Original example
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df = df[sample(nrow(df)),]
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.243   0.001   0.245 
dim(result)
## [1] 501501      2

## updated example from comments
df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L
df <- rbind(df, data.frame(parent_id = 1000L, child_id = 1002L))
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.195   0.001   0.195 
dim(result)
## [1] 502502      2

## problematic case from @jblood94
df <- data.frame(
    parent_id=c(1, 1, 2),
    child_id = c(2, 3, 3)
)
ancestor_descendant(df)
##   ancestor_id descendant_id
## 1           1             1
## 2           1             2
## 3           1             3
## 4           2             2
## 5           2             3
## 6           3             3

## previously failed without filtering re-visited nodes
df <- data.frame(
    parent_id = rep(1:100, each = 2),
    child_id = c(2, rep(3:101, each = 2), 102)
)
system.time(result <- ancestor_descendant(df))
##  user  system elapsed 
## 0.005   0.000   0.006 
dim(result)
## [1] 5252    2

How to extract all code from an RMarkdown (.Rmd) file?

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knitr::purl(input = "Report.Rmd", output = "Report.R",documentation = 0)

knitr::opts_chunk$set(echo = TRUE)

a = 1
print(a)
summary(cars)

plot(pressure)

-----------------------

knitr::purl(input = "Report.Rmd", output = "Report.R",documentation = 0)

knitr::opts_chunk$set(echo = TRUE)

a = 1
print(a)
summary(cars)

plot(pressure)

-----------------------

```{r setup, include=FALSE}
knitr::knit_hooks$set(purl = knitr::hook_purl)
knitr::opts_chunk$set(echo = TRUE)
```

Convincing R that the .dbf file associated with a .shp file is not an executable during command checks

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fn = "myfile.dbf"
sz = file.info(fn)$size
r = readBin(fn, raw(), sz)
r[2] = as.raw(121) ## make it 2021 instead of 2022
writeBin(r, fn)

Equivalent of the Rstudio `browser()` function in Julia for debugging

Copy

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julia> using Debugger

julia> @run for i in 1:5
           println(i)
           if i > 3
               @bp
           end
       end
1
2
3
4
Hit breakpoint:
In ##thunk#257() at REPL[4]:1
  9  │         Base.println(i)
 10  │   %10 = i > 3
 11  └──       goto #4 if not %10
●12  3 ─       nothing
>13  4 ┄       @_2 = Base.iterate(%1, %8)
 14  │   %14 = @_2 === nothing
 15  │   %15 = ($(QuoteNode(Core.Intrinsics.not_int)))(%14)
 16  └──       goto #6 if not %15
 17  5 ─       goto #2

About to run: (iterate)(1:5, 4)
1|debug>

-----------------------

julia> using Infiltrator

julia> for i in 1:5
         println(i)
         if i > 3
           @infiltrate
         end
       end
1
2
3
4
Infiltrating top-level scope at REPL[1]:4:

infil> i
4

Tensorflow setup on RStudio/ R | CentOS

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sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

Configuring compilers on Mac M1 (Big Sur, Monterey) for Rcpp and other tools

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$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

-----------------------

$ sudo xcode-select --install

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

Why is this task faster in Python than Julia?

Copy

Download

arr = reduce(vcat, eachrow(Matrix(string.(df[2:570, 1:3198]))))

-----------------------

function tostringvector(d)
    r, c = size(d)
    result = Vector{String}(undef, r*c)
    v = reshape(result, r, c)
    for (rcol, dcol) in zip(eachcol(v), eachcol(d))
        @inbounds rcol .= string.(dcol)
    end
    return result
end

tostringvector(d) = vec(Matrix(string.(d)))

-----------------------

function tostringvector(d)
    r, c = size(d)
    result = Vector{String}(undef, r*c)
    v = reshape(result, r, c)
    for (rcol, dcol) in zip(eachcol(v), eachcol(d))
        @inbounds rcol .= string.(dcol)
    end
    return result
end

tostringvector(d) = vec(Matrix(string.(d)))

See all related Code Snippets

Community Discussions

Trending Discussions on rstudio

Fastest way to edit multiple lines of code at the same time
Convert multiple lines to single line using RStudio text editor
Is it possible to break 1 line of code into multiple in atom IDE, just like in Rstudio
How can I make a Shiny app W3C compliant?
Fast method of getting all the descendants of a parent
How to extract all code from an RMarkdown (.Rmd) file?
Convincing R that the .dbf file associated with a .shp file is not an executable during command checks
Equivalent of the Rstudio `browser()` function in Julia for debugging
Tensorflow setup on RStudio/ R | CentOS
Configuring compilers on Mac M1 (Big Sur, Monterey) for Rcpp and other tools

Trending Discussions on rstudio

QUESTION

Fastest way to edit multiple lines of code at the same time

Asked 2022-Mar-18 at 15:52

What is the best way to do the same action across multiple lines of code in the RStudio source editor?

Example 1

Let's say that I copy a list from a text file and paste it into R (like the list below). Then, I want to add quotation marks around each word and add a comma to each line, so that I can make a vector.

Krista Hicks
Miriam Cummings
Ralph Lamb
Jaylene Gilbert
Jordon Sparks
Kenna Melton

Expected Output

"Krista Hicks",
"Miriam Cummings",
"Ralph Lamb",
"Jaylene Gilbert",
"Jordon Sparks",
"Kenna Melton"

Example 2

How can I add missing parentheses on multiple lines. For example, if I have an if statement, then how can I add the missing opening parentheses for names on line 1 and line 4.

if (!is.null(names pattern))) {
  vec <- FALSE
  replacement <- unname(pattern)
  pattern[] <- names pattern)
}

Expected Output

if (!is.null(names(pattern))) {
  vec <- FALSE
  replacement <- unname(pattern)
  pattern[] <- names(pattern)
}

*Note: These names are just from a random name generator.

ANSWER

Answered 2022-Mar-16 at 16:20

RStudio has support for multiple cursors, which allows you to write and edit multiple lines at the same time.

Example 1

You can simply click Alt on Windows/Linux (or option on Mac) and drag your mouse to make your selection, or you can use Alt+Shift to create a rectangular selection from the current location of the cursor to a clicked position.

Example 2

Another multiple cursor option is for selecting all matching instances of a term. So, you can select names and press Ctrl+Alt+Shift+M. Then, you can use the arrow keys to move the cursors to delete the space and add in the parentheses.

Source https://stackoverflow.com/questions/71472412

QUESTION

How can I make a Shiny app W3C compliant?

Asked 2022-Mar-04 at 08:05

I've written and optimized a Shiny app, and now I'm struggling with the IT section of the organization where I work to have it published on their servers. Currently, they are claiming that the app is not W3C compliant, which is true, according to the W3C validator.

The errors I'm trying to solve, with no success, are:

<form class="well" role="complementary"> Bad value “complementary” for attribute “role” on element “form”.
<label class="control-label" id="foo-label" for="foo"> The value of the “for” attribute of the “label” element must be the ID of a non-hidden form control.

Such errors can be seen also in very minimal shiny apps, like:

# Reprex adapted from https://shiny.rstudio.com/gallery/tabsets.html
library(shiny)

# Define UI for random distribution app ----
ui <- fluidPage(
  
  # App title ----
  titlePanel("Tabsets"),
  
  # Sidebar layout with input and output definitions ----
  sidebarLayout(
    
    # Sidebar panel for inputs ----
    sidebarPanel(
      
      # Input: Select the random distribution type ----
      radioButtons("dist", "Distribution type:",
                   c("Normal" = "norm",
                     "Uniform" = "unif",
                     "Log-normal" = "lnorm",
                     "Exponential" = "exp")),
      
      # br() element to introduce extra vertical spacing ----
      br(),
      
      # Input: Slider for the number of observations to generate ----
      sliderInput("n",
                  "Number of observations:",
                  value = 500,
                  min = 1,
                  max = 1000)
      
    ),
    
    # Main panel for displaying outputs ----
    mainPanel(
      
      # Output: Tabset w/ plot, summary, and table ----
      tabsetPanel(type = "tabs",
                  tabPanel("Plot", plotOutput("plot")),
                  tabPanel("Summary", verbatimTextOutput("summary")),
                  tabPanel("Table", tableOutput("table"))
      )
      
    )
  )
)

# Define server logic for random distribution app ----
server <- function(input, output) {
  
  # Reactive expression to generate the requested distribution ----
  d <- reactive({
    dist <- switch(input$dist,
                   norm = rnorm,
                   unif = runif,
                   lnorm = rlnorm,
                   exp = rexp,
                   rnorm)
    
    dist(input$n)
  })
  
  # Generate a plot of the data ----
  output$plot <- renderPlot({
    dist <- input$dist
    n <- input$n
    
    hist(d(),
         main = paste("r", dist, "(", n, ")", sep = ""),
         col = "#75AADB", border = "white")
  })
}

# Create Shiny app ----
shinyApp(ui, server)

The second error seems to be related, somehow, to radiobuttons only, whereas the first one seems to afflict all the shiny apps I've found on the web and tested with the W3C validator so far. For completeness, I report also the HTML code generated by the shiny app in the reprex:

<!DOCTYPE html>
<html>
<head>
  <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
  <script type="application/shiny-singletons"></script>
  <script type="application/html-dependencies">jquery[3.6.0];shiny-css[1.7.1];shiny-javascript[1.7.1];ionrangeslider-javascript[2.3.1];strftime[0.9.2];ionrangeslider-css[2.3.1];bootstrap[3.4.1]</script>
<script src="shared/jquery.min.js"></script>
<link href="shared/shiny.min.css" rel="stylesheet" />
<script src="shared/shiny.min.js"></script>
<script src="shared/ionrangeslider/js/ion.rangeSlider.min.js"></script>
<script src="shared/strftime/strftime-min.js"></script>
<link href="shared/ionrangeslider/css/ion.rangeSlider.css" rel="stylesheet" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="shared/bootstrap/css/bootstrap.min.css" rel="stylesheet" />
<link href="shared/bootstrap/accessibility/css/bootstrap-accessibility.min.css" rel="stylesheet" />
<script src="shared/bootstrap/js/bootstrap.min.js"></script>
<script src="shared/bootstrap/accessibility/js/bootstrap-accessibility.min.js"></script>  <title>Tabsets</title>
</head>
<body>
  <div class="container-fluid">
    <h2>Tabsets</h2>
    <div class="row">
      <div class="col-sm-4">
        <form class="well" role="complementary">
          <div id="dist" class="form-group shiny-input-radiogroup shiny-input-container" role="radiogroup" aria-labelledby="dist-label">
            <label class="control-label" id="dist-label" for="dist">Distribution type:</label>
            <div class="shiny-options-group">
              <div class="radio">
                <label>
                  <input type="radio" name="dist" value="norm" checked="checked"/>
                  <span>Normal</span>
                </label>
              </div>
              <div class="radio">
                <label>
                  <input type="radio" name="dist" value="unif"/>
                  <span>Uniform</span>
                </label>
              </div>
              <div class="radio">
                <label>
                  <input type="radio" name="dist" value="lnorm"/>
                  <span>Log-normal</span>
                </label>
              </div>
              <div class="radio">
                <label>
                  <input type="radio" name="dist" value="exp"/>
                  <span>Exponential</span>
                </label>
              </div>
            </div>
          </div>
          <br/>
          <div class="form-group shiny-input-container">
            <label class="control-label" id="n-label" for="n">Number of observations:</label>
            <input class="js-range-slider" id="n" data-skin="shiny" data-min="1" data-max="1000" data-from="500" data-step="1" data-grid="true" data-grid-num="9.99" data-grid-snap="false" data-prettify-separator="," data-prettify-enabled="true" data-keyboard="true" data-data-type="number"/>
          </div>
        </form>
      </div>
      <div class="col-sm-8" role="main">
        <div class="tabbable">
          <ul class="nav nav-tabs" data-tabsetid="9747">
            <li class="active">
              <a href="#tab-9747-1" data-toggle="tab" data-bs-toggle="tab" data-value="Plot">Plot</a>
            </li>
            <li>
              <a href="#tab-9747-2" data-toggle="tab" data-bs-toggle="tab" data-value="Summary">Summary</a>
            </li>
            <li>
              <a href="#tab-9747-3" data-toggle="tab" data-bs-toggle="tab" data-value="Table">Table</a>
            </li>
          </ul>
          <div class="tab-content" data-tabsetid="9747">
            <div class="tab-pane active" data-value="Plot" id="tab-9747-1">
              <div id="plot" class="shiny-plot-output" style="width:100%;height:400px;"></div>
            </div>
            <div class="tab-pane" data-value="Summary" id="tab-9747-2">
              <pre class="shiny-text-output noplaceholder" id="summary"></pre>
            </div>
            <div class="tab-pane" data-value="Table" id="tab-9747-3">
              <div id="table" class="shiny-html-output"></div>
            </div>
          </div>
        </div>
      </div>
    </div>
  </div>
</body>
</html>

Do you have any suggestions? Thanks!

This message was also posted on RStudio Community: sorry for cross-posting.

ANSWER

Answered 2022-Mar-04 at 08:05

The following only deals with the first of the errors you mention (as this one is pretty clear thanks to @BenBolkers comment), but hopefully it points you to the right tools to use.

I'd use htmltools::tagQuery to make the needed modifications - please check the following:

# Reprex adapted from https://shiny.rstudio.com/gallery/tabsets.html
library(shiny)
library(htmltools)

# Define UI for random distribution app ----
ui <- fluidPage(
  
  # App title ----
  titlePanel("Tabsets"),
  
  # Sidebar layout with input and output definitions ----
  sidebarLayout(
    
    # Sidebar panel for inputs ----
    {querySidebarPanel <- tagQuery(sidebarPanel(
      # Input: Select the random distribution type ----
      radioButtons("dist", "Distribution type:",
                   c("Normal" = "norm",
                     "Uniform" = "unif",
                     "Log-normal" = "lnorm",
                     "Exponential" = "exp")),
      
      # br() element to introduce extra vertical spacing ----
      br(),
      
      # Input: Slider for the number of observations to generate ----
      sliderInput("n",
                  "Number of observations:",
                  value = 500,
                  min = 1,
                  max = 1000)
    ))
    querySidebarPanel$find(".well")$removeAttrs("role")$addAttrs("role" = "none")$allTags()},
    
    # Main panel for displaying outputs ----
    mainPanel(
      
      # Output: Tabset w/ plot, summary, and table ----
      tabsetPanel(type = "tabs",
                  tabPanel("Plot", plotOutput("plot")),
                  tabPanel("Summary", verbatimTextOutput("summary")),
                  tabPanel("Table", tableOutput("table"))
      )
      
    )
  )
)

# Define server logic for random distribution app ----
server <- function(input, output) {
  
  # Reactive expression to generate the requested distribution ----
  d <- reactive({
    dist <- switch(input$dist,
                   norm = rnorm,
                   unif = runif,
                   lnorm = rlnorm,
                   exp = rexp,
                   rnorm)
    
    dist(input$n)
  })
  
  # Generate a plot of the data ----
  output$plot <- renderPlot({
    dist <- input$dist
    n <- input$n
    
    hist(d(),
         main = paste("r", dist, "(", n, ")", sep = ""),
         col = "#75AADB", border = "white")
  })
}

# Create Shiny app ----
shinyApp(ui, server)

Please also see this related chapter from "Outstanding User Interfaces with Shiny".

Source https://stackoverflow.com/questions/71343768

QUESTION

Fast method of getting all the descendants of a parent

Asked 2022-Feb-25 at 08:17

With the parent-child relationships data frame as below:

  parent_id child_id
1         1        2
2         2        3
3         3        4

The goal is to achieve the following, i.e. expanded version of previous data frame where all the descendants (children, grandchildren, etc.) are assigned to each parent (and incl. the parent/child itself):

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

The question I have: What is the fastest possible way (or one of them) of achieving that in R?

I've already tried various methods - from a for loop, SQL recursion to using igraph (as described here). They are all rather slow, and some of them are also prone to crashing when dealing with a larger number of combinations.

Below are examples with sqldf and igraph, benchmarked on a slightly larger data frame than above.

library(sqldf)
library(purrr)
library(dplyr)
library(igraph)

df <- data.frame(parent_id = 1:1000L)
df$child_id <- df$parent_id + 1L

# SQL recursion

sqlQuery <- 'with recursive
             dfDescendants (parent_id, child_id)
             as
             (select parent_id, child_id from df
             union all
             select d.parent_id, s.child_id from dfDescendants d
             join df s
             on d.child_id = s.parent_id)
             select distinct parent_id, parent_id as child_id from dfDescendants
             union
             select distinct child_id as parent_id, child_id from dfDescendants
             union
             select * from dfDescendants;'

sqldf(sqlQuery)

# igraph with purrr

df_g = graph_from_data_frame(df, directed = TRUE)

map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>% 
  map_df(~ data.frame(child_id = .x), .id = "parent_id")

Benchmark (excl. query creation in sqldf and conversion to graph in igraph):

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>% 
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  times = 5
)

#    Unit: seconds
#           expr      min       lq     mean   median       uq      max neval
#          sqldf 7.815179 8.002836 8.113392 8.084038 8.315207 8.349701     5
#     tidyigraph 5.784239 5.806539 5.883241 5.889171 5.964906 5.971350     5

ANSWER

Answered 2022-Feb-25 at 08:17

We can use ego like below

library(igraph)
df <- data.frame(parent_id = 1:3, child_id = 2:4)
g <- graph_from_data_frame(df)

setNames(
  rev(
    stack(
      Map(
        names,
        setNames(
          ego(g,
            order = vcount(g),
            mode = "out"
          ),
          names(V(g))
        )
      )
    )
  ),
  names(df)
)

which gives

   parent_id child_id
1          1        1
2          1        2
3          1        3
4          1        4
5          2        2
6          2        3
7          2        4
8          3        3
9          3        4
10         4        4

Benchmarking

set.seed(23423)

microbenchmark::microbenchmark(
  sqldf = sqldf(sqlQuery),
  tidyigraph = map(V(df_g), ~ names(subcomponent(df_g, .x, mode = "out"))) %>%
    map_df(~ data.frame(child_id = .x), .id = "parent_id"),
  ego = setNames(
    rev(
      stack(
        Map(
          names,
          setNames(
            ego(df_g,
              order = vcount(df_g),
              mode = "out"
            ),
            names(V(df_g))
          )
        )
      )
    ),
    names(df)
  ),
  times = 5
)

shows

Unit: milliseconds
       expr       min       lq      mean    median         uq        max neval
      sqldf 7156.2753 9072.155 9402.6904 9518.2796 10206.3683 11060.3738     5
 tidyigraph 2483.9943 2623.558 3136.7490 2689.8388  2879.5688  5006.7853     5
        ego  182.5941  219.151  307.2481  253.2171   325.8721   555.4064     5

The code using pipes for readability :

g |>
  ego(order = vcount(g), mode = "out") |>
  setNames(names(V(g))) |>
  Map(f = names) |>
  stack() |>
  rev() |>
  setNames(names(df))

Source https://stackoverflow.com/questions/71022350

QUESTION

Tensorflow setup on RStudio/ R | CentOS

Asked 2022-Feb-11 at 09:36

For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?

Following are the steps I am using to install tensorflow in RStudio.

Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.

devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/keras") # This package also installs tensorflow
library(reticulate)
reticulate::install_miniconda()
reticulate::use_miniconda("r-reticulate")
library(tensorflow)
tensorflow::tf_config() **# Crashes at this point**

sessionInfo()


R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
[17] base64enc_0.1-3

Update 1 The only way RStudio does not crash while installing tensorflow is by executing following steps -

First, I created a new virtual environment using conda

conda create --name py38 python=3.8.0
conda activate py38
conda install tensorflow=2.4

Then from within RStudio, I installed reticulate and activated the virtual environment which I earlier created using conda

devtools::install_github("rstudio/reticulate")
library(reticulate)
reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
reticulate::py_available(initialize = TRUE)
ts <- reticulate::import("tensorflow")

As soon as I try to import tensorflow in RStudio, it loads the library /lib64/libstdc++.so.6 instead of /root/.conda/envs/py38/lib/libstdc++.so.6 and I get the following error -

Error in py_module_import(module, convert = convert) : 
  ImportError: Traceback (most recent call last):
  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
    module = _import(
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/errors

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

Here is what inside /lib64/libstdc++.so.6

> strings /lib64/libstdc++.so.6 | grep GLIBC

GLIBCXX_3.4
GLIBCXX_3.4.1
GLIBCXX_3.4.2
GLIBCXX_3.4.3
GLIBCXX_3.4.4
GLIBCXX_3.4.5
GLIBCXX_3.4.6
GLIBCXX_3.4.7
GLIBCXX_3.4.8
GLIBCXX_3.4.9
GLIBCXX_3.4.10
GLIBCXX_3.4.11
GLIBCXX_3.4.12
GLIBCXX_3.4.13
GLIBCXX_3.4.14
GLIBCXX_3.4.15
GLIBCXX_3.4.16
GLIBCXX_3.4.17
GLIBCXX_3.4.18
GLIBCXX_3.4.19
GLIBC_2.3
GLIBC_2.2.5
GLIBC_2.14
GLIBC_2.4
GLIBC_2.3.2
GLIBCXX_DEBUG_MESSAGE_LENGTH

To resolve the library issue, I added the path of the correct libstdc++.so.6 library having GLIBCXX_3.4.20 in RStudio.

system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')

and, also

Sys.setenv("LD_LIBRARY_PATH" = "/root/.conda/envs/py38/lib")

But still I get the same error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20'. Somehow RStudio still loads /lib64/libstdc++.so.6 first instead of /root/.conda/envs/py38/lib/libstdc++.so.6

Instead of RStudio, if I execute the above steps in the R console, then also I get the exact same error.

Update 2: A solution is posted here

ANSWER

Answered 2022-Jan-16 at 00:08

Perhaps my failed attempts will help someone else solve this problem; my approach:

boot up a clean CentOS 7 vm
install R and some dependencies

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

Download and install Anaconda via linux installer script
Create a new conda env

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

**From within this conda env you can import tensorflow in python without error; now to access tf via R

install an updated gcc via devtoolset

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

attempt to use tensorflow in R via the reticulate package

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

try older versions of tensorflow/keras

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

Try an updated version of R (v4.0)

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

I guess the issue is with R/CentOS, as you can import and use tensorflow via python normally, but I'm not sure what else to try.

I would also like to say that I had no issues with Ubuntu (which is specifically supported by tensorflow, along with macOS and Windows), and I came across these docs that might be some help: https://wiki.hpcc.msu.edu/display/ITH/Installing+TensorFlow+using+anaconda / https://wiki.hpcc.msu.edu/pages/viewpage.action?pageId=22709999

Source https://stackoverflow.com/questions/70645074

QUESTION

Configuring compilers on Mac M1 (Big Sur, Monterey) for Rcpp and other tools

Asked 2022-Feb-10 at 21:07

I'm trying to use packages that require Rcpp in R on my M1 Mac, which I was never able to get up and running after purchasing this computer. I updated it to Monterey in the hope that this would fix some installation issues but it hasn't. I tried running the Rcpp check from this page but I get the following error:

> Rcpp::sourceCpp("~/github/helloworld.cpp")

ld: warning: directory not found for option '-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0'
ld: warning: directory not found for option '-L/opt/R/arm64/gfortran/lib'
ld: library not found for -lgfortran
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make: *** [sourceCpp_4.so] Error 1
clang++ -arch arm64 -std=gnu++14 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include   -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/Rcpp/include" -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppArmadillo/include" -I"/Users/afredston/github" -I/opt/R/arm64/include   -fPIC  -falign-functions=64 -Wall -g -O2  -c helloworld.cpp -o helloworld.o
clang++ -arch arm64 -std=gnu++14 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/arm64/lib -o sourceCpp_4.so helloworld.o -L/Library/Frameworks/R.framework/Resources/lib -lRlapack -L/Library/Frameworks/R.framework/Resources/lib -lRblas -L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
Error in Rcpp::sourceCpp("~/github/helloworld.cpp") : 
  Error 1 occurred building shared library.

I get that it can't "find" gfortran. I installed this release of gfortran for Monterey. When I type which gfortran into Terminal, it returns /opt/homebrew/bin/gfortran. (Maybe this version of gfortran requires Xcode tools that are too new—it says something about 13.2 and when I run clang --version it says 13.0—but I don't see another release of gfortran for Monterey?)

I also appended /opt/homebrew/bin: to PATH in R so it looks like this now:

> Sys.getenv("PATH")

[1] "/opt/homebrew/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/Library/TeX/texbin:/Applications/RStudio.app/Contents/MacOS/postback"

Other things I checked:

Xcode command line tools is installed (which clang returns /usr/bin/clang).
Files ~/.R/Makevars and ~/.Renviron don't exist.

Here's my session info:

R version 4.1.1 (2021-08-10)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
[1] compiler_4.1.1           tools_4.1.1              RcppArmadillo_0.10.7.5.0
[4] Rcpp_1.0.7

ANSWER

Answered 2022-Feb-10 at 21:07

Background

Currently (2022-02-05), CRAN builds R binaries for Apple silicon using Apple clang (from Command Line Tools for Xcode 12.4) and an experimental build of gfortran.

If you obtain R from CRAN (i.e., here), then you need to replicate CRAN's compiler setup on your system before building R packages that contain C/C++/Fortran code from their sources (and before using Rcpp, etc.). This requirement ensures that your package builds are compatible with R itself.

A further complication is the fact that Apple clang doesn't support OpenMP, so you need to do even more work to compile programs that make use of multithreading. You could circumvent the issue by building R itself and all R packages from sources with LLVM clang, which does support OpenMP, but this approach is onerous and "for experts only". There is another approach that has been tested by a few people, including Simon Urbanek, the maintainer of R for macOS. It is experimental and also "for experts only", but seems to work on my machine and is simpler than trying to build R yourself.

Instructions for obtaining a working toolchain

Warning: These instructions come with no warranty and could break at any time. They assume some level of familiarity with C/C++/Fortran program compilation, Makefile syntax, and Unix shells. As usual, sudo at your own risk.

I will try to address compilers and OpenMP support at the same time. I am going to assume that you are starting from nothing. Feel free to skip steps you've already taken, though you might find a fresh start helpful.

I've tested these instructions on a machine running Big Sur, and at least one person has tested them on a machine running Monterey. I would be glad to hear from others.

Download an R binary from CRAN here and install. Be sure to select the binary built for Apple silicon.
Run

$ sudo xcode-select --install

in Terminal to install the latest release version of Apple's Command Line Tools for Xcode, which includes Apple clang. You can obtain earlier versions from your browser here. The version that you install should not be older than the one that CRAN used to build your R binary.

Download the gfortran binary recommended here and install by unpacking to root:

$ wget https://mac.r-project.org/libs-arm64/gfortran-f51f1da0-darwin20.0-arm64.tar.gz
$ sudo tar xvf gfortran-f51f1da0-darwin20.0-arm64.tar.gz -C /
$ sudo ln -sfn $(xcrun --show-sdk-path) /opt/R/arm64/gfortran/SDK

The last command updates a symlink inside of the gfortran installation so that it points to the SDK inside of your Command Line Tools installation.

Download an OpenMP runtime suitable for your Apple clang version here and install by unpacking to root. You can query your Apple clang version with clang --version. For example, I have version 1300.0.29.30, so I did:

$ wget https://mac.r-project.org/openmp/openmp-12.0.1-darwin20-Release.tar.gz
$ sudo tar xvf openmp-12.0.1-darwin20-Release.tar.gz -C /

After unpacking, you should find these files on your system:

/usr/local/lib/libomp.dylib
/usr/local/include/ompt.h
/usr/local/include/omp.h
/usr/local/include/omp-tools.h

Add the following lines to $(HOME)/.R/Makevars, creating the file if necessary.

CPPFLAGS+=-I/usr/local/include -Xclang -fopenmp
LDFLAGS+=-L/usr/local/lib -lomp

FC=/opt/R/arm64/gfortran/bin/gfortran -mtune=native
FLIBS=-L/opt/R/arm64/gfortran/lib/gcc/aarch64-apple-darwin20.2.0/11.0.0 -L/opt/R/arm64/gfortran/lib -lgfortran -lemutls_w -lm

Run R and test that you can compile a program with OpenMP support. For example:

if (!requireNamespace("RcppArmadillo", quietly = TRUE)) {
    install.packages("RcppArmadillo")
}
Rcpp::sourceCpp(code = '
#include <RcppArmadillo.h>
#ifdef _OPENMP
# include <omp.h>
#endif

// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
void omp_test()
{
#ifdef _OPENMP
    Rprintf("OpenMP threads available: %d\\n", omp_get_max_threads());
#else
    Rprintf("OpenMP not supported\\n");
#endif
}
')
omp_test()

OpenMP threads available: 8

If the C++ code fails to compile, or if it compiles without error but you get linker warnings or you find that OpenMP is not supported, then something is likely wrong. Please report any issues.

References

Everything is a bit scattered:

R Installation and Administration manual [link]
R for macOS Developers page [link]

Source https://stackoverflow.com/questions/70638118

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install rstudio

You can download it from GitHub.
You can use rstudio like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the rstudio component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .