The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Functiondepends - usage

library(functiondepends)
# Create environment for loaded functions 
envir <- new.env()
# Search recursively source files
functions <- find_functions(".", envir = envir, recursive = TRUE)
functions
#> # A tibble: 5 × 3
#>   Path  Function          SourceFile         
#>   <chr> <chr>             <chr>              
#> 1 R     find_dependencies find-dependencies.R
#> 2 R     is_function       find-functions.R   
#> 3 R     get_function_name find-functions.R   
#> 4 R     is_assign         find-functions.R   
#> 5 R     find_functions    find-functions.R

Search for dependencies of function find_functions within parsed functions:

dependency <- find_dependencies("find_functions", envir = envir, in_envir = TRUE)
dependency
#> # A tibble: 2 × 5
#>   Source            SourceRep SourceNamespace Target         TargetInDegree
#>   <chr>                 <int> <chr>           <chr>                   <int>
#> 1 get_function_name         1 user-defined    find_functions              2
#> 2 is_function               1 user-defined    find_functions              2

Note that SourceNamespace column has value user-defined as the functions are searched within source of the package.

Search for all dependencies of find_functions function:

library(ggplot2)
library(dplyr)

dependency <- find_dependencies("find_functions", envir = envir, in_envir = FALSE)
dependency %>% 
  slice_max(SourceRep, n = 10) %>% 
  mutate(Source = reorder(Source, SourceRep)) %>% 
  ggplot(aes(x = Source, y = SourceRep, fill = SourceNamespace)) +
  geom_col() +
  coord_flip() +
  labs(caption = "Top 10 most repeated calls in 'find_functions'.")

Note that name df is often used to store object of type data.frame. df is also a name of F distribution density function from stats package. If you suspect that given function ought not to use a specific package, see the source code of function to check the context. To do so, one can execute find_dependencies function with add_info argument set to TRUE.

library(tidyr)

dependency <- find_dependencies("find_functions", envir = envir, in_envir = FALSE, add_info = TRUE)
dependency %>% 
  filter(SourceNamespace == "stats") %>% 
  select(Source, SourcePosition, SourceContext) %>% 
  unnest(c(SourcePosition, SourceContext)) 
#> # A tibble: 6 × 3
#>   Source SourcePosition SourceContext                                           
#>   <chr>           <dbl> <chr>                                                   
#> 1 df                 10 "    df <- purrr::map_dfr(sourceFiles, function(file) {"
#> 2 df                 19 "    source_name <- basename(df$Path)"                  
#> 3 df                 21 "    df <- df %>% dplyr::mutate(Path = stringr::str_rem…
#> 4 df                 23 "        paths <- stringr::str_split(df$Path, \"/|\\\\\…
#> 5 df                 25 "        df <- tidyr::separate(df, \"Path\", into = pas…
#> 6 df                 27 "    df %>% dplyr::mutate(SourceFile = source_name)"

One can see that indeed df is not a call to function stats::df.

dependency <- find_dependencies(unique(functions$Function), envir = envir, in_envir = FALSE)
dependency %>% 
  distinct(Target, TargetInDegree) %>%
  mutate(Target = reorder(Target, TargetInDegree)) %>%
  ggplot(aes(x = Target, y = TargetInDegree)) +
  geom_col() +
  coord_flip() + 
  labs(caption = "Functions with most function calls.")

dependency <- find_dependencies(unique(functions$Function), envir = envir, in_envir = FALSE)
dependency %>% 
  group_by(SourceNamespace) %>% 
  tally(name = "Count") %>% 
  slice_max(Count, n = 10) %>% 
  mutate(SourceNamespace = reorder(SourceNamespace, Count)) %>% 
  ggplot(aes(x = SourceNamespace, y = Count)) +
  geom_col() +
  coord_flip() +
  labs(caption = "Top 10 used namespaces.")

See which user-defined functions depend most on other user-defined functions within searched codebase.

dependency <- find_dependencies(unique(functions$Function), envir = envir, in_envir = TRUE)
dependency %>% 
  distinct(Target, TargetInDegree) %>% 
  arrange(-TargetInDegree)
#> # A tibble: 5 × 2
#>   Target            TargetInDegree
#>   <chr>                      <dbl>
#> 1 find_functions                 2
#> 2 is_function                    1
#> 3 find_dependencies              0
#> 4 get_function_name              0
#> 5 is_assign                      0
library(igraph)

edges <- dependency %>% 
  select(Source, Target) %>% 
  na.omit()

vertices <- unique(c(dependency$Source, dependency$Target))
vertices <- vertices[!is.na(vertices)]

g <- graph_from_data_frame(d = edges, vertices = vertices)
deg <- degree(g, mode = "in")
V(g)$size <- deg * 10 + 5
V(g)$label.cex <- (degree(g, mode = "in", normalized = TRUE) + 1)

plot(
  g,
  vertex.color = "grey",
  edge.color = "grey",
  edge.arrow.size = .4,
  main = "Functions dependency graph"
)

dependency <- find_dependencies(unique(functions$Function), envir = envir, in_envir = FALSE)
edges <- dependency %>% 
  select(Source, Target) %>% 
  na.omit()

vertices <- unique(c(edges$Source, edges$Target))

g <- graph_from_data_frame(edges)
deg <- degree(g, mode = "in")
V(g)$size <- deg
V(g)$label.cex <- (degree(g, mode = "in", normalized = TRUE) + 1) / 1.8

plot(
  g,
  vertex.color = "grey",
  edge.color = "grey",
  edge.arrow.size = .4,
  main = "Full functions dependency graph"
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.