forestgeo / fgeo

[Meta R-package on CRAN] Analyse forest diversity and dynamics

Analyze forest diversity and dynamics

fgeo helps you to install, load, and access the documentation of multiple packages to analyze forest diversity and dynamics (fgeo.analyze, fgeo.plot, fgeo.tool, fgeo.x). This package-collection allows you to manipulate and plot ForestGEO data, and to do common analyses including abundance, demography, and species-habitats associations.

• Search functions and datasets
• Ask questions, report bugs, or propose features

Installation

Make sure your R environment is as follows:

• R version is recent
• All packages are updated (run update.packages(); maybe use ask = FALSE)
• No other R session is running
• Current R session is clean (click Session > Restart R)

Install the latest stable version of fgeo from CRAN with:

install.packages("fgeo")

Or install the development version of fgeo from GitHub with:

# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.x")

• How to setup .Rprofile for easiest installation of fgeo?
• How to update fgeo?
• How to remove fgeo?
• How to avoid or fix common installation problems?

Example

library(fgeo)
#> ── Attaching packages ────────────────────────────────────────── fgeo 1.1.3.9000 ──
#> ✔ fgeo.analyze 1.1.10.9000     ✔ fgeo.tool    1.2.5      
#> ✔ fgeo.plot    1.1.8           ✔ fgeo.x       1.1.4
#> ── Conflicts ────────────────────────────────────────────────── fgeo_conflicts() ──
#> ✖ fgeo.tool::filter() masks stats::filter()

Explore fgeo

On an interactive session, fgeo_help() and fgeo_browse_reference() help you to search documentation.

if (interactive()) {
  # To search on the viewer; accepts keywords
  fgeo_help()
  # To search on a web browser
  fgeo_browse_reference() 
}

Access and manipulate data

example_path() allows you to access datasets stored in your R libraries.

example_path()
#>  [1] "csv"           "mixed_files"   "rdata"         "rdata_one"    
#>  [5] "rds"           "taxa.csv"      "tsv"           "vft_4quad.csv"
#>  [9] "view"          "weird"         "xl"

(vft_file <- example_path("view/vft_4quad.csv"))
#> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/3.6/fgeo.x/extdata/view/vft_4quad.csv"

read_<table>()

read_vft() and read_taxa() import a ViewFullTable and ViewTaxonomy from .tsv or .csv files.

read_vft(vft_file)
#> # A tibble: 500 x 32
#>     DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies
#>     <int> <chr>     <int> <chr>  <chr> <chr>       <chr>    <chr>     
#>  1 385164 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  2 385261 luquillo      1 Urtic… Cecr… schreberia… CECSCH   <NA>      
#>  3 384600 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  4 608789 luquillo      1 Rubia… Psyc… berteroana  PSYBER   <NA>      
#>  5 388579 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>      
#>  6 384626 luquillo      1 Arali… Sche… morototoni  SCHMOR   <NA>      
#>  7 410958 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  8 385102 luquillo      1 Piper… Piper glabrescens PIPGLA   <NA>      
#>  9 353163 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>      
#> 10 481018 luquillo      1 Salic… Case… arborea     CASARB   <NA>      
#> # … with 490 more rows, and 24 more variables: SpeciesID <int>,
#> #   SubspeciesID <chr>, QuadratName <chr>, QuadratID <int>, PX <dbl>,
#> #   PY <dbl>, QX <dbl>, QY <dbl>, TreeID <int>, Tag <chr>, StemID <int>,
#> #   StemNumber <int>, StemTag <int>, PrimaryStem <chr>, CensusID <int>,
#> #   PlotCensusNumber <int>, DBH <dbl>, HOM <dbl>, ExactDate <date>,
#> #   Date <int>, ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>,
#> #   Status <chr>

pick_<what>() and drop_<what>()

fgeo is pipe-friendly. You may not use pipes but often they make code easier to read.

Use %>% to emphasize a sequence of actions, rather than the object that the actions are being performed on.

– https://style.tidyverse.org/pipes.html

pick_dbh_under(), drop_status() and friends pick and drop rows from a ForestGEO ViewFullTable or census table.

(census <- fgeo.x::tree5)
#> # A tibble: 30 x 19
#>    treeID stemID tag   StemTag sp    quadrat    gx    gy MeasureID CensusID
#>     <int>  <int> <chr> <chr>   <chr> <chr>   <dbl> <dbl>     <int>    <int>
#>  1   7624 160987 1089… 175325  TRIP… 722     139.  425.     486675        5
#>  2   8055  10036 1094… 109482  CECS… 522      94.8 424.     468874        5
#>  3  19930 117849 1234… 165576  CASA… 425      61.3 496.     471979        5
#>  4  23746  29677 14473 14473   PREM… 617     100.  328.     442571        5
#>  5  31702  39793 22889 22889   SLOB… 304      53.8  73.8    447307        5
#>  6  35355  44026 27538 27538   SLOB… 1106    203.  110.     449169        5
#>  7  35891  44634 282   282     DACE… 901     172.   14.7    434266        5
#>  8  39705  48888 33371 33370   CASS… 1010    184.  194.     451067        5
#>  9  50184  60798 5830  5830    MATD… 1007    191.  132.     437645        5
#> 10  57380 155867 66962 171649  SLOB… 1414    274.  279.     459427        5
#> # … with 20 more rows, and 9 more variables: dbh <dbl>, pom <chr>,
#> #   hom <dbl>, ExactDate <date>, DFstatus <chr>, codes <chr>,
#> #   nostems <dbl>, status <chr>, date <dbl>

census %>% 
  pick_dbh_under(100)
#> # A tibble: 18 x 19
#>    treeID stemID tag   StemTag sp    quadrat    gx    gy MeasureID CensusID
#>     <int>  <int> <chr> <chr>   <chr> <chr>   <dbl> <dbl>     <int>    <int>
#>  1   7624 160987 1089… 175325  TRIP… 722     139.  425.     486675        5
#>  2  19930 117849 1234… 165576  CASA… 425      61.3 496.     471979        5
#>  3  31702  39793 22889 22889   SLOB… 304      53.8  73.8    447307        5
#>  4  35355  44026 27538 27538   SLOB… 1106    203.  110.     449169        5
#>  5  39705  48888 33371 33370   CASS… 1010    184.  194.     451067        5
#>  6  57380 155867 66962 171649  SLOB… 1414    274.  279.     459427        5
#>  7  95656 129113 1315… 131519  OCOL… 402      79.7  22.8    474157        5
#>  8  96051 129565 1323… 132348  HIRR… 1403    278    40.6    474523        5
#>  9  96963 130553 1347… 134707  TETB… 610     114.  182.     475236        5
#> 10 115310 150789 1652… 165286  MANB… 225      24.0 497.     483175        5
#> 11 121424 158579 1707… 170701  CASS… 811     146.  218.     484785        5
#> 12 121689 158871 1712… 171277  INGL… 515      84.2 285.     485077        5
#> 13 121953 159139 1718… 171809  PSYB… 1318    247.  354.     485345        5
#> 14 124522 162698 1742… 174224  CASS… 1411    279.  210.     488386        5
#> 15 125038 163236 1753… 175335  CASS… 822     153.  426.     488924        5
#> 16 126087     NA 1773… <NA>    CASA… 521      89.8 408.         NA       NA
#> 17 126803     NA 1785… <NA>    PSYB… 622     113.  426          NA       NA
#> 18 126934     NA 1787… <NA>    MICR… 324      47   480.         NA       NA
#> # … with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>,
#> #   status <chr>, date <dbl>

pick_main_stem() and pick_main_stemid() pick the main stem or main stemid(s) of each tree in each census.

stem <- download_data("luquillo_stem6_random")

dim(stem)
#> [1] 1320   19
dim(pick_main_stem(stem))
#> [1] 1000   19

add_<column(s)>()

add_status_tree()adds the column status_tree based on the status of all stems of each tree.

stem %>% 
  select(CensusID, treeID, stemID, status) %>% 
  add_status_tree()
#> # A tibble: 1,320 x 5
#>    CensusID treeID stemID status status_tree
#>       <int>  <int>  <int> <chr>  <chr>      
#>  1        6    104    143 A      A          
#>  2        6    119    158 A      A          
#>  3       NA    180    222 G      A          
#>  4       NA    180    223 G      A          
#>  5        6    180    224 G      A          
#>  6        6    180    225 A      A          
#>  7        6    602    736 A      A          
#>  8        6    631    775 A      A          
#>  9        6    647    793 A      A          
#> 10        6   1086   1339 A      A          
#> # … with 1,310 more rows

add_index() and friends add columns to a ForestGEO-like dataframe.

stem %>% 
  select(gx, gy) %>% 
  add_index()
#> Guessing: plotdim = c(320, 500)
#> * If guess is wrong, provide the correct argument `plotdim`
#> # A tibble: 1,320 x 3
#>       gx    gy index
#>    <dbl> <dbl> <dbl>
#>  1  10.3  245.    13
#>  2 183.   410.   246
#>  3 165.   410.   221
#>  4 165.   410.   221
#>  5 165.   410.   221
#>  6 165.   410.   221
#>  7 149.   414.   196
#>  8  38.3  245.    38
#>  9 143.   411.   196
#> 10  68.9  253.    88
#> # … with 1,310 more rows

Plot data

For simplicity, we will focus on only a few species.

stem_2sp <- stem %>% 
  filter(sp %in% c("PREMON", "CASARB"))

autoplot() and friends produce different output depending on the class of input. You can create different input classes, for example, with sp() and sp_elev():

• Use sp(census) to plot the column sp of a census dataset – i.e. to plot species distribution.

class(sp(stem_2sp))
#> [1] "sp"         "tbl_df"     "tbl"        "data.frame"

autoplot(sp(stem_2sp))

• Use sp_elev(census, elevation) to plot the columns sp and elev of a census and elevation dataset, respectively – i.e. to plot species distribution and topography.

elevation <- fgeo.x::elevation
class(sp_elev(stem_2sp, elevation))
#> [1] "sp_elev" "list"

autoplot(sp_elev(stem_2sp, elevation))

Analyze

Abundance

abundance() and basal_area() calculate abundance and basal area, optionally by groups.

abundance(
  pick_main_stem(census)
)
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1    30

by_species <- group_by(census, sp)

basal_area(by_species)
#> # A tibble: 18 x 2
#> # Groups:   sp [18]
#>    sp     basal_area
#>    <chr>       <dbl>
#>  1 CASARB      437. 
#>  2 CASSYL     4146. 
#>  3 CECSCH   144150. 
#>  4 DACEXC    56832. 
#>  5 GUAGUI     9161. 
#>  6 HIRRUG      131. 
#>  7 INGLAU      141. 
#>  8 MANBID      167. 
#>  9 MATDOM    45239. 
#> 10 MICRAC        0  
#> 11 OCOLEU      437. 
#> 12 PREMON    78864. 
#> 13 PSYBER        0  
#> 14 PSYBRA      154. 
#> 15 SCHMOR    41187. 
#> 16 SLOBER    23377. 
#> 17 TETBAL      272. 
#> 18 TRIPAL       93.3

Demography

recruitment_ctfs(), mortality_ctfs(), and growth_ctfs() calculate recruitment, mortality, and growth. They all output a list. as_tibble() converts the output from a list to a more convenient dataframe.

tree5 <- fgeo.x::tree5

as_tibble(
  mortality_ctfs(tree5, tree6)
)
#> Detected dbh ranges:
#>   * `census1` = 10.9-323.
#>   * `census2` = 10.5-347.
#> Using dbh `mindbh = 0` and above.
#> # A tibble: 1 x 9
#>       N     D    rate   lower  upper  time  date1  date2 dbhmean
#>   <dbl> <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>  <dbl>   <dbl>
#> 1    27     1 0.00834 0.00195 0.0448  4.52 18938. 20590.    101.

Species-habitats association

tt_test() runs a torus translation test to determine habitat associations of tree species. as_tibble() converts the output from a list to a more convenient dataframe. summary() helps you to interpret the result.

# This analysis makes sense only for tree tables
tree <- download_data("luquillo_tree5_random")

habitat <- fgeo.x::habitat
result <- tt_test(tree, habitat)
#> Using `plotdim = c(320, 500)`. To change this value see `?tt_test()`.
#> Using `gridsize = 20`. To change this value see `?tt_test()`.

as_tibble(result)
#> # A tibble: 292 x 8
#>    habitat sp     N.Hab Gr.Hab Ls.Hab Eq.Hab Rep.Agg.Neut Obs.Quantile
#>  * <chr>   <chr>  <dbl>  <dbl>  <dbl>  <dbl>        <dbl>        <dbl>
#>  1 1       ALCFLO     2   1443    153      4            0        0.902
#>  2 2       ALCFLO     1    807    778     15            0        0.504
#>  3 3       ALCFLO     0      0    715    885           -1        0    
#>  4 4       ALCFLO     0      0    402   1198           -1        0    
#>  5 1       ALCLAT     0      0    544   1056           -1        0    
#>  6 2       ALCLAT     1   1432    156     12            0        0.895
#>  7 3       ALCLAT     0      0    324   1276           -1        0    
#>  8 4       ALCLAT     0      0    144   1456           -1        0    
#>  9 1       ANDINE     1   1117    466     17            0        0.698
#> 10 2       ANDINE     1   1081    510      9            0        0.676
#> # … with 282 more rows

summary(result)
#> # A tibble: 292 x 3
#>    sp     habitat association
#>    <chr>  <chr>   <chr>      
#>  1 ALCFLO 1       neutral    
#>  2 ALCFLO 2       neutral    
#>  3 ALCFLO 3       repelled   
#>  4 ALCFLO 4       repelled   
#>  5 ALCLAT 1       repelled   
#>  6 ALCLAT 2       neutral    
#>  7 ALCLAT 3       repelled   
#>  8 ALCLAT 4       repelled   
#>  9 ANDINE 1       neutral    
#> 10 ANDINE 2       neutral    
#> # … with 282 more rows

Downloads of fgeo packages

Related projects

Additional packages maintained by ForestGEO but not included in fgeo:

• fgeo.data: Open datasets of ForestGEO.
• fgeo.krig: Analyze soils.

Other packages not maintained by ForestGEO:

• CTFS-R Package: The original package of CTFS functions. No longer supported by ForestGEO.
• BIOMASS: An R package to estimate above-ground biomass in tropical forests.

R code from recent publications by ForestGEO partners

Data have been made available as required by the journal to enable reproduction of the results presented in the paper. Please do not share these data without permission of the ForestGEO plot Principal Investigators (PIs). If you wish to publish papers based on these data, you are also required to get permission from the PIs of the corresponding ForestGEO plots.

• Soil drivers of local-scale tree growth in a lowland tropical forest (Zemunik et al., 2018).
• Plant diversity increases with the strength of negative density dependence at the global scale (LaManna et al., 2018)
  • Response #1: LaManna et al. 2018. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale” Science Vol. 360, Issue 6391, eaar3824. DOI: 10.1126/science.aar3824
  • Response #2: LaManna et al. 2018. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science Vol. 360, Issue 6391, eaar5245. DOI: 10.1126/science.aar5245

Information

• Getting help.
• Contributing.
• Contributor Code of Conduct.

Acknowledgments

Thanks to all partners of ForestGEO for sharing their ideas and code. For feedback on fgeo, special thanks to Gabriel Arellano, Stuart Davies, Lauren Krizel, Sean McMahon, and Haley Overstreet. For all other help, I thank contributors in the the documentation of the features they helped with.

fgeo's Issues

Remove dependency on ctfs

Downloading GitHub repo forestgeo/ctfs@master
Error in utils::download.file(url, path, method = download_method(), quiet = quiet,  : 
  cannot open URL 'https://api.github.com/repos/forestgeo/ctfs/zipball/master'

Problems is in fgeo.demography()

library(fgeo)
Error in library(fgeo) : there is no package called ‘fgeo’
> library(fgeo.map)
> library(fgeo.tool)
> library(soilkrig)
Warning message:
In fun(libname, pkgname) : couldn't connect to display ":0"
> library(fgeo.demography)
Error in library(fgeo.demography) : 
  there is no package called ‘fgeo.demography’
> library(fgeo.abundance)

Record a 1' video for the Home menu of fgeo

This is the home page of fgeo, an R package that helps you Analyze forest diversity and dynamics.

You may bookmark this page so you can find it quickly.

To find out what you can do with fgeo -- even before you install it -- try a Quick Search for
• packages,
• functios, or
• datasets.
Follow the links for information on any topic.

To install fgeo by running this code.

Then in any R session you can load fgeo with library(fgeo) and find anything run fgeo_help().

You can also narrow down your search with specific keywords, for example fgeo_help ("map").

Each help file has a similar structure -- generally with a title, description, arguments and examples.

With latest versions of RStudio you can run examples directly from the Help tab. Highlight the code you want run and press Control/Cmd enter.

If what you found is not quite what you expected, try the links to related topics.

You can try fgeo on rstudio.cloud for free at http://bit.ly/fgeo-demo

Now go ahead and Get Started

Write fun to index all functions by package.

add by_treeid_pick_dbh_max() to fgeo.Rmd

Update standarized documents accross packages.

Standardize infrastructure following this post.

---

README

Logo:

• Use logo on the left of package name

<img src="https://i.imgur.com/39pvr4n.png" align="right" height=44 /> 

Badges

EDIT: ADD BADGES CREATED WITH:

use_lifecycle_badge()
use_cran_badge()

EDIT: CONSIDER ADDING BADGES CREATED WITH:

# In .travis.yml consider adding `warnings_are_errors: false`
use_travis()

use_coverage("coveralls")

Installation

# install.packages("remotes")
remotes::install_github("EDIT-OWNER/EDIT-PACKAGE-NAME")

For details on how to install packages from GitHub, see [this article](https://goo.gl/dQKEeg).

Code of Conduct

EDIT: Run this:

use_tidy_coc()

use_directory(".github")
# move coc manually to .github/

test fgeo_help()

React with emoji to learning resources at https://github.com/forestgeo/learn/issues

What is your favorite resource?

The goal of the learn repository is to store learning resources as issues.

Your job is to explore learning resources as issues and react with an emoji. For example, this is how you can up-vote the resources you like:

By contributing to this issue you will help others discover the best resources.

If you have any question, please ask me by referring to me as @maurolepore.

---

Search related issues on https://github.com with: label:hacktoberfest maurolepore

Fix badge for test coverage

Write a helper to specify columns when reading fastfield output.

In all fgeo_index_*() add `pattern` to filter index

add argument build_vignettes in fgeo_update()?

what does tidyverse_update() do?

Write fgeo_index_vignettes()

Tweak websites README

Simplify readme even further. E.g. don't repeat package name. See http://dplyr.tidyverse.org/.

Remove fgeo_index_*() ; they are replaced by fgeo_docs()

write funcition to index tutorials in inst/tutorials and to open them if installed

add fgeo_docs() to fgeo.Rmd (Get started)

Rename or hide fgeo_packages() then replace fgeo_index_*() by fgeo_*()

Programatically include links to contributors.

Links can be added programatically. This can be useful to add links to packages in README, to funcitons in Get Started; to people's websites on acknowledgements.

For inspiration see:

• code to generate the list of reviewers here.
• fgeo README, with links created with fgeo_link() and stored in a knitr::kable().

Add spatial the most common spatial statistics?

Instead of adding code I may point to good packages. E.g.:

• See this paper presenting the ads package.
• See spatstat. It has a long version history on CRAN going back to 2002.
• Consider using spatstat, recommended by Alexandre (adalardo).
• Research uses of ppm point pattern models.

Improve labels of learning resources at https://github.com/forestgeo/learn/issues

What labels would you add/remove?

The goal of the learn repository is to store learning resources as issues. To make those easy to find I am using labels. The resources and labels accumulated organically, and now it's time to get organized. What labels would you add/remove?

If you have any question, please ask me by referring to me as @maurolepore.

---

Search related issues on https://github.com with: label:hacktoberfest maurolepore

Show in readme `apropos()`

> apropos("pick")
character(0)
> library(fgeo)
── Attaching packages ─────────────────────────────────────── fgeo 0.0.0.9000 ──
✔ bciex           0.0.0.9000     ✔ fgeo.demography 0.0.0.9000
✔ fgeo.abundance  0.0.0.9004     ✔ fgeo.habitat    0.0.0.9006
✔ fgeo.base       0.0.0.9001     ✔ fgeo.map        0.0.0.9204
✔ fgeo.data       0.0.0.9002     ✔ fgeo.tool       0.0.0.9003

Warning message:
In fun(libname, pkgname) : couldn't connect to display ":0"
> apropos("pick")
[1] "pick_dbh_max"   "pick_dbh_min"   "pick_dbh_over"  "pick_dbh_under"
[5] "pick_plotname"  "pick_recensus"  "pick_status"    "pick_top"   

In README provide a visualization of the structure of fgeo packages

I think this comes from cwickham tidyverse course:

This ...

install.packages("fgeo")

... is equivalent to this

install.packages("fgeo.tool")
install.packages("fgeo.map")
...

And this ...

library(fgeo)

... is equivalent to this

library(fgeo.tool)
library(fgeo.map)
...

reexport tibble

Write function to table all functions in each of fgeo's packages

Add table of partner's contributions.

E.g. code of the paper of Joe LaManna.

Website: Simplify README by adding Get Started

Get Started appears automatically when the package contains an article with the same name of the package.

• Simplify README to the bare bones. Give detailed installation instructions in blog .
• Move all the rest of the information to Get Started.
• Add links to packages.
• Remove links to packages from References.
• Leave references only for functions.
• Add list of all functions to get started.
• Add list of all datasets to get started.

fgeo cleanup

Find what functions are used and not used in each package:

• May use pkginspector.
• May use the shell.

---

Reorganize functions in website:

• Hide developer-oriented functions with @Keywords internal.
• Redefine families and use_concept().
• Label useless functions to be moved to PR.
  • Functions that work with fastField seem too specific to live in fgeo.tool. Extract them into a PR.
  • Functions to read/write from/to files/directories should move to its own package fgeo.io

---

• Moved functions from ctfs to fgeo.ctfs (not a core package).
• Renamed funcitons.
• Removed functions.
• Some functions are exported but not shown in website.

Rename enline() to anchor() or anchor_regex()

Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/, https://community.rstudio.com/, https://github.com/forestgeo/forum/ or email Mauro Lepore at [email protected].

Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

---

Brief description of the problem

# insert reprex here

Startup message shows fgeo.abundance twice

library(fgeo)
#> -- Attaching packages ------------------------------------------- fgeo 0.0.0.9000 --
#> v fgeo.abundance  0.0.0.9005     v fgeo.demography 0.0.0.9100
#> v fgeo.base       0.0.0.9001     v fgeo.habitat    0.0.0.9006
#> v fgeo.data       0.0.0.9005     v fgeo.map        0.0.0.9204
#> v fgeo.abundance  0.0.0.9005     v fgeo.tool       0.0.0.9003
#> 

Created on 2018-10-19 by the reprex package (v0.2.1)

Add search with shift + / (?)

Configure following http://pkgdown.r-lib.org/articles/pkgdown.html?q=search%20shift#docsearch-configuration

---

On Tue, Jun 26, 2018 at 7:47 AM Sylvain Pace [email protected] wrote:
Hi Mauro,

Congratulations, your search is now ready!
I've successfully configured the underlying crawler and it will now run every 24h.

You're now a few steps away from having it working on your website:

• Copy the following CSS/JS snippets and add them to your page

<!-- at the end of the HEAD --> 
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsearch.js@2/dist/cdn/docsearch.min.css" /> 

<!-- at the end of the BODY --> 
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/docsearch.js@2/dist/cdn/docsearch.min.js"></script> 
<script type="text/javascript"> docsearch({ 
 apiKey: 'ec79b1c00aff9cd841703edc746e1a71', 
 indexName: 'fgeo', 
 inputSelector: '### REPLACE ME ####', 
 debug: false // Set debug to true if you want to inspect the dropdown 
}); 
</script> 

• Add a search input in your page if you don't have any yet. Then update the inputSelector value in JS snippet to a CSS selector that targets your search input field.
• Optionally customize the look and feel by following the DocSearch documentation (https://community.algolia.com/docsearch/documentation/)
• You can also check your configuration in our github repo (https://github.com/algolia/docsearch-configs/blob/master/configs/fgeo.json).

Please open a pull request if want to leverage your configuration!

Feel free to get back to us if you have any issues or questions regarding the integration.

We'd also be happy to get your feedback and thoughts about DocSearch - so we can continue to improve it.

Have a nice day :)

Sylvain Pace
Software Engineer
| www.algolia.com | @algolia

Website: Simplify package-website templates to be closer to default

This is closer to defaults, and is achieved adding nothing to the left side of the navbar.

Fix search box in home

Search box finds stuff but the links to found stuff don't work.

Style errors consistently

http://style.tidyverse.org/error-messages.html#problem-statement

Take the chance and replace stop(), warning() and message() by rlang's abor(), warn() and inform().

Review list of functions to see if some exported functions from rlang can be not-exported.

Ideas for tutorials

Record videos for an overview of fgeo.

• fgeo home
• searching functions, packages and datasets
• understanding helpfiles
• Write. Finish blog on packages
• Continue adapting rstudio.cloud/primers

Remov exported functions that are not indexed.

From README remove internal functions.

Write fun to show the specific fgeo-pkgs in imports, suggests, ...

Export fgeo_imports _sugests _core, to do things from other pkgs. See ?fgeo_pkg_deps().

fgeo_help()

#' if (interactive()) {
#'   fgeo_help("pick")
#' }
fgeo_help <- function(pattern = "", package = fgeo::fgeo_core()) {
  help.search(pattern = pattern, package = fgeo::fgeo_core())
}

Create a shared space in rstudio.cloud

• Find out how to create a workspace that is temporary, e.g. this one

Style website

(info @)

• How to tweak a theme
• Where to place the files in the root directory

Add pillar to imports?

Write an object to set and get description of fgeo-fun

fgeo_nms_conventions()
fgeo_abreviations()
fgeo_funs()

Tutorials - videos.

• Workflow:
  • google sheets
  • Project oriented workflow
  • Row oriented workflow
• Managing data
• Exploratory data analysis
• dplyr: wrangling
• ggplot2: visualizing
• janitor: Counting things

Index datasets

To index datasets -- and even their descriptions -- I can wrap data()

get_datasets <- function(package) {
    dinfo <- data(package = package)
    dinfo[["results"]][, "Item"]
}

get_datasets("ggplot2")

Thanks to Nathan Werth via rstudio community.

In fgeo_functions() add titles

Ideas:

• See how pkgdown parses topic's titles.
• Get functionality from ctfs

Text review

Incorporate text edits from Stuart and Haley.

Update README template.

Use this instead.

use_lifecycle_badge("EDIT-STAGE")
use_travis()

Remove this (.github/ is created automatically with use_tidy_coc()):

use_directory(".github")
# move coc manually to .github/

Acknowledgements

Label issues for hacktoberfest

https://twitter.com/stefaniebutland/status/1050561344418676737?s=12

Provide shortcut links to featured packages.

Write a function to create package links from package names and list those links as shortcuts to package website.