Merge pull request #7 from p2m2/develop

CI/CD implementation

.Rbuildignore

@@ -8,3 +8,6 @@ devel.md$
 ^.*\.Rproj$
 ^\.circleci\.yml$
 ^LICENSE\.md$
+.circleci
+exampleOligopeptides*
+Data

.circleci/config.yml

@@ -7,26 +7,94 @@ workflows:
       - build:
           context:
             - DOCKER_CONTEXT
+      - publish-github-release:
+          requires:
+            - build
+          context:
+            - DOCKER_CONTEXT
+            - GITHUB_CONTEXT
+          filters:
+            branches:
+              only:
+                - main
+      - publish-shinyapp:
+          requires:
+            - build
+          context:
+            - DOCKER_CONTEXT
+            - SHINYAPP_CONTEXT
+          filters:
+            tags:
+              only: /.*/
+            branches:
+              only:
+                - develop
 jobs:
   build:
+    working_directory: ~/tmp
     docker:
-      - image: inraep2m2/ci-r-base-devtools:4.2.2
+      - image: inraep2m2/ci-r-base-devtools:4.2.3
     steps:
       - checkout
       - run:
           name: Install package dependencies
           command: R -e 'devtools::install_deps(dependencies = TRUE)'
       - run:
           name: Check package
-          command: R -e 'devtools::check()'
-#      Probleme de generation du PDF avec le "R CMD check *tar.gz"
-#      todo: ajouter la bonne lib dans inraep2m2/ci-r-base-devtools
-#      - run:
-#          name: Build package
-#          command: R CMD build .
-#      - run:
-#          name: Check package
-#          command: R CMD check *tar.gz
-      - store_artifacts:
-          path: man/
-          destination: man
+          command: |
+            R -e 'devtools::check()'
+            NAMEPACKAGE=$(grep Package DESCRIPTION | awk '{print $2}')
+            VERSION=$(grep Version DESCRIPTION | awk '{print $2}')
+            R CMD build --no-manual .
+            R CMD check --no-manual ${NAMEPACKAGE}_${VERSION}.tar.gz
+            mkdir -p workspace
+            mv ${NAMEPACKAGE}_${VERSION}.tar.gz workspace/
+
+      - persist_to_workspace:
+          root: workspace
+          paths: 
+            - .
+
+  publish-github-release:
+    working_directory: ~/tmp
+    docker:
+      - image: circleci/golang:1.17
+    steps:
+      - checkout
+      - attach_workspace:
+          at: workspace
+      - run:
+          name: "Publish Release on GitHub"
+          command: |
+            LAST_VERSION=$(git ls-remote --tags --sort="v:refname" https://${GITHUB_TOKEN}@github.com/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}.git | tail -n1  | sed 's/.*\///; s/\^{}//') 
+            NAMEPACKAGE=$(grep Package DESCRIPTION | awk '{print $2}')
+            VERSION=$(grep Version DESCRIPTION | awk '{print $2}')
+            # -------------------------------------------------------
+            # utiliser gh ? => https://github.com/cli/cli/blob/trunk/docs/install_linux.md
+            #                  https://cli.github.com/manual/gh_release_create
+            # -------------------------------------------------------
+            if [ "LAST_VERSION" != "VERSION" ]; then
+              go get github.com/tcnksm/ghr
+              BODY="# ${NAMEPACKAGE} ${VERSION}"
+              ghr -t ${GITHUB_TOKEN} \
+                  -u ${CIRCLE_PROJECT_USERNAME} \
+                  -r ${CIRCLE_PROJECT_REPONAME} \
+                  -c ${CIRCLE_SHA1} \
+                  -n $VERSION \
+                  -b "$BODY" \
+                  -delete $VERSION workspace/${NAMEPACKAGE}_${VERSION}.tar.gz
+            fi
+
+  publish-shinyapp:
+    docker:
+      - image: inraep2m2/ci-r-base-devtools:4.2.3
+    steps:
+      - checkout
+      - run:
+          name: Install package dependencies
+          command: R -e 'devtools::install_deps(dependencies = TRUE)'
+      - run:
+          name: deployment on https://www.shinyapps.io/
+          command: |
+            [ -z "${CIRCLE_TAG}" ] && export APP_NAME="${CIRCLE_PROJECT_REPONAME}-${CIRCLE_BRANCH}" || export APP_NAME="${CIRCLE_PROJECT_REPONAME}"
+            ./publish_rshinyapp.sh

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: oligopeptidesMatching
 Title: Library of functions for in silico construction and detection of oligopeptides
-Description: In order to compute from amino acids molecular weight the putative oligomers of various oligomerization degree
+Description: In order to compute from amino acids molecular weight the putative oligomers of various oligomerization degree.
 Version: 0.0.1
 Authors@R: c(
     person(given = "Kevin",

NAMESPACE

@@ -2,6 +2,12 @@
 
 export(build_mz_peptides)
 export(d_ppm)
+export(get_combination_compounds)
 export(get_oligopeptides)
-export(mz_matching)
+export(match_mz_obs)
 export(oligopeptides_building)
+importFrom(dplyr,"%>%")
+importFrom(dplyr,filter)
+importFrom(dplyr,mutate)
+importFrom(stringr,str_sort)
+importFrom(stringr,str_split)

R/build_mz_peptides.R

@@ -1,23 +1,20 @@
-#' Builds an informative matrix of Oligopeptides mapping to Mass Spectrometry output
+#' Builds an informative matrix of Oligopeptides mapping to
+#' Mass Spectrometry output
 #'
 #' Builds a matrix containing : 
 #'  - Oligomerization Degree
 #'  - Id (Combinaison of Amino Acid names)
 #'  - Mz (ionized mass)
-#'  - [M - X ionization[1]] negative mode ionization
-#'  - [M + X ionization[1]] positive mode ionization 
-#'  - [M - X ionization[2]] negative mode ionization
-#'  - [M + X ionization[2]] positive mode ionization 
-#'  - ...
-#' 
-#' 
 #' @param peptide list of oligopeptide, Named num (column with names)
 #' @param oligomerization_degree integer : oligomerization degree  
 #' @param ionization : list of ionization
-#' @return a data.frame containing OD, Id , Mz , ([M - Xionization ], [M + Xionization])+ .
+#' @return a data.frame containing OD, Id , Mz ,
+#' M + Xionization, M - Xionization
 #' @export
-#' @examples 
-#' build_mz_peptides(stats::setNames(c(160.0848,192.0569),c("A_A","A_C")), 2, stats::setNames(c(1.007825),c("H")))
+#' @examples
+#' build_mz_peptides(
+#' stats::setNames(c(160.0848,192.0569),c("A_A","A_C")), 2,
+#' stats::setNames(c(1.007825),c("H")))
 build_mz_peptides <- function(peptide,
                               oligomerization_degree,
                               ionization){

R/d_ppm.R

@@ -2,12 +2,13 @@
 #'
 #' @description Calculate the δppm between an observed mz and a theorical one
 #' @param mz_obs is the observed mz of the metabolomic study
-#' @param mz_theoric is the theorical mz (calculated by get_arrangement_oligopeptides and or get_combination_compounds)
-#' @return the δppm of the difference between the mz observed and a theorical one
+#' @param mz_theoric theorical mz (calculated by get_arrangement_oligopeptides
+#' and or get_combination_compounds)
+#' @return δppm of the difference between the mz observed and a theorical one
 #' @export
-#' @examples for the glucose in negative ionization mode: d_ppm(179.0564, 179.0561)
-d_ppm <- function(mz_obs, mz_theoric){
-  if(mz_theoric==0){
+#' @examples d_ppm(179.0564, 179.0561)
+d_ppm <- function(mz_obs, mz_theoric) {
+  if (mz_theoric == 0) {
     return(0)
   }
   ppm_error <- abs(((mz_obs - mz_theoric) / mz_theoric) * 10^6)

R/get_combination_compounds.R

@@ -1,16 +1,24 @@
-# compounds_1, compounds_2 = R column with a name (Named num)
+#' get_combination_compounds
+#'
+#' compounds_1, compounds_2 = R column with a name (Named num)
+#' @param compounds_1 compounds_1
+#' @param compounds_2 compounds_2
+#' @param chemical_reaction chemical_reaction
+#' @param ionization ionization
+#' @return δppm of the difference between the mz observed and a theorical one
+#' @export
 get_combination_compounds <- function(compounds_1,
                                       compounds_2,
                                       chemical_reaction,
-                                      ionization){
+                                      ionization) {
 
-n = length(compounds_1)
-m = length(compounds_2)
+n <- length(compounds_1)
+m <- length(compounds_2)
 
 combined_compounds_names <- vector("character", m*n)
 combined_compounds_mass <- vector("numeric", m*n)
 
-k = 1
+k <- 1
 
 for (i in 1:n){
   for (j in 1:m){
@@ -24,7 +32,6 @@ combined_compounds <- (stats::setNames(combined_compounds_mass,combined_compound
 combined_compounds <- combined_compounds[unique(names(combined_compounds))]
 
 df <- as.data.frame(combined_compounds)
-# df[order(df$combined_compounds_mass)]
 return(df)
 }
 

R/get_arrangement_oligopeptides.R → R/get_oligopeptides.R

@@ -1,32 +1,37 @@
-#' Builds an informative and complete matrix of Oligopeptides mapping to Mass Spectrometry output
-#' 
+#' Builds an informative and complete matrix of Oligopeptides
+#' mapping to Mass Spectrometry output
+#'
 #' Builds a matrix containing : 
 #'  - Oligomerization Degree
 #'  - Id (Combinaison of Amino Acid names)
 #'  - Mz (ionized mass)
-#'  - [M - X ionization[1]] negative mode ionization
-#'  - [M + X ionization[1]] positive mode ionization 
-#'  - [M - X ionization[2]] negative mode ionization
-#'  - [M + X ionization[2]] positive mode ionization 
+#'  - M - X ionization 1 negative mode ionization
+#'  - M + X ionization 1 positive mode ionization
+#'  - M - X ionization 2 negative mode ionization
+#'  - M + X ionization 2 positive mode ionization
 #'  - ...
-#' 
-#' 
+#'
 #' @param aminoacids list of amino acids.
-#' @param chemical_reaction reaction linking the combination of oligopepid and amino acids. 
+#' @param chemical_reaction reaction linking the combination of
+#' oligopepid and amino acids.
 #' @param ionization list of ionization
-#' @param oligomerization_degree integer : oligomerization degree  
-#' @return matrix containing OD, Id , Mz , ([M - Xionization ], [M + Xionization])+ .
+#' @param oligomerization_degree integer : oligomerization degree
+#' @return matrix containing OD, Id , Mz ,M - Xionization,M + Xionization .
 #' @export
-#' @examples 
-#' get_oligopeptides(aminoacids = stats::setNames(c(89.047679),c("A")),chemical_reaction = 18.010565,ionization = stats::setNames(c(1.007825),c("H")),oligomerization_degree = 4)
+#' @examples
+#' get_oligopeptides(aminoacids = stats::setNames(c(89.047679),c("A")),
+#' chemical_reaction = 18.010565,
+#' ionization = stats::setNames(c(1.007825),c("H")),
+#' oligomerization_degree = 4)
 get_oligopeptides <- function(aminoacids,
                               chemical_reaction,
                               ionization,
                               oligomerization_degree){
   tmp_oligopeptides <- aminoacids
   all_mz_peptides <-  build_mz_peptides(aminoacids, 1, ionization)
   for (i in 1:(oligomerization_degree-1)){
-    tmp_oligopeptides <- oligopeptides_building(tmp_oligopeptides, aminoacids, chemical_reaction)
+    tmp_oligopeptides <- oligopeptides_building(tmp_oligopeptides, aminoacids,
+    chemical_reaction)
     tmp_mz_peptides <- build_mz_peptides(tmp_oligopeptides, i+1, ionization)
     all_mz_peptides <- rbind(all_mz_peptides, tmp_mz_peptides)
   }

R/match_mz_obs.R

@@ -1,20 +1,22 @@
 #' Match an observed mz against the combined compounds Robject using a δppm
 #'
-#' @description Match an observed mz against the combined compounds Robject using a δppm
+#' @description Match an observed mz against the combined 
+#' compounds Robject using a δppm
 #' @param mz_obs is the observed mz of the metabolomic study
 #' @param combined_compounds is the combined compounds array to match
 #' @param ppm_error is the criteria to match compounds
-#' @return the list as dataframe of all the matches 
+#' @return the list as dataframe of all the matches
+#' @importFrom dplyr %>%
+#' @importFrom dplyr mutate
+#' @importFrom dplyr filter
 #' @export
-#' @examples 
-
 match_mz_obs <- function(mz_obs,
                          combined_compounds,
-                         ppm_error=5){
+                         ppm_error=5) {
   return(combined_compounds %>%
-           filter(combined_compounds<round(mz_obs, digits=0)+1) %>%
-           filter(combined_compounds>=round(mz_obs, digits=0)) %>%
-           mutate(round = round(combined_compounds, digits=4)) %>%
-           mutate(ppm_error_value = d_ppm(round, mz_obs)) %>%
-           filter(ppm_error_value<ppm_error))
+           dplyr::filter(combined_compounds < mz_obs + 1.0) %>%
+           dplyr::filter(combined_compounds >= mz_obs - 1.0) %>%
+           dplyr::mutate(round = round(combined_compounds, digits=4)) %>%
+           dplyr::mutate(ppm_error_value = d_ppm(round, mz_obs)) %>%
+           dplyr::filter(ppm_error_value<ppm_error))
 }

R/mass_oligo_vector_np1.R → R/oligopeptides_building.R

@@ -2,19 +2,23 @@
 #'
 #' Builds an N-size amino acid oligopeptide list from the combination between 
 #' an N-size oligopeptide and an amino acid list
-#' 
+#'
 #' All vectors are named with a combination of amino acids. 
 #' the values ​​correspond to the theoretical mass.
-#' 
+#'
 #' Formula : oligo^n = aa^n - H2O^n-1 
-#' 
+#'
 #' @param mass_oligo_vector_np1 list of oligopeptide.
 #' @param mass_aa_vector list of amino acids.
-#' @param additional_reaction additional reaction linking the combination of oligopepid and amino acids.
+#' @param additional_reaction additional reaction linking the 
+#' combination of oligopepid and amino acids.
 #' @return new oligo_vector with mass values .
+#' @importFrom stringr str_split
+#' @importFrom stringr str_sort
 #' @export
-#' @examples 
-#' oligopeptides_building(stats::setNames(c(160,085358),c("A-A")),stats::setNames(c(89.047679),c("A")),18.010565)
+#' @examples
+#' oligopeptides_building(stats::setNames(c(160,085358),
+#' c("A-A")),stats::setNames(c(89.047679),c("A")),18.010565)
 oligopeptides_building <- function(mass_oligo_vector_np1,
                                    mass_aa_vector,
                                    additional_reaction){