Restore the data-raw folder from manuscript and zadd it to .Rbuildignore

.Rbuildignore

 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENCE-CECILL-2.1.txt$
+^data-raw$

data-raw/Soil_litter/prepare_data.R

+library(vegan)
+library(ROBITools)
+library(ProcMod)
+
+litere_col_clean <- readRDS("litere_col_clean.rds")
+litere_col_clean <- metabarcoding.data(reads = litere_col_clean$reads,
+                                       motus = litere_col_clean$motus,
+                                       samples = litere_col_clean$samp)
+
+litere_bac_clean <- readRDS("litere_bac_clean.rds")
+litere_bac_clean <- metabarcoding.data(reads = litere_bac_clean$reads,
+                                       motus = litere_bac_clean$motus,
+                                       samples = litere_bac_clean$samp)
+
+
+
+
+litere_ins_clean <- readRDS("litere_ins_clean.rds")
+litere_ins_clean <- metabarcoding.data(reads = litere_ins_clean$reads,
+                                       motus = litere_ins_clean$motus,
+                                       samples = litere_ins_clean$samp)
+
+litere_euk_clean <- readRDS("litere_euk_clean.rds")
+litere_euk_clean <- metabarcoding.data(reads = litere_euk_clean$reads,
+                                       motus = litere_euk_clean$motus,
+                                       samples = litere_euk_clean$samp)
+
+
+# Remove rare motus
+
+litere_col_norare <- litere_col_clean[,apply(decostand(litere_col_clean$reads,method = "total"),MARGIN = 2,max) >= 0.001]
+litere_euk_norare <- litere_euk_clean[,apply(decostand(litere_euk_clean$reads,method = "total"),MARGIN = 2,max) >= 0.001]
+litere_ins_norare <- litere_ins_clean[,apply(decostand(litere_ins_clean$reads,method = "total"),MARGIN = 2,max) >= 0.001]
+litere_bac_norare <- litere_bac_clean[,apply(decostand(litere_bac_clean$reads,method = "total"),MARGIN = 2,max) >= 0.001]
+
+
+litere_euk_classes <- lapply(levels(litere_euk_norare$motus$class_name),
+                             function(x) litere_euk_norare[,which(litere_euk_norare$motus$class_name == x)])
+names(litere_euk_classes)=levels(litere_euk_norare$motus$class_name)
+
+litere_euk_classes <- litere_euk_classes[! sapply(litere_euk_classes, is.numeric)]
+litere_euk_classes.diverse = litere_euk_classes[sapply(litere_euk_classes,ncol) >=30]
+
+litere_euk_dist <- lapply(litere_euk_classes.diverse,
+                          function(metabar)
+                            vegdist(decostand(metabar@reads,"hellinger"),"euclidean"))
+
+litere_euk_dist <- as.procmod.frame(litere_euk_dist)
+
+litere_euk_cor <- corls(litere_euk_dist)
+ilitere_euk_cor <- solve(litere_euk_cor)
+litere_euk_cor.partial = ilitere_euk_cor/sqrt(diag(ilitere_euk_cor) %*% t(diag(ilitere_euk_cor)))
+
+plot(hclust(as.dist(1- ilitere_euk_cor.partial^2)))

data-raw/climat.csv

+"","Aspect","TempSeasonality","MaxMonTemp","MeanMonTempRange","AnnMeanTemp","Isothemality"
+"AA1",103.6505,0.9665,30.71,9.62,22.5,0.5483
+"AA2",187.3814,0.9971,27.14,8.64,19.13,0.5107
+"AA3",87.605,0.8224,31.9,9.12,24.46,0.5699
+"AA4",270.3022,0.8563,26.97,7.92,19.8,0.5285
+"ABR",142.1181,1.4235,24.98,9.5,14.01,0.4607
+"ADR",105.0941,1.4423,26.4,11.94,15.92,0.5151
+"AE1",310.0796,1.2487,27.89,9.16,19.21,0.4793
+"AE2",203.875,1.2345,27.62,8.91,19.11,0.4752
+"AI1",170.3255,0.5761,32.52,8.41,25.95,0.6182
+"AI2",344.4032,0.5752,32.9,8.84,26.12,0.6285
+"AML",152.9057,1.1795,27.71,9.93,19,0.5164
+"AN1",137.0323,1.2401,28.53,10.81,19.05,0.5231
+"AN2",188.1505,1.1367,25.76,8.84,17.3,0.495
+"AN3",189.0312,1.1072,24.35,8,16.26,0.478
+"AN4",129.9723,1.1237,24.45,8.23,16.19,0.4817
+"AN5",155.2782,1.1312,23.57,7.91,15.3,0.4715
+"APL",179.9313,1.1008,26.66,7.78,19.31,0.4675
+"ARY",127.4023,1.2902,26.15,10.07,16.02,0.4965
+"AW1",107.2608,1.4214,26.04,11.57,15.82,0.5108
+"AW2",53.0343,1.5126,25.57,11.96,14.4,0.5039
+"AWO",132.1917,1.2578,25.17,9.29,15.85,0.483

data-raw/data.prep.R

+require(vegan)
+
+bacteria <- read.csv("data-raw/bacteria.csv",
+  header = TRUE,
+  row.names = 1,
+  na.strings = "NA"
+)
+
+eukaryotes <- read.csv("data-raw/eukaryotes.csv",
+  header = TRUE,
+  row.names = 1,
+  na.strings = "NA"
+)
+
+climat <- read.csv("data-raw/climat.csv",
+  header = TRUE,
+  row.names = 1,
+  na.strings = "NA"
+)
+
+soil <- read.csv("data-raw/soil.csv",
+  header = TRUE,
+  row.names = 1,
+  na.strings = "NA"
+)
+
+geography <- read.csv("data-raw/geography.csv",
+  header = TRUE,
+  row.names = 1,
+  na.strings = "NA"
+)
+
+usethis::use_data(bacteria, overwrite = TRUE)
+usethis::use_data(eukaryotes, overwrite = TRUE)
+usethis::use_data(climat, overwrite = TRUE)
+usethis::use_data(soil, overwrite = TRUE)
+usethis::use_data(geography, overwrite = TRUE)
+
+#
+# MicroCurvulatum datasets
+#
+
+mc.bacteria <- read.table("data-raw/MicroCurvulatum/rarefied_bacterial_community_matrix.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.bacteria = as.matrix(mc.bacteria)
+
+mc.fungi <- read.table("data-raw/MicroCurvulatum/rarefied_fungal_community_matrix.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.fungi <- as.matrix(mc.fungi[row.names(mc.bacteria),])
+
+mc.plants <- read.table("data-raw/MicroCurvulatum/Phytosocio.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.plants <- as.matrix(mc.plants[substr(row.names(mc.bacteria),
+                              1,
+                              nchar(row.names(mc.bacteria))-1),])
+
+mc.soil <- read.table("data-raw/MicroCurvulatum/SOIL_CHEM.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.soil <- as.matrix(mc.soil[row.names(mc.bacteria),5:10])
+
+mc.geography <- read.table("data-raw/MicroCurvulatum/Coord_geo.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.geography <- as.matrix(mc.geography[substr(row.names(mc.bacteria),
+                              1,
+                              nchar(row.names(mc.bacteria))-1),])
+
+MicroCurvulatum = ProcMod::procmod.frame(bacteria = mc.bacteria,
+                                         fungi = mc.fungi,
+                                         plants = mc.plants,
+                                         soil = mc.soil)
+
+usethis::use_data(MicroCurvulatum, overwrite = TRUE)

data-raw/geography.csv

+"","Latitude","Longitude"
+"AA1",-17.609964,145.766577
+"AA2",-17.68226,145.53411
+"AA3",-16.167472,145.404203
+"AA4",-16.594605,145.281487
+"ABR",-32.122698,151.681657
+"ADR",-30.36654,152.730009
+"AE1",-21.25014,148.54388
+"AE2",-21.118972,148.508602
+"AI1",-12.714988,143.319181
+"AI2",-12.739328,143.241397
+"AML",-26.735437,152.89289
+"AN1",-28.64008,153.332294
+"AN2",-28.581605,153.375615
+"AN3",-28.568607,153.344726
+"AN4",-28.544691,153.289707
+"AN5",-28.460052,153.133512
+"APL",-18.994224,146.164664
+"ARY",-34.6369444,150.7208333
+"AW1",-30.361,152.795083
+"AW2",-29.476051,152.335134
+"AWO",-34.402028,150.843187

data-raw/soil.csv

+"","KLg","pH","AlLg","FeLg","PLg","SLg","CaLg","MgLg","MnLg","CNratio","CLg","NLg"
+"AA1",2.336459734,5.51,4.752824104,5.123854906,3.152594078,2.995196292,2.80140371,2.923244019,3.127428778,2.59371350857308,2.15443305197019,-0.439280456602895
+"AA2",2.225309282,5.38,5.021193435,5.206828574,3.238297068,2.902546779,2.26245109,2.99343623,3.187802639,2.52997764835275,2.05949390387014,-0.470483744482613
+"AA3",2.600972896,5.59,4.442495447,4.212214247,1.883661435,2.499687083,1.996511672,2.178976947,1.824125834,2.77979061738005,0.862467925176648,-1.9173226922034
+"AA4",2.491361694,4.79,4.136752266,3.532881719,2.371067862,2.444044796,2.136720567,2.257678575,1.959041392,2.65877734673006,0.979453306831795,-1.67932403989827
+"ABR",3.130655349,4.4,4.60853673,4.399691024,2.825426118,2.933993164,3.374931554,2.98721923,2.835690571,2.74704992700182,2.55334381134123,-0.193706115660593
+"ADR",2.760422483,4.5,4.860343997,4.916985305,3.633569443,3.053462605,3.742803658,3.385784959,3.124178055,2.58561770154574,2.60933422816305,0.0237165266173161
+"AE1",2.59439255,5.82,4.964264346,5.075550611,3.456517858,2.963787827,3.045714059,2.617000341,3.320354033,2.48703632681194,2.14651590734416,-0.340520419467782
+"AE2",2.481442629,6.12,4.754355982,4.594403601,2.963787827,2.816903839,3.01745073,2.610660163,3.285782274,2.44241829197956,1.59168119084484,-0.850737101134716
+"AI1",2.588831726,6,3.893817224,4.348324338,2.342422681,2.484299839,3.243286146,2.803457116,2.562292864,2.5925052251585,1.04133622084838,-1.55116900431012
+"AI2",3.086715664,4.76,3.894371454,4.113976758,2.178976947,2.460897843,2.696356389,2.693726949,2.002597981,2.63340227213191,1.15499262210422,-1.4784096500277
+"AML",2.933487288,5.3,4.499700826,4.638499241,3.193402903,2.98721923,3.854366778,3.706803097,3.360025089,2.43523094205837,2.33408376005342,-0.101147182004958
+"AN1",2.641474111,5.4,4.849425557,5.029387836,3.363799945,2.951337519,3.817631467,3.083144143,3.416806872,2.391482313416,2.06242253651024,-0.329059776905759
+"AN2",2.547774705,4.4,4.61910377,4.187548921,2.190331698,2.799340549,3.262688344,2.618048097,1.703291378,2.76547430488356,2.23001440015921,-0.535459904724352
+"AN3",2.51054501,4.7,4.57635287,4.243062865,2.214843848,2.773786445,3.100715087,2.563481085,1.631443769,2.70588053221028,2.08654127879506,-0.619339253415225
+"AN4",2.833784375,4.4,4.872744649,4.900372591,3.114277297,2.993876915,3.546666025,3.25791845,3.255513713,2.44530482454504,2.31154483436552,-0.133759990179524
+"AN5",2.790988475,4.2,5.021193435,4.965676671,3.179264464,3.049605613,3.563599729,3.269746373,2.903632516,2.66378277732491,2.48740352998659,-0.176379247338325
+"APL",2.170261715,5.44,4.447173542,4.07191881,2.11058971,2.587710965,2.198657087,2.139879086,1.804139432,2.74373079612205,1.35583515363518,-1.38789564248686
+"ARY",2.63447727,5.08,4.190359716,4.26009525,2.622214023,2.640481437,3.037824751,2.63447727,2.42975228,2.61195103000548,1.77053583277643,-0.841415197229048
+"AW1",2.675778342,4.2,4.626350634,4.545319489,2.5132176,2.916453949,3.137037455,2.797959644,1.322219295,2.74478221037094,2.39607543608138,-0.348706774289555
+"AW2",2.607455023,5.3,4.571720475,4.68034458,2.86332286,2.769377326,3.632558515,2.778874472,3.053462605,2.5423619161776,1.97602358248135,-0.566338333696249
+"AWO",3.360025089,5.5,3.931508722,4.330434067,2.971739591,2.671172843,3.487279816,3.009025742,3.120902818,2.60195648126179,1.65803758227016,-0.943918898991629