change import function in R

.gitignore

@@ -0,0 +1 @@
+planemo/

tools/regionalgam/ab-index.R

@@ -1,14 +1,13 @@
 #!/usr/bin/env Rscript
 
-args = commandArgs(trailingOnly=TRUE)
+args <- commandArgs(trailingOnly = TRUE)
 source(args[1])
 
+input <- data.frame(data.table::fread(args[2]))
+pheno <- read.table(args[3], header = TRUE, sep = "	")
 
-tryCatch({input = read.table(args[2], header=TRUE,sep=" ")},finally={input = read.table(args[2], header=TRUE,sep=",")})
-pheno = read.table(args[3], header=TRUE,sep="	")
-
-if("TREND" %in% colnames(input)){
-    input <- input[input$TREND==1,c("SPECIES","SITE","YEAR","MONTH","DAY","COUNT")]
+if ("TREND" %in% colnames(input)) {
+    input <- input[input$TREND == 1, c("SPECIES", "SITE", "YEAR", "MONTH", "DAY", "COUNT")]
 }
-data.index <- abundance_index(input, pheno)
-write.table(data.index, file="data.index", row.names=FALSE, sep="	")
+data_index <- abundance_index(input, pheno)
+write.table(data_index, file = "data_index", row.names = FALSE, sep = "	")

tools/regionalgam/ab-index.xml

@@ -3,20 +3,22 @@
     <macros>
         <import>regionalgam_macros.xml</import>
     </macros>
-    <expand macro="rg_r_requirements"/>
+    <requirements>
+        <requirement type="package" version="1.12.2">r-data.table</requirement>
+    </requirements>
     <command detect_errors="exit_code"><![CDATA[
         Rscript '$__tool_directory__/ab-index.R'
-            '$__tool_directory__/dennis-gam-initial-functions.R' 
-            '$count_file' '$input2' 
-            '$output' 
+            '$__tool_directory__/dennis-gam-initial-functions.R'
+            '$count_file' '$input2'
+            '$output'
     ]]>
     </command>
     <inputs>
         <expand macro="rg_count_file"/>
         <param format="tabular" name="input2" type="data" label="Regional expected pattern of abundance" help="Flight curve output, tabular file."/>
     </inputs>
     <outputs>
-        <data format="tabular" name="output" from_work_dir="data.index" />
+        <data format="tabular" name="output" from_work_dir="data_index" />
     </outputs>
     <tests>
         <test>

tools/regionalgam/autocorr-res-acf.R

@@ -1,10 +1,10 @@
 #!/usr/bin/env Rscript
-args = commandArgs(trailingOnly=TRUE)
+args <- commandArgs(trailingOnly = TRUE)
 load(args[1])
 
-png('output-acf.png',width = 480, height = 480, units = "px")
-graph<-acf(residuals(mod,type="normalized"),plot=FALSE)
-plot(graph, main="Acf residuals")
+png("output_acf.png", width = 480, height = 480, units = "px")
+graph <- acf(residuals(mod, type = "normalized"), plot = FALSE)
+plot(graph, main = "Acf residuals")
 invisible(dev.off())
-sink("output-acf.txt")
+sink("output_acf.txt")
 print(graph)

tools/regionalgam/autocorr-res-acf.xml

@@ -6,17 +6,17 @@
     <expand macro="rg_r_requirements"/>
     <command detect_errors="exit_code"><![CDATA[
         Rscript '$__tool_directory__/autocorr-res-acf.R'
-            '$gls_model' 
-            '$output' 
-            '$output_res_values' 
+            '$gls_model'
+            '$output'
+            '$output_res_values'
     ]]>
     </command>
     <inputs>
         <expand macro="rg_gls_model"/>
     </inputs>
     <outputs>
-        <data format="png" name="output" from_work_dir="output-acf.png" />
-        <data format="txt" name="output_res_values" from_work_dir="output-acf.txt" />
+        <data format="png" name="output" from_work_dir="output_acf.png" />
+        <data format="txt" name="output_res_values" from_work_dir="output_acf.txt" />
     </outputs>
     <tests>
         <test>
@@ -31,7 +31,7 @@
     </edam_topics>
     <help><![CDATA[
 ======================================
-Model residuals autocorrelation check 
+Model residuals autocorrelation check
 ======================================
 
 This tool is an implementation of the autocorr-res_acf function from RegionalGAM package:  https://github.com/RetoSchmucki/regionalGAM/

tools/regionalgam/flight-curve.R

@@ -1,8 +1,8 @@
 #!/usr/bin/env Rscript
-args = commandArgs(trailingOnly=TRUE)
+args <- commandArgs(trailingOnly = TRUE)
 source(args[1]) #TODO replace by library(regionalGAM) if available as official package from bioconda
-tryCatch({input = read.table(args[2], header=TRUE,sep="	")},finally={input = read.table(args[2], header=TRUE,sep=",")})
-dataset1 <- input[,c("SPECIES", "SITE", "YEAR", "MONTH", "DAY", "COUNT")]
+input <- data.frame(data.table::fread(args[2]))
+dataset1 <- input[, c("SPECIES", "SITE", "YEAR", "MONTH", "DAY", "COUNT")]
 pheno <- flight_curve(dataset1, MinVisit = args[3], MinOccur = args[4])
 
-write.table(pheno, file="pheno", row.names=FALSE, sep="	")
+write.table(pheno, file = "pheno", row.names = FALSE, sep = "	")

tools/regionalgam/flight-curve.xml

@@ -5,14 +5,15 @@
     </macros>
     <requirements>
         <requirement type="package" version="1.8_28">r-mgcv</requirement>
+        <requirement type="package" version="1.12.2">r-data.table</requirement>
     </requirements>
     <command detect_errors="exit_code"><![CDATA[
         Rscript '$__tool_directory__/flight-curve.R' 
-            '$__tool_directory__/dennis-gam-initial-functions.R' 
-            '$count_file' 
-            $minvisit 
-            $minoccur 
-            '$output' 
+            '$__tool_directory__/dennis-gam-initial-functions.R'
+            '$count_file'
+            $minvisit
+            $minoccur
+            '$output'
     ]]>
     </command>
     <inputs>
@@ -34,7 +35,7 @@
                     <has_n_columns n="6"/>
                     <has_text_matching expression="&quot;Pyronia&#032;tithonus&quot;&#009;2003&#009;[0-9]&#009;[0-9]&#009;[0-9]&#009;[0-9]"/>
                 </assert_contents>
-            </output>   
+            </output>
         </test>
     </tests>
     <edam_topics>
@@ -50,7 +51,7 @@ This tool is an implementation of the flight_curve function RegionalGAM package:
 
 This function computes the annual phenology on a specific region.
 
-The output tabular file can be used to impute expected count values with the abundance index computation tool. 
+The output tabular file can be used to impute expected count values with the abundance index computation tool.
 
 |
 

tools/regionalgam/glmmpql.R

@@ -2,15 +2,15 @@
 library(nlme)
 library(MASS)
 
-args = commandArgs(trailingOnly=TRUE)
-input = read.table(args[1], header=TRUE,sep="	") 
-glmm.mod_fullyear <- glmmPQL(regional_gam~ as.factor(YEAR)-1,data=input,family=quasipoisson,random=~1|SITE, correlation = corAR1(form = ~ YEAR | SITE),verbose = FALSE)
+args <- commandArgs(trailingOnly = TRUE)
+input <- read.table(args[1], header = TRUE, sep = "	")
+glmm_mod_fullyear <- glmmPQL(regional_gam ~ as.factor(YEAR) - 1, data = input, family = quasipoisson, random = ~ 1 | SITE, correlation = corAR1(form = ~ YEAR | SITE), verbose = FALSE)
 
-col.index <- as.numeric(glmm.mod_fullyear$coefficients$fixed)
+col_index <- as.numeric(glmm_mod_fullyear$coefficients$fixed)
 year <- unique(input$YEAR)
 
-write.table(col.index, file="output-glmmpql", row.names=FALSE, sep=" ")
+write.table(col_index, file = "output-glmmpql", row.names = FALSE, sep = " ")
 
-png('output-plot.png')
-plot(year,col.index,type='o', xlab="year",ylab="collated index")
+png("output-plot.png")
+plot(year, col_index, type = "o", xlab = "year", ylab = "collated index")
 invisible(dev.off())

tools/regionalgam/gls-adjusted.R

@@ -2,15 +2,15 @@
 library(nlme)
 library(MASS)
 
-args = commandArgs(trailingOnly=TRUE)
-input1 = read.table(args[1], header=TRUE) #input1=col.index =glmmpql-output
-input2 = read.table(args[2], header=TRUE,sep="	") #input2=data.index =abundance_index-output
+args <- commandArgs(trailingOnly = TRUE)
+input1 <- read.table(args[1], header = TRUE)
+input2 <- read.table(args[2], header = TRUE, sep = "	")
 
-input1<-as.matrix(input1)
+input1 <- as.matrix(input1)
 
 year <- unique(input2$YEAR)
-mod <- gls(input1~year, correlation = corARMA(p=2))
-summary<-summary(mod)
+mod <- gls(input1 ~ year, correlation = corARMA(p = 2))
+summary <- summary(mod)
 
 save(mod, file = "mod_adjusted.rda")
-capture.output(summary, file="mod_adjusted-summary.txt")
+capture.output(summary, file = "mod_adjusted-summary.txt")

tools/regionalgam/gls.R

@@ -2,15 +2,15 @@
 library(nlme)
 library(MASS)
 
-args = commandArgs(trailingOnly=TRUE)
-input1 = read.table(args[1], header=TRUE) #input1=col.index =glmmpql-output
-input2 = read.table(args[2], header=TRUE,sep="	") #input2=data.index =abundance_index-output
+args <- commandArgs(trailingOnly = TRUE)
+input1 <- read.table(args[1], header = TRUE)
+input2 <- read.table(args[2], header = TRUE, sep = "	")
 
-input1<-as.matrix(input1)
+input1 <- as.matrix(input1)
 
 year <- unique(input2$YEAR)
-mod <- gls(input1~year)
-summary<-summary(mod)
+mod <- gls(input1 ~ year)
+summary <- summary(mod)
 
 save(mod, file = "mod.rda")
-capture.output(summary, file="mod-summary.txt")
+capture.output(summary, file = "mod-summary.txt")

tools/regionalgam/plot-trend.R

@@ -2,22 +2,22 @@
 library(nlme)
 library(MASS)
 
-args = commandArgs(trailingOnly=TRUE)
-input = read.table(args[1], header=TRUE,sep="	") #input=data.index =ab_index-output
+args <- commandArgs(trailingOnly = TRUE)
+input <- read.table(args[1], header = TRUE, sep = "	")
 load(args[2])
 
-glmm.mod_fullyear <- glmmPQL(regional_gam~ as.factor(YEAR)-1,data=input,family=quasipoisson,random=~1|SITE, correlation = corAR1(form = ~ YEAR | SITE),verbose = FALSE)
+glmm_mod_fullyear <- glmmPQL(regional_gam ~ as.factor(YEAR) - 1, data = input, family = quasipoisson, random = ~ 1 | SITE, correlation = corAR1(form = ~ YEAR | SITE), verbose = FALSE)
 
-col.index <- as.numeric(glmm.mod_fullyear$coefficients$fixed)
+col_index <- as.numeric(glmm_mod_fullyear$coefficients$fixed)
 year <- unique(input$YEAR)
 
-png('output-plot-trend.png')
-plot(year,col.index, type='o',xlab="year",ylab="collated index")
-abline(mod,lty=2,col="red")
+png("output-plot-trend.png")
+plot(year, col_index, type = "o", xlab = "year", ylab = "collated index")
+abline(mod, lty = 2, col = "red")
 invisible(dev.off())
 
-d<-data.frame(year,col.index)
-colnames(d)<-c("year","collated index")
-write.table(d,"s_trend.tsv",sep="	",row.names=FALSE)
+d <- data.frame(year, col_index)
+colnames(d) <- c("year", "collated index")
+write.table(d, "s_trend.tsv", sep = "	", row.names = FALSE)
 
 cat("Show trend line on abundance plot")