I am currently working on an R code that uses the YAML package to create a config file. I need to import and use certain packages like any other code but for some reason when I try to run the program on my server at work it does not work properly. I have 4 packages I need but I went ahead an ran the code with just lubridate to keep the output short. If I can load lubridate I am sure I can load the others too.
Here is what I get from the terminal and after that is my code. I know its a bit messy but I am just trying to get it to work right now.
Terminal output: PS I cant post more than 2 links so I am cutting the output some
unable to load shared object '/nfs/aer/prg/r/lib-linux-x86_64/stringi/libs/stringi.so':
/lib64/libc.so.6: version `GLIBC_2.7' not found (required by /nfs/aer/prg/r/lib-linux-x86_64/stringi/libs/stringi.so)
ERROR: lazy loading failed for package ‘evaluate’
* removing ‘/nfs/aer/prg/r/lib-linux-x86_64/evaluate’
* restoring previous ‘/nfs/aer/prg/r/lib-linux-x86_64/evaluate’
ERROR: failed to lock directory ‘/nfs/aer/prg/r/lib-linux-x86_64’ for modifying
Try removing ‘/nfs/aer/prg/r/lib-linux-x86_64/00LOCK-lubridate’
* installing *source* package ‘knitr’ ...
** package ‘knitr’ successfully unpacked and MD5 sums checked
** R
** demo
** inst
** preparing package for lazy loading
Error in dyn.load(file, DLLpath = DLLpath, ...) :
unable to load shared object '/nfs/aer/prg/r/lib-linux-x86_64/stringi/libs/stringi.so':
/lib64/libc.so.6: version `GLIBC_2.7' not found (required by /nfs/aer/prg/r/lib-linux-x86_64/stringi/libs/stringi.so)
Error : unable to load R code in package ‘knitr’
ERROR: lazy loading failed for package ‘knitr’
* removing ‘/nfs/aer/prg/r/lib-linux-x86_64/knitr’
installation of package ‘knitr’ had non-zero exit status
Error in library(lubridate) : there is no package called ‘lubridate’
Execution halted
Code:
#install.packages('lubridate', repos="http://cran.rstudio.com/")
#install.packages('ggplot2', repos="http://cran.rstudio.com/")
#install.packages('plyr', repos="http://cran.rstudio.com/")
#install.packages('yaml', repos="http://cran.rstudio.com/")
install.packages('lubridate', dependencies = TRUE, repos="http://cran.rstudio.com/")
#install.packages("lubridate", repos="http://cran.rstudio.com/")
#install.packages('ggplot2', dependencies = TRUE, repos="http://cran.rstudio.com/")
#install.packages('plyr', dependencies = TRUE, repos="http://cran.rstudio.com/")
#install.packages('yaml', dependencies = TRUE, repos="http://cran.rstudio.com/")
library(lubridate)
#library(lubridate, lib.loc="Y:/work/anne/bin/R Library")
#library(ggplot2)
#library(plyr)
#library(yaml)
#install.packages("lubridate", repos = "http://cran.rstudio.com/", type="source")
#require(lubridate)
#require(ggplot2)
#require(plyr)
#require(yaml)
# install.packages("lubridate", repos="C:/Users/hadi/Documents/R/R-3.1.1/library")
#library("lubridate")
#install.packages("ggplot2", repos="http://cran.rstudio.com/")
#library("ggplot2")
#install.packages("plyr", repos="http://cran.rstudio.com/")
#library("plyr")
#install.packages("yaml", repos="http://cran.rstudio.com/")
#library("yaml")
#install_github("lubridate")
#install_github("ggplot2")
#install_github("plyr")
#install_github("yaml")
#Set Working Directory
#setwd('~/Desktop/Front End Testing/Binning Data')######
#Read Config File
config = yaml.load_file('BinConfig.yaml')
#Create Dataframe of the data
dataframe<-read.csv(config$db$dataframe)
#Filter up to 5 Variables at a time or as few as 1 or even 0 variables
dataframe<-dataframe[which(dataframe[config$db$filter1VarName]>=config$db$filter1greaterthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter1VarName]<=config$db$filter1lessthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter2VarName]>=config$db$filter2greaterthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter2VarName]<=config$db$filter2lessthanorequalto),]
#If you do not need this many filters just leave as is with "#" in front. You will get an error but it does not affect the program
dataframe<-dataframe[which(dataframe[config$db$filter3VarName]>=config$db$filter3greaterthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter3VarName]<=config$db$filter3lessthanorequalto),]
#If you do not need this many filters just leave as is with "#" in front. You will get an error but it does not affect the program
dataframe<-dataframe[which(dataframe[config$db$filter4VarName]>=config$db$filter4greaterthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter4VarName]<=config$db$filter4lessthanorequalto),]
#If you do not need this many filters just leave as is with "#" in front. You will get an error but it does not affect the program
dataframe<-dataframe[which(dataframe[config$db$filter5VarName]>=config$db$filter5greaterthanorequalto),]
dataframe<-dataframe[which(dataframe[config$db$filter5VarName]<=config$db$filter5lessthanorequalto),]
#Best kept small. This way if you have narrow bins you can still hit the min # of bins
xBinSize<-config$db$xMinBinSize;
#This is the number of bins you want provided the xBinSize allows for the number of bins
minXBins<- config$db$minXBins;
stationColors <- rainbow(length(stations));
plotX<-vector('list',length(stations));
plotY<-vector('list',length(stations));
stn<-stations
#dataframe<-subset(dataframe,ZAngSGR0_XI<2.7)
#Absorption_Angstrom_Exponent_BG_PM10
#For our application change these two red lines below to fit the column name you wissh to use. Name must be the same as in the data frame
rawX <- c(dataframe[paste(c(config$db$XVar), collapse="")][TRUE,1],
dataframe[paste(c(config$db$XVar), collapse="")][TRUE,1]);
rawY <- c(dataframe[paste(c(config$db$YVar), collapse="")][TRUE,1],
dataframe[paste(c(config$db$YVar), collapse="")][TRUE,1]);
dataX <- rawX[!is.na(rawX) & !is.na(rawY)];
dataY <- rawY[!is.na(rawX) & !is.na(rawY)];
# Figure out the bin range and bin size
rX <- range(dataX);
useBinSize <- xBinSize;
rX[1] <- floor(rX[1] / useBinSize) * useBinSize;
rX[2] <- ceiling(rX[2] / useBinSize) * useBinSize;
if ((rX[2] - rX[1]) > useBinSize * minXBins) {
rX <- range(dataX);
useBinSize <- (rX[2] - rX[1]) / minXBins;
}# < useBinSize * minXBins
nBins <- (rX[2] - rX[1]) / useBinSize ;
# Bin the data
bins <- seq(rX[1],rX[2], useBinSize);
outY <- seq(1,nBins);
outX <- seq(1,nBins);#bins
rows<-length(dataX)
outXBin1<-NULL
outXBindims=NULL
for (bin in seq(1,nBins)) {
outY[bin] <- mean(dataY[dataX>=bins[bin] & dataX<bins[bin+1]],na.rm=T);
outXBin1[bin] <- as.data.frame(dataX[dataX>=bins[bin] & dataX<bins[bin+1]]);
outXBindims[bin]<-length(outXBin1[[bin]])
#outYBinF[,bin] <- (dataY[dataX>=bins[bin] & dataX<bins[bin+1]]);
outX[bin] <- mean(c(bins[bin], bins[bin+1]));
#outXBinF[,bin] <- (c(bins[bin], bins[bin+1]));
}
plotX <- data.frame(outX[!is.na(outY)]);
plotY <- data.frame(outY[!is.na(outY)]);
weight<-data.frame(outXBindims[!is.na(outY)]);
DF<-cbind(plotX,plotY,weight)
names(DF)[names(DF)=='outX..is.na.outY..']<-config$db$XVar
names(DF)[names(DF)=='outY..is.na.outY..']<-config$db$YVar
names(DF)[names(DF)=='outXBindims..is.na.outY..']<-config$db$Y2Var
#DF$FrequencyPercent<-(DF[config$db$Y2Var]/(sum(DF[config$db$Y2Var]))*100)
#names(DF)[names(DF)=='FrequencyPercent']<-c("Frequency Percent")
write.csv(DF,config$db$BinnedDataName)
