pierreqi/r_code_50k · Datasets at Hugging Face

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Bdiag.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.r \name{Bdiag} \alias{Bdiag} \title{Create block diagonal matrix} \usage{ Bdiag(A, k) } \arguments{ \item{A}{a numeric matrix forming each block.} \item{k}{an integer value indicating the number of blocks.} } \value{ Return a block diagonal matrix from the matrix A. } \description{ Create block diagonal matrix } \examples{ Bdiag(matrix(1:4,2,2), 3) }

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/PLOT4.R

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nsamniegorojas/ExData_Plotting1

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PLOT4.R

rm(list = ls()) #reading file csv setwd ("C:/R_PROJECTS/EXPLORATORY_DATA") #install.packages ("readr") #library("readr") data <-read.csv2 ("C:/R_PROJECTS/EXPLORATORY_DATA/PROJECT1_EDA/household_power_consumption.txt", header = TRUE) data <- na.omit(data) #subsetting data subdata<-subset(data, data$Date=="1/2/2007"|data$Date=="2/2/2007") datetime <- paste(as.Date(subdata$Date, format="%d/%m/%Y"), subdata$Time) datatime1 <- as.POSIXct(datetime) datetime png("plot4.png", width=480, height=480) par(mfrow = c(2, 2)) # P4.1 plot(datatime1, subdata$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power (kilowatts)") # P4.2 plot(datatime1, subdata$Voltage, type="l", xlab="datetime", ylab="Voltage") # P4.3 plot(datatime1, subdata$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering") lines(datatime1, subdata$Sub_metering_2, col = "red") lines(datatime1, subdata$Sub_metering_3, col = "blue") legend("topright", col = c("black","red","blue"), c("Sub_metering_1 ", "Sub_metering_2", "Sub_metering_3"), lty = c(1,1), lwd = c(1,1), bty = "n", cex = 0.6) # P4.4 plot(datatime1, subdata$Global_reactive_power, type="l", xlab="datetime", ylab="Global_reactive_power") dev.off() file.show("plot4.png")

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/man/check_inds.Rd

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USAID-OHA-SI/Wavelength

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check_inds.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/validate_output.R \name{check_inds} \alias{check_inds} \title{Validate indicators for export} \usage{ check_inds(df) } \arguments{ \item{df}{HFR data framed created by \code{hfr_process_template()}} } \description{ Check whether there are any rows/records with missing indicators and provides readout }

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LeoCai/MultiDeviceAlign-R

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exp_newdata.R

source("./utils.R") source("./getMatrixByMag.R") source("./readData_new_1.R") source("./addPCAData.R") lacctop = cbind(top$LinearAcc0,top$LinearAcc1,top$LinearAcc2) gacctop = getGlobalAccByMag(top) plot(gacctop[200:1000,2],type="l",main="global top forward") cor(gacctop[200:1000,2],lacctop[200:1000,3]) plot(lacctop[200:1000,3],type="l",main = "top forward") matplot(lacctop[100:800,],type="l",main = "top")

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/Main/Level1/scripts/GenerateDrug_ExposureReport_QueryWise.R

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GenerateDrug_ExposureReport_QueryWise.R

flog.info(Sys.time()) generateDrugExposureReport <- function() { table_name<-"drug_exposure" data_tbl <- cdm_tbl(req_env$db_src, table_name) concept_tbl <- vocab_tbl(req_env$db_src, "concept") #writing to the final DQA Report fileConn<-file(paste(normalize_directory_path( g_config$reporting$site_directory), "./reports/",table_name,"_Report_Automatic.md",sep="")) fileContent <-get_report_header(table_name, g_config) logFileData<-data.frame(g_data_version=character(0), table=character(0),field=character(0), issue_code=character(0), issue_description=character(0), alias=character(0) , finding=character(0), prevalence=character(0)) logFileData<- custom_rbind(logFileData,applyCheck(DrugClass(), c("drug_exposure"),c("drug_concept_id"))) #PRIMARY FIELD field_name<-"drug_exposure_id" df_total_measurement_count<-retrieve_dataframe_count(data_tbl,field_name) current_total_count<-as.numeric(df_total_measurement_count[1][1]) fileContent<-c(fileContent,paste("The total number of",field_name,"is:", formatC(current_total_count, format="d", big.mark=','),"\n")) ###########DQA CHECKPOINT############## difference from previous cycle logFileData<-custom_rbind(logFileData,applyCheck(UnexDiff(), c(table_name), NULL,current_total_count)) ## write current total count to total counts write_total_counts(table_name, current_total_count) df_total_patient_count<-retrieve_dataframe_count(data_tbl,"person_id", distinction = T) fileContent<-c(fileContent,paste("The drug exposure to patient ratio is ",round(df_total_measurement_count[1][1]/df_total_patient_count[1][1],2),"\n")) df_total_visit_count<-retrieve_dataframe_count(data_tbl,"visit_occurrence_id", distinction = T) fileContent<-c(fileContent,paste("The drug exposure to visit ratio is ",round(df_total_measurement_count[1][1]/df_total_visit_count[1][1],2),"\n")) # visit concept id df_visit <-retrieve_dataframe_clause(concept_tbl,c("concept_id" ,"concept_name") ,"vocabulary_id =='Visit' | (vocabulary_id == 'PCORNet' & concept_class_id == 'Encounter Type') | (concept_class_id == 'Encounter Type' & domain_id == 'Visit') | (vocabulary_id == 'PCORNet' & concept_class_id == 'Undefined')") #condition / person id by visit types fileContent <-c(fileContent,paste("## Barplot for Drug:Patient ratio by visit type\n")) df_drug_patient_ratio <- retrieve_dataframe_ratio_group_join(data_tbl, cdm_tbl(req_env$db_src,"visit_occurrence"), num ="drug_exposure_id", den = "person_id", "visit_concept_id", "visit_occurrence_id") for(i in 1:nrow(df_drug_patient_ratio)) { label<-df_visit[df_visit$concept_id==df_drug_patient_ratio[i,1],2] df_drug_patient_ratio[i,1]<-paste(df_drug_patient_ratio[i,1],"(",label,")",sep="") } describeOrdinalField(df_drug_patient_ratio,table_name,"drug_exposure_id_person_id_ratio", group_ret = 1); fileContent<-c(fileContent,paste_image_name(table_name,"drug_exposure_id_person_id_ratio")); #NOMINAL Fields field_name<-"person_id" # fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) df_table<-retrieve_dataframe_group(data_tbl,field_name) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name), paste_image_name_sorted(table_name,field_name),message); field_name<-"drug_source_value" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent_source_value<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeOrdinalField(df_table, table_name,field_name,ggplotting = F) fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); field_name<-"drug_source_concept_id" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- no matching concept ############## logFileData<-custom_rbind(logFileData,applyCheck(MissConID(), c(table_name),c(field_name), data_tbl)) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) if(nrow(df_table)>1) { fileContent<-c(fileContent,paste("\n The source vocabulary is",get_vocabulary_name(concept_tbl, df_table[2,1]),"\n")) } if(nrow(df_table)==1){ if(is.na(df_table[1,1])) { fileContent<-c(fileContent,paste("\n The source vocabulary is NA \n")) } else { fileContent<-c(fileContent,paste("\n The source vocabulary is",get_vocabulary_name(concept_tbl, df_table[1,1]),"\n")) } } message<-describeOrdinalField(df_table, table_name,field_name, ggplotting = F) new_message<-"" if(length(message)>0) { # create meaningful message new_message<-create_meaningful_message_concept_id(concept_tbl, message,field_name) } fileContent<-c(fileContent,new_message,paste_image_name(table_name,field_name)); flog.info(Sys.time()) field_name<-"drug_concept_id" # df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) ###########DQA CHECKPOINT -- no matching concept ############## logFileData<- custom_rbind(logFileData,applyCheck(MissConID(), c(table_name),c(field_name),data_tbl)) ### DQA CHECKPOINT ########## logFileData<-custom_rbind(logFileData,applyCheck(InvalidVocab(), c(table_name),c(field_name), c('Drug','RxNorm', 'RxNorm Extension','NDC'), concept_tbl, data_tbl)) message<-describeOrdinalField(df_table, table_name,field_name,ggplotting = F) # create meaningful message new_message<-create_meaningful_message_concept_id(concept_tbl, message,field_name) fileContent<-c(fileContent,new_message,paste_image_name(table_name,field_name)); null_message<-reportNullFlavors(df_table,table_name,field_name,44814653,44814649,44814650) ###########DQA CHECKPOINT############## source value Nulls and NI concepts should match logFileData<-custom_rbind(logFileData,applyCheck(InconSource(), c(table_name), c(field_name, "drug_source_value"), data_tbl)) fileContent<-c(fileContent,"###Distribution of drug concept class:") df_concept_class<-retrieve_dataframe_join_clause_group(data_tbl, concept_tbl, "drug_concept_id", "concept_class_id","drug_concept_id!=0") fileContent<-c(fileContent,print_2d_dataframe(df_concept_class)) ### ingredient-level normalization # also draw distribution of drug concept id vs person_id field_name<-"drug_concept_id" df_table_new<-retrieve_dataframe_count_group(data_tbl,"person_id", field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"by person_id","\n")) message<-describeOrdinalField(df_table_new, "person_id",field_name, ggplotting = F) # create meaningful message new_message<-create_meaningful_message_concept_id(concept_tbl, message,field_name) fileContent<-c(fileContent,new_message,paste_image_name("person_id",field_name)); field_name<-"drug_exposure_start_datetime" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-describeDateField(df_table, table_name,field_name, datetime = 1) ### DQA checkpoint - future date logFileData<-custom_rbind(logFileData,applyCheck(ImplFutureDate(), c(table_name), c(field_name),data_tbl)) fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); message<-describeTimeField(df_table, table_name,field_name) fileContent<-c(fileContent,message,paste_image_name(table_name,paste(field_name,"_datetime",sep=""))); field_name<-"drug_exposure_end_datetime" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) if(missing_percent<100){ message<-describeDateField(df_table, table_name,field_name, datetime = 1) ### DQA checkpoint - future date logFileData<-custom_rbind(logFileData,applyCheck(ImplFutureDate(), c(table_name), c(field_name),data_tbl)) fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); message<-describeTimeField(df_table, table_name,field_name) fileContent<-c(fileContent,message,paste_image_name(table_name,paste(field_name,"_datetime",sep=""))); } #implausible event clause logFileData<-custom_rbind(logFileData,applyCheck(ImplEvent(), c(table_name), c('drug_exposure_start_date','drug_exposure_end_date'), data_tbl)) # drug exposure end date field_name<-"drug_exposure_end_date" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeDateField(df_table, table_name,field_name) field_name<-"drug_exposure_order_datetime" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeDateField(df_table, table_name,field_name) ###########DQA CHECKPOINT -- future dates ############## if(missing_percent!=100){ ### DQA checkpoint - future date logFileData<-custom_rbind(logFileData,applyCheck(ImplFutureDate(), c(table_name), c(field_name),data_tbl)) } fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); message<-describeTimeField(df_table, table_name,field_name) fileContent<-c(fileContent,message,paste_image_name(table_name,paste(field_name,"_datetime",sep=""))); # drug exposure order date field_name<-"drug_exposure_order_date" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeDateField(df_table, table_name,field_name) #drug type concept id field_name="drug_type_concept_id" df_table<-retrieve_dataframe_group(data_tbl,field_name) #### Check for unexpected differences from prev cycle fact_type_count<-df_table[df_table$drug_type_concept_id==38000175,2] write_total_fact_type_counts(table_name,"Dispensing" , fact_type_count) logFileData<-custom_rbind(logFileData,applyCheck(UnexDiffFactType(), c(table_name), c(field_name) ,c("Dispensing",fact_type_count))) fact_type_count<-df_table[df_table$drug_type_concept_id==38000180,2] write_total_fact_type_counts(table_name,"InpatientMAR" , fact_type_count) logFileData<-custom_rbind(logFileData,applyCheck(UnexDiffFactType(), c(table_name), c(field_name) ,c("InpatientMAR",fact_type_count))) fact_type_count<-df_table[df_table$drug_type_concept_id==38000177,2] write_total_fact_type_counts(table_name,"Prescriptions" , fact_type_count) logFileData<-custom_rbind(logFileData,applyCheck(UnexDiffFactType(), c(table_name), c(field_name) ,c("Prescriptions",fact_type_count))) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) ###########DQA CHECKPOINT -- no matching concept ############## logFileData<-custom_rbind(logFileData,applyCheck(MissConID(), c(table_name),c(field_name), data_tbl)) logFileData<-custom_rbind(logFileData,applyCheck(InvalidConID(), c(table_name),c(field_name) ,"drug_type_concept_id.csv", concept_tbl, data_tbl)) describeNominalField(df_table,table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name)); ## DQA check for recall of various drug types logFileData<-custom_rbind(logFileData,applyCheck(MissFact(), c(table_name),c(field_name), list( list(38000175, "Dispensing"), list(38000180, "Inpatient Administration"), list(38000177, "prescription")), data_tbl)) field_name<-"stop_reason" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeOrdinalField(df_table, table_name,field_name, ggplotting = F) fileContent<-c(fileContent, paste_image_name(table_name,field_name),message); field_name="refills" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeRatioField(df_table, table_name,field_name,"") fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); field_name="quantity" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeRatioField(df_table, table_name,field_name,"") fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); field_name="days_supply" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeRatioField(df_table, table_name,field_name,"") fileContent<-c(fileContent,message,paste_image_name(table_name,field_name)); field_name<-"lot_number" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name), data_tbl)) field_name<-"frequency" df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name),message) field_name<-"route_source_value" fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) df_table<-retrieve_dataframe_group(data_tbl,field_name) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent_source_value<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) describeNominalField(df_table, table_name, field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name)); df_table<-retrieve_dataframe_top_5(data_tbl, field_name) fileContent<-c(fileContent,paste("The most frequent values for",field_name,"are:")) for(row_count in 1:5) { fileContent<-c(fileContent,paste(df_table[row_count,1],"(count=",df_table[row_count,2],")")) } field_name="route_concept_id" fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) ######## DQA Checkpoint #################### logFileData<-custom_rbind(logFileData,applyCheck(InvalidConID(), c(table_name),c(field_name) ,"route_concept_id_dplyr.txt", concept_tbl, data_tbl)) df_route_concept_id <-generate_df_concepts(table_name,"route_concept_id_dplyr.txt", concept_tbl) df_table<-retrieve_dataframe_group(data_tbl,field_name) ###########DQA CHECKPOINT -- missing information############## message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) ###########DQA CHECKPOINT -- no matching concept ############## logFileData<-custom_rbind(logFileData,applyCheck(MissConID(), c(table_name),c(field_name), data_tbl)) df_table_route_enhanced<-EnhanceFieldValues(df_table,field_name,df_route_concept_id); describeNominalField(df_table_route_enhanced,table_name,field_name); fileContent<-c(fileContent,paste_image_name(table_name,field_name)); null_message<-reportNullFlavors(df_table,table_name,field_name,44814653,44814649,44814650) ###########DQA CHECKPOINT############## source value Nulls and NI concepts should match logFileData<-custom_rbind(logFileData,applyCheck(InconSource(), c(table_name), c(field_name, "route_source_value"), data_tbl)) field_name<-"dose_unit_source_value" # 3 minutes df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent_source_value<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) describeNominalField(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name)); df_table<-retrieve_dataframe_top_5(data_tbl, field_name) fileContent<-c(fileContent,paste("The most frequent values for",field_name,"are:")) for(row_count in 1:5) { fileContent<-c(fileContent,paste(df_table[row_count,1],"(count=",df_table[row_count,2],")")) } field_name<-"dose_unit_concept_id" # ########## DQA checkpoint ################## field_name="dose_unit_concept_id" logFileData<-custom_rbind(logFileData,applyCheck(InvalidConID(), c(table_name),c(field_name) ,"dose_unit_concept_id_dplyr.txt", concept_tbl, data_tbl)) df_table<-retrieve_dataframe_group(data_tbl,field_name) df_dose_unit_concept_id <-generate_df_concepts(table_name,"dose_unit_concept_id_dplyr.txt", concept_tbl) df_table<-retrieve_dataframe_group(data_tbl,field_name) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) ###########DQA CHECKPOINT############## fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) ###########DQA CHECKPOINT -- no matching concept ############## logFileData<-custom_rbind(logFileData,applyCheck(MissConID(), c(table_name),c(field_name),data_tbl)) df_table_unit_enhanced<-EnhanceFieldValues(df_table,field_name,df_dose_unit_concept_id); describeNominalField(df_table_unit_enhanced,table_name,field_name); fileContent<-c(fileContent,paste_image_name(table_name,field_name)); df_table_top_5<-retrieve_dataframe_top_5(data_tbl, field_name) fileContent<-c(fileContent,paste("The most frequent values for",field_name,"are:")) for(row_count in 1:5) { fileContent<-c(fileContent,paste(df_table_top_5[row_count,1],"(count=",df_table_top_5[row_count,2],")")) } null_message<-reportNullFlavors(df_table,table_name,field_name,44814653,44814649,44814650) ###########DQA CHECKPOINT############## source value Nulls and NI concepts should match logFileData<-custom_rbind(logFileData,applyCheck(InconSource(), c(table_name),c(field_name, "dose_unit_source_value"), data_tbl)) fileContent<-c(fileContent,"\nDQA NOTE: There should be a one-to-one correspondence between dose_unit source value and concept id fields; compare the top 5 values") fileContent<-c(fileContent,"\nDQA NOTE: Look for cases where dose_unit_concept_id =0, this would happen when dose_unit_source_value is either NULL or cannot be mapped") field_name<-"effective_drug_dose" # df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name),message); fileContent<-c(fileContent,"\nDQA NOTE: effective_drug_dose cannot be generated if dose_unit_source_value is NULL") field_name<-"eff_drug_dose_source_value" # df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name), data_tbl)) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name),message); field_name<-"provider_id" # fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) df_table<-retrieve_dataframe_group(data_tbl,field_name) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name),data_tbl)) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name), paste_image_name_sorted(table_name,field_name),message); field_name<-"visit_occurrence_id" fileContent <-c(fileContent,paste("## Barplot for",field_name,"\n")) df_table<-retrieve_dataframe_group(data_tbl,field_name) message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) fileContent<-c(fileContent,message) ###########DQA CHECKPOINT -- missing information############## missing_percent<-extract_numeric_value(message) logFileData<-custom_rbind(logFileData,applyCheck(MissData(), c(table_name),c(field_name), data_tbl)) message<-describeForeignKeyIdentifiers(df_table, table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name), paste_image_name_sorted(table_name,field_name),message); flog.info(Sys.time()) field_name="lot_number" fileContent <-c(fileContent,paste("## Description for",field_name,"","\n")) df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent<-c(fileContent,reportMissingCount(df_table,table_name,field_name, group_ret = 1)) # this is a nominal field - work on it field_name<-"dispense_as_written_concept_id" # if(field_name %in% colnames(data_tbl)){ ##field currently is not in Oracle test db df_table<-retrieve_dataframe_group(data_tbl,field_name) fileContent <-c(fileContent,paste("## Barplot for",field_name,"","\n")) missing_percent_message<-reportMissingCount(df_table,table_name,field_name, group_ret = 1) missing_percent<- extract_numeric_value(missing_percent_message) fileContent<-c(fileContent,missing_percent_message) ###########DQA CHECKPOINT############## logFileData<-custom_rbind(logFileData,applyCheck(InvalidConID(), c(table_name),c(field_name) ,"dispense_as_written_concept_id.csv", concept_tbl, data_tbl)) describeNominalField(df_table,table_name,field_name) fileContent<-c(fileContent,paste_image_name(table_name,field_name)); } # other fields not plotted : operator concept id, value as concept id, value source value, measurement source concept id #write all contents to the report file and close it. writeLines(fileContent, fileConn) close(fileConn) colnames(logFileData)<-c("g_data_version", "table","field", "issue_code", "issue_description","alias","finding", "prevalence") logFileData<-subset(logFileData,!is.na(issue_code)) write.csv(logFileData, file = paste(normalize_directory_path( g_config$reporting$site_directory),"./issues/",table_name,"_issue.csv",sep="") ,row.names=FALSE) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/api.R \name{qapi_connect} \alias{qapi_connect} \title{qapi_connect} \usage{ qapi_connect(org_id, api_key, auth_file = ".qapi_auth.R", verbose = FALSE) } \arguments{ \item{org_id}{Qualtrics org_id with which to get surveys} \item{api_key}{Qualtrics API key} \item{auth_file}{File from which to source Qualtrics API auth info} } \description{ Open a connection to Qualtrics API with login info }

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# mean of 100 random numbers mean(abs(rnorm(100))) # 10 random normal variables rnorm(10) x = c(1,2,4) q = c(x, x, 8) x[1] x[1:3] mean(x) sd(x) #Internal R datasets data() #Nile Dataset mean(Nile) sd(Nile) hist(Nile) hist(Nile, breaks = 20) #Functions oddcount = function(x){ k = 0 for(n in x){ if(n%%2 == 1) k = k + 1 } return(k) } oddcount(c(1,2,3,4,5,6,7,8,9)) oddcount(c(1,3,5,7)) #Scope y = 5 #Y is global variable. func = function(x) return(x+y) # y: global variable returned in the function func(3) #Default Arguments func2 = function (a, b = 10){ return(a+b) } func2(2,10) func2(10) #String y = c("abc"," def"," ghi") y u = paste("abc","def","ghi") u k = strsplit(u," ") k #Matrices m = rbind(c(1,4),c(2,2)) #row bind m m[1,] m[,1] m = cbind(c(1,4),c(2,2)) #row bind m m[1,] m[,1] #Lists: #Contents can be different data types x = list(u = 2, v= "abc") x hn = hist(Nile) print(hn) hn$density #DataFrames d = data.frame(list(kids = c("jack","annie"),ages = c(10,12))) d$kids d$ages #Classes hn = hist(Nile) print(hn)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/finspace_operations.R \name{finspace_update_kx_user} \alias{finspace_update_kx_user} \title{Updates the user details} \usage{ finspace_update_kx_user(environmentId, userName, iamRole, clientToken = NULL) } \arguments{ \item{environmentId}{[required] A unique identifier for the kdb environment.} \item{userName}{[required] A unique identifier for the user.} \item{iamRole}{[required] The IAM role ARN that is associated with the user.} \item{clientToken}{A token that ensures idempotency. This token expires in 10 minutes.} } \description{ Updates the user details. You can only update the IAM role associated with a user. See \url{https://www.paws-r-sdk.com/docs/finspace_update_kx_user/} for full documentation. } \keyword{internal}

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# PLL Groundball Analysis # Sejin Kim # STAT 306 Sports Analytics # This script is for the gradient boosted regressor library(mosaic) library(dplyr) library(gbm) library(parallel) library(leaps) library(caret) library(MASS) # Read in data pll <- readRDS(url('https://github.com/kim3-sudo/pll_analysis/blob/main/data/pll.RDS?raw=true')) # Prepare multicore processing numCores <- detectCores() print(paste('Will use ', numCores, ' cores in GBM')) # Generate models to use with GBM ## Start with full model using reasonable data fullmod <- lm(GB ~ Pos + GP + P + G1 + G2 + A + Sh + SOG + TO + CT + Team, data = pll) ## Generate stepwise model stepmod <- stepAIC(fullmod, direction = "both", trace = FALSE) summary(stepmod) ## Generate max subsetted model using leaps on sequential replacement seqrepmod <- regsubsets(GB ~ Pos + GP + P + G1 + G2 + A + Sh + SOG + TO + CT + Team, data = pll, nvmax = 10, method = "seqrep") summary(seqrepmod) ## Generate model via k-fold x-validation trainCtrl <- trainControl(method = "cv", number = 10) newstepmod <- train(GB ~ Pos + GP + P + G1 + G2 + A + Sh + SOG + TO + CT + Team, data = pll, method = "leapSeq", tuneGrid = data.frame(nvmax = 1:10), trControl = trainCtrl) backmod <- train(GB ~ Pos + GP + P + G1 + G2 + A + Sh + SOG + TO + CT + Team, data = pll, method = "leapBackward", tuneGrid = data.frame(nvmax = 1:10), trControl = trainCtrl) forwmod <- train(GB ~ Pos + GP + P + G1 + G2 + A + Sh + SOG + TO + CT + Team, data = pll, method = "leapForward", tuneGrid = data.frame(nvmax = 1:10), trControl = trainCtrl) summary(newstepmod) summary(backmod) summary(forwmod) ## View optimal nvmax value - finds the model with the lowest RMSE newstepmod$bestTune backmod$bestTune forwmod$bestTune ## View best models summary(newstepmod$finalModel) summary(backmod$finalModel) summary(forwmod$finalModel) # Generate gradient boosted modifier model # For interactions, use P:G1, GP:G1 and G1:SOG gbmmod <- gbm(formula = (GB ~ A + Sh + SOG + P + G1 + G2 + (P * G1) + (GP * G1) + (G1 * SOG)), distribution = "poisson", data = pll, n.trees = 100000, interaction.depth = 5, cv.folds = 10, verbose = TRUE, n.cores = numCores) summary(gbmmod) gbmmod interpmod <- glm(formula = (GB ~ A + Sh + SOG + P + G1 + G2 + (P * G1) + (GP * G1) + (G1 * SOG)), family = poisson(), data = pll) summary(interpmod)

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################################################################### # prepare the data-matrix DM and replace the rownames ################################################################### # remove all objects before processing rm(list=ls()) # list of experiment names varieties <- c('fuji','golden', 'pinklady') modes <- c('pos', 'neg') path.root <- '/home/mylonasr/work/ager_melo/' path.metams <- paste0(path.root, 'data/metams/') path.sampledesc <- paste0(path.root, 'data/sample_desc/') path.perl <- paste0(path.root, 'perl/add_factor.pl') for(variety in varieties){ for(mode in modes){ exp <- paste0(variety, '_', mode) # load and prepare data load(paste0(path.metams, exp, '.RData')) sample.nr <- dim(out$PeakTable)[2] DM <- t(out$PeakTable[,6:sample.nr]) rt <- out$PeakTable[,"rt"] mz <- out$PeakTable[,"mz"] pcgroup <- out$PeakTable[,"pcgroup"] adduct <- out$PeakTable[,"adduct"] isotopes <- out$PeakTable[,"isotopes"] # write current row names write.table(rownames(DM), paste0("/tmp/", exp, "_rownames.csv"), row.names=FALSE, col.names=FALSE, sep=",") # call external perl script system(paste0(path.perl, " /tmp/", exp, "_rownames.csv ", path.sampledesc, "sample_descr_", variety, ".csv > /tmp/", exp, "_new_rownames.csv")) rownames.new <- read.table(paste0("/tmp/", exp, "_new_rownames.csv"), header=FALSE) rownames(DM) <- rownames.new[,1] # save prepared data save(DM, rt, mz, pcgroup, adduct, isotopes, file=paste0(path.metams, exp, '_prepro.RData')) } }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/GO_terms.R \name{GO_terms} \alias{GO_terms} \title{GO Term Number from Gene Name} \usage{ GO_terms(filtered_info_df) } \arguments{ \item{filtered_info_df}{input data.frame which contain only one PDB entries} } \value{ Number of GO Terms which related with the input genes } \description{ Function finds the number of GO Terms which related with the input genes. }

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new_ml_evaluator <- function(jobj, ..., class = character()) { structure( list( uid = invoke(jobj, "uid"), type = jobj_info(jobj)$class, param_map = ml_get_param_map(jobj), ..., .jobj = jobj ), class = c(class, "ml_evaluator") ) } #' @export spark_jobj.ml_evaluator <- function(x, ...) { x$.jobj } #' @export print.ml_evaluator <- function(x, ...) { cat(ml_short_type(x), "(Evaluator) \n") cat(paste0("<", x$uid, ">"),"\n") ml_print_column_name_params(x) cat(" (Evaluation Metric)\n") cat(paste0(" ", "metric_name: ", ml_param(x, "metric_name"))) } #' Spark ML -- Evaluate prediction frames with evaluators #' #' Evaluate a prediction dataset with a Spark ML evaluator #' #' @param x A \code{ml_evaluator} object. #' @param dataset A \code{spark_tbl} with columns as specified in the evaluator object. #' @export ml_evaluate <- function(x, dataset) { x %>% spark_jobj() %>% invoke("evaluate", spark_dataframe(dataset)) }

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readPriceVolChangesCVSSAS.r

readPriceVolChangesCVSSAS <- function(Portfolio, Date) { # browser() options$SASdir <- "S:/Risk/Projects/SAS/DATASTORE/SASIVaRProcm/" fileList <- list.files(options$SASdir) fileCorePortfolio <- paste(strsplit(toupper(Portfolio)," ")[[1]], sep = "", collapse = "-") indPortfolio <- grep(fileCorePortfolio, fileList) # fileCoreDate <- paste("HDV-vSOR_", toupper(as.character(format(Date, "%d%b%y"))), sep = "") fileCoreDate <- paste("HDV-vSOR_", toupper(as.character(format(Date, "%d%b%y"))), sep = "") indDate <- grep(fileCoreDate, fileList[indPortfolio]) # Delta # browser() indDateP <- grep(paste(fileCorePortfolio, "_price", sep = ""), fileList[indPortfolio[indDate]]) if(length(indDateP) == 1) { fileNameP <- paste(options$SASdir,fileList[indPortfolio[indDate[indDateP]]], sep = "") dataDelta <- read.csv(fileNameP) dataDelta$contract_month <- as.Date(dataDelta$contract_month,"%d%b%Y") cols <- c(1,3, c((length(colnames(dataDelta)) -69):length(colnames(dataDelta)))) dataDelta <- dataDelta[,cols] colnames(dataDelta)[1:2] <- c("CURVE_NAME", "VALUATION_MONTH") } else { dataDelta <- NULL } # Vega indDateV <- grep(paste(fileCorePortfolio, "_vol", sep = ""), fileList[indPortfolio[indDate]]) if(length(indDateV) == 1) { fileNameV <- paste(options$SASdir, fileList[indPortfolio[indDate[indDateV]]], sep = "") dataVega <- read.csv(fileNameV) dataVega$contract_month <- as.Date(dataVega$contract_month,"%d%b%Y") cols <- c(1,3, c((length(colnames(dataVega)) -69):length(colnames(dataVega)))) dataVega <- dataVega[,cols] colnames(dataVega)[1:2] <- c("CURVE_NAME", "VALUATION_MONTH") } else { dataVega <- NULL } return(list(dataDelta, dataVega)) }

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/dplyr mtcars.R

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dplyr mtcars.R

#analysis of dataset mtcars using mtcars library(dplyr) ?mtcars mtcars%>%summarise(mean(cyl)) mtcars%>%group_by(gear)%>%summarise(mean(am),mean(drat)) mtcars #structure of a data frame str(mtcars) dim(mtcars)#dimensions names(mtcars)#column names rownames(mtcars)#rownames summary(mtcars) #summary activities on mtcars t1=table(mtcars$am) pie(t1) 19/32*360 pie(t1, labels = c('auto','manual')) t2=table(mtcars$gear) pie(t2) pie(t2, labels = c('3 gear transmission','4gear transmission','5 gear transmission')) barplot(t2) barplot(t2,col = 23:33) barplot(t2,col = c('red','yellow','blue'),xlab = 'no. of gears',ylab = 'no. of cars') ?barplot title('Gear Distribution') plot(mtcars$gear,mtcars$cyl) ?plot boxplot(mtcars$gear,mtcars$cyl) #using dplyr %>% is chaining function mtcars%>%select(mpg,gear)%>%slice(1:5) #select for rows and column and slice for row selection ?slice mtcars%>%arrange(mpg)# arrange in ascending order for columns mtcars%>%arrange(am,desc(mpg))%>%select(am,mpg) # descending order and selected two columns ?mtcars mtcars%>%arrange(drat,hp)%>% select(carb,hp) mtcars%>%arrange(desc(disp))%>% select(vs,qsec,hp) mtcars%>%mutate(rn=rownames(mtcars))%>%select(rn,vs,qsec,hp)#mutate adds row names mtcars%>%slice(seq(1,32,4))# select rows after interval of 4 mtcars%>%sample_n(4)#select random 4 rows mtcars%>%sample_frac(.1)#selct randomly 10% of the row data mtcars%>%select(sample(x=c(1:11),size = 2)) %>% head mtcars%>%mutate(newmpg=mpg*1.2) #new column with 1.2 tmes mpg #types of tx, mean mtcars%>%group_by(am)%>%summarise(MEANMPG=mean(mpg),MAXHP=max(hp),MinGear=min(gear)) mtcars%>%group_by(gear,vs)%>%summarise(SUMwt=sum(wt),MEANdrat=mean(drat),MAXqsec=max(qsec)) ?filter

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/scripts/brahman_angus_bionano_corrected_cut.R

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/Users/lloyd/Documents/lloyd_2017/Research/Brahman_Angus/brahman_angus_bionano_corrected_cut.R

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dendro_rpart.R

# # ggdendro/R/dendro_rpart.R by Andrie de Vries Copyright (C) 2011-2015 # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 or 3 of the License # (at your option). # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ # # Classification Tree with rpart #' Extract data from classification tree object for plotting using ggplot. #' #' Extracts data to plot line segments and labels from a #' [rpart::rpart()] classification tree object. This data can then be #' manipulated or plotted, e.g. using [ggplot2::ggplot()]. #' #' This code is in essence a copy of [rpart::plot.rpart()], retaining #' the plot data but without plotting to a plot device. #' #' @param model object of class "tree", e.g. the output of tree() #' #' @param uniform if TRUE, uniform vertical spacing of the nodes is used; this #' may be less cluttered when fitting a large plot onto a page. The default is #' to use a non-uniform spacing proportional to the error in the fit. #' #' @param branch controls the shape of the branches from parent to child node. #' Any number from 0 to 1 is allowed. A value of 1 gives square shouldered #' branches, a value of 0 give V shaped branches, with other values being #' intermediate. #' #' @param compress if FALSE, the leaf nodes will be at the horizontal plot #' coordinates of 1:nleaves. If TRUE, the routine attempts a more compact #' arrangement of the tree. The compaction algorithm assumes uniform=TRUE; #' surprisingly, the result is usually an improvement even when that is not #' the case. #' #' @param nspace the amount of extra space between a node with children and a #' leaf, as compared to the minimal space between leaves. Applies to #' compressed trees only. The default is the value of branch. #' #' @param minbranch set the minimum length for a branch to minbranch times the #' average branch length. This parameter is ignored if uniform=TRUE. Sometimes #' a split will give very little improvement, or even (in the classification #' case) no improvement at all. A tree with branch lengths strictly #' proportional to improvement leaves no room to squeeze in node labels. #' #' @param ... ignored #' @export #' @return #' A list of three data frames: #' \item{segments}{a data frame containing the line segment data} #' \item{labels}{a data frame containing the label text data} #' \item{leaf_labels}{a data frame containing the leaf label text data} #' #' @seealso [ggdendrogram()] #' @family dendro_data methods #' @family rpart functions #' @example inst/examples/example_dendro_rpart.R #' dendro_data.rpart <- function(model, uniform = FALSE, branch = 1, compress = FALSE, nspace, minbranch = 0.3, ...) { x <- model if (!inherits(x, "rpart")) stop("Not a legitimate \"rpart\" object") if (nrow(x$frame) <= 1L) stop("fit is not a tree, just a root") if (compress & missing(nspace)) nspace <- branch if (!compress) nspace <- -1L # means no compression # if(!interactive()) if (dev.cur() == 1L) dev.new() # not needed in R parms <- list( uniform = uniform, branch = branch, nspace = nspace, minbranch = minbranch ) ## define the plot region temp <- rpartco(x, parms = parms) xx <- temp$x yy <- temp$y # temp1 <- range(xx) + diff(range(xx)) * c(-margin, margin) # temp2 <- range(yy) + diff(range(yy)) * c(-margin, margin) # plot(temp1, temp2, type = "n", axes = FALSE, xlab = "", ylab = "", ...) ## Save information per device, once a new device is opened. # assign(paste0("device", dev.cur()), parms, envir = rpart_ggdendro_env) # Draw a series of horseshoes or V's, left son, up, down to right son # NA's in the vector cause lines() to "lift the pen" node <- as.numeric(row.names(x$frame)) temp <- rpart.branch(xx, yy, node, branch) # if (branch > 0) text(xx[1L], yy[1L], "|") # lines(c(temp$x), c(temp$y)) # invisible(list(x = xx, y = yy)) labels <- text.rpart(x, parms = parms) segments <- rpart_segments(temp) # labels <- rpart_labels(xx, ...) as.dendro( segments = segments, labels = labels$labels, leaf_labels = labels$leaf_labels, class = "rpart" ) } #' Extract data frame from rpart object for plotting using ggplot. #' @param model object of class "tree", e.g. the output of tree() #' @param ... ignored #' @keywords internal #' @seealso [ggdendrogram()] #' @family rpart functions rpart_segments <- function(x, ...) { dat <- data.frame( stack(as.data.frame(x$x)), stack(as.data.frame(x$y)) )[, c("ind", "values", "values.1")] dat <- cbind(head(dat, -1), tail(dat, -1)) dat <- dat[complete.cases(dat), -4] names(dat) <- c("n", "x", "y", "xend", "yend") dat } #' Extract labels data frame from rpart object for plotting using ggplot. #' #' This code is modified from the original plot.rpart in package rpart. #' @param model object of class "rpart", e.g. the output of rpart() #' @param ... ignored #' @return a list with two elements: $labels and $leaf_labels #' @author Original author Brian Ripley #' @keywords internal #' @seealso [ggdendrogram()] #' @family dendro_data methods #' @family rpart functions rpart_labels <- function(x) { labelSplits <- labelLeaves <- NULL list( labels = labelSplits, leaf_labels = labelLeaves ) }

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inflate.R

#' @export inflate <- function(x, n){ if (is.matrix(x)) { y <- apply(x, 2, function(x) rep(x, each = n)) } else if (is.data.frame(x)) { y <- as.data.frame(apply(x, 2, function(x) rep(x, each = n)), stringsAsFactors = FALSE) # copy class if different if (!isTRUE(base::all.equal(sapply(x, class), sapply(y, class)))){ y <- as.data.frame(mapply(FUN = as, y, sapply(x, class), SIMPLIFY = FALSE)) } } else break return(y) }

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data_cleaning.R

### Subset BBS data for community trajectory analysis library(tidyverse) library(purrr) library(spdep) library(tmap) library(sf) library(vegclust) library(ecospat) ### Read in data ##### ## NA map na <- world %>% filter(continent == "North America") ## BBS 2017 Version # Append correct BioArk path info <- sessionInfo() bioark <- ifelse(grepl("apple", info$platform), "/Volumes", "\\\\ad.unc.edu\\bio") routes <- read.csv(paste0(bioark, "/HurlbertLab/Databases/BBS/2017/bbs_routes_20170712.csv")) counts <- read.csv(paste0(bioark, "/HurlbertLab/Databases/BBS/2017/bbs_counts_20170712.csv")) species <- read.csv(paste0(bioark, "/HurlbertLab/Databases/BBS/2017/bbs_species_20170712.csv")) weather <- read.csv(paste0(bioark, "/HurlbertLab/Databases/BBS/2017/bbs_weather_20170712.csv")) routes$stateroute <- routes$statenum*1000 + routes$route weather$stateroute <-weather$statenum*1000 + weather$route RT1 <- subset(weather, runtype == 1, select = c("stateroute", "year")) RT1.routes <- merge(RT1, routes[ , c("countrynum", "statenum", "stateroute", "latitude", "longitude","bcr")], by = "stateroute", all.x = TRUE) counts$stateroute <- counts$statenum*1000 + counts$route # species list species_list <- species %>% filter(aou > 2880) %>% filter(aou < 3650 | aou > 3810) %>% filter(aou < 3900 | aou > 3910) %>% filter(aou < 4160 | aou > 4210) %>% filter(aou != 7010) %>% filter(sporder != "Accipitriformes", sporder != "Falconiformes", sporder != "Anseriformes", sporder != "Cathartiformes") # write.csv(species_list, "data/species_list.csv", row.names = F) # four letter species codes fourletter_codes <- read.csv("data/four_letter_codes_birdspp.csv", stringsAsFactors = F) %>% left_join(species_list, by = c("COMMONNAME" = "english_common_name")) %>% filter(!is.na(aou)) # write.csv(fourletter_codes, "data/four_letter_codes_aous.csv", row.names = F) # Filter BBS to rpid = 101, runtype = 1, land birds != birds of prey counts.subs <- counts %>% filter(rpid == 101) %>% filter(aou %in% species_list$aou) %>% right_join(RT1.routes, by = c("countrynum", "statenum", "stateroute", "year")) ### sample sizes for route time series rtes_per_year <- counts.subs %>% group_by(year) %>% summarize(n_rtes = n_distinct(stateroute)) ### routes sampled continuously 1970-present cont_routes <- counts.subs %>% filter(year >= 1970) %>% mutate(year_bin = 5*floor(year/5)) %>% group_by(countrynum, stateroute) %>% mutate(n_bins = n_distinct(year_bin)) %>% filter(n_bins == 10) route_sf <- cont_routes %>% ungroup() %>% distinct(stateroute, latitude, longitude) %>% st_as_sf(coords = c("longitude", "latitude")) bbs_map <- tm_shape(na) + tm_polygons() + tm_shape(route_sf) + tm_dots() # tmap_save(bbs_map, "figures/bbs_route_map_1970-2016.pdf") ### Route density 1990-2016 - 1-4 years in every four year time window # countrynum 124 = Canada, 840 = US counts_landcover_years <- counts.subs %>% mutate(y1 = case_when(countrynum == 124 ~ 1990, countrynum == 840 ~ 1992), y2 = case_when(countrynum == 124 ~ 2010, countrynum == 840 ~ 2016), max_bins = case_when(countrynum == 124 ~ 5, countrynum == 840 ~ 6)) ## Count number of routes that meet a threshhold (surveys_per_window) of surveys per five year time window across study period counts_per_window <- function(surveys_per_window) { cont_routes <- counts_landcover_years %>% filter(year >= y1, year <= y2) %>% group_by(countrynum) %>% nest() %>% mutate(year_bins = map2(countrynum, data, ~{ country <- .x df <- .y if(country == 124) { df %>% mutate(year_bin = case_when(year >= 1990 & year <= 1993 ~ 1990, year >= 1994 & year <= 1997 ~ 1994, year >= 1998 & year <= 2001 ~ 1998, year >= 2002 & year <= 2005 ~ 2002, TRUE ~ 2006)) } else { df %>% mutate(year_bin = case_when(year >= 1992 & year <= 1995 ~ 1992, year >= 1996 & year <= 1999 ~ 1996, year >= 2000 & year <= 2003 ~ 2000, year >= 2004 & year <= 2007 ~ 2004, year >= 2008 & year <= 2011 ~ 2008, year >= 2012 & year <= 2016 ~ 2012)) } })) %>% select(-data) %>% unnest(cols = c(year_bins)) %>% group_by(max_bins, stateroute, year_bin) %>% summarize(n_years = n_distinct(year)) %>% filter(n_years >= surveys_per_window) %>% group_by(stateroute) %>% mutate(n_bins = n_distinct(year_bin)) %>% filter(n_bins == max_bins) return(list(df = cont_routes, n_routes = length(unique(cont_routes$stateroute)))) } bbs_density <- data.frame(years_per_window = 1:4) %>% mutate(routes = map_dbl(years_per_window, ~{ output <- counts_per_window(.) output$n_routes })) ggplot(bbs_density, aes(x = years_per_window, y = routes)) + geom_point() + geom_line(cex = 1) + theme_classic(base_size = 15) ggsave("figures/bbs_route_density.pdf") pdf("figures/bbs_route_map_1990-2016_surveys_per_window.pdf") for(i in c(1:4)) { output <- counts_per_window(i) cont_routes <- output$df routes_short <- route_sf %>% filter(stateroute %in% cont_routes$stateroute) map <- tm_shape(na) + tm_polygons() + tm_shape(routes_short) + tm_dots() + tm_layout(title = paste0("Surveys per window = ", i)) print(map) } dev.off() counts_subs <- counts_per_window(3) routes_subs <- counts_subs$df # write.csv(routes_subs, "data/bbs_route_subset_1990-2016.csv", row.names = F) bbs_subset <- counts.subs %>% filter(stateroute %in% routes_subs$stateroute) %>% filter(year >= 1990) # write.csv(bbs_subset, "data/bbs_counts_subset_1990-2016.csv", row.names = F) ## Sample size per BCR bcr_n <- bbs_subset %>% group_by(bcr) %>% summarize(n_routes = n_distinct(stateroute)) ## Figure: map of BCRs with BBS routes, number of routes per BCR that meet sampling density thresholds bcrs <- read_sf(paste0(bioark, "/HurlbertLab/DiCecco/bcr_terrestrial_shape/BCR_terrestrial_master.shp")) %>% filter(COUNTRY == "USA" | COUNTRY == "CANADA") %>% st_crop(c(xmin = -178, ymin = 18.9, xmax = -53, ymax = 60)) %>% filter(PROVINCE_S != "ALASKA" & PROVINCE_S != "HAWAIIAN ISLANDS" & PROVINCE_S != "NUNAVUT" & PROVINCE_S != "NORTHWEST TERRITORIES" & PROVINCE_S != "YUKON") %>% filter(WATER == 3) %>% left_join(bcr_n, by = c("BCR" = "bcr")) bcr_route_density <- tm_shape(bcrs) + tm_polygons(col = "n_routes", palette = "YlGnBu", title = "BBS routes") + tm_layout(scale = 2) tmap_save(bcr_route_density, "figures/bcr_route_density.pdf") ## Log abundance log_abund <- cont_routes %>% group_by(countrynum, stateroute, year) %>% mutate(log_abund = log10(speciestotal)) %>% dplyr::select(stateroute, year, aou, log_abund) # write.csv(log_abund, "data/derived_data/bbs_subset_1970-2016_logabund.csv", row.names = F)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ssm_operations.R \name{ssm_list_tags_for_resource} \alias{ssm_list_tags_for_resource} \title{Returns a list of the tags assigned to the specified resource} \usage{ ssm_list_tags_for_resource(ResourceType, ResourceId) } \arguments{ \item{ResourceType}{[required] Returns a list of tags for a specific resource type.} \item{ResourceId}{[required] The resource ID for which you want to see a list of tags.} } \value{ A list with the following syntax:\preformatted{list( TagList = list( list( Key = "string", Value = "string" ) ) ) } } \description{ Returns a list of the tags assigned to the specified resource. } \section{Request syntax}{ \preformatted{svc$list_tags_for_resource( ResourceType = "Document"|"ManagedInstance"|"MaintenanceWindow"|"Parameter"|"PatchBaseline"|"OpsItem", ResourceId = "string" ) } } \keyword{internal}

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/expand.R \name{expand} \alias{expand} \title{expand grid with numerical component} \usage{ expand(formula, data, res, ...) } \arguments{ \item{formula}{a formula} \item{data}{the data set} \item{res}{numerical, the resolution for the numerical variable, i.e. the length of the sequence between minimum and maximum} \item{...}{further columns to be added with their value} } \value{ a data frame with columns as specified in \code{formula} and if given as in \code{\dots} } \description{ expand combinations with range of numerical variable } \details{ this function is an extension of expand.grid, i.e. it takes all the possible combinations of factors and binary variables in the data set and matches the numerical variable accordingly along a sequence from its minimum to its maximum only combinations that actually occur in the data are returned if \code{res = 2}, the numerical variable will be returned as range } \examples{ set.seed(123) xdata <- data.frame(pred1 = rnorm(100), pred2 = sample(c(0, 1), 100, TRUE), fac1 = sample(letters[1:3], 100, TRUE), fac2 = sample(LETTERS[7:8], 100, TRUE)) expand(~pred1 + fac1 + fac2, xdata, res = 3) expand(~pred2 + fac1 + fac2, xdata, res = 3) # adding columns expand(~pred2 + fac1 + fac2, xdata, res = 3, x = -17, y = "gg") }

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r

DistritosLima.R

structure(list(IDDPTO = c("15", "15", "15", "15", "15", "15", "15", "15", "07", "15", "15", "15", "15", "15", "15", "15", "15", "15", "15", "15", "15", "07", "15", "15", "15", "15", "15", "15", "15", "15", "07", "15", "15", "15", "07", "15", "15", "15", "15", "15", "15", "07", "15", "15", "15", "15", "15", "07", "15", "07" ), DEPARTAMEN = c("LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "CALLAO"), IDPROV = c("1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "0701", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "0701", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "1501", "0701", "1501", "1501", "1501", "0701", "1501", "1501", "1501", "1501", "1501", "1501", "0701", "1501", "1501", "1501", "1501", "1501", "0701", "1501", "0701" ), PROVINCIA = c("LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "LIMA", "LIMA", "LIMA", "LIMA", "CALLAO", "LIMA", "CALLAO"), IDDIST = c("150119", "150102", "150131", "150120", "150130", "150116", "150140", "150121", "070105", "150139", "150106", "150113", "150124", "150138", "150108", "150104", "150143", "150133", "150122", "150141", "150127", "070104", "150134", "150136", "150142", "150126", "150129", "150123", "150115", "150114", "070102", "150105", "150137", "150109", "070103", "150101", "150111", "150128", "150103", "150107", "150112", "070101", "150135", "150117", "150110", "150118", "150132", "070106", "150125", "070107"), DISTRITO = c("LURIN", "ANCON", "SAN ISIDRO", "MAGDALENA DEL MAR", "SAN BORJA", "LINCE", "SANTIAGO DE SURCO", "PUEBLO LIBRE", "LA PUNTA", "SANTA ROSA", "CARABAYLLO", "JESUS MARIA", "PUCUSANA", "SANTA MARIA DEL MAR", "CHORRILLOS", "BARRANCO", "VILLA MARIA DEL TRIUNFO", "SAN JUAN DE MIRAFLORES", "MIRAFLORES", "SURQUILLO", "PUNTA NEGRA", "LA PERLA", "SAN LUIS", "SAN MIGUEL", "VILLA EL SALVADOR", "PUNTA HERMOSA", "SAN BARTOLO", "PACHACAMAC", "LA VICTORIA", "LA MOLINA", "BELLAVISTA", "BREŅA", "SANTA ANITA", "CIENEGUILLA", "CARMEN DE LA LEGUA REYNOSO", "LIMA", "EL AGUSTINO", "RIMAC", "ATE", "CHACLACAYO", "INDEPENDENCIA", "CALLAO", "SAN MARTIN DE PORRES", "LOS OLIVOS", "COMAS", "LURIGANCHO", "SAN JUAN DE LURIGANCHO", "VENTANILLA", "PUENTE PIEDRA", "MI PERU"), CAPITAL = c("LURIN", "ANCON", "SAN ISIDRO", "MAGDALENA DEL MAR", "SAN FRANCISCO DE BORJA", "LINCE", "SANTIAGO DE SURCO", "PUEBLO LIBRE", "LA PUNTA", "SANTA ROSA", "CARABAYLLO", "JESUS MARIA", "PUCUSANA", "SANTA MARIA DEL MAR", "CHORRILLOS", "BARRANCO", "VILLA MARIA DEL TRIUNFO", "CIUDAD DE DIOS", "MIRAFLORES", "SURQUILLO", "PUNTA NEGRA", "LA PERLA", "SAN LUIS", "SAN MIGUEL", "VILLA EL SALVADOR", "PUNTA HERMOSA", "SAN BARTOLO", "PACHACAMAC", "LA VICTORIA", "LA MOLINA", "BELLAVISTA", "BREŅA", "SANTA ANITA - LOS FICUS", "CIENEGUILLA", "CARMEN DE LA LEGUA REYNOSO", "LIMA", "EL AGUSTINO", "RIMAC", "VITARTE", "CHACLACAYO", "INDEPENDENCIA", "CALLAO", "BARRIO OBRERO INDUSTRIAL", "LAS PALMERAS", "LA LIBERTAD", "CHOSICA", "SAN JUAN DE LURIGANCHO", "VENTANILLA", "PUENTE PIEDRA", "MI PERU"), CODCCPP = c("0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001", "0001" ), AREA = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), FUENTE = c("INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI", "INEI" ), geometry = structure(list(structure(list(list(structure(c(-76.7054892359999, -76.7042894859999, -76.703127611, -76.702900111, -76.702378611, -76.7007363609999, -76.700156611, -76.698779111, -76.697739486, -76.696068361, -76.693979736, -76.6915356109999, -76.6902467359999, -76.688735986, 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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Pareto.R \name{ExposureCurvePareto} \alias{ExposureCurvePareto} \title{Exposure Curve from a Pareto severity distribution} \usage{ ExposureCurvePareto(x, scale, shape) } \arguments{ \item{x}{A positive real number - the claim amount where the exposure curve will be evaluated.} \item{scale}{A positive real number - the scale parameter of the Claim Severity's Pareto distribution.} \item{shape}{A positive real number - the shape parameter of the Claim Severity's Pareto distribution.} } \value{ The value of the Exposure curve at \code{x} with Claim Severity from a Pareto distribution with parameters \code{scale} and \code{shape}. } \description{ Exposure Curve from a Pareto severity distribution } \examples{ ExposureCurvePareto(700,500,1.2) ExposureCurvePareto(20000,200,1.1) }

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/data/genthat_extracted_code/rdhs/examples/dhs_countries.Rd.R

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dhs_countries.Rd.R

library(rdhs) ### Name: dhs_countries ### Title: API request of DHS Countries ### Aliases: dhs_countries ### ** Examples ## Not run: ##D # A common use for the countries API endpoint is to query which countries ##D # ask questions about a given topic. For example to find all countries that ##D # record data on malaria prevalence by RDT: ##D ##D dat <- dhs_countries(indicatorIds = "ML_PMAL_C_RDT") ##D ##D # Additionally you may want to know all the countries that have conducted ##D # MIS (malaria indicator surveys): ##D ##D dat <- dhs_countries(surveyType="MIS") ##D ##D # A complete list of examples for how each argument to the countries API ##D # endpoint can be provided is given below, which is a copy of each of ##D # the examples listed in the API at: ##D ##D # https://api.dhsprogram.com/#/api-countries.cfm ##D ##D ##D dat <- dhs_countries(countryIds="EG",all_results=FALSE) ##D dat <- dhs_countries(indicatorIds="FE_FRTR_W_TFR",all_results=FALSE) ##D dat <- dhs_countries(surveyIds="SN2010DHS",all_results=FALSE) ##D dat <- dhs_countries(surveyYear="2010",all_results=FALSE) ##D dat <- dhs_countries(surveyYearStart="2006",all_results=FALSE) ##D dat <- dhs_countries(surveyYearStart="1991", surveyYearEnd="2006", ##D all_results=FALSE) ##D dat <- dhs_countries(surveyType="DHS",all_results=FALSE) ##D dat <- dhs_countries(surveyCharacteristicIds="32",all_results=FALSE) ##D dat <- dhs_countries(tagIds="1",all_results=FALSE) ##D dat <- dhs_countries(f="html",all_results=FALSE) ## End(Not run)

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#import libraries library(shiny) library(shinydashboard) library(gganimate) library(ggplot2) library(stringr) library(gifski) #import RScripts for cleaning and recoding data source("newdash.R") linebreaks <- function(n){HTML(strrep(br(), n))} #define UI for application ui <- fluidPage( #define title and inputs titlePanel("Outcomes Charts"), selectInput("grant","Choose a Grant", choices=c("SOAR 2 Young Adult", "SOAR 3 Adult", "SOAR 3 Young Adult", "SOAR 4 Adult"), selected=""), #define layout of images fluidRow(column(width = 6, offset = 0, style='padding:50px;', imageOutput("chart1", inline=TRUE), linebreaks(2), imageOutput("chart3", inline=TRUE)), column(width = 6, offset = 0, style='padding:50px;', imageOutput("chart2", inline=TRUE), linebreaks(2), imageOutput("chart4", inline=TRUE))) ) # Run the application server <- function(input, output) { output$chart1 <- renderImage({ #create temp .gif file to save output outfile <- tempfile(fileext='.gif') #create chart based on input from UI Image1 <- if (input$grant == "SOAR 2 Young Adult") { ggplot(data=S2Enroll, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF33CC", "#6699CC"), labels=c("Enrollment Goal", "Enrollments")) + labs(x="Site", y="Number of Enrollments") + ggtitle("Enrollment Goals and Numbers by Site") + #coord_fixed(ratio=0.009) + scale_y_continuous(limits = c(0,300)) } else if (input$grant == "SOAR 3 Adult") { ggplot(data=S3AEnroll, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#339966", "#9966CC"), labels=c("Enrollment Goal", "Enrollments")) + labs(x="Site", y="Number of Enrollments") + ggtitle("Enrollment Goals and Numbers by Site") + #coord_fixed(ratio=0.009) + scale_y_continuous(limits = c(0,200)) } else if (input$grant == 'SOAR 3 Young Adult') { ggplot(data=S3YEnroll, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FFCC66", "#FF6666"), labels=c("Enrollment Goal", "Enrollments")) + labs(x="Site", y="Number of Enrollments") + ggtitle("Enrollment Goals and Numbers by Site") + #coord_fixed(ratio=0.009) + scale_y_continuous(limits = c(0,200)) } else if (input$grant == 'SOAR 4 Adult') { ggplot(data=S4Enroll, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF9966", "#99FF66"), labels=c("Enrollment Goal", "Enrollments")) + labs(x="Site", y="Number of Enrollments") + ggtitle("Enrollment Goals and Numbers by Site") + #coord_fixed(ratio=0.009) + scale_y_continuous(limits = c(0,250)) } #animate the chart and save as .gif image in the temp file anim_save("outfile.gif", animate(Image1, width = 550, height = 450, renderer = gifski_renderer())) #return a list containing file name list(src = "outfile.gif", width = input$shiny_width, height=input$shiny_height, contentType = 'image/gif')}, deleteFile = TRUE) #repeat process for next 4 aminated images output$chart2 <- renderImage({ outfile <- tempfile(fileext='.gif') Image2 <- if (input$grant == "SOAR 2 Young Adult") { ggplot(data=S2IRC, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF33CC", "#6699CC"), labels=c("Industry\nRecogonized\nCredential Goal", "Attained Industry\nRecogonized\nCredential")) + labs(x="Site", y="Number of Participants") + ggtitle("Credential Goals and Numbers by Site") + #coord_fixed(ratio=0.018) + theme(legend.key.size = unit(1.3, "cm")) + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 3 Adult") { ggplot(data=S3AIRC, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#339966", "#9966CC"), labels=c("Industry\nRecogonized\nCredential Goal", "Attained Industry\nRecogonized\nCredential")) + labs(x="Site", y="Number of Participants") + ggtitle("Credential Goals and Numbers by Site") + #coord_fixed(ratio=0.018) + theme(legend.key.size = unit(1.3, "cm")) + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 3 Young Adult") { ggplot(data=S3YIRC, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FFCC66", "#FF6666"), labels=c("Industry\nRecogonized\nCredential Goal", "Attained Industry\nRecogonized\nCredential")) + labs(x="Site", y="Number of Participants") + ggtitle("Credential Goals and Numbers by Site") + #coord_fixed(ratio=0.018) + theme(legend.key.size = unit(1.3, "cm")) + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 4 Adult") { ggplot(data=S4IRC, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF9966", "#99FF66"), labels=c("Industry\nRecogonized\nCredential Goal", "Attained Industry\nRecogonized\nCredential")) + labs(x="Site", y="Number of Participants") + ggtitle("Credential Goals and Numbers by Site") + #coord_fixed(ratio=0.018) + theme(legend.key.size = unit(1.3, "cm")) + scale_y_continuous(limits = c(0, 150)) } anim_save("outfile.gif", animate(Image2, width = 550, height = 450, renderer = gifski_renderer())) list(src = "outfile.gif", width = input$shiny_width, height=input$shiny_height, contentType = 'image/gif')}, deleteFile = TRUE) output$chart3 <- renderImage({ outfile <- tempfile(fileext='.gif') Image3 <- if (input$grant == "SOAR 2 Young Adult") { ggplot(data=S2Training, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF33CC", "#6699CC"), labels=c("Training Goal", "In Trainings")) + labs(x="Site", y="Number of Participants") + #coord_equal(ratio=0.0135) + ggtitle("Training Attendance Goals and Numbers by Site") + scale_y_continuous(limits = c(0, 200)) } else if (input$grant == "SOAR 3 Adult") { ggplot(data=S3ATraining, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#339966", "#9966CC"), labels=c("Training Goal", "In Trainings")) + labs(x="Site", y="Number of Participants") + #coord_equal(ratio=0.0135) + ggtitle("Training Attendance Goals and Numbers by Site") + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 3 Young Adult") { ggplot(data=S3YTraining, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FFCC66", "#FF6666"), labels=c("Training Goal", "In Trainings")) + labs(x="Site", y="Number of Participants") + #coord_equal(ratio=0.0135) + ggtitle("Training Attendance Goals and Numbers by Site") + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 4 Adult") { ggplot(data=S4Training, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF9966", "#99FF66"), labels=c("Training Goal", "In Trainings")) + labs(x="Site", y="Number of Participants") + #coord_equal(ratio=0.0135) + ggtitle("Training Attendance Goals and Numbers by Site") + scale_y_continuous(limits = c(0, 150)) } anim_save("outfile.gif", animate(Image3, width = 550, height = 450, renderer = gifski_renderer())) list(src = "outfile.gif", width = input$shiny_width, height=input$shiny_height, contentType = 'image/gif')}, deleteFile = TRUE) output$chart4 <- renderImage({ outfile <- tempfile(fileext='.gif') Image4 <- if (input$grant == "SOAR 2 Young Adult") { ggplot(data=S2JobP, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF33CC", "#6699CC"), labels=c("Job Placement\nGoal", "Job Placements")) + labs(x="Site", y="Number of Participants") + #coord_fixed(ratio=0.0135) + ggtitle("Job Placement Goals and Numbers by Site") + theme(legend.key.size = unit(0.8, "cm")) + scale_y_continuous(limits = c(0, 200)) } else if (input$grant == "SOAR 3 Adult") { ggplot(data=S3AJobP, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#339966", "#9966CC"), labels=c("Job Placement\nGoal", "Job Placements")) + labs(x="Site", y="Number of Participants") + #coord_fixed(ratio=0.0135) + ggtitle("Job Placement Goals and Numbers by Site") + theme(legend.key.size = unit(0.8, "cm")) + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 3 Young Adult") { ggplot(data=S3YJobP, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FFCC66", "#FF6666"), labels=c("Job Placement\nGoal", "Job Placements")) + labs(x="Site", y="Number of Participants") + #coord_fixed(ratio=0.0135) + ggtitle("Job Placement Goals and Numbers by Site") + theme(legend.key.size = unit(0.8, "cm")) + scale_y_continuous(limits = c(0, 150)) } else if (input$grant == "SOAR 4 Adult") { ggplot(data=S4JobP, aes(x=Site, y=count, fill= Outcome)) + geom_bar(stat="identity") + geom_text(aes(label=count), vjust=(-0.5), size=3) + transition_states(Outcome, transition_length = 4, state_length = 20) + enter_fade() + exit_shrink() + scale_fill_manual(values=c("#FF9966", "#99FF66"), labels=c("Job Placement\nGoal", "Job Placements")) + labs(x="Site", y="Number of Participants") + #coord_fixed(ratio=0.0135) + ggtitle("Job Placement Goals and Numbers by Site") + theme(legend.key.size = unit(0.8, "cm")) + scale_y_continuous(limits = c(0, 150)) } anim_save("outfile.gif", animate(Image4, width = 550, height = 450, renderer = gifski_renderer())) list(src = "outfile.gif", width = input$shiny_width, height=input$shiny_height, contentType = 'image/gif')}, deleteFile = TRUE) } shinyApp(ui, server)

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r

Baby Mama Money - Project.R

# name the database files in the "MonetDB" folder of the current working directory dbfolder <- paste0( getwd() , "/MonetDB" ) ####################################### # survey design for replicate weights # ####################################### # create survey design object with CPS design information # using existing data frame of CPS data y <- svrepdesign( weights = ~marsupwt, repweights = "pwwgt[1-9]", type = "Fay", rho = (1-1/sqrt(4)), data = "asec16" , combined.weights = T , dbtype = "MonetDBLite" , dbname = dbfolder ) #pwsswgt #marsupwt # workaround for a bug in survey::svrepdesign.character y$mse <- TRUE females.above15.w_child <- subset( y , a_age > 15 & # age 16+ a_sex %in% 2 & # females hunder18 > 0 & # children in house a_pfrel %in% 5 # single ) females.above15.w_child2 <- subset( y , a_age > 15 & # age 16+ a_sex %in% 2 & # females hunder18 > 0 & # children in house h_type %in% 4 # single female household ) females.above15.w_child <- update( povll = factor( povll ) , females.above15.w_child ) females.above15.w_child2 <- update( povll = factor( povll ) , females.above15.w_child2 ) svymean( ~povll , design = females.above15.w_child ) svymean( ~povll , design = females.above15.w_child2 ) svyquantile( ~hwsval, design = females.above15.w_child2 , c( 0 , .05 , .10, .15, .20, .25 , .30, .35, .40, .45, .5 , .55, .60, .65, .70, .75, .80, .85, .9 , .95 , 1 ) )

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/man/create_entity_all.Rd

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BLE-LTER/MetaEgress

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refs/heads/main

2023-06-23T05:59:20.330729

2023-06-12T18:14:33

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create_entity_all.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/create_entity_all.R \name{create_entity_all} \alias{create_entity_all} \title{Quickly create all EML entity list objects.} \usage{ create_entity_all( meta_list, file_dir = getwd(), dataset_id, entity_numbers = NULL, skip_checks = FALSE ) } \arguments{ \item{meta_list}{(character) A list of dataframes containing metadata returned by \code{\link{get_meta}}.} \item{file_dir}{(character) Path to directory containing flat files (data files). Defaults to current R working directory.} \item{dataset_id}{(numeric) A dataset ID.} \item{entity_numbers}{(numeric) Vector of entity numbers to include. Defaults to all of them. Use only when you need to delibrately exclude a couple entities.} \item{skip_checks}{(logical) Whether to skip checking for attribute congruence. Defaults to FALSE.} } \value{ (list) A list containing all data entities from dataset. Use this in the `entity_list` argument for \code{\link{create_EML}}. First level list elements are grouped by entity types present in dataset and named accordingly. Each first level element is a list of unnamed lists; the number of elements correspond to how many entities of each type are present in dataset. Second level elements are analogous to output from \code{\link{create_entity}}. } \description{ . Use to quickly create EML entity list objects from all entities listed in dataset. } \examples{ \dontrun{ # continued from \code{\link{get_meta}} entities <- create_entity_all(meta_list = metadata, dataset_id = 1) } }

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/LogicOpt/R/logicopt.R

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ingted/R-Examples

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logicopt.R

'logicopt' <- function(in_tt=NULL, n_in=0, n_out=0, find_dc=FALSE, input_sizes=NULL, exact_cover=TRUE, esp_file="", mode="espresso") ######################################################################## ######################################################################## #' @title Truth Table Logic Optimization # #' @description This function provides various options to optimize and #' analyze an input truth table that represents a sum of Boolean or #' multi-valued input product terms. #' This function leverages the powerful logic minimization #' algorithms from Espresso. These algorithms are a standard for optimizing #' large functions in digital logic synthesis and have been modified to #' handle general logic minimization problems to serve the R community. #' The input truth table is an R data frame or alteratively an #' Espresso format file. See examples section for more details. # #' @return The logicopt function returns a list of two items: an #' truth table and a vector. The vector represents the size and #' number of solutions in the output truth table. For example, a vector [10] #' means there is a single solution in the truth table which has 10 rows. #' A vector [5 8 2] means there are three solutions in the truth table #' with 5, 8, and 2 rows respectively. # #' @param in_tt #' An R data frame table representing a sum of product terms (PTs) truth table. #' The PTs have one or more inputs with a positive integer #' value or a "-" which means the input is not specified. The outputs are #' Boolean and have possible values 1, 0, "-", or "~" and specify that the #' corresponding PT is part of the ON set, the OFF set, the DC (don't care) set, #' or is unspecified (not a part of any set) respectively. PTs should not #' be in both the ON set and OFF set. When logicopt optimizes, it attempts to #' find the fewest number of PTs in the ON set and uses PTs in the #' DC set to further reduce the solution. #' #' @param n_in #' Integer number of input columns in the truth table. Inputs #' must come first in the truth table. # #' @param n_out #' Integer number of output columns in the truth table. #' Outputs must come after the n_in inputs of the truth table. #' The number of columns in the in_tt must be n_in + n_out. # #' @param find_dc #' FALSE (default) means any unspecified input conditions are #' added to the OFF set for that output. TRUE means that any unspecified input #' conditions are added to DC set for that output. The DC set #' is used to further optimize the ON set. Don't cares can also be explicitly #' defined by using "-" for the output in the input truth table. # #' @param input_sizes #' Integer vector which represents the number of possible values for each #' input. Default is NULL which means the size for each input will be #' determined automatically by the software by analyzing the input truth table #' and counting the number of values used. #' Specifying input_sizes #' is important when the input table has unspecified ("-") or unused input #' values and all possible values are not used. #' #' @param exact_cover #' Do an exact covering of prime implicant table. Option applies to QM based #' algorithms (mode = "qm", "multi-min", and "multi-full"). If FALSE, the covering #' algoirthm is heuristic and runs faster but may not find an exact solution. #' If TRUE, algorithm is exact and finds an exact minimum solution. #' Default is TRUE. #' #' @param esp_file #' File name for espresso format file to read and process. If esp_file is #' specified, the input truth table options (in_tt, n_in, n_out, input_sizes, #' and find_dc) are ignored. The mode and exact_cover options still apply. #' #' @param mode #' A string that specifies the mode to use for optimization: #' \itemize{ #' \item{"espresso"}{ -- Use the classic espresso algorithm to optimize #' the input truth table in_tt or the espresso format table in esp_file. #' Returns a single solution of the optimized ON set. This option should #' be used for very large truth tables.} #' \item{"qm"}{ -- Use Quine-McCluskey (QM) algorithm to optimize in_tt #' or esp_file and return a single solution of the optimized ON set. Use #' with caution for large truth tables.} #' \item{"primes"}{ -- Return set of prime implicants for in_tt or esp_file. #' Three solutions are returned in a single truth table. These represent the #' ESSENTIAL PRIMES, the PARTIALLY REDUNDANT PRIMES, and the TOTALLY REDUNDANT #' PRIMES. Use the \code{\link{print_primes_tt}} function to print the results.} #' \item{"multi-min"}{ -- Use QM to find the minimum set of non-redundant #' solutions that cover all the ESSENTIAL PRIMES and the minimal set of #' PARTIALLLY REDUNDANT PRIMES. Solutions are ordered #' by size. The number of solutions found is capped at 50.} #' \item{"multi-full"}{ -- Find additional coverings of prime implicants #' beyond what is found in multi-min. A exhaustive covering of all PARTIALLY #' REDUNDANT PRIMES is found. The number of solutions is capped at 50.} #' \item{"echo"}{ -- Echo the ON, OFF, and DC sets for the in_tt or esp_file #' truth table without any optimization. Note the resulting truth table #' can be extremely large because it will cover the complete Boolean (or MV) #' space for all inputs and outputs. Use with caution.} #' } # #' @examples #' #' ######################### EXAMPLE #1 ############################### #' # create a truth table with 4 inputs A, B, C, D and 2 outputs X and Y #' e.ex1 <- data.frame( #' A = c(0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1), #' B = c(0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0), #' C = c(0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1), #' D = c(0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0), #' X = c(1,1,1,1,0,1,1,1,"-",1,1,0,1,1,0,0), #' Y = c("-",1,1,1,1,1,1,1,1,1,1,0,0,0,0,"-")) #' #' # show the unoptimized equations #' tt2eqn(e.ex1,4,2) #' #' # optimize the truth table #' tte <- logicopt(e.ex1,4,2) #' #' # show the optimized equations #' tt2eqn(tte[[1]],4,2) #' #' # generate and print the prime implicants from optimized tte #' ttp <- logicopt(tte[[1]],4,2,mode="primes") #' print_primes_tt(ttp,TRUE,4,2) #' #' ######################### EXAMPLE #2 ############################### #' # get path to an espresso format file #' file <- system.file("extdata/espresso/small2.esp", package="LogicOpt") #' #' # get the espresso truth table without optimization #' small2 <- logicopt(esp_file=file,mode="echo") #' #' # print the unoptimized equations #' print_multi_tt(small2,TRUE,4,3) #' #' # optimize with espresso algorithm #' small2_opt <- logicopt(small2[[1]],4,3,mode="espresso") #' #' # print the optimized equations #' print_multi_tt(small2_opt,TRUE,4,3) #' #' ######################### EXAMPLE #3 ############################### #' # load up truth table created from a QCA dataset #' data(l.represent.1) #' #' # read documentation on how truth table was created #' ?l.represent.1 #' #' # find the set of minimum solutions that cover QCA dataset (mode="multi-min") #' # treat unspecified input conditions as don't cares (find_dc=TRUE) #' # find a exact covering (exact_cover=TRUE) #' lomm <- logicopt(l.represent.1,n_in=5,n_out=1,find_dc=TRUE, #' exact_cover=1,mode="multi-min") #' #' # print the solutions in equation format #' print_multi_tt(lomm,TRUE,5,1,QCA=TRUE) #' #' ######################### EXAMPLE #4 ############################### #' # optimize a truth table from Genetic Programming #' inpath <- system.file("extdata/espresso/robot1_in.esp", package="LogicOpt") #' robot1 <- logicopt(esp_file=inpath,mode="echo") #' #' # unoptimized truth table has 273 rows (256 in ON set and 18 in OFF set) #' robot1[2] #' #' # optimize l.robot1 #' robot1_opt <- logicopt(robot1[[1]],8,3) #' #' # optimized results have 13 rows that cover outputs zero, one, and minus #' robot1_opt[2] #' #' # print optimized equations (where each output is 1) #' print_multi_tt(robot1_opt,TRUE,8,3) #' #' ######################### EXAMPLE #5 ############################### #' # show how to use input_sizes #' #' # get vector of number of unique values for each input #' data(l.partybans.1) #' pb_in_vals <- num_input_values(l.partybans.1,5) #' pb_in_vals #' #' # optimize with mode=espresso #' epb <- logicopt(l.partybans.1,5,1,find_dc=TRUE,mode="espresso") #' epb #' pb_in_opt_vals <- num_input_values(epb[[1]],5) #' #' # note how some input values have been optimized away and are no longer used! #' pb_in_opt_vals #' #' # we need original input sizes to process the optimized truth table #' qmpb <- logicopt(epb[[1]],5,1,find_dc=FALSE, input_sizes=pb_in_vals,mode="qm") #' print_multi_tt(epb,TRUE,5,1) #' print_multi_tt(qmpb,TRUE,5,1) #' #' @keywords Espresso Logic Minimization Quine-McKluskey QCA #' @export #' @importFrom utils write.table # ############################################################## { ############################################################## # sanity checks ############################################################## if (esp_file == "") { if (is.data.frame(in_tt) == FALSE) stop('Bad input truth table in_tt.\n') if ((n_in + n_out) != ncol(in_tt)) stop('Number of inputs (',n_in,') + number of outputs (', n_out,') is not equal to number of in_tt columns (', ncol(in_tt),').\n') if (nrow(in_tt) == 0) stop('in_tt has no rows.\n') file_name <- "esptemp" write_esp_file(file_name, input_sizes, in_tt, n_in, n_out, find_dc) } else { if ((find_dc == TRUE) || (n_in > 0) || (n_out > 0) || length(input_sizes) >0) warning("When esp_file is used, options find_dc, n_in, n_out, and input_sizes are ignored.") if (! file.exists(esp_file)) stop('File "', esp_file, '" does not exist.\n') file_name <- esp_file } ############################################################## # call espresso ############################################################## if (exact_cover) Sys.setenv(ESP_EXACT_COVER=1) else Sys.unsetenv("ESP_EXACT_COVER") ret <- esp_system(mode, file_name, use_system=FALSE) if ((ret != 0) || (! file.exists("esptemp.out"))) { unlink("esptemp") stop('Call to espresso failed.\n') } ############################################################## # create truth table from espresso results ############################################################## esptt <- readLines("esptemp.out") # get solution sizes from .p meta commands dot_p <- grep("^.p ", esptt) p_str <- esptt[dot_p] p_size <- as.numeric(gsub("\\D", "", p_str)) # get input and output names from esp_file .ilb and .ob if (esp_file != "") { dot_ilb <- as.numeric(grep(".ilb ", esptt)[1]) ilb_str <- esptt[dot_ilb] in_names <- unlist((strsplit(ilb_str,split=" ")))[-1] dot_ob <- grep(".ob ", esptt)[1] ob_str <- esptt[dot_ob] out_names <- unlist((strsplit(ob_str,split=" ")))[-1] col_names <- c(in_names,out_names) } # use names from input truth table else { col_names <- colnames(in_tt)[seq(ncol(in_tt))] } # ignore espresso meta commands and comments esptt <- esptt[!grepl("[.#]", esptt)] esptt <- t(sapply(esptt, function(x) { unlist(strsplit(x, split=" ")) })) if ((ncol(esptt) == 0) || (nrow(esptt) == 0)) { unlink("esptemp") unlink("esptemp.out") stop('All terms of input truth table optimized away.\n') } rownames(esptt) <- seq(nrow(esptt)) if (length(col_names) == ncol(esptt)) colnames(esptt) <- col_names else warning("No input and output names. Will use system generated names." ,call.=FALSE) ############################################################## # clean up and return results ############################################################## if (Sys.getenv("LOGOPT_SAVE_TEMP_FILES") == "") { unlink("esptemp") unlink("esptemp.out") } # return list of truth table and solution sizes return(list(as.data.frame(esptt),p_size)) } 'write_esp_file' <- function(file_name, input_sizes, in_tt, n_in, n_out, find_dc) ############################################################## # write an espresso format file from a R truth table ############################################################## { espfile <- file(file_name, "w") cat(".mv", n_in+1,"0", file=espfile) n_sizes = length(input_sizes) if (n_sizes > 0) { if (n_sizes != n_in) stop('Vector paramater input_sizes has ', n_sizes, ' elements -- it neeeds to have n_in=', n_in, ' elements.\n') } for (j in 1:n_in) { # if input_sizes specified, use it, otherwise set size must be at least 2 (Boolean) if (n_sizes > 0) size = input_sizes[j] else size = 2 nval <- length(unique_int(in_tt[j],n_vals=size)) if (nval < 2) stop('Input ', j, ' must have at least two values. Specify input_sizes.\n') cat(" -", nval, sep="", file=espfile) } cat(" ", n_out, "\n", file=espfile) for (j in 1:n_in) { if (n_sizes > 0) size = input_sizes[j] else size = 2 valj <- unique_int(in_tt[j], n_vals=size) cat('.label var=', j-1, valj,'\n', file=espfile) } if (find_dc) cat(".type fr\n", file=espfile) else cat(".type fd\n", file=espfile) close(espfile) write.table(in_tt, file=file_name,sep=" ",append=TRUE, row.names=FALSE, col.names=FALSE, quote = FALSE) espfile <- file(file_name, "a") cat(".e\n", file=espfile) close(espfile) } 'num_input_values' <- function(tt, n_in) ############################################################################ #' @title Find size of input values #' @description Find number of unique input values for each input in #' tt. #' @param tt a truth table where first n_in columns are inputs #' @param n_in number of inputs #' @return returns a vector of number of unique input values for each input #' @export ############################################################################ { valv <- NULL for (j in 1:n_in) { valj <- length(unique_int(tt[j], strip_dc=TRUE)) valv <- c(valv, valj) } return (valv) } 'unique_int' <- function(col, strip_dc=TRUE, n_vals=0) ######################################################################## ######################################################################## # Return a vector of the unique values in the input column 'col'. # The col contains possible values from an Espresso optimized # truth table. Don't care values ("-" or "dc") are converted to -1. # If strip_dc=TRUE, we remove the don't care value from the return # vector. If n_vals is > 0, the return vector is filled with values # (v1 .. vn) to make the vector size n_vals. ######################################################################## { uniq <- unique(col) uint <- as.data.frame(lapply(uniq, function(x) { x <- as.character(x) x[x %in% c("-", "dc")] <- -1 return(as.numeric(x)) })) vect <- uint[,1] if (strip_dc) vect <- vect[vect != -1] if (n_vals > 0) { vlen <- length(vect) if ((n_vals-vlen) > 0) for (i in 1:(n_vals-vlen)) vect <- c(vect,paste('v',i,sep='')) } return (sort(vect)) } 'print_multi_tt' <- function(esp_multi, eqn=FALSE, n_in, n_out, max_sol=50, QCA=FALSE) ############################################################################ #' @title Print logicopt() results #' #' @description This function prints the the results from logicopt() #' in truth table or equation format. # #' @return None # #' @param esp_multi #' An R data frame table representing a truth table with 1 or more #' solutions. #' #' @param eqn #' Print in equation format. Default is FALSE. #' #' @param n_in #' Number of inputs in the esp_multi truth table. #' #' @param n_out #' Number of outputs in the esp_multi truth table. #' #' @param max_sol #' Maximum number of solutions to print. Default is 50. #' #' @param QCA #' Attempt to print out in a QCA like format #' #' @examples #' data(l.partybans.0) #' tt <- logicopt(l.partybans.0,n_in=5,n_out=1,find_dc=TRUE,mode="multi-full") #' print_multi_tt(tt,5,1,eqn=TRUE,max_sol=5) #' #' @export ############################################################################ { ett <- esp_multi[[1]] esize <- esp_multi[[2]] n_solutions = length(esize) if (max_sol < n_solutions) { n_solutions = max_sol cat("\nPrinting", n_solutions,"of", length(esize), "solutions.\n") } first <- 1 for (i in 1:n_solutions) { if (QCA) cat("M", i, ": ", sep="") else cat("SOLUTION", i,"\n") last <- esize[i]+first-1 tt <- ett[first:last,] if (eqn) cat(tt2eqn(tt,n_in,n_out,QCA),sep="\n") else print(tt) first <- last+1 if (i == max_sol) return; } } 'tt2eqn' <- function(tt,n_in,n_out,QCA=FALSE) ############################################################################ #' @title Equations from a Truth Table #' #' @description This function generates the ON set equations for a truth table. #' Inputs are uppercase if they are positive and lowercase for negative. # #' @return Vector of equations strings. One for each output. # #' @param tt #' R data frame truth table. #' #' @param n_in #' Number of inputs in the tt. #' #' @param n_out #' Number of outputs in the tt. #' #' @param QCA #' Print in QCA format. #' #' @examples #' data(l.small) #' tt <- logicopt(l.small,n_in=4,n_out=3) #' eqn <- tt2eqn(tt[[1]],4,3) #' #' @export ############################################################################ { have_mv <- any(apply(tt,2,function(x) x > 1)) eqn <- "" tot_eqn <- NULL for (j in 1:n_out) { out_nm <- colnames(tt)[n_in+j] out_on <- tt[tt[out_nm]=="1",] n_rows <- nrow(out_on) if (n_rows > 0) { if (! QCA) eqn <- paste(out_nm,"= ") for (i in 1:n_rows) { row <- as.character(t(out_on[i,])) first_and <- TRUE for (k in 1:n_in) { ch <- row[k] if (ch != "-") { if (first_and == FALSE) # eqn <- paste(eqn,sep="") eqn <- paste(eqn,"*",sep="") eqn <- paste(eqn,literal(colnames(tt)[k],ch,have_mv),sep="") first_and <- FALSE } } if ((first_and == FALSE) && (i != n_rows)) eqn <- paste(eqn," + ",sep="") # eqn <- paste(eqn,"+",sep="") } } if (QCA) eqn <- paste(eqn,"<=>",out_nm) tot_eqn <- c(tot_eqn,eqn) eqn <- "" } return(tot_eqn) } 'literal' <- function(name,value,have_mv) { if (have_mv) lit <- paste(name,"{",value,"}", sep="") else { if (value == 0) lit <- tolower(name) else if (value == 1) lit <- toupper(name) } return (lit) } 'print_primes_tt' <- function(primes,eqn=FALSE,n_in,n_out) ############################################################################ #' @title Print the Primes from logicopt(mode="primes") #' #' @description This function prints the results from logicopt(..,,mode="primes") #' option. # #' @return None # #' @param primes #' An R data frame table generated by logicopt(...,mode="primes"). #' #' @param eqn #' Print in equation format. Default is FALSE. #' #' @param n_in #' Number of inputs in the primes truth table. #' #' @param n_out #' Number of outputs in the primes truth table. #' #' #' @examples #' data(l.small) #' ptt <- logicopt(l.small,n_in=4,n_out=3,find_dc=TRUE,mode="primes") #' print_primes_tt(ptt,eqn=TRUE,4,3) #' #' @export ############################################################################ { ptt <- primes[[1]] psize <- primes[[2]] if (length(psize) != 3) stop("Don't have a primes truth table.\n") first <- 1 cat("\nESSENTIAL PRIMES\n") if (psize[1] > 0) { last <- psize[1]+first-1 tt <- ptt[first:last,] if (eqn) cat(tt2eqn(tt,n_in,n_out),sep="\n") else print(tt) first <- last+1 } else cat(" <empty>\n") cat("\nPARTIALLY REDUNDANT PRIMES\n") if (psize[2] > 0) { last <- psize[2]+first-1 tt <-ptt[first:last,] if (eqn) cat(tt2eqn(tt,n_in,n_out),sep="\n") else print(tt) first <- last+1 } else cat(" <empty>\n") cat("\nTOTALLY REDUNDANT PRIMES\n") if (psize[3] > 0) { last <- psize[3]+first-1 tt <- ptt[first:last,] if (eqn) cat(tt2eqn(tt,n_in,n_out),sep="\n") else print(tt) } else cat(" <empty>\n") } 'esp_system' <- function(mode, file_name, use_system=TRUE) { if (use_system) { if (mode == "qm") esp_command <- paste('yespresso -srmv -Dqm', file_name,'>esptemp.out') else if (mode == "primes") esp_command <- paste('yespresso -srmv -Dprimes', file_name,'>esptemp.out') else if (mode == "espresso") esp_command <- paste('yespresso -srmv ', file_name,'>esptemp.out') else if (mode == "multi-min") esp_command <- paste('yespresso -srmv -Dmulti-min', file_name,'>esptemp.out') else if (mode == "multi-full") esp_command <- paste('yespresso -srmv -Dmulti-full', file_name,'>esptemp.out') else if (mode == "echo") esp_command <- paste('yespresso -srmv -Decho', file_name,'>esptemp.out') else stop('Mode "', mode, '" not supported.\n', call. = FALSE) ret = system(esp_command) } else { ret <- esp_R(mode, file_name) # need to fix return ret <- 0 } return(ret) } #' @useDynLib LogicOpt esp_main esp_R <- function(mode, file_name) { #libpath <- system.file("libs", package="LogicOpt") #lib <- paste0(libpath,"/LogicOpt",.Platform$dynlib.ex) #dyn.load(lib) if (!is.character(mode)) { stop("mode must be of type character.\n") } if ((mode == "qm") || (mode == "primes") || (mode == "espresso") || (mode == "multi-full") || (mode == "multi-min") || (mode == "echo")) { result <- .C(esp_main, esp_mode=mode, esp_file=file_name, PACKAGE="LogicOpt") return(result) } else { stop("Unsupported mode\n") } }

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/man/check.ordered.to.pa.Rd

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cran/bnpa

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check.ordered.to.pa.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/check.ordered.to.pa.R \name{check.ordered.to.pa} \alias{check.ordered.to.pa} \title{Verifies if there are ordered factor variables to be declared in the pa model building process} \usage{ check.ordered.to.pa(bn.structure, data.to.work) } \arguments{ \item{bn.structure}{is a BN structure learned from data used to identify if the variable is endogenous or exogenous when building the PA model.} \item{data.to.work}{is a data set containing the variables of the BN.} } \value{ a data frame with ordered variables. } \description{ Receives a BN structure and a data set, then verifies if there are ordered variables. In a positive case return TRUE. } \examples{ # Clean environment closeAllConnections() rm(list=ls()) # Set enviroment # setwd("~/your working directory") # Load packages library(bnpa) # Load the dataset data(dataQualiN) # Pre-Loaded # Build the BN structure bn.structure<-bnlearn::hc(dataQualiN) # Show the BN structure learned bnlearn::graphviz.plot(bn.structure) # Tranforms variables A and B in ordered factor dataQualiN$A <- as.ordered(dataQualiN$A) dataQualiN$B <- as.ordered(dataQualiN$B) # Generates a list with variables to be ordered and exogenous variables cat.var.to.use.in.pa <- bnpa::check.ordered.to.pa(bn.structure, dataQualiN) # Show the variables cat.var.to.use.in.pa } \references{ HAYES, A F; PREACHER, K J. Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, v. 67, n. 3, p. 451-470, 2014. } \author{ Elias Carvalho }

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kayleahaynes/TidyTuesday

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# set up ----------------------------------------------------------------- library(tidyverse) library(lubridate) library(extrafont) loadfonts() # load data -------------------------------------------------------------- tuesdata <- tidytuesdayR::tt_load(2021, week = 27) df_data_raw <- tuesdata$animal_rescues glimpse(df_data_raw) View(df_data_raw) # transform data --------------------------------------------------------- df_data <- df_data_raw %>% filter(cal_year < 2021) %>% mutate(animal_group_parent = ifelse(animal_group_parent == "cat", "Cat", animal_group_parent), animal_group_parent = fct_lump(animal_group_parent, 4)) %>% mutate(date = dmy_hm(date_time_of_call), month = month(date, label = TRUE)) %>% mutate(year2020 = ifelse(cal_year == 2020, "2020", "<2020")) df_data %>% group_by(animal_group_parent, month, cal_year, year2020) %>% count() %>% group_by(animal_group_parent, month, year2020) %>% summarise(average = median(n)) %>% ungroup() %>% group_by(month, year2020) %>% mutate(total = sum(average)) %>% ggplot(aes(x = month, y = average, group = fct_reorder(animal_group_parent, average), fill = animal_group_parent)) + geom_col(width = 0.98, col = "black", size = 0.1) + geom_text(aes(y = total + 8, label = month), size = 2) + coord_polar() + facet_wrap(~year2020, strip.position="bottom") + theme_void() + labs(title = "Average number of animals rescued", subtitle = "Pre 2020 vs 2020", y = "", x = "", caption = "#TidyTuesday week 27 | source: London Fire Brigade | datavis: @kayleahaynes") + theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5, margin = margin(10,0,30,0)), plot.caption = element_text(size = 8, margin = margin(30,0,0,0)), panel.border = element_blank(), legend.position = "top", panel.grid = element_line(size = 0.05), axis.text.x = element_blank(), axis.text.y = element_text(size = 8), text = element_text(family = "Catamaran", size = 14), plot.background = element_rect(fill = "white", color = "white")) + scale_fill_manual(values = c("#d1b490", "#8aa8a1", "#885a89", "#ee7b30", "#cbcbd4"), name = "") ggsave("week27.png", height = 5, width = 10)

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/src/assignments/spa1/code/crypta2.R

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supersubscript/compbio

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crypta2.R

### Returns possible permutations of input permutations <- function(n) { if (n == 1) { return(matrix(1)) } else { sp <- permutations(n - 1) p <- nrow(sp) A <- matrix(nrow = n * p, ncol = n) for (i in 1:n) { A[(i - 1) * p + 1:p, ] <- cbind(i, sp + (sp >= i)) } return(A) } } ### Get the number of row duplicates we will have getUnique = function(x){ if(x==0){ return(1) } x * getUnique(x-1) } getFirstCharacters = function(string) { string = gsub("[[:punct:]]", " ", string) # remove symbols string = gsub("\\s+"," ", string) # remove extra whitespaces #string = paste(string,collapse=" ") # string = strsplit(string," ")[[1]] unique(sapply(string, function(x) substring(x,1,1))) } m = permutations(10) - 1 uniqueLetters = function(inString) { inString = strsplit(inString, "")[[1]] inString = inString[!inString %in% c(" ", "+", "*", "-", "=", "/", "&")] inString = unique(inString) } inString = "AB * C = DE & DE + FG = HI" #inString = "one + two + two + three + three = eleven" #"send + more = money" letters = uniqueLetters(inString) firstCharacters = getFirstCharacters(inString) inString = strsplit(inString, "")[[1]] colnames(m) = c(letters, rep("NA", 10 - length(letters))) m = m[, -grep("NA", colnames(m))] # Slice the bread! (Take off edge.) m = m[seq(1,nrow(m),getUnique(10 - length(letters))),] # Comb the desert! (Remove duplicates.) for(ii in firstCharacters){ m = m[-which(m[, ii] == 0),] # Kill off those w 0's at beginning. } inString = inString[!inString %in% c(" ")] strings = matrix(inString, ncol = length(inString), nrow = nrow(m), byrow = TRUE) colnames(strings) = strings[1,] count = 1 for(ii in colnames(strings)){ if(ii %in% colnames(m)){ strings[, count] = m[,ii] } else if(ii == "=") { strings[, count] = rep("==", nrow(m)) } count = count + 1 } strings = matrix(do.call(paste0, as.data.frame(strings))) output = sapply(strings, function(x) eval(parse(text=x))) print(m[which(output == TRUE),]) #findSolution = function(str){ # # #}

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/R/tm1_run_chore.R

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catgopal/tm1r

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tm1_run_chore.R

tm1_run_chore <- function(tm1_connection, chore = "") { tm1_adminhost <- tm1_connection$adminhost tm1_httpport <- tm1_connection$port tm1_auth_key <- tm1_connection$key tm1_ssl <- tm1_connection$ssl # added because some http does not know space chore <- gsub(" ", "%20", chore, fixed=TRUE) u1 <- ifelse(tm1_ssl==TRUE, "https://", "http://") #u1 <- "https://" u2 <- tm1_adminhost u3 <- ":" u4 <- tm1_httpport u5 <- "/api/v1/Chores('" u6 <- chore u7 <- "')/tm1.Execute" # url development url <- paste0(u1, u2, u3, u4, u5, u6, u7) #url = "https://localhost:8881/api/v1/Chores('create_Y2Ksales_cube')/tm1.Execute" # post request tm1_chore_return <- httr::POST(url, httr::add_headers("Authorization" = tm1_auth_key), httr::add_headers("Content-Type" = "application/json")) # return manipulation # if content is empty; then success # else get the error message to differentiate abortion and minor error if(httr::content(tm1_chore_return, "text", encoding = "UTF-8") == "") { tm1_chore_message <- "ChoreCompletedSuccessfully" print(tm1_chore_message) } else { # check return if error if (is.null(jsonlite::fromJSON(httr::content(tm1_chore_return, "text"))$error$message) == FALSE) { message(jsonlite::fromJSON(httr::content(tm1_chore_return, "text"))$error$message) stop() } } }

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neptune_describe_db_clusters.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/neptune_operations.R \name{neptune_describe_db_clusters} \alias{neptune_describe_db_clusters} \title{Returns information about provisioned DB clusters, and supports pagination} \usage{ neptune_describe_db_clusters( DBClusterIdentifier = NULL, Filters = NULL, MaxRecords = NULL, Marker = NULL ) } \arguments{ \item{DBClusterIdentifier}{The user-supplied DB cluster identifier. If this parameter is specified, information from only the specific DB cluster is returned. This parameter isn't case-sensitive. Constraints: \itemize{ \item If supplied, must match an existing DBClusterIdentifier. }} \item{Filters}{A filter that specifies one or more DB clusters to describe. Supported filters: \itemize{ \item \code{db-cluster-id} - Accepts DB cluster identifiers and DB cluster Amazon Resource Names (ARNs). The results list will only include information about the DB clusters identified by these ARNs. \item \code{engine} - Accepts an engine name (such as \code{neptune}), and restricts the results list to DB clusters created by that engine. } For example, to invoke this API from the Amazon CLI and filter so that only Neptune DB clusters are returned, you could use the following command:} \item{MaxRecords}{The maximum number of records to include in the response. If more records exist than the specified \code{MaxRecords} value, a pagination token called a marker is included in the response so that the remaining results can be retrieved. Default: 100 Constraints: Minimum 20, maximum 100.} \item{Marker}{An optional pagination token provided by a previous \code{\link[=neptune_describe_db_clusters]{describe_db_clusters}} request. If this parameter is specified, the response includes only records beyond the marker, up to the value specified by \code{MaxRecords}.} } \description{ Returns information about provisioned DB clusters, and supports pagination. See \url{https://www.paws-r-sdk.com/docs/neptune_describe_db_clusters/} for full documentation. } \keyword{internal}

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mainClusterKMLparameter.R

library(sf) #library(tmap) library(rgdal) library(dplyr) library(xml2) library(XML) library(fpc) source("D:/Test/clusterKml2Polygon/readKML.r") # tfile <- list.files(zipfileDir, full.names = T, recursive = T, pattern = "zip") # tfile <- tfile[!grepl("TAB", tfile)] #tFileName <- tfile[1] kml2Tab = function(tempKml, eps, minPts, poorRsrpThr, topTraffic){ ######config parameters tabDsn <- "//192.168.1.12/e$/rf/polygonBackup/Backup/" fname <- strsplit(tempKml, split = "/") %>% unlist() fname <- fname[9] # eps = 0.0014 # minPts = 3 # poorRsrpThr = 20 # topTraffic = 0.6 # # tempFileDir <- "//192.168.1.12/e$/rf/kml2tab" # tabDsn <- "//192.168.1.12/f$/FTP Data/Cellrefs_and_NBR/Nokia/Cell Trace Reports/PoorCoveragePolygons_20200304/TAB/" ##################################################### # fileName <- tools::file_path_sans_ext(basename(tFileName)) # tempUnzipDir <- paste0(tempFileDir,"/", fileName) # # cat(fileName, "\n") # # unzip(tFileName, exdir = tempUnzipDir) # tempKml <- list.files(fileName, # recursive = T, # full.names = T, # pattern = ".kml") #lyr <- ogrListLayers(tempKml) data <- readKML(tempKml, layer = "CE_FCN_PoorRSRP__RLP_over_PAST") %>% st_as_sf() colnames(data)[3] <- "PoorRSRP" data$PoorRSRP <- as.numeric(as.character(data$PoorRSRP)) data <- data[data$PoorRSRP > poorRsrpThr,] data2 <- readKML(tempKml, layer = "CE_FCN_TotalErlangsLTE_RLP_ove") %>% st_as_sf() colnames(data2)[3] <- "TotalErlang" data2$TotalErlang <- as.numeric(as.character(data2$TotalErlang)) #Urban: 0.6/suburban: 0.4/rural:0.2 data2 <- data2[data2$TotalErlang > quantile(data2$TotalErlang, topTraffic, na.rm = T),] data.f <- data[data2, , op = st_intersects] data.f.point <- st_centroid(data.f) data.f.point <- do.call(rbind, st_geometry(data.f.point)) %>% as_tibble() %>% setNames(c("lon", "lat")) tcluster <- dbscan(cbind(data.f.point$lat, data.f.point$lon), eps = eps, MinPts = minPts) data.f.cluster <- cbind(data.f, tcluster$cluster) data.union <- st_buffer(data.f.cluster[data.f.cluster$tcluster.cluster > 0,], dist = 0.00001, endCapStyle = "SQUARE") %>% st_union() st_write(data.union, dsn = paste0(tabDsn, fname,"minPts", minPts, "eps", eps/0.0014*100, "topTraffic", topTraffic), driver = "MapInfo File", delete_dsn = T) #delete files under tempfile directory #unlink(tempFileDir, recursive = T) #delete the zip file from original directory #unlink(tFileName) } #===main=== #zipfileDir <- "//192.168.1.12/f$/FTP Data/Cellrefs_and_NBR/Nokia/Cell Trace Reports/PoorCoveragePolygons_20200304" fileName <- list.files("//192.168.1.12/e$/rf/polygonBackup/Backup/", recursive = T, pattern = ".kml", full.names = T) eps = 0.0014 minPts = c(3, 10, 20) poorRsrpThr = 20 topTraffic = c0.6 for (temp in fileName) { kml2Tab(temp, eps, minPts, poorRsrpThr, topTraffic) }

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/R/set_age_comp.R

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MatthVeron/wham

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set_age_comp = function(input, age_comp) { data = input$data par = input$par if(is.null(age_comp)){ data$age_comp_model_fleets = rep(1, data$n_fleets) # multinomial by default data$age_comp_model_indices = rep(1, data$n_indices) # multinomial by default } else { if(is.character(age_comp)){ # all use the same model themod <- match(age_comp, c("multinomial","dir-mult","dirichlet","logistic-normal-01-infl","logistic-normal-pool0","logistic-normal-01-infl-2par","logistic-normal-miss0")) if(is.na(themod)) stop("age_comp option not recognized. See ?prepare_wham_input.") data$age_comp_model_fleets = rep(themod, data$n_fleets) data$age_comp_model_indices = rep(themod, data$n_indices) } else { if(all(names(age_comp) == c("fleets","indices"))){ themods <- match(age_comp$fleets, c("multinomial","dir-mult","dirichlet","logistic-normal-01-infl","logistic-normal-pool0","logistic-normal-01-infl-2par","logistic-normal-miss0")) if(any(is.na(themods))) stop("age_comp$fleets option not recognized. See ?prepare_wham_input for available options.") if(length(themods) != data$n_fleets) stop("age_comp$fleets must have length = the number of fleets") data$age_comp_model_fleets = themods themods <- match(age_comp$indices, c("multinomial","dir-mult","dirichlet","logistic-normal-01-infl","logistic-normal-pool0","logistic-normal-01-infl-2par","logistic-normal-miss0")) if(any(is.na(themods))) stop("age_comp$indices option not recognized. See ?prepare_wham_input for available options.") if(length(themods) != data$n_indices) stop("age_comp$indices must have length = the number of indices") data$age_comp_model_indices = themods } else { stop("age_comp must either be a character or a named list. See ?prepare_wham_input.") } } } data$n_age_comp_pars_fleets = c(0,1,1,3,1,2,1)[data$age_comp_model_fleets] data$n_age_comp_pars_indices = c(0,1,1,3,1,2,1)[data$age_comp_model_indices] # age comp pars n_catch_acomp_pars = c(0,1,1,3,1,2,1)[data$age_comp_model_fleets[which(apply(data$use_catch_paa,2,sum)>0)]] n_index_acomp_pars = c(0,1,1,3,1,2,1)[data$age_comp_model_indices[which(apply(data$use_index_paa,2,sum)>0)]] par$catch_paa_pars = rep(0, sum(n_catch_acomp_pars)) par$index_paa_pars = rep(0, sum(n_index_acomp_pars)) if(all(data$age_comp_model_fleets %in% c(5,7))){ # start tau/neff at 0 neff <- data$catch_Neff neff[neff <= 0] <- NA neff <- apply(neff,2,mean, na.rm=TRUE)[which(apply(data$use_catch_paa,2,sum)>0)] par$catch_paa_pars = 0.5*log(neff) # exp(age_comp_pars(0)-0.5*log(Neff)) } if(all(data$age_comp_model_indices %in% c(5,7))){ # start tau/neff at 0 neff <- data$index_Neff neff[neff <= 0] <- NA neff <- apply(neff,2,mean, na.rm=TRUE)[which(apply(data$use_index_paa,2,sum)>0)] par$index_paa_pars = 0.5*log(neff) # exp(age_comp_pars(0)-0.5*log(Neff)) } input$data = data input$par = par return(input) }

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drawdown.R

#' The Three Biggest Drawdowns in the portfolio #' #' Show the top 3 drawdowns including start and end dates, as well as #' decrease in returns during the drawdown period. All the information will be #' returned in a table, with all the numbers properly formatted. #' #' If the data set is not big enough that there are fewer drawdowns than #' required by the user, the function will throw NA's into the table so that the #' table will still contain as many rows as the user demands. #' #' @param x A data frame with date and return columns #' #' @return A data frame that contains the starting date, end date and values of #' the three biggest drawdowns. drawdown <- function(x) { ## Create a new column. Each value contains the biggest drawdowns that end ## exactly at the corresponding date of that row res <- list() x[, "pnl"] <- cumsum(x[["pnl"]]) diff <- cummax(x[["pnl"]]) - x[["pnl"]] ## Start the output table of the function with labels res[[1]] <- data.frame(start = "Start Date", end = "End Date", pnl = "P&L ($)") for (i in 1:3){ ## Fill the output table with NA's if there is no more drawdown available if (length(diff) != 0){ ## Find the end date of the biggest drawdown dd.end.idx <- max(which.max(diff), length(diff) - which.max(rev(diff)) + 1) } if (nrow(x) > 1 & length(which(diff == 0 & seq(diff) < dd.end.idx)) != 0) { ## Look up the starting date of the biggest drawdown and calculate the ## P&L loss of that drawdown dd.start.idx <- max(which(diff == 0 & seq(diff) < dd.end.idx)) dd.pnl <- x[["pnl"]][dd.end.idx] - x[["pnl"]][dd.start.idx] ## Find the dates of the recovery period from the biggest drawdown dd.recover.idx <- which(diff == 0 & seq(diff) > dd.end.idx) ## Find the start date of the recovery period if (length(dd.recover.idx) == 0) { dd.recover.idx <- length(diff) } else { dd.recover.idx <- min(dd.recover.idx) } ## Store the results of the drawdown in a layer res[[i+1]] <- data.frame(start = as.character(next_trading_day(x[["date"]][dd.start.idx])), end = as.character(x[["date"]][dd.end.idx]), pnl = as.character(dd.pnl)) ## Remove the data of the biggest drawdown and the recovery period x <- x[-1 * c(dd.start.idx:dd.recover.idx), ] diff <- diff[-1 * c(dd.start.idx:dd.recover.idx)] } } ## Make a table do.call("rbind", res) }

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demography merge.R

setwd("C:/Users/amykr/Box Sync/Amy Krystosik's Files/Data Managment/redcap/ro1 lab results long") demo<-read.csv("R01CHIKVDENVProject_DATA_2017-08-17_1647_demo_merge.csv") demo_wide<-reshape(demo, direction = "wide", idvar = "ï..person_id", timevar = "redcap_event_name", sep = "_") demo_wide$gender_equal <- ifelse(demo_wide$gender_visit_a_arm_1 != demo_wide$dem_child_gender_patient_informatio_arm_1 | demo_wide$gender_visit_b_arm_1 != demo_wide$dem_child_gender_patient_informatio_arm_1 | demo_wide$gender_visit_c_arm_1 != demo_wide$dem_child_gender_patient_informatio_arm_1 | demo_wide$gender_visit_d_arm_1 != demo_wide$dem_child_gender_patient_informatio_arm_1 | demo_wide$gender_visit_e_arm_1 != demo_wide$dem_child_gender_patient_informatio_arm_1, 0, 1) table(demo_wide$gender_equal) demo_wide <-demo_wide[!sapply(demo_wide, function (x) all(is.na(x) | x == ""| x == "NA"))] write.csv(as.data.frame(demo_wide), "demo_wide.csv")

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LafArt/R-util-functions

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prueba_Simula_Likert.R

library(ggplot2) library(tidyverse) source("C:/wmd/R-util-functions/Simula_Likert.R") num_de_items=20 num_de_items2=16 escala_interpretada_1 = c("Muy malo","Malo","Regular","Bueno","Muy bueno") escala_interpretada_2 = c("Muy baja","Baja","Media","Alta","Muy Alta") entorno_familiar<-Simula_Likert(escala=1:5, escala_interpretada = escala_interpretada_1, probabilidad = c(.2,.2,.2,.2,.2), numero_de_items = num_de_items, cantidad_respuestas = 120, dsBase = NULL, escala2 = 1:5, escala_interpretada2 = escala_interpretada_2, numero_de_items2 = num_de_items2, porcentaje_aleatorio = 10, corr_pos_neg = "+") names(entorno_familiar)<-c(rep(paste("x",1:num_de_items,sep = "")), "suma_v1","Baremo_1", rep(paste("y",1:num_de_items2,sep = "")), "suma_v2","Baremo_2") # ggplot(data=entorno_familiar,aes(x=suma_v1,y=suma_v2)) + # geom_point(aes(color=Baremo_1)) + # geom_smooth(method = "lm") + # annotate("text", x = 60, y = 80,label = paste("r =", round(crr$estimate,4))) + # theme_bw() + # labs(x = "Variable 1", y = "Variable 2") baremo_1<-factor(x=entorno_familiar$Baremo_1, levels = escala_interpretada_1, labels = c(1,2,3,4,5)) baremo_1<-factor(x=as.character(baremo_1),levels = c(1,2,3,4,5), labels = escala_interpretada_1) baremo_2<-factor(x=entorno_familiar$Baremo_2, levels = escala_interpretada_2, labels = c(1,2,3,4,5)) baremo_2<-factor(x=as.character(baremo_2),levels = c(1,2,3,4,5), labels = escala_interpretada_2) corrSpearman=cor.test(as.numeric(baremo_1),as.numeric(baremo_2),method = "spearman",exact = FALSE) crr<-cor.test(entorno_familiar$suma_v1,entorno_familiar$suma_v2) ggplot(data=entorno_familiar,aes(x=suma_v1,y=suma_v2)) + geom_jitter(width = 1,height = 1,color="darkred") + geom_smooth(method = "lm") + annotate("text", x = min(entorno_familiar$suma_v1)+30, y = max(entorno_familiar$suma_v1), label = paste("r =", round(crr$estimate,4),",", "p = ", sprintf("%.3f",round(crr$p.value,3)) )) + annotate("text", x = min(entorno_familiar$suma_v1)+30, y = max(entorno_familiar$suma_v1)-10, label = paste("Rho =", round(corrSpearman$estimate,4), ",", "p = ", sprintf("%.3f",round(corrSpearman$p.value,3)) )) + theme_bw() + labs(x = "Variable 1", y = "Variable 2")

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rpivotAddin.R

getDataFrames = function() { if ((length(ls()) == 0) | (length(sapply(.GlobalEnv, is.data.frame)) == 0) | (any(sapply(.GlobalEnv, is.data.frame))==F)) data(iris) return(names(which(sapply(.GlobalEnv, is.data.frame)))) } rpivotAddin <- function() { library(shiny) library(rpivotTable) library(miniUI) library(rstudioapi) ui <- miniPage( # css hack to provide space for a select input in the gadgetTitleBar tags$head(tags$style(HTML(" .gadget-title .shiny-input-container { position: relative; height: 30px; margin: 6px 10px 0; z-index: 10; }"))), gadgetTitleBar("Pivot Table Gadget", left=miniTitleBarButton("done", "Done", primary=T), right=selectInput("dataset", NULL, choices = getDataFrames())), miniContentPanel( rpivotTableOutput("mypivot") ) ) # minipage server <- function(input, output, session) { output$mypivot <- renderRpivotTable({ rpivotTable(getSelectedDF()) }) observeEvent(input$done, { stopApp(TRUE) }) getSelectedDF <- reactive({ eval(parse(text = input$dataset)) }) } # server runGadget(shinyApp(ui, server), viewer = paneViewer()) }

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mozzarellaV8/ATF-FFL

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usa-map-prep.R

# ATF-FFL - United States Map Prep library(maps) library(mapproj) library(maptools) library(sp) library(fiftystater) library(dplyr) library(ggplot2) # Function: Capwords ---------------------------------------------------------- # from tolower() documentation capwords <- function(s, strict = FALSE) { cap <- function(s) paste(toupper(substring(s, 1, 1)), {s <- substring(s, 2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } # Alaska and Hawaii Maps----------------------------------------------------------- # load fifty states data data("fifty_states") fifty_states # rename columns for binding later colnames(fifty_states) <- c("lon", "lat", "order", "hole", "piece", "NAME", "group") fifty_states$NAME <- capwords(fifty_states$NAME) # test map out ggplot(fifty_states, aes(lon, lat, group = group)) + geom_path() + coord_map("polyconic") # USA by county -------------------------------------------------------------- us.county <- map_data("county") colnames(us.county)[5:6] <- c("NAME", "County") us.county$NAME <- capwords(us.county$NAME) us.county$County <- capwords(us.county$County) ggplot(us.county, aes(long, lat, group = group)) + geom_path() + coord_map("polyconic")

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jgooding/ProgrammingAssignment2

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cachematrix.R

## makeCacheMatrix: Returns a list of functions that: ## Set the value of the matrix ## Get the value of the matrix ## Set the value of the matrix inverse ## Get the value of the matrix inverse makeCacheMatrix <- function(x = matrix()) { ## object to store the cached inverse and set to null store_inv <- NULL ## Set the Matrix function ## can be used to set a new matrix, default is that ## the matrix is passed as originating function arguement ## if new matrix provided, also clear any cached inverse setmatrix <- function(y) { x <<- y store_inv <<- NULL } ## Get Matrix function (passes matrix as arguement) getmatrix <- function() x ## Set Matrix Inverse function (inverse passed as arguement) setinv <- function(solved_inv) store_inv <<- solved_inv ## Get the Matrix inverse function (passes inverse as arguement) getinv <-function() store_inv ## Return the list of defined functions list(set=setmatrix, get=getmatrix, setinv=setinv, getinv=getinv) } ## CacheSolve: Returns the inverse of the matrix if already solved, ## Otherwise solves and caches the matrix inverse cacheSolve <- function(x, ...) { ## call the getinv() function to get the inverse cache object store_inv <-x$getinv() ## Check value to see if inverse is already cached and ## return cache if exists if(!is.null(store_inv)) { message("getting cached data") return(store_inv) } ## Otherwise if not already cached ## call the get() function to get the matrix data_matrix <- x$get() ## create the inverse using solve() function store_inv <- solve(data_matrix, ...) ## call setinv() function to cache the inverse x$setinv(store_inv) ## Return solved inverse store_inv } ## :Test case: This is the test script executed to validate ## Define a matrix ## > x <- rbind((1:2),(3:4)) ## call makeCacheMatrix function with matrix as arguement ## > tst <- makeCacheMatrix(x) ## Check object returns the four functions ## > tst ## $set ## function (y) ## { ## x <<- y ## store_inv <<- NULL ## } ## <environment: 0x11080d88> ## ## $get ## function () ## x ## <environment: 0x11080d88> ## ## $setinv ## function (solved_inv) ## store_inv <<- solved_inv ## <environment: 0x11080d88> ## ## $getinv ## function () ## store_inv ## <environment: 0x11080d88> ## ## Check the object get function returns the Matrix as passed ## > tst$get() ## [,1] [,2] ## [1,] 1 2 ## [2,] 3 4 ## Call the cacheSolve function the first time ## > cacheSolve(tst) ## [,1] [,2] ## [1,] -2.0 1.0 ## [2,] 1.5 -0.5 ## ## Call the cacheSolve function again ## Shows inverse came from cache second time round ## > cacheSolve(tst) ## getting cached data ## [,1] [,2] ## [1,] -2.0 1.0 ## [2,] 1.5 -0.5

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borgo-larici/ExData_Plotting1

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getEPC.R

# Function "getEPC" # Used for Course Project 1 in "Exploratory Data Analysis". # Checks if object "epc" exists already. # If "epc" does not exist, downloads data from # https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip # to working directory. Then unzips and # reads specific rows--want to include data from dates 2007-02-01 and 2007-02-02. # Stores data in object "epc". # Converts character-based date and time vars into Date/Time classes. getEPC <- function() { # Return the existing "epc" object if exists if (exists("epc")) return(epc); # Execution continues here if "epc" object does not exist. # Download data download.file( "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", "epc.zip" ) # Unzip data unzip("epc.zip") # Read unzipped file "household_power_consumption.txt". # Omit lines that don't correspond to Feb. 1 and 2, 2007. epc <- read.csv( file = "household_power_consumption.txt", header = TRUE, sep = ";", col.names = c( "DateText", "TimeText", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3" ), skip = 66636, nrows = 2880 ) # Create new "epcDate" vector by converting "DateText" column. epcDate = strptime( paste( epc$DateText, epc$TimeText ), format = "%d/%m/%Y %H:%M:%S" ) # Output "epc" but with new "epcDate" vector prepended and the # text-based date/time columns removed. cbind(epcDate, epc[,3:9]) }

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bbanbury/phyloextra

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matchTreeTaxa.R

#' Check taxa match in two trees #' #' This function will check that taxonomic names in two trees match and if they do not, it will write the pruned trees to file. #' @param phy1 A phylogenetic tree in the class "phylo" #' @param phy2 A phylogenetic tree in the class "phylo" #' @param toFile If TRUE, it will save pruned tree files in the working directory #' @param treeFormat either "newick" or "nexus" depending on how you want the tree written #' @export #' @return Returns either "OK" if all taxa match, or it returns a list with taxa from each tree that does not match the other. #' @seealso \link{phyDataMatch} \link{TreeNameCheck} #' @examples #' tree1 <- rtree(10) #' tree2 <- rtree(9) #' matchTreeTaxa(tree1, tree2, toFile=FALSE) matchTreeTaxa <- function(phy1, phy2, toFile=TRUE, treeFormat=match.arg(treeFormat,choices=c("newick", "nexus"), several.ok=FALSE)){ a <- TreeNameCheck(phy1, phy2) if(a[1] == "OK") warning("You do not need to drop taxa from either phylogeny") else if (length(a$phy1.not.phy2) > 0){ pruned.phy1 <- drop.tip(phy1, a$phy1.not.phy2) if(toFile){ if (treeFormat=="newick") write.tree(pruned.phy1, file=paste("pruned_phy1.tree")) if (treeFormat=="nexus") write.nexus(pruned.phy1, file=paste("pruned_phy1.nex")) print("Saved pruned_phy1") } } else if (length(a$phy2.not.phy1) > 0){ pruned.phy2 <- drop.tip(phy2, a$phy2.not.phy1) if(toFile){ if (treeFormat=="newick") write.tree(pruned.phy2, file=paste("pruned_phy2.tree")) if (treeFormat=="nexus") write.nexus(pruned.phy2, file=paste("pruned_phy2.nex")) print("Saved pruned_phy2") } } return(a) }

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print.facet_trelliscope.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/facet_trelliscope.R \name{print.facet_trelliscope} \alias{print.facet_trelliscope} \title{Print facet trelliscope object} \usage{ \method{print}{facet_trelliscope}(x, ...) } \arguments{ \item{x}{plot object} \item{...}{ignored} } \description{ Print facet trelliscope object }

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SShDFunc.R

#' Calculating Sexual Shape Dimorphism #' #' This function directly calculates SShD. However it may be a poor estimator for unbalanced designs where there are more of one sex than the other, SShDLM() is more robust. #' @param Coords Two-dimensional array of coordinates from geometric morphometric analysis (see two.d.array() in geomorph for correct formatting) #' @param Sex A character or factor vector recording sex for each individual as 'm' or 'f', individuals should be in the same order as the rows of the Coords #' @param Zeroed a logical value stating whether you wish to correct the SShD for the variation among individuals regardless of sex (defaults to TRUE) #' #' @export #' #' @examples #' SShDFunc(Coords, Sex, Zeroed = TRUE) #' SShDFunc(Coords, Sex, TRUE) SShDFunc <- function(Coords, Sex, Zeroed = TRUE) { if (!is.element("m", Sex) | !is.element("f", Sex)) { return(NA_real_) warning("No data found for at least one sex.") } FData <- Coords[as.character(Sex)=='f', , drop = FALSE] MData <- Coords[as.character(Sex)=='m', , drop = FALSE] ConsensusF <- colMeans(FData) ## Average male and female coordinates ConsensusM <- colMeans(MData) DiffFM <- ConsensusF-ConsensusM SShD <- euclidean(DiffFM) if (isTRUE(Zeroed)) { Zero <- replicate(1000, zeroSShD(Coords, Sex)) return(SShD-mean(Zero)) } SShD }

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skyformat99/msgpack2R

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tests.r

# Tests for testing out the functionality of the package, to make sure it isn't broken # some references # //https://github.com/msgpack/msgpack-c/blob/401460b7d99e51adc06194ceb458934b359d2139/include/msgpack/v1/adaptor/boost/msgpack_variant.hpp # //https://stackoverflow.com/questions/44725299/messagepack-c-how-to-iterate-through-an-unknown-data-structure # //https://stackoverflow.com/questions/37665361/how-to-determine-the-class-of-object-stored-in-sexp-in-rcpp # //https://stackoverflow.com/questions/12954852/booststatic-visitor-with-multiple-arguments # //https://stackoverflow.com/questions/25172419/how-can-i-get-the-sexptype-of-an-sexp-value # //https://github.com/wch/r-source/blob/48536f1756a88830076023db9566fbb2c1dbb29b/src/include/Rinternals.h#L1178-L1214 # //https://stackoverflow.com/questions/16131462/how-to-use-boost-library-in-c-with-rcpp library(msgpack2R) # library(Rcpp) # sourceCpp("src/msgpack_unpack.cpp") # sourceCpp("src/msgpack_pack.cpp") # source("R/functions.r") catn <- function(...) cat(..., "\n") # Test atomic types # integer xpk <- msgpack_pack(1) catn(identical(msgpack_unpack(xpk), 1)) # double xpk <- msgpack_pack(1.54) catn(identical(msgpack_unpack(xpk), 1.54)) # string xpk <- msgpack_pack("sdfsdf") catn(identical(msgpack_unpack(xpk), "sdfsdf")) # raw xpk <- msgpack_pack(as.raw(c(0x28, 0x4F))) catn(identical(msgpack_unpack(xpk), as.raw(c(0x28, 0x4F)))) # boolean xpk <- msgpack_pack(T) catn(msgpack_unpack(xpk)) # nil xpk <- msgpack_pack(NULL) catn(is.null(msgpack_unpack(xpk))) # ext x <- as.raw(c(0x28, 0x4F)) attr(x, "EXT") <- 1L xpk <- msgpack_pack(x) catn(identical(msgpack_unpack(xpk), x)) # unicode or something characters - note this doesn't always work if you copy/paste into a terminal because of how terminals encode text, but if you source this file it works x <- list('图书，通常在狭义上的理解是带有文字和图像的纸张的集合。书通常由墨水、纸张、羊皮纸或者其他材料固定在书脊上组成。组成书的一张纸称为一张，一张的一面称为一页。但随着科学技术的发展，狭义图书的概念也在扩展，制作书的材料也在改变，如电子格式的电子书。从广义理解的图书，则是一切传播讯息的媒介。书也指文学作品或者其中的一部分。在图书馆信息学中，书被称为专著，以区别于杂志、学术期刊、报纸等连载期刊。所有的书面作品（包括图书）的主体是文学。在小说和一些类型（如传记）中，书可能还要分成卷。对书特别喜爱的人被称为爱书者或藏书家，更随意的称呼是书虫或者书呆子。买书的地方叫书店，图书馆则是可以借阅书籍的地方。2010年，谷歌公司估计，从印刷术发明至今，大概出版了一亿三千万本不同书名的书籍。[1]') xpk <- msgpack_pack(x) xu <- msgpack_unpack(xpk) catn(identical(x, xu)) # Complex nested object with lists and map x <- as.raw(c(0x28, 0x4F)) attr(x, "EXT") <- 1L xmap <- msgpack_map(key=letters[1:10], value=1:10) xmap$value[[3]] <- list(NULL) xmap$value[[4]] <- as.list(1:10) xmap$value[[4]][[3]] <- xmap xmap$value[[5]] <- x y <- 1:10 names(y) <- letters[1:10] x <- list(1:10, y, "a", list(3,raw(4)), xmap) x <- msgpack_format(x) xpk <- msgpack_pack(x) xu <- msgpack_unpack(xpk) xs <- msgpack_simplify(x) xus <- msgpack_simplify(xu) catn(identical(xs, xus)) # named list can be used directly as input - should come out to a map, simplify to get a named vector x <- list(a=1L, b=2L) xpk <- msgpack_pack(x) catn(identical(msgpack_simplify(msgpack_unpack(xpk)),c(a=1L, b=2L))) # multiple objects xpk <- msgpack_pack(1,2,3,5,"a", msgpack_format(1:10)) xu <- msgpack_unpack(xpk) catn(identical(msgpack_simplify(xu[[6]]), 1:10)) # speed test require(microbenchmark) x <- as.list(1:1e7) print(microbenchmark(xpk <- msgpack_pack(x), times=3)) # 0.5 seconds print(microbenchmark(xu <- msgpack_unpack(xpk), times=3)) #2.4 seconds

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refs/heads/master

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2017-11-21T23:42:13

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as3_v3.R

# I confirm that the attached work is my own, except where clearly indicated in the text. ############################################### #CONFIDENCE INTERVALS & #COVERAGE FNS ############################################## b.ests.np <- function(B = 99, data, h = sd(data)/3, method) { #Purpose: Creates the bootstrap estimates for a dataset by taking B samples of # size n from the dataset and taking the mean of each resample to create our samples #Inputs: # B - a scalar - number bootstrap resamples to be taken # n - a scalar - size of resamples # data - vector of data you wish to create bootstraps from # h - scalar - degree of smoothing # method - named vector of methods, indicating what method of bootstrap confidence # interval you wish to use. Must be one of "percentile", "bca", "t" and "smooth" #Outputs: # boot.est - vector of length B containing all the bootstrap estimates of interest # from each sample if(!is.numeric(data)){ stop("data must be numeric") } x.star <- matrix(sample(x = data, length(data)*B, replace = T), ncol = B) if (method == "smooth"){ x.star <- x.star + rnorm(prod(dim(x.star))) } if (method == "t"){ B.ests <- apply(x.star, 2, b.t, data = data) } else{ B.ests <- apply(x.star, 2, mean) } return(B.ests) } #--------------------------------------------------------------------------------- b.t <- function(x, data){ #Purpose: function to be used in a apply call in b.ests.np to create the bootstrap-t # estimates #Inputs: # x - vector - bootstrap sample used to created estimate # data - vector - dataset from which we are sampling and using to create bootstrap # estimates #Output: # scalar - the bootstrap-t estimate for the bootstrap sample from the data return((mean(x)-mean(data))/sd(x)) } #---------------------------------------------------------------------------------- r.add <- function(x, h){ #Purpose: adds noise to a scalar value x using random deviates from the standard # normal distribution. This function is to be used in the smooth bootstrap # in a apply call in b.ests.np #Inputs: # x - scalar that has noise added to it # h - scalar that indicates the degree of smoothing #Output: # scalar - original value of x with noise added return(x + rnorm(1, 0, sd = h)) } #---------------------------------------------------------------------------------- #Using len's functions: get.zhat0<-function(est, boot.est){ #Purpose: Return the bias correction factor, zhat0, in BCa bootstrap CI method #Inputs: # est - estimated quantity of interest from data # boot.est - vector of bootstrap estimates of quantity of interest #Outputs: # zhat - scalar - bias correction factor if (!is.numeric(est)){ stop("est must be numeric") } if (length(est) > 1){ stop("est must be a scalar") } if (!is.numeric(boot.est)){ stop("boot.est must be numeric") } prop.less<-sum(boot.est<est)/length(boot.est) zhat<-qnorm(prop.less) return(zhat) } #------------------------------------------------------------------------------------------ get.ahat <- function(data){ #Purpose: Return the acceleration factor, ahat, in BCa bootstrap CI method #Inputs: # data - vector of data # est - estimated quantity of interest from data # fun - function that can be used to produce est from data via fun(data) # dist - character indicating the distribution that the data follows # so that the correct est is created to be used in fun #Implementation note: # 1. The routine calls fun(data,...) and expects it to return a scalar equal to est # 2. Requires a data vector of length at least 2 #Check data vector length n <- length(data) if (n < 2) stop("data vector must be at least length 2\n") #Get jacknife estimates of quantity of interest jack.est <- numeric(n) for (i in 1:n){ jack.data <- data[-i] jack.est[i] <- mean(x = jack.data) } #Compute ahat mean.jack.est <- mean(jack.est) ahat.numerator <- sum((mean.jack.est - jack.est)^3) ahat.denominator <- 6*(sum((mean.jack.est - jack.est)^2))^1.5 ahat <- ahat.numerator / ahat.denominator return(ahat) } #--------------------------------------------------------------------------------- alpha.perc <- function(alpha){ #Purpose: gives the correct percentiles to use in the quantile function # for the percentile method #Inputs: # alpha - scalar - gives the size of the interval so alpha = 0.025 gives # a 95% CI ie. a (1-2*alpha)*100% CI #Outputs: # vector of size 2 containing alpha and 1-alpha alpha. <- c(alpha, (1-alpha)) return((alpha.)) } #---------------------------------------------------------------------------------- alpha.bca <- function(alpha = 0.025, data, est, boot.est){ #Purpose: gives the correct percentiles to use in the quantile fn # for the BCa method #Inputs: # alpha - scalar - gives the size of the interval # data - vector of dataset you are using to create CIs # est - estimated quanity of interest from data # boot.est - vector of bootstrap estimates #Outputs: # vector of size 2 containing the BCa percentiles if (!is.numeric(est)){ stop("est must be numeric") } if (length(est) > 1){ stop("est must be a scalar") } if (!is.numeric(boot.est)){ stop("boot.est must be numeric") } ahat <- get.ahat(data) zhat <- get.zhat0(est = est, boot.est = boot.est) alpha1 <- pnorm(zhat + (zhat + qnorm(alpha)) / (1 - ahat*(zhat + qnorm(alpha)))) alpha2 <- pnorm(zhat + (zhat + qnorm(1 - alpha)) / (1 - ahat*(zhat + qnorm(1 - alpha)))) alpha. <- c(alpha1, alpha2) return(alpha.) } #--------------------------------------------------------------------------------- CI <- function(alpha, boot.est){ #Purpose: Creates CIs for the BCa, percentile and smooth methods by using the # quantile function using the appropriate alphas for each method. #Inputs: # alpha - the percentiles for the method to be used in the quantile function to create the CI # boot.est - list of 2 lists of bootstrap estimates for each level of B and n #Output: # CI.bca - output of the quantile function, vector of size 2 with named entries # that is the percentile used CI.bca <- quantile(boot.est, probs = alpha) return(CI.bca) } #---------------------------------------------------------------------------------- CI.t <- function(alpha, est, data, boot.est){ #Purpose: Generates the confidence interval for the bootstrap-t method #Inputs: # alpha - vector of the percentiles for the percentile method to be used in # the quantile function to create the CI # est - scalar - the estimated mean from the data # data - vector of the dataset sampled from to create the bootstrap estimates # boot.est - vector of bootstrap estimates for each sample #Output: # CI.t - vector of upper and lower limits of the confidence interval for bootstrap-t quantiles <- CI(1-alpha, boot.est) CI.t <- est - sd(data)*quantiles return(CI.t) } #---------------------------------------------------------------------------------- bootstrap.ci <- function(B = 99, data, alpha = 0.025, method, dist = "normal", h = sd(data)/3){ #Purpose: wrapper function that creates bootstrap estimates for each level of B and n # and calculates percentiles for each method and then the corresponding CI. #Inputs: #Inputs: # B - a scalar - number of bootstraps resamples to be taken # data - vector of dataset you wish to create bootstraps from # alpha - scalar - indicatess the size of the interval so alpha = 0.025 gives # a 95% CI ie. a (1-2*alpha)*100% CI for the percentile method # dist - character giving distribution of simulated data, must be one of "normal", # "poisson" or "gamma" # h - scalar - degree of smoothing # method - named vector of methods, indicating what method of bootstrap CI # you wish to use. Must be one of "percentile", "bca", "smooth" or "t" #Outputs: # list of: # CI - vector of size 2 containing the upper and lower limits of the CI if (!is.numeric(data)){ stop("data must be numeric") } if (length(data) < 2){ stop("data must have length greater than 2") } if (h < 0){ stop("h must be positive") } if (length(h) > 1){ stop("h must be a scalar") } if(!is.numeric(h)){ stop("h must be numeric") } #generate bootstrap estimates boot.est <- b.ests.np(B, data, method = method, h = h) est <- mean(data) #getting right arguments for alpha fn args <- switch(method, percentile = list(alpha), smooth = list(alpha), t = list(alpha), bca = list(alpha = alpha, data = data, est = est, boot.est = boot.est)) #getting right percentiles for the method alpha.fn <- switch(method, percentile = match.fun(alpha.perc), smooth = match.fun(alpha.perc), t = match.fun(alpha.perc), bca = match.fun(alpha.bca)) alpha <- do.call(alpha.fn, args) # get the percentiles # getting arguments for CI fn args.CI <- switch(method, percentile = list(alpha = alpha, boot.est = boot.est), smooth = list(alpha = alpha, boot.est = boot.est), t = list(alpha = alpha, boot.est = boot.est, data = data, est = est), bca = list(alpha = alpha, boot.est = boot.est)) # getting CI method CI.method <- switch(method, percentile = match.fun(CI), smooth = match.fun(CI), bca = match.fun(CI), t = match.fun(CI.t)) CI <- do.call(CI.method, args = args.CI) # get CI return(CI = CI) } #--------------------------------------------------------------------- data.gen <- function(dist, mean, sd, lambda, shape, rate, n){ #Purpose: Generates the data to be used as the resample for which a CI is created. # This is the data argument used in b.ests.np and from which est is calculated #Inputs: # dist - character giving distribution of simulated data, must be one of "normal", # "poisson" or "gamma" # mean - scalar - mean of the normal distribution that you want to generate samples from # sd - scalar - standard deviation of the normal dist for the sample # lambda - scalar - rate parameter for the poisson dist for the sample # shape - scalar - the shape parameter or alpha for the gamma dist for the sample # rate - scalar - the rate parameter or beta for the gamma dist for the sample # n - scalar - the number of random deviates to be sampled from the distribution # ie. size of the bootstrap sample if (n%%1!=0 | n < 1){ stop("n must be a positive non-zero integer") } if (!is.numeric(n)){ stop("n must be numeric") } if (length(n) > 1){ stop("n must be a scalar") } #getting correct arguments to be used in the random deviate fn args.dist <- switch(dist, normal = list(n, mean, sd), poisson = list(n, lambda), gamma = list(n, shape, rate)) #getting right distribution fn dist.fn <- switch(dist, normal = match.fun(rnorm), poisson = match.fun(rpois), gamma = match.fun(rgamma)) #sample created data.fn <- do.call(dist.fn, args = args.dist) return(data.fn) } #-------------------------------------------------------------------------------------- input.checks <- function(sims, B.v, B, n.v, n, alpha, method, dist, mean, sd, lambda, shape, rate){ #Purpose: checks inputs into do.sim.np function are of the correct type and form. #Input: # sims - scalar - number of simulations to be performed # B.v - vector of the levels of B - number of bootstrap samples - to be used to create CIs # B - scalar - the B to kept constant as n varies in n.v # n.v - vector of the levels of n - size of resamples - to be used in simulation # n - scalar - the size of resamples to be kept constant as B changes in B.v # alpha - scalar - gives the size of the interval so alpha = 0.025 gives # a 95% CI ie. a (1-2*alpha)*100% CI for the percentile method # method - character giving the method to be used to create the CI. Must be one of "percentile", # "bca" and "t" # dist - character that determines the distribution that the data are to be simulated from. # One of "normal", "poisson" and "gamma" # h - scalar - degree of smoothing # mean - scalar - the mean of the normal distribution that the samples are simulated from # sd - scalar - the standard deviation of the normal distribution that the samples # are simulated from # lambda - scalar - the rate parameter of the poisson distribution that the samples are generated from # shape - scalar - shape parameter (alpha) of the gamma dist the samples are simualted from # rate - scalar - rate parameter (beta) of the gamma dist the samples are generated from #Output: # if all the checks pass then nothing happens and the code carries on to run. If any fail # then an error message is produced. if (!is.numeric(sims) | !is.numeric(B.v) | !is.numeric(B) | !is.numeric(n.v) | !is.numeric(n) | !is.numeric(alpha) | !is.numeric(mean) | !is.numeric(sd) | !is.numeric(lambda) | !is.numeric(shape) | !is.numeric(rate)){ stop("input must be numeric") } if (sims%%1!=0 | B%%1!=0 | n%%1!=0){ stop("input must be an integer") } if (sims < 1 | B < 1 | n < 1){ stop("input must be postive non-zero integer") } if (sd <= 0 | lambda <= 0 | shape <= 0 | rate <= 0){ stop("input must be positive and non zero") } if (alpha <= 0 | alpha > 1){ stop("alpha must be between 0 and 1") } if (!(all(B.v > 0)) | !(all(n.v > 0))){ stop("all elements in vector must be positive and non zero") } if (!(all(B.v%%1==0)) | !(all(n.v%%1==0))){ stop("all elements in vector must be integers") } if (length(B) > 1 | length(n) > 1 | length(mean) > 1 | length(sd) > 1 | length(lambda) > 1 | length(alpha) > 1 | length(shape) > 1 | length(rate) > 1){ stop("input must be a scalar") } if (!method %in% c("percentile", "bca", "t", "smooth")){ stop("method is not valid") } if (!dist %in% c("normal", "poisson", "gamma")){ stop("distribution is not valid") } if (!is.character(method) | !is.character(dist)){ stop("input must be character") } } #-------------------------------------------------------------------------------------- do.sim.np <- function(sims = 100, B.v = c(10, 50, 100), B = 100, n.v = c(10, 50, 100), n = 100, alpha = 0.025 , method = "percentile", dist = "normal", h = sd(data)/3, mean = 0 , sd = 1, lambda = 10, shape = 1, rate = 1, check = T){ #Purpose: Generates confidence intervals for each simulation and each level of B.v # and n.v Creates sims CIs. # Data is generated from the distribution specified and this is our sample. # Calls bootstrap.ci to create the CI for that sample for that B and n, using the method # specified. The lower and upper limits of this CI are then stored in a matrix # which is then stored in a list. #Inputs: # sims - scalar - number of simulations to be performed # B.v - vector of the levels of B - number of bootstrap samples - to be used to create CIs # B - scalar - the B to kept constant as n varies in n.v # n.v - vector of the levels of n - size of resamples - to be used in simulation # n - scalar - the size of resamples to be kept constant as B changes in B.v # alpha - scalar - gives the size of the interval so alpha = 0.025 gives # a 95% CI ie. a (1-2*alpha)*100% CI for the percentile method # method - character giving the method to be used to create the CI. Must be one of "percentile", # "bca" and "t" # dist - character that determines the distribution that the data are to be simulated from. # One of "normal", "poisson" and "gamma" # smooth - boolean T/F that determines whether a smooth bootstrap is used # h - scalar - degree of smoothing # mean - scalar - the mean of the normal distribution that the samples are simulated from # sd - scalar - the standard deviation of the normal distribution that the samples # are simulated from # lambda - scalar - the rate parameter of the poisson distribution that the samples are generated from # shape - scalar - shape parameter (alpha) of the gamma dist the samples are simualted from # rate - scalar - rate parameter (beta) of the gamma dist the samples are generated from # check - boolean T/F indicating whether input checks should take place. #Outputs: # list of 6 of : # dist - character indicating the distribution of the samples. Returning this to use in plotting # functions later # method - character giving the method used to create bootstrap simulations. Used in plotting # B.levels - vector of each level of B # n.levels - vector of each level of n # CI.B - list for each level of B where each element is a matrix. Rows are CI for each simulation. Columns are # lower and upper limits of CI for that level of B. #CI.B - list for each level of n where each element is a matrix. Rows are CI for each simulation. Columns are # lower and upper limits of CI for that level of n. if (!is.logical(check)){ stop("check must be TRUE or FALSE") } if (check == TRUE){ input.checks(sims = sims, B.v = B.v, B = B, n.v = n.v, n = n, alpha = alpha, method = method, dist = dist, mean = mean, sd = sd, lambda = lambda, shape = shape, rate = rate) } sim.CIs.B <- matrix(nrow = sims, ncol = 2) #matrix to store CIs for each level of B sim.CIs.n <- matrix(nrow = sims, ncol = 2) #matrix to store CIs for each level of n CI.B <- list() CI.n <- list() for (i in 1:length(B.v)){ for (simulation in 1:sims){ # each row is CI for that simulation data <- data.gen(dist = dist, mean, sd, lambda, shape, rate, n) CI <- bootstrap.ci(B = B.v[i], data = data, alpha = alpha, method = method, dist = dist, h = h) sim.CIs.B[simulation,1] <- CI[1] # first column is lower limit of CIs sim.CIs.B[simulation,2] <- CI[2] # 2nd column is upper limit of CIs } CI.B[[i]] <- sim.CIs.B # all simulations for that level of B are stored in a matrix # and so each element in list is matrix for each level of B } for (j in 1:length(n.v)){ for (simulation in 1:sims){ data <- data.gen(dist = dist, mean, sd, lambda, shape, rate, n.v[j]) CI <- bootstrap.ci(B = B, data = data, alpha = alpha, method = method, dist = dist) sim.CIs.n[simulation,1] <- CI[1] sim.CIs.n[simulation,2] <- CI[2] } CI.n[[j]] <- sim.CIs.n # same as above but for each level of n } return(list(dist = dist, method = method, B.levels = B.v, n.levels = n.v, CI.B = CI.B, CI.n = CI.n)) } #----------------------------------------------------------------------------------------- coverage <- function(true_mean, simulation){ #Purpose: Calculates the average coverage, the proportion of times that the simulated # confidence interval includes the true mean for each simulated dataset. #Input: # true_mean - a scalar that is the true mean for the simulated dataset # simulation - an output from do.sim.np function that is a list of 4 elements, # B.levels - vector of each level of B - number of bootstrap resamples # n. levels - vector of each level of n - size of resample # CI.B - list of a list for each level of B containing a matrix with #sims rows # and 2 columns with the first element being the lower bound # and the 2nd element in the row being the upper bound of the CI # for each bootstrap sample # CI.n - same as for CI.B but for each level of n instead #Ouput: # list of 6 elements: # dist - character - the distribution of the simulated data. I'm returning this so I can plot by # distribution for each method type. # method - character of the method used to create CI # B.levels - vector of each level of B # n.levels - vector of each level of n # B.cov - vector, length = number of B levels, containing the average coverage # for each level of B # n.cov - vector, length = number of n levels, containing the average coverage # for each level of n sims <- length(simulation$CI.B[[1]][,1]) dist <- simulation$dist method <- simulation$method B.levels <- simulation$B.levels n.levels <- simulation$n.levels no.B.levels <- length(simulation$CI.B) no.n.levels <- length(simulation$CI.n) B.results <- rep(NA, sims) n.results <- rep(NA, sims) B.cov <- rep(NA, no.B.levels) n.cov <- rep(NA, no.n.levels) for (j in 1:no.B.levels){ for (i in 1:sims){ # is true_mean within CI? B.results[i] <- with(simulation, CI.B[[j]][i,1] <= true_mean & CI.B[[j]][i,2] >= true_mean) } #proportion of times true_mean is in CI B.cov[j] <- length(B.results[B.results == TRUE])/length(B.results) } for (y in 1:no.n.levels){ for (i in 1:sims){ n.results[i] <- with(simulation, CI.n[[y]][i,1] <= true_mean & CI.n[[y]][i,2] >= true_mean) } n.cov[y] <- length(n.results[n.results == TRUE])/length(n.results) } return(list(dist = dist, method = method, B.levels = B.levels, n.levels = n.levels, B.cov = B.cov, n.cov = n.cov)) } #--------------------------------------------------------------------------------------------------- ################################################################## #PLOT FNS ################################################################## format.data<- function(cov_objects){ #Purpose: puts the coverage data into a format that can be used to create ggplots. # Creates a data frame from a list of coverage outputs. #Input: # cov_objects - list of outputs from the coverage function #Output: # df - data frame with columns of B - levels of B, n - levels of n, coverage.B - # the coverage of the simulation for the corresponding level of B, coverage.n - # same as for coverage.B but for each corresponding level of n, method - the method # of the coverage simulation object, dist - distribution of the data used in the coverage object B <- cov_objects[[1]]$B.levels n <- cov_objects[[1]]$n.levels N <- length(cov_objects) cov.B.L <- rep(NA, N) cov.n.L <- rep(NA, N) for (j in 1:N){ if(B!=n){ stop("There must be the same number of B and n levels") } cov.B.L[j] <- length(cov_objects[[j]]$B.cov) cov.n.L[j] <- length(cov_objects[[j]]$n.cov) } coverage.B <- rep(NA, sum(cov.B.L)) coverage.n <- rep(NA, sum(cov.n.L)) method <- rep(NA, sum(cov.n.L)) dist <- rep(NA, sum(cov.n.L)) for (i in 1:N){ B.len <- length(cov_objects[[i]]$B.cov) n.len <- length(cov_objects[[i]]$n.cov) coverage.B[(B.len*(i-1)+1):(B.len*i)] <- cov_objects[[i]]$B.cov coverage.n[(n.len*(i-1)+1):(n.len*i)] <- cov_objects[[i]]$n.cov method[(B.len*(i-1)+1):(B.len*i)] <- cov_objects[[i]]$method dist[(B.len*(i-1)+1):(B.len*i)] <-cov_objects[[i]]$dist } df <- data.frame(B, coverage.B, n, coverage.n, method, dist) return(df) } #----------------------------------------------------------------------------------------- #this doesn't work :/ plot.fn2 <- function(df, x, y = coverage.B, method.plot = T, dist.plot = F){ #Purpose: creates plot that show # a) how coverage changes as either B or n changes, for multiple distributions using the same # method or # b) how coverage changes as either B or n changes, for multiple methods using the same # distribution #Inputs: # df - data frame, the output from the data.format function # x - the variable to be plotted along the x-axis, must be B or n # y - variable to be plotted along the y-axis must be coverage.B or coverage.n # method.plot - logical T/F indicating whether you want a plot to compare methods for one dist # dist.plot - logical T/F indicating whether you want a plot to compare dists for one method #Output: # ggplot of a) or b) if (method.plot == TRUE && dist.plot == TRUE){ stop("both method.plot and dist.plot cannot be TRUE") } if (method.plot == FALSE && dist.plot == FALSE){ return(NULL) } if (method.plot == TRUE && dist.plot == FALSE){ p <- ggplot(data = df, aes(x = x, y = y)) + geom_point() + geom_line(aes(color = method)) + coord_cartesian(ylim = c(0.7,1)) } if (dist.plot == TRUE && method.plot == FALSE){ p <- ggplot(data = df, aes(x = x, y = y, dist = dist, group = dist)) + geom_point() + geom_line() + coord_cartesian(ylim = c(0.7,1)) } p }

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survey <- read.delim("D:/Data-Science/09_Youth, technology, and social media/Raw survey responses.txt", header = TRUE, stringsAsFactors = FALSE) dim(survey) #580 1599

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/micRowave.R \name{readAbundanceFile} \alias{readAbundanceFile} \title{Read in an abundance file} \usage{ readAbundanceFile(abundanceFile) } \arguments{ \item{abundanceFile}{.tsv file with first column as feature. Columns are samples} } \value{ a data frame } \description{ Reads in data file } \examples{ readAbundanceFile("abundances.tsv") }

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combined_alpha_mayjulyonly.R

# Combine alpha plots MS setwd("/Users/jamiemcdevitt-irwin/Documents/Git_Repos/McDevittIrwinetal_Kiritimati16S") getwd() library("ggplot2") # clear my environment rm(list=ls()) theme_set(theme_bw()) # load the data # May Alpha load("data/secondmito/lowreads_pruned/may_coral_alphamodel_combined.Rdata") # July Alpha load("data/secondmito/lowreads_pruned/july_coral_alphamodel_combined.Rdata") ls() may_p2=may_p2 + ggtitle("Low heat stress") july_p1=july_p1 + ggtitle("High heat stress") # shared legend code library(ggplot2) library(gridExtra) library(grid) grid_arrange_shared_legend <- function(...) { plots <- list(...) g <- ggplotGrob(plots[[1]] + theme(legend.position="bottom"))$grobs legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]] lheight <- sum(legend$height) grid.arrange( do.call(arrangeGrob, lapply(plots, function(x) x + theme(legend.position="none"))), legend, ncol = 1, heights = unit.c(unit(1, "npc") - lheight, lheight)) } ##put them all together require(gtable) legend = gtable_filter(ggplotGrob(may_p2), "guide-box") grid.draw(legend) g_legend<-function(a.gplot){ tmp <- ggplot_gtable(ggplot_build(a.gplot)) leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") legend <- tmp$grobs[[leg]] return(legend)} legend <- g_legend(may_p2) lwidth <- sum(legend$width) pdf(file="figures/secondmito/top10removed/diversity/combined_alphadiv_for.ms_mayjulyonly_v2.pdf", height=8, width=11, onefile=FALSE) grid.arrange(arrangeGrob(may_p2+ theme(legend.position="none") + annotate("text", x=c(2.05,2.1), y=c(1.5, 1.3), label= c("Disturbance: p=0.0001", "Species: p=0.0007"), size=5), july_p1 + labs(x="") + annotate("text", x=2.1, y= 1.3, label= "Species: p=0.0001", size=5) + theme(legend.position="none"), layout_matrix=rbind(c(1,2),c(1,2))), legend, widths=unit.c(unit(1, "npc") - lwidth, lwidth), nrow=1) dev.off()

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is_date.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils_testers.R \name{is_date} \alias{is_date} \title{Checks if an object is a date object.} \usage{ is_date(x) } \arguments{ \item{x}{object to be tested.} } \value{ TRUE or FALSE depending on whether its arguments is a date object or not. } \description{ Checks if an object is a date object. }

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test-conversion.R

context("conversion") spectrum <- unname(t(col2rgb(rainbow(10)))) reconvert <- function(data, space) { unname(round(convert_colour(convert_colour(data, 'rgb', space), space, 'rgb'))) } test_that("basic io works", { expect_error(convert_colour(spectrum, 'test', 'lab')) expect_error(convert_colour(spectrum, 'rgb', 'test')) expect_equal(nrow(spectrum), nrow(convert_colour(spectrum, 'rgb', 'lab'))) }) test_that("cmy works", { expect_equal(spectrum, reconvert(spectrum, 'cmy')) }) test_that("cmyk works", { expect_equal(spectrum, reconvert(spectrum, 'cmyk')) }) test_that("hsl works", { expect_equal(spectrum, reconvert(spectrum, 'hsl')) }) test_that("hsb works", { expect_equal(spectrum, reconvert(spectrum, 'hsb')) }) test_that("hsv works", { expect_equal(spectrum, reconvert(spectrum, 'hsv')) }) test_that("lab works", { expect_equal(spectrum, reconvert(spectrum, 'lab')) }) test_that("hunterlab works", { expect_equal(spectrum, reconvert(spectrum, 'hunterlab')) }) test_that("lch works", { expect_equal(spectrum, reconvert(spectrum, 'lch')) }) test_that("luv works", { expect_equal(spectrum, reconvert(spectrum, 'luv')) }) test_that("rgb works", { expect_equal(spectrum, reconvert(spectrum, 'rgb')) }) test_that("xyz works", { expect_equal(spectrum, reconvert(spectrum, 'xyz')) }) test_that("yxy works", { expect_equal(spectrum, reconvert(spectrum, 'yxy')) })

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capitalhumano.R

# Nivel educativo de los integrantes del hogar ---------------------------- capitalHumano <- function(df_hogares, df_personas){ # Por personas educacion <- df_personas %>% select( c( CONSECUTIVO:A00, starts_with("p1708") ) ) %>% mutate( p1708 = replace_na(p1708, 1), p1708 = factor(p1708, labels = c("Ninguno", "Preescolar", "Básica\nPrimaria", "Básica\nSecundaria", "Educación media\n(vocacional\ndiversificada o técnica)", "Tecnológica", "Superior o\nuniversitaria")) ) %>% ggplot(aes(as.factor(p1708)))+ geom_bar(col = "#005117", fill = "#8CBD0E")+ labs(x="Nivel educativo", y="Número de personas") # Por tramo df_nivel_educativo_tramos <- df_personas %>% select(c( CONSECUTIVO:A00, starts_with("p1708") )) %>% mutate( CONSECUTIVO = as.character(CONSECUTIVO), A00 = as.character(A00), p1708 = replace_na(p1708, 1), p1708 = factor(p1708, labels = c("Ninguno", "Preescolar", "Básica\nPrimaria", "Básica\nSecundaria", "Educación media\n(vocacional\ndiversificada o técnica)", "Tecnológica", "Superior o\nuniversitaria")) ) %>% inner_join(df_hogares, by = c("CONSECUTIVO","A00")) %>% select( CONSECUTIVO:A00, starts_with("p1708"), Nombre ) %>% rename(tramo = Nombre) educacion_tramos <- create_barplot_tramo(df_nivel_educativo_tramos, "p1708", var_cat="tramo", "Nivel educativo", "Número de personas") table(df_nivel_educativo_tramos$p1708) %>% prop.table() # El 16% de las personas encuestadas no tiene ningún tipo de educación formal, # el 45% completó estudios de preescolar y primaria, 35% culminó sus estudios de secundaria, # o educación media; y un 4% de los encuestados tiene un nivel tecnológico o universitario # Organización familiar para la producción -------------------------------- df_org_familiar <- df_personas %>% select(c( CONSECUTIVO:A00, starts_with("p1709") )) %>% mutate( p1709 = factor(p1709, labels = c("Trabajador familiar\nsin remuneración", "Trabajador familiar\nremunerado", "Ayudante familiar\nocasional", "Otro")) ) contribucion_produccion <- ggplot(df_org_familiar,aes(as.factor(p1709)))+ geom_bar(col = "#005117", fill = "#8CBD0E")+ labs(x="Tipo de contribución a la producción económica", y="Número de personas") df_org_familiar$p1709 %>% table() %>% prop.table() # Aproxiamdamente el 37% de las personas contribuyen en la producción de su familia # en calidad de trabajador familiar remunerado, 16% contribuye como trabajador familiar # sin remuneración, 11% son ayudantes familiares ocasionales, y 34% contribuye de otras formas. # 3% de los encuestados no tienen información relacionada con la contribución df_org_familiar$p1709other %>% wordcloud_graph() # Porcentaje del tiempo dedicado a tareas del hogar por persona ----------- df_tiempo_dedicado <- df_personas %>% select(c( CONSECUTIVO:A00, starts_with("p1706") )) plt1 <- df_tiempo_dedicado %>% select(p1706) %>% ggplot(aes(x="", y = p1706)) + geom_boxplot(col = "#005117", fill = "#8CBD0E") + coord_flip() + theme_classic() + ylab("Porcentaje de tiempo dedicado a tareas del hogar") + xlab("")+ scale_y_continuous(labels = scales::comma)+ theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) plt2 <- ggplot(df_tiempo_dedicado, aes(as.numeric(p1706)))+ geom_histogram(col = "#005117", fill = "#8CBD0E", bins = 10)+ labs(x="", y="Frecuencia")+ scale_x_continuous(labels = scales::comma)+ theme_classic() tiempo_tareas_domesticas <- egg::ggarrange(plt2, plt1, heights = 2:1) # Número de personas que componen el hogar -------------------------------- table(df_hogares$P16) %>% prop.table() numero_personas_hogar <- df_hogares %>% ggplot(aes(as.integer(P16)))+ geom_bar(col = "#005117", fill = "#8CBD0E")+ scale_x_continuous(breaks = seq(1, 15, 1))+ labs(x="Número de personas que compone el hogar", y="Frecuencia") # El gráfico y la tabla anterior muestra la distrbución del número de personas en el hogar. # Se observa que casi el 15% de los hogares de los encuestados se componen de una o dos # personas, casi el 60% de los hogares se componen de tres, cuatro o cinco miembros, # 18% de los hogares están compuestos por seis o siete personas, y el restante 7% lo # conforman hogares con ocho personas o más df_hogares %>% ggplot(aes(as.integer(P16)))+ geom_bar(col = "#005117", fill = "#8CBD0E")+ scale_x_continuous(breaks = seq(1, 15, 1))+ labs(x="Número de personas que compone el hogar", y="Frecuencia")+ facet_wrap(vars(P18)) table(df_hogares$P18) %>% prop.table() # Del total de hogares, 32.4% de ellos afirman que la totalidad de personas mayores de 12 años que componen # el hogar participan en las actividades económicas que realiza el hogar para adquirir # sus medios de vida. El restante 67.6% de los hogares emplea únicamente a algunos de # sus miembros para el desarrollo de las actividades. actividades_economicas_quienes <- df_hogares %>% ggplot(aes(as.integer(P16), fill = factor(P18, labels = c('Todos','Algunos'))))+ geom_bar(col = "#005117", position="fill")+ geom_text(data = . %>% group_by(P16, P18) %>% tally() %>% mutate(p = n / sum(n)) %>% ungroup(), aes(y = p, label = scales::percent(p), colour = P18), position = position_stack(vjust = 0.5), show.legend = FALSE, size=2.7)+ scale_x_continuous(breaks = seq(1, 15, 1))+ scale_color_manual(values = c("black", "white"))+ scale_fill_manual(name = "¿Quiénes participan\nen las actividades\neconómicas del hogar?", values = c("Todos" = "#8CBD0E", "Algunos" = "#005117"))+ labs(x="Número de personas", y="Frecuencia") # En el gráfico anterior se observa por número de personas en el hogar, la proporción # de hogares en los que todos sus miembros participan en las actividades de adquisición # de medios de vida; y la propoción de los hogares en los que algunos de sus miembros # participan. Se puede obserevar que por lo # general, en uno de cada cuatro hogares todos los miembros de la familia participan en # las actividades de adquisición de medios de vida df_prop_trabajadores <- df_hogares %>% select(c("P16", "P18", "P18B")) %>% mutate_all(as.integer) %>% mutate( prop_trabajadores = ifelse(P18==1,100,round(P18B*100/P16,2)) ) plt1 <- df_prop_trabajadores %>% select(prop_trabajadores) %>% ggplot(aes(x="", y = prop_trabajadores)) + geom_boxplot(col = "#005117", fill = "#8CBD0E") + coord_flip() + theme_classic() + xlab("") + ylab("Proporción de personas que trabajan en el hogar") + scale_y_continuous(labels = scales::comma)+ theme(axis.text.y=element_blank(), axis.ticks.y=element_blank()) plt2 <- ggplot(df_prop_trabajadores, aes(as.numeric(prop_trabajadores)))+ geom_histogram(col = "#005117", fill = "#8CBD0E", bins = 15)+ labs(x="", y="Frecuencia")+ scale_x_continuous(labels = scales::comma)+ theme_classic() proporcion_personas_que_trabajan <- egg::ggarrange(plt2, plt1, heights = 2:1) # En el gráfico anterior se observa la distribución de la proporción de personas que participan en # las actividades de adquisición de medios de vida. Hay que tener en cuenta que el número # de hogares con 100% de participación de los miembros en actividades económicas son 1540, # y el número de hogares donde solo algunos miembros participan es 3202, que son los que se # pueden observar a la izquierda de la distribución df_otras_actividades <- df_hogares %>% select(c( CONSECUTIVO:A00, starts_with(c("P1801","P181")) )) %>% pivot_longer( cols = starts_with(c("P1801","P181")), names_to = "num_actividad", values_to = "actividad" ) %>% filter(!is.na(actividad)) (df_otras_actividades$actividad == "NINGUNA") %>% table() %>% prop.table() ## Entre las otras actividades productivas se dedican por fuera de la parcela ## o terreno familiar, destaca la respuesta NINGUNA, la cual aparece en el 55% ## de las respuestas actividades_otras <- df_otras_actividades %>% filter(actividad!="NINGUNA" & actividad != "#N/D") %>% select(actividad) wordcloud_graph(actividades_otras) ## Las anteriores son las otras actividades productivas a las que las personas ## que no están participando en las actividades de adquisición de medios de vida ## se dedican por fuera de la parcela o terreno familiar # Retribución económica de las otras actividades -------------------------- df_hogares %>% select(c( CONSECUTIVO:A00, starts_with(c("P1802")) )) table(df_hogares$P18)[[2]] ## Para los siguientes cálculos, se tiene en cuenta que el numero de personas que ocupa ## parcialmente su tiempo en otras actividades es 3202, y de estos, un 45% hace otra ## actividad diferente de ninguna. Estas personas son quienes indican una clasificación para ## la actividad productiva que hacen fuera de la parcela o terreno familiar aporte_hogar <- df_hogares %>% select(c("P1802A","P1802B","P1802C")) %>% `colnames<-`(c("Dinero", "Productos", "Disminución\nen gastos")) %>% mutate_all(as.integer) %>% summarise_all(sum, na.rm = TRUE) %>% pivot_longer( cols = everything(), names_to = 'actividad', names_prefix = "acti", values_to = "num_hogares", values_drop_na = TRUE ) %>% mutate( porcentaje=num_hogares/table(df_hogares$P18)[[2]] ) %>% ggplot(aes(x=reorder(actividad, -porcentaje), y=porcentaje))+ geom_bar( stat = "identity", position = "fill", fill = "#8CBD0E")+ geom_bar( stat = "identity", fill='#005117')+ geom_text(aes(label = paste0(round(porcentaje*100,2),'%')), stat = "identity", vjust = 0.5,hjust = 2, colour = "white",position = position_dodge(.9), size=2.7)+ labs(x='Actividad', y='Porcentaje')+ scale_y_continuous(labels = scales::percent_format())+ coord_flip() ## Del total de personas que ocupa parcialmente su tiempo en otras actividades, ## casi el 20% es retribuído con dinero por hacer esas actividades, el 7% de personas es retribuído con ## reducción de gastos de alimentación, y un 6% se retribuye con productos df_hogares$P1802OTHER %>% wordcloud_graph() ## Las actividades que indican en opción Otra, son ninguna, sustento, hogar, responsabilidad, producción, remuneración df_hogares$P1802E %>% table() # Menores de 12 años que contribuyen en el hogar -------------------------- table(df_hogares$P19) %>% prop.table() ## 92.2% de los hogares encuestados no emplean a ningún miembro de la familia menor de 12 ## años en labores de adquisición de medios de vida, mientras que 1.5% ocupa a algunos ## menores de 12 años, y 6.3% ocupa a todos los miembros menores de 12 años en las labores ## de producción table(df_hogares$P19B) ## No hay datos df_otras_actividades_menores <- df_hogares %>% select(c( CONSECUTIVO:A00, starts_with(c("P1901","P191")) )) %>% pivot_longer( cols = starts_with(c("P1901","P191")), names_to = "num_actividad", values_to = "actividad" ) %>% filter(!is.na(actividad)) df_otras_actividades_menores$actividad %>% wordcloud_graph() ## Entre las otras actividades productivas se dedican por fuera de la parcela ## o terreno familiar los menores de 12 años, se pueden encontrar labores como la venta ## labores del hogar y oficios, preparación de alimentos, estudiar, seimbra y cultivo, entre otras. df_hogares %>% select(c("P1902A","P1902B","P1902C")) %>% `colnames<-`(c("Dinero", "Productos", "Disminución\nen gastos")) %>% mutate_all(as.integer) %>% summarise_all(sum, na.rm = TRUE) ## De los hogares encuestados, en 23 se retribuye con dinero, en 11 con productos, y en ## 11 con disminución de gastos df_hogares$P1902OTHER %>% table() # No hay datos ## Las actividades que indican en opción Otra, son ninguna, sustento, hogar, responsabilidad, producción, remuneración df_hogares$P1802E %>% table() # Datos raros return(list(educacion = educacion, educacion_tramos = educacion_tramos, contribucion_produccion = contribucion_produccion, tiempo_tareas_domesticas = tiempo_tareas_domesticas, numero_personas_hogar = numero_personas_hogar, actividades_economicas_quienes = actividades_economicas_quienes, proporcion_personas_que_trabajan = proporcion_personas_que_trabajan, aporte_hogar = aporte_hogar)) # df_org_familiar$p1709other %>% wordcloud_graph() # wordcloud_graph(actividades_otras) # df_hogares$P1802OTHER %>% wordcloud_graph() # df_otras_actividades_menores$actividad %>% wordcloud_graph() }

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mosaic-tools/nhd_net

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library(nhdplusTools) library(tidyverse) library(dplyr) library(magrittr) #to download full nhdplusV2 on my computer nhdplus_path("D:/projects/TSS/data/NHDPlusNationalData/NHDPlusV21_National_Seamless_Flattened_Lower48.gdb") nhd_paths <- stage_national_data() network <- readRDS(nhd_paths$flowline) #to download nhdplusv2 to your machine, specify the filepath in outdir argument #network <- download_nhdplusv2(outdir=) #function to create network for tinkertoy model given the outlet reach (i.e. comid) makeNetwork <- function(network, comid) { up_id <- get_UT(network,comid ) net <- network %>% filter(StreamOrde == StreamCalc) %>% filter(COMID %in% up_id) %>% left_join(select(network %>% as_tibble %>% filter(StreamOrde == StreamCalc) %>% filter(COMID %in% up_id) %>% select(-Shape), toCOMID = COMID, FromNode), by = c("ToNode" = "FromNode")) %>% left_join(select(network %>% as_tibble %>% filter(StreamOrde == StreamCalc) %>% filter(COMID %in% up_id) %>% select(-Shape), fromCOMID = COMID, ToNode), by = c("FromNode" = "ToNode")) %>% group_by(COMID) %>% mutate( fromCOMID = as.character(list(unique(fromCOMID)))) %>% ungroup() %>% st_set_geometry(NULL) %>% distinct_at(vars(COMID:Enabled), .keep_all = T) return(net) } savannah <- makeNetwork(network, comid = 18242767) write_csv(savannah, "D:/Dropbox/projects/mosaics/savannah_river_v2.csv") ############### MUNGE ################# # sav_1 <- network %>% # filter(StreamOrde == StreamCalc) %>% # filter(COMID %in% up_id_sav) %>% # left_join(select(network %>% # as_tibble %>% # filter(StreamOrde == StreamCalc) %>% # filter(COMID %in% up_id_sav) %>% # select(-Shape), # toCOMID = COMID, FromNode), # by = c("ToNode" = "FromNode")) %>% # left_join(select(network %>% # as_tibble %>% # filter(StreamOrde == StreamCalc) %>% # filter(COMID %in% up_id_sav) %>% # select(-Shape), # fromCOMID = COMID, ToNode), # by = c("FromNode" = "ToNode")) %>% # group_by(COMID) %>% # mutate( fromCOMID = as.character(list(unique(fromCOMID)))) %>% # ungroup() %>% # st_set_geometry(NULL) %>% # distinct_at(vars(COMID:Enabled), .keep_all = T) # # write_csv(sav_1, "D:/Dropbox/projects/mosaics/savannah_river_v2.csv")

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kkondo1981/l0araxx

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summary.R

predict.l0araxx <- function(obj, newx, type=c("link", "response", "coefficients", "class"), offset=NULL, ...) { np = dim(newx) type <- match.arg(type) # check offset if (is.null(offset)) { offset <- rep(0, np[1]) } else if (length(offset) != np[1]) { stop("length of offset not equal to the length of newx") } # getting coefficients beta <- coef.l0araxx(obj) if (type=="coefficients") return(beta) # getting newx if(missing(newx)) newx <- obj$x if (obj$standardize) newx <- scale(newx) newx <- cbind(rep(1, np[1]), newx) # calculates link value eta <- newx %*% beta + offset if (type=="link") return(drop(eta)) # calculates response var response <- switch(obj$family, gaussian = eta, poisson = exp(eta), gamma = 1/eta, 'gamma(log)' = exp(eta)) if(obj$family == "gaussian" & obj$standardize) response <- response + mean(obj$y) if (type=="response") return(drop(response)) # calculates binary class if (type=="class") { if (object$family=="logit") { return(drop(1*(eta>0))) } else { stop("type='class' can only be used with family='logit'") } } } coef.l0araxx <- function(obj, ...) { coefs <- as.vector(obj$beta) p <- length(coefs) names(coefs)[1] <- "Intercept" names(coefs)[2:p] <- paste0("X",1:(length(coefs)-1)) return(coefs) } print.l0ara <- function(x, ...) { family <- switch(x$family, gaussian = "Linear regression", logit = "Logistic regression", poisson = "Poisson regression", inv.gaussian = "Inverse gaussian regression", gamma = "Gamma regression") cat("Lambda used : ", x$lambda, "\n") cat("Model : ", family, "\n") cat("Iterations : ", x$iter, "\n") cat("Degree of freedom : ", x$df,"\n") }

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shrink-output-trunk.R

library("atlantistools") file_fgs <- "SETasGroupsDem_NoCep.csv" file_init <- "INIT_VMPA_Jan2015" file_gen <- "outputSETAS" file_prod <- "outputSETASPROD" # utility functions ------------------------------------------------------------------------------- find_flags <- function(chars) { ids <- sort(unlist(lapply(c("double", "short", "int "), grep, x = chars))) flags <- chars[ids] flags <- stringr::str_split(flags, pattern = " ", n = 2) if (!all(sapply(flags, length) == 2)) stop("wrong split.") flags <- sapply(flags, function(x) x[2]) flags <- stringr::str_split(flags, pattern = "\\(", n = 2) if (!all(sapply(flags, length) == 2)) stop("wrong split.") flags <- sapply(flags, function(x) x[1]) return(paste0(" ", flags)) } find_block <- function(chars, var) { flags <- find_flags(chars) if (which(flags == var) == length(flags)) { block <- grep(var, chars, ignore.case = FALSE) block <- c(block, (block[length(block)] + 1):(length(chars) - 1)) } else { next_var <- flags[which(flags == var) + 1] ids <- lapply(c(var, next_var), grep, x = chars, ignore.case = FALSE) if (any(sapply(ids, length) != 2)) stop(paste(var, "found multiple times.")) ids_min <- c(ids[[1]][1], ids[[1]][length(ids[[1]])]) ids_max <- c(ids[[2]][1], ids[[2]][length(ids[[2]])]) block <- unlist(Map(seq, ids_min, ids_max - 1)) } return(block) } # functionalGroups file --------------------------------------------------------------------------- fgs <- load_fgs(fgs = file_fgs) fgs <- fgs[fgs$IsTurnedOn == 1, ] fgs$Index <- 1:nrow(fgs) write.csv(fgs, file = file.path("new", file_fgs), quote = FALSE, row.names = FALSE) # initial conditions file ------------------------------------------------------------------------- vars <- paste0(" ", c("porosity", "topk", "sedbiodepth", "seddetdepth", "sedoxdepth", "sedbiodens", "sedirrigenh", "sedturbenh", "erosion_rate", "reef", "flat", "canyon", "soft", "eddy", "water", "DON", "MicroNut", "Stress", "DiagNGain", "DiagNLoss", "DiagNFlux", "Light_Adaptn_MB", "Light_Adaptn_PL", "Light_Adaptn_DF", "Light_Adaptn_PS", "t", "Light", "Oxygen", "Si", "Det_Si")) chars <- readLines(paste0(file_init, ".cdf")) flags <- find_flags(chars) ff <- load_fgs(fgs = file.path("new", file_fgs)) keep_vars <- c( paste0(" ", c(sort(as.vector(outer(as.vector(outer(ff$Name[ff$NumCohorts == 10], 1:10, FUN = paste0)), c("Nums", "ResN", "StructN"), FUN = paste, sep = "_"))), sort(paste(ff$Name[ff$NumCohorts != 2], "N", sep = "_")), sort(as.vector(outer(paste(ff$Name[ff$NumCohorts == 2], "N", sep = "_"), 1:2, FUN = paste0))))), flags[1:45][!flags[1:45] %in% vars]) ids <- lapply(keep_vars, find_block, chars = chars) length(keep_vars[sapply(ids, length) > 38]) == 0 res <- vector(mode = "logical", length = length(ids)) for (i in seq_along(ids)) { dummy <- FALSE for (j in seq(1:length(ids))[-i]) { dummy <- dummy + any(ids[[i]] %in% ids[[j]]) } res[i] <- dummy } length(keep_vars[res != 0]) == 0 ids <- unlist(ids) length(ids) == length(unique(ids)) gl_at <- grep(chars, pattern = "global attributes") new_init <- c(chars[1:8], chars[sort(c(unlist(ids), gl_at:(gl_at + 9)))], chars[length(chars)]) writeLines(new_init, con = file.path("new", paste0(paste0(file_init, ".cdf")))) # general output file ----------------------------------------------------------------------------- chars <- readLines(paste0(file_gen, ".cdf")) ids <- lapply(keep_vars, find_block, chars = chars) ids <- unlist(ids) length(ids) == length(unique(ids)) gl_at <- grep(chars, pattern = "global attributes") new_init <- c(chars[1:8], chars[sort(c(unlist(ids), gl_at:(gl_at + 11)))], chars[length(chars)]) writeLines(new_init, con = file.path("new", paste0(paste0(file_gen, ".cdf")))) # productivity output file ------------------------------------------------------------------------ chars <- readLines(paste0(file_prod, ".cdf")) flags <- find_flags(chars) ff <- load_fgs(fgs = file.path("new", file_fgs)) keep_vars <- c( paste0(" ", c(sort(as.vector(outer(as.vector(outer(ff$Name[ff$NumCohorts == 10], 1:10, FUN = paste0)), c("Growth", "Eat"), FUN = paste, sep = "_"))), sort(paste0(ff$Name[ff$NumCohorts != 10 & ff$IsPredator != 0], "Prodn")), sort(paste0(ff$Name[ff$NumCohorts != 10 & ff$IsPredator != 0], "Grazing")), c("dz", "volume", "numlayers")))) ids <- lapply(keep_vars, find_block, chars = chars) ids <- unlist(ids) length(ids) == length(unique(ids)) gl_at <- grep(chars, pattern = "global attributes") new_init <- c(chars[1:8], chars[sort(c(unlist(ids), gl_at:(gl_at + 11)))], chars[length(chars)]) writeLines(new_init, con = file.path("new", paste0(paste0(file_prod, ".cdf"))))

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/scripts/extractAllRowsWithOnlyOneSnpGenotype.R

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benranco/docs

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extractAllRowsWithOnlyOneSnpGenotype.R

options(stringsAsFactors = FALSE, warn = 1) args <- commandArgs(trailingOnly = TRUE) write("Running extractAllRowsWithOnlyOneSnpGenotype.R.", stdout()) # ######################################################## # Input Parameters: path <- "/home/benrancourt/Desktop/junjun/region45-SNPpipelineOct2018/reports/LepMAP2/extractSingleSnpRows" #inputFile <- "R-38-M32178-38329_p0.01-postLepMAP2-lod10and6-allChr.csv" #numMetaColumns <- 5 #outputFile <- "R-38-M32178-38329_p0.01-postLepMAP2-lod10and6-allChr-withPercentMissingData.csv" inputFile <- "R45-for-Cr4-co-seg-extraction.csv" numMetaColumns <- 6 outputFileMostlyUniformSnpRows <- "R45-for-Cr4-co-seg-extraction-mostlyUniformSnpRows.csv" outputFileAllOtherRows <- "R45-for-Cr4-co-seg-extraction-allOtherRows.csv" # data cells in the input .csv files (both fathers and seeds) which contain these # strings will be interpreted as NA/missing data: myNaString <- c("-") # a number indicating how many of the second SNP (if there's more than one) are allowed numAltSnpAllowed <- 2 ############################################################## input <- read.csv(paste(path.expand(path),inputFile,sep="/"),header=TRUE, na.strings=myNaString, check.names=FALSE) # using check.names=FALSE in case the column names have dashes (-) in them. This will prevent them from being converted to periods. However, a column name with a dash in it will not be able to be used as a variable name, so we'll have to refer to columns by their index if accessing them. firstDataCol <- numMetaColumns + 1 rowsWithOneSnpType <- logical(length=nrow(input)) # initializes all elements to FALSE for(rowNum in 1:nrow(input)) { rowData <- as.matrix(input[rowNum,firstDataCol:ncol(input)]) # count and tabulate the SNP types in the row, in descending order of frequency (should be only one or two types) tabulatedRowData <- sort(table(rowData, useNA = "no"),decreasing=TRUE) tabulatedRowDataNames <- names(tabulatedRowData) # if there's only one type of non-NA data if( length(tabulatedRowDataNames) == 1) { rowsWithOneSnpType[rowNum] <- TRUE } else if( length(tabulatedRowDataNames) > 1 && tabulatedRowData[2] <= numAltSnpAllowed) { rowsWithOneSnpType[rowNum] <- TRUE } } onlyRowsWithOneSnp <- input[rowsWithOneSnpType, ] allOtherRows <- input[!rowsWithOneSnpType, ] write.csv(onlyRowsWithOneSnp, paste(path.expand(path), outputFileMostlyUniformSnpRows,sep="/"), row.names=FALSE, na=myNaString) write.csv(allOtherRows, paste(path.expand(path), outputFileAllOtherRows,sep="/"), row.names=FALSE, na=myNaString) write(paste0("================================================"), stdout()) write(paste0("FINISHED."), stdout())

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domingos86/job-listings

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server.R

library(shiny) library(leaflet) library(leaflet.extras) library(dplyr) # library(htmltools) # library(htmlwidgets) # # heatPlugin <- htmlDependency("Leaflet.heat", "99.99.99", # src = c(href = "http://leaflet.github.io/Leaflet.heat/dist/"), # script = "leaflet-heat.js" # ) # # registerPlugin <- function(map, plugin) { # map$dependencies <- c(map$dependencies, list(plugin)) # map # } companies_locations <- read.csv('data/company_job_locations.csv') companies_locations <- companies_locations %>% select(company, latitude, longitude) %>% group_by(company, latitude, longitude) %>% summarize(quantity = n()) aggregator_locations <- read.csv('data/aggregator_job_locations.csv') aggregator_locations$date_posted <- as.Date(aggregator_locations$date_posted) max_date <- max(aggregator_locations$date_posted) aggregator_locations['week'] = trunc((max_date - aggregator_locations$date_posted) / 7) aggregator_locations <- aggregator_locations %>% group_by(week, latitude, longitude) %>% summarize(quantity = n()) %>% ungroup() col_names = c('A', 'B', 'C', 'D', 'E', 'F') locations_index <- unique(aggregator_locations[c('latitude', 'longitude')]) locations_index[3] = 0 colnames(locations_index)[3] = col_names[1] for(i in 1:5) { locations_index <- locations_index %>% left_join(aggregator_locations %>% filter(week == i - 1) %>% select(longitude, latitude, quantity), by = c('longitude', 'latitude')) col = col_names[i+1] colnames(locations_index)[3+i] = col locations_index[col] <- replace(locations_index[col], which(is.na(locations_index[col]), arr.ind = T), 0) locations_index[col] <- locations_index[col] + locations_index[col_names[i]] } update_companiesMap <- function(map, companiesGroup = c('apple', 'facebook', 'amazon')) { locations <- companies_locations %>% filter(company %in% companiesGroup) %>% group_by(latitude, longitude) %>% summarize(quantity = sum(quantity)) return(map %>% removeWebGLHeatmap(layerId = 'heat') %>% addWebGLHeatmap(lng = ~longitude, lat = ~latitude, intensity = ~quantity, size = 40000, opacity = 0.8, layerId = 'heat', alphaRange = 0.01, data = locations)) } update_aggregatorsMap <- function(map, date_slider = c(0, 4), size_slider = 10000) { locations <- locations_index %>% select(longitude, latitude) locations['quantity'] = locations_index[col_names[date_slider[2]+1]] - locations_index[col_names[date_slider[1]+1]] return(map %>% removeWebGLHeatmap(layerId = 'heat') %>% addWebGLHeatmap(lng = ~longitude, lat = ~latitude, intensity = ~quantity, size = size_slider, opacity = 0.8, layerId = 'heat', alphaRange = 0.01, data = locations)) } # update_aggregatorsMap <- function(map, date_slider = c(0, 4), size_slider = 10000) { # locations <- locations_index %>% # select(longitude, latitude) # locations['quantity'] = locations_index[col_names[date_slider[2]+1]] - # locations_index[col_names[date_slider[1]+1]] # return(map %>% registerPlugin(heatPlugin) %>% # onRender("function(el, x, data) { # if(\"heat_layer\" in window) { # heat_layer.remove(); # } # data = HTMLWidgets.dataframeToD3(data); # data = data.map(function(val) { return [val.lat, val.long, val.mag]; }); # heat_layer = L.heatLayer(data, {radius: 25}).addTo(this); # }", data = locations %>% select(lat = latitude, long = longitude, mag = quantity))) # } shinyServer(function(input, output, session) { output$companiesMap = renderLeaflet({ leaflet() %>% addProviderTiles('Hydda.Base') %>% addProviderTiles('Stamen.TonerHybrid') %>% setView(-95, 37, 4) %>% update_companiesMap() }) observe({ leafletProxy('companiesMap', session) %>% update_companiesMap(input$companiesGroup) }) output$aggregatorsMap = renderLeaflet({ leaflet() %>% addProviderTiles('Hydda.Base') %>% addProviderTiles('Stamen.TonerHybrid') %>% setView(-95, 37, 4) %>% update_aggregatorsMap() }) observe({ leafletProxy('aggregatorsMap', session) %>% update_aggregatorsMap(input$date_slider, round(10^(input$size_slider/4))) }) #output$size_output <- renderText(input$size_slider) })

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/scripts/qtl_overlap.R

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qtl_overlap.R

################################################################################ # Plot of QTL overlap per chr. # Daniel Gatti # 2021-02-14 # dmgatti@coa.edu ################################################################################ library(GenomicRanges) library(tidyverse) # Set up base directories. base_dir = '/media/dmgatti/hdb/projects/TB/' qtl_dir = file.path(base_dir, 'results', 'qtl2', 'gen_factor2') fig_dir = file.path(base_dir, 'figures') qtl_file = file.path(qtl_dir, 'tb_qtl_peaks.csv') peaks = read_csv(qtl_file) png(file.path(fig_dir, 'qtl_overlap.png'), width = 800, height = 800) print(ggplot(peaks) + geom_segment(aes(x = ci_lo, xend = ci_hi, y = lodcolumn, yend = lodcolumn)) + geom_point(aes(pos, lodcolumn)) + facet_wrap(~chr)) + labs(title = 'DO TB QTL Overlap', x = '', y = '') dev.off() peaks_gr = GRanges(seqnames = peaks$chr, ranges = IRanges(start = peaks$ci_lo, end = peaks$ci_hi)) ol = findOverlaps(peaks_gr, peaks_gr) result = select(peaks[queryHits(ol),], lodcolumn:ci_hi) %>% rename_with(.fn = ~str_c(., '_q'), .cols = everything()) %>% bind_cols(select(peaks[subjectHits(ol),], lodcolumn:ci_hi) %>% rename_with(.fn = ~str_c(., '_s'), .cols = everything())) %>% filter(lodcolumn_q != lodcolumn_s) %>% arrange(chr_q, pos_q)

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/man/load_grr.Rd

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load_grr.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/create.R \name{load_grr} \alias{load_grr} \title{Load \code{grr} object from a template} \usage{ load_grr(template, modules = "all") } \arguments{ \item{template}{Name of template. Run \code{list_templates()} to see current options} \item{modules}{A character vector indicating modules from the template to include.} } \value{ A S3 object of class \code{grr} built upon a list. } \description{ Loads a new \code{grr} map from one of several template options. }

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stat133-sp19/hw-stat133-mallikakolar

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bin_cumulative.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/main-functions.R \name{bin_cumulative} \alias{bin_cumulative} \title{bin_cumulative} \usage{ bin_cumulative(trials, prob) } \arguments{ \item{int}{integer trials} \item{int}{integer prob} } \value{ dataframe with bincum class } \description{ returns a dataframe with the probability distribution and the cumulative probabilities } \examples{ bin_cumulative(trials = 5, prob = 0.5) }

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/cachematrix.R

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Chloex7/ProgrammingAssignment2

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cachematrix.R

## This function creates a matrix and its inverse, which will be cached makeCacheMatrix <- function(x = matrix()) { a <- NULL set <- function(y) { x <<- y a <<- NULL } get <- function() x setinverse <- function(inverse) a <<- inverse getinverse <- function() a list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## This function computes the inverse of the matrix returned by makeCacheMatrix ## and will retrieve the cached version if available cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' a <- x$getinverse() if(!is.null(a)) { message("getting cached data") return(a) } data <- x$get() a <- inverse(data, ...) x$setinverse(a) a }

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/competitor.R

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Coelacanss/C_Digital_Marketing

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competitor.R

### This script is to study the use of social media of Genesee's competitor Small Town Brewery library(Rfacebook) library(httpuv) library(RCurl) library(rjson) library(httr) library(RColorBrewer) library(twitteR) library(tm) library(SnowballC) library(plyr) ################## Facebook #################### ## ## API and Access fb_oauth <- fbOAuth(app_id="1504407903188968", app_secret="a2f31c81a5bb1c740680b26c779782a7", extended_permissions = TRUE) myaccess_token = 'CAACEdEose0cBAMR1XQzVAY4Q5yyZCiHdIDHq6VNUQsZBX9bUqNAM9cDOFNiDpz9nYU10sJVgrDXftR7AfL4plikSHbtB3Hz2JK6NdFzctLcxZBaWW3O0pVO6jj3u6wiNhgvVaBEn8BCMLLl68Aw3pFGR9bJi6CErMHsZBmMwLeNzKOCAPVpzAL1uSWZAEZAfO20UBN0qlCaKWy0NTEOU1X' post_stb <- getPage(page = 'smalltownbrewery', token = myaccess_token, n = 1030, since = NULL, until = NULL, feed = FALSE) # save(post_stb, file = 'post_stb.Rda') created_time = post_stb$created_time created_time = gsub('T', ' ', created_time) created_time = gsub('+0000', '', created_time) created_year = year(created_time) created_month = month(created_time) created_day = day(created_time) created_hour = hour(created_time) created_minute = minute(created_time) created_second = second(created_time) created_weekday = wday(created_time) created_weekday = created_weekday -1 created_weekday[created_weekday == 0] = 7 post_stb$created_time = created_time post_stb$year = created_year post_stb$month = created_month post_stb$day = created_day post_stb$hour = created_hour post_stb$minute = created_minute post_stb$second = created_second post_stb$weekday = created_weekday # save(post_stb, file = 'post_stb.Rda') atTF = grepl('@', post_stb$message) hashtagTF = grepl('#', post_stb$message) post_stb$atOrNot = atTF post_stb$hashtagOrNot = hashtagTF post_stb$hashtagContent = NULL post_stb$atContent = NULL ## hashtag hashList = str_extract_all(post_stb$message[hashtagTF], '#\\w{1,20}') index = which(hashtagTF == T) count = 0 for (i in index){ count = count + 1 post_stb[i, "hashtagContent"] = paste(unlist(hashList[count]), collapse = ' ') print(i) } # save(post_stb, file = 'post_stb.Rda') ## at atList = str_extract_all(post_stb$message[atTF], '@\\w{1,20}') index = which(atTF == T) count = 0 for (i in index){ count = count + 1 post_stb[i, "atContent"] = paste(unlist(atList[count]), collapse = ' ') print(i) } # save(post_stb, file = 'post_stb.Rda') ################################################### load(file = 'post_stb.Rda') emptyData = matrix(data = 0, nrow = 4, ncol = 3) emptyData = as.data.frame(emptyData) colnames(emptyData) <- c('hour', 'comments_count', 'post') emptyData$hour = c(7:10) ## likes by hour likesByHour <- aggregate(likes_count ~ hour, data = post_stb, mean) postNum = count(post_stb$hour) likesByHour$post = postNum$freq likesByHour = rbind(likesByHour, emptyData) ggplot(data = likesByHour) + theme_bw() + geom_line(aes(x = hour, y = likes_count), color = 'red') + geom_line(aes(x = hour, y = post), color = 'blue') + ggtitle('Average Likes By Post Time (Hours)') ## Blue line is the number of posts by hours. ## shares by hour sharesByHour <- aggregate(shares_count ~ hour, data = post_stb, mean) postNum = count(post_stb$hour) sharesByHour$post = postNum$freq sharesByHour = rbind(sharesByHour, emptyData) ggplot(data = sharesByHour) + theme_bw() + geom_line(aes(x = hour, y = shares_count), color = 'red') + geom_line(aes(x = hour, y = post), color = 'blue') + ggtitle('Average Shares By Post Time (Hours)') ## Blue line is the number of posts by hours. ## comments by hours commentsByHour <- aggregate(comments_count ~ hour, data = post_stb, mean) postNum = count(post_stb$hour) commentsByHour$post = postNum$freq commentsByHour = rbind(commentsByHour, emptyData) ggplot(data = commentsByHour) + theme_bw() + geom_line(aes(x = hour, y = comments_count), color = 'red') + geom_line(aes(x = hour, y = post), color = 'blue') + ggtitle('Average Comments By Post Time (Hours)') ## Blue line is the number of posts by hours. ## comments by weekday commentsByDay <- aggregate(comments_count ~ weekday, data = post_stb, mean) postNum = count(post_stb$weekday) commentsByDay$post = postNum$freq ggplot(data = commentsByDay) + theme_bw() + geom_line(aes(x = weekday, y = comments_count), color = 'red') + geom_line(aes(x = weekday, y = post), color = 'blue') + ggtitle('Average Comments By Post Time (Weekdays)') ## Blue line is the number of posts by weekdays. ## likes by weekdays likesByDay <- aggregate(likes_count ~ weekday, data = post_stb, mean) postNum = count(post_stb$weekday) likesByDay$post = postNum$freq ggplot(data = likesByDay) + theme_bw() + geom_line(aes(x = weekday, y = likes_count), color = 'red', lty = 1) + geom_line(aes(x = weekday, y = post), color = 'blue', lty = 1) + ggtitle('Average Likes By Post Time (Weekdays)') ## Blue line is the number of posts by weekdays. ## shares by weekdays sharesByDay <- aggregate(shares_count ~ weekday, data = post_stb, mean) postNum = count(post_stb$weekday) sharesByDay$post = postNum$freq ggplot(data = sharesByDay) + theme_bw() + geom_line(aes(x = weekday, y = shares_count), color = 'red', lty = 1) + geom_line(aes(x = weekday, y = post), color = 'blue', lty = 1) + ggtitle('Average Likes By Post Time (Weekdays)') ## Blue line is the number of posts by weekdays. ## comments by type commentsByType <- aggregate(comments_count ~ type, data = post_stb, mean) postNum = count(post_stb$type) commentsByType$post = postNum$freq ## shares by type sharesByType <- aggregate(shares_count ~ type, data = post_stb, mean) postNum = count(post_stb$type) sharesByType$post = postNum$freq ## likes by type likesByType <- aggregate(likes_count ~ type, data = post_stb, mean) postNum = count(post_stb$type) likesByType$post = postNum$freq ####################### Hashtag Analysis ########################## ## hashMessy = unlist(hashList) sort(table(hashMessy), decreasing = T) test = count(hashMessy) ## sub1 with hashtag, sub2 without hashtag data.sub1 = post_stb[post_stb$hashtagOrNot == T,] data.sub2 = post_stb[post_stb$hashtagOrNot == F,] (mean(data.sub1$likes_count) - mean(data.sub2$likes_count)) / mean(data.sub2$likes_count) (mean(data.sub1$comments_count) - mean(data.sub2$comments_count)) / mean(data.sub2$comments_count) (mean(data.sub1$shares_count) - mean(data.sub2$shares_count)) / mean(data.sub2$shares_count) ## sub1 with @, sub2 without @ data.sub1 = post_stb[post_stb$atOrNot == T,] data.sub2 = post_stb[post_stb$atOrNot == F,] (mean(data.sub1$likes_count) - mean(data.sub2$likes_count)) / mean(data.sub2$likes_count) (mean(data.sub1$comments_count) - mean(data.sub2$comments_count)) / mean(data.sub2$comments_count) (mean(data.sub1$shares_count) - mean(data.sub2$shares_count)) / mean(data.sub2$shares_count) brandHashtagTF = grepl('smalltownbrewery', post_stb$hashtagContent, ignore.case = T) data.sub1 = post_stb[brandHashtagTF,] data.sub2 = post_stb[-brandHashtagTF,] (mean(data.sub1$likes_count) - mean(data.sub2$likes_count)) / mean(data.sub2$likes_count) (mean(data.sub1$comments_count) - mean(data.sub2$comments_count)) / mean(data.sub2$comments_count) (mean(data.sub1$shares_count) - mean(data.sub2$shares_count)) / mean(data.sub2$shares_count) ################### Distribution by Time ###################### ## post_stb$created_time = as.Date(post_stb$created_time) ggplot(data = post_stb) + geom_point(aes(x = created_time, y = likes_count)) + ylim(0,800)

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library(shiny) ui = fluidPage( titlePanel('Central Limit Theorem'), sidebarLayout( sidebarPanel( sliderInput('n', 'Sample size:', min=1, max=50, step=1, value=1, animate=animationOptions(interval=300,loop=FALSE)) ), mainPanel( plotOutput("plot") ) ) ) server = function(input, output) { output$plot <- renderPlot({ x = seq(0, 20, 0.001) y = dchisq(x, df=2) z = dnorm(x, mean=2, sd=2/sqrt(input$n)) getmeans = function(n){ mean(rchisq(n, df=2)) } means = replicate(n=1000, getmeans(input$n)) hist(means, col='grey', border='white', xlab='', xlim=c(0,15), yaxt='n', ylab='', ylim=c(0,1.5), main=ifelse(input$n==1,'Population distribution', paste('Sampling distribution for n =',input$n)), prob=TRUE) abline(h=0, col='grey') lines(x, y, col='blue') lines(density(means, from=0), col='red') lines(x, z, col='darkgreen') legend(x='topright', lty=1, col=c('blue','darkgreen','red'), legend=c('Population','Normal','Sampling distribution'), bty='n') axis(side=1, at=2) }) } shinyApp(ui=ui, server=server)

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#' Functions to get median and IQR #' @description Functions to get median and IQR used by createNiceTable() printMedianQ13 <- function(vec) { q <- quantile(vec, na.rm=TRUE) sprintf("%.1f [%.1f-%.1f]", median(vec,na.rm=TRUE), q["25%"], q["75%"]) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plsda.R \name{plsda_auroc_vip_compare} \alias{plsda_auroc_vip_compare} \title{Compare PLSDA auroc VIP results} \usage{ plsda_auroc_vip_compare(...) } \arguments{ \item{...}{Results of \link{nmr_data_analysis} to be combined. Give each result a name.} } \value{ A plot of the AUC for each method } \description{ Compare PLSDA auroc VIP results }

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source(here::here("Automation/00_Functions_automation.R")) #install.packages("archive") library(archive) # assigning Drive credentials in the case the script is verified manually if (!"email" %in% ls()){ email <- "gatemonte@gmail.com" } # info country and N drive address ctr <- "Peru" dir_n <- "N:/COVerAGE-DB/Automation/Hydra/" # Drive credentials drive_auth(email = Sys.getenv("email")) gs4_auth(email = Sys.getenv("email")) # load data m_url1 <- "https://www.datosabiertos.gob.pe/dataset/casos-positivos-por-covid-19-ministerio-de-salud-minsa" m_url2 <- "https://www.datosabiertos.gob.pe/dataset/fallecidos-por-covid-19-ministerio-de-salud-minsa" html1 <- read_html(m_url1) html2 <- read_html(m_url2) # locating the links for Excel files cases_url <- html_nodes(html1, xpath = '//*[@id="data-and-resources"]/div/div/ul/li/div/span/a') %>% html_attr("href") deaths_url <- html_nodes(html2, xpath = '//*[@id="data-and-resources"]/div/div/ul/li/div/span/a') %>% html_attr("href") #JD: updating the vaccine link #vacc_url <- "https://cloud.minsa.gob.pe/s/ZgXoXqK2KLjRLxD/download" #vacc_url <- "https://cloud.minsa.gob.pe/s/To2QtqoNjKqobfw/download" data_source_c <- paste0(dir_n, "Data_sources/", ctr, "/cases_",today(), ".csv") data_source_d <- paste0(dir_n, "Data_sources/", ctr, "/deaths_",today(), ".csv") #source changed to provide data in 7z file #data_source_v <- paste0(dir_n, "Data_sources/", ctr, "/vacc_",today(), ".7z") #data_source_v <- paste0(dir_n, "Data_sources/", ctr, "/vacc_",today(), ".csv") # EA: needed to add the index [1] because there is more than one link, while the first one is the # full database that we need ## MK: 06.07.2022: large file and give download error, so stopped this step and read directly instead #download.file(cases_url[1], destfile = data_source_c, mode = "wb") #download.file(deaths_url[1], destfile = data_source_d, mode = "wb") #download.file(vacc_url, destfile = data_source_v, mode = "wb") #JD: read in from Url was failing, I changed it to reading in the downloaded csv # cases #db_c <- read_delim(cases_url, delim = ";") %>% # as_tibble() # deaths #db_d <- read_delim(deaths_url, delim = ";") %>% #as_tibble() # Vaccines #db_v <- read_csv(data_source_v) #db_c <- read.csv(data_source_c, sep = ";") #db_d <- read.csv(data_source_d, sep = ";") #db_v <- read.csv(data_source_v, sep = ",") #db_v=read_csv(archive_read(data_source_v), col_types = cols()) #db_v <- read_csv(vacc_url) ## MK: 07.07.2022: due to large file size (use fread to read first, and then write a copy), and ## .7z (vaccination file), we need to download it first then read. db_c <- bigreadr::fread2(cases_url[1], select = c("FECHA_RESULTADO", "SEXO", "EDAD", "DEPARTAMENTO")) db_d <- data.table::fread(deaths_url[1], select = c("FECHA_FALLECIMIENTO", "SEXO", "EDAD_DECLARADA", "DEPARTAMENTO")) # cases ---------------------------------------------- db_c2 <- db_c %>% rename(date_f = FECHA_RESULTADO, Sex = SEXO, Age = EDAD, Region = DEPARTAMENTO) %>% select(date_f, Sex, Age, Region) %>% mutate(date_f = ymd(date_f), Sex = case_when(Sex == "MASCULINO" ~ "m", Sex == "FEMENINO" ~ "f", TRUE ~ "UNK"), Age = case_when(Age < 0 ~ NA_integer_, Age > 100 ~ 100L, is.na(Age) ~ NA_integer_, TRUE ~ as.integer(Age)), Region = str_to_title(Region)) %>% group_by(date_f, Sex, Age, Region) %>% summarise(new = n()) %>% ungroup() dates <- db_c2 %>% drop_na(date_f) %>% select(date_f) %>% unique() dates_f <- seq(min(dates$date_f),max(dates$date_f), by = '1 day') ages <- 0:100 db_c3 <- db_c2 %>% tidyr::complete(Region, Sex, Age = ages, date_f = dates_f, fill = list(new = 0)) %>% group_by(Region, Sex, Age) %>% mutate(Value = cumsum(new), Measure = "Cases", Age = case_when(is.na(Age) ~ "UNK", TRUE ~ as.character(Age))) %>% ungroup() %>% select(-new) # deaths ---------------------------------------------- db_d2 <- db_d %>% rename(date_f = FECHA_FALLECIMIENTO, Sex = SEXO, Age = EDAD_DECLARADA, Region = DEPARTAMENTO) %>% select(date_f, Sex, Age, Region) %>% mutate(date_f = ymd(date_f), Sex = case_when(Sex == "MASCULINO" ~ "m", Sex == "FEMENINO" ~ "f", TRUE ~ "UNK"), Age = case_when(Age < 0 ~ NA_integer_, Age > 100 ~ 100L, is.na(Age) ~ NA_integer_, TRUE ~ as.integer(Age)), Region = str_to_title(Region)) %>% group_by(date_f, Sex, Age, Region) %>% summarise(new = n()) %>% ungroup() dates_f <- seq(min(db_d2$date_f),max(db_d2$date_f), by = '1 day') ages <- 0:100 db_d3 <- db_d2 %>% tidyr::complete(Region, Sex, Age = ages, date_f = dates_f, fill = list(new = 0)) %>% group_by(Region, Sex, Age) %>% mutate(Value = cumsum(new), Measure = "Deaths", Age = case_when(is.na(Age) ~ "UNK", TRUE ~ as.character(Age))) %>% ungroup() %>% select(-new) # template for database ------------------------------------------------------------ db_dc <- bind_rows(db_d3, db_c3) db_pe <- db_dc %>% group_by(date_f, Sex, Age, Measure) %>% summarise(Value = sum(Value)) %>% ungroup() %>% mutate(Region = "All") # 5-year age intervals for regional data ------------------------------- db_dc2 <- db_dc %>% # mutate(Age = ifelse(Age <= 4, Age, floor(Age/5) * 5)) %>% group_by(date_f, Region, Sex, Age, Measure) %>% summarise(Value = sum(Value)) %>% ungroup() %>% arrange(date_f, Region, Measure, Sex, Age) # ---------------------------------------------------------------------- db_pe_comp <- bind_rows(db_dc2, db_pe) %>% mutate(Age = as.character(Age)) db_tot_age <- db_pe_comp %>% group_by(Region, date_f, Sex, Measure) %>% summarise(Value = sum(Value)) %>% ungroup() %>% mutate(Age = "TOT") db_tot_sex <- db_pe_comp %>% group_by(Region, date_f, Age, Measure) %>% summarise(Value = sum(Value)) %>% ungroup() %>% mutate(Sex = "b") db_tot <- db_pe_comp %>% group_by(Region, date_f, Measure) %>% summarise(Value = sum(Value)) %>% ungroup() %>% mutate(Sex = "b", Age = "TOT") db_inc <- db_tot %>% filter(Measure == "Deaths", Value >= 100) %>% group_by(Region) %>% summarise(date_start = ymd(min(date_f))) db_all <- bind_rows(db_pe_comp, db_tot_age, db_tot_sex, db_tot) db_all2 <- db_all %>% left_join(db_inc) %>% drop_na() %>% filter((Region == "All" & date_f >= "2020-03-01") | date_f >= date_start) out <- db_all2 %>% mutate(Country = "Peru", AgeInt = case_when(Region == "All" & !(Age %in% c("TOT", "100")) ~ 1, Region != "All" & !(Age %in% c("0", "1", "TOT")) ~ 5, Region != "All" & Age == "0" ~ 1, Region != "All" & Age == "1" ~ 4, Age == "100" ~ 5, Age == "TOT" ~ NA_real_), Date = ddmmyyyy(date_f), Code = case_when( Region == "All" ~ paste0("PE"), Region == "Amazonas" ~ paste0("PE-AMA"), Region == "Ancash" ~ paste0("PE-ANC"), Region == "Apurimac" ~ paste0("PE-APU"), Region == "Arequipa" ~ paste0("PE-ARE"), Region == "Ayacucho" ~ paste0("PE-AYA"), Region == "Cajamarca" ~ paste0("PE-CAJ"), Region == "Callao" ~ paste0("PE-CAL"), Region == "Cusco" ~ paste0("PE-CUS"), Region == "Huancavelica" ~ paste0("PE-HUV"), Region == "Huanuco" ~ paste0("PE-HUC"), Region == "Ica" ~ paste0("PE-ICA"), Region == "Junin" ~ paste0("PE-JUN"), Region == "La Libertad" ~ paste0("PE-LAL"), Region == "Lambayeque" ~ paste0("PE-LAM"), Region == "Lima" ~ paste0("PE-LIM"), Region == "Loreto" ~ paste0("PE-LOR"), Region == "Madre De Dios" ~ paste0("PE-MDD"), Region == "Moquegua" ~ paste0("PE-MOQ"), Region == "Pasco" ~ paste0("PE-PAS"), Region == "Piura" ~ paste0("PE-PIU"), Region == "Puno" ~ paste0("PE-PUN"), Region == "San Martin" ~ paste0("PE-SAM"), Region == "Tacna" ~ paste0("PE-TAC"), Region == "Tumbes" ~ paste0("PE-TUM"), Region == "Ucayali" ~ paste0("PE-UCA"), TRUE ~ "Other" ), Metric = "Count") %>% sort_input_data() # test <- db_final %>% # filter(Sex == "b", # Age == "TOT") ######################### # save processed data in N: ------------------------------------------------- ######################### log_update(pp = ctr, N = nrow(out)) write_rds(out, paste0(dir_n, ctr, ".rds")) ######################### # Push zip file to Drive ------------------------------------------------- ######################### # Saving original Cases & Deaths datafiles to N readr::write_csv(db_c, file = data_source_c) readr::write_csv(db_d, file = data_source_d) # saving compressed data to N: drive data_source <- c(data_source_c, data_source_d) zipname <- paste0(dir_n, "Data_sources/", ctr, "/", ctr, "_data_", today(), ".zip") zipr(zipname, data_source, recurse = TRUE, compression_level = 9, include_directories = TRUE) # clean up file chaff file.remove(data_source)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/util_FindCorrelatedRegions.R \name{FindCorrelatedRegions} \alias{FindCorrelatedRegions} \title{Find contiguous co-edited subregions.} \usage{ FindCorrelatedRegions( sites_df, featureType = c("site", "cpg"), minSites_int = 3 ) } \arguments{ \item{sites_df}{An output data frame from function \code{MarkCoeditedSites}, with variables \code{site, keep, ind, r_drop}. Please see \code{\link{MarkCoeditedSites}} for details.} \item{featureType}{Feature type, Defaults to \code{"site"}.} \item{minSites_int}{An integer indicates the minimum number of sites to be considered a contiguous co-edited region.} } \value{ A data frame with the following columns: \itemize{ \item{\code{site} : }{site ID.} \item{\code{subregion} : }{index for each output contiguous co-edited region.} } } \description{ Find contiguous co-edited subregions based on the output file from function \code{\link{MarkCoeditedSites}}. } \examples{ data(t_rnaedit_df) ordered_cols <- OrderSitesByLocation( sites_char = colnames(t_rnaedit_df), output = "vector" ) exm_data <- t_rnaedit_df[, ordered_cols] exm_sites <- MarkCoeditedSites( rnaEditCluster_mat = exm_data, method = "spearman" ) FindCorrelatedRegions( sites_df = exm_sites, featureType = "site" ) } \keyword{internal}

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library(grid) library(ggplot2) library(readr) library(dplyr) candleGrob <- function(xc, yc, openc, closec, highc, lowc) { } candleGrobs <- function(x, y, open, close, high, low) { #x,y are the center of the candle rectangle #high,low define the top and bottom of the glyph #open/close can be in either order and define which #of two colors fills the rect #total_height <- high - low #rect_height <- max(open,close) - min(open,close) t <- ifelse( open > close, open, close ) td <- high - t b <- ifelse( open > close, close, open ) bd <- b - low h <- t - b f <- ifelse( open > close, "red", "green" ) #TODO: update this to return a gList() of two # lineGrob() and the rectGrob() #TODO: do it the same way as "arrow_geom", i.e. # calling mapply() to generate the gList of # gTrees generated by candleGrob. best thing to # do is pass in x, y, t, td, bd, b, h and color # and let candleGrob generate a gTree with three # children - one for the upper line, one for the # lower line, and one for the rectangle gTree( children = gList( rectGrob(x, y, 0.005, h, gp = gpar(fill = f, col = 0)) ) ) } candle_Drawpanel <- function(data, panel_scales, coord, na.rm = FALSE) { coords <- coord$transform(data, panel_scales) # print(head(coords)) ggplot2:::ggname("geom_candlesticks", candleGrobs(coords$x, coords$y, coords$open, coords$close, coords$high, coords$low)) } geom_candlesticks <- function(mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { layer( data = data, mapping = mapping, stat = stat, geom = gCandles, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) } gCandles <- ggproto( "gCandles", Geom, draw_panel = candle_Drawpanel, non_missing_aes = c("x", "y", "open", "close", "high", "low"), default_aes = aes(fill = "lightgreen", alpha = "0.5", size = 1), icon = function(.) {}, desc_params = list(), seealso = list(), examples = function(.) {}) lhsif <- read_csv("LHSIF.csv") %>% mutate(y_med = Low + (High - Low)/2) ggplot(lhsif, aes(x = Date, y = y_med, open = Open, high = High, close = `Adj Close`, low = Low)) + geom_candlesticks()

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Azure/Azure-MachineLearning-ClientLibrary-R

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refs/heads/master

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2023-03-28T16:48:18

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MSFTstock.r

## Linear model for MSFT stock dataset ## # You can use the setwd() command to change your working directory. Examples below # Currently using identification for an account on studio.azureml-int.net # If you would like to see the web services published, please create an account there # and substitute in your identification wsID = "" # Insert workspace ID wsAuth = "" # Insert workspace authorization token require(quantmod) || install.packages(quantmod) library(quantmod) getSymbols('MSFT') class(MSFT) chartSeries(MSFT) str(MSFT) #plot(MSFT[,1],MSFT[,2]) data = as.data.frame(cbind(MSFT[,4],MSFT[,5]/100000)) # Train model model = lm(MSFT.Close~.,data=data) summary(model) # Create prediction function MSFTpredict <- function(close, volume) { return(predict(model, data.frame("MSFT.Close"=close, "MSFT.Volume"=volume))) } #Publish MSFT prediction function msftWebService <- publishWebService("MSFTpredict", "MSFTdemo", list("close"="float", "volume"="float"), list("number"="float"), wsID, wsAuth) # Discover endpoints msftEndpoints <- msftWebService[[2]] # msftConsumeSingleRows <- consumeLists(msftEndpoints[[1]]["PrimaryKey"], msftEndpoints[[1]]$ApiLocation, list("close", "volume"), list(25, 300), list(30, 100)) msftDF <- data.frame("close"=c(107,208,300), "volume"=c(400,569,665)) msftConsumeDF <- consumeDataframe(msftWebService[[2]][[1]]$PrimaryKey, msftWebService[[2]][[1]]$ApiLocation, msftDF)

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/FinalCode_v2.R

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caomao1111/6101-bts

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refs/heads/master

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FinalCode_v2.R

#' --- #' title: 6101 Project 2 Final #' author: TeamBestTeam #' date: 31Oct17 #' output: #' html_document: #' toc: true #' highlight: haddock #' --- #'####################################### #http://rpubs.com/jassalak/TeamBestTeam_Proj2 #'####################################### #' ## Environment Preparation #Remove any objects in the Environment rm(list = ls()) ### Set working directory setwd("C:/Users/akash/Desktop/GWU/6101_DataScience_RShah/Proj_2") #setwd("/Users/jimgrund/6101-bts/data/") ### Knitr global options library(knitr) opts_chunk$set(eval = TRUE, echo = TRUE, warning = FALSE, tidy = TRUE, results = "hold", cache = TRUE) ### Set the overall seed for reproducibility set.seed(6101) #'####################################### #' #' ### Load Necessary Libraries #install.packages("BSDA") suppressPackageStartupMessages(library(BSDA)) #install.packages("MASS") suppressPackageStartupMessages(library(MASS)) #install.packages("Hmisc") suppressPackageStartupMessages(library(Hmisc)) #install.packages("caret") suppressPackageStartupMessages(library(caret)) #install.packages("ROCR") suppressPackageStartupMessages(library(ROCR)) #install.packages("rpart") suppressPackageStartupMessages(library(rpart)) #install.packages("Amelia") suppressPackageStartupMessages(library(Amelia)) #install.packages("openxlsx") suppressPackageStartupMessages(library(openxlsx)) #install.packages("pROC") suppressPackageStartupMessages(library(pROC)) #'####################################### ### # read in data that has been filtered down to only DCA, IAD, BWI airports data <- data.frame(read.csv("DMV_On_Time_Performance_2015-2017.csv", header = TRUE)) d2 <- subset(data,select = c(Year, Quarter, #smote this Month, #smote this DayofMonth, #smote this DayOfWeek, #smote this AirlineID, Origin, #smote this Distance, DepDel15, #smote this DepartureDelayGroups, DepDelay, Carrier, #smote this Dest, #smote this DestAirportID )) ### # parse this down to only where DCA, IAD, BWI are the origin airports d2.1 <- subset(d2, Origin == "IAD" | Origin == "BWI" | Origin == "DCA") ### # write this out to a new CSV write.csv(d2.1,"DMV-origin_On_Time_Performance_2015-2017.csv") # remove the old dataframes to save on memory consumption rm(data,d2,d2.1) ### # read in the DVM origin data into a data frame for processing d2 <- data.frame(read.csv("DMV-origin_On_Time_Performance_2015-2017.csv", header = TRUE)) ### # remove any nulls and na data from the dataframe row.has.na <- apply(d2, 1, function(x){any(is.na(x))}) d2 <- d2[!row.has.na,] rm(row.has.na) d2 <- d2[!apply(is.na(d2) | d2 == "", 1, all),] ### # perform some garbage collection gc() ### # prior to any further processing # display some graphics of the relation between Departure Delay and some of the key independent variables that we’re looking at colors = rainbow(length(unique(d2))) barchart(d2$Origin, ylab="Name of Airport", main="Barchart of Airport Name Frequency (pre-smote)", col=rainbow(3), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) bartable <- table(d2$DepDel15, d2$Origin) barplot(bartable,xlab="Name of Airport", ylab="Frequency", main="Stacked barchart of Airport Name Frequency vs DepDelay15 (pre-smote)", col = c("Green4","Blue4"), legend = rownames(bartable), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) bartable <- table(d2$DepDel15, d2$DayOfWeek) barplot(bartable,xlab="Day Of Week", ylab="Frequency", main="Day Of Week vs DepDelay15 (pre-smote)", col = c("Green2","Blue2"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) bartable <- table(d2$DepDel15, d2$Month) barplot(bartable,xlab="Month", ylab="Frequency", main="Month vs DepDelay15 (pre-smote)", col = c("Green2","Blue2"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) bartable <- table(d2$DepDel15, d2$Carrier) barplot(bartable,xlab="Carrier", ylab="Frequency", main="Carrier vs DepDelay15 (pre-smote)", col = c("Green3","Blue3"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) bartable <- table(d2$DepDel15, d2$DayofMonth) barplot(bartable,xlab="Day of Month", ylab="Frequency", main="Day of Month vs DepDelay15 (pre-smote)", col = c("Green2","Blue2"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) hist(d2$DepDel15,xlab="Delay Flag", breaks=10, main="Histogram of Delay Flag Frequency", col = c("Gold3","Brown1"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) #' ## Partition Data # create a 70% training set, 30% test set subsamples <- createDataPartition(y=d2$DepDel15, p=0.7, list=FALSE) TrainSet <- d2[subsamples, ] TestSet <- d2[-subsamples, ] rm(subsamples) rm(d2) #hist(TrainSet$DepDel15) #’ ## One-hot-encode the training set library(ade4) library(data.table) ohe_feats = c('DayOfWeek','DayofMonth','Month','Quarter','Carrier') for (f in ohe_feats){ df_all_dummy = acm.disjonctif(TrainSet[f]) TrainSet[f] = NULL TrainSet = cbind(TrainSet, df_all_dummy) } rm(df_all_dummy,ohe_feats) #’ SMOTE the data ### # SMOTE wants the dependent variable to be a factor TrainSet$DepDel15 <- as.factor(TrainSet$DepDel15) # Use Smote to increase the minority to be better balanced with the majority. # use only the key independent variables that we care about # in this execution, we remove the ones we don’t care about (notice the usage of the minus sign) rather than listing all the ones we want. #newData <- SMOTE(DepDel15 ~ . - Year - AirlineID - Distance - DepartureDelayGroups - DepDelay - DestAirportID, TrainSet, perc.over = 100,perc.under=200, k=7) # write the smoked data out to disk so we can read it in later without having to re-run smote #write.csv(newData,"smoted-one-hot-trainset.csv") #prop.table(table(newData$DepDel15)) #’ Model ### # Now that we’ve got all the data one-hot-encoded, and training data has been smote’d, # let’s clean up the environment and # let’s run the modeling #' ## Model Environment Preparation #Remove any objects in the Environment rm(list = ls()) ######################################## #' Load Test Data TestSet <- data.frame(read.csv("TestSet-OneHot.csv", header = TRUE)) ######################################## #' Load Smoted Data .. this will be the training data d3 <- data.frame(read.csv("smoted-one-hot-trainset.csv", header = TRUE)) TrainSet <- d3 #View(d3) #' Create Random Sample d3_rand15 <- d3[sample(nrow(d3),15),] ######################################## #' ## Model 5 (Logistic Regression) # join the smoted data with the test test to create a complete dataset # for running the baseline model against finalModel<-rbind(d3,TestSet) ######################################## #' Histogram of DV #hist(d3$DepDel15) #' ## Baseline Model #(length(d3$DepDelay15[d3$DepDelay15>=1]) / nrow(d3) * 100 ) #What is the percentage the plane will be delayed without a model (at random) #+ echo = FALSE, fig.width=4, fig.height=4, dpi=100 sum(finalModel$DepDel15)/length(finalModel$DepDel15) *100 ##################################### #' EDA diagrams colors = rainbow(length(unique(finalModel))) # Airport frequency barchart(finalModel$Origin, ylab="Name of Airport", main="Barchart of Airport Name Frequency (post-smote)", col=rainbow(3), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) # Airport on-time and delay stacked barchart bartable <- table(finalModel$DepDel15, finalModel$Origin) barplot(bartable,xlab="Name of Airport", ylab="Frequency", main="Stacked barchart of Airport Name Frequency vs DepDelay15 (post-smote)", col = c("Green4","Blue4"), legend = rownames(bartable), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) #' Histogram of DV # Delay Flag Frequency after SMOTE hist(finalModel$DepDel15,xlab="Delay Flag", breaks=10, main="Histogram of Delay Flag Frequency (Final Model Data)", col = c("Purple3","Pink3"), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) barchart(finalModel$Origin,ylab="Name of Airport", main="Barchart of Airport Name Frequency", col = rainbow(3), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) #bartable <-- table(finalModel$DepDel15, finalModel$Origin) #barplot(bartable,ylab="Name of Airport", main="Stacked barchart of Airport Name Frequency vs DepDelay15", # col = c("Green4","Blue4"), legend = rownames(bartable), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) #' MODEL # remove Year, Quarter AirlineID, Distance, DepartureDelayGroups, DepDelay, DestAirportID from the model # all other fields from the TrainSet are fair game for use in the model m5 <- glm(DepDel15 ~ . - Year - Quarter.1 - Quarter.2 - Quarter.3 - Quarter.4 - AirlineID - Distance - DepartureDelayGroups - DepDelay - DestAirportID, family=binomial(link = "logit"), data=TrainSet) summary(m5) #' ### Using anova() for feature importance #anova(m5, test="Chisq") # remove TTN and MSN from TestSet since not found in TrainSet, else testing the test set against the trainset will fail TestSet<-TestSet[!(TestSet$Dest=="TTN"),] TestSet<-TestSet[!(TestSet$Dest=="MSN"),] #' ### Prediction p5 <- predict(m5, newdata = TestSet,type = 'response') p5 <- ifelse(p5 > 0.5,1,0) str(p5) #' ### Confusion Matrix to Check Accuracy confusionMatrix(data=p5, reference = TestSet$DepDel15) #' ### ROC and AUC response_predict <- predict(m5, newdata=TestSet, type = "response") link_predict <- predict(m5, newdata=TestSet, type = "link") terms_predict <- predict(m5, newdata=TestSet, type = "terms") qplot(x=response_predict, geom="histogram") qplot(x=link_predict, geom="histogram") qplot(x=terms_predict, geom="histogram") predictions <- predict(m5, newdata=TestSet, type="response") #' ROC prediction ROCRpred <- prediction(response_predict, TestSet$DepDel15) ROCRperf <- performance(ROCRpred, measure = "tpr", x.measure = "fpr") plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7),print.cutoffs.at = seq(0,1,0.1), main="Receiver Operator Characteristic Curve", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5) ################################ #' ### Custom Prediction options(warn=-1) # create a customQuery dataframe with defaults to zero for all the one-hot-encoded independent vars rm(customQuery) customQuery <- data.frame(matrix(ncol = 77, nrow = 0)) cols <- c("X", "Year", "AirlineID", "Origin", "Distance", "DepDel15", "DepartureDelayGroups", "DepDelay", "Dest", "DestAirportID", "DayOfWeek.1", "DayOfWeek.2", "DayOfWeek.3", "DayOfWeek.4", "DayOfWeek.5", "DayOfWeek.6", "DayOfWeek.7", "DayofMonth.1", "DayofMonth.2", "DayofMonth.3", "DayofMonth.4", "DayofMonth.5", "DayofMonth.6", "DayofMonth.7", "DayofMonth.8", "DayofMonth.9", "DayofMonth.10", "DayofMonth.11", "DayofMonth.12", "DayofMonth.13", "DayofMonth.14", "DayofMonth.15", "DayofMonth.16", "DayofMonth.17", "DayofMonth.18", "DayofMonth.19", "DayofMonth.20", "DayofMonth.21", "DayofMonth.22", "DayofMonth.23", "DayofMonth.24", "DayofMonth.25", "DayofMonth.26", "DayofMonth.27", "DayofMonth.28", "DayofMonth.29", "DayofMonth.30", "DayofMonth.31", "Month.1", "Month.2", "Month.3", "Month.4", "Month.5", "Month.6", "Month.7", "Month.8", "Month.9", "Month.10", "Month.11", "Month.12", "Quarter.1", "Quarter.2", "Quarter.3", "Quarter.4", "Carrier.AA", "Carrier.AS", "Carrier.B6", "Carrier.DL", "Carrier.EV", "Carrier.F9", "Carrier.MQ", "Carrier.NK", "Carrier.OO", "Carrier.UA", "Carrier.US", "Carrier.VX", "Carrier.WN") colnames(customQuery) <- cols customQuery <- data.frame(X=0, Year=0, AirlineID=0, Origin="0", Distance=0, DepDel15=0, DepartureDelayGroups=0, DepDelay=0, Dest="0", DestAirportID=0, DayOfWeek.1=0, DayOfWeek.2=0, DayOfWeek.3=0, DayOfWeek.4=0, DayOfWeek.5=0, DayOfWeek.6=0, DayOfWeek.7=0, DayofMonth.1=0, DayofMonth.2=0, DayofMonth.3=0, DayofMonth.4=0, DayofMonth.5=0, DayofMonth.6=0, DayofMonth.7=0, DayofMonth.8=0, DayofMonth.9=0, DayofMonth.10=0, DayofMonth.11=0, DayofMonth.12=0, DayofMonth.13=0, DayofMonth.14=0, DayofMonth.15=0, DayofMonth.16=0, DayofMonth.17=0, DayofMonth.18=0, DayofMonth.19=0, DayofMonth.20=0, DayofMonth.21=0, DayofMonth.22=0, DayofMonth.23=0, DayofMonth.24=0, DayofMonth.25=0, DayofMonth.26=0, DayofMonth.27=0, DayofMonth.28=0, DayofMonth.29=0, DayofMonth.30=0, DayofMonth.31=0, Month.1=0, Month.2=0, Month.3=0, Month.4=0, Month.5=0, Month.6=0, Month.7=0, Month.8=0, Month.9=0, Month.10=0, Month.11=0, Month.12=0, Quarter.1=0, Quarter.2=0, Quarter.3=0, Quarter.4=0, Carrier.AA=0, Carrier.AS=0, Carrier.B6=0, Carrier.DL=0, Carrier.EV=0, Carrier.F9=0, Carrier.MQ=0, Carrier.NK=0, Carrier.OO=0, Carrier.UA=0, Carrier.US=0, Carrier.VX=0, Carrier.WN=0 ) ########## #' populate your custom query attributes here customQuery$Origin="IAD" #customQuery$Distance=283 customQuery$Dest="JFK" #customQuery$DestAirportID=11298 customQuery$DayOfWeek.1=1 customQuery$DayofMonth.11=1 customQuery$Month.12=1 customQuery$Carrier.DL=1 # execute the custom prediction request predict(m5,newdata=customQuery,type = "response")

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/data/genthat_extracted_code/balance/examples/vlr.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

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vlr.Rd.R

library(balance) ### Name: vlr ### Title: Calculate Log-ratio Variance ### Aliases: vlr ### ** Examples library(balance) data(iris) x <- iris[,1:4] vlr(x)

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/Graph/Rigidity.R

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discoleo/R

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Rigidity.R

################### ### ### Rigidity Theory ### ### Leonard Mada ### ### draft v.0.1l-formatted ### Rigidity Theory ### Global Rigidity ################### ### 1D Rigidity ### ################### ### Satisfiability: # - let x be n positive integers; # - is there a set of coefficients b # with values in {-1, 1}, # such that sum(b*x) = 0; ### Very Naive Approach # - iterate through all permutations of b[i]; # - Complexity: 2^n permutations; ### Improved Very Naive Approach: # - sum(b*x) is a solution <=> sum((-b)*x) is a solution; # - iterate only through the quasi-unique permutations; # - Complexity: 2^(n-1) permutations; ### Example: n=4 nr = 4 m = matrix(c( 1, 1, 1, 1, -1, 1, 1, 1, 1,-1, 1, 1, 1, 1,-1, 1, 1, 1, 1,-1, -1,-1, 1, 1, -1, 1,-1, 1, -1, 1, 1,-1 ), nrow=nr) # all quasi-unique permutations sort(sapply(seq(ncol(m)), function(id) abs(sum(m[,id] * sqrt(2:(nr+1)))))) ### Modular Arithmetic ### Constraints Mod 2: # Note: all b = 1 (MOD 2); # => sum(b*x) = sum(x) (MOD 2); # => sum(b*x) = 0 <=> sum(x) = 0 (MOD 2); # TODO ################### library(pracma) ### Helper functions ### generate 1D Framework rpx1D = function(n, lim=c(1, 20), replace=TRUE) { sample(seq(lim[1], lim[2]), n, replace=replace) } # connect the points rpx1D.con = function(r, p=NULL) { # does NOT check if a framework is already possible; len = length(r); p = if(missing(p) || is.null(p)) NULL else if(length(p) >= 2) p[1:2] else c(p, 1-p); b = sample(c(1,-1), len, TRUE, prob=p); s = sum(b*r); if(s != 0) { b = c(b, -sign(s)); r = c(r, abs(s)); } return(list(p=r, b=b)); } ### Framework Realizations ### Problem: # - massive redundancy! # - the reductions simplify greatly the subproblems, # but create massive redundancy between the subproblems; solve.fr1D = function(f) { l = mod(f, 2); if(l$sum == 1) { l$SAT = FALSE; return(l); } l = mod(f, 3); m = as.integer(names(l$tbl)); len1 = l$tbl[m == 1]; len2 = l$tbl[m == 2]; if(length(len1) == 0) len1 = 0; if(length(len2) == 0) len2 = 0; l$R = list(); if(len1 == 0 && len2 == 0) { len = l$tbl[1]; # few Elements vs Many if(len <= 3) { if(len == 1) { isSat = (f[1] == 0); id = 1; } else if(len == 2) { isSat = (f[1] == f[2]); id = c(1, -2); } else { iSat = c(f[1]+f[2]-f[3], f[1]-f[2]+f[3], f[1]-f[2]-f[3]); isSat.all = (iSat == 0); isSat = any(isSat.all); id = list(c(1,2,-3), c(1,-2,3), c(1,-2,-3)); id = if(isSat) unlist(id[isSat.all]) else 0; } l$SAT = isSat; l$R = list(id=id); } else { l2 = solve.fr1D(f / 3); if(l2$SAT) { l$SAT = TRUE; l$R = c(l$R, l2$R); } else { l$SAT = FALSE; } } return(l); } # TODO: m3 = f %% 3; if(len1 == 0) { if(len2 == 1) { l$SAT = FALSE; return(l); } if(len2 %% 2 == 0) { f2 = combine.diff2(f[m3 == 2], add=f[m3 == 0], p=3); l2 = solve.fr1D(f2); if(l2$SAT) { l$R = c(l$R, l2$R); } } if(len2 %% 3 == 0) { f2 = combine.sum3(f[m3 == 2], add=f[m3 == 0], p=3); l2 = solve.fr1D(f2); if(l2$SAT) { l$R = c(l$R, l2$R); } } } else if(len2 == 0) { } return(l) } combine.diff2 = function(v, add, p=3) { # TODO: resolve massive redundancy! # - creating all combinations generates massive redundancy; id = seq(1, length(v), by=2); v2 = c(add, abs(v[id] - v[id + 1])) / p; return(v2); } combine.sum3 = function(v, add, p=3) { # TODO: resolve massive redundancy! # - creating all combinations generates massive redundancy; id = seq(1, length(v), by=3); v2 = c(add, abs(v[id] + v[id + 1] + v[id + 2])) / p; return(v2); } # exploratory test to reduce redundancy # - unique decomposition into subproblems; # [similar somehow to a matrix factorization] latin6 = function() { m = matrix(c( 1, 1, 1, 1, 1, 1, # s3 + s3 1, 1, 1,-1,-1,-1, # s3 - s3 1, 1,-1, 1,-1,-1, 1, 1,-1,-1, 1,-1, 1, 1,-1,-1,-1, 1, 1,-1, 1, 1,-1,-1, 1,-1, 1,-1, 1,-1, 1,-1, 1,-1,-1, 1, -1,1, 1, 1,-1,-1, -1,1, 1,-1, 1,-1, -1,1, 1,-1,-1, 1 ), nrow=6) return(m); } latin5 = function() { m = matrix(c( 1, 1, 1, 1,-1, # s3 + s2 1, 1, 1,-1, 1, # s3 - s2 1, 1,-1, 1, 1, 1,-1, 1, 1, 1, -1,1, 1, 1, 1 ), nrow=6) return(m); } # Test sort(abs(apply(latin6() * sqrt(2:7), 2, sum))) ### Test Sat mod = function(x, p) { list(sum=sum(x) %% p, tbl=table(x %% p), p=p) } mod.all = function(x, p) { list(tbl=tabulate(x %% p), p=p) } test = function(x) { UNSAT = function(p) { return(list(SAT=FALSE, p=p)); } fail.mod = function(s, nm, p) { len = length(nm); if(len == 1 && s < p && s %% 2 == 1) return(TRUE); return(FALSE); } # t2 = mod(x, 2); if(t2$sum == 1) return(UNSAT(2)); ### 3 tp = mod.all(x, 3)$tbl; if(length(tp) == 3) tp = tp[-1]; if(sum(tp) == 1) return(UNSAT(3)); ### 5 p = 5; tp = mod.all(x, p)$tbl; if(length(tp) == p) tp = tp[-1]; s = sum(tp); nm = which(tp != 0); if(fail.mod(s, nm, p)) return(UNSAT(p)); if(s == 2 && length(nm) == 2 && sum(nm) != p) return(UNSAT(p)); if(s == 3 && length(nm) == 2 && (sum(nm) %% p == 0)) return(UNSAT(p)); ### 7 p = 7; tp = mod.all(x, p)$tbl; if(length(tp) == p) tp = tp[-1]; s = sum(tp); nm = which(tp != 0); if(fail.mod(s, nm, p)) return(UNSAT(p)); if(s == 2 && length(nm) == 2 && sum(nm) != p) return(UNSAT(p)); if(s == 3 && length(nm) == 2 && (sum(nm) %% p == 0)) return(UNSAT(p)); # TODO: (1,2*2), (1,2*5), (2*1,3), ...; # TODO: combination of 3; # did NOT fail by congruence! return(list(SAT=TRUE, p=c(2,3,5,7))); } simplify = function(x) { if(x$sum == 0) return(0); # works only with p = prime number; # Note: NOT fully correct! # 2 types of simplification: %% p or %% 2; tbl = x$tbl %% 2; # correction for: (%% p)-times; m = as.integer(names(tbl)); # Note: should do the sum(tbl %% p == 0); # but even then it is only = 0 (mod p)! doCorrect = (tbl == 1) & (m != 0); cum.sum = sum((x$tbl[doCorrect] %/% x$p) * m[doCorrect]) + tbl[m == 0]; tbl[m == 0] = cum.sum %% 2; tbl[doCorrect] = (x$tbl[doCorrect] %% x$p) %% 2; r = sum(tbl * m) %% x$p; # TODO: more simplifications possible; # TODO: all correct combinations; return(list(tbl=tbl, r=r)); } simplify.p3 = function(x) { # full implementation of congruence (mod 3); # SAT: only for (mod 3)! if( ! is.list(x)) { l = mod(x, 3); } else l = x; m = as.integer(names(l$tbl)); simple.mod = function(l, mlog) { s2 = l$tbl[mlog]; l$SAT = TRUE; l$type = "Simple"; if(length(s2) == 0) return(l); if(s2 %% 2 == 0) { l$tbl[mlog] = 0; } else if(s2 %% 3 == 0) { l$tbl[mlog] = 0; } else { # else NO realization possible! l$SAT = FALSE; } return(l); } if(all(m != 1)) { return(simple.mod(l, m == 2)); } else if(all(m != 2)) { return(simple.mod(l, m == 1)); } print("Non-simple") # both present: = 1 (mod 3) & = 2 (mod 3); s1 = l$tbl[m == 1]; s2 = l$tbl[m == 2]; l$SAT = TRUE; l$tbl0 = l$tbl; if((s1 %% 3 == 0 || s1 %% 2 == 0) && (s2 %% 3 == 0 || s2 %% 2 == 0)) { l$tbl = l$tbl[m == 0]; return(l); } if(s1 %% 2 == 0) { s2 = s2 %% 2; # == 1; s1 = -2; l$tbl[m == 1] = s1; l$tbl[m == 2] = s2; return(l); } if(s2 %% 2 == 0) { s1 = s1 %% 2; # == 1; s2 = -2; l$tbl[m == 1] = s1; l$tbl[m == 2] = s2; return(l); } s1 = s1 %% 2; # == 1; s2 = s2 %% 2; # == 1; l$tbl[m == 1] = s1; l$tbl[m == 2] = s2; return(l); } ### Analyse gcd.v = function(v, p) { gcd(v, p) } gcd.all = function(v) { len = length(v) - 1; d = sapply(seq(1, len), function(id) gcd(tail(v,-id), v[id])); d = unlist(d); m = diag(v); len = length(v); id = expand.grid(1:len, 1:len); m[id[,1] > id[,2]] = d; return(m); } ##################### ##################### ##################### ### 1D Frameworks ### ##################### n = 10; f = rpx1D(n) ### Q: Is this a valid cyclic framework? # (exists) b[i] = {-1, 1} such that sum(b*f) = 0; mod(f, 2)$tbl; l = simplify.p3(f) l ### Example: # - at least 2 realizations (for any order); f = c(1, 3, 8, 10, 13, 14, 17, 18, 18, 20) l = simplify.p3(f) l ### Solution: # {1, 3, 13, 17}: {(1+/-3), (13+/-17)}, ...; # (mod 2)-variant; # {1, 10, 13}, {8, 14, 17, 20}: # "3x1" + "2-2" => {(1+10+13), (8-14), (17-20)}, ...; # 3 variants # "3x2" + "1+2" => {(1-10), (13+8), (14+17+20)}, ...; # 4*3 = 12 variants; ### Case 1: "3x1" + "2-2" # C.1.1: {3, 18, 18, 24, -6, -3} => scale by +/-1/3 # => {1, 6, 6, 8, 2, 1}; # smaller problem; # => (1-1), {6,6,8,2} => NO realization! # => (1+1), {6,6,8,2} => realization: see below; # C.1.2: {3, 18, 18, 24, -9, -6} => scale by +/-1/3 # = > {1, 6, 6, 8, 3, 2}; # smaller problem; # => (1-3), {6,6,8,2} => 6+6-(8+2+3-1) 18 + 18 - (1+10+13) - (20-14 + (17-8) - 3); # = 0; # C.1.3: {3, 18, 18, 24, -12, -3} => scale by +/-1/3 # => {1, 6, 6, 8, 4, 1}; # smaller problem; # => (1-1), {6,6,8,4} => 6+6-8-4 + (1-1) 18 + 18 - (1+10+13) - (20-8) + 3 - (17-14) # (same as previous) OR 18 + 18 - (1+10+13) - (20-8) - 3 + (17-14); # = 0; ### Case 2: "3x2" + "1+2" # C.2.1: {3, 18, 18, 14+17+20, 8+1, 10-13} => scale by +/-1/3 # => {1, 6, 6, 17, 3, 1}; # smaller problem; # => (1-3), {6,6,8,2} => 17 - (6+6+1+3+1) 14 + 17 + 20 - (18+18+3+8+1+13-10); # = 0; ### TODO: solve recursive problem f = c(1, 3, 8, 10, 13, 14, 17, 18, 18, 20) # sol = c(1,1,1,-1,1,-1,-1,1,1,-1) b1 = 1 A = matrix(c( 3, 8, 10, 13, 14, 0, 2, 1, 1, 2, # mod 3 3, 3, 0, 3, 4, # mod 5 3, 1, 3, 6, 0, # mod 7 3, 8, 10, 2, 3 # mod 11 ), ncol=5, byrow=T) B = matrix(c( -1,-17,-18,-18,-20, 0, 0, 0, 0, # + 0 * c(3*k3, 5*k5, 7*k7, 11*k11) -1, -2, 0, 0, -2, 3, 0, 0, 0, -1, -2, -3, -3, 0, 0, 5, 0, 0, -1, -3, -4, -4, -6, 0, 0, 7, 0, -1, -6, -7, -7, -9, 0, 0, 0,11 ), ncol=9, byrow=T) det(A) # (b2, b3, b4, b5, b6) = {+/- 1}; S = solve(A, B) S # p = 5; sum((f %% p) * sol) / p # 1, b7, ..., b10, k3, ..., k11 b7_10 = c(-1,1,1,-1); bk = c(1, b7_10, -1,2,1,0) b2_6 = S %*% bk b = c(b1, b2_6, b7_10) b # Test sum(b*f) ### TODO: # - develop theory/techniques to test/solve; # for M[6*6] det == 0 ### # p = prime number # if(sum(p) %% p == 1): # NOT realizable (for odd n)! # for n = even => more work; n = 10; f = rpx1D(n) l = test(f) l ### TODO: different algorithm simplify(l) ### TODO: # - design full algorithm; ### generate a valid framework rpx1D.con(f) ######################## ############# ### MOD 2 ### ############# ### Example: x = c(2,4,3,5,7,7) ### 0 (MOD 2) c(2, 4) # possible combinations => c(4+2, 4-2) ### 1 (MOD 2) c(3,5,7,7) # possible combinations => matrix(c( 3+5, 7+7, 3+5, 7-7, 5-3, 7+7, 5-3, 7-7 ), nrow=2) ### Combinations: 1*0(MOD 2) + 2*1(MOD 2): c(6, 8,14) # => c(3,4,7) # SAT => (2+4)+(3+5)-(7+7) c(2, 8,14) # => c(1,4,7) # NO (other); c(6, 8, 0) # => c(3,4,0) # NO (MOD 2); c(2, 8, 0) # => c(1,4,0) # NO (MOD 2); c(6, 2,14) # => c(3,1,7) # NO (MOD 2); c(2, 2,14) # => c(1,1,7) # NO (MOD 2); c(6, 2, 0) # => c(3,1,0) # NO (other); c(2, 2, 0) # => c(1,1,0) # SAT => (4-2)-(5-3)+(7-7) # 8 simpler subproblems; ### Notes: # Note 1: # - testing subproblems is simpler (fewer terms); # - gain in efficiency: compute each of the MOD subgroups only once! # Note 2: # - it may be possible to avoid the actual computations # using (MOD 4) arithmetic: eliminate unfeasible permutations; ######################## ### Complex Analysis gcd.all(f) ######################## ########## ### LP ### ########## # install.packages("lpSolve") library(lpSolve) ########## solve.lp = function(x, objective.coeff) { n = length(x); if(missing(objective.coeff)) objective.coeff = rep(1, n); # (1/2 - bl)*x = 0 => 2*bl*x = sum(x) constr.mat = matrix(2*x, nrow=1) constr.val = c(sum(x)) constr.dir = c("="); b.logic = rep(1, n); # not used; optimum = lp(direction="max", objective.coeff, constr.mat, constr.dir, constr.val, all.bin=TRUE) optimum } n = 20 x = rpx1D(n, lim=c(1,2*n)) mod(x, 2) # basic SAT ### objective.coeff = rep(1, n) # alternative: # n2 = n %/% 2; objective.coeff = rep(c(1,-1), c(n2, n-n2)); optimum = solve.lp(x, objective.coeff) optimum # b = 1 - 2*optimum$solution (b*x)[order(x)] ### Test sum(b*x)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/listr.R \name{listr} \alias{listr} \alias{olistr} \title{Convert to list} \usage{ listr() olistr() } \value{ \code{listr()} returns an unordered markdown list \code{olistr()} returns an ordered markdown list } \description{ Convert selected text into an (un)ordered list. } \examples{ \dontrun{ #unordered list remedy_example(c('line 1','line 2'),listr) #ordered list remedy_example(c('line 1','line 2'),olistr) } }

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#### ### ### ## #### ### ### ## #### ### ### ## # Created by Marc-Olivier Beausoleil # 03 Nomvember 2020 # Why: Get the qqplot for any data # Output: # Requires: # NOTES: Inspired from a course named "Learning Statistics with R" from "The Great Courses" #### ### ### ## #### ### ### ## #### ### ### ## # Get the data ir.virg.data.plgth = iris$Petal.Length[iris$Species == "virginica"] # Get sample size n = length(ir.virg.data.plgth) # Generate probabilities ps = seq(from = 0, to = 1, length.out = n/2) # Calculate the quantiles q.virg = quantile(ir.virg.data.plgth, probs = ps) # Get the qqplot qqnorm(q.virg, pch = 20, cex = 2) # add qqline for normal distribution (implied) qqline(q.virg)

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x <- read.csv("scores.csv", as.is = T) y <- read.csv("tournament.csv", as.is = T) x$rundiff <- x$teamscore - x$oppscore # Create Model lm.bb <- lm(rundiff ~ team + opponent + location, data = x) # Point Spread to Win Percentage Model x$winprob <- NA x$winprob[x$rundiff > 0] <- 1 x$winprob[x$rundiff < 0] <- 0 x$predrundiff <- as.numeric(round(predict(lm.bb, newdata = x), 1)) glm.bb<- glm(winprob ~ predrundiff, data = x, family=binomial(link=logit)) x$winprob[is.na(x$winprob)] <- predict(glm.bb, newdata = x[is.na(x$winprob),], type = "response") bb.sim <- function(a, G3W, G4L, G4W, G5W){ sims <- 1000 Games<- x[1:7, c("team", "opponent", "location")] Games[1:7,] <- NA Games$winprob <- 0 Games$predrundiff <- 0 totals <- rep(0, 4) data <- y[y$Regional == a, ] home <- data$Team[1] for(i in 1:sims) { print(i) ### Game 1 # Games[1, c("team", "opponent", "location")] <- c(data$Team[1], data$Team[4], "H") # Games$predrundiff[1] <- as.numeric(predict(lm.bb, newdata = Games[1,])) # Games$winprob[1] <- predict(glm.bb, newdata = Games[1,], type = "response") # rand <- runif(1) # # if(rand < Games$winprob[1]){ # Games$team[3] <- G1L # Games$location[3] <- L3 # Games$team[4] <- G1W # Games$location[4] <- L4 # }else{ # Games$team[4] <- G1W # Games$location[4] <- L4 # Games$team[3] <- G1L # Games$location[3] <- L3 # } # # ### Game 2 # Games[2, c("team", "opponent", "location")] <- c(data$Team[2], data$Team[3], "N") # Games$predrundiff[2] <- as.numeric(predict(lm.bb, newdata = Games[2,])) # Games$winprob[2] <- predict(glm.bb, newdata = Games[2,], type = "response") # rand <- runif(1) # # if(rand < Games$winprob[2]){ # Games$opponent[3] <- G2L # Games$opponent[4] <- G2W # }else{ # Games$opponent[4] <- G2W # Games$opponent[3] <- G2L # } # # ### Game 3 and 4 # Games$predrundiff[3] <- as.numeric(predict(lm.bb, newdata = Games[3,])) # Games$winprob[3] <- predict(glm.bb, newdata = Games[3,], type = "response") # Games$predrundiff[4] <- as.numeric(predict(lm.bb, newdata = Games[4,])) # Games$winprob[4] <- predict(glm.bb, newdata = Games[4,], type = "response") # # rand <- runif(1) # if(rand < Games$winprob[3]){ # Games$opponent[5] <- Games$team[3] # # }else{ # Games$opponent[5] <- Games$opponent[4] # } # # # rand <- runif(1) # if(rand < Games$winprob[4]){ # Games$team[5] <- Games$opponent[4] # Games$team[6] <- Games$team[4] # # }else{ # Games$team[5] <- Games$team[4] # Games$team[6] <- Games$opponent[4] # } Games$team[5] <- G4L Games$team[6] <- G4W Games$opponent[5] <- G3W ### Game 5 if(Games$opponent[5] == home){ Games$location[5] <- "V" }else if (Games$team[5] == home){ Games$location[5] <- "H" }else{ Games$location[5] <- "N" } Games$predrundiff[5] <- as.numeric(predict(lm.bb, newdata = Games[5,])) Games$winprob[5] <- predict(glm.bb, newdata = Games[5,], type = "response") rand <- runif(1) if(rand < Games$winprob[5]){ Games$opponent[6] <- G5W }else{ Games$opponent[6] <- G5W } ### Game 6 if(Games$opponent[6] == home){ Games$location[6] <- "V" }else if (Games$team[6] == home){ Games$location[6] <- "H" }else{ Games$location[6] <- "N" } Games$predrundiff[6] <- as.numeric(predict(lm.bb, newdata = Games[6,])) Games$winprob[6] <- predict(glm.bb, newdata = Games[6,], type = "response") rand <- runif(1) if(rand < Games$winprob[6]) { seed <- data$Seed[data$Team == Games$team[6]] totals[seed] <- totals[seed] + 1 }else{ Games[7,] <- Games[6,] rand <- runif(1) if(rand < Games$winprob[7]) { seed <- data$Seed[data$Team == Games$team[7]] totals[seed] <- totals[seed] + 1 }else{ seed <- data$Seed[data$Team == Games$opponent[7]] totals[seed] <- totals[seed] + 1 } } } return(round(100 * totals/sims, 1)) } bb.sim("Fort Worth", "TCU", "Central Conn. St.", "N", "Virginia", "DBU", "H") bb.sim("Corvallis", "Holy Cross", "Yale", "Oregon St.", "Yale") bb.sim("Clemson", "UNCG", "Clemson", "Vanderbilt") bb.sim("Chapel Hill", "North Carolina", "FGCU", "Davidson") bb.sim("Baton Rouge", "Rice", "Southeastern La.", "LSU") bb.sim("Fayetteville\xe6", "Oral Roberts", "Arkansas", "Missouri St.") bb.sim("Hattiesburg", "Southern Miss.", "Ill.-Chicago", "N", "South Ala.", "Mississippi St.", "H") bb.sim("Gainesville", "South Fla.", "Bethune-Cookman", "Florida") bb.sim("Houston", "Houston", "Iowa", "Texas A&M") bb.sim("Lexington", "Indiana", "Kentucky", "North Carolina St.") bb.sim("Long Beach", "San Diego St.", "Long Beach St.", "Texas") bb.sim("Lubbock", "Arizona", "Sam Houston St.", "Texas Tech") bb.sim("Tallahassee", "Florida St.", "Tennessee Tech", "Auburn") bb.sim("Winston-Salem","Maryland", "West Virginia", "Wake Forest") bb.sim("Louisville", "Xavier", "Oklahoma", "Louisville") z <- read.csv("corvallis.csv", as.is = T) plot(OSU ~ Game, data = z, col = "Orange", type = "l", lwd = 3, xaxt='n', ylim = c(0,100), main = "Corvallis, OR NCAA Baseball Regional", ylab = "Advance to Super Regional (%)") par(new = T) plot(Nebraska ~ Game, data = z, col = "Red", type = "l", lwd = 3, xaxt='n', ylim = c(0,100), main = "Corvallis, OR NCAA Baseball Regional", ylab = "Advance to Super Regional (%)") par(new = T) plot(Yale ~ Game, data = z, col = "Blue", type = "l", lwd = 3, xaxt='n', ylim = c(0,100), main = "Corvallis, OR NCAA Baseball Regional", ylab = "Advance to Super Regional (%)") par(new = T) plot(HC ~ Game, data = z, col = "Purple", type = "l", lwd = 3, xaxt='n', ylim = c(0,100), main = "Corvallis, OR NCAA Baseball Regional", ylab = "Advance to Super Regional (%)") LAB = c("1", "2", "3", "4", "5","6") pos = 1:6 axis(1, labels = LAB, at = pos) labels<-c("Oregon St.", "Nebraska", "Yale", "Holy Cross") colors <-c("Orange", "red", "blue", "purple") legend("topleft", xjust = 0, cex = .4, title="Legend", labels, lwd=2, lty=c(1, 1, 1, 1, 1), col=colors)

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granovagg.ds.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/granovagg.ds.R \name{granovagg.ds} \alias{granovagg.ds} \title{Elemental Graphic for Display of Dependent Sample Data} \usage{ granovagg.ds(data = NULL, revc = FALSE, main = "default_granova_title", xlab = NULL, ylab = NULL, conf.level = 0.95, plot.theme = "theme_granova_ds", northeast.padding = 0, southwest.padding = 0, ...) } \arguments{ \item{data}{is an n X 2 dataframe or matrix. First column defines X (intially for horzontal axis), the second defines Y.} \item{revc}{reverses X,Y specifications} \item{main}{optional main title (as character); can be supplied by user. The default value is \code{"default_granova_title"}, which will print a generic title for the graphic.} \item{xlab}{optional label (as character) for horizontal axis. If not defined, axis labels are taken from colnames of data.} \item{ylab}{optional label (as character) for vertical axis. If not defined, axis labels are taken from colnames of data.} \item{conf.level}{The confidence level at which to perform a dependent sample t-test. Defaults to \code{0.95} (95\% Confidence)} \item{plot.theme}{argument indicating a ggplot2 theme to apply to the graphic; defaults to a customized theme created for the dependent sample graphic} \item{northeast.padding}{(numeric) extends axes toward lower left, effectively moving data points to the southwest. Defaults to zero padding.} \item{southwest.padding}{(numeric) extends axes toward upper right, effectively moving data points to the southwest. Defaults to zero padding. Making both southwest and northeast padding smaller moves points farther apart, while making both larger moves data points closer together.} \item{...}{Optional arguments to/from other functions} } \value{ Returns a plot object of class \code{ggplot}. } \description{ Plots dependent sample data beginning from a scatterplot for the X,Y pairs; proceeds to display difference scores as point projections; also X and Y means, as well as the mean of the difference scores. } \details{ Paired X and Y values are plotted as scatterplot. The identity reference line (for Y = X) is drawn. Parallel projections of data points to (a lower-left) line segment show how each point relates to its X-Y = D difference; semitransparent "shadow" points are used to display the distribution of difference scores, with thin grey lines leading from each raw datapoint to its shadow projection on the difference distribution. The range of that difference score distribution is drawn as a blue line beneath the shadow points and the mean difference is displayed as a heavy dashed purple line, parallel to the identity reference line. Means for X and Y are also plotted (as thin dashed vertical and horizontal lines), and rug plots are shown for the distributions of X (at the top of graphic) and Y (on the right side). The 95\% confidence interval for the population mean difference is also shown graphically as a green band, perpendicular to the mean treatment effect line. Because all data points are plotted relative to the identity line, and summary results are shown graphically, clusters, data trends, outliers, and possible uses of transformations are readily seen, possibly to be accommodated. In summary, the graphic shows all initial data points relative to the identity line, adds projections (to the 'north' and 'east') showing the marginal distributions of X and Y, as well as projections to the 'southwest' where the difference scores for each point are drawn. Means for all three distributions are shown using straight lines; the confidence interval for the population mean difference score is also shown. Summary statistics are printed as side effects of running the function for the dependent sample analysis. } \examples{ ### Using granovagg.ds to examine trends or effects for repeated measures data. # This example corresponds to case 1b in Pruzek and Helmreich (2009). In this # graphic we're looking for the effect of Family Treatment on patients with anorexia. data(anorexia.sub) granovagg.ds(anorexia.sub, revc = TRUE, main = "Assessment Plot for weights to assess \\ Family Therapy treatment for Anorexia Patients", xlab = "Weight after therapy (lbs.)", ylab = "Weight before therapy (lbs.)" ) ### Using granovagg.ds to compare two experimental treatments (with blocking) # This example corresponds to case 2a in Pruzek and Helmreich (2009). For this # data, we're comparing the effects of two different virus preparations on the # number of lesions produced on a tobacco leaf. data(tobacco) granovagg.ds(tobacco[, c("prep1", "prep2")], main = "Local Lesions on Tobacco Leaves", xlab = "Virus Preparation 1", ylab = "Virus Preparation 2" ) ### Using granovagg.ds to compare two experimental treatments (with blocking) # This example corresponds to case 2a in Pruzek and Helmreich (2009). For this # data, we're comparing the wear resistance of two different shoe sole # materials, each randomly assigned to the feet of 10 boys. data(shoes) granovagg.ds(shoes, revc = TRUE, main = "Shoe Wear", xlab = "Sole Material B", ylab = "Sole Material A", ) ### Using granovagg.ds to compare matched individuals for two treatments # This example corresponds to case 2b in Pruzek and Helmreich (2009). For this # data, we're examining the level of lead (in mg/dl) present in the blood of # children. Children of parents who had worked in a factory where lead was used # in making batteries were matched by age, exposure to traffic, and neighborhood # with children whose parents did not work in lead-related industries. data(blood_lead) granovagg.ds(blood_lead, sw = .1, main = "Dependent Sample Assessment Plot Blood Lead Levels of Matched Pairs of Children", xlab = "Exposed (mg/dl)", ylab = "Control (mg/dl)" ) } \author{ Brian A. Danielak \email{brian@briandk.com}\cr Robert M. Pruzek \email{RMPruzek@yahoo.com} with contributions by:\cr William E. J. Doane \email{wil@drdoane.com}\cr James E. Helmreich \email{James.Helmreich@Marist.edu}\cr Jason Bryer \email{jason@bryer.org} } \references{ Pruzek, R. M., & Helmreich, J. E. (2009). Enhancing Dependent Sample Analyses with Graphics. Journal of Statistics Education, 17(1), 21. Wickham, H. (2009). Ggplot2: Elegant Graphics for Data Analysis. New York: Springer. Wilkinson, L. (1999). The Grammar of Graphics. Statistics and computing. New York: Springer. } \seealso{ \code{\link{granovagg.1w}}, \code{\link{granovagg.ds}}, \code{\link{granovaGG}} }

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######################################## #File: Problem Set 1 #Author: Nishidh Lad #Date: September 7,2017 ######################################## rm(list=ls(all=TRUE)) library(data.table) context1 <- fread('WAGE1.csv') summary(context1) lwage <- log(context1$wage) model1 <- lm(wage~educ, data=context1) summary(model1) #Coefficients: # Estimate Std. Error t value Pr(>|t|) #(Intercept) -0.93389 0.68769 -1.358 0.175 #educ 0.54470 0.05346 10.189 <2e-16 model2 <- lm(wage~educ+exper+tenure, data = context1) summary(model2) #Coefficients: # Estimate Std. Error t value Pr(>|t|) #(Intercept) -2.91354 0.73172 -3.982 7.81e-05 *** # educ 0.60268 0.05148 11.708 < 2e-16 *** # exper 0.02252 0.01210 1.861 0.0633 . # tenure 0.17002 0.02173 7.825 2.83e-14 *** model3 <- lm(lwage~educ+exper+tenure, data = context1) summary(model3) #Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 0.282635 0.104331 2.709 0.00697 ** # educ 0.092256 0.007340 12.569 < 2e-16 *** # exper 0.004137 0.001726 2.397 0.01687 * # tenure 0.022112 0.003098 7.138 3.19e-12 *** coef(model3)*100 # (Intercept) educ exper tenure # 28.2634948 9.2256203 0.4136804 2.2111673

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stein-shrinkage.r \name{risk_stein} \alias{risk_stein} \title{Stein Risk function from Pang et al. (2009).} \usage{ risk_stein(N, K, var_feature, num_alphas = 101, t = -1) } \arguments{ \item{N}{the sample size.} \item{K}{the number of classes.} \item{var_feature}{a vector of the sample variances for each dimension.} \item{num_alphas}{The number of values used to find the optimal amount of shrinkage.} \item{t}{a constant specified by the user that indicates the exponent to use with the variance estimator. By default, t = -1 as in Pang et al. See the paper for more details.} } \value{ list with \itemize{ \item \code{alpha}: the alpha that minimizes the average risk under a Stein loss function. If the minimum is not unique, we randomly select an \code{alpha} from the minimizers. \item \code{risk}: the minimum average risk attained. } } \description{ This function finds the value for \eqn{\alpha \in [0,1]} that empirically minimizes the average risk under a Stein loss function, which is given on page 1023 of Pang et al. (2009). } \references{ Pang, H., Tong, T., & Zhao, H. (2009). "Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data," Biometrics, 65, 4, 1021-1029. \url{http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2009.01200.x/abstract} }

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############################################################################ # ############################################################################ ## for the fullNamesTranslator fntFUN <- function(names, ...) { pattern <- "^(TCGA-[0-9]{2}-[0-9]{4})-([0-9]{2}[A-Z])[-]*(.*)"; gsub(pattern, "\\1,\\2,\\3", names); }; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Setting up fracB data sets # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!exists("fracBDsList", mode="list")) { verbose && enter(verbose, "Loading 'fracBDsList'"); pattern <- sprintf("|,%s.*", names(postProcessing)); pattern <- paste(pattern, collapse=""); pattern <- sprintf("^%s(%s)$", dataSet, pattern); fracBDsList <- loadAllDataSets(dataSet, chipType=chipType, pattern=pattern, type="fracB", rootPath=rootPath); rm(pattern); fracBDsList <- lapply(fracBDsList, setFullNamesTranslator, fntFUN); verbose && print(verbose, fracBDsList); verbose && enter(verbose, "Identifying normals"); dsN0 <- fracBDsList[[dataSet]]; types <- sapply(dsN0, function(df) getTags(df)[1]); keep <- grep("^1[01][A-Z]$", types); dsN <- extract(dsN0, keep); verbose && print(verbose, dsN); # Sanity check stopifnot(length(dsN) > 0); rm(dsN0, types); verbose && exit(verbose); ## Keep only tumors verbose && enter(verbose, "Keeping only tumors"); fracBDsList <- lapply(fracBDsList, function(ds) { types <- sapply(ds, function(df) getTags(df)[1]); keep <- grep("^01[A-Z]$", types); ds <- extract(ds, keep); ds; }); verbose && print(verbose, fracBDsList); verbose && exit(verbose); # Set names verbose && enter(verbose, "Updating data set names"); names <- names(fracBDsList); names <- sapply(names, FUN=strsplit, split=",", fixed=TRUE); while(TRUE) { ns <- sapply(names, FUN=length); if (any(ns == 0)) break; first <- unname(sapply(names, FUN=function(x) x[1])); if (length(unique(first)) > 1) break; names <- lapply(names, FUN=function(x) x[-1]); } names <- sapply(names, FUN=paste, collapse=","); names[(names == "") | sapply(names, length)==0] <- "raw"; names(fracBDsList) <- names; rm(names, ns, first); verbose && exit(verbose); verbose && exit(verbose); } # Sanity check if (length(fracBDsList) == 0) { throw("No matching data sets found."); } # Filter out the data sets matching the method pattern if (!is.null(methodPattern)) { verbose && enter(verbose, "Filtering out data sets matching the method pattern"); verbose && cat(verbose, "Method pattern: ", methodPattern); verbose && cat(verbose, "Before:"); verbose && print(verbose, fracBDsList); dsList <- fracBDsList; verbose && print(verbose, names(dsList)); keep <- (regexpr(methodPattern, names(dsList)) != -1); verbose && print(verbose, keep); dsList <- dsList[keep]; # Sanity check if (length(dsList) == 0) { throw("No data sets remaining after name pattern filtering."); } fracBDsList <- dsList; rm(dsList); verbose && cat(verbose, "After:"); verbose && print(verbose, fracBDsList); verbose && exit(verbose); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Setting up total CN data sets # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!exists("cnDsList", mode="list")) { dsList <- loadAllDataSets(dataSet, chipType=chipType, pattern=dataSet, type="total", rootPath=rootPath, verbose=verbose); for (acs0 in dsList) { setFullNamesTranslator(acs0, fntFUN); types <- sapply(acs0, function(df) getTags(df)[1]); isTumor <- grep("^01[A-Z]$", types); isNormal <- grep("^1[01][A-Z]$", types); acsN <- extract(acs0, isNormal); acsT <- extract(acs0, isTumor); rm(acs0, types, isTumor, isNormal); exportTotalCnRatioSet(acsT, acsN, verbose=verbose); rm(acsN, acsT); } rm(dsList) pattern <- sprintf("^%s(|,%s)(|.lnk)$", dataSet, paste(postTags, collapse="|")) cnDsList <- loadAllDataSets(dataSet, chipType=chipType, pattern=pattern, type="total", rootPath="rawCnData", verbose=verbose); rm(pattern); } # Sanity check if (length(cnDsList) == 0) { throw("No matching data sets found."); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Setting up normal genotype data set # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!exists("gcDsList", mode="list")) { pattern <- sprintf("^%s,", dataSet) gcDsList <- loadAllDataSets(dataSet, chipType=chipType, pattern=pattern, type="genotypes", rootPath="callData", verbose=verbose); rm(pattern); # Keep only those that match the target chip type if (!is.null(targetChipType)) { keep <- sapply(gcDsList, function(ds) { (getChipType(ds) == targetChipType); }); gcDsList <- gcDsList[keep]; } gcDsList <- lapply(gcDsList, setFullNamesTranslator, fntFUN); ## Keep only normals gcDsList <- lapply(gcDsList, function(ds) { types <- sapply(ds, function(df) getTags(df)[1]); keep <- grep("^1[01][A-Z]$", types); ds <- extract(ds, keep); ds; }); # Set names by caller algorithm names <- names(gcDsList); names <- gsub(".*,", "", names); names(gcDsList) <- names; rm(names); # keep those who match genTags and update genTags accordingly m <- match(genTags, names(gcDsList)); if (sum(is.na(m))) { warning("No matching genotype data set found for tag: ", paste(genTags[is.na(m)], collapse=",")); } genTags <- genTags[!is.na(m)]; gcDsList <- gcDsList[genTags]; # Sanity check ns <- sapply(gcDsList, FUN=function(ds) { nbrOfUnits(getFile(ds,1)); }); stopifnot(length(unique(ns)) == 1); rm(m); } # Sanity check if (length(gcDsList) == 0) { throw("No matching data sets found."); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. Setting up normal genotype call confidence scores data set # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (confQuantile < 1) { if (!exists("gcsDsList", mode="list")) { pattern <- sprintf("^%s,", dataSet) gcsDsList <- loadAllDataSets(dataSet, chipType=chipType, pattern=pattern, type="confidenceScores", rootPath="callData", verbose=verbose); rm(pattern); # Keep only those that match the target chip type if (!is.null(targetChipType)) { keep <- sapply(gcsDsList, function(ds) { (getChipType(ds) == targetChipType); }); gcsDsList <- gcsDsList[keep]; } gcsDsList <- lapply(gcsDsList, setFullNamesTranslator, fntFUN); ## Keep only normals gcsDsList <- lapply(gcsDsList, function(ds) { types <- sapply(ds, function(df) getTags(df)[1]); keep <- grep("^1[01][A-Z]$", types); ds <- extract(ds, keep); ds; }); # Set names by caller algorithm names <- names(gcsDsList); names <- gsub(".*,", "", names); names(gcsDsList) <- names; rm(names); # keep those who match genTags m <- match(genTags, names(gcsDsList)); gcsDsList <- gcsDsList[genTags[!is.na(m)]]; if (sum(is.na(m))) { warning("No matching genotype confidence score data set found for tag: ", paste(genTags[is.na(m)], collapse=",")); } rm(m); } # Drop non existing confidence scores gcsDsList <- gcsDsList[sapply(gcsDsList, FUN=length) > 0]; # Sanity check if (length(gcsDsList) == 0) { throw("Stratification on genotype confidence scores is requests but there are no confidence score files available: ", confQuantile); } # Drop incomplete cases if (length(gcsDsList) > 0) { keep <- intersect(names(gcDsList), names(gcsDsList)); gcDsList <- gcDsList[keep]; gcsDsList <- gcsDsList[keep]; rm(keep); # Sanity check if (length(gcDsList) == 0) { throw("After matching genotypes with available confidence scores, there is no data."); } # Sanity check ns <- sapply(gcsDsList, FUN=function(ds) { nbrOfUnits(getFile(ds,1)); }); stopifnot(length(unique(ns)) == 1); } } else { # Empty dummy gcsDsList <- list(); } # if (confQuantile < 1) genTags <- names(gcDsList); names <- names(fracBDsList); names <- strsplit(names, split=",", fixed=TRUE); keep <- sapply(names, FUN=function(tags) { is.element("raw", tags) || is.element("CalMaTe", tags) || any(is.element(genTags, tags)) }); fracBDsList <- fracBDsList[keep]; rm(keep); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Sanity checks # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Assert that only one sample is studied regionsList <- lapply(regions, FUN=parseRegion); sampleNames <- sapply(regionsList, FUN=function(x) x$name); sampleName <- sampleNames[1]; stopifnot(all(sampleNames == sampleName)); rm(regions, sampleNames); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Drop all samples but the one of interest # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - cnDsList <- lapply(cnDsList, FUN=function(ds) { ds <- extract(ds, indexOf(ds, sampleName)); }); print(cnDsList); fracBDsList <- lapply(fracBDsList, FUN=function(ds) { ds <- extract(ds, indexOf(ds, sampleName)); }); print(fracBDsList); gcDsList <- lapply(gcDsList, FUN=function(ds) { ds <- extract(ds, indexOf(ds, sampleName)); }); print(gcDsList); # Infer the tumor and normal type df <- getFile(cnDsList[[1]], 1); tags <- getTags(df); tags <- grep("^[0-9]{2}[A-Z]$", tags, value=TRUE); tags <- sort(tags); tumorType <- tags[1]; normalType <- tags[2]; rm(df, tags); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Document path # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ds <- cnDsList[[1]]; platform <- getPlatform(ds); chipType <- getChipType(ds, fullname=FALSE); chipTypeEsc <- gsub("_", "\\_", chipType, fixed=TRUE); rm(ds); docTags <- c(platform, chipType, docTags); if (confQuantile < 1) { docTags <- c(docTags, confQuantileTag); } docTags <- paste(docTags, collapse=","); docPath <- sprintf("doc,%s", docTags); docPath <- Arguments$getWritablePath(docPath); docName <- sprintf("BengtssonH_2009c-SupplementaryNotes,%s", docTags); pdfName <- sprintf("%s.pdf", docName); pdfPathname <- filePath(docPath, pdfName); figPath <- file.path(docPath, "figures", "col"); setOption("devEval/args/path", figPath); figForce <- 3; figDev <- function(..., force=(figForce > 0)) { epsDev(..., path=figPath, force=force) } figDev <- function(..., force=(figForce > 0)) { pngDev(..., device=png, path=figPath, force=force) } docTags <- strsplit(docTags, split=",", fixed=TRUE)[[1]]; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # CLEAN UP # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rm(fntFUN); ############################################################################ # HISTORY: # 2012-02-24 [PN] # o Now able to load several TCN data sets # 2012-02-19 # o Now the data set name pattern for loading fracB data is generated # from the 'postProcessing' strings. # 2011-03-18 # o Prepared to utilize new devEval(). # 2009-12-06 # o Updated to also handle BeadStudio genotype calls. # 2009-12-04 # o Now only one sample per data set is kept. # 2009-06-18 # o Now use 'loadAllDataSets' to retrieve genotype calls and confidence scores. # 2009-06-18 # o Now use 'loadAllDataSets' to retrieve total copy number data. # 2009-06-13 # o More cleanups. # 2009-06-09 # o Genotype calls now assumed to be stored in AromaUnitGenotypeCallFile:s. # 2009-06-08 # o Created from aroma.aroma.cn.eval/inst/vignettes/GSE13372,2CHCC1143,fracB. ############################################################################

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subset_proteins.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/TwoPart_MultiMS.R \name{subset_proteins} \alias{subset_proteins} \title{Subset proteins} \usage{ subset_proteins(mm_list, prot.info, prot_col_name) } \arguments{ \item{mm_list}{list of matrices for each experiment, length = number of datasets to compare internal dataset dimentions: numpeptides x numsamples for each dataset} \item{prot.info}{list of protein and peptide mapping for each matrix in mm_list, in same order as mm_list} \item{prot_col_name}{column name in prot.info that contains protein identifiers that link all datasets together. Not that Protein IDs will differ across different organizms and cannot be used as the linking identifier. Function match_linker_ids() produces numeric identifyers that link all datasets together} } \value{ data frame with the following columns \describe{ \item{sub_mm_list}{list of dataframes of intensities for each of the datasets passed in with proteins present in all datasets} \item{sub_prot.info}{list of dataframes of metadata for each of the datasets passed in with proteins present in all datasets. Same order as sub_mm_list} \item{sub_unique_mm_list}{list of dataframes of intensities not found in all datasets} \item{sub_unique_prot.info}{ist of dataframes of metadata not found in all datasets} \item{common_list}{list of protein IDs commnon to all datasets} } } \description{ Subset proteins into ones common to all datasets passed into the function and unique to each dataset. Note: for 3+ datasets no intermediate combinations of proteins are returned, only proteins common to all datasets, the rest are returned as unique to each dataset. } \examples{ # Load mouse dataset data(mm_peptides) head(mm_peptides) # different from parameter names as R uses # outer name spaces if variable is undefined intsCols = 8:13 metaCols = 1:7 # reusing this variable m_logInts = make_intencities(mm_peptides, intsCols) # will reuse the name m_prot.info = make_meta(mm_peptides, metaCols) m_logInts = convert_log2(m_logInts) grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG')) set.seed(173) mm_m_ints_eig1 = eig_norm1(m=m_logInts,treatment=grps,prot.info=m_prot.info) mm_m_ints_eig1$h.c # check the number of bias trends detected mm_m_ints_norm = eig_norm2(rv=mm_m_ints_eig1) mm_prot.info = mm_m_ints_norm$normalized[,1:7] mm_norm_m = mm_m_ints_norm$normalized[,8:13] set.seed(131) imp_mm = MBimpute(mm_norm_m, grps, prot.info=mm_prot.info, pr_ppos=2, my.pi=0.05, compute_pi=FALSE) # Load human dataset data(hs_peptides) head(hs_peptides) intsCols = 8:13 metaCols = 1:7 # reusing this variable m_logInts = make_intencities(hs_peptides, intsCols) # will reuse the name m_prot.info = make_meta(hs_peptides, metaCols) m_logInts = convert_log2(m_logInts) grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG')) hs_m_ints_eig1 = eig_norm1(m=m_logInts,treatment=grps,prot.info=m_prot.info) hs_m_ints_eig1$h.c # check the number of bias trends detected hs_m_ints_norm = eig_norm2(rv=hs_m_ints_eig1) hs_prot.info = hs_m_ints_norm$normalized[,1:7] hs_norm_m = hs_m_ints_norm$normalized[,8:13] set.seed(131) imp_hs = MBimpute(hs_norm_m, grps, prot.info=hs_prot.info, pr_ppos=2, my.pi=0.05, compute_pi=FALSE) # Multi-Matrix Model-based differential expression analysis # Set up needed variables mms = list() treats = list() protinfos = list() mms[[1]] = imp_mm$y_imputed mms[[2]] = imp_hs$y_imputed treats[[1]] = grps treats[[2]] = grps protinfos[[1]] = imp_mm$imp_prot.info protinfos[[2]] = imp_hs$imp_prot.info subset_data = subset_proteins(mm_list=mms, prot.info=protinfos, 'MatchedID') mms_mm_dd = subset_data$sub_unique_mm_list[[1]] protinfos_mm_dd = subset_data$sub_unique_prot.info[[1]] # DIfferential expression analysis for mouse specific protiens DE_mCG_CG_mm_dd = peptideLevel_DE(mms_mm_dd, grps, prot.info=protinfos_mm_dd, pr_ppos=2) }

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/tests/testthat/test.utilities.R

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livioivil/resamplingMCP

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refs/heads/master

2021-01-18T11:28:53.636162

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test.utilities.R

context("Utilities") expect_aboutequal <- function(object,expected,digits=4,...){ ro <- round(object,digits) re <- round(expected,digits) expect_equivalent(ro,re,...) } ## test mean diff n <- 30 m <- 100 x <- rnorm(2*n) X <- matrix(rnorm(2*n*m),nc=m) largeX <- matrix(rnorm(2*n*10^5),nc=10^5) g <- rep(c(0,1),each=n) g2 <- rep(0:2,each=2*n/3) test_that("Test of meandiff function", { expect_equivalent(meandiff(x,g),mean(x[(n+1):(2*n)])-mean(x[1:n])) expect_equivalent(meandiff(X,g),colMeans(X[(n+1):(2*n),])-colMeans(X[1:n,])) }) test_that("Test of pooled variances function", { n1 <- sum(g>0) n2 <- sum(g<=0) pv <- function(x,g)(var(x[g>0])*(n1-1)+var(x[g<=0])*(n2-1))/(n1+n2-2) expect_equivalent(pooled_variance(x,g),pv(x,g)) expect_equivalent(pooled_variance(X,g),apply(X,2,function(x) pv(x,g))) x <- rnorm(4) G <- omega(rep(0:1,each=2)) X <- apply(G,2,function(i) c(x[i>0],x[i<=0])) expect_equivalent(pooled_variance(X,G[,1]),pooled_variance(x,G)) expect_equivalent(sumdiff(X,G[,1]),sumdiff(x,G)) expect_equivalent(meandiff(X,G[,1]),meandiff(x,G)) expect_equivalent(zstat(X,G[,1]),zstat(x,G)) expect_equivalent(tstat(X,G[,1]),tstat(x,G)) }) test_that("Test of tstat function", { expect_equivalent(tstat(x,g),-t.test(x~g,var.equal=T)$statistic) expect_equivalent(tstat(X,g),apply(X,2,function(x) -t.test(x~g,var.equal=T)$statistic)) }) test_that("Test of zstat function", { expect_equivalent(zstat(x,g),sqrt(n/2)*(mean(x[(n+1):(2*n)])-mean(x[1:n]))) expect_equivalent(zstat(X,g),sqrt(n/2)*(colMeans(X[(n+1):(2*n),])-colMeans(X[1:n,]))) expect_aboutequal(sd(zstat(largeX,g)),1,2) expect_aboutequal(mean(zstat(largeX,g)),0,2) }) test_that("Test pairwise stuff", { expect_equivalent(pw_meandiff(x,g2),c(mean(x[g2==1])-mean(x[g2==0]),mean(x[g2==2])-mean(x[g2==0]))) expect_equivalent(pw_meandiff(X,g2),cbind(colMeans(X[g2==1,])-colMeans(X[g2==0,]),colMeans(X[g2==2,])-colMeans(X[g2==0,]))) expect_equivalent(pw_zstat(x,g2),c(sqrt(n/3)*(mean(x[g2==1])-mean(x[g2==0])),sqrt(n/3)*(mean(x[g2==2])-mean(x[g2==0])))) expect_equivalent(pw_zstat(X,g2),sqrt(n/3)*cbind(colMeans(X[g2==1,])-colMeans(X[g2==0,]),colMeans(X[g2==2,])-colMeans(X[g2==0,]))) expect_aboutequal(pw_meandiff(x,g2)*n*2/3,flip(x,make_pw_contrasts(g2),statTest='sum')@res$Stat) expect_aboutequal(as.numeric(t(pw_meandiff(X,g2)*n*2/3)),flip(X,make_pw_contrasts(g2),statTest='sum')@res$Stat) })

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/cachematrix.R

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aaditya22/ProgrammingAssignment2

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refs/heads/master

2020-05-28T04:14:58.695004

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cachematrix.R

#The first function, makeCacheMatrix creates a special "matrix", which is really a list containing a function to #1set the value of the matrix #2get the value of the matrix #3set the value of the inverse #4get the value of the inverse makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y){ x <<- y inv <<- NULL } get <- function() x setInv <- function(solve) inv <<- solve getInv <- function() inv list(set = set, get = get, setInv = setInv, getInv = getInv) } #The following function calculates the inverse of the special "matrix" created with the above function. #However, it first checks to see if the inverse has already been calculated. #If so, it gets the mean from the cache and skips the computation. #Otherwise, it calculates the inverse of the data and sets the value of the inverse in the cache via the setinv function. cacheSolve <- function(x, ...) { inv <- x$getInv() if(!is.null(inv)){ message("getting cached data") return(inv) } data <- x$get() inv <- solve(data) x$setInv(inv) inv }

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/Visualization.R

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Franca97/Web_Data_and-_Digital_Analytics

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refs/heads/master

2022-07-22T15:49:31.500990

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Visualization.R

remove(list = ls()) library("readxl") library("writexl") library(assertthat) library(dplyr) library(purrr) library(igraph) library(ggplot2) library(ggraph) library(ggmap) library(maps) getwd() Loc <- read_excel("countries_Location.xlsx") Loc <- data.frame(Loc) #Changed the excel file into a data frame colnames(Loc) <- c("from","to", "latfrom","longfrom","latto","longto") edges <- Loc[,c(1,2)] nodes <- Loc[,c(1,3,4)] nodes2 <- Loc[,c(2,5,6)] colnames(nodes2) <- c("from", "latfrom","longfrom") nodes <- bind_rows(nodes,nodes2) nodes <- unique(nodes) nodes$ID <- seq.int(nrow(nodes)) nodes <- nodes[,c(4,3,2,1)] colnames(nodes) <- c("id", "lon","lat","name") edges$fromid <- nodes$id[match(edges$from,nodes$name)] edges$toid <- nodes$id[match(edges$to,nodes$name)] edgesname <- edges edges <- edges[,c(3,4)] colnames(edges) <- c("from", "to") edges$weight <- 1 edges <- edges %>% group_by(from, to) %>% summarise(weight = sum(weight)) g <- graph_from_data_frame(edges, directed = TRUE, vertices = nodes) edges_for_plot <- edges %>% inner_join(nodes %>% select(id, lon, lat), by = c('from' = 'id')) %>% rename(x = lon, y = lat) %>% inner_join(nodes %>% select(id, lon, lat), by = c('to' = 'id')) %>% rename(xend = lon, yend = lat) assert_that(nrow(edges_for_plot) == nrow(edges)) important_edges <- edges_for_plot %>% filter(weight > 5) nodes$weight = degree(g) maptheme <- theme(panel.grid = element_blank()) + theme(axis.text = element_blank()) + theme(axis.ticks = element_blank()) + theme(axis.title = element_blank()) + theme(legend.position = "bottom") + theme(panel.grid = element_blank()) + theme(panel.background = element_rect(fill = "#596673")) + theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm')) country_shapes <- geom_polygon(aes(x = long, y = lat, group = group), data = map_data('world'), fill = "#CECECE", color = "#515151", size = 0.15) mapcoords <- coord_fixed(xlim = c(-150, 180), ylim = c(-55, 80)) #Plot with only countries size based on degree jpeg("DegreeCentrality.jpg", width = 1500) ggplot(nodes) + country_shapes + geom_point(aes(x = as.numeric(lon), y = as.numeric(lat), size = weight), # draw nodes shape = 21, fill = 'red', color = 'black', stroke = 0.5) + scale_size_continuous(guide = FALSE, range = c(1, 8)) + # scale for node size geom_text(aes(x = as.numeric(lon), y = as.numeric(lat), label = name), # draw text labels hjust = 0, nudge_x = 1, nudge_y = 4, size = 1.5, color = "black", fontface = "bold") + mapcoords + maptheme dev.off() #Plot with most important connections (>5) jpeg("MainConnections.jpg", width = 1500) ggplot(nodes) + country_shapes + geom_curve(aes(x = as.numeric(x), y = as.numeric(y), xend = as.numeric(xend), yend = as.numeric(yend), size = weight), # draw edges as arcs data = important_edges, curvature = 0.33, alpha = 0.5) + scale_size_continuous(guide = FALSE, range = c(0.25, 2)) + # scale for edge widths geom_point(aes(x = as.numeric(lon), y = as.numeric(lat)), # draw nodes shape = 21, fill = 'white', color = 'black', stroke = 0.5) + mapcoords + maptheme dev.off()

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/man/expmFrechet.Rd

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cran/expm

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refs/heads/master

2023-01-28T14:42:11.533892

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expmFrechet.Rd

\name{expmFrechet} \title{Frechet Derivative of the Matrix Exponential} \alias{expmFrechet} \encoding{UTF-8} \description{ Compute the Frechet (actually \sQuote{Fréchet}) derivative of the matrix exponential operator. } \usage{ expmFrechet(A, E, method = c("SPS", "blockEnlarge"), expm = TRUE) } \arguments{ \item{A}{square matrix (\eqn{n \times n}{n x n}).} \item{E}{the \dQuote{small Error} matrix, used in \eqn{L(A,E) = f(A + E, A)}}%% FIXME \item{method}{string specifying the method / algorithm; the default \code{"SPS"} is \dQuote{Scaling + Pade + Squaring} as in the algorithm 6.4 below; otherwise see the \sQuote{Details} section.} \item{expm}{logical indicating if the matrix exponential itself, which is computed anyway, should be returned as well.} } \details{ Calculation of \eqn{e^A} and the Exponential Frechet-Derivative \eqn{L(A,E)}. When \code{method = "SPS"} (by default), the with the Scaling - Padé - Squaring Method is used, in an R-Implementation of Al-Mohy and Higham (2009)'s Algorithm 6.4. \describe{ \item{Step 1:}{Scaling (of A and E)} \item{Step 2:}{Padé-Approximation of \eqn{e^A} and \eqn{L(A,E)}} \item{Step 3:}{Squaring (reversing step 1)} } \code{method = "blockEnlarge"} uses the matrix identity of %% FIXME use nice LaTeX \deqn{f(\left{ .... \right} ) } \deqn{f([A E ; 0 A ]) = [f(A) Df(A); 0 f(A)]} for the \eqn{2n \times 2n}{(2n) x (2n)} block matrices where \eqn{f(A) := expm(A)} and \eqn{Df(A) := L(A,E)}. Note that \code{"blockEnlarge"} is much simpler to implement but slower (CPU time is doubled for \eqn{n = 100}). } \value{ a list with components \item{expm}{if \code{expm} is true, the matrix exponential (\eqn{n \times n}{n x n} matrix).} \item{Lexpm}{the Exponential-Frechet-Derivative \eqn{L(A,E)}, a matrix of the same dimension.} } \references{see \code{\link{expmCond}}.} \author{Michael Stadelmann (final polish by Martin Maechler).} \seealso{ \code{\link{expm.Higham08}} for the matrix exponential. \code{\link{expmCond}} for exponential condition number computations which are based on \code{expmFrechet}. } \examples{ (A <- cbind(1, 2:3, 5:8, c(9,1,5,3))) E <- matrix(1e-3, 4,4) (L.AE <- expmFrechet(A, E)) all.equal(L.AE, expmFrechet(A, E, "block"), tolerance = 1e-14) ## TRUE } \keyword{algebra} \keyword{math}

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/R/extract_poc_token.R

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cssat/oliveRconnect

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refs/heads/master

2020-04-16T12:24:06.364665

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extract_poc_token.R

#' Extract token from Oliver sign-in GET response #' #' @param response A response from a GET to one of the oliver Sign-in Domains containing a "poc.t" cookie. #' #' @return #' @export #' #' @examples extract_poc_token <- function(response) { dat_cookies <- httr::cookies(response) dat_cookies[dat_cookies$name == "poc.t","value"] }

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/utilities.R

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NIH-IRP-SingleCell/SC-UsersGroup

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refs/heads/master

2023-08-04T11:27:50.907025

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utilities.R

# Load required biconductor and CRAN packages require(BiocGenerics) # Enable only semi-serious parallel processing - only max of 2 cores suppressPackageStartupMessages(require("BiocParallel")) n.cores <- min(2, parallel::detectCores()) BiocParallel::register(BiocParallel::MulticoreParam(n.cores)) # Enable keeping only 1GB blocks of input in RAM - rest randomly accessible from file system suppressPackageStartupMessages(require("DelayedArray")) DelayedArray:::set_verbose_block_processing(TRUE) options(DelayedArray.auto.block.size = 1e9) # Load some other libraries required by monocle suppressPackageStartupMessages(require("dplyr")) suppressPackageStartupMessages(require("garnett")) suppressPackageStartupMessages(require("ggplot2")) suppressPackageStartupMessages(require("ggrastr")) suppressPackageStartupMessages(require("igraph")) # Load monocle suppressPackageStartupMessages(require("Matrix")) suppressPackageStartupMessages(require("monocle")) # Load dB's required for mouse annotation suppressPackageStartupMessages(require("org.Mm.eg.db")) org_db <- org.Mm.eg.db # Load reticulate suppressPackageStartupMessages(require("reticulate")) # Load scran (for MNN) suppressPackageStartupMessages(require("scran")) # Load scMCA suppressPackageStartupMessages(require(scMCA)) # Load Seurat suppressPackageStartupMessages(require("Seurat")) # Load sva (for batch correction via combat) suppressPackageStartupMessages(require("sva")) suppressPackageStartupMessages(require("VGAM")) # Load required python modules py_scrublet <- reticulate::import("louvain") # Define some convenience functions #################### # acquire_raw_data # #################### # Reads in all count matrices (assumed to be in compressed .gz or .zip) from a specified directory # into a list of Serurat or CellDataSet objects (as specified by the user) acquire_raw_data <- function(base.data.dir, data.format = c("10X", "txt"), object.type = c("Seurat", "monocle"), selection.criteria = character(0)) # Example of valid selection criteria (if vector of criteria is specified, then evaluates the logical "AND") # # selection.criteria = c("species == \"mouse\"") { # Argument validation data.format <- match.arg(data.format) object.type <- match.arg(object.type) if(grepl("10X", data.format, ignore.case = TRUE) && length(selection.criteria)) { cat("[acquire_raw_data] Warning Subselection of 10X data not enabled in this version of scripts.") selection.criteria <- character(0) } # Determine whether metadata file exists if(file.exists(file.path(base.data.dir, "metadata.txt"))) { metadata <- read_metadata(metadata.file = file.path(base.data.dir, "metadata.txt"), selection.criteria = selection.criteria) print(metadata) } else { stop("[acquire_raw_data] Check that (required) metadata file exists and is placed in the same directory as data.") } # Ensure that the base data directory actually exists if(!dir.exists(base.data.dir)) { stop("[acquire_raw_data] base.data.dir", base.data.dir, "not found.") } # Determine the format of the data if(grepl("10X", data.format, ignore.case = TRUE)) { # Data is in 10X format - Get list of sub-directories containing the data data.files <- sapply(system(paste('ls -d ', file.path(base.data.dir, '/*/')), intern = TRUE), function(s){tail(strsplit(s, '[/]')[[1]], 1)}) } else { # Data is in canonical text format - Obtain the list of data files data.files <- metadata$sample.name } # Instantiate [monocle or Seurat] object list object_list <- list() for(file in data.files) { origin <- strsplit(file, "[.]")[[1]][1] cat("Obtaining data from", origin, " ... ") # Read in the data if(grepl("10X", data.format, ignore.case = TRUE)) { # Data is in HD5 format, we'll use Seurat function to read it data.dir <- file.path(base.data.dir, file, paste0('filtered_feature_bc_matrix')) if(!dir.exists(data.dir)) { compressed.data.dir <- file.path(base.data.dir, file, paste0(file, '_filtered_feature_bc_matrix.tar.gz')) if(file.exists(compressed.data.dir)) { # Uncompress and unarchive the count matrix - do this within the local directory cwd <- getwd() setwd(dir = file.path(base.data.dir, file)) system(paste0('tar -xvzf ', compressed.data.dir), wait = TRUE) setwd(dir = cwd) } } if(dir.exists(data.dir)) { counts <- Read10X(data.dir = data.dir) } else { stop("[acquire_raw_data] No filtered feature count matrix found for ", file) } } else if(grepl("txt", data.format, ignore.case = TRUE)) { # Data is in flat txt file format if(grepl(".zip$", file, ignore.case = TRUE)) { # Compression method is 'zip' rather than 'gzip', will need to convert file_gz <- gsub(".zip$", ".gz", file) # Convert to gz file and read in system(paste("unzip -c", file.path(base.data.dir, file), "| gzip > ", file_gz), wait = TRUE) dense.counts <- read.table(file = file.path(base.data.dir, file_gz), header = TRUE, sep = "", stringsAsFactors = FALSE) # Delete the temporary gz file system(paste("rm", file.path(base.data.dir, file_gz))) } else { dense.counts <- read.table(file = file.path(base.data.dir, file), header = TRUE, sep = "", stringsAsFactors = FALSE) } if(all(is.character(dense.counts[, 1]))) { genes <- dense.counts[, 1] dense.counts <- dense.counts[, -1] rownames(dense.counts) <- genes } counts <- Seurat::as.sparse(dense.counts) } else { stop("[acquire_raw_data] Unrecognized data format ", data.format) } # Fix up cell and gene names colnames(counts) <- gsub("_", "-", colnames(counts)); cells <- colnames(counts) rownames(counts) <- gsub("_", "-", rownames(counts)); genes <- rownames(counts) # Generate some phenotypic data # ... fraction of mitochondrial reads mito.genes <- grep(pattern = "^MT-", x = rownames(counts), ignore.case = TRUE, value = TRUE) fraction.mito <- Matrix::colSums(counts[mito.genes, ])/Matrix::colSums(counts) this.md <- data.frame(fraction.mito = fraction.mito) # Determine whether additional metadata is available if(!is.null(metadata)) { for(m in names(metadata)) { this.md <- data.frame(this.md, rep(metadata[file, m], ncol(counts))) } names(this.md) <- c("fraction.mito", names(metadata)) } # Convert into Seurat or monocle object if(grepl("seur", object.type, ignore.case = TRUE)) { object_list[[origin]] <- Seurat::CreateSeuratObject(counts = counts, meta.data = this.md) } else if(grepl("mono", object.type, ignore.case = TRUE)) { # ... Assemble feature data feature.data <- data.frame(gene_id = genes, gene_short_name = genes); rownames(feature.data) <- genes fd <- new("AnnotatedDataFrame", data = feature.data) # ... Assemble phenotypic data pd <- new("AnnotatedDataFrame", data = this.md) # ... Instantiate the CellDataSet object_list[[origin]] <- monocle::newCellDataSet(cellData = counts, phenoData = pd, featureData = fd, expressionFamily = VGAM::negbinomial.size()) } else { stop("[acquire_raw_mca_data] Profuse apologies - I don't yet know how to instantiate objects of type", object.type) } cat("Done.\n") } invisible(object_list) } ################### # basic.filtering # ################### basic.filtering <- function(sol, feature.sd = 2.5, max.fraction.mito = 0.05, percent.cells = 1) { for(n in names(sol)) { cat("Filtering ", n, " ... ") so <- sol[[n]] # Get total number of cells num_cells <- ncol(so) # Filter features min_cells <- percent.cells*num_cells/100 num_cells <- rowSums(GetAssayData(so) > 0) so <- so[num_cells > min_cells, ] # Filter percent mitochondria so <- so[, so@meta.data$fraction.mito < max.fraction.mito] # Filter cells num_features <- colSums(GetAssayData(so)) min_features <- quantile(num_features, 1 - pnorm(feature.sd)) so <- so[, num_features > min_features] sol[[n]] <- so cat("Done.\n") } invisible(sol) } ################ # calculate_it # ################ calculate_it <- function(force.recalculation, RData.file) { calc_it <- force.recalculation || !file.exists(RData.file) if(!calc_it) { cat("Loading", RData.file, " ... ") load(file = RData.file) cat("Done.\n") } return(calc_it) } ############################# # dimensionally_reduce_data # ############################# dimensionally_reduce_data <- function(so, data_type = c("Merged", "Integrated"), dim.reduc = 30L, k = 1.0, md = 0.001, n.iter = 1, prune.SNN = 1/15, res = 0.5, output.dir = "~/Downloads", plot_title = "") { # Sanity checks on anrguments data_type <- match.arg(data_type) if(data_type == "Integrated") { # Scale Data scaled_data <- Seurat::ScaleData(object = so, vars.to.regress = c("nUMI", "fraction.mito")) } else { # Normalize the data between the cells normalized_data <- Seurat::NormalizeData(object = so) # Find variable features normalized_data <- Seurat::FindVariableFeatures(object = normalized_data, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 8, y.low.cutoff = 0.5, y.high.cutoff = 8) # Scale Data scaled_data <- Seurat::ScaleData(object = normalized_data, vars.to.regress = c("nUMI", "fraction.mito")) } # Perform PCA scaled_data <- Seurat::RunPCA(object = scaled_data, npcs = dim.reduc, verbose = TRUE, seed.use = 123) # Dimensionally reduce via UMAP scaled_data <- Seurat::RunUMAP(object = scaled_data, min.dist = md) # Cluster the data nn <- 10*k*round(sqrt(ncol(scaled_data))/10) scaled_data <- Seurat::FindNeighbors(object = scaled_data, dims = 1:dim.reduc, k.param = nn) scaled_data <- Seurat::FindClusters(object = scaled_data, resolution = res, n.iter = n.iter) # Plot dimensional reduction grouped by "cluster ID" p1_umap <- Seurat::DimPlot(scaled_data, reduction = "umap", group.by = "ident") # Plot dimensional reduction grouped by "sample.name" p2_umap <- Seurat::DimPlot(scaled_data, reduction = "umap", group.by = "clean.name") # Plot dimensional reduction grouped by "treatment" p3_umap <- Seurat::DimPlot(scaled_data, reduction = "umap", group.by = "selection") # PDF plot_title <- paste0(plot_title, " - ", data_type, " UMAP -- md = ", md, " nn = ", nn, " res = ", res, ".pdf") pdf(file = file.path(output.dir, plot_title)) plot(p1_umap) plot(p2_umap) plot(p3_umap) dev.off() invisible(scaled_data) } ###################### # estimate_root_node # ###################### estimate_root_node <- function(cds, cell_phenotype, root_type) { # Note this function is lifted from Trapnell's tutorial. One very interesting fact to keep in mind: # # pr_graph_cell_proj_closest_vertex is just a matrix with a single column that stores for each cell, # the ID of the principal graph node it's closest to. # # This is handy for computing statistics (e.g. with dplyr) about the principal graph nodes and which # cells of what type map to them. cell_ids <- which(pData(cds)[, cell_phenotype] == root_type) closest_vertex <- cds@auxOrderingData[[cds@rge_method]]$pr_graph_cell_proj_closest_vertex closest_vertex <- as.matrix(closest_vertex[colnames(cds), ]) root_pr_nodes <- V(cds@minSpanningTree)$name[as.numeric(names (which.max(table(closest_vertex[cell_ids,]))))] root_pr_nodes } ############# # merge_sol # ############# # Merges a list of Seurat objects into a single Seurat object merge_sol <- function(seurat_object_list, project_name = "Mouse HSPCs") { for(n in names(seurat_object_list)) { cat("Processing library: ", n, " ... ") # Rename the cells seurat_object_list[[n]] <- Seurat::RenameCells(object = seurat_object_list[[n]], add.cell.id = n) if(n == names(seurat_object_list)[1]) { so_merged <- seurat_object_list[[1]] } else { so_merged <- merge(x = so_merged, y = seurat_object_list[[n]], project = project_name) } cat("Done.\n") } invisible(so_merged) } ################# # read_metadata # ################# read_metadata <- function(metadata.file, selection.criteria = character(0)) { metadata <- read.table(file = metadata.file, header = TRUE, sep = "\t", stringsAsFactors = TRUE) if(!any(grepl("sample.name", names(metadata), ignore.case = FALSE))) { stop("[read_metadata] Metadata is missing column \"sample.data\".") } metadata.cols <- names(metadata) clean.name <- gsub(".txt|.txt.gz|.txt.zip", "", metadata$sample.name) metadata <- data.frame(clean.name = clean.name, metadata) names(metadata) <- c("clean.name", metadata.cols) rownames(metadata) <- metadata$sample.name if(length(selection.criteria)) { metadata <- eval(parse(text = paste("subset(metadata, subset =", paste0(selection.criteria, collapse = " & "), ")"))) } invisible(metadata) } ############# # so_to_cds # ############# so_to_cds <- function(so) { # ... Assemble feature data genes <- rownames(so) feature.data <- data.frame(gene_id = genes, gene_short_name = genes); rownames(feature.data) <- genes fd <- new("AnnotatedDataFrame", data = feature.data) # ... Assemble phenotypic data pd <- new("AnnotatedDataFrame", data = so@meta.data) # ... Instantiate the CellDataSet cds <- monocle::newCellDataSet(cellData = GetAssayData(so), phenoData = pd, featureData = fd, expressionFamily = VGAM::negbinomial.size()) invisible(cds) }

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/Super-Computer-Analysis-With-R-Language/Super-Computer-Analysis-With-R-Language.R

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library(tidyverse) library(readxl) ham_veri <- read_excel("/Users/cenkatlig/Super_Bilgisayar.xlsx") %>% tbl_df() # Then making the raw data smoother # It is provided to write separately on each line (originally, systems would sell 2 series) ara_veri <- ham_veri %>% slice(seq(1,nrow(.),by=2)) %>% cbind(.,ham_veri %>% slice(seq(2,nrow(.),by=2)) %>% select(Country=Site,Company=System)) %>% mutate_at(vars(Cores:`Power (kW)`),funs(as.numeric(gsub(",","",.)))) %>% tbl_df() # ara_veri # ggplot(ara_veri, aes(x = Site, y = Country)) + geom_point() # country_list <- ara_veri %>% filter(Country == "United States" & Rank < 100 ) # ggplot(country_list, aes(x = Rank, y = Cores)) + geom_point() ggplot(ara_veri,aes(x=Country)) + geom_bar() + theme(axis.text.x = element_text(angle=90,hjust = 1)) company_list <- ara_veri %>% filter(Company == "IBM" & Rank < 100 ) ggplot(company_list, aes(x=Company)) + geom_bar() # + theme(axis.text.x = element_text(angle=90,hjust = 1)) # Number of top 100 systems from the USA country_list_100_US <- ara_veri %>% filter(Country == "United States" & Rank < 10 ) ggplot(country_list_100_US, aes(x = System)) + geom_bar(fill = "white", colour = "green") + theme(axis.text.x = element_text(angle=30, hjust = 1)) # theme_stata() # Number of systems by non-Chinese, Japanese, US companies Non_CN_JP_US <- ara_veri %>% filter(Country != "Japan" & Country != "United States" & Country != "China") ggplot(Non_CN_JP_US, aes(x=Company)) + geom_bar(fill = "white", colour = "red") + theme(axis.text.x = element_text(angle=45,hjust = 1)) + ggtitle("Number of systems by non-Chinese, Japanese, US companies") minimum_core = min(ara_veri$Cores) max_core = max(ara_veri$Cores) message("Minimum CORE (Core) NUMBER: ", minimum_core) ## Minimum CORE (Core) NUMBER: 9.792 message("Maximum CORE (Core) NUMBER: ", max_core) ##Maximum CORE (Core) NUMBER: 19860000 minimum_enerji = min(ara_veri$`Power (kW)`,na.rm=TRUE) max_enerji = max(ara_veri$`Power (kW)`,na.rm=TRUE) message("THE CONSUMPTION OF THE ENERGY CONSUMER SYSTEM (kW): ", minimum_enerji) ## THE CONSUMPTION OF THE ENERGY CONSUMER SYSTEM(kW): 1 message(" CONSUMPTION AMOUNT OF THE MOST ENERGY CONSUMING SYSTEM (kW): ", max_enerji) ## CONSUMPTION AMOUNT OF THE MOST ENERGY CONSUMING SYSTEM (kW): 997

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cquigley/rPerfFunc

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/performance.R \name{model_fun_no_rebal} \alias{model_fun_no_rebal} \title{identical to model_fun, except does not rebalance} \usage{ model_fun_no_rebal(vsn, model, namer = "ret", custom_rets = NULL) } \description{ identical to model_fun, except does not rebalance }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MetFragConfigR.R \name{MetFragConfig.CompToxCSV} \alias{MetFragConfig.CompToxCSV} \title{Create MetFrag Config Files with LocalCSV and Scoring Terms from CompTox MetFrag XLS Export} \usage{ MetFragConfig.CompToxCSV(mass, adduct_type, results_filename, peaklist_path, base_dir, CompToxLocalCSVterms, ...) } \arguments{ \item{mass}{The mass with which to search the candidate database (\code{DB}). Use \code{neutralPrecursorMass} and \code{adduct_type} to set whether this is monoisotopic mass or an adduct species. Defaults to \code{adduct_type}.} \item{adduct_type}{The adduct species used to define mass (if \code{neutralPrecursorMass=FALSE}) and fragmentation settings in the config file, entered as either \code{PrecursorIonType} (text) or \code{PrecursorIonmode} (a number). The available options are given in the system file \code{MetFragAdductTypes.csv} in the \code{extdata} folder. Recommended default values (if ion state is unclear) are \code{[M+H]+} (1) for positive and \code{[M-H]-} (-1) for negative mode.} \item{results_filename}{Enter a base filename for naming the results files - do not include file endings} \item{peaklist_path}{Enter the full path and file name to the peak list for this config file} \item{base_dir}{Enter the directory name to set up the subfolders for MetFrag batch results. If the folders don't exist, subfolders \code{config}, \code{log} and \code{results} are created; the output of this function is saved in \code{config}.} \item{CompToxLocalCSVterms}{The output of \code{\link{CompToxXLStoLocalCSVterms}}, \code{\link{CompToxCSVtoLocalCSVterms}} or \code{\link{CompToxFullCSVtoLocalCSVterms}}, used to set \code{DB, localDB_path, UDS_Category and UDS_Weights} in \code{\link{MetFragConfig}}} } \value{ Returns a MetFrag config file name } \description{ This is a CompTox XLS or CSV specific wrapper function for \code{\link{MetFragConfig}}. } \details{ Remaining parameters are described in \code{\link{MetFragConfig}} } \examples{ # Example from DOI: 10.1021/acs.est.7b01908 # Note that this scores automatically with all metadata fields in the example file, which # is not necessarily ideal as not all predicted values are relevant for ranking the best candidate. CompToxXLS <- system.file("extdata","CompToxBatchSearch_MetFrag_MSready_C10H14N2.xls",package="ReSOLUTION") LocalCSVterms <- CompToxXLStoLocalCSVterms(CompToxXLS) peaklist <- system.file("extdata","EQ300804_Nicotine_peaks.txt",package="ReSOLUTION") test_dir <- "C:/DATA/Workflow/MetFrag22/metfrag_test_results" config_file <- MetFragConfig.CompToxCSV(163.1230, "[M+H]+","Nicotine_PrecMass_MpHp_XLS",peaklist, test_dir, LocalCSVterms) metfrag_dir <- "C:/DATA/Workflow/MetFrag22/" MetFragCL_name <- "MetFrag2.4.4-msready-CL.jar" runMetFrag(config_file, metfrag_dir, MetFragCL_name) # Example of Simazine # Note that this uses a CSV file with fewer scoring terms that are more relevant for candidate selection. CompToxFullCSV_test <- system.file("extdata","dsstox_MS_Ready_MetFragTestCSV5.csv",package="ReSOLUTION") LocalCSVterms <- CompToxFullCSVtoLocalCSVterms(CompToxFullCSV_test) peaklist <- system.file("extdata","EA026206_Simazine_peaks.txt",package="ReSOLUTION") rt_file_path <- system.file("extdata","Eawag_rt_inchi.csv",package="ReSOLUTION") test_dir <- "C:/DATA/Workflow/MetFrag22/metfrag_test_results" MBrecord <- system.file("extdata","EA026206_Simazine.txt",package="ReSOLUTION") MBinfo <- getMBRecordInfo.MetFragConfig(MBrecord,peaklist, writePeaklist=FALSE) IsPosMode <- grepl(MBinfo$ion_mode,"POSITIVE") adduct_type <- MBinfo$prec_type config_file <- MetFragConfig.CompToxCSV(mass=MBinfo$exact_mass,adduct_type = adduct_type, results_filename = paste0("Simazine", "_byExactMass_5ppm"),peaklist_path = MBinfo$peaklist,neutralPrecursorMass=TRUE, base_dir = test_dir, CompToxLocalCSVterms = LocalCSVterms, IsPosMode = IsPosMode,rt_file_path = rt_file_path, rt_exp = MBinfo$ret_time,filter_by_InChIKey = FALSE) metfrag_dir <- "C:/DATA/Workflow/MetFrag22/" MetFragCL_name <- "MetFrag2.4.4-msready-CL.jar" runMetFrag(config_file, metfrag_dir, MetFragCL_name) # Example of diclofenac peaklist <- system.file("extdata","EA020161_Diclofenac_peaks.txt",package="ReSOLUTION") MBrecord <- system.file("extdata","EA020161_Diclofenac.txt",package="ReSOLUTION") MBinfo <- getMBRecordInfo.MetFragConfig(MBrecord,peaklist, writePeaklist=FALSE) IsPosMode <- grepl(MBinfo$ion_mode,"POSITIVE") adduct_type <- MBinfo$prec_type config_file <- MetFragConfig.CompToxCSV(mass=MBinfo$prec_mass,adduct_type = adduct_type, results_filename = paste0("Diclofenac", "_byPrecMass_5ppm"),peaklist_path = MBinfo$peaklist,neutralPrecursorMass=FALSE, base_dir = test_dir, CompToxLocalCSVterms = LocalCSVterms, IsPosMode = IsPosMode,rt_file_path = rt_file_path, rt_exp = MBinfo$ret_time,filter_by_InChIKey = FALSE) metfrag_dir <- "C:/DATA/Workflow/MetFrag22/" MetFragCL_name <- "MetFrag2.4.4-msready-CL.jar" runMetFrag(config_file, metfrag_dir, MetFragCL_name) } \seealso{ \code{\link{MetFragConfig}}, \code{\link{runMetFrag}}, \code{\link{CompToxXLStoLocalCSVterms}}, \code{\link{CompToxCSVtoLocalCSVterms}}, \code{\link{CompToxFullCSVtoLocalCSVterms}} } \author{ Emma Schymanski <emma.schymanski@uni.lu> in partnership with Christoph Ruttkies (MetFragCL), Antony J. Williams and team (CompTox Dashboard) }

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evaluate_cps.R

##################################### #####Analytical Validity Evaluations# ##################################### eval<-function(data){ ####quantities of interest ####mean income by gender ####mean age by gender ####returns to schooling ###descriptive statistics income<-cbind(as.numeric(by(data$income,data$sex,mean)), as.numeric(by(data$income,data$sex,var)/table(data$sex))) age<-cbind(as.numeric(by(data$age,data$sex,mean)), as.numeric(by(data$age,data$sex,var)/table(data$sex))) ###regression if(is.factor(data$educ)){ data$educ<-as.numeric(levels(data$educ))[data$educ] } reg1<-lm(log(income)~educ+race+as.factor(sex)+age+I(tax>0),data=data) res<-cbind(summary(reg1)$coef[,1],summary(reg1)$coef[,2]^2) results<-rbind(income,age,res) return(results) } ###load original and synthetic data ("syndata.RData") ###get estimates from original data org.res<-eval(cps) l.95.org<-org.res[,1]-1.96*sqrt(org.res[,2]) u.95.org<-org.res[,1]+1.96*sqrt(org.res[,2]) ci.length.org<-u.95.org-l.95.org ###get estimates from synthetic data m<-length(syn.data) syn.temp<-array(NA,dim=c(nrow(org.res),ncol(org.res),length(syn.data))) for (i in 1:length(syn.data)){ syn.temp[,,i]<-eval(syn.data[[i]]) } q.bar<-apply(syn.temp[,1,],1,mean) u.bar<-apply(syn.temp[,2,],1,mean) b.m<-apply(syn.temp[,1,],1,var) T.p<-u.bar+b.m/m ###compute the degrees of freedom dfs<-(m-1)*(1+u.bar/(b.m/m))^2 l.95.syn<-q.bar-qt(0.975,df=dfs)*sqrt(T.p) u.95.syn<-q.bar+qt(0.975,df=dfs)*sqrt(T.p) ci.length.syn<-u.95.syn-l.95.syn ########compare the results ##compute the overlap U.max.beta<-apply(cbind(u.95.syn,u.95.org),1,max) L.max.beta<-apply(cbind(l.95.syn,l.95.org),1,max) U.min.beta<-apply(cbind(u.95.syn,u.95.org),1,min) L.min.beta<-apply(cbind(l.95.syn,l.95.org),1,min) overlap.beta<-apply(cbind((U.max.beta-L.min.beta)-(U.max.beta-U.min.beta)- (L.max.beta-L.min.beta),0),1,max) J.k.beta<-1/2*(overlap.beta/ci.length.org+overlap.beta/ci.length.syn) beta.coef<-cbind(org.res[,1],q.bar,J.k.beta,ci.length.syn/ci.length.org) print(beta.coef)

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/R/telfer_func.r

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# Internal function that uses the implementation of Telfer written by Gary Powney GP_telfer <- function (taxa_data,time_periods,iterations=10,useIterations=TRUE,min_sq=5){ # Create a vector of all the years to include for (i in 1:length(row.names(time_periods))) { run<-time_periods[i,1]:time_periods[i,2] if (exists('all_years')){ all_years <- c(all_years,run) }else{ all_years <- run } } # Subset data to years needed taxa_data<-taxa_data[taxa_data$year %in% all_years,] # The year to split is taken to be the average of the max of earlier time period # and min of the later time period splityr <- mean(c(max(time_periods[1,]),min(time_periods[2,]))) # subset data to one object for each time perod T1 <- taxa_data[taxa_data$year<splityr,] T2 <- taxa_data[taxa_data$year>splityr,] # remove gridcells that are only found in one time period T1cells <- unique(T1$site) T2cells <- unique(T2$site) allGood <- T1cells[T1cells %in% T2cells] T1 <- T1[T1$site %in% allGood,] T2 <- T2[T2$site %in% allGood,] # Change to GP format (2 col, $species and $gcell) T1 <- unique(T1[c('CONCEPT','site')]) T2 <- unique(T2[c('CONCEPT','site')]) names(T1) <- c('species','gcell') names(T2) <- c('species','gcell') ### Identify the number of grid cells each species occupies in each time period. ### T1_species<-as.vector(unique(T1$species)) # creates species list for time period 1 rangesT1<-NULL # create the vector to be filled for (x in T1_species){ # for each species in time period 1 temp<-T1[T1$species==x,] # create a mini table for species x in time period 1 size<-length(temp[,1]) # identify the number of cells the species occupies (species range size) rangesT1<-c(rangesT1,size) # concatenate all species range sizes } T1_range<-data.frame(T1_species,rangesT1) # create a dataframe of species and range size for time period 1 ## Do the same for the second period, but using the species list for the first period this will put 0 in counts for species with no record in second period ## rangesT2<-NULL # create the vector to be filled for (x in T1_species){ # for each species in time period 1 temp<-T2[T2$species==x,] # create a mini table for species x in time period 2 size<-length(temp[,1]) # identify the number of cells the species occupies (species range size) rangesT2<-c(rangesT2,size) # concatenate all species range sizes } T2_range<-data.frame(T1_species,rangesT2) # create a dataframe of species and range size for time period 2 ### Remove species which have less than min_sq grid cells in first period ### T1_range_good<-T1_range[T1_range$ranges>=min_sq,] ### Link the two tables on species then identify the change in number of grid squares between time periods. ### names(T1_range_good)[1]<-"CONCEPT" # re-naming columns names(T2_range)[1]<-"CONCEPT" # re-naming columns spp_table<-merge(T1_range_good,T2_range) # merge T1 with T2 removing species with less than 5 gridcells in the first period. ### Identify the simple difference between the two time periods ### spp_table$range_change<-(spp_table$rangesT2-spp_table$rangesT1) # identify the difference between T1 and T2 ### convert the grid cell number to proportion of total number fo cells surveyed ### total.cells<-length(unique(T1$gcell)) # this is the total number of cells surveyed spp_table$T1_range_prop<-(spp_table$rangesT1+0.5)/(total.cells+1) # identify the proportion of number of cells in T1 of the total cells surveyed. To avoid the problems associated with 0 proportions they were calculated as (x+0.5)/(n+1). spp_table$T2_range_prop<-(spp_table$rangesT2+0.5)/(total.cells+1) # identify the proportion of number of cells in T2 of the total cells surveyed spp_table$T1_logit_range<-log(spp_table$T1_range_prop/(1-spp_table$T1_range_prop)) # logit transform the proportions spp_table$T2_logit_range<-log(spp_table$T2_range_prop/(1-spp_table$T2_range_prop)) # logit transform the proportions ### To account for non constant variance we must do the following steps to create a variable to weight the final regression ### row.names(spp_table) <- spp_table$CONCEPT #name rows helps make sense of the model output m1<-lm(T2_logit_range~T1_logit_range,data=spp_table) # linear regression of two time periods if(!is.logical(useIterations)){ stop('useIterations must be a logical variable') } else if(!useIterations){ spp_table$change_index<-rstandard(m1) final_output_table<-spp_table[,c(1,9)] return(final_output_table) } else if(useIterations){ residual_m1<-resid(m1) # take the residuals from the lm spp_table$sq_residual_m1<-residual_m1^2 # square the residuals spp_table$fitted_proportions<-m1$fitted # calculated the fitted proportions and add them into the dataframe P<-ilt(spp_table$fitted_proportions) # exponential function, shown above. spp_table$fitted_proportions<-P # overwrite fitted values to fitted proportions total.cell.1<-total.cells+1 # total grid cells surveyed +1 spp_table$NP_test<-1/(total.cell.1*spp_table$fitted_proportions*(1-spp_table$fitted_proportions)) # 1/[NP (1-P)] in telfer paper m2<-lm(sq_residual_m1~NP_test,data=spp_table) # second model which is the squared residuals of m1 on 1/[NP (1-P)] c_<-coef(m2)[1] # take the intercept of m2 d_<-coef(m2)[2] # take the slope of m2 V_<-NULL # prepare variance vector rep_loop<-1:(iterations-1) # repeat the process C_all<-c_ # prepare c vector D_all<-d_ # prepare d vector V_all<-V_ # prepare variance vector for (i in rep_loop){ # start loop # c_ + d_ should not be in brackets as they were in a previous version V_<- c_ + d_ / (total.cell.1*spp_table$fitted_proportions*(1-spp_table$fitted_proportions)) # work out the variance (modification of the binomial proportion variance structure) inv_sq_V<-1/(V_^2) # take the inverse of variance squared. m4<-lm(sq_residual_m1~NP_test,weight=inv_sq_V,data=spp_table) # run the new model weighting by the inverse variance identifeid above. c_<-coef(m4)[1] # take the intercept of the new model d_<-coef(m4)[2] # take the slope of the new model C_all<-c(C_all,c_) # build a vector with all of the new intercepts D_all<-c(D_all,d_) # build a vector with all of the new slopes V_all<-c(V_all,V_) # build a vector with all of the new inverse variances squared } ## final model to take the residuals from spp_table$recip_V<-1/V_ # take the reciprocal of the "settled" variance (Telfer et al 2002) m1<-lm(T2_logit_range~T1_logit_range,weight=recip_V,data=spp_table) # use the reciprocal of the "settled" variance as weight for the final model. spp_table$change_index<-rstandard(m1) # take the standardised residuals from the model. For each species, the standardised residual from the fitted regression line provides the index of relative change in range size. A species with a negative change index has been recorded in relatively fewer grid cells in the later period, whereas a species with a positive change index has been recorded in relatively more. final_output_table<-spp_table[,c(1,13)] } }

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hiphop <- readLines("~/Desktop/R/data/hiphop1.txt"); hiphop hiphop1 <- str_replace_all(hiphop, "\\W", " "); hiphop1 #단어가 아닌 모든 것 hiphop2 <- extractNoun(hiphop1); hiphop2 wordcount <- table(unlist(hiphop2)); wordcount df_word <- as.data.frame(wordcount, stringsAsFactors = FALSE); head(df_word) df_word1 <- rename(df_word, word = Var1, freq = Freq); head(df_word1) df_word2 <- filter(df_word1, nchar(word) >= 2); #2자 이상 단어 추출 top_20 <- df_word2 %>% arrange(desc(freq)) %>% head(20); top_20 pal <- brewer.pal(8,"Blues") [5:9] wordcloud(words = df_word2$word, freq = df_word2$freq, min.freq = 10, max.words = 100, random.order = FALSE, rot.per = .1, scale = c(4,0.3), colors = pal, family = "AppleGothic") dev.new() #새 창 띄우기 display.brewer.all() #워드 클라우드 색 조합 목록 보기 ### dplyr 패키지 함수 filter(mtcars, cyl == 4) head(arrange(mtcars, wt)) head(select(mtcars, am,gear)) head(mutate(mtcars, years = "1974")) distinct(mtcars, cyl) summarize(mtcars, cyl_mean = mean(cyl), cyl_min = min(cyl), cyl_max = max(cyl)) sample_n(mtcars, 10) group_by(mtcars, cyl) %>% summarize(n()) # 연습문제 p.248 middle_mid_exam <- read_xlsx("~/Desktop/R/data/middle_mid_exam.xlsx"); middle_mid_exam MATHEMATICS <- dcast group_by(middle_mid_exam, CLASS) %>% summarise(mean_eng = mean(ENGLISH), sum_eng = sum(ENGLISH), mean_math = mean(MATHEMATICS), sum_math = sum(MATHEMATICS)) middle_mid_exam %>% filter(CLASS == "class1" & MATHEMATICS >= 80) %>% summarise(n()) arrange(middle_mid_exam, desc(MATHEMATICS), ENGLISH) middle_mid_exam %>% filter(MATHEMATICS >= 80 & ENGLISH >= 85) %>% summarise(n()) ### ck <- read_xlsx("~/Desktop/R/data/치킨집_가공.xlsx") address <- substr(ck$소재지전체주소, 11,16); head(address) address_num <- gsub("[0-9]","",address) address_trim <- gsub(" ","", address_num); head(address_trim) address_count <- address_trim %>% table() %>% data.frame(); address_count # = data.frame(table(address_trim)) # treemap(데이터세트, index = 구분열, vSize = 분포열, vColor = 색상, title = 제목) treemap(address_count, index = ".", vSize = "Freq", title = "서대문구 동별 치킨집 분포", fontfamily.labels = "AppleGothic") hospital <- read_xlsx("~/Desktop/R/data/hospital.xlsx"); hospital gn_street <- substr(hospital$도로명전체주소, 11,16); head(gn_street) gn_street1 <- gsub("[0-9길 ]","",gn_street); gn_street1 gn_street_count <- data.frame(table(gn_street1)); gn_street_count treemap(gn_street_count, index = "gn_street1", vSize = "Freq", title = "Hospital numbers of Gangnam arrondissement by street", fontfamily.labels = "AppleGothic") hospital <- read_xlsx("~/Desktop/R/data/hospital.xlsx"); hospital gn_quartier <- substr(hospital$소재지전체주소, 11,14); head(gn_quartier) gn_quartier1 <- gsub(" ","", gn_quartier); head(gn_quartier1) gn_quartier_count <- gn_quartier1 %>% table() %>% data.frame(); gn_quartier_count treemap(gn_quartier_count, index = ".", vSize = "Freq", title = "Hospital numbers of Gangnam arrondissement by quartier", fontfamily.labels = "AppleGothic") data("GNI2014") ??GNI2014 ls(GNI2014) head(GNI2014) treemap(GNI2014, index = c("continent","iso3"), vSize = "population", title = "World population", bg.labels = "blue", fontfamily.labels = "AppleGothic") GNI2014_po <- head(arrange(GNI2014, desc(population)), 20); GNI2014_po treemap(GNI2014_po, index = c("continent", "iso3"), vSize = "population", title = "World population top 20 in 2014", bg.labels = "white", fontfamily.labels = "AppleGothic") GNI2014_per <- mutate(GNI2014, per_pop = GNI2014$GNI / GNI2014$population) %>% arrange(desc(per_pop)) %>% head(20); GNI2014_per treemap(GNI2014_per, index = c("country", "continent"), vSize = "per_pop", title = "GNI per capita in 2014", border.col = 'white', fontfamily.labels = "AppleGothic")

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#Function makes a scatter plot. Takes in 4 paramters: #The data to be made into a plot, the x variable, the y variable, the type of flower to be displayed build_scatter <- function(data, xvar = 'Petal.Length', yvar = 'Sepal.Length', flower = 'all') { #Filters down to match the selected flower. Only filters if all is not selected if(flower != 'all') { data <- data %>% filter(Species == flower) } data %>% #Makes the plotly scatterplot. Sets x and y values, size plot_ly(x = eval(parse(text = xvar)), y = eval(parse(text = yvar)), size = eval(parse(text = xvar)), mode = "markers", color = Species) %>% layout(xaxis = list(title = xvar), yaxis = list(title = yvar) ) %>% return() }

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library(Rcpp) sourceCpp("C:/Users/Jay/Desktop/CompRisk/R/cumh_ED.cpp") #sourceCpp("C:/Users/Jay/Desktop/CompRisk/R/cumh_IS.cpp") source("C:/Users/Jay/Desktop/CompRisk/R/Fit_Model.R") source("C:/Users/Jay/Desktop/CompRisk/R/Logliklihood.R") ### Raw data BCraw <- read.csv("C:/Users/jay/Desktop/New Era/July31-2017/July31-2017DM/df6.csv") ### Load data b1 <- read.csv("C:/Users/jay/Dropbox/Jay/New Era/CompetingRisk Logliklihood/Data/dfcomp_ScreenUpto5_March9.csv") # load data b1_NC <- read.csv("C:/Users/jay/Dropbox/Jay/New Era/CompetingRisk Logliklihood/dfbreast_removal_ScreenUpto3_Jan16.csv") ### Fit model Competing Risk brca1.1frailty6 <- FitModelEM( data = b1, #breast (ovarian, death) ~ mgene (TID) + screen (TD) + oophorectomy (TD) init.Parms = list( cause1 = list(base = c(0.008,2.581), time_indep=c(1.960), time_dep=list(main=c(2,-2,1,-1) , interaction=c() )), cause2 = list(base = c(0.008,3.220), time_indep=c(1.331), time_dep=list(main=0)), cause3 = list(base = c(0.016,4.061), time_indep=c(-0.095), time_dep=list(main=0)) ), time.dep.cov = list( time.dep.cov.type = "ED" , # choose among ("PE","ED","CO") reccur.type = "IS" # choose among ("CM","IS") ), missing.method = "data", # "mendelian" base.dist = "Weibull", # "Piece_Wise", frailty=TRUE, # weight=FALSE ) brca1.9frailty_NC = FitModelEM_NC( data = b1_NC,#### <- run this line for model fitting #### #You can change initial parameter Here init.Parms = list( #lam1 rho1 gene screen1,2,3,oopho cause1 = list(base = c(0.008,2.22), time_indep=c(1.4), time_dep=list(main=c(1,1,1,-0.6) , interaction=c() )) ), time.dep.cov = list(time.dep.cov.type = "ED" , #You can change between "PE" and "ED" reccur.type = "IS" #You can change intercal specific(IS) or cumulative effect(CM) ), missing.method = "data", base.dist = "Weibull", frailty=TRUE, # Fit with Gamma frailty? Yes=TRUE No=FALSE weight=FALSE, # Fit with Sampling weight? Yes=TRUE No=FALSE mutation.prediction=FALSE) # Mutation status prediction results 7720.937 indep 7581.388 frailty ### Debugging (Comprisk model) Run below lines for variable settings for likelihood function missing.method = "data";base.dist = "Weibull";frailty=FALSE;weight=FALSE data=carrierprobgeno(data=b1); data2=Data_preparation(b1); agemin=16; time.dep.cov = list(time.dep.cov.type = "ED" ,reccur.type = "IS") base.parms <- c(init.Parms[[1]]$base, init.Parms[[2]]$base, init.Parms[[3]]$base) # total 6, lambda1,rho1 lambda2,rho2, lambda3,rho3 vbeta_b <- c( init.Parms[[1]]$time_indep, init.Parms[[1]]$time_dep$main )#, init.Parms[[1]]$time_dep$interaction ) # breast cancer parameter vbeta_o <- c( init.Parms[[2]]$time_indep, init.Parms[[2]]$time_dep$main ) # ovarian cancer parameter vbeta_d <- c( init.Parms[[3]]$time_indep, init.Parms[[3]]$time_dep$main ) # death parameter vbeta <- c(vbeta_b,vbeta_o,vbeta_d) theta = theta0 = c(log(base.parms),vbeta) ### Debugging (Noncompeting risk model), Run below lines for variable settings for likelihood function missing.method = "data";base.dist = "Weibull";frailty=FALSE;weight=FALSE data=carrierprobgeno_NC(data=b1_NC); data2=Data_preparation_NC(b1_NC); agemin=16; time.dep.cov = list(time.dep.cov.type = "PE" ,reccur.type = "IS") base.parms <- c(init.Parms[[1]]$base, init.Parms[[2]]$base, init.Parms[[3]]$base) # total 6, lambda1,rho1 lambda2,rho2, lambda3,rho3 vbeta_b <- c( init.Parms[[1]]$time_indep, init.Parms[[1]]$time_dep$main )#, init.Parms[[1]]$time_dep$interaction ) # breast cancer parameter # death parameter vbeta <- c(vbeta_b) theta = theta0 = c(log(base.parms),vbeta) #Results plot(seq(0,20,by=0.01),dgamma(seq(0,20,by=0.01), shape=0.1 , scale=1/0.1))#stong dependency plot(seq(0,5,by=0.01),dgamma(seq(0,5,by=0.01), shape=1.86 , scale=1/1.86))#breast plot(seq(0,20,by=0.01),dgamma(seq(0,20,by=0.01), shape=2.6 , scale=1/2.6))#ovarian plot(seq(0,20,by=0.01),dgamma(seq(0,20,by=0.01), shape=30 , scale=1/30))#Death weak dependency?

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/timestreamwrite_operations.R \name{timestreamwrite_create_table} \alias{timestreamwrite_create_table} \title{Adds a new table to an existing database in your account} \usage{ timestreamwrite_create_table( DatabaseName, TableName, RetentionProperties = NULL, Tags = NULL, MagneticStoreWriteProperties = NULL, Schema = NULL ) } \arguments{ \item{DatabaseName}{[required] The name of the Timestream database.} \item{TableName}{[required] The name of the Timestream table.} \item{RetentionProperties}{The duration for which your time-series data must be stored in the memory store and the magnetic store.} \item{Tags}{A list of key-value pairs to label the table.} \item{MagneticStoreWriteProperties}{Contains properties to set on the table when enabling magnetic store writes.} \item{Schema}{The schema of the table.} } \description{ Adds a new table to an existing database in your account. In an Amazon Web Services account, table names must be at least unique within each Region if they are in the same database. You might have identical table names in the same Region if the tables are in separate databases. While creating the table, you must specify the table name, database name, and the retention properties. \href{https://docs.aws.amazon.com/timestream/latest/developerguide/ts-limits.html}{Service quotas apply}. See \href{https://docs.aws.amazon.com/timestream/latest/developerguide/code-samples.create-table.html}{code sample} for details. See \url{https://www.paws-r-sdk.com/docs/timestreamwrite_create_table/} for full documentation. } \keyword{internal}

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insert_na.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/insert_na.R \name{insert_na} \alias{insert_na} \title{Add NA values to a dataframe} \usage{ insert_na(.dataset, columns, .p = 0.01, seed = 123) } \arguments{ \item{.dataset}{data frame.} \item{columns}{vector that indicates the name of the columns where the NA values will be added, in the format: c("X1", "X2") for variables X1, X2.} \item{.p}{value between 0 and 1, indicating the proportion of NA values that will be added.} \item{seed}{random number seed.} } \value{ the original data frame, but with the NA values added in the indicated columns. } \description{ allows adding NA values to a data frame, selecting the columns and the proportion of desired NAs. } \examples{ insert_na(.dataset = iris, columns = c("Sepal.Length","Petal.Length"), .p = 0.25) }

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/single proportion hyp test and confidence.R

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sharmash937/R-Basics

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refs/heads/master

2021-04-09T15:00:33.746993

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single proportion hyp test and confidence.R

rm(list = ls()) prop.test(98, 162) #one tailed test with 90% confidence interval prop.test(98, 162, alt = "greater", conf.level = 0.90) #----------------------------- quakes[1:5,] mag <- quakes$mag mag[1:5] t.test(mag) t.test(mag, alternative = "greater", mu =4)

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/FinID/server.R

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idelgado2/gws_finID_portal

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refs/heads/master

2020-06-23T01:50:19.341042

2019-08-22T18:41:42

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server.R

### This is the SERVER file for our groundUP file ### library(shiny) source("fin_shiny_fxns2.R") shinyServer( function(input, output, session){ ##~~~~~~~~~~~~~~~~~~~~~~~~## ## VARIABLES HERE ## ##~~~~~~~~~~~~~~~~~~~~~~~~## phid <- reactiveValues() #reactive value to hold majority of user input values hide("reviewButton") #hide review button to ensure it is not clicked before masfins is clicked once so that the temp csv file is created and site crash is avoided if(file.exists(paste0(finCSVPath,"test.csv"))){ file.remove(paste0(finCSVPath,"test.csv")) } ##~~~~~~~~~~~~~~~~~~~~~~## ## OUTPUTS HERE ## ##~~~~~~~~~~~~~~~~~~~~~~## output$finishTable <- renderDataTable({read.csv(paste0(finCSVPath,"test.csv"), row.names = NULL)}) ##may not need this here, I will check right now ############################################################## ## HIDE DATA entry table until essential infor is included ### ############################################################## output$finuploaded <- reactive({ return(!is.null(finUP())) }) ######################################################## ## Fin upload will continue to run even when hidden, ### ## not exactly sure why we need this but we need it ### ######################################################## outputOptions(output, 'finuploaded', suspendWhenHidden=FALSE) ####################################################################### ## Ouputs for chossen SURVEY SITE, SURVEY DATE, and generated FindID ## ####################################################################### output$siteOutput.phid = renderUI(tags$p(tags$span(style="color:red", "SURVEY SITE"), "assigned as: ", tags$span(style="color:red", input$site.phid))) output$dateOutput.phid = renderUI(tags$p(tags$span(style="color:red", "SURVEY DATE"), "assigned as: ", tags$span(style="color:red", as.Date(input$date.phid, format = "%m-%d-%Y")))) output$path = renderText({ inFile <- input$fin.photo ifelse (is.null(inFile), return(NULL), return(inFile$datapath)) }) ##~~~~~~~~~~~~~~~~~~~~## ## FUNCTIONS HERE ## ##~~~~~~~~~~~~~~~~~~~~## #################################################################### ## DATA SHOWING Function called 'finUP', This will check ### ## if the a photo has been uploaded. Once a photo has been ### ## uploaded it will assign all inputed values to a ### ## reavctive values structure and will output the uploaded ### ## photo on the the screen as well as the the Photo ID givin ### ## to the photo, the leaflet map, to pinpoint location of ### ## fin photo capture and a table at the bottom summarizing ### ## the current fin photo information that will be submitted ### ## to the current session. All map functionality is included ### ## in this function, the actual map and OBSERVE EVENT associated ### ## with the map when clicking. ### #################################################################### finUP <- reactive({ if(is.null(input$fin.photo)){ #if there is no photo uploaded do not assign values yet return(NULL) } else{ #assign reactive values phid$site <- input$site.phid phid$date <- input$date.phid phid$val <- paste0(toupper(input$site.phid), #create Photo ID and assign to reactive value 'val' format(input$date.phid, "%y%m%d"), ifelse(nchar(input$sighting.phid==1), paste0("0", input$sighting.phid), input$sighting.phid)) output$FinShot <- renderImage({list(src = input$fin.photo$datapath)}, deleteFile = FALSE) #print uploaded photo output$PhotoID = renderUI(tags$p(tags$span(style="color:red", "PHOTO ID"), "assigned as: ", tags$span(style="color:red", phid$val))) #print Photo ID ### LEAFLET MAP FUNCTIONALITY HERE ### ctr <- flds$coords[[input$site.phid]] output$map <- renderLeaflet({ if(input$lat =="" || input$long ==""){ leaflet() %>% addProviderTiles(providers$Stamen.TonerLite, options = providerTileOptions(noWrap=T)) %>% setView(lng=ctr[1,1], lat = ctr[1,2], zoom = 14) } else{ phid$lat <- as.numeric(input$lat) phid$long <- as.numeric(input$long) leaflet() %>% addProviderTiles(providers$Stamen.TonerLite, options = providerTileOptions(noWrap=T)) %>% setView(lng=input$long, lat = input$lat, zoom = 14) %>% addPulseMarkers(data = click, lng=as.numeric(input$long), lat=as.numeric(input$lat), icon = makePulseIcon(), options = leaflet::markerOptions(draggable = F)) } }) output$dataentry <- renderDataTable(formData()) #print preview table to current information to view befor submitting to current session ######################################### ## OBSERVER EVENT FOR MAP CLICK HERE, ### ## within the same function, I don't ### ## know if we can move it out of this ### ## function, but this works here ### ######################################### observeEvent(input$map_click,{ #capture click click <- input$map_click phid$lat <- click$lat phid$long <- click$lng #add to map leafletProxy('map') %>% clearMarkers() %>% addPulseMarkers(data = click, lng=~lng, lat=~lat, icon = makePulseIcon(), options = leaflet::markerOptions(draggable = F)) output$xyloc <- renderText({paste("lat: ", round(click$lat, 4), "| long: ", round(click$lng, 4) ) }) updateTextInput(session, "lat", value = round(click$lat, 4)) #update coordinates in input boxes depending on map click updateTextInput(session, "long", value = round(click$lng, 4)) }) } }) ################################################# ## DATA MAKING function 'formData', this will ### ## take all the inputed values and put ### ## them into a data.frame called 'data' ### ################################################# formData <- reactive({ if(is.null(finUP)){ return(NULL) }else{ data <- c(refID = "UNMATCHED", name = "NONE_YET", match.sugg = as.character(input$match.sugg), time.obs = as.character(input$time), PhotoID = as.character(phid$val), site = toupper(as.character(input$site.phid)), date = as.character(input$date.phid), sighting = as.character(input$sighting.phid), sex = as.character(input$sex), size = as.character(round(input$size/0.5)*0.5), tag.exists = as.character(input$tag.exists), tag.deployed = as.character(input$tagdeployed), tag.id = as.character(input$tag.id), tag.side = as.character(input$tag.side), biopsy = as.character(input$biopsy), biopsy.id = as.character(input$biopsy.id), notes = as.character(input$notes), tagging.notes = as.character(input$tag.notes), user = as.character(input$user), lat.approx = as.character(round(as.numeric(phid$lat), 4)), long.approx = as.character(round(as.numeric(phid$long), 4)), timestamp = epochTime(), dfN = file.path(paste0("CCA_GWS_PHID_", phid$val, "_",as.integer(Sys.time()),".csv")), #pFn = file.path(dropfin, paste0(phid$val, ".", tools::file_ext(input$fin.photo$datapath))), survey.vessel = as.character(input$vessel), survey.crew = as.character(paste(input$crew, collapse = "|")), survey.effortON = as.character(input$effort[1]), #w/ slider range survey.effortOFF = as.character(input$effort[2]), survey.notes = as.character(input$survey.notes) ) data <- t(data) data } }) ##~~~~~~~~~~~~~~~~~~~~~## ## OBSERVE EVENTS HERE ## ##~~~~~~~~~~~~~~~~~~~~~## ################################################ ## OBSERVE EVENT for 'addFins' button ##### ## in SURVEY INFO Tab this will push the ##### ## user to the 'FIN ID ENTRY' Tab ##### ################################################ observeEvent( input$addfins, {updateTabsetPanel( session = session, "form", selected = "Fin Photo Entry" )} ) ################################################################## ## OBSERVE EVENT for 'masfins' button in FIN ID ENTRY Tab, ### ## this will take the data.frame 'data' and pass it to ### ## saveData() function as well as the photo uploaded to ### ## savePhoto() function. These functions are located in ### ## file fin_shiny_fxns2.R and this will save the the data ### ## to a temporary csv file and the photo to a separate folder. ### ## The paths can be changed at the top of fin_shiny_fxns2.R ### ################################################################## observeEvent(input$masfins, { data <- data.frame(formData(), stringsAsFactors = F) #save data to a dataframe in local varibale to pass to functions showNotification(paste(data$dfN, "being uploaded to server"), closeButton = F, type = "message", duration=2, id = "datUP") saveData(data) #save data savePhoto(input$fin.photo, phid$val) #save photo show("reviewButton") #Show review button after initial masfins/addfin click is made to ensure that temp csv file is created to prevent from potential crashing #reset fields sapply(c("sex", "size", "tag.exists", "tagdeployed", "tag.id","tag.side", "biopsy", "biopsy.id", "notes", "tag.notes","finuploaded", "fin.photo", "PhotoID", "match.sugg", "time", "FinShot"), reset) updateNumericInput(session, "sighting.phid", value = (input$sighting.phid + 1)) #increase sigting by one everytime we want to add another fin entry runjs("window.scrollTo(0, 50)") #scroll to top of the window after reseting everything output$FinShot <- NULL output$dataentry <- NULL output$PhotoID <- NULL phid$val <- NULL #reset("data") #reset("masfins") }) #################################################################### ## OBSERVE EVENT for 'r2submit' (review to submit) button in ### ## FIN ID ENTRY Tab, this will read the temporary csv file ### ## created by 'masfins' button into a data.frame called 'mydata' ### ## and then render an editable table (handsontable) in the ### ## DATA SUBMISSION tab. This even will also push the user to the ### ## DATA SUBMISSION tab as well. ### #################################################################### observeEvent(input$r2submit, { mydata <- read.csv(file=paste0(finCSVPath,"test.csv"), header=TRUE, sep=",", stringsAsFactors = FALSE, row.names = NULL) #this is called to load the data table in the Data Submission page Sys.sleep(1) #forcing program to sleep for a second in order to let test csv file to be created and identified in time to render the table output$hotTable <- renderRHandsontable({ rhandsontable(mydata) %>% # converts the R dataframe to rhandsontable object hot_col("PhotoID", readOnly = TRUE) %>% hot_col("site", readOnly = TRUE) %>% hot_col("date", readOnly = TRUE) %>% hot_col("sighting", readOnly = TRUE) %>% hot_context_menu(allowRowEdit = TRUE, allowColEdit = FALSE) }) updateTabsetPanel(session, "form", selected = "Data Submission") #move user to submission page }) ############################################################################# ## OBSERVE EVENT for 'serverSubmit' button in DATA SUBMISSION Tab, ### ## this will write to a new permenant CSV file using the newly ### ## created table. We use the table here instead of the previous ### ## data.frame because the user may edit the information in the table. ### ## This event will also delete the temporary csv file previously created. ### ## Lastly, this will push the user to the SURVEY INFO tab to start over. ### ############################################################################# observeEvent(input$serverSubmit,{ write.table(hot_to_r(input$hotTable), file = paste0(finCSVPath, paste0(as.character(Sys.time()),"_FinID.csv")), row.names = FALSE, col.names = TRUE, quote = TRUE, append=FALSE, sep = ",") # write table to new csv file file.remove(paste0(finCSVPath,"test.csv")) #delete temporary filed shinyalert::shinyalert(title = "Uploading To Server", text = "", type = "success", closeOnEsc = FALSE, closeOnClickOutside = FALSE, html = FALSE, showCancelButton = FALSE, showConfirmButton = FALSE, timer = 3000, animation = TRUE) #notification to user updateTabsetPanel(session, "form", selected = "Survey Info") #move to initial tab }) ######################################################################## ## OBSERVER for insuring that all mandatory items are filled in. ### ## This may need to be revisioned, all fields are now filled in by ### ## default to ensure editability in the table in DATA SUBMISSION tab ### ## Thus, this observer may not be necessary at this point ### ######################################################################## observe({ mandatoryFilled <- vapply(flds$mandatory, function(x) { !is.null(input[[x]]) && input[[x]] != "" && !is.null(finUP()) }, logical(1)) mandatoryFilled <- all(mandatoryFilled) }) } )

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/R/aggregation_operators.R

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cran/mongopipe

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refs/heads/master

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aggregation_operators.R

#' Conditional expression ($cond) #' #' Evaluates a boolean expression to return one #' of the two specified return expressions. #' #' @param test Expression which returns a boolean value. #' @param yes Return this if the test returns true. #' @param no Return this if the test returns false. #' #' @examples #' \dontrun{ #' cond <- condition(test = list("$isArray"="$chart"), #' yes = list("$size"="$chart"), #' no = 0) #' jsonlite::toJSON(cond) #' } #' #' @return Return a list for using in mongopipe. #' #' @export condition <- function(test, yes, no) { query <- check_query(list("if"=test, "then"=yes, "else"=no)) list("$cond" = query) }