如何在R的季节中使用函数monthmean计算平均月平均值

如何解决如何在R的季节中使用函数monthmean计算平均月平均值

问题：

我有一个名为 FID 的数据框（请参见下文），我想生成一个表格，显示所有三年（2015-2017年）FID的平均每月平均值

运行由Adrian Barnett编写的函数后，出现以下错误消息：

Error in monthmean(data = New_Blue_Edited_Final,resp = New_Blue_Edited_Final$FID,: 
  data set must contain a variable with the numeric month called 'month'

如果有人可以提供帮助，我将非常感激。

#Load package
library(season)

#R code for the monthmean()function

   monthmean<-function(data,resp,offsetpop=NULL,adjmonth=FALSE){
  # checks
  if (is.null(data)==TRUE){stop("must have an input data set (data)")}
  if (is.null(resp)==TRUE){stop("must have an input variable (resp)")}
  nnn<-names(data)
  if (any(nnn=='year')==FALSE){stop("data set must contain a variable with the 4 digit year called 'year'")}
  if (any(nnn=='month')==FALSE){stop("data set must contain a variable with the numeric month called 'month'")}
  # calculations
  days<-flagleap(data) # get the number of days in each month
  mean<-vector(length=12,mode='numeric')
  if (adjmonth=='thirty') adjf=30  
  if (adjmonth=='average') adjf=365.25/12
  if (is.null(offsetpop)==TRUE) adjp=1 else adjp=with(data,eval(offsetpop)) # population adjustment
  xxxx=subset(data,select=resp)[,1] # instead of with
  for (i in 1:12){
    if (adjmonth!=FALSE) mean[i]<-mean(xxxx[data$month==i]*(adjf/days$ndaysmonth[i])/adjp)
    else mean[i]<-mean(xxxx[data$month==i]/adjp) # no monthly adjustment
  }
  # return
  toret<-list()
  toret$mean<-as.vector(mean)
  class(toret)<-'Monthmean'
  return(toret)
}

 #Model

 FID$year <- FID$Year

 mean_FID<-monthmean(data=FID,resp=FID$FID,adjmonth = "thirty")

错误消息

Error in monthmean(data = New_Blue_Edited_Final,: 
  data set must contain a variable with the numeric month called 'month'

数据框称为“ FID”

structure(list(FID = c(1L,2L,3L,4L,5L,6L,7L,8L,9L,10L,11L,12L,13L,14L,15L,16L,17L,18L,19L,20L,21L,22L,23L,24L,25L,26L,27L,28L,29L,30L,31L,32L,33L,34L,35L,36L,37L,38L,39L,40L,41L,42L,43L,44L,45L,46L,47L,48L,49L,50L,51L,52L,53L,54L,55L,56L,57L,58L,59L,60L,61L,62L,63L,64L,65L,66L,67L,68L,69L,70L,71L,72L,73L,74L,75L,76L,77L,78L,79L,80L,81L,82L,83L,84L,85L,86L,87L,88L,89L,90L,91L,92L,93L,94L,95L,96L,97L,98L,99L,100L,101L,102L,103L,104L,105L,106L,107L,108L,109L,110L,111L,112L,113L,114L,115L,116L,117L,118L,119L,120L,121L,122L,123L,124L,125L,126L,127L,128L,129L,130L,131L,132L,133L,134L,135L,136L,137L,138L,139L,140L,141L,142L,144L,145L,146L,147L,148L,149L,150L,151L,152L,153L,154L,155L,156L,157L,158L,159L,160L,161L,162L,163L,164L,165L,166L,167L,168L,169L,170L,171L,172L,173L,174L,175L,176L,177L,178L,179L,180L,181L,182L,183L,184L,185L,186L,187L,188L,189L,190L,191L,192L,193L,194L,195L,196L,197L,198L,199L,200L,201L,202L,203L,204L,205L,206L,207L,208L,209L,210L,211L,212L,213L,214L,215L,216L,217L,218L,219L,220L,221L,222L,223L,224L,225L,226L,227L,228L,229L,230L,231L,232L,233L,234L,235L,236L,237L,238L,239L,240L,241L,242L,243L,244L,245L,246L,247L,248L,249L,250L,251L,252L,253L,254L,255L,256L,257L,258L,259L,260L,261L,262L,263L,264L,265L,266L,267L,268L,269L,270L,271L,272L,273L,274L,275L,276L,277L,278L,279L,280L,281L,282L,283L,284L,285L,286L,287L,288L,289L,290L,291L,292L,293L,294L,295L,296L,297L,298L,299L,300L,301L,302L,303L,304L,305L,306L,307L,308L,309L,310L,311L,312L,313L,314L,315L,316L,317L,318L,319L,320L,321L,322L,323L,324L,325L,326L,327L,328L,329L,330L,331L,332L,333L,334L,335L,336L,337L,338L,339L,340L,341L,342L,343L,344L,345L,346L,347L,348L,349L,350L,351L,352L,353L,354L,355L,356L,357L,358L,359L,360L,361L,362L,363L,364L,365L,366L,367L,368L,369L,370L,371L,372L,373L,374L,375L,376L,377L,378L,379L,380L,381L,382L,383L,384L,385L,386L,387L,388L,389L,390L,391L,392L,393L,394L,395L,396L,397L,398L,399L,400L,401L,402L,403L,404L,405L,406L,407L,408L,409L,410L,411L,412L,413L,414L,415L,416L,417L,418L,419L,420L,421L,422L,423L,424L,425L,426L,427L,428L,429L,430L,431L,432L,433L,434L,435L,436L,437L,438L,439L,440L,441L,442L,443L,444L,445L,446L,447L,448L,449L,450L,451L,452L,453L,454L,455L,456L,457L,458L,459L,460L,461L,462L,463L,464L,465L,466L,467L,468L,469L,470L,471L,472L,473L,474L,475L,476L,477L,478L,479L,480L,481L,482L,483L,484L,485L,486L,487L,488L,489L,490L,491L,492L,493L,494L,495L,496L,497L,498L,499L,500L,501L,502L,503L,504L,505L,506L,507L,508L,509L,510L,511L,512L,513L,514L,515L,516L,517L,518L,519L,520L,521L,522L,523L,524L,525L,526L,527L,528L,529L,530L,531L,532L,533L,534L,535L,536L,537L,538L,539L,540L,541L,542L,543L,544L,545L,546L,547L,548L,549L,550L,551L,552L,553L,554L,555L,556L,557L,558L,559L,560L,561L,562L,563L,564L,565L,566L,567L,568L,569L,570L,571L,572L,573L,574L,575L,576L,577L,578L,579L,580L,581L,582L,583L,584L,585L,586L,587L,588L,589L,590L,591L,592L,593L,594L,595L,596L,597L,598L,599L,600L,601L,602L,603L,604L,605L,606L,607L,608L,609L,610L,611L,612L,613L,614L,615L,616L,617L,618L,619L,620L,621L,622L,623L,624L,625L,626L,627L,628L,629L,630L,631L,632L,633L,634L,635L,636L,637L,638L,639L,640L,641L,642L,643L,644L,645L,646L,647L,648L,649L,650L,651L,652L,653L,654L,655L,656L,657L,658L,659L,660L,661L,662L,663L,664L,665L,666L,667L,668L,669L,670L,671L,672L,673L,674L,675L,676L,677L,678L,679L,680L,681L,682L,683L,684L,685L,686L,687L,688L,689L,690L,691L,692L,693L,694L,695L,696L,697L,698L,699L,700L,701L,702L,703L,704L,705L,706L,707L,708L,709L,710L,711L,712L,713L,714L,715L,716L,717L,718L,719L,720L,721L,722L,723L,724L,725L,726L,727L,728L,729L,730L,731L,732L,733L,734L,735L,736L,737L,738L,739L,740L,741L,742L,743L,744L,745L,746L,747L,748L,749L,750L,751L,752L,753L,754L,755L,756L,757L,758L,759L,760L,761L,762L,763L,764L,765L,766L,767L,768L,769L,770L,771L,772L,773L,774L,775L,776L,777L,778L,779L,780L,781L,782L,783L,784L,785L,786L,787L,788L,789L,790L,791L,792L,793L,794L,795L,796L,797L,798L,799L,800L,801L,802L,803L,804L,805L,806L,807L,808L,809L,810L,811L,812L,813L,814L,815L,816L,817L,818L,819L,820L,821L,822L,823L,824L,825L,826L,827L,828L,829L,830L,831L,832L,833L,834L,835L,836L,837L,838L,839L,840L,841L,842L,843L,844L,845L,846L,847L,848L,849L,850L,851L,852L,853L,854L,855L,856L,857L,858L,859L,860L,861L,862L,863L,864L,865L,866L,867L,868L,869L,870L,871L,872L,873L,874L,875L,876L,877L,878L,879L,880L,881L,882L,883L,884L,885L,886L,887L,888L,889L,890L,891L,892L,893L,894L,895L,896L,897L,898L,899L,900L,901L,902L,903L,904L,905L,906L,907L,908L,909L,910L,911L,912L,913L,914L,915L,916L,917L,918L),Year = c(2015L,2015L,2016L,2017L,2017L),Month = structure(c(5L,1L,3L),.Label = c("April","August","December","February","January","July","March","May","November","October","September"),class = "factor"),year = c(2015L,2017L)),row.names = c(NA,-917L),class = "data.frame")