r-根据名字对性别(以及可能的年龄范围)进行分类

Question

在尝试将数据集（作为CSV）上传到H2O后，发现FirstName列被转换为null/missing，我了解到当前版本的H2O不支持类字符串的列，并且因子只能达到65k个唯一值。所以现在我正在寻找另一种方法来解决这个问题。

我想以一个模型结束，给定任何FirstName，都会返回：

• 该人是男性/女性的概率（+1.0到-1.0）
• 如果可能的话，此人可能的年龄（平均值，stdev）

哪些R函数（或包：：函数）适用于此？最好是文档齐全的包/函数，这样我就可以边走边了解更多。

这是R中的数据集示例。列类型有：Numerical、factor、factor和Numerical。

> head(TrainingNames)
  Year FirstName Gender Freq
1 1880      Mary      F 7065
2 1880      Anna      F 2604
3 1880      Emma      F 2003
4 1880 Elizabeth      F 1939
5 1880    Minnie      F 1746
6 1880  Margaret      F 1578
> summary(TrainingNames)
      Year        FirstName       Gender           Freq        
 Min.   :1880   Francis:    268   F:1062432   Min.   :    5.0  
 1st Qu.:1948   James  :    268   M: 729659   1st Qu.:    7.0  
 Median :1981   Jean   :    268               Median :   12.0  
 Mean   :1972   Jesse  :    268               Mean   :  186.1  
 3rd Qu.:2000   Jessie :    268               3rd Qu.:   32.0  
 Max.   :2013   John   :    268               Max.   :99674.0  
                (Other):1790483                                

以下是用于提取/处理数据源的R代码。

# Create data dir, download and extract data source
dir.create('Data Files', showWarnings = F)
if(!file.exists('Data Files/names.zip')) {
  download.file(url = 'http://www.ssa.gov/oact/babynames/names.zip', destfile = 'Data Files/names.zip', cacheOK = T)
  setwd('Data Files/')
  unzip(zipfile = 'names.zip')
  setwd('../') 
}
FileList <- list.files(path = "Data Files/", pattern = ".txt") # List of data files
# Create data-source of names for R/Tableau
munge <- function(f) { # Return data frame of single data file
  y <- as.numeric(gsub(pattern = '[^0-9]', replacement = "", x = f))
  l <- read.csv(file = paste0("Data Files/", f), header = F, quote = "'")
  d <- cbind(y, l)
  colnames(d) <- c("Year", "FirstName", "Gender", "Freq")
  return(data.frame(d))
}
if(!file.exists('TrainingNames.csv')) {
  pb <- txtProgressBar(min = 1, max = length(FileList), style = 3) # Start progress bar
  TrainingNames <- munge(FileList[[1]]) # Munge first data file
  for(n in 2:length(FileList)) { # Munge remaining data files
    TrainingNames <- rbind(TrainingNames, munge(FileList[[n]]))
    setTxtProgressBar(pb, n)
  }
  close(pb) # Close progress bar
  rm(n, pb)
  write.table(x = TrainingNames, file = "TrainingNames.csv", sep = ";", row.names = F, col.names = T) # Write results to CSV file
}
summary(TrainingNames)

Answer 1

这里我定义了一个函数name_stats，它可以根据您的请求执行操作。您需要先运行问题中的代码来创建TrainingNames，然后函数才能工作。

您可以编辑任何您喜欢的内容，使其符合您的特定需求。

name_stats=function(name){
    df=subset(TrainingNames,FirstName==name)
    gender=tapply(df[,'Freq'],df[,'Gender'],sum)
    prob_male=gender['M']/sum(gender)
    prob_female=gender['F']/sum(gender)
    age=tapply(df[,'Freq'],as.factor(df[,'Year']),sum)
    dimnames(age)=list(age=round((Sys.Date()-as.Date(unlist(dimnames(age)),format='%Y'))/365))
    mean_age=mean(rep(as.numeric(unlist(dimnames(age))),age))
    sd_age=sd(rep(as.numeric(unlist(dimnames(age))),age))
    cat('Probability',name,'is male is',round(prob_male,6),'n','Probability',name,'is female is',round(prob_female,6),'n','Mean age of',name,'is',round(mean_age,6),'n','SD age of',name,'is',round(sd_age,6))
}