将列类型转换为 R 中的 read_csv() 列类型答案

【问题标题】：Convert column types to their read_csv() column type in R将列类型转换为 R 中的 read_csv() 列类型
【发布时间】：2018-05-13 23:16:40
【问题描述】：

关于 library(readr) 和 R 中的 read_csv() 函数，我最喜欢的一点是它几乎总是将我的数据的列类型设置为正确的类。但是，我目前正在使用 R 中的 API，它将数据作为所有字符类的数据框返回给我，即使数据显然是数字。以这个数据框为例，它有一些体育数据：

dput(mydf)
structure(list(isUnplayed = c("false", "false", "false"), isInProgress = 
c("false", "false", "false"), isCompleted = c("true", "true", "true"), awayScore = c("106", 
"95", "95"), homeScore = c("94", "97", "111"), game.ID = c("31176", 
"31177", "31178"), game.date = c("2015-10-27", "2015-10-27", 
"2015-10-27"), game.time = c("8:00PM", "8:00PM", "10:30PM"), 
    game.location = c("Philips Arena", "United Center", "Oracle Arena"
    ), game.awayTeam.ID = c("88", "86", "110"), game.awayTeam.City = c("Detroit", 
    "Cleveland", "New Orleans"), game.awayTeam.Name = c("Pistons", 
    "Cavaliers", "Pelicans"), game.awayTeam.Abbreviation = c("DET", 
    "CLE", "NOP"), game.homeTeam.ID = c("91", "89", "101"), game.homeTeam.City = c("Atlanta", 
    "Chicago", "Golden State"), game.homeTeam.Name = c("Hawks", 
    "Bulls", "Warriors"), game.homeTeam.Abbreviation = c("ATL", 
    "CHI", "GSW"), quarterSummary.quarter = list(structure(list(
        `@number` = c("1", "2", "3", "4"), awayScore = c("25", 
        "23", "34", "24"), homeScore = c("25", "18", "23", "28"
        )), .Names = c("@number", "awayScore", "homeScore"), class = "data.frame", row.names = c(NA, 
    4L)), structure(list(`@number` = c("1", "2", "3", "4"), awayScore = c("17", 
    "23", "28", "27"), homeScore = c("26", "20", "25", "26")), .Names = c("@number", 
    "awayScore", "homeScore"), class = "data.frame", row.names = c(NA, 
    4L)), structure(list(`@number` = c("1", "2", "3", "4"), awayScore = c("35", 
    "14", "26", "20"), homeScore = c("39", "20", "35", "17")), .Names = c("@number", 
    "awayScore", "homeScore"), class = "data.frame", row.names = c(NA, 
    4L)))), .Names = c("isUnplayed", "isInProgress", "isCompleted", 
"awayScore", "homeScore", "game.ID", "game.date", "game.time", 
"game.location", "game.awayTeam.ID", "game.awayTeam.City", "game.awayTeam.Name", 
"game.awayTeam.Abbreviation", "game.homeTeam.ID", "game.homeTeam.City", 
"game.homeTeam.Name", "game.homeTeam.Abbreviation", "quarterSummary.quarter"
), class = "data.frame", row.names = c(NA, 3L))

考虑到类类型，一旦 API 返回此数据帧，处理它就相当麻烦。我想出了一种更新列类的技巧，如下所示：

write_csv(mydf, 'mydf.csv')
mydf <- read_csv('mydf.csv')

通过写入 CSV，然后使用 read_csv() 重新读取 CSV，数据框列会更新。不幸的是，我在我的目录中留下了一个我不想要的 CSV 文件。有没有一种方法可以将 R 数据框的列更新为它们的“read_csv()”列类，而无需实际编写 CSV？

感谢任何帮助！

【问题讨论】：

在写入和读取 csv 时要小心以到达您想要的位置，因为——在你的代码中——你会丢失你的（如果有的话）因子（它们将被转换为字符）。如果 API 设置正确，我希望得到格式正确的数据，因此因素应该是因素。这对于绘图等时的一致顺序可能至关重要。

标签： r data-manipulation readr

【解决方案1】：

试试这段代码，type.convert 将字符向量转换为适当的逻辑、整数、数字、复数或因子。

indx <- which(sapply(df, is.character))
df[, indx] <- lapply(df[, indx], type.convert)
indx <- which(sapply(df, is.factor))
df[, indx] <- lapply(df[, indx], as.character)

【讨论】：

【解决方案2】：

如果您只想让readr 猜测您的列类型，则无需写入和读取数据。您可以为此使用readr::type_convert：

iris %>% 
  dplyr::mutate(Sepal.Width = as.character(Sepal.Width)) %>% 
  readr::type_convert() %>% 
  str()

比较：

iris %>% 
  dplyr::mutate(Sepal.Width = as.character(Sepal.Width)) %>% 
  str()

【讨论】：