刪除“。” 使用 JSON 時來自 R 中的列名

Question

我正在嘗試清理 R 中的列名。我正在處理一個 JSON 數據集，我使用名為“stream_in”的jsonlite函數將其導入到 R 中。

首先，我嘗試了“gsub”命令和“paste”命令，但都不起作用。

在我看來，問題是這樣的：當我使用命令頭檢查數據時，它會向我讀取所有列名，甚至包括包含“.”的列名。 而且，奇怪的是，“空格”，但如果我使用名稱命令，它只會讀取沒有“點”或“空格”的那些。 有什么建議？ 我有名稱的列，例如

hours.Monday.open attributes.Alcohol

我想刪除“。”

我試過這樣的事情

名稱（restaurant.data）[3] <- paste（“HoursMondayOpen”）

但這只是刪除了第一個“。”之前的單詞。 新列名稱為“HoursMondayOpen.Monday.Open”

我也試過

名稱（餐廳.數據）<- gsub（“\\.”，“”，名稱（餐廳.數據））

但這根本沒有改變任何東西，也沒有給我一個錯誤。

這有幫助嗎？

這是 dput() 的輸出

> dput(head(restaurant.data))
structure(list(business_id = c("5UmKMjUEUNdYWqANhGckJw", "UsFtqoBl7naz8AVUBZMjQQ", 
"3eu6MEFlq2Dg7bQh8QbdOg", "cE27W9VPgO88Qxe4ol6y_g", "HZdLhv6COCleJMo7nPl-RA", 
"mVHrayjG3uZ_RLHkLj-AMg"), FullAddress = c("4734 Lebanon Church Rd\nDravosburg, PA 15034", 
"202 McClure St\nDravosburg, PA 15034", "1 Ravine St\nDravosburg, PA 15034", 
"1530 Hamilton Rd\nBethel Park, PA 15234", "301 South Hills Village\nPittsburgh, PA 15241", 
"414 Hawkins Ave\nrankin, PA 15104"), HoursFridayClose = structure(list(
    Friday = structure(list(close = c("21:00", NA, NA, NA, "17:00", 
    "20:00"), open = c("11:00", NA, NA, NA, "10:00", "10:00")), .Names = c("close", 
    "open"), row.names = c(NA, 6L), class = "data.frame"), Tuesday = structure(list(
        close = c("21:00", NA, NA, NA, "21:00", "19:00"), open = c("11:00", 
        NA, NA, NA, "10:00", "10:00")), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame"), Thursday = structure(list(
        close = c("21:00", NA, NA, NA, "17:00", "19:00"), open = c("11:00", 
        NA, NA, NA, "10:00", "10:00")), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame"), Wednesday = structure(list(
        close = c("21:00", NA, NA, NA, "21:00", "19:00"), open = c("11:00", 
        NA, NA, NA, "10:00", "10:00")), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame"), Monday = structure(list(
        close = c("21:00", NA, NA, NA, "21:00", NA), open = c("11:00", 
        NA, NA, NA, "10:00", NA)), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame"), Sunday = structure(list(
        close = c(NA, NA, NA, NA, "18:00", NA), open = c(NA, 
        NA, NA, NA, "11:00", NA)), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame"), Saturday = structure(list(
        close = c(NA, NA, NA, NA, "21:00", "16:00"), open = c(NA, 
        NA, NA, NA, "10:00", "10:00")), .Names = c("close", "open"
    ), row.names = c(NA, 6L), class = "data.frame")), .Names = c("Friday", 
"Tuesday", "Thursday", "Wednesday", "Monday", "Sunday", "Saturday"
), row.names = c(NA, 6L), class = "data.frame"), open = c(TRUE, 
TRUE, TRUE, FALSE, TRUE, TRUE), categories = list(c("Fast Food", 
"Restaurants"), "Nightlife", c("Auto Repair", "Automotive"), 
    c("Active Life", "Mini Golf", "Golf"), c("Shopping", "Home Services", 
    "Internet Service Providers", "Mobile Phones", "Professional Services", 
    "Electronics"), c("Bars", "American (New)", "Nightlife", 
    "Lounges", "Restaurants")), city = c("Dravosburg", "Dravosburg", 
"Dravosburg", "Bethel Park", "Pittsburgh", "rankin"), review_count = c(4L, 
4L, 3L, 5L, 5L, 20L), name = c("Mr Hoagie", "Clancy's Pub", "Joe Cislo's Auto", 
"Cool Springs Golf Center", "Verizon", "Emil's Lounge"), neighborhoods = list(
    character(0), character(0), character(0), character(0), character(0), 
    character(0)), longitude = c(-79.9007057, -79.8868138, -79.889059, 
-80.0146597, -80.05998, -79.8802474), state = c("PA", "PA", "PA", 
"PA", "PA", "PA"), stars = c(4.5, 3.5, 5, 2.5, 2.5, 5), latitude = c(40.3543266, 
40.3505527, 40.3509559, 40.3541155, 40.35762, 40.4134643), attributes = structure(list(
    `Take-out` = c(TRUE, NA, NA, NA, NA, TRUE), `Drive-Thru` = c(FALSE, 
    NA, NA, NA, NA, NA), `Good For` = structure(list(dessert = c(FALSE, 
    NA, NA, NA, NA, FALSE), latenight = c(FALSE, NA, NA, NA, 
    NA, FALSE), lunch = c(FALSE, NA, NA, NA, NA, TRUE), dinner = c(FALSE, 
    NA, NA, NA, NA, FALSE), brunch = c(FALSE, NA, NA, NA, NA, 
    FALSE), breakfast = c(FALSE, NA, NA, NA, NA, FALSE)), .Names = c("dessert", 
    "latenight", "lunch", "dinner", "brunch", "breakfast"), row.names = c(NA, 
    6L), class = "data.frame"), Caters = c(FALSE, NA, NA, NA, 
    NA, TRUE), `Noise Level` = c("average", NA, NA, NA, NA, "average"
    ), `Takes Reservations` = c(FALSE, NA, NA, NA, NA, FALSE), 
    Delivery = c(FALSE, NA, NA, NA, NA, FALSE), Ambience = structure(list(
        romantic = c(FALSE, NA, NA, NA, NA, FALSE), intimate = c(FALSE, 
        NA, NA, NA, NA, FALSE), classy = c(FALSE, NA, NA, NA, 
        NA, FALSE), hipster = c(FALSE, NA, NA, NA, NA, FALSE), 
        divey = c(FALSE, NA, NA, NA, NA, FALSE), touristy = c(FALSE, 
        NA, NA, NA, NA, FALSE), trendy = c(FALSE, NA, NA, NA, 
        NA, FALSE), upscale = c(FALSE, NA, NA, NA, NA, FALSE), 
        casual = c(FALSE, NA, NA, NA, NA, FALSE)), .Names = c("romantic", 
    "intimate", "classy", "hipster", "divey", "touristy", "trendy", 
    "upscale", "casual"), row.names = c(NA, 6L), class = "data.frame"), 
    Parking = structure(list(garage = c(FALSE, NA, NA, NA, FALSE, 
    FALSE), street = c(FALSE, NA, NA, NA, FALSE, FALSE), validated = c(FALSE, 
    NA, NA, NA, FALSE, FALSE), lot = c(FALSE, NA, NA, NA, FALSE, 
    FALSE), valet = c(FALSE, NA, NA, NA, FALSE, FALSE)), .Names = c("garage", 
    "street", "validated", "lot", "valet"), row.names = c(NA, 
    6L), class = "data.frame"), `Has TV` = c(FALSE, NA, NA, NA, 
    NA, TRUE), `Outdoor Seating` = c(FALSE, FALSE, NA, NA, NA, 
    FALSE), Attire = c("casual", NA, NA, NA, NA, "casual"), Alcohol = c("none", 
    NA, NA, NA, NA, "full_bar"), `Waiter Service` = c(FALSE, 
    NA, NA, NA, NA, TRUE), `Accepts Credit Cards` = c(TRUE, TRUE, 
    NA, NA, FALSE, TRUE), `Good for Kids` = c(TRUE, NA, NA, TRUE, 
    NA, TRUE), `Good For Groups` = c(TRUE, TRUE, NA, NA, NA, 
    TRUE), `Price Range` = c(1L, 1L, NA, NA, 2L, 1L), `Happy Hour` = c(NA, 
    TRUE, NA, NA, NA, FALSE), `Good For Dancing` = c(NA, NA, 
    NA, NA, NA, FALSE), `Coat Check` = c(NA, NA, NA, NA, NA, 
    FALSE), Smoking = c(NA, NA, NA, NA, NA, "no"), `Wi-Fi` = c(NA, 
    NA, NA, NA, NA, "no"), Music = structure(list(dj = c(NA, 
    NA, NA, NA, NA, FALSE), background_music = c(NA, NA, NA, 
    NA, NA, NA), jukebox = c(NA, NA, NA, NA, NA, NA), live = c(NA, 
    NA, NA, NA, NA, NA), video = c(NA, NA, NA, NA, NA, NA), karaoke = c(NA, 
    NA, NA, NA, NA, NA)), .Names = c("dj", "background_music", 
    "jukebox", "live", "video", "karaoke"), row.names = c(NA, 
    6L), class = "data.frame"), `Wheelchair Accessible` = c(NA, 
    NA, NA, NA, NA, NA), `Dogs Allowed` = c(NA, NA, NA, NA, NA, 
    NA), BYOB = c(NA, NA, NA, NA, NA, NA), Corkage = c(NA, NA, 
    NA, NA, NA, NA), `BYOB/Corkage` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Order at Counter` = c(NA, NA, NA, NA, NA, NA), `By Appointment Only` = c(NA, 
    NA, NA, NA, NA, NA), `Open 24 Hours` = c(NA, NA, NA, NA, 
    NA, NA), `Hair Types Specialized In` = structure(list(coloring = c(NA, 
    NA, NA, NA, NA, NA), africanamerican = c(NA, NA, NA, NA, 
    NA, NA), curly = c(NA, NA, NA, NA, NA, NA), perms = c(NA, 
    NA, NA, NA, NA, NA), kids = c(NA, NA, NA, NA, NA, NA), extensions = c(NA, 
    NA, NA, NA, NA, NA), asian = c(NA, NA, NA, NA, NA, NA), straightperms = c(NA, 
    NA, NA, NA, NA, NA)), .Names = c("coloring", "africanamerican", 
    "curly", "perms", "kids", "extensions", "asian", "straightperms"
    ), row.names = c(NA, 6L), class = "data.frame"), `Accepts Insurance` = c(NA, 
    NA, NA, NA, NA, NA), `Ages Allowed` = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_
    ), `Dietary Restrictions` = structure(list(`dairy-free` = c(NA, 
    NA, NA, NA, NA, NA), `gluten-free` = c(NA, NA, NA, NA, NA, 
    NA), vegan = c(NA, NA, NA, NA, NA, NA), kosher = c(NA, NA, 
    NA, NA, NA, NA), halal = c(NA, NA, NA, NA, NA, NA), `soy-free` = c(NA, 
    NA, NA, NA, NA, NA), vegetarian = c(NA, NA, NA, NA, NA, NA
    )), .Names = c("dairy-free", "gluten-free", "vegan", "kosher", 
    "halal", "soy-free", "vegetarian"), row.names = c(NA, 6L), class = "data.frame")), .Names = c("Take-out", 
"Drive-Thru", "Good For", "Caters", "Noise Level", "Takes Reservations", 
"Delivery", "Ambience", "Parking", "Has TV", "Outdoor Seating", 
"Attire", "Alcohol", "Waiter Service", "Accepts Credit Cards", 
"Good for Kids", "Good For Groups", "Price Range", "Happy Hour", 
"Good For Dancing", "Coat Check", "Smoking", "Wi-Fi", "Music", 
"Wheelchair Accessible", "Dogs Allowed", "BYOB", "Corkage", "BYOB/Corkage", 
"Order at Counter", "By Appointment Only", "Open 24 Hours", "Hair Types Specialized In", 
"Accepts Insurance", "Ages Allowed", "Dietary Restrictions"), row.names = c(NA, 
6L), class = "data.frame"), type = c("business", "business", 
"business", "business", "business", "business")), .Names = c("business_id", 
"FullAddress", "HoursFridayClose", "open", "categories", "city", 
"review_count", "name", "neighborhoods", "longitude", "state", 
"stars", "latitude", "attributes", "type"), row.names = c(NA, 
6L), class = "data.frame")
> 

一切盡在其中，盡顯榮耀！

Answer 1

您的數據非常復雜（我會說很亂...），因為您有一個data.frame ，其中一些列又是一個data.frame其列又是data.frame的...還有一些列只是列出內部不同長度的元素（即列"neighborhoods"和"categories" ）

所以，我會盡可能用這個自定義函數展平：

poormansUnnest <- function(nestedDF){
  toBind <- list()
  for(col in names(nestedDF)){
    if(is.data.frame(nestedDF[[col]])){
      df <- poormansUnnest(nestedDF[[col]])
      names(df) <- paste0(col,'.',names(df))
      toBind[[length(toBind)+1]] <- df
    }else{
      toBind[[length(toBind)+1]] <- nestedDF[col]
    }
  }
  final <- do.call(cbind.data.frame,toBind)
  return(final)
}

res <- poormansUnnest(restaurant.data)

# store list columns in separate object (then you would do whatever you need with them...)
categories <- res$categories
neighborhoods<- res$neighborhoods

# remove the list columns from the data.frame
res$categories <- NULL
res$neighborhoods <- NULL

所以現在，您應該能夠使用gsub重命名res的列