使用 JSON API 源的 R 循环

Question

我正在尝试根据 ISBN 从 API (http://www.knigoed.info/api-prices.html) 获取图书价格数据。

这个想法是向函数提交 ISBN 的向量，以获取包含所有可用信息的数据框（或至少包含来自不同供应商的价格的 Data.Frame）

isbns<- c("9785170922789", "9785170804801", "9785699834174", "9785699717255", "9785170869237")


getISBNprice <- function(ISBN, source="http://www.knigoed.info/api/Prices?code=") {
        pathA <- source
        for (i in 1:length(ISBN)) {

                ISB <- ISBN[i]
                AAA <- paste(pathA, ISB, "&sortPrice=DESC&country=RU", sep="")
                document <- fromJSON(AAA, flatten = FALSE)

                dfp <- document$prices
                dfp <- cbind(dfp,ISB ) 
                # dfp <- cbind(dfp,BookID=document$bookId) 
                # dfp <- cbind(dfp,Title=document$title) 
                # dfp <- cbind(dfp,Author=document$author) 
                # dfp <- cbind(dfp,Publisher=document$publisher)
                # dfp <- cbind(dfp,Series=document$series)
                # dfp <- cbind(dfp,Picture=document$picture)

                if (!exists("AAAA")) {AAAA<- dfp} else {bind_rows(AAAA, dfp) }
        }

        AAAA
}        

但是函数返回错误： 1. 在 bind_rows_(x, .id) 中：不等因子级别：强制转换为字符 2：在 bind_rows_(x, .id) 中：不等因子级别：强制转换为字符 3：在 bind_rows_(x, .id) 中：不等因子级别：强制转换为字符 4：在 bind_rows_(x, .id) 中：不等因子级别：强制转换为字符

Answer 1

这是最简单的make a list from the start，这将使以后的简化变得更容易。 purrr 包可以使处理列表变得更加容易，尽管如果您愿意，可以将这里的用法替换为 base 的 lapply 和 mapply/Map。

library(purrr)

# Paste is vectorized, so make a list of URLs all at once. 
# `httr` can make a URL out of a list of named parameters, if it's more convenient.
results <- paste0("http://www.knigoed.info/api/Prices?code=", 
                  isbns, 
                  "&sortPrice=DESC&country=RU") %>% 
    # Iterate over vector of URLs, using fromJSON to pull and parse the request.
    # map, like lapply, will put the results into a list. 
    map(jsonlite::fromJSON, flatten = FALSE)

            # Grab "prices" element of each top-level list element
results %>% map('prices') %>% 
    # Iterate in parallel (like mapply/Map) over prices and isbns, making a data.frame of
    # each. map2_df will coerce the resulting list of data.frames to a single data.frame.
    map2_df(isbns, ~data.frame(isbn = .y, .x, stringsAsFactors = FALSE)) %>%
    # For pretty printing
    tibble::as_data_frame()

## # A tibble: 36 x 10
##             isbn shopId       name      domain
##            <chr>  <chr>      <chr>       <chr>
## 1  9785170922789     29    Магистр    booka.ru
## 2  9785170922789      3   Лабиринт labirint.ru
## 3  9785170922789     20  LitRes.ru   litres.ru
## 4  9785170804801     29    Магистр    booka.ru
## 5  9785170804801      2    Read.ru     read.ru
## 6  9785170804801      3   Лабиринт labirint.ru
## 7  9785170804801     63      Эксмо    eksmo.ru
## 8  9785170804801      1    OZON.ru     ozon.ru
## 9  9785170804801      4 My-shop.ru  my-shop.ru
## 10 9785170804801      1    OZON.ru     ozon.ru
## # ... with 26 more rows, and 6 more variables: url <chr>, available <lgl>, downloadable <lgl>,
## #   priceValue <dbl>, priceSuffix <chr>, year <int>