Question

我尝试使用https://data.worldbank.org/indicator/IS.AIR.PSGR

中的read.csv导入csv格式的数据

然而，read.csv函数返回：

read.table(file = file, header = header, sep = sep, quote = quote,中的错误：比列名更多的列。

我搜索过以前的帖子，但看起来答案在实际data tables的情况下有所不同，那么这个问题出了什么问题？

Answer 1

问题是由于前4行有随机文本。您需要使用skip = 4。使用read_csv包中的readr会更好，因为它会保留原始列名称。

library(readr)

dat <- read_csv("API_IS.AIR.PSGR_DS2_en_csv_v2.csv", skip = 4)

#> Warning: Missing column names filled in: 'X63' [63]
#> Parsed with column specification:
#> cols(
#>   .default = col_integer(),
#>   `Country Name` = col_character(),
#>   `Country Code` = col_character(),
#>   `Indicator Name` = col_character(),
#>   `Indicator Code` = col_character(),
#>   `1960` = col_character(),
#>   `1961` = col_character(),
#>   `1962` = col_character(),
#>   `1963` = col_character(),
#>   `1964` = col_character(),
#>   `1965` = col_character(),
#>   `1966` = col_character(),
#>   `1967` = col_character(),
#>   `1968` = col_character(),
#>   `1969` = col_character(),
#>   `1995` = col_double(),
#>   `2007` = col_double(),
#>   `2008` = col_double(),
#>   `2009` = col_double(),
#>   `2010` = col_double(),
#>   `2011` = col_double()
#>   # ... with 7 more columns
#> )
#> See spec(...) for full column specifications.

head(dat)

#> # A tibble: 6 x 63
#>   `Country Name` `Country Code` `Indicator Name`   `Indicator Code` `1960`
#>   <chr>          <chr>          <chr>              <chr>            <chr> 
#> 1 Aruba          ABW            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> 2 Afghanistan    AFG            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> 3 Angola         AGO            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> 4 Albania        ALB            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> 5 Andorra        AND            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> 6 Arab World     ARB            Air transport, pa~ IS.AIR.PSGR      <NA>  
#> # ... with 58 more variables: `1961` <chr>, `1962` <chr>, `1963` <chr>,
#> #   `1964` <chr>, `1965` <chr>, `1966` <chr>, `1967` <chr>, `1968` <chr>,
#> #   `1969` <chr>, `1970` <int>, `1971` <int>, `1972` <int>, `1973` <int>,
#> #   `1974` <int>, `1975` <int>, `1976` <int>, `1977` <int>, `1978` <int>,
#> #   `1979` <int>, `1980` <int>, `1981` <int>, `1982` <int>, `1983` <int>,
#> #   `1984` <int>, `1985` <int>, `1986` <int>, `1987` <int>, `1988` <int>,
#> #   `1989` <int>, `1990` <int>, `1991` <int>, `1992` <int>, `1993` <int>,
#> #   `1994` <int>, `1995` <dbl>, `1996` <int>, `1997` <int>, `1998` <int>,
#> #   `1999` <int>, `2000` <int>, `2001` <int>, `2002` <int>, `2003` <int>,
#> #   `2004` <int>, `2005` <int>, `2006` <int>, `2007` <dbl>, `2008` <dbl>,
#> #   `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>,
#> #   `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <chr>, X63 <chr>

由reprex package（v0.2.0）于2018-03-05创建。

Answer 2

在通过docker运行时，我遇到了类似的问题。因此，我必须先下载文件，然后再读取csv文件。

# download data
download.file("https://data.worldbank.org/indicator/IS.AIR.PSGR", dest = "file.csv")

# load data
gm = read.table("file.csv", header = T, stringsAsFactors = F, skipNul = F)

read.csv错误列比列名更多？

2 个答案: