I tried to import data in csv format using read.csv from https://data.worldbank.org/indicator/IS.AIR.PSGR
However, the read.csv function gives back:
Error in
read.table(file = file, header = header, sep = sep, quote = quote,: more columns than column names.
I searched previous posts but it looks like the answers are different case by case by the actual data tables, so what's wrong with this one?
The problem was due to the first 4 lines having random text. You need to use skip = 4. Using read_csv from readr package is better as it preserves original column names.
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>
Created on 2018-03-05 by the reprex package (v0.2.0).
I had similar issue while running through docker. So, I had to download the file first then read csv file.
# 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)
