title: Override column types when importing data using readr::read_csv() when there are many columns
body: <p>I am trying to read a csv file using readr::read_csv in R. The csv file that I am importing has about 150 columns, I am just including the first few columns for the example. I am looking to override the second column from the default type (which is date when I do read_csv) to character, or other date format.  </p>
  
  <pre><code>GIS Join Match Code Data File Year  State Name  State Code  County Name County   Code   Area Name   Persons: Total
  G0100010    2008-2012   Alabama 1   Autauga County  1   Autauga County, Alabama 54590
  
  df &lt;- data.frame("GIS Join Match Code"="G0100010", "Data File" = "2008-2012", "State" = "Alabama", "County" = "Autauga County", "Population" = 54590)
  </code></pre>
  
  <p>The issue is that when I use readr::read_csv, it seems I may have to use all variables while overriding in the col_types (see error below). That is need to specify overriding all the 150 columns individually(?).. The question is that : Is there a way to specify overriding the col_type of just specific columns, or a named list of objects? In my case, it would be just overriding the column "Data File Year".</p>
  
  <p>I understand that any omitted columns will be automatically parsed, which is fine for my analysis. I think it gets further complex as the column names have a space in them in the file I downloaded (For e.g., "Data File Year", "State Code") etc.</p>
  
  <pre><code>tempdata &lt;- read_csv(df, col_types = "cc")
  Error: You have 135 column names, but 2 columns
  </code></pre>
  
  <p>The Other option I guess, if possible, is to just skip reading the second column all together? </p>