title: how to select columns which share specific name in a data frame
body: <p>I am sorry to ask question which could be very simple. I have a data frame which I try to put the dput below. </p>
  
  <pre><code>mydf &lt;- structure(list(br.Id = c(1992.0001, 1992.0002, 1992.0003, 1992.0004, 
  1992.0005, 1992.0006, 1992.0007, 1992.0008, 1992.0009, 1992.001
  ), si.month = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), br.day = c(23L, 
  23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L), br.year = c(1992L, 
  1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L
  ), branch = 1:10, br.location = c(160170L, 160170L, 160170L, 160170L, 
  160170L, 160170L, 160170L, 160170L, 160170L, 160170L), si.length = c(90L, 
  128L, 112L, 68L, 56L, 58L, 111L, 111L, 115L, 65L), si.weight = c(9.3, 
  32.5, 19, 4.4, 2.1, 2.8, 16.1, 17.9, 22.7, 3.4), si.sex = structure(c(2L, 
  1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L), .Label = c("female", "male", 
  "unknown"), class = "factor"), maturity = structure(c(7L, 7L, 
  7L, 7L, 10L, 7L, 7L, 7L, 7L, 2L), .Label = c("developing", "immature", 
  "mature", "nearly.ripe", "nearly.spent", "recovering", "ripe", 
  "running", "spent", "unknown", "yoy"), class = "factor"), age = c(NA, 
  NA, NA, NA, NA, NA, NA, NA, NA, 1L)), .Names = c("br.Id", "si.month", 
  "br.day", "br.year", "branch", "br.location", "si.length", "si.weight", 
  "si.sex", "maturity", "age"), row.names = c(NA, 10L), class = "data.frame")
  </code></pre>
  
  <p>what I am trying to do is to select specific columns which share the same name as br. so the output should look like </p>
  
  <pre><code>      br.Id br.day br.year branch br.location
  1  1992.000     23    1992      1      160170
  2  1992.000     23    1992      2      160170
  3  1992.000     23    1992      3      160170
  4  1992.000     23    1992      4      160170
  5  1992.001     23    1992      5      160170
  6  1992.001     23    1992      6      160170
  7  1992.001     23    1992      7      160170
  8  1992.001     23    1992      8      160170
  9  1992.001     23    1992      9      160170
  10 1992.001     23    1992     10      160170
  </code></pre>
  
  <p>I thought maybe grep can be used to get these columns but I could not figure out how to use it. I thank you for any help </p>