Proportion Table

We will now continue to work with the table that we made on the previous page.

As a first step, we can save the table as an object for further processing:

table.theme <- table(songs$THEME, 
                     exclude = NULL)

If we now use the command prop.table() on the object table.theme, we will see the proportions of the themes. This indicates how large the share of each theme is (with a number between 0 and 1).

prop.table(table.theme)

          Heartbreak       Life_and_death                 Love 
               0.145                0.131                0.139 
         Party_songs    People_and_places Politics_and_protest 
               0.162                0.145                0.141 
                 Sex                 <NA> 
               0.131                0.006

If we multiply the proportions by 100, we will get the percentages.

prop.table(table.theme)*100

          Heartbreak       Life_and_death                 Love 
                14.5                 13.1                 13.9 
         Party_songs    People_and_places Politics_and_protest 
                16.2                 14.5                 14.1 
                 Sex                 <NA> 
                13.1                  0.6

What percentage of the total number of songs in the Guardian list are about ‘People_and_places’?

  • Wrong
  • Wrong
  • Correct
  • Wrong

Now, you can try to make these two tables yourself. First make a frequency table of the variable YEAR. Save it as an object and make a proportion table using the object.

Practice

Make a frequency table and a proportion table for the variable YEAR. Use again theexclude = NULL argument.

table.year <- table(songs$YEAR, 
                    exclude = NULL)
table.year

1916 1922 1928 1929 1931 1932 1935 1936 1938 1939 1940 1941 1944 1946 1949 1950 
   1    1    6    2    2    2    1    1    2    3    1    2    2    2    1    2 
1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 
   3    1    1    4    3    9    3    6   10    5   14    7   17   27   33   37 
1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 
  33   39   31   23   30   27   24   27   23   18   27   25   34   25   19   18 
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 
  25   22   18   17   18   15   19   10   10   14   11   18   12    7    8    7 
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 <NA> 
  11   12    7   12   14    9   15   14   16   19    6 
prop.table(table.year)

 1916  1922  1928  1929  1931  1932  1935  1936  1938  1939  1940  1941  1944 
0.001 0.001 0.006 0.002 0.002 0.002 0.001 0.001 0.002 0.003 0.001 0.002 0.002 
 1946  1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  1960 
0.002 0.001 0.002 0.003 0.001 0.001 0.004 0.003 0.009 0.003 0.006 0.010 0.005 
 1961  1962  1963  1964  1965  1966  1967  1968  1969  1970  1971  1972  1973 
0.014 0.007 0.017 0.027 0.033 0.037 0.033 0.039 0.031 0.023 0.030 0.027 0.024 
 1974  1975  1976  1977  1978  1979  1980  1981  1982  1983  1984  1985  1986 
0.027 0.023 0.018 0.027 0.025 0.034 0.025 0.019 0.018 0.025 0.022 0.018 0.017 
 1987  1988  1989  1990  1991  1992  1993  1994  1995  1996  1997  1998  1999 
0.018 0.015 0.019 0.010 0.010 0.014 0.011 0.018 0.012 0.007 0.008 0.007 0.011 
 2000  2001  2002  2003  2004  2005  2006  2007  2008  <NA> 
0.012 0.007 0.012 0.014 0.009 0.015 0.014 0.016 0.019 0.006

As you can see, the proportion table for the variable year is not very informative due to the large number of categories of the variable. Cumulative frequency tables can help with this problem. How to make them, you will learn on the next page.

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