Cumulative frequencies or proportions can be a clearer way to present categorical variables. This allow us to see how many songs in total, or what proportion of all songs, were released before a certain year.
To do this, we use the cumsum() function on the tables. First the frequency table:
<- table (songs$ YEAR, exclude = NULL )cumsum (table.year)
1916 1922 1928 1929 1931 1932 1935 1936 1938 1939 1940 1941 1944 1946 1949 1950
1 2 8 10 12 14 15 16 18 21 22 24 26 28 29 31
1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
34 35 36 40 43 52 55 61 71 76 90 97 114 141 174 211
1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
244 283 314 337 367 394 418 445 468 486 513 538 572 597 616 634
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
659 681 699 716 734 749 768 778 788 802 813 831 843 850 858 865
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 <NA>
876 888 895 907 921 930 945 959 975 994 1000
We can use the same on a proportion table:
cumsum (prop.table (table.year))
1916 1922 1928 1929 1931 1932 1935 1936 1938 1939 1940 1941 1944
0.001 0.002 0.008 0.010 0.012 0.014 0.015 0.016 0.018 0.021 0.022 0.024 0.026
1946 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
0.028 0.029 0.031 0.034 0.035 0.036 0.040 0.043 0.052 0.055 0.061 0.071 0.076
1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
0.090 0.097 0.114 0.141 0.174 0.211 0.244 0.283 0.314 0.337 0.367 0.394 0.418
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986
0.445 0.468 0.486 0.513 0.538 0.572 0.597 0.616 0.634 0.659 0.681 0.699 0.716
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
0.734 0.749 0.768 0.778 0.788 0.802 0.813 0.831 0.843 0.850 0.858 0.865 0.876
2000 2001 2002 2003 2004 2005 2006 2007 2008 <NA>
0.888 0.895 0.907 0.921 0.930 0.945 0.959 0.975 0.994 1.000
Wrong
Correct
Wrong
Wrong