[R] R in the NY Times

[hadley wickham]

Here's a couple of similar plots created with ggplot2.  I chose to
turn the data into a data frame with an explicit date column.  Using a
log scale somewhat stabilises the variability.

## SAS-L traffic
sas <- structure(list(Jan = c(NA, 546L, 548L, 853L, 1007L, 894L, 514L,
1720L, 1826L, 1941L, 1832L, 1636L, 2122L, 2722L, 2750L, 2305L,
357L), Feb = c(NA, 511L, 734L, 1024L, 1150L, 1068L, 493L, 1519L,
1537L, 1845L, 1846L, 1652L, 1960L, 1645L, 926L, 2255L, NA), Mar = c(NA,
658L, 963L, 805L, 1108L, 945L, 659L, 1177L, 1915L, 2010L, 1755L,
2188L, 629L, 1711L, 1728L, 2712L, NA), Apr = c(NA, 681L, 792L,
1052L, 1315L, 784L, 1077L, 1163L, 1467L, 2199L, 1757L, 1826L,
2169L, 2796L, 2766L, 2789L, NA), May = c(NA, 712L, 945L, 1163L,
1212L, 448L, 778L, 1963L, 1735L, 2373L, 1863L, 1836L, 2283L,
3147L, 2974L, 2025L, NA), Jun = c(NA, 751L, 1002L, 999L, 1127L,
813L, 540L, 1615L, 1905L, 2133L, 1701L, 2606L, 2407L, 2723L,
2691L, 2368L, NA), Jul = c(15L, 763L, 775L, 1184L, 1074L, 896L,
476L, 1572L, 2027L, 2445L, 1926L, 1843L, 2061L, 761L, 2435L,
2607L, NA), Aug = c(458L, 975L, 969L, 1053L, 692L, 823L, 612L,
1696L, 1976L, 1492L, 1689L, 2143L, 1793L, 2027L, 2592L, 2584L,
NA), Sep = c(330L, 703L, 745L, 1176L, 947L, 894L, 1351L, 1491L,
1439L, 1864L, 1646L, 1784L, 1365L, 2714L, 1868L, 2554L, NA),
Oct = c(219L, 805L, 691L, 1197L, 900L, 1129L, 1708L, 1669L,
1592L, 2133L, 1832L, 1712L, 1427L, 2983L, 2320L, 2434L, NA
), Nov = c(472L, 752L, 773L, 911L, 853L, 733L, 1720L, 1490L,
1636L, 1663L, 1545L, 1786L, 1518L, 2848L, 2112L, 1984L, NA
), Dec = c(517L, 666L, 765L, 844L, 677L, 492L, 1595L, 1298L,
1424L, 1520L, 1445L, 2148L, 1524L, 2374L, 1948L, 1921L, NA
)), .Names = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), class = "data.frame",
row.names = c("1993",
"1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001",
"2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009"
))

## s-news traffic
s <- structure(c(NA, 210, 264, 246, 230, 189, 197, 174, 109, 51, 48,
5, 273, 173, 313, 232, 255, 179, 230, 161, 87, 59, 63, NA, 378,
313, 285, 252, 242, 218, 257, 193, 99, 74, 58, NA, 293, 300,
264, 300, 228, 196, 151, 182, 123, 48, 47, NA, 330, 334, 306,
331, 219, 189, 164, 174, 107, 46, 31, NA, 243, 254, 247, 282,
248, 217, 175, 109, 96, 34, 27, NA, 219, 284, 245, 258, 230,
221, 154, 159, 84, 47, 40, NA, 209, 270, 302, 260, 207, 187,
187, 144, 97, 39, 28, NA, 191, 300, 204, 260, 221, 186, 195,
107, 68, 35, 41, NA, 241, 253, 251, 229, 280, 295, 150, 98, 73,
70, 30, NA, 181, 300, 261, 232, 228, 197, 176, 82, 53, 56, 27,
NA, 141, 194, 176, 194, 177, 142, 176, 84, 20, 41, 36, NA), .Dim = c(12L,
12L), .Dimnames = list(c("1998", "1999", "2000", "2001", "2002",
"2003", "2004", "2005", "2006", "2007", "2008", "2009"), c("Jan",
"Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct",
"Nov", "Dec")))

r <- structure(c(NA, 135, 226, 205, 558, 884, 1017, 1116, 1746,
2075, 1714, 2490, 462, NA, 79, 145, 355, 583, 697, 1137, 1580, 1724,
1920, 1907, 2583, NA, NA, 114, 195, 377, 651, 880, 1203, 1946,
1703, 2270, 2191, 2740, NA, 92, 101, 189, 377, 470, 965, 1488,
1657, 2057, 1818, 2145, 2487, NA, 36, 90, 161, 504, 552, 1057,
1268, 1561, 1887, 2029, 2210, 2517, NA, 47, 105, 186, 418, 550,
926, 1319, 1714, 2056, 1811, 2307, 2774, NA, 41, 110, 184, 293,
615, 918, 1344, 1618, 1872, 1785, 2138, 3268, NA, 37, 64, 148,
356, 562, 824, 1210, 1493, 1777, 1898, 2241, 2813, NA, 40, 94,
203, 434, 678, 705, 1443, 1534, 1709, 1902, 2028, 2990, NA, 76,
96, 231, 418, 657, 1055, 1567, 1712, 1810, 2328, 2708, 3037,
NA, 61, 184, 318, 433, 825, 1038, 1605, 1895, 1907, 2127, 2594,
2730, NA, 57, 105, 221, 422, 530, 742, 1158, 1481, 1508, 1450,
2028, 2399, NA), .Dim = c(13L, 12L), .Dimnames = list(c("1997",
"1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005",
"2006", "2007", "2008", "2009"), c("Jan", "Feb", "Mar", "Apr",
"May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")))

library(reshape)
sas <- melt(as.matrix(sas), na.rm = TRUE)
r <- melt(r, na.rm = TRUE)
s <- melt(s, na.rm = TRUE)
names(r) <- names(s) <- names(sas) <- c("year", "month", "count")

sas$software <- "sas"
s$software <- "s"
r$software <- "r"
all <- rbind(sas, s, r)
all$date <- with(all,
  as.Date(paste(year, month, 15, sep = "-"), "%Y-%b-%d"))


library(ggplot2)
qplot(date, count, data = all, geom = "line", colour = software) +
   geom_smooth(se = F, size = 1)
last_plot() + scale_y_log10(breaks = 10^(1:3), labels = 10^(1:3))

yearly <- ddply(all, .(year, software), function(df) c(count = sum(df$count)))
qplot(year, count, data = yearly, geom = "line", colour = software)


Hadley

-- 
http://had.co.nz/