d <- read.table("dat/boarfish_catch_at_age.csv",header=TRUE,sep=",")
names(d) <- c("age",2007:2013)
d <- melt(d,"age")
names(d) <- c("age","year","oC")
d$year <- as.integer(as.character(d$year))
dcast(d,age ~ year,value.var="oC")
## age 2007 2008 2009 2010 2011 2012 2013
## 1 1 0 0 1575 2415 0 28 301
## 2 2 352 5488 15043 11229 2894 893 7148
## 3 3 2114 21140 65744 72709 41913 5467 156680
## 4 4 40851 105575 338931 294382 28148 41278 58522
## 5 5 48915 141300 475619 567689 30116 110272 59797
## 6 6 62713 195339 543707 878363 175696 146582 68949
## 7 7 26132 104031 307333 522703 143967 492078 302967
## 8 8 29766 66570 172783 293719 107126 365840 250341
## 9 9 56075 53159 155477 276672 77861 271916 212318
## 10 10 44875 46893 130148 232122 60022 173486 160137
## 11 11 14019 15289 42521 78588 46079 69396 63025
## 12 12 32359 21178 61350 114600 40468 40968 41490
## 13 13 4848 11854 39609 59932 24352 58888 59380
## 14 14 16837 13570 31569 59060 19724 30277 30355
## 15 15 109481 112947 196967 349320 157707 217260 239366
x <- d
x$year <- x$year + 1
x$age <- x$age + 1
names(x)[3] <- c("oC2")
d <- join(d,x)
## Joining by: age, year
d$logC <- log(d$oC2/d$oC)
i <- d$age %in% c(3:12)
dcast(d[i,], age ~ year,value.var = "logC")
## age 2007 2008 2009 2010 2011 2012 2013
## 1 3 NA -4.0953 -2.48320 -1.57555 -1.3171 -0.63609 -5.1674
## 2 4 NA -3.9108 -2.77463 -1.49911 0.9490 0.01527 -2.3707
## 3 5 NA -1.2410 -1.50520 -0.51578 2.2798 -1.36547 -0.3706
## 4 6 NA -1.3847 -1.34753 -0.61344 1.1728 -1.58253 0.4696
## 5 7 NA -0.5061 -0.45320 0.03940 1.8085 -1.02988 -0.7260
## 6 8 NA -0.9351 -0.50735 0.04531 1.5850 -0.93261 0.6758
## 7 9 NA -0.5799 -0.84824 -0.47080 1.3277 -0.93149 0.5441
## 8 10 NA 0.1788 -0.89538 -0.40077 1.5281 -0.80117 0.5295
## 9 11 NA 1.0767 0.09787 0.50445 1.6169 -0.14512 1.0126
## 10 12 NA -0.4125 -1.38946 -0.99145 0.6637 0.11757 0.5144
ggplot(d[i,],aes(year,logC)) + geom_text(aes(label=age)) + geom_line(aes(group=age))
## Warning: Removed 10 rows containing missing values (geom_text).
## Warning: Removed 10 rows containing missing values (geom_path).
i <- d$age %in% c(5:13)
ggplot(d[i,],aes(year,logC)) + geom_text(aes(label=age)) + geom_point() + geom_smooth()
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## Warning: Removed 9 rows containing missing values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_text).
## Warning: Removed 9 rows containing missing values (geom_point).
