-
Notifications
You must be signed in to change notification settings - Fork 0
/
make_tables.R
104 lines (94 loc) · 5.81 KB
/
make_tables.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
## Source thsi file to make the tables, note it creates csv files in the#
# results folder
## table of MARES since now dropped from the paper
mare.table.long <- ddply(binning.long.figure, .(species,data,binmatch,variable, data.binwidth),
summarize,
median_ = median(value, na.rm = TRUE),
l2 = quantile(value, 0.25, na.rm = TRUE),
u2 = quantile(value, 0.75, na.rm = TRUE),
mare = 100 * round(median(abs(value), na.rm = TRUE), 3),
mre= 100*round(median(value, na.rm=TRUE),3),
pct.converged=pct.converged[1],
count = length(value))
## cast it into wide for easier reading
mare.table.long$binmatch <- ifelse(mare.table.long$binmatch!='pbin=1cm', 'Match', 'No Match')
mare.table.wide <- reshape2::dcast(mare.table.long,
formula=species+data+variable+binmatch~data.binwidth,
value.var='mare')
write.csv(mare.table.wide, 'results/table_mares.csv')
mre.table.wide <- reshape2::dcast(mare.table.long,
formula=species+data+variable+binmatch~data.binwidth,
value.var='mre')
write.csv(mre.table.wide, 'results/table_mres.csv')
## tried a quick test of plotting age vs CAAL but didn't really help understand
## datatype <- rep("Age", nrow(mare.table.long))
## datatype[grep("C", as.character(mare.table.long$data))] <- "CAAL"
## dataquant <- rep("Rich", nrow(mare.table.long))
## dataquant[grep("Poor", as.character(mare.table.long$data))] <- "Limited"
## mare.table.long$datatype <- datatype
## mare.table.long$dataquant <- dataquant
## mare.table.data <- reshape2::dcast(mare.table.long,
## formula=species+data.binwidth+variable+binmatch+dataquant~datatype,
## value.var='mare')
## mare.table.data <- subset(mare.table.data, binmatch=='No Match' | data.binwidth==1)
## ggplot(mare.table.data, aes(Age, CAAL, color=data.binwidth))+geom_point() + facet_grid(species~dataquant+variable)
## table of converged binning scenarios
write.table(dcast(binning.counts, value.var='pct.converged',
formula=species+data+binmatch~data.binwidth),
file="results/table_binning_convergence.csv",
sep=",", row.names=FALSE)
## table of run time
binning.runtime.table <- dcast(binning.runtime, value.var='median.runtime', formula=species+data~B)
write.table(x=binning.runtime.table,
file='results/table_binning_runtime.csv', sep=',',
row.names=FALSE)
## divide by B0 to standardize across species and scenarios
binning.runtime.table[,-(1:2)] <- 100*round(binning.runtime.table[,-(1:2)]/binning.runtime.table[,3],2)
write.table(x=binning.runtime.table,
file='results/table_binning_runtime_normalized.csv', sep=',',
row.names=FALSE)
binning.growth.table <- dcast(ddply(binning.long.growth, .(species, data, B, variable), summarize,
median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~B)
write.table(x=binning.growth.table,
file='results/table_binning_growth.csv', sep=',',
row.names=FALSE)
binning.selex.table <- dcast(ddply(binning.long.selex, .(species, data, B, variable), summarize,
median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~B)
write.table(x=binning.selex.table,
file='results/table_binning_selex.csv', sep=',',
row.names=FALSE)
binning.management.table <- dcast(ddply(binning.long.management, .(species, data, B, variable), summarize,
median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~B)
write.table(x=binning.management.table,
file='results/table_binning_management.csv', sep=',',
row.names=FALSE)
## tcomp.growth.table <- dcast(ddply(tcomp.long.growth, .(species, data, tvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~tvalue)
## write.table(x=tcomp.growth.table,
## file='results/table_tcomp_growth.csv', sep=',',
## row.names=FALSE)
## tcomp.selex.table <- dcast(ddply(tcomp.long.selex, .(species, data, tvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~tvalue)
## write.table(x=tcomp.selex.table,
## file='results/table_tcomp_selex.csv', sep=',',
## row.names=FALSE)
## tcomp.management.table <- dcast(ddply(tcomp.long.management, .(species, data, tvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~tvalue)
## write.table(x=tcomp.management.table,
## file='results/table_tcomp_management.csv', sep=',',
## row.names=FALSE)
## robust.growth.table <- dcast(ddply(robust.long.growth, .(species, data, rvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~rvalue)
## write.table(x=robust.growth.table,
## file='results/table_robust_growth.csv', sep=',',
## row.names=FALSE)
## robust.selex.table <- dcast(ddply(robust.long.selex, .(species, data, rvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~rvalue)
## write.table(x=robust.selex.table,
## file='results/table_robust_selex.csv', sep=',',
## row.names=FALSE)
## robust.management.table <- dcast(ddply(robust.long.management, .(species, data, rvalue, variable), summarize,
## median.re=round(median(value),3)), value.var="median.re", formula=species+data+variable~rvalue)
## write.table(x=robust.management.table,
## file='results/table_robust_management.csv', sep=',',
## row.names=FALSE)