README.md
In fishvice/pax: tidy fish data wrangling functions
pax
Preamble
... pax: population analysis in unix. This is a resuscitation of an old name - but likely going to do more.
Have you wished for a painless way to generate survey indices. Give the following a try.
Needed packages
devtools::install_github("fishvice/husky", dependencies = FALSE) # A temporary measure
devtools::install_github("fishvice/pax", dependencies = FALSE)
In addition it is assumed you have the fjolst-package.
Loading needed libraries
library(tidyverse)
library(fjolst) # for the time being
library(pax)
attach("/net/hafkaldi/export/u2/reikn/R/SurveyWork/SMB/Stations.rdata")
Length based indices
Specify the stations and strata
OK, here you have to have some background knowledge. But we select "all" SMB stations:
Station <-
STODVAR.all %>%
filter(tognumer %in% 1:39) %>%
select(id = synis.id, year = ar, towlength = toglengd, strata = oldstrata) %>%
mutate(towlength = pax:::trim_towlength(towlength),
mult = 1) %>%
arrange(id)
And get the appropriate strata as well (the old strata, which is not the same as the very old strata):
Stratas <-
husky::stratas_df %>%
select(strata, area = rall.area)
And then we leave the rest to the smx:calc_length_indices-function:
Cod spring survey index
res <- calc_length_indices(Station, Stratas, SPECIES = 1)
Extracting the tidy information and preparing the plot:
tidy_fixed <-
res$aggr %>%
filter(length == 5) %>%
mutate(source = "tidy")
p <-
tidy_fixed %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1")
Lets add the official calculations for comparison:
attach("/net/hafkaldi/export/u2/reikn/Splus5/SMB/Allindices.RData")
mri <-
All.indices %>%
filter(species == 1,
svaedi == "Heild",
lengd == 5,
type == "all") %>%
select(year = ar, cb = bio.staerri, cb.cv = cv.bio.staerri, type) %>%
mutate(source = "mri")
p +
geom_pointrange(data = mri,
aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "yellow") +
geom_line(data = mri, colour = "yellow") +
labs(x = NULL, y = NULL,
title = "Biomass indices",
subtitle = "Comparison of the tidyverse (red) and the Bible (grey)")
Something to worry about:?
tidy_fixed %>%
select(year, cb) %>%
left_join(mri %>% select(year, cb2 = cb)) %>%
mutate(diff = (cb - cb2)/cb2 * 100) %>%
summary()
## year cb cb2 diff
## Min. :1985 Min. :160666 Min. :160877 Min. :-2.68817
## 1st Qu.:1993 1st Qu.:243030 1st Qu.:241671 1st Qu.:-0.14274
## Median :2001 Median :294071 Median :288828 Median : 0.16847
## Mean :2001 Mean :337992 Mean :326905 Mean :-0.03683
## 3rd Qu.:2009 3rd Qu.:408901 3rd Qu.:387380 3rd Qu.: 0.31040
## Max. :2017 Max. :697694 Max. :626306 Max. : 0.80048
## NA's :1 NA's :1
So the tidyverse is 0.2% higher than the mri - some may want to dig into that.
Grásleppa spring survey index
Here lets try to calculate the index for Grásleppa. We use the same station set and strata as above. Hence only need to do specify in addition the sex:
res <- calc_length_indices(Station, Stratas, SPECIES = 48, Sex = 2)
# and a quick plot
res$aggr %>%
filter(length == 5) %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
labs(x = NULL, y = NULL,
title = "Grásleppa: Spring survey biomass index",
subtitle = "All stations")
"non-standard" species spring survey index
spe88 <- calc_length_indices(Station, Stratas, 88, lwcoeff = c(0.01, 3))
spe562 <- calc_length_indices(Station, Stratas, 562, lwcoeff = c(0.01, 3))
spe150 <- calc_length_indices(Station, Stratas, 150, lwcoeff = c(0.01, 3))
spe71 <- calc_length_indices(Station, Stratas, 71, lwcoeff = c(0.01, 3))
spe88$aggr %>%
mutate(Species = "Rauða sævesla") %>%
bind_rows(spe562$aggr %>% mutate(Species = "Ljóskjafta")) %>%
bind_rows(spe150$aggr %>% mutate(Species = "Svartgóma")) %>%
bind_rows(spe71$aggr %>% mutate(Species = "Ískóð")) %>%
filter(length == 140) %>%
select(year, Species, cn, cn.cv) %>%
ggplot(aes(year, cn)) +
geom_pointrange(aes(ymin = cn * (1 - cn.cv),
ymax = cn * (1 + cn.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
facet_wrap(~ Species, scale = "free_y") +
labs(x = NULL, y = NULL,
title = "Spring survey abundance index",
subtitle = "All stations")
## Warning: Removed 57 rows containing missing values (geom_pointrange).
So we have an invasion of Ljóskjafta and Svartgóma in recent years. That must explain the mackerel invasion :-)
NOTE: There is a bug when it comes to the biomass estimates of the two last species. And one needs to double test for when some species were only counted.
And at last be not the least - the fall survey
Stations <-
smh_strata_setup() %>%
select(id = synis.id, year = ar, towlength = toglengd, strata = newstrata) %>%
mutate(towlength = pax:::trim_towlength(towlength),
mult = 1) %>%
arrange(id)
## Strata fannst ekki fyrir stodvar 76,77,154,484,925,1099,1101,1303,2061,2566,2674,3059,3102,3316,3712,4309,4398,4835,5085,5484,5793,6367,6375,6906,6914,7289
Stratas <-
husky::newstratas_df %>%
select(strata, area = rall.area)
res <- calc_length_indices(Stations, Stratas, SPECIES = 1)
res$aggr %>%
filter(length == 5) %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
labs(x = NULL, y = NULL,
title = "Cod: Fall survey biomass index",
subtitle = "All stations")
Age based indices
Still under development (needs further testing), but here is one trial.
# Lest start from scratch
stratas <-
husky::stratas_df %>%
select(strata, area = rall.area)
SPECIES <- 1
lengthclass <- c(seq(4.5, 109.5, by = 5), 119.5, 139.5)
ind <- c(31931, 31932, 32131, 36731, 37031, 37131, 37132, 37231, 41431, 41531, 42231, 42232, 47431, 52331)
st1 <-
bind_rows(husky::STODVAR) %>%
filter(tognumer < 20 | index %in% ind) %>%
mutate(region = ifelse(area %in% c(1, 9, 10), "south",
ifelse(area %in% c(2:8), "north", NA))) %>%
filter(!is.na(region)) %>%
select(synis.id, ar, towlength = toglengd, region, strata = newstrata)
st2 <-
bind_rows(husky::STODVAR) %>%
filter(area %in% 1:10) %>%
bind_rows(lesa.stodvar(leidangur="A4-2001")) %>%
mutate(region = ifelse(area %in% c(1, 9, 10), "south",
ifelse(area %in% c(2:8), "north", NA))) %>%
filter(!is.na(region)) %>%
select(synis.id, ar, towlength = toglengd, region, strata = newstrata)
x <- calc_age_indices(st_length = st1, st_ototliths = st2, species = 1)
x$aggr %>%
mutate(n = round(n/1e6, 2)) %>%
select(-n.cv) %>%
filter(aldur %in% 1:11) %>% # just to fit things on the screen
spread(aldur, n) %>%
as.data.frame()
## ar 1 2 3 4 5 6 7 8 9 10 11
## 1 1985 16.58 110.73 35.52 48.60 64.65 23.07 15.31 5.07 3.40 1.84 0.29
## 2 1986 15.10 60.71 96.15 22.51 21.53 27.55 7.17 2.80 0.94 0.82 0.30
## 3 1987 3.66 28.32 104.66 83.00 21.38 12.75 12.95 2.80 0.99 0.42 0.44
## 4 1988 3.45 7.07 72.67 103.77 69.68 8.39 6.45 7.22 0.67 0.28 0.12
## 5 1989 4.05 16.43 22.10 80.04 74.32 39.26 4.85 1.70 1.41 0.27 0.19
## 6 1990 5.57 11.82 26.16 14.20 28.04 35.24 16.77 1.75 0.58 0.47 0.12
## 7 1991 3.95 16.06 18.24 30.28 15.53 19.03 22.47 4.91 0.94 0.31 0.22
## 8 1992 0.72 16.94 33.65 18.96 16.71 6.90 6.35 5.78 1.48 0.23 0.03
## 9 1993 3.57 4.78 30.97 36.84 13.55 10.64 2.43 2.04 1.40 0.41 0.13
## 10 1994 14.44 15.02 9.07 26.97 22.48 6.09 3.97 0.80 0.54 0.51 0.18
## 11 1995 1.08 29.37 24.85 9.08 24.57 18.48 4.03 1.92 0.38 0.19 0.24
## 12 1996 3.73 5.45 42.70 29.49 12.91 14.66 14.05 3.82 1.04 0.17 0.05
## 13 1997 1.18 22.23 13.53 56.28 29.22 9.54 8.81 6.71 0.58 0.27 0.15
## 14 1998 8.08 5.34 30.06 16.16 62.24 28.92 6.65 5.27 3.05 0.68 0.16
## 15 1999 7.41 33.05 7.03 42.32 13.11 23.72 11.11 2.34 1.29 0.75 0.18
## 16 2000 18.92 27.67 55.12 6.92 30.08 8.41 8.20 4.14 0.49 0.28 0.09
## 17 2001 12.30 23.50 36.49 38.17 4.86 15.26 3.34 2.01 0.76 0.23 0.11
## 18 2002 0.97 38.57 41.41 40.32 36.75 7.17 8.06 1.48 0.72 0.29 0.06
## 19 2003 11.20 4.21 46.58 36.61 28.45 16.87 3.76 4.42 0.98 0.35 0.15
## 20 2004 7.02 26.51 8.13 64.68 38.50 27.97 15.94 2.81 3.27 0.56 0.34
## 21 2005 2.70 17.84 41.81 9.99 46.83 25.24 12.02 6.25 0.98 0.98 0.24
## 22 2006 9.12 7.45 25.12 40.62 11.76 31.69 11.77 3.95 1.58 0.28 0.18
## 23 2007 5.67 19.05 9.05 23.03 30.12 10.11 11.50 6.05 2.41 0.84 0.27
## 24 2008 6.76 12.44 23.08 9.87 22.92 22.89 9.27 7.95 3.00 0.78 0.44
## 25 2009 22.01 12.66 16.66 22.97 15.79 26.48 16.50 4.54 3.11 1.12 0.26
## 26 2010 18.73 21.59 18.94 18.28 24.67 14.27 18.53 9.94 3.07 1.88 0.55
## 27 2011 3.58 23.05 27.64 20.17 23.05 26.84 14.74 13.43 4.91 0.98 0.98
## 28 2012 20.41 11.04 39.53 57.23 42.31 31.53 28.16 10.79 6.97 3.16 0.91
## 29 2013 10.96 33.72 18.18 44.68 47.12 26.06 17.64 14.64 7.03 3.36 1.68
## 30 2014 3.31 24.29 38.93 23.82 47.67 38.28 18.14 8.29 4.33 2.22 0.86
## 31 2015 21.10 10.98 28.11 42.51 21.33 42.08 29.35 16.99 5.11 3.16 1.48
## 32 2016 31.63 31.65 15.25 37.72 54.88 28.05 38.38 19.41 6.94 2.36 1.21
Above it not a perfect match with the husky-approach, but who says the latter is correct :-)
Some info
devtools::session_info()
## setting value
## version R version 3.3.2 (2016-10-31)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate is_IS.UTF-8
## tz Atlantic/Reykjavik
## date 2017-05-24
##
## package * version date source
## assertthat 0.2.0 2017-04-11 cran (@0.2.0)
## backports 1.0.4 2016-10-24 cran (@1.0.4)
## colorspace 1.2-6 2015-03-11 CRAN (R 3.2.0)
## DBI 0.6 2017-03-09 cran (@0.6)
## devtools 1.12.0 2016-12-05 CRAN (R 3.3.1)
## digest 0.6.12 2017-01-27 CRAN (R 3.3.2)
## dplyr * 0.5.0 2016-06-24 CRAN (R 3.3.1)
## evaluate 0.10 2016-10-11 cran (@0.10)
## fjolst * 1.0 2017-05-14 local
## geo * 1.4-3 2015-07-03 CRAN (R 3.3.1)
## ggplot2 * 2.2.1.9000 2017-04-12 Github (hadley/ggplot2@f4398b6)
## gtable 0.2.0 2016-02-26 cran (@0.2.0)
## htmltools 0.3.5 2016-03-21 CRAN (R 3.3.0)
## husky 0.0.3.9000 2017-05-24 Github (fishvice/husky@f0b9fb9)
## knitr 1.15.1 2016-11-22 cran (@1.15.1)
## labeling 0.3 2014-08-23 CRAN (R 3.2.0)
## lazyeval 0.2.0 2016-06-12 cran (@0.2.0)
## magrittr 1.5 2014-11-22 CRAN (R 3.1.2)
## mapdata * 2.2-6 2016-01-14 cran (@2.2-6)
## maps * 3.1.1 2016-07-27 cran (@3.1.1)
## memoise 1.0.0 2016-01-29 CRAN (R 3.3.0)
## munsell 0.4.3 2016-02-13 cran (@0.4.3)
## ora 2.0-1 2014-04-10 CRAN (R 3.1.2)
## pax * 0.0.1.9000 2017-05-24 Github (fishvice/pax@5a9af82)
## plyr 1.8.4 2016-06-08 cran (@1.8.4)
## purrr * 0.2.2.2 2017-05-11 cran (@0.2.2.2)
## R6 2.2.0 2016-10-05 cran (@2.2.0)
## Rcpp 0.12.11 2017-05-22 cran (@0.12.11)
## readr * 1.0.0 2016-08-03 CRAN (R 3.3.1)
## rlang 0.1.1 2017-05-18 cran (@0.1.1)
## rmarkdown 1.5 2017-04-26 CRAN (R 3.3.2)
## ROracle 1.2-2 2016-02-17 CRAN (R 3.3.0)
## rprojroot 1.1 2016-10-29 cran (@1.1)
## scales 0.4.1 2016-11-09 CRAN (R 3.3.1)
## stringi 1.1.5 2017-04-07 CRAN (R 3.3.2)
## stringr 1.2.0 2017-02-18 cran (@1.2.0)
## tibble * 1.3.1 2017-05-17 CRAN (R 3.3.2)
## tidyr * 0.6.3 2017-05-15 cran (@0.6.3)
## tidyverse * 1.0.0 2016-09-09 CRAN (R 3.3.1)
## withr 1.0.2 2016-06-20 CRAN (R 3.3.0)
## yaml 2.1.14 2016-11-12 cran (@2.1.14)
fishvice/pax documentation built on May 16, 2019, 1:13 p.m.
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pax
Preamble
... pax: population analysis in unix. This is a resuscitation of an old name - but likely going to do more.
Have you wished for a painless way to generate survey indices. Give the following a try.
Needed packages
devtools::install_github("fishvice/husky", dependencies = FALSE) # A temporary measure
devtools::install_github("fishvice/pax", dependencies = FALSE)
In addition it is assumed you have the fjolst-package.
Loading needed libraries
library(tidyverse)
library(fjolst) # for the time being
library(pax)
attach("/net/hafkaldi/export/u2/reikn/R/SurveyWork/SMB/Stations.rdata")
Length based indices
Specify the stations and strata
OK, here you have to have some background knowledge. But we select "all" SMB stations:
Station <-
STODVAR.all %>%
filter(tognumer %in% 1:39) %>%
select(id = synis.id, year = ar, towlength = toglengd, strata = oldstrata) %>%
mutate(towlength = pax:::trim_towlength(towlength),
mult = 1) %>%
arrange(id)
And get the appropriate strata as well (the old strata, which is not the same as the very old strata):
Stratas <-
husky::stratas_df %>%
select(strata, area = rall.area)
And then we leave the rest to the smx:calc_length_indices-function:
Cod spring survey index
res <- calc_length_indices(Station, Stratas, SPECIES = 1)
Extracting the tidy information and preparing the plot:
tidy_fixed <-
res$aggr %>%
filter(length == 5) %>%
mutate(source = "tidy")
p <-
tidy_fixed %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1")
Lets add the official calculations for comparison:
attach("/net/hafkaldi/export/u2/reikn/Splus5/SMB/Allindices.RData")
mri <-
All.indices %>%
filter(species == 1,
svaedi == "Heild",
lengd == 5,
type == "all") %>%
select(year = ar, cb = bio.staerri, cb.cv = cv.bio.staerri, type) %>%
mutate(source = "mri")
p +
geom_pointrange(data = mri,
aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "yellow") +
geom_line(data = mri, colour = "yellow") +
labs(x = NULL, y = NULL,
title = "Biomass indices",
subtitle = "Comparison of the tidyverse (red) and the Bible (grey)")
Something to worry about:?
tidy_fixed %>%
select(year, cb) %>%
left_join(mri %>% select(year, cb2 = cb)) %>%
mutate(diff = (cb - cb2)/cb2 * 100) %>%
summary()
## year cb cb2 diff
## Min. :1985 Min. :160666 Min. :160877 Min. :-2.68817
## 1st Qu.:1993 1st Qu.:243030 1st Qu.:241671 1st Qu.:-0.14274
## Median :2001 Median :294071 Median :288828 Median : 0.16847
## Mean :2001 Mean :337992 Mean :326905 Mean :-0.03683
## 3rd Qu.:2009 3rd Qu.:408901 3rd Qu.:387380 3rd Qu.: 0.31040
## Max. :2017 Max. :697694 Max. :626306 Max. : 0.80048
## NA's :1 NA's :1
So the tidyverse is 0.2% higher than the mri - some may want to dig into that.
Grásleppa spring survey index
Here lets try to calculate the index for Grásleppa. We use the same station set and strata as above. Hence only need to do specify in addition the sex:
res <- calc_length_indices(Station, Stratas, SPECIES = 48, Sex = 2)
# and a quick plot
res$aggr %>%
filter(length == 5) %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
labs(x = NULL, y = NULL,
title = "Grásleppa: Spring survey biomass index",
subtitle = "All stations")
"non-standard" species spring survey index
spe88 <- calc_length_indices(Station, Stratas, 88, lwcoeff = c(0.01, 3))
spe562 <- calc_length_indices(Station, Stratas, 562, lwcoeff = c(0.01, 3))
spe150 <- calc_length_indices(Station, Stratas, 150, lwcoeff = c(0.01, 3))
spe71 <- calc_length_indices(Station, Stratas, 71, lwcoeff = c(0.01, 3))
spe88$aggr %>%
mutate(Species = "Rauða sævesla") %>%
bind_rows(spe562$aggr %>% mutate(Species = "Ljóskjafta")) %>%
bind_rows(spe150$aggr %>% mutate(Species = "Svartgóma")) %>%
bind_rows(spe71$aggr %>% mutate(Species = "Ískóð")) %>%
filter(length == 140) %>%
select(year, Species, cn, cn.cv) %>%
ggplot(aes(year, cn)) +
geom_pointrange(aes(ymin = cn * (1 - cn.cv),
ymax = cn * (1 + cn.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
facet_wrap(~ Species, scale = "free_y") +
labs(x = NULL, y = NULL,
title = "Spring survey abundance index",
subtitle = "All stations")
## Warning: Removed 57 rows containing missing values (geom_pointrange).
So we have an invasion of Ljóskjafta and Svartgóma in recent years. That must explain the mackerel invasion :-)
NOTE: There is a bug when it comes to the biomass estimates of the two last species. And one needs to double test for when some species were only counted.
And at last be not the least - the fall survey
Stations <-
smh_strata_setup() %>%
select(id = synis.id, year = ar, towlength = toglengd, strata = newstrata) %>%
mutate(towlength = pax:::trim_towlength(towlength),
mult = 1) %>%
arrange(id)
## Strata fannst ekki fyrir stodvar 76,77,154,484,925,1099,1101,1303,2061,2566,2674,3059,3102,3316,3712,4309,4398,4835,5085,5484,5793,6367,6375,6906,6914,7289
Stratas <-
husky::newstratas_df %>%
select(strata, area = rall.area)
res <- calc_length_indices(Stations, Stratas, SPECIES = 1)
res$aggr %>%
filter(length == 5) %>%
select(year, cb, cb.cv) %>%
ggplot(aes(year, cb)) +
geom_pointrange(aes(ymin = cb * (1 - cb.cv),
ymax = cb * (1 + cb.cv)),
colour = "red", lwd = 1) +
geom_line(colour = "red", lwd = 1) +
scale_colour_brewer(palette = "Set1") +
labs(x = NULL, y = NULL,
title = "Cod: Fall survey biomass index",
subtitle = "All stations")
Age based indices
Still under development (needs further testing), but here is one trial.
# Lest start from scratch
stratas <-
husky::stratas_df %>%
select(strata, area = rall.area)
SPECIES <- 1
lengthclass <- c(seq(4.5, 109.5, by = 5), 119.5, 139.5)
ind <- c(31931, 31932, 32131, 36731, 37031, 37131, 37132, 37231, 41431, 41531, 42231, 42232, 47431, 52331)
st1 <-
bind_rows(husky::STODVAR) %>%
filter(tognumer < 20 | index %in% ind) %>%
mutate(region = ifelse(area %in% c(1, 9, 10), "south",
ifelse(area %in% c(2:8), "north", NA))) %>%
filter(!is.na(region)) %>%
select(synis.id, ar, towlength = toglengd, region, strata = newstrata)
st2 <-
bind_rows(husky::STODVAR) %>%
filter(area %in% 1:10) %>%
bind_rows(lesa.stodvar(leidangur="A4-2001")) %>%
mutate(region = ifelse(area %in% c(1, 9, 10), "south",
ifelse(area %in% c(2:8), "north", NA))) %>%
filter(!is.na(region)) %>%
select(synis.id, ar, towlength = toglengd, region, strata = newstrata)
x <- calc_age_indices(st_length = st1, st_ototliths = st2, species = 1)
x$aggr %>%
mutate(n = round(n/1e6, 2)) %>%
select(-n.cv) %>%
filter(aldur %in% 1:11) %>% # just to fit things on the screen
spread(aldur, n) %>%
as.data.frame()
## ar 1 2 3 4 5 6 7 8 9 10 11
## 1 1985 16.58 110.73 35.52 48.60 64.65 23.07 15.31 5.07 3.40 1.84 0.29
## 2 1986 15.10 60.71 96.15 22.51 21.53 27.55 7.17 2.80 0.94 0.82 0.30
## 3 1987 3.66 28.32 104.66 83.00 21.38 12.75 12.95 2.80 0.99 0.42 0.44
## 4 1988 3.45 7.07 72.67 103.77 69.68 8.39 6.45 7.22 0.67 0.28 0.12
## 5 1989 4.05 16.43 22.10 80.04 74.32 39.26 4.85 1.70 1.41 0.27 0.19
## 6 1990 5.57 11.82 26.16 14.20 28.04 35.24 16.77 1.75 0.58 0.47 0.12
## 7 1991 3.95 16.06 18.24 30.28 15.53 19.03 22.47 4.91 0.94 0.31 0.22
## 8 1992 0.72 16.94 33.65 18.96 16.71 6.90 6.35 5.78 1.48 0.23 0.03
## 9 1993 3.57 4.78 30.97 36.84 13.55 10.64 2.43 2.04 1.40 0.41 0.13
## 10 1994 14.44 15.02 9.07 26.97 22.48 6.09 3.97 0.80 0.54 0.51 0.18
## 11 1995 1.08 29.37 24.85 9.08 24.57 18.48 4.03 1.92 0.38 0.19 0.24
## 12 1996 3.73 5.45 42.70 29.49 12.91 14.66 14.05 3.82 1.04 0.17 0.05
## 13 1997 1.18 22.23 13.53 56.28 29.22 9.54 8.81 6.71 0.58 0.27 0.15
## 14 1998 8.08 5.34 30.06 16.16 62.24 28.92 6.65 5.27 3.05 0.68 0.16
## 15 1999 7.41 33.05 7.03 42.32 13.11 23.72 11.11 2.34 1.29 0.75 0.18
## 16 2000 18.92 27.67 55.12 6.92 30.08 8.41 8.20 4.14 0.49 0.28 0.09
## 17 2001 12.30 23.50 36.49 38.17 4.86 15.26 3.34 2.01 0.76 0.23 0.11
## 18 2002 0.97 38.57 41.41 40.32 36.75 7.17 8.06 1.48 0.72 0.29 0.06
## 19 2003 11.20 4.21 46.58 36.61 28.45 16.87 3.76 4.42 0.98 0.35 0.15
## 20 2004 7.02 26.51 8.13 64.68 38.50 27.97 15.94 2.81 3.27 0.56 0.34
## 21 2005 2.70 17.84 41.81 9.99 46.83 25.24 12.02 6.25 0.98 0.98 0.24
## 22 2006 9.12 7.45 25.12 40.62 11.76 31.69 11.77 3.95 1.58 0.28 0.18
## 23 2007 5.67 19.05 9.05 23.03 30.12 10.11 11.50 6.05 2.41 0.84 0.27
## 24 2008 6.76 12.44 23.08 9.87 22.92 22.89 9.27 7.95 3.00 0.78 0.44
## 25 2009 22.01 12.66 16.66 22.97 15.79 26.48 16.50 4.54 3.11 1.12 0.26
## 26 2010 18.73 21.59 18.94 18.28 24.67 14.27 18.53 9.94 3.07 1.88 0.55
## 27 2011 3.58 23.05 27.64 20.17 23.05 26.84 14.74 13.43 4.91 0.98 0.98
## 28 2012 20.41 11.04 39.53 57.23 42.31 31.53 28.16 10.79 6.97 3.16 0.91
## 29 2013 10.96 33.72 18.18 44.68 47.12 26.06 17.64 14.64 7.03 3.36 1.68
## 30 2014 3.31 24.29 38.93 23.82 47.67 38.28 18.14 8.29 4.33 2.22 0.86
## 31 2015 21.10 10.98 28.11 42.51 21.33 42.08 29.35 16.99 5.11 3.16 1.48
## 32 2016 31.63 31.65 15.25 37.72 54.88 28.05 38.38 19.41 6.94 2.36 1.21
Above it not a perfect match with the husky-approach, but who says the latter is correct :-)
Some info
devtools::session_info()
## setting value
## version R version 3.3.2 (2016-10-31)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate is_IS.UTF-8
## tz Atlantic/Reykjavik
## date 2017-05-24
##
## package * version date source
## assertthat 0.2.0 2017-04-11 cran (@0.2.0)
## backports 1.0.4 2016-10-24 cran (@1.0.4)
## colorspace 1.2-6 2015-03-11 CRAN (R 3.2.0)
## DBI 0.6 2017-03-09 cran (@0.6)
## devtools 1.12.0 2016-12-05 CRAN (R 3.3.1)
## digest 0.6.12 2017-01-27 CRAN (R 3.3.2)
## dplyr * 0.5.0 2016-06-24 CRAN (R 3.3.1)
## evaluate 0.10 2016-10-11 cran (@0.10)
## fjolst * 1.0 2017-05-14 local
## geo * 1.4-3 2015-07-03 CRAN (R 3.3.1)
## ggplot2 * 2.2.1.9000 2017-04-12 Github (hadley/ggplot2@f4398b6)
## gtable 0.2.0 2016-02-26 cran (@0.2.0)
## htmltools 0.3.5 2016-03-21 CRAN (R 3.3.0)
## husky 0.0.3.9000 2017-05-24 Github (fishvice/husky@f0b9fb9)
## knitr 1.15.1 2016-11-22 cran (@1.15.1)
## labeling 0.3 2014-08-23 CRAN (R 3.2.0)
## lazyeval 0.2.0 2016-06-12 cran (@0.2.0)
## magrittr 1.5 2014-11-22 CRAN (R 3.1.2)
## mapdata * 2.2-6 2016-01-14 cran (@2.2-6)
## maps * 3.1.1 2016-07-27 cran (@3.1.1)
## memoise 1.0.0 2016-01-29 CRAN (R 3.3.0)
## munsell 0.4.3 2016-02-13 cran (@0.4.3)
## ora 2.0-1 2014-04-10 CRAN (R 3.1.2)
## pax * 0.0.1.9000 2017-05-24 Github (fishvice/pax@5a9af82)
## plyr 1.8.4 2016-06-08 cran (@1.8.4)
## purrr * 0.2.2.2 2017-05-11 cran (@0.2.2.2)
## R6 2.2.0 2016-10-05 cran (@2.2.0)
## Rcpp 0.12.11 2017-05-22 cran (@0.12.11)
## readr * 1.0.0 2016-08-03 CRAN (R 3.3.1)
## rlang 0.1.1 2017-05-18 cran (@0.1.1)
## rmarkdown 1.5 2017-04-26 CRAN (R 3.3.2)
## ROracle 1.2-2 2016-02-17 CRAN (R 3.3.0)
## rprojroot 1.1 2016-10-29 cran (@1.1)
## scales 0.4.1 2016-11-09 CRAN (R 3.3.1)
## stringi 1.1.5 2017-04-07 CRAN (R 3.3.2)
## stringr 1.2.0 2017-02-18 cran (@1.2.0)
## tibble * 1.3.1 2017-05-17 CRAN (R 3.3.2)
## tidyr * 0.6.3 2017-05-15 cran (@0.6.3)
## tidyverse * 1.0.0 2016-09-09 CRAN (R 3.3.1)
## withr 1.0.2 2016-06-20 CRAN (R 3.3.0)
## yaml 2.1.14 2016-11-12 cran (@2.1.14)
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