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pollen is a set of functions for working with aerobiological data. It takes care of some of the most widely use aerobiological calculations, such as determination of pollen season limits or replacement of outliers in a pollen count data.
library(pollen)In the examples below, we will use the pollen_count dataset available in the pollen package. It has five variables:
data("pollen_count")
head(pollen_count)
#> site date alder birch hazel
#> 1 Oz 2007-01-01 0 0 0
#> 2 Oz 2007-01-02 0 0 0
#> 3 Oz 2007-01-03 0 0 0
#> 4 Oz 2007-01-04 0 0 0
#> 5 Oz 2007-01-05 0 0 0
#> 6 Oz 2007-01-06 0 0 0The most important function in this package, pollen_season() determines pollen season limits. It can be calculated independently for each site, for example Oz:
df <- subset(pollen_count, site == "Oz")
pollen_season(value = df$birch, date = df$date, method = "95")
#> year start end
#> 1 2007 2007-03-31 2007-05-03
#> 2 2008 2008-04-19 2008-05-07
#> 3 2009 2009-04-09 2009-05-09
#> 4 2010 2010-04-14 2010-05-07
#> 5 2011 2011-04-20 2011-05-17
#> 6 2012 2012-04-09 2012-05-14
#> 7 2013 2013-04-09 2013-05-09
#> 8 2014 2014-04-08 2014-05-10
#> 9 2015 2015-04-08 2015-04-30
#> 10 2016 2016-04-06 2016-05-09… and Atlantis:
df2 <- subset(pollen_count, site == "Atlantis")
pollen_season(value = df2$alder, date = df2$date, method = "95")
#> Warning in pollen_season(value = df2$alder, date = df2$date, method = "95"): NA
#> values were found in the input data.
#> year start end
#> 1 2007 <NA> <NA>
#> 2 2008 2008-03-23 2008-04-14
#> 3 2009 2009-03-16 2009-04-03
#> 4 2010 2010-03-26 2010-04-07
#> 5 2011 2011-03-28 2011-04-14
#> 6 2012 2012-02-13 2012-04-05
#> 7 2013 2013-02-05 2013-03-16
#> 8 2014 2014-02-11 2014-04-29
#> 9 2015 2015-03-19 2015-04-04
#> 10 2016 2016-03-14 2016-04-23NA is returned for years with missing values in the data, as you can see above.
In combination with the purrr package (or the base apply() function), it is possible to calculate pollen season limits for many sites:
library(purrr)
pollen_count %>%
split(., .$site) %>%
map_dfr(~pollen_season(value = .$hazel, date = .$date, method = "95"), .id = "site")
#> site year start end
#> 1 Atlantis 2007 2007-01-29 2007-03-19
#> 2 Atlantis 2008 2008-03-23 2008-04-14
#> 3 Atlantis 2009 2009-03-15 2009-04-11
#> 4 Atlantis 2010 2010-03-24 2010-04-14
#> 5 Atlantis 2011 2011-03-26 2011-04-12
#> 6 Atlantis 2012 2012-01-21 2012-03-26
#> 7 Atlantis 2013 2013-02-02 2013-03-29
#> 8 Atlantis 2014 2014-02-07 2014-04-09
#> 9 Atlantis 2015 2015-03-01 2015-03-30
#> 10 Atlantis 2016 2016-03-11 2016-04-06
#> 11 Hundred Acre Wood 2007 2007-01-29 2007-03-31
#> 12 Hundred Acre Wood 2008 2008-03-10 2008-05-10
#> 13 Hundred Acre Wood 2009 2009-02-08 2009-03-31
#> 14 Hundred Acre Wood 2010 2010-01-24 2010-04-16
#> 15 Hundred Acre Wood 2011 2011-03-25 2011-04-16
#> 16 Hundred Acre Wood 2012 2012-01-10 2012-03-29
#> 17 Hundred Acre Wood 2013 2013-01-24 2013-03-12
#> 18 Hundred Acre Wood 2014 2014-03-04 2014-03-31
#> 19 Hundred Acre Wood 2015 2015-02-26 2015-03-31
#> 20 Hundred Acre Wood 2016 2016-02-06 2016-03-31
#> 21 Oz 2007 2007-02-03 2007-03-18
#> 22 Oz 2008 2008-03-10 2008-04-03
#> 23 Oz 2009 2009-02-17 2009-03-26
#> 24 Oz 2010 2010-03-18 2010-04-10
#> 25 Oz 2011 2011-03-27 2011-04-13
#> 26 Oz 2012 2012-01-12 2012-03-14
#> 27 Oz 2013 2013-01-22 2013-03-25
#> 28 Oz 2014 2014-03-05 2014-04-05
#> 29 Oz 2015 2015-03-19 2015-04-02
#> 30 Oz 2016 2016-03-10 2016-03-30
#> 31 Shire 2007 2007-01-28 2007-03-24
#> 32 Shire 2008 2008-02-22 2008-04-01
#> 33 Shire 2009 2009-02-03 2009-03-27
#> 34 Shire 2010 2010-02-07 2010-04-07
#> 35 Shire 2011 2011-02-20 2011-04-12
#> 36 Shire 2012 2012-01-10 2012-03-18
#> 37 Shire 2013 2013-01-21 2013-03-02
#> 38 Shire 2014 2014-03-01 2014-03-27
#> 39 Shire 2015 2015-02-19 2015-03-30
#> 40 Shire 2016 2016-01-17 2016-03-28Next possibility is to compare many methods for determination of pollen season limits for one measurement site. Let’s try it for Oz:
df <- subset(pollen_count, site == "Oz")We just need to provide a vector of names with the methods and use it in the map_dfr() function:
ps_methods <- c("90", "95", "98", "Mesa", "Jager", "Lejoly")
names(ps_methods) <- ps_methods
df_seasons <- ps_methods %>%
map_dfr(~pollen_season(method = ., value = df$birch, date = df$date), .id = "method")
head(df_seasons)
#> method year start end
#> 1 90 2007 2007-03-31 2007-04-29
#> 2 90 2008 2008-04-20 2008-05-03
#> 3 90 2009 2009-04-10 2009-05-04
#> 4 90 2010 2010-04-14 2010-05-03
#> 5 90 2011 2011-04-21 2011-05-13
#> 6 90 2012 2012-04-11 2012-05-07The pollen package also implements a method for replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>) with the outliers_replacer() function. outliers_replacer() accepts a column with concentration, a column with date, and a threshold - a number indicating how many times outlying value needs to be larger than the background to be replaced. This method can be applied on a single site:
df <- subset(pollen_count, site == "Shire")
new_df <- outliers_replacer(value = df$alder, date = df$date)
identical(df, new_df)
#> [1] FALSEOr a group of sites:
library(purrr)
new_pollen_count <- pollen_count %>%
split(., .$site) %>%
map_dfr(~outliers_replacer(value = .$hazel, date = .$date, threshold = 4))These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.