Functions to import/export various formats of snow profiles, with
basic formatting and visualization functions.
1. Snowprofile objects
The package uses S3 classes for individual snow profiles (class
snowprofile) or lists of multiple snow profiles (class
snowprofileSet). Objects with these classes can be created,
manipulated, and visualized.
A snowprofile object contains data about a snow
stratigraphy profile. It is structured as a list with
metadata (e.g. profile name and location) and a data.frame
containing layer properties. Mandatory parts of a
snowprofile object include:
station and station_id provide a profile
namedatetime timestamplatlon, elev, angle,
aspect location informationhs total snow heightlayers a data.frame of class
snowprofileLayers that contains layer properties (each row
is a layer and each column is a property such as depth or grain
size).
A snowprofileSet object is simply a list of
multiple snowprofile objects.
The package includes sample data packaged into three
snowprofileSet objects:
SPgroup contains 12 profiles from different locations
with the same timestampSPtimeline contains 11 profiles from the same location
with different timestampsSPpairs contains various other snow profiles
## Print the structure of a single `snowprofile` object
Profile <- SPpairs$C_day1
str(Profile)
## List of 14
## $ station : int 112233
## $ station_id : chr "VIR112233"
## $ datetime : POSIXct[1:1], format: "2018-11-21 05:00:00"
## $ latlon : num [1, 1:2] 52.3 -119
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr ""
## .. ..$ : chr [1:2] "latitude" "longitude"
## $ elev : int 1917
## $ angle : num 0
## $ aspect : num 0
## $ hs : num 111
## $ type : chr "vstation"
## $ layers :Classes 'snowprofileLayers' and 'data.frame': 30 obs. of 11 variables:
## ..$ height : num [1:30] 1.8 3.54 5.08 5.42 6.83 ...
## ..$ ddate : POSIXct[1:30], format: "2018-09-12 17:00:00" "2018-09-15 17:00:00" ...
## ..$ density : int [1:30] 422 354 408 332 404 357 313 294 312 359 ...
## ..$ temperature: num [1:30] -0.1 -0.1 -0.2 -0.2 -0.3 -1 -1.1 -1.1 -1.4 -1.5 ...
## ..$ lwc : int [1:30] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ gsize : num [1:30] 1.37 1.62 0.96 1.69 0.82 0.98 1.38 1.93 1.25 1.32 ...
## ..$ hardness : num [1:30] 4 5 4 2 4 5 5 5 2.15 3 ...
## ..$ ssi : num [1:30] 5.43 3.93 1.37 1.39 4.12 5.59 4.68 1.57 3.4 3.03 ...
## ..$ gtype : Factor w/ 8 levels "DF","DH","FC",..: 5 6 4 2 4 6 6 6 3 4 ...
## ..$ depth : num [1:30] 109 107 106 105 104 ...
## ..$ thickness : num [1:30] 1.8 1.74 1.54 0.34 1.41 ...
## $ zone : chr "BONE_NORTH"
## $ band : chr "TL"
## $ date : Date[1:1], format: "2018-11-21"
## $ maxObservedDepth: num 111
## - attr(*, "class")= chr "snowprofile"
Check out an empty snowprofile object to understand which information
can potentially go where:
snowprofile(dropNAs = FALSE)
## MetaData
## station NA
## station_id NA
## datetime NA
## latlon NA NA
## elev NA
## angle NA
## aspect NA
## hs NA
## maxObservedDepth NA
## type manual
## band NA
## zone NA
## comment NA
## hn24 NA
## hn72 NA
## ski_pen NA
## date NA
## layers
## depth height thickness temperature density lwc gsize gsize_max gsize_avg
## 1 NA NA NA NA NA NA NA NA NA
## gtype gtype_sec hardness ddate bdate datetag ssi sphericity v_strain_rate
## 1 <NA> <NA> NA <NA> <NA> <NA> NA NA NA
## crit_cut_length tsa tsa_interface rta rta_interface layerOfInterest comment
## 1 NA NA NA NA NA NA <NA>
## tests
## [1] type result score fract_char depth comment
## <0 rows> (or 0-length row.names)
## instabilitySigns
## [1] type present comment
## <0 rows> (or 0-length row.names)
2. Profile creation
The package includes functions to create a snowprofile
object by importing common file formats as well as provides constructor
functions to manually create a snowprofile.
2.1 Import profiles from file
Import functions for generic snow profiles include::
- CAAML (
snowprofileCaaml)
- csv (
snowprofileCsv)
and import functions for simulated profiles produced with the snow
cover model SNOWPACK
include:
- prf (
snowprofilePrf)
- pro (
snowprofilePro)
- sno (
snowprofileSno)
Note that prf and pro files contain multiple profiles and thus the
import functions return a snowprofileSet, while CAAML, csv,
and sno files return a single snowprofile.
## Import a CAAML file
# Filename <- "path/to/file.caaml"
# Profile <- snowprofileCaaml(Filename)
# ## Import all profiles from a directory of CAAML files and create a `snowprofileSet`
# CaamlFiles <- list.files('path/to/caamlprofiles', full.names = T)
# Profiles <- lapply(CaamlFiles, snowprofileCaaml)
# Profiles <- snowprofileSet(Profiles)
An additional parser readSmet is also provided to read
other input and output files from SNOWPACK.
2.2 Manually construct profiles
To manually create a snowprofile object see the help
pages for the constructor functions snowprofile() and
snowprofileLayers() where metadata and layer properties are
provided as function arguments.
3. Profile manipulation
print, summary, and rbind
methods exist to summarize and extract contents from
snowprofile and snowprofileSet objects.
3.1 Print
Print contents of a snowprofile to the console.
Profile <- SPpairs$A_manual
print(Profile)
## MetaData
## station SarpOfficeStation
## station_id niviz_station
## datetime 2019-02-22 09:24:00
## latlon 49.2765643 -122.9139272
## elev 339
## angle 38
## aspect 180
## hs 260
## type manual
## observer Sarp|FH
## comment_general don't feel like commenting
## comment_location test caaml file
## date 2019-02-22
## maxObservedDepth 260
## layers
## depth thickness height hardness gtype gsize_avg gsize_max lwc gtype_sec
## 1 250 10 10 5.00 MFcr 1.5 1.5 D <NA>
## 2 230 20 30 3.00 FC 2.0 2.0 D <NA>
## 3 220 10 40 5.00 MFcr 1.5 1.5 D <NA>
## 4 157 63 103 4.00 RG 0.3 0.3 D <NA>
## 5 155 5 105 2.00 SH 5.0 5.0 D <NA>
## 6 95 60 165 4.00 RG 0.3 0.3 D <NA>
## 7 75 20 185 3.00 RG 0.3 0.3 D <NA>
## 8 60 15 200 1.00 FC 2.0 2.0 D <NA>
## 9 42 18 218 2.00 RG 1.0 1.0 D <NA>
## 10 40 2 220 1.50 FC 2.0 2.5 D <NA>
## 11 38 2 222 1.00 SH 5.0 5.0 D DH
## 12 0 35 260 1.25 DF 1.5 1.5 D <NA>
## ddate
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## 7 <NA>
## 8 <NA>
## 9 <NA>
## 10 <NA>
## 11 2019-02-20
## 12 <NA>
3.2 Summary
Extract metadata from a single profile or set of profiles into a
data.frame.
## station station_id datetime lat lon elev
## 1 SarpOfficeStation niviz_station 2019-02-22 09:24:00 49.27656 -122.9139 339
## angle aspect hs type nLayers observer comment_general
## 1 38 180 260 manual 12 Sarp|FH don't feel like commenting
## comment_location date maxObservedDepth
## 1 test caaml file 2019-02-22 260
## station station_id datetime elev angle aspect hs type
## 1 116093 VIR116093 2019-02-02 05:00:00 1907 0 0 156.48 vstation
## 2 116094 VIR116094 2019-02-02 05:00:00 1979 0 0 150.41 vstation
## 3 116095 VIR116095 2019-02-02 05:00:00 2011 0 0 117.89 vstation
## 4 116096 VIR116096 2019-02-02 05:00:00 1859 0 0 102.42 vstation
## 5 116643 VIR116643 2019-02-02 05:00:00 1867 0 0 179.87 vstation
## 6 116644 VIR116644 2019-02-02 05:00:00 1969 0 0 179.22 vstation
## 7 116645 VIR116645 2019-02-02 05:00:00 2019 0 0 156.85 vstation
## 8 116646 VIR116646 2019-02-02 05:00:00 1983 0 0 113.26 vstation
## 9 117194 VIR117194 2019-02-02 05:00:00 1915 0 0 156.02 vstation
## 10 117197 VIR117197 2019-02-02 05:00:00 1941 0 0 116.94 vstation
## 11 117746 VIR117746 2019-02-02 05:00:00 1911 0 0 142.78 vstation
## 12 117747 VIR117747 2019-02-02 05:00:00 1957 0 0 128.56 vstation
## zone band date maxObservedDepth
## 1 ALBREDA TL 2019-02-02 156.48
## 2 ALBREDA TL 2019-02-02 150.41
## 3 ALBREDA TL 2019-02-02 117.89
## 4 ALBREDA TL 2019-02-02 102.42
## 5 ALBREDA TL 2019-02-02 179.87
## 6 ALBREDA TL 2019-02-02 179.22
## 7 ALBREDA TL 2019-02-02 156.85
## 8 ALBREDA TL 2019-02-02 113.26
## 9 ALBREDA TL 2019-02-02 156.02
## 10 ALBREDA TL 2019-02-02 116.94
## 11 ALBREDA TL 2019-02-02 142.78
## 12 ALBREDA TL 2019-02-02 128.56
Summary methods are useful to extract subsets of a
snowprofileSet based on some attribute (e.g. location,
time).
## Subset all profiles from SPgroup with elevation > 2000 m
Metadata <- summary(SPgroup)
Alpine <- SPgroup[Metadata$elev > 2000]
print(paste(length(Alpine), 'of', length(SPgroup), 'profiles in SPgroup are above 2000 m'))
## [1] "2 of 12 profiles in SPgroup are above 2000 m"
3.3 Rbind
rbind methods merge metadata and layer properties from
one or many profiles into a large data.frame that is
convenient for analysis tasks.
## Rbind SPgroup
TabularProfile <- rbind(SPgroup)
names(TabularProfile)
## [1] "station" "station_id" "datetime" "lat"
## [5] "lon" "elev" "angle" "aspect"
## [9] "hs" "type" "nLayers" "zone"
## [13] "band" "date" "maxObservedDepth" "height"
## [17] "ddate" "density" "temperature" "lwc"
## [21] "gtype" "hardness" "ssi" "gsize"
## [25] "depth" "thickness"
## Tabulate all grain types
table(TabularProfile$gtype)
##
## DF DH FC FCxr MF MFcr PP RG SH
## 29 45 148 96 1 73 24 63 12
## Get elevations of profiles with SH layers > 5 mm
SH_layers <- subset(TabularProfile, gtype == 'SH' & gsize > 5)
sort(SH_layers$elev)
## [1] 1859 1911 1941 1957
4. Profile visualization
Plot methods exist for individual profiles and sets of profiles:
plot.snowprofile produces a hardness profile for a
single snowprofile objectplot.snowprofileSet produces several types of plots for
a snowprofileSet including timelines and side-by-side
stratigraphies
## Plot a single hardness profile
plot(Profile)
## Plot a timeline
plot(SPtimeline)
## Plot all profiles in the group and sort by height
plot(SPgroup, SortMethod = 'hs')
The package also includes several getColour... and
setColour... functions to define colour palettes for
various profile properties.