From 6c66baedf50af46f1270e08f36d00b3110e1c93a Mon Sep 17 00:00:00 2001 From: nickmckay Date: Sat, 6 Jul 2024 22:19:30 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20nickmcka?= =?UTF-8?q?y/lipdR@854e65a926156fdcb8232ed258bf3142aca44327=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/Query.html | 60 ++++++++++++++++++++++--------------------- articles/Summary.html | 21 +++++++-------- pkgdown.yml | 2 +- 3 files changed, 43 insertions(+), 40 deletions(-) diff --git a/articles/Query.html b/articles/Query.html index 62ff142..d253582 100644 --- a/articles/Query.html +++ b/articles/Query.html @@ -98,8 +98,9 @@

LiPD Queries

Let’s get a sense for what the table holds

 dim(queryTable)
-#> Error in eval(expr, envir, enclos): object 'queryTable' not found
-names(queryTable)
+#> Error in eval(expr, envir, enclos): object 'queryTable' not found
+
+names(queryTable)
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

That’s a lot of rows! There’s a row for every time series in the LiPDverse database

@@ -111,7 +112,7 @@

LiPD Queries

session

If you tell queryLipdverse to skip.update once, it will not prompt you again in a given R session

-
+
 qt <- queryLipdverse(variable.name = c("δ18O"),
                      skip.update = TRUE)
 #> Based on your query parameters, there are 0 available time series in 0 datasets

@@ -119,12 +120,12 @@ 
LiPD Queries

 
Let’s have a look at the unique paleoData
 variable.name

 
We’ll just look at the first 250, there are quite a few

-
+
 unique(queryTable$paleoData_variableName)[1:250]
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

 
There’s quite a few notation styles, but they all have “18o” in
 common, so let’s try that

-
+
 qt <- queryLipdverse(variable.name = c("18o"))
 #> Based on your query parameters, there are 1688 available time series in 1069 datasets

 
Now that is a lot more data

@@ -135,7 +136,7 @@ 
LiPD Queries

 
For the variable.name parameter, multiple entries are
 combined with “OR” logic, so more entries will generally pull more
 datasets

-
+
 qt <- queryLipdverse(variable.name = c("δ18O", "d18O"))
 #> Based on your query parameters, there are 1688 available time series in 1069 datasets

 
This gets us almost the same result, but we see the simplified filter
@@ -143,24 +144,24 @@ 
LiPD Queries

 
Also note that capitalization makes no difference here

 
The archive.type filter works similarly, let’s look at
 our options

-
+
 unique(queryTable$archiveType)
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

 
Note that we have distinct options: “Lake”, “LakeSediment”,
 “LakeDeposits”, “LakeDeposit”, and “Lake Sediment”

 
We can grab the archive.type names and search for all of
 them like this

-
+
 qt <- queryLipdverse(archive.type = unique(queryTable$archiveType)[c(2,3,5,6,39)])
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

 
Or we can use the simplification strategy again

-
+
 qt <- queryLipdverse(archive.type = c("lake"))
 #> Based on your query parameters, there are 46308 available time series in 2309 datasets

 
As we saw last time, the results are similar

 
We can also filter based on publications: Author, DOI, Title, etc.
 using pub.info

-
+
 qt <- queryLipdverse(pub.info = c("10.1016/j.quascirev.2008.09.005"))
 #> Based on your query parameters, there are 20 available time series in 7 datasets

 
This query can be a little slow if you don’t narrow the results with
@@ -170,11 +171,11 @@ 
LiPD Queries

 coord, country, continent, and
 ocean

 
Let’s grab all the North American datasets

-
+
 qt <- queryLipdverse(continent = "North America")
 #> Based on your query parameters, there are 46652 available time series in 1817 datasets

 
Let’s grab just those from Mexico

-
+
 qt <- queryLipdverse(country = c("Mexico"))
 #> Based on your query parameters, there are 512 available time series in 36 datasets

 
Note that the country and contient filters are not filtered based on
@@ -185,12 +186,12 @@ 
LiPD Queries

 continent with unique()

 
We can also use latitude and longitude directly with a bounding
 box

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110))
 #> Based on your query parameters, there are 14062 available time series in 772 datasets

 
We can limit this to only marine data by setting ocean
 to TRUE

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
          ocean = TRUE)
 #> Based on your query parameters, there are 1485 available time series in 85 datasets

@@ -199,11 +200,11 @@ 
LiPD Queries

 unreliable in coastal areas

 
We can also grab all the data from a compilation, such
 as the Western North America (WNAm)

-
+
 qt <- queryLipdverse(compilation = c("wNAm"))
 #> Based on your query parameters, there are 392 available time series in 184 datasets

 
or multiple compilations

-
+
 qt <- queryLipdverse(compilation = c("wnam", "temp12k"))
 #> Based on your query parameters, there are 1717 available time series in 799 datasets

 
the compilation filter uses “OR” logic

@@ -212,13 +213,13 @@ 
LiPD Queries

 
We can pull summer, commonly defined as June, July, and August, for
 the Northern Hemisphere

 
seasonality input is taken as a list

-
+
 qt <- queryLipdverse(continent = "North America",
                seasonality = list("June", "July", "August"))
 #> Based on your query parameters, there are 1 available time series in 1 datasets

 
Okay, so this must not be a good choice of format, let’s see how LiPD
 authors define their seasons

-
+
 unique(queryTable$interpretation1_seasonality)
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

 
It looks like numeric months are most common. Season names,
@@ -233,7 +234,7 @@ 
LiPD Queries

 data with ANY of these months

 
Let’s try to get all of the summer datasets by entering a few
 different notations

-
+
 qt <- queryLipdverse(continent = "North America",
                seasonality = list(list("6", "7", "8"), list("summer"), list("JJA")))
 #> Based on your query parameters, there are 219 available time series in 165 datasets

@@ -244,7 +245,7 @@ 
LiPD Queries

 season.not

 
The input for for season.not works the same as
 seasonality

-
+
 qt <- queryLipdverse(continent = "North America",
                seasonality = list(list("6", "7", "8"), list("summer"), list("JJA")),
                season.not = list(list("annual"), list("December"), list("12","1","2"), list("winter"), list("cold")))
@@ -256,15 +257,16 @@ 
LiPD Queries

 
These variables are expressed in two different formats:
 interpretation variable and interpretation detail

 
Each of these variables has four possible interpretation slots

-
+
 unique(queryTable$interp_Vars)
-#> Error in eval(expr, envir, enclos): object 'queryTable' not found
-
+#> Error in eval(expr, envir, enclos): object 'queryTable' not found

+
+
 unique(queryTable$interp_Details)
 #> Error in eval(expr, envir, enclos): object 'queryTable' not found

 
Let’s look at some marine interp.vars in the northeast
 Pacific

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
                ocean = TRUE,
                interp.vars =  c("SST", "upwelling", "SSS"))
@@ -272,7 +274,7 @@ 
LiPD Queries

 
That gives us just a few datasets

 
Perhaps we’ll have more luck with interp.details, which
 is more standardized

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
                ocean = TRUE,
                interp.details = c("sea@surface", "elNino"))
@@ -280,7 +282,7 @@ 
LiPD Queries

 
let’s see if we grab more using both

 
These inputs combine with “OR” logic, so we may gather more datasets
 by using both parameters

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
                ocean = TRUE,
                interp.details = c("sea@surface", "elNino"),
@@ -292,7 +294,7 @@ 
LiPD Queries

 
We’ll look for marine archives in the northeast Pacific, with
 interpretations related to marine climate variables in the summer months
 only

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
                archive.type = c("marine", "ocean"),
                ocean = TRUE,
@@ -305,7 +307,7 @@ 
LiPD Queries

 have what effect on filtering

 
We’ll narrow the results further by adding author names to find
 within the publication info

-
+
 qt <- queryLipdverse(coord = c(0,90,-180,-110),
                archive.type = c("marine", "ocean"),
                ocean = TRUE,
@@ -349,7 +351,7 @@ 
LiPD Queries

 
When you’re satisfied with the query results, we can simply put the
 filtered query table into the readLipd() function to
 download the datasets

-
+
 D <- readLipd(qt)
 #> Error in value[[3L]](cond): Error: get_src_or_dst: Error in if (!dir.exists(path)) {: argument is of length zero

   

diff --git a/articles/Summary.html b/articles/Summary.html
index 165d122..3e10418 100644
--- a/articles/Summary.html
+++ b/articles/Summary.html
@@ -396,8 +396,9 @@ 
Storing the summary table#>     filter, lag
 #> The following objects are masked from 'package:base':
 #> 
-#>     intersect, setdiff, setequal, union
-arrange(MLSummary,desc(NumEns))
+#>     intersect, setdiff, setequal, union

+
+arrange(MLSummary,desc(NumEns))
 #> # A tibble: 47 × 8
 #>    Dataset                  `Archive Type` NumPalTabs NumChrTabs NumEns AgeMin        AgeMax           `Paleo Vars`                                                                                                                                                                                                                                                                                                                                                                                                 
 #>    <chr>                    <chr>               <dbl>      <dbl>  <dbl> <chr>         <chr>            <chr>                                                                                                                                                                                                                                                                                                                                                                                                        
@@ -417,7 +418,7 @@ 
Storing the summary table
 
NPM: we might have to kill this section because of the gt
 package, or just not show that. 

-
+
 library(gt)
 MLSummary %>% 
   select(-starts_with("Num"),-"Paleo Vars") %>% 
@@ -1167,17 +1168,17 @@ 
LiPD Timeseries objects
-
+
 
Let’s quickly check the size of this tibble

-
+
 dim(tibTS)
 #> [1] 583 382

 
Wow - that’s pretty big. That means that there are 583 variables
 across all our datasets, and 382 data or metadata fields present in one
 or more of the datasets.

 
Again, let’s print out a quick overview:

-
+
 tibTS
 #> LiPD TS object containing 47 datasets
 #> 583 variables and 382 data and metadata fields.
@@ -1195,7 +1196,7 @@ 
LiPD Timeseries objectssummary(). This works the same for lipd_ts and
 lipd_ts_tibble objects. Let’s limit the table to show details of the
 first 10 rows only

-
+
 summary(tibTS, print.length = 10)
 #> LiPD TS object containing 47 datasets
 #> 583 variables and 382 data and metadata fields.
@@ -1224,7 +1225,7 @@ 
LiPD Timeseries objectsadd.variable parameter.

-
+
 summary(tibTS,
         print.length = 10, 
         add.variable = c("archiveType", "paleoData_meanValue12k","geo_continent", "geo_elevation", "agesPerKyr", "dataSetName"))
@@ -1272,7 +1273,7 @@ 
Filtering the TS objectcover the early Holocene
 
  • Have some climate interpretation
    • 
 
-
    +
     library(magrittr) #We'll use the pipe for clarity
 subsetTs <- tibTS %>% 
   filter(between(geo_latitude,30,50),
@@ -1283,7 +1284,7 @@ 
    Filtering the TS objectGreat - we found 29 timeseries that meet these criteria. Let’s
 explore their interpretations in a bit more detail by saving the table
 output.
    
-
    +
     sumTable <- summary(subsetTs,
                     add.variable = c("interpretation1_variable",
                                      "interpretation1_variableDetail",
diff --git a/pkgdown.yml b/pkgdown.yml
index 3bcb75e..6bd4e5d 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,5 +5,5 @@ articles:
   lipd2neotoma: lipd2neotoma.html
   Query: Query.html
   Summary: Summary.html
-last_built: 2024-05-14T00:43Z
+last_built: 2024-07-06T22:18Z