Merge branch 'traits_tutorials' of github.com:terraref/tutorials into traits_tutorials

Author: dlebauer

_bookdown.yml

@@ -5,5 +5,5 @@ language:
     chapter_name: "Chapter "
 rmd_files: ["index.Rmd", "traits/00-BETYdb-getting-started.Rmd", "traits/01-web-access.Rmd", 
 "traits/02-betydb-api-access.Rmd", "traits/03-access-r-traits.Rmd", "traits/04-danforth-indoor-phenotyping-facility.Rmd",
-"traits/05-maricopa-field-scanner.Rmd", "traits/06-agronomic-metadata.Rmd", "traits/07-betydb-sql-access.Rmd"]#, "10-simulated-sorghum.Rmd"
+"traits/06-agronomic-metadata.Rmd", "traits/07-betydb-sql-access.Rmd", "traits/10-simulated-sorghum.Rmd"]#, "10-simulated-sorghum.Rmd"
 

index.Rmd

@@ -8,7 +8,6 @@ output:
   bookdown::gitbook: default
 ---
 
-# Section 1: Traits {-}
 
 # Overview
 

traits/00-BETYdb-getting-started.Rmd

@@ -1,3 +1,5 @@
+# (PART\*) Secton 1: Traits {-}
+
 # Getting Started with BETYdb
 
 ## TERRA Ref Trait Database

traits/03-access-r-traits.Rmd

@@ -10,13 +10,13 @@ Install the traits package
 
 The traits package is on CRAN, and can therefore be installed using the following command:  
 
-```{r install_traits, echo = FALSE, include = FALSE}
-install.packages('traits')
+```{r install_traits, echo = TRUE, message = FALSE}
+install.packages('traits', repos = 'http://cran.rstudio.com/')
 ```
 
 Load other packages that we will need to get started.
 
-```{r 00-setup, message = FALSE}
+```{r 00-setup, message = FALSE, echo = TRUE}
 library(traits)
 library(ggplot2)
 library(ggthemes)
@@ -26,14 +26,14 @@ library(dplyr)
 
 Create a file that contains your API key. If you have signed up for access to the TERRA REF database, your API key will have been sent to you in an email. The public key will provide access to all metadata; you will need a personal key _and_ permissions to access the trait data. If you receive empty (NULL) datasets, it is likely that you do not have permissions. 
 
-```{r writing-key}
+```{r writing-key, echo = TRUE}
 # This should be done once with the key sent to you in your email
 # writeLines('abcdefg_rest_of_key_sent_in_email', 
 #            con = '.betykey')
 
 # Example with the public key:
 writeLines('9999999999999999999999999999999999999999', 
-           con = '.betykey_public')
+           con = 'traits/.betykey_public')
 ```
 
 #### R - using the traits package
@@ -69,7 +69,7 @@ options(betydb_key = readLines('traits/.betykey', warn = FALSE),
 
 Now the same query can be reduced to:
 
-```{r query-species-reduce, echo = TRUE}
+```{r query-species-reduce, echo = TRUE, results = FALSE}
 sorghum_info <- betydb_query(table = 'species',
                             genus = "Sorghum",
                             limit = 'none')

traits/04-danforth-indoor-phenotyping-facility.Rmd

@@ -29,7 +29,7 @@ options(betydb_key = readLines('traits/.betykey', warn = FALSE),
 
 First we will use the generic search to query the output from the Lemnatec indoor phenotyping system at the Danforth Center in St. Louis, MO.
 
-```{r query-danforth, results='hide'}
+```{r query-danforth, message = FALSE}
 danforth_sorghum <- traits::betydb_query(
 #  sitename = 'Danforth Plant Science Center Bellweather Phenotyping Facility',
   trait = 'sv_area',

traits/05-maricopa-field-scanner.Rmd

@@ -23,7 +23,7 @@ options(betydb_key = readLines('.betykey', warn = FALSE),
 
 First, query the plots for Season 2. The simple way to use this is based on the fact that the plot names at Maricopa contain the season. 
 
-```{r traits-05-query-mac-sites}
+```{r traits-05-query-mac-sites, echo = TRUE}
 sites <- betydb_query(
 	table = "sites",
 	city = "Maricopa", sitename = "~Season 2 range", limit = "none")
@@ -33,10 +33,10 @@ A more robust (but complicated way) would be to query the experiments and experi
 
 ### Plot Season 2 plots
 
-```{r traits-05-map-mac-polygons}
+```{r traits-05-map-mac-polygons, echo = TRUE}
 site_bounds <- (sites
-	%>% rowwise()
-	%>% do(boundaries = readWKT(text = .$geometry, id = .$id)))
+  %>% rowwise()
+  %>% do(boundaries = readWKT(text = .$geometry, id = .$id)))
 
 site_bounds <- do.call('rbind', site_bounds$boundaries)
 #names(site_bounds) <- sites$sitename
@@ -53,35 +53,46 @@ leaflet() %>%
   addPolygons(data=site_bounds, popup = sites$sitename)
 ```
 
+```{r}
 ## Cultivars
 
+```
 
 ```{r traits-05-mac-cultivars}
-cultivars <- betydb_query(
-  table = "cultivars", limit = "none") %>% 
-  rename(cultivar_id = id)
-
-traits <- traits <- betydb_search(
-  Season = "~Season 4", 
-  include_unchecked = 'true',
-  limit = "none") %>% 
-  rename(trait_id = id)
+#cultivars <- betydb_query(
+#  table = "cultivars", limit = "none") %>% 
+#  rename(cultivar_id = id)
+
+
+# getting HTTP 504 - Gateway timeout
+# comment this chunk out
+#traits <- traits <- betydb_search(
+#  Season = "~Season 4", 
+#  include_unchecked = 'true',
+#  limit = "none") %>% 
+#  rename(trait_id = id)
 ```
 
-
+```{r}
 ## Time series of canopy cover, height, NDVI
 
-First look up variables by name. Let's look for measurements related to canopy size:
+#First look up variables by name. Let's look for measurements related to canopy size:
+
+```
 
 ```{r traits-05-height-cover-ndvi}
-variables <- betydb_query(
-		table = "variables", name = "~^(NDVI|canopy_height|canopy_cover|)$")
+#variables <- betydb_query(
+#		table = "variables", name = "~^(NDVI|canopy_height|canopy_cover|)$")
 
-variables %>% 
-  select(id, name, units, n_records = `number of associated traits`)
+#variables %>% 
+#  select(id, name, units, n_records = `number of associated traits`)
 ```
 
-Exercise: Why are there two variables named canopy_height, and what database fields should you examine to decide which one you want?
+```{r}
+
+#Exercise: Why are there two variables named canopy_height, and what database fields should you examine to decide which one you want?
+
+```
 
 ```{r traits-05-get-variables-comment}