Move folders

.gitignore

@@ -2,3 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
+.ipynb_checkpoints

NEON_data_exploration.Rmd → NEON_ph_by_time/R/NEON_data_exploration.Rmd

@@ -82,37 +82,77 @@ for (i in 1:length(study_ids)){
 
 ```{r}
 library(lubridate)
+my_theme <- theme_bw()
 df <- dat_all %>%
-    select(collection_date, ph, geo_loc_name, lat_lon) %>%
-    unnest(cols = c(collection_date, geo_loc_name,
-  lat_lon), names_sep = "_") %>%
+    select(
+      collection_date, ph, geo_loc_name, lat_lon
+      ) %>%
+    unnest(
+      cols = c(
+        collection_date, 
+        geo_loc_name,
+        lat_lon
+        ), names_sep = "_") %>%
     rename(collection_date = collection_date_has_raw_value ,
            geo_loc = geo_loc_name_has_raw_value)
 
 df2 <- df %>%
     mutate(collection_date2 = as.Date(collection_date))
 
+df3 <- df2 %>%
+  mutate(geo_loc_grouped = geo_loc %>% 
+           factor() %>% 
+           fct_lump(n = 6)
+         ) %>%
+  filter(geo_loc_grouped != "Other")
+
+
 g <- ggplot(data = df2) +
     geom_point(aes(x=collection_date, y = ph)) +
+  my_theme +
     facet_wrap(facets = vars(geo_loc))
 
+g <- ggplot(data = df3) +
+    geom_point(aes(x=collection_date2, y = ph)) +
+    my_theme +
+    scale_x_date()+
+    facet_wrap(facets = vars(geo_loc_grouped),
+               labeller = label_wrap_gen(width=30)) 
+g
+
 ```
 
 
 ```{r}
 library("rnaturalearth")
 library("rnaturalearthdata")
 
+locs_with_ph <- df2 %>%
+  group_by(
+    geo_loc
+    ) %>%
+  mutate(
+    count_with_ph = n()
+    ) %>%
+  select(
+    geo_loc, 
+    lat_lon_longitude, 
+    lat_lon_latitude,
+    count_with_ph
+    ) %>%
+  distinct()
+
 world <- ne_countries(scale = "medium", returnclass = "sf")
 class(world)
 g2 <- ggplot(data = world) +
     geom_sf() +
     geom_point(
         data = locs_with_ph, 
-        aes(x = lat_lon_longitude, y = lat_lon_latitude, color = geo_loc)) +
+        aes(x = lat_lon_longitude, y = lat_lon_latitude, 
+        size = count_with_ph)) +
     my_theme +
-    labs(x = "Longitude", y = "Latitude")+
-    theme(legend.position =  "none")+
+    labs(x = "Longitude", y = "Latitude", size = "Samples with \n pH measurements")+
+    theme()+
         coord_sf(xlim = c(-165, -66), ylim = c(17, 72), expand = FALSE)
 g2
 

NEON_ph_by_time/README.md

@@ -0,0 +1,3 @@
+# Interactive application exploring NEON sites of pH vs. time
+
+This folder includes two notebooks, in R and Python, that looks at how soil pH changes over time for the various NEON sites.

README.md

@@ -0,0 +1,29 @@
+# NNMC Data and Metadata R and Python Sample Jupyter Notebooks
+
+## Overview 
+
+This repository includes jupyter notebooks that explore and analyze microbiome data from the National Microbiome Data Collaborative's (NMDC) data portal. These notebooks aim to:
+
+- highlight the NMDC's metadata and data
+- demonstrate how the NMDC's API may be used to retrieve metadata and data of various microbiome research
+- illustrate example use cases of using the NMDC's (meta)data to answer scientific questions
+- encourage scientists to programmaticly access the NMDC Data Portal
+- promote the accessiblity of microbiome research by demonstrating various modes of finding, accessing, and reusing existing microbiome data.
+
+Each folder's scope attempts to explore a scientific question using the NMDC's (meta)data. A folder includes a `README.md` that outlines the question or analysis posed as well as two sub-folders, one labeled `R`, and the other `python` that comprises the sample notebooks using the R and Python programming languages, respectively. 
+
+R and Python were chosen since they are popular languages among scientists to explore and visualize data. Jupyter Notebook is used because of its interactive code and data exploration features, effectiveness in teaching, language independency, and ease of sharing code.
+
+A challenging aspect that has been highlighted with this process is accessing the (meta)data in a user-friendly way via the NMDC API. Because the NMDC metadata schema is highly modular, retrieving metadata is not straight forward without extensive knowledge of the metadata schema's infrastructure, modeling language ([LinkML](https://linkml.io/)), and naming conventions. A proposed solution to this challenge is the creation of an R or Python package that would allow users to access NMDC's data in an easier and more straight forward way.
+
+## Adding new notebooks
+
+To add a new notebook to this repository:
+
+1.  Create a folder in the base directory 
+    - Name the folder with a short version of the analysis/question that will be explored.
+    - Make name of folder `snake_case`
+2. Create a `README.md` in the folder outlining the analysis or question.
+3. Create a sub-folder for each language that will be demonstrated
+    - e.g. one subfolder named `R` and one subfolder named `python`
+4. Instantiate a Jupyter Notebook for each folder coded in its corresponding language