Challenge 1 Instructions

challenge_1
railroads
faostat
wildbirds
Author

Meredith Rolfe

Published

August 15, 2022

Code 
library(tidyverse)
library(readxl)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in the Data and Summarize

Code 
#reading in wild_bird_data dataset with readxl

wild_bird_data <- as_tibble(read_excel("~/GIT/601_Fall_2022/posts/_data/wild_bird_data.xlsx", skip = 1))
Error: `path` does not exist: '~/GIT/601_Fall_2022/posts/_data/wild_bird_data.xlsx'
Code 
#previewing data

head(wild_bird_data)
Error in head(wild_bird_data): object 'wild_bird_data' not found

This data appears to be derived from that used to produce the first figure in a research paper authored by researcher(s) Nee et al. It could be interpreted as the average wet body weight, measured in grams, for different species of birds and the size of each species population. I suspect that this is likely derived from a project to gather data on wild bird populations that frequent (or outright inhabit) aquatic or semi-aquatic environments, such as marshes or similar wetland environments. I imagine this would be a port of a catch and release program to collect biological data and potentially biological samples (potentially tagging future data collection/generation). Wet body weight is an ambiguous measurement and may be directly a measurement of wet mass, or may be related to metabolism, fat retention (through various seasons), a catalog of museum wet specimens, etc., it is difficult to determine without additional information.

The first variable, ‘wet body weight’ is a collection of continuous numerical values, and the other variable, ‘population size’ is also a collection of continuous numerical values (not seen here due setting a digit limit for display purposes). They may represent average population sizes, and this data could be from a meta analysis of various wild bird population studies’ data, as I imagine a single count would more likely produce a discrete count value (counting whole animals) than a continuous one, unless they were averaging multiple population counts together.

Code 
#getting data dimensions and column names

dim(wild_bird_data)
Error in eval(expr, envir, enclos): object 'wild_bird_data' not found
Code 
colnames(wild_bird_data)
Error in is.data.frame(x): object 'wild_bird_data' not found

By taking the dimensions of the dataset, we can conclude some additional information. It that may be seen that the researchers took wet body weight and population size observations for 146 wild bird species populations.

Code 
#attempting to arrange data to get wet body weight values in ascending order

wild_bird_data %>%
  arrange('Wet body weight [g]')
Error in arrange(., "Wet body weight [g]"): object 'wild_bird_data' not found
Code 
#attempt failed, reason unknown




:::{#quarto-navigation-envelope .hidden}
[DACSS 601: Data Science Fundamentals - FALL 2022]{.hidden render-id="quarto-int-sidebar-title"}
[DACSS 601: Data Science Fundamentals - FALL 2022]{.hidden render-id="quarto-int-navbar-title"}
[Course information]{.hidden render-id="quarto-int-sidebar:quarto-sidebar-section-1"}
[Overview]{.hidden render-id="quarto-int-sidebar:/Syllabus.html"}
[Instructional Team]{.hidden render-id="quarto-int-sidebar:/instructional_team.html"}
[Course Schedule]{.hidden render-id="quarto-int-sidebar:/course_schedule.html"}
[Weekly materials]{.hidden render-id="quarto-int-sidebar:quarto-sidebar-section-2"}
[Fall 2022 posts]{.hidden render-id="quarto-int-sidebar:/index.html"}
[final posts]{.hidden render-id="quarto-int-sidebar:https://dacss.github.io/601_Fall_2022_final_posts/"}
[Fall 2022 Posts]{.hidden render-id="quarto-int-navbar:Fall 2022 Posts"}
[Contributors]{.hidden render-id="quarto-int-navbar:Contributors"}
[DACSS]{.hidden render-id="quarto-int-navbar:DACSS"}
:::



:::{#quarto-meta-markdown .hidden}
[DACSS 601: Data Science Fundamentals - FALL 2022 - Challenge 1 Instructions]{.hidden render-id="quarto-metatitle"}
[DACSS 601: Data Science Fundamentals - FALL 2022]{.hidden render-id="quarto-metasitename"}
:::




<!-- -->

::: {.quarto-embedded-source-code}
```````````````````{.markdown shortcodes="false"}
---
title: "Challenge 1 Instructions"
author: "Meredith Rolfe"
desription: "Reading in data and creating a post"
date: "08/15/2022"
format:
  html:
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_1
  - railroads
  - faostat
  - wildbirds
---

quarto-executable-code-5450563D

```r
#| label: setup
#| warning: false
#| message: false

library(tidyverse)
library(readxl)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in the Data and Summarize

quarto-executable-code-5450563D

#reading in wild_bird_data dataset with readxl

wild_bird_data <- as_tibble(read_excel("~/GIT/601_Fall_2022/posts/_data/wild_bird_data.xlsx", skip = 1))

quarto-executable-code-5450563D

#previewing data

head(wild_bird_data)

This data appears to be derived from that used to produce the first figure in a research paper authored by researcher(s) Nee et al. It could be interpreted as the average wet body weight, measured in grams, for different species of birds and the size of each species population. I suspect that this is likely derived from a project to gather data on wild bird populations that frequent (or outright inhabit) aquatic or semi-aquatic environments, such as marshes or similar wetland environments. I imagine this would be a port of a catch and release program to collect biological data and potentially biological samples (potentially tagging future data collection/generation). Wet body weight is an ambiguous measurement and may be directly a measurement of wet mass, or may be related to metabolism, fat retention (through various seasons), a catalog of museum wet specimens, etc., it is difficult to determine without additional information.

The first variable, ‘wet body weight’ is a collection of continuous numerical values, and the other variable, ‘population size’ is also a collection of continuous numerical values (not seen here due setting a digit limit for display purposes). They may represent average population sizes, and this data could be from a meta analysis of various wild bird population studies’ data, as I imagine a single count would more likely produce a discrete count value (counting whole animals) than a continuous one, unless they were averaging multiple population counts together.

quarto-executable-code-5450563D

#getting data dimensions and column names

dim(wild_bird_data)
colnames(wild_bird_data)

By taking the dimensions of the dataset, we can conclude some additional information. It that may be seen that the researchers took wet body weight and population size observations for 146 wild bird species populations.

quarto-executable-code-5450563D

#attempting to arrange data to get wet body weight values in ascending order

wild_bird_data %>%
  arrange('Wet body weight [g]')

#attempt failed, reason unknown

:::