Challenge 1 Solutions Final

challenge_1
railroads
faostat
wildbirds
Moira Chiong
Reading in data and creating a post
Author

Moira Chiong

Published

June 6, 2023

Code 
library(tidyverse)
library(readxl)
library(dplyr)

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

Challenge Overview

Today’s challenge is to

  1. describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

Read in one (or more) of the following data sets, using the correct R package and command.

  • railroad_2012_clean_county.csv ⭐
  • birds.csv ⭐⭐
  • FAOstat*.csv ⭐⭐
  • wild_bird_data.xlsx ⭐⭐⭐
  • StateCounty2012.xls ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

#read in excel file and remove first observation library(readxl) wild_bird_data <- read_excel(“posts/_data/wild_bird_data.xlsx”, skip=1) View(wild_bird_data) library(“dplyr”)


Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

## Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

1)  read in a dataset, and
#This data set looks at weight and population size of Wild Birds. There are two variables, weight and population size. There are 146 
# observations with 2 variables. The mean and median of the weight variable are 363.69 and 69.23 respectively. The mean and median
# of the population size variable are 382874 and 24353.21 respectively. The minimum and maximum of the weight variable is 5.46 and 9639.85 
#respectively. The minimum and maximum of the population size variable are 4.92 and 5093378 respectively. The Interquartile range for 
#weight and population size is 291 and 196694 respectively.



#install packages to read in an excel file

install.packages("readxl")
install.packages("dplyr")
library("readxl")

#rename columns to easier names
names(wild_bird_data) <- NULL           
wild_bird_data
colnames(wild_bird_data) <- c("weight","population_size")
print(wild_bird_data)

#round the variables to 2 significant figures
round(wild_bird_data_SANDBOX,digits=2)

#find the mean of each variable
mean(wild_bird_data_SANDBOX$weight)
mean(wild_bird_data_SANDBOX$popsize)

#find the median of each variable
median(wild_bird_data_SANDBOX$weight)
median(wild_bird_data_SANDBOX$popsize)

#find the number of observations
nrow(wild_bird_data_SANDBOX)
#find the number of variables
ncol(wild_bird_data_SANDBOX)

#find the min of each variable
min(wild_bird_data_SANDBOX$weight)
min(wild_bird_data_SANDBOX$popsize)

#find the max of each variable
max(wild_bird_data_SANDBOX$weight)
max(wild_bird_data_SANDBOX$popsize)

library(dplyr)
#calculating mean value
data <- wild_bird_data_SANDBOX
data %>%
summarize_all(mean)

#calculating median value
data <- wild_bird_data_SANDBOX
data %>%

summarize_all(median)

#calculating Interquartile Range (IQR) value
data <- wild_bird_data_SANDBOX
data %>%
summarize_all(IQR)
#END




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<!-- -->

::: {.quarto-embedded-source-code}
```````````````````{.markdown shortcodes="false"}
---
title: "Challenge 1 Solutions Final"
author: "Moira Chiong"
description: "Reading in data and creating a post"
date: "06/06/2023"
format:
  html:
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_1
  - railroads
  - faostat
  - wildbirds
  - Moira Chiong
---

quarto-executable-code-5450563D

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

library(tidyverse)
library(readxl)
library(dplyr)

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

Challenge Overview

Today’s challenge is to

  1. describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

Read in one (or more) of the following data sets, using the correct R package and command.

  • railroad_2012_clean_county.csv ⭐
  • birds.csv ⭐⭐
  • FAOstat*.csv ⭐⭐
  • wild_bird_data.xlsx ⭐⭐⭐
  • StateCounty2012.xls ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

#read in excel file and remove first observation library(readxl) wild_bird_data <- read_excel(“posts/_data/wild_bird_data.xlsx”, skip=1) View(wild_bird_data) library(“dplyr”)


Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

## Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

1)  read in a dataset, and
#This data set looks at weight and population size of Wild Birds. There are two variables, weight and population size. There are 146 
# observations with 2 variables. The mean and median of the weight variable are 363.69 and 69.23 respectively. The mean and median
# of the population size variable are 382874 and 24353.21 respectively. The minimum and maximum of the weight variable is 5.46 and 9639.85 
#respectively. The minimum and maximum of the population size variable are 4.92 and 5093378 respectively. The Interquartile range for 
#weight and population size is 291 and 196694 respectively.



#install packages to read in an excel file

install.packages("readxl")
install.packages("dplyr")
library("readxl")

#rename columns to easier names
names(wild_bird_data) <- NULL           
wild_bird_data
colnames(wild_bird_data) <- c("weight","population_size")
print(wild_bird_data)

#round the variables to 2 significant figures
round(wild_bird_data_SANDBOX,digits=2)

#find the mean of each variable
mean(wild_bird_data_SANDBOX$weight)
mean(wild_bird_data_SANDBOX$popsize)

#find the median of each variable
median(wild_bird_data_SANDBOX$weight)
median(wild_bird_data_SANDBOX$popsize)

#find the number of observations
nrow(wild_bird_data_SANDBOX)
#find the number of variables
ncol(wild_bird_data_SANDBOX)

#find the min of each variable
min(wild_bird_data_SANDBOX$weight)
min(wild_bird_data_SANDBOX$popsize)

#find the max of each variable
max(wild_bird_data_SANDBOX$weight)
max(wild_bird_data_SANDBOX$popsize)

library(dplyr)
#calculating mean value
data <- wild_bird_data_SANDBOX
data %>%
summarize_all(mean)

#calculating median value
data <- wild_bird_data_SANDBOX
data %>%

summarize_all(median)

#calculating Interquartile Range (IQR) value
data <- wild_bird_data_SANDBOX
data %>%
summarize_all(IQR)
#END

:::