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.xlsx ⭐⭐⭐⭐
I will be working on the “wild_bird_data” dataset.
Code
library(readxl)wild_bird_data <-read_xlsx("_data/wild_bird_data.xlsx")# View the datasetwild_bird_data
# A tibble: 147 × 2
Reference `Taken from Figure 1 of Nee et al.`
<chr> <chr>
1 Wet body weight [g] Population size
2 5.45887180052624 532194.395145161
3 7.76456810683605 3165107.44544653
4 8.63858738018464 2592996.86778979
5 10.6897349302105 3524193.2266336
6 7.41722577905587 389806.168891807
7 9.1169347252776 604765.97978904
8 8.03684333000353 192360.511579436
9 8.70473119796067 250452.449623033
10 8.89032317828959 16997.4156415239
# … with 137 more rows
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).
Code
# Use dim() to get dimensions of datasetdim(wild_bird_data)
[1] 147 2
There are 147 cases in 2 columns(Reference and Taken from Figure 1 of Nee et al). Actually the second row has the real column names so we will now make second row as column names and remove the first row.
Code
#Rename the column namescolnames(wild_bird_data) <- wild_bird_data[1,]#Removing the first rowwild_bird_data <- wild_bird_data[-1,]#New dimensions of datasetdim(wild_bird_data)
Wet body weight [g] Population size
Length:146 Length:146
Class :character Class :character
Mode :character Mode :character
Code
#Converting datset to numericwild_bird_data$`Wet body weight [g]`<-as.numeric(wild_bird_data$`Wet body weight [g]`)wild_bird_data$`Population size`<-as.numeric(wild_bird_data$`Population size`)#Summary of the converted datasetsummary(wild_bird_data)
Wet body weight [g] Population size
Min. : 5.459 Min. : 5
1st Qu.: 18.620 1st Qu.: 1821
Median : 69.232 Median : 24353
Mean : 363.694 Mean : 382874
3rd Qu.: 309.826 3rd Qu.: 198515
Max. :9639.845 Max. :5093378
Brief summary of the wild_bird dataset.
Source Code
---title: "Challenge 1_Jyoti Rani"author: "Jyoti Rani"desription: "Reading in data and creating a post"date: "08/15/2022"format: html: toc: true code-fold: true code-copy: true code-tools: truecategories: - challenge_1---```{r}library(tidyverse)knitr::opts_chunk$set(echo =TRUE, warning=FALSE, message=FALSE)```## Challenge OverviewToday's challenge is to1) read in a dataset, and2) describe the dataset using both words and any supporting information (e.g., tables, etc)## Read in the DataRead in one (or more) of the following data sets, using the correct R package and command.- railroad_2012_clean_county.csv ⭐- birds.csv ⭐⭐- FAOstat\*.csv ⭐⭐- wild_bird_data.xlsx ⭐⭐⭐- StateCounty2012.xlsx ⭐⭐⭐⭐I will be working on the “wild_bird_data” dataset.```{r}library(readxl)wild_bird_data <-read_xlsx("_data/wild_bird_data.xlsx")# View the datasetwild_bird_data```## Describe the dataUsing a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).```{r}# Use dim() to get dimensions of datasetdim(wild_bird_data)```There are 147 cases in 2 columns(Reference and Taken from Figure 1 of Nee et al). Actually the second row has the real column names so we will now make second row as column names and remove the first row.```{r}#Rename the column namescolnames(wild_bird_data) <- wild_bird_data[1,]#Removing the first rowwild_bird_data <- wild_bird_data[-1,]#New dimensions of datasetdim(wild_bird_data)``````{r}#View the datasetwild_bird_data``````{r}#Summary of datasetsummary(wild_bird_data)``````{r}#Converting datset to numericwild_bird_data$`Wet body weight [g]`<-as.numeric(wild_bird_data$`Wet body weight [g]`)wild_bird_data$`Population size`<-as.numeric(wild_bird_data$`Population size`)#Summary of the converted datasetsummary(wild_bird_data)```Brief summary of the wild_bird dataset.