Read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
Identify what needs to be done to tidy the current data
Anticipate the shape of pivoted data
Pivot the data into tidy format using pivot_longer
Read in data
Upon reading in the data set, I made some initial changes to clean up the data frame and make it easier to use later on. Specifically, I removed/renamed the column headers and removed the empty column.
This data set outlines the monthly prices (cents per pound) paid for USDA certified organic eggs & poultry from 2004-2013. There are four types of eggs: extra large dozen, extra large half dozen, large dozen, and large half dozen. There are also five types of chickens: whole, boneless/skinless breast, bone-in breast, whole legs, and thighs. This data set is sourced from the U.S. Department of Agriculture, Agricultural Marketing Service (AMS) Market News, Organic Poultry and Eggs (Weekly reports).
In an ideal state, I am planning to pivot this data set so it neatly displays in one row the year, month, item, item type, and cost. Right now, even though I cleaned up the data, the reader cannot easily understand what story the data is telling.
Generated by summarytools 1.0.1 (R version 4.2.2) 2023-03-07
Anticipate the End Result
To reach the ideal state mentioned above, I will first need to break out the year and month from the date column. Additionally, there are non-numeric data points in the chicken columns so I will recode those as well. Each of these changes will ensure that I can properly pivot the data from wide to long in the last section.
I pivoted the data successfully from wide to long. I can check this by comparing it to the data table above. It now clearly outlines the breakdown of organic egg and poultry prices (cent per pound) by month from 2004-2013. A new “case” in this instance is the monthly price of a type of egg or poultry.
---title: "Challenge 3 - Organiceggpoultry Dataset"author: "Megan Galarneau"description: "Tidy Data: Pivoting"date: "03/07/2023"format: html: df-print: paged toc: true code-fold: true code-copy: true code-tools: true css: "style.css"categories: - challenge_3 - organiceggpoultry - Megan Galarneau---```{r}#| label: setup#| warning: false#| message: falselibrary(tidyverse)library(dplyr)library(lubridate)knitr::opts_chunk$set(echo =TRUE, warning=FALSE, message=FALSE)```## Challenge OverviewToday's challenge is to:1. Read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)2. Identify what needs to be done to tidy the current data3. Anticipate the shape of pivoted data4. Pivot the data into tidy format using `pivot_longer`## Read in dataUpon reading in the data set, I made some initial changes to clean up the data frame and make it easier to use later on. Specifically, I removed/renamed the column headers and removed the empty column.```{r}library(readxl) raw_organiceggpoultry <-read_excel("_data/organiceggpoultry.xls", skip =5, col_names =c("Date", "Eggxl_12", "Eggxl_6", "Egglg_12", "Egglg_6", "Remove", "Chkn_Whl", "Chkn_BSBrst", "Chkn_BnInBrst", "Chkn_WhlLg", "Chkn_Thigh")) %>%select(-c(Remove))```### Briefly describe the dataThis data set outlines the monthly prices (cents per pound) paid for USDA certified organic eggs & poultry from 2004-2013. There are four types of eggs: extra large dozen, extra large half dozen, large dozen, and large half dozen. There are also five types of chickens: whole, boneless/skinless breast, bone-in breast, whole legs, and thighs. This data set is sourced from the U.S. Department of Agriculture, Agricultural Marketing Service (AMS) Market News, Organic Poultry and Eggs (Weekly reports).In an ideal state, I am planning to pivot this data set so it neatly displays in one row the year, month, item, item type, and cost. Right now, even though I cleaned up the data, the reader cannot easily understand what story the data is telling.```{r}print(summarytools::dfSummary(raw_organiceggpoultry,varnumbers =FALSE,plain.ascii =FALSE, style ="grid", graph.magnif =0.70, valid.col =FALSE),method ='render',table.classes ='table-condensed')```## Anticipate the End ResultTo reach the ideal state mentioned above, I will first need to break out the year and month from the date column. Additionally, there are non-numeric data points in the chicken columns so I will recode those as well. Each of these changes will ensure that I can properly pivot the data from wide to long in the last section.```{r}raw_organiceggpoultry <- raw_organiceggpoultry %>%separate(Date, into =c("Month", "Year"), sep =" ") %>%mutate(Year =as.integer(Year))%>%fill(Year)``````{r}raw_organiceggpoultry<-raw_organiceggpoultry%>%mutate(Chkn_BnInBrst =recode(Chkn_BnInBrst, `too few`="0"),Chkn_Thigh =recode(Chkn_Thigh, `too few`="0"))``````{r}raw_organiceggpoultry$Chkn_BnInBrst <-as.numeric(raw_organiceggpoultry$Chkn_BnInBrst)``````{r}raw_organiceggpoultry$Chkn_Thigh <-as.numeric(raw_organiceggpoultry$Chkn_Thigh)raw_organiceggpoultry``````{r}str(raw_organiceggpoultry)```## Pivot the DataI pivoted the data successfully from wide to long. I can check this by comparing it to the data table above. It now clearly outlines the breakdown of organic egg and poultry prices (cent per pound) by month from 2004-2013. A new "case" in this instance is the monthly price of a type of egg or poultry.```{r}raw_organiceggpoultry <-pivot_longer(raw_organiceggpoultry, col =c("Eggxl_12", "Eggxl_6", "Egglg_12", "Egglg_6", "Chkn_Whl", "Chkn_BSBrst", "Chkn_WhlLg", "Chkn_BnInBrst", "Chkn_Thigh"),names_to="Organic Eggs & Poultry",values_to ="Cost, Cent/Lb")raw_organiceggpoultry```
