Challenge 2 - FAO Stats, Chickens

Code

library(tidyverse)

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

Challenge Overview

1. Read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
2. Provide summary statistics for different interesting groups within the data, and interpret those statistics

Read in the Data

Table 1: EggOrChicken

Code

#initial data set import
library(readr)
EggOrChicken<-read_csv("_data/FAOSTAT_egg_chicken.csv")
EggOrChicken

Describe the data

Through various R commands, I cleaned up the data set so I may work with it later on in this Challenge (EggAfChicken & ChickenBfEgg).

Table 2: EggAfChicken

Code

#remove columns with "code" in the name
EggAfChicken<-EggOrChicken%>%
  select(-contains("Code"))

Code

EggAfChicken<-rename(EggAfChicken, Country = Area)
EggAfChicken

Table 3: ChickenBfEgg

Code

#create new element columns (Laying, Yield,& Production) and fill with corresponding values
pivot_wider(EggAfChicken, names_from = "Element", values_from = "Value")

Code

#assign new name to data set
ChickenBfEgg<-pivot_wider(EggAfChicken, names_from = "Element", values_from = "Value")

There are 38,170 rows and 14 columns (9 without “Code” in the name) in this data set. The columns are Domain, Area, Element, Item, Year, Unit, Value, Flag, and Flag Description. Note, I renamed Area to Country for this assignment. The data set spans from 1961-2020 (not all years are represented for each country).

The data sources include aggregate, may include official, semi-official, estimated or calculated data (3186 cases), calculated data (13344 cases), data not available (40 cases), FAO data based on imputation methodology (2079 cases), FAO estimate (10538 cases), official data (7548 cases), and unofficial figure (1435 cases).

Code

#data sources
table((select(ChickenBfEgg, "Flag Description")))

Flag Description
Aggregate, may include official, semi-official, estimated or calculated data 
                                                                        3186 
                                                             Calculated data 
                                                                       13344 
                                                          Data not available 
                                                                          40 
                                    FAO data based on imputation methodology 
                                                                        2079 
                                                                FAO estimate 
                                                                       10538 
                                                               Official data 
                                                                        7548 
                                                           Unofficial figure 
                                                                        1435 

Additionally, there are three element types regarding egg-laying that are analyzed in this Challenge: Laying, Production, and Yield of chicken eggs. From that, the item is chicken eggs, hen, in shell.

Provide Grouped Summary Statistics

Using the ChickenBfEgg data set, I conducted exploratory data analysis on the values of “Laying, Yield, and Production” per Country. Each table contains the mean, median, min, max, standard deviation, variance & interquartile range.

Table 1a: Laying

Code

#mean, median, min, max, standard deviation, variance & interquartile range for Laying per Country
ChickenBfEgg%>%
group_by(Country)%>%
summarize(mean.Laying = mean(Laying, na.rm = TRUE), median.Laying = median(Laying, na.rm = TRUE), min.Laying = min(Laying, na.rm = TRUE), max.Laying = max(Laying, na.rm = TRUE), sd.Laying = sd(Laying, na.rm = TRUE), var.Laying = var(Laying, na.rm = TRUE), IQR.Laying = IQR(Laying, na.rm = TRUE))

Table 2a: Yield

Code

#mean, median, min, max, standard deviation, variance & interquartile range for Yield per Country
ChickenBfEgg%>%
group_by(Country)%>%
summarize(mean.Yield = mean(Yield, na.rm = TRUE), median.Yield = median(Yield, na.rm = TRUE), min.Yield = min(Yield, na.rm = TRUE), max.Yield = max(Yield, na.rm = TRUE), sd.Yield = sd(Yield, na.rm = TRUE), var.Yield = var(Yield, na.rm = TRUE), IQR.Yield = IQR(Yield, na.rm = TRUE))

Table 3a: Production

Code

#mean, median, min, max, standard deviation, variance & interquartile range for Production per Country
ChickenBfEgg%>%
group_by(Country)%>%
summarize(mean.Production = mean(Production, na.rm = TRUE), median.Production = median(Production, na.rm = TRUE), min.Production = min(Production, na.rm = TRUE), max.Production = max(Production, na.rm = TRUE), sd.Production = sd(Production, na.rm = TRUE), var.Production = var(Production, na.rm = TRUE), IQR.Production = IQR(Production, na.rm = TRUE))

Explain and Interpret

I conclude from this analysis that this data set is reporting on egg-laying in three categories (laying, yield, and production) measured in units of 1000 Head globally (245 areas) from year to year.