Challenge 2 Instructions

Code

library(tidyverse)
library(summarytools)

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

Challenge Overview

Today’s challenge is to

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

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

• railroad*.csv or StateCounty2012.xls ⭐
• FAOstat*.csv or birds.csv ⭐⭐⭐
• hotel_bookings.csv ⭐⭐⭐⭐

Code

birds_df <- read_csv("_data/birds.csv")
birds_df

Code

#temp <- birds_df[c('Item Code', 'Item', 'Year')]
#temp
print(unique(birds_df$Domain))

[1] "Live Animals"

Describe the data

This is a huge data set with 30977 rows and 14 columns. It shows the different type of birds in different countries of the world. Flag column defines the gender of the bird.The data is defined according to the years and value column shows the number of animals in that particular year.

Code

#grouping the items according to the countries
df<-birds_df%>%
  group_by(Area,Item)
#Summation of the item categories
new_df<-df%>%summarise(Sum=sum(Value, na.rm = TRUE))
new_df

Code

new2<-filter(new_df, `Item` == "Chickens")
new2

Code

arrange(new2,desc(Sum))

Code

median_value<-median(new2$Sum)
median_value

[1] 627823.5

Code

mean_value<-mean(new2$Sum)
mean_value

[1] 10874446

Code

summary(new2)

     Area               Item                Sum           
 Length:248         Length:248         Min.   :      150  
 Class :character   Class :character   1st Qu.:    96443  
 Mode  :character   Mode  :character   Median :   627824  
                                       Mean   : 10874446  
                                       3rd Qu.:  3862730  
                                       Max.   :674215634  

Code

sd(new2$Sum)

[1] 51802479

From this dataset I want to calculate the sum of particular animal according to their area. So I applied group by function to select the Area and Item column from the dataset.By Sum function I get the total number of animals in that area, through filter function I am able to calculate the particular item “Chickens” with respect to area. According to filtered data set I can interpret that area “World” has the max number of chickens “674215634” whereas its lowest at Aruba “150”.