Challenge 8 Submission

challenge_8

military

CamNeedels

Joining Data

Author

Cam Needels

Published

May 1, 2023

library(tidyverse)
library(ggplot2)
library(readxl)
library(readr)

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

Briefly describe the data

All these data sets are the amount of a certain item that are produced from livestock, dairy cattle, and eggs.

Tidy Data (as needed)

Data is already tidy, all I needed to do is join it together. It makes it easy that they all have the same variables so merging them was pretty simple!

#assigning the eggs data set to an object
Eggs <- read_csv(("B:/Needels/Documents/DACCS 601/DACSS_601_New/posts/_data/FAOSTAT_egg_chicken.csv"))
dim(Eggs)

[1] 38170    14

Eggs

# A tibble: 38,170 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QL           Lives…       2 Afgh…    5313 Laying     1062 Eggs…    1961  1961
 2 QL           Lives…       2 Afgh…    5410 Yield      1062 Eggs…    1961  1961
 3 QL           Lives…       2 Afgh…    5510 Produc…    1062 Eggs…    1961  1961
 4 QL           Lives…       2 Afgh…    5313 Laying     1062 Eggs…    1962  1962
 5 QL           Lives…       2 Afgh…    5410 Yield      1062 Eggs…    1962  1962
 6 QL           Lives…       2 Afgh…    5510 Produc…    1062 Eggs…    1962  1962
 7 QL           Lives…       2 Afgh…    5313 Laying     1062 Eggs…    1963  1963
 8 QL           Lives…       2 Afgh…    5410 Yield      1062 Eggs…    1963  1963
 9 QL           Lives…       2 Afgh…    5510 Produc…    1062 Eggs…    1963  1963
10 QL           Lives…       2 Afgh…    5313 Laying     1062 Eggs…    1964  1964
# … with 38,160 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`

#assigning the livestock data set to an object
livestock<- read_csv("B:/Needels/Documents/DACCS 601/DACSS_601_New/posts/_data/FAOSTAT_livestock.csv")
#the dimensions of livestock
dim(livestock)

[1] 82116    14

livestock

# A tibble: 82,116 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1961  1961
 2 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1962  1962
 3 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1963  1963
 4 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1964  1964
 5 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1965  1965
 6 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1966  1966
 7 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1967  1967
 8 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1968  1968
 9 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1969  1969
10 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1970  1970
# … with 82,106 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`

#assinging the dairy cattle dataset to an object
dairy<- read_csv("B:/Needels/Documents/DACCS 601/DACSS_601_New/posts/_data/FAOSTAT_cattle_dairy.csv")
#the dimensions for dairy
dim(dairy)

[1] 36449    14

dairy

# A tibble: 36,449 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1961  1961
 2 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1961  1961
 3 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1961  1961
 4 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1962  1962
 5 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1962  1962
 6 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1962  1962
 7 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1963  1963
 8 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1963  1963
 9 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1963  1963
10 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1964  1964
# … with 36,439 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`

Join Data

To double check the dimensions I should expect with math that the sum of the rows in both the dairy cattle and livestock should be 36449 + 82116 = 118565

#joining the dairy cattle and livestock datasets
cattlestock <- full_join(dairy,livestock)
#the dimension of the joining
dim(cattlestock)

[1] 118565     14

cattlestock

# A tibble: 118,565 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1961  1961
 2 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1961  1961
 3 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1961  1961
 4 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1962  1962
 5 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1962  1962
 6 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1962  1962
 7 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1963  1963
 8 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1963  1963
 9 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1963  1963
10 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1964  1964
# … with 118,555 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`

We should expect 36449 + 38170 = 74619 for cattle_eggs

#joining the dairy cattle and eggs datasets
cattle_eggs <- full_join(dairy, Eggs)
#the dimensions of the joining
dim(cattle_eggs)

[1] 74619    14

cattle_eggs

# A tibble: 74,619 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1961  1961
 2 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1961  1961
 3 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1961  1961
 4 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1962  1962
 5 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1962  1962
 6 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1962  1962
 7 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1963  1963
 8 QL           Lives…       2 Afgh…    5420 Yield       882 Milk…    1963  1963
 9 QL           Lives…       2 Afgh…    5510 Produc…     882 Milk…    1963  1963
10 QL           Lives…       2 Afgh…    5318 Milk A…     882 Milk…    1964  1964
# … with 74,609 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`

We should expect 82116 + 38170 = 120286

#joining the livestock and eggs datasets
stockeggs <- full_join(livestock, Eggs)
#the dimensions of the joining
dim(stockeggs)

[1] 120286     14

stockeggs

# A tibble: 120,286 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1961  1961
 2 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1962  1962
 3 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1963  1963
 4 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1964  1964
 5 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1965  1965
 6 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1966  1966
 7 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1967  1967
 8 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1968  1968
 9 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1969  1969
10 QA           Live …       2 Afgh…    5111 Stocks     1107 Asses    1970  1970
# … with 120,276 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹`Domain Code`, ²`Area Code`, ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`