Challenge 8

challenge_8

bird remake

faostat

Joining Data

Author

Connor Landreth

Published

April 30, 2023

library(knitr)
library(tidyverse)
library(ggplot2)
library(readxl)
library(lubridate)
library(here)
library(ggthemes)

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

getwd()

[1] "C:/Github Projects/601_Spring_2023/posts"

setwd("C:/Github Projects/601_Spring_2023/posts/_data")

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

FAO
bird

library(readxl)
setwd("C:/Github Projects/601_Spring_2023/posts/_data")
#milmar <- read_excel("ActiveDuty_MaritalStatus.xls")
#Amar <- read.csv("australian_marriage_tidy.csv")
birds <- read.csv("birds.csv")
fao <- read.csv("FAOSTAT_country_groups.csv")

Going to work through the fao and bird joining example. First, rename variables

fao <- fao %>% 
  select(Country.Group, Country)%>%
  rename(country_group = "Country.Group")%>%
  distinct()

fao %>% 
  filter(country_group == "Southern Europe")

     country_group                      Country
1  Southern Europe                      Albania
2  Southern Europe                      Andorra
3  Southern Europe       Bosnia and Herzegovina
4  Southern Europe                      Croatia
5  Southern Europe                    Gibraltar
6  Southern Europe                       Greece
7  Southern Europe                     Holy See
8  Southern Europe                        Italy
9  Southern Europe                       Kosovo
10 Southern Europe                        Malta
11 Southern Europe                   Montenegro
12 Southern Europe              North Macedonia
13 Southern Europe                     Portugal
14 Southern Europe                   San Marino
15 Southern Europe                       Serbia
16 Southern Europe        Serbia and Montenegro
17 Southern Europe                     Slovenia
18 Southern Europe Southern Europe, unspecified
19 Southern Europe                        Spain
20 Southern Europe                 Yugoslav SFR

Knit table with country groups and associated country groups

temp<-fao%>%
  group_by(country_group)%>%
  summarize(n=n())%>%
  arrange(desc(n))
half <-c(1:round(nrow(temp)/2))
knitr::kable(list(temp[half,],  
           matrix(numeric(), nrow=0, ncol=1),
           temp[-half,]), 
           caption = "# Coutnries by Country Groups")%>%
  kableExtra::kable_styling(font_size=15)

# Coutnries by Country Groups

country_group	n
World	277
Non-Annex I countries	161
Net Food Importing Developing Countries	81
Annex I countries	78
High-income economies	64
Africa	63
Europe	63
Americas	61
Small Island Developing States	58
Upper-middle-income economies	56
Low Income Food Deficit Countries	55
Asia	54
Sub-Saharan Africa	53
Latin America and the Caribbean	52
Least Developed Countries	51
Sub-Saharan Africa (including Sudan)	49
Lower-middle-income economies	46
Northern America and Europe	44
OECD	36
Oceania	35
Low income economies	34
Land Locked Developing Countries	32
Caribbean	30
European Union (27)	30
Western Asia and Northern Africa	24
Eastern Africa	23
Eastern Asia and South-eastern Asia	20
Southern Europe	20
Western Asia	20

country_group	n
Northern Europe	18
Oceania excluding Australia and New Zealand	18
Western Africa	18
South America	17
Central Asia and Southern Asia	14
North and Central America	14
Western Asia (exc. Armenia, Azerbaijan, Cyprus, Israel and Georgia)	13
Eastern Europe	12
Polynesia	12
South-eastern Asia	11
Western Europe	11
Micronesia	10
Eastern Asia	9
Middle Africa	9
Southern Asia	9
Caucasus and Central Asia	8
Central America	8
Northern Africa	8
Southern Asia (excluding India)	8
Australia and New Zealand	6
Eastern Asia (excluding Japan)	6
Northern Africa (excluding Sudan)	6
Northern America	6
Central Asia	5
Melanesia	5
Southern Africa	5
Eastern Asia (excluding China)	3
Eastern Asia (excluding Japan and China)	3
Antarctic Region	1

fao%>%
  summarise(n=n())/277

        n
1 7.01444

Nested sub regions within regions:

fao%>%
  filter(str_detect(country_group, "[aA]nnex"))%>%
  group_by(country_group)%>%
  summarise(n=n())

# A tibble: 2 × 2
  country_group             n
  <chr>                 <int>
1 Annex I countries        78
2 Non-Annex I countries   161

fao%>%
  filter(str_detect(country_group, "[aA]nnex"))%>%
  summarise(n=n())

    n
1 239

Join Data - Combine data sets (marriage & debt)

fao%>%
  filter(str_detect(country_group, "[iI]ncome"))%>%
  group_by(country_group)%>%
  summarise(n=n())

# A tibble: 5 × 2
  country_group                         n
  <chr>                             <int>
1 High-income economies                64
2 Low income economies                 34
3 Low Income Food Deficit Countries    55
4 Lower-middle-income economies        46
5 Upper-middle-income economies        56

fao%>%
  filter(str_detect(country_group, "[iI]ncome"))%>%
  summarise(n=n())

    n
1 255

fao%>%
  filter(str_detect(country_group, "[Dd]evelop|OECD"))%>%
  group_by(country_group)%>%
  summarise(n=n())

# A tibble: 5 × 2
  country_group                               n
  <chr>                                   <int>
1 Land Locked Developing Countries           32
2 Least Developed Countries                  51
3 Net Food Importing Developing Countries    81
4 OECD                                       36
5 Small Island Developing States             58

fao%>%
  filter(str_detect(country_group, "[Dd]evelop|OECD"))%>%
  summarise(n=n())

    n
1 258

major_regions<-c("Africa", "Asia", "Europe", "Americas", 
                 "Oceania", "Antarctic Region")

fao%>%
  filter(country_group %in% major_regions)%>%
  summarise(n=n())

    n
1 277

Create new categorical variables for the country groups for data set.

fao_regions_wide<-fao%>%
  filter(country_group!="World")%>%
  pivot_wider(names_from=country_group, values_from = 1)%>%
  unite("gp_annex", contains("Annex"), 
        sep="", na.rm=TRUE, remove=TRUE)%>%
  unite("gp_major_region", any_of(major_regions), 
        sep="", na.rm=TRUE, remove=TRUE)%>%
  unite("gp_income", contains("Income")|contains("income"),
        sep="", na.rm=TRUE, remove=TRUE)%>%
  unite("gp_develop", contains("Develop")|contains("OECD"),
        sep="", na.rm=TRUE, remove=TRUE)%>%
  select(Country, starts_with("gp"))

nrow(birds)

[1] 30977

birds <- left_join(birds, fao_regions_wide,
                   by = c("Area" = "Country"))

Then we can summarize the data across the countries

eggs_chicken <- here("posts","_data","FAOSTAT_egg_chicken.csv") %>%
  read_csv()
cattle <- here("posts","_data","FAOSTAT_egg_chicken.csv") %>%
  read_csv()
data <- left_join(eggs_chicken, cattle, by="Area")