Presentation exercise

Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data. “Chicken meat production – UN FAO” [dataset]. Food and Agriculture Organization of the United Nations, “Production: Crops and livestock products” [original data]. Retrieved February 18, 2026 from https://archive.ourworldindata.org/20260119-235736/grapher/chicken-meat-production.html (archived on January 19, 2026).

# Loading used libraries
library(ggplot2)
library(maps)
library(countries)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(here)

here() starts at C:/Users/marco/Documents/marcoreina-portafolio

# Loading data and exploring variables
original_data <- read.csv(here("presentation-exercise/chicken-meat-production/chicken-meat-production.csv"))
str(original_data)

'data.frame':   14385 obs. of  4 variables:
 $ region                 : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ Code                   : chr  "AFG" "AFG" "AFG" "AFG" ...
 $ Year                   : int  1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 ...
 $ Chicken.meat.production: num  5600 6000 6160 6400 6800 7200 7600 8000 9600 9600 ...

summary(original_data)

    region              Code                Year      Chicken.meat.production
 Length:14385       Length:14385       Min.   :1961   Min.   :        0      
 Class :character   Class :character   1st Qu.:1977   1st Qu.:     4300      
 Mode  :character   Mode  :character   Median :1993   Median :    40589      
                                       Mean   :1993   Mean   :  1421667      
                                       3rd Qu.:2008   3rd Qu.:   373800      
                                       Max.   :2023   Max.   :126530270      

# Determining if entity is a country name
original_data$is_country <- is_country(x = original_data$region)

# Adding ISO code for all the countries
original_data$iso_code <- country_name(x= original_data$region, to="ISO3")

Some country IDs have no match in one or more of the requested country naming conventions, NA returned.

Multiple country IDs have been matched to the same country name.

Set - verbose - to TRUE for more details

# Creating bins for chicken production: code fine-tuned with ChatGPT

breaks_prod <- c(0, 100000, 200000, 500000, 1000000, 2000000, 5000000, 10000000, 100000000)

# Mutating data to include bins: code fine-tuned with ChatGPT
original_data <- original_data |>
  mutate(
    prod_bin = cut(
      Chicken.meat.production,
      breaks = breaks_prod,
      include.lowest = TRUE,
      right = FALSE
    )
  )

# Changing bins names to categories: code fine-tuned with ChatGPT
levels(original_data$prod_bin) <- c(
  "0 – 100k t",
  "100k – 200k t",
  "200k – 500k t",
  "500k – 1M t",
  "1 – 2M t",
  "2 – 5M t",
  "5 – 10M t",
  "> 10M t"
)

# Creating clean data vector and exploring variables
clean_data <- subset(original_data, is_country == TRUE)
str(clean_data)

'data.frame':   11432 obs. of  7 variables:
 $ region                 : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ Code                   : chr  "AFG" "AFG" "AFG" "AFG" ...
 $ Year                   : int  1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 ...
 $ Chicken.meat.production: num  5600 6000 6160 6400 6800 7200 7600 8000 9600 9600 ...
 $ is_country             : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ iso_code               : chr  "AFG" "AFG" "AFG" "AFG" ...
 $ prod_bin               : Factor w/ 8 levels "0 – 100k t","100k – 200k t",..: 1 1 1 1 1 1 1 1 1 1 ...

summary(clean_data)

    region              Code                Year      Chicken.meat.production
 Length:11432       Length:11432       Min.   :1961   Min.   :       0       
 Class :character   Class :character   1st Qu.:1977   1st Qu.:    2700       
 Mode  :character   Mode  :character   Median :1994   Median :   19084       
                                       Mean   :1993   Mean   :  274513       
                                       3rd Qu.:2009   3rd Qu.:  108000       
                                       Max.   :2023   Max.   :20197090       
                                                                             
 is_country       iso_code                  prod_bin   
 Mode:logical   Length:11432       0 – 100k t   :8450  
 TRUE:11432     Class :character   100k – 200k t: 911  
                Mode  :character   200k – 500k t: 861  
                                   500k – 1M t  : 558  
                                   1 – 2M t     : 404  
                                   2 – 5M t     : 146  
                                   (Other)      : 102  

# Creating 2023 dataset, just to mimic the first plot shown
data_2023 <- subset(clean_data, Year == 2023)

# Loading world map  and removing Antarctica
world_map <- map_data("world") %>% 
  filter(region != "Antarctica")

# Adding ISO code for all the countries
world_map$iso_code <- country_name(x= world_map$region, to="ISO3")

Some country IDs have no match in one or more of the requested country naming conventions, NA returned.

Multiple country IDs have been matched to the same country name.

Set - verbose - to TRUE for more details

# Merging dataframes based on the ISO code per country
map_2023 <- left_join(world_map, data_2023, by = "iso_code")

Chicken Production around the world 2023 (Recreated from FAO data)

# Recreating plot for 2023: code fine-tuned with ChatGPT

plot_2023 <- ggplot(map_2023, aes(long, lat, group = group, fill = prod_bin)) +
  geom_polygon(color = "lightgrey", linewidth = 0.1) +
    labs(fill = "Chicken production"
  ) +
  theme_void() +
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#FFF5F0", "#FEE0D2", "#FCBBA1", "#FC9272", "#FB6A4A", "#EF3B2C", "#CB181D", "#99000D")
  )
 
plot_2023

Creating a table using this same information

ChatGPT prompt: “I want to make an interactive table for chicken consumption from around the world that integrates some graphs within the table, i have information from 1961 to 2023”

ChatGPT response:

library(reactable) library(sparkline) library(dplyr) library(tidyr)

# Loading suggested libraries by MR
library(reactable)
library(sparkline)
library(tidyr)

ChatGPT response:

table_data <- chicken_data %>% group_by(country) %>% summarise( latest_2023 = chicken_consumption[year == 2023], trend = list(chicken_consumption) )

Code adapted by MR

clean_data <- clean_data %>%
  mutate(
    change = round(100 * (Chicken.meat.production - lag(Chicken.meat.production))/lag(Chicken.meat.production), 1)
  )

clean_data_table <- na.omit(clean_data)
  
table_chicken <- clean_data_table %>%
  group_by(region) %>%
  summarise(
    latest_2023 = Chicken.meat.production[Year == 2023],
    change = change[Year == 2023],
    trend = list(Chicken.meat.production)
  )

Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
  always returns an ungrouped data frame and adjust accordingly.

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

ChatGPT response:

reactable( table_data, columns = list( country = colDef(name = “Country”),

latest_2023 = colDef( name = “Consumption (2023)”, format = colFormat(digits = 1) ),

trend = colDef( name = “1961–2023 Trend”, cell = function(values) { sparkline(values, type = “line”) }, html = TRUE ) ), searchable = TRUE, sortable = TRUE, highlight = TRUE, striped = TRUE )

Code adapted by MR

reactable(
  table_chicken,
  columns = list(
    region = colDef(name = "Country"),
    
    latest_2023 = colDef(
      name = "2023 Production (t)",
      format = colFormat(digits = 0),
      defaultSortOrder = "desc"
    ),
    
    change = colDef(
      name = "2023 change (%)",
      format = colFormat(digits = 1),
      style = function(value) {
        color <- if (value > 5) "green"
        else if (value > -5) "black"
        else "red"
        list(
          color = color,
          fontWeight = "bold"
        )
      },
    ),
    
    trend = colDef(
      name = "Historic Trend",
      cell = function(values) {
        sparkline(values, type = "line")
      },
      html = TRUE
    )
  ),
  searchable = TRUE,
  sortable = TRUE,
  highlight = TRUE,
  striped = TRUE
)

Table shows the 2023 production and the change percentage compared to 2022. Conditional formatting is shown as black for for -5% to 5%, green for +5% and above, red for -5% and under. Table can be searched and sorted.