Challenge 19: Using purrr

Load required libraries

library(conflicted)
library(purrr)
library(dplyr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ readr     2.1.4

conflicts_prefer(dplyr::filter)

## [conflicted] Will prefer dplyr::filter over any other package.

Read data

cattle_dairy <- read.csv("challenge_datasets/FAOSTAT_cattle_dairy.csv")
egg_chicken <- read.csv("challenge_datasets/FAOSTAT_egg_chicken.csv")
livestock <- read.csv("challenge_datasets/FAOSTAT_livestock.csv")

purrr

analyze_trends <- function(country, cattle_dairy, egg_chicken, livestock) {
  # Extract data for the given country from each dataset
  dairy_data <- filter(cattle_dairy, Area == country)
  egg_data <- filter(egg_chicken, Area == country)
  livestock_data <- filter(livestock, Area == country)

  # Create a summary of trends
  summary <- list(
    Country = country,
    DairyTrend = summary(dairy_data$Value),
    EggTrend = summary(egg_data$Value),
    LivestockTrend = summary(livestock_data$Value)
  )

  return(summary)
}

# List of top countries
top_countries <- c("Afghanistan", "China", "India", "Germany", "Japan")

# Apply the function to each country using map
results <- map(top_countries, analyze_trends, cattle_dairy, egg_chicken, livestock)

results

## [[1]]
## [[1]]$Country
## [1] "Afghanistan"
## 
## [[1]]$DairyTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3592    5459  600000  903662 1361100 4050000 
## 
## [[1]]$EggTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4000    8800   14325   15203   21225   29048 
## 
## [[1]]$LivestockTrend
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    20000   200000  1300000  3597216  3787500 21500000 
## 
## 
## [[2]]
## [[2]]$Country
## [1] "China"
## 
## [[2]]$DairyTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##                                                 
## 
## [[2]]$EggTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##                                                 
## 
## [[2]]$LivestockTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##                                                 
## 
## 
## [[3]]
## [[3]]$Country
## [1] "India"
## 
## [[3]]$DairyTrend
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     3298    10491 18322000 19678511 32565440 89833590 
## 
## [[3]]$EggTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   35000   83542  119868  608512  447500 5236935 
## 
## [[3]]$LivestockTrend
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##     52813    825500  10246848  48618161  76858006 204584000 
## 
## 
## [[4]]
## [[4]]$Country
## [1] "Germany"
## 
## [[4]]$DairyTrend
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    30992    66294  6635978 11923510 28064424 34538496 
## 
## [[4]]$EggTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   33400   83929  151722  387334  810025 1213480 
## 
## [[4]]$LivestockTrend
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##     4200   440546  2740200  9617191 19666752 37342448       49 
## 
## 
## [[5]]
## [[5]]$Country
## [1] "Japan"
## 
## [[5]]$DairyTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   37513   70972 1219500 2638715 4955461 8657000 
## 
## [[5]]$EggTrend
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   55922  139260  173922  816778 1814000 2627764 
## 
## [[5]]$LivestockTrend
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     9000    22625   133848  2658424  4529250 11866000