Commodity Analysis Report

## Summary Statistics
library(tidyverse)

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file_path <- "wfp_commodities_global.csv"
data <- read.csv(file_path)
category_counts <- data %>%
group_by(category) %>%
summarise(count = n())
category_counts

## # A tibble: 9 × 2
##   category              count
##   <chr>                 <int>
## 1 #item+type                1
## 2 cereals and tubers      271
## 3 meat, fish and eggs     158
## 4 milk and dairy           28
## 5 miscellaneous food      127
## 6 non-food                121
## 7 oil and fats             51
## 8 pulses and nuts         110
## 9 vegetables and fruits   265

category_counts <- data %>%
group_by(category) %>%
summarise(count = n())
category_counts

## # A tibble: 9 × 2
##   category              count
##   <chr>                 <int>
## 1 #item+type                1
## 2 cereals and tubers      271
## 3 meat, fish and eggs     158
## 4 milk and dairy           28
## 5 miscellaneous food      127
## 6 non-food                121
## 7 oil and fats             51
## 8 pulses and nuts         110
## 9 vegetables and fruits   265

library(ggplot2)
ggplot(category_counts, aes(x = reorder(category, -count), y = count)) +
geom_bar(stat = "identity", fill = "skyblue") +
theme_minimal() +
labs(title = "Commodity Counts by Category",
x = "Category",
y = "Count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))