About the Data

THis is a datset of monthly values of each country’s food prices since 2017. I’m trying to see how food prices have varied in past years.

Dataset is available at:

https://datacatalog.worldbank.org/search/dataset/0060165/Monthly-food-price-inflation-estimates-by-country

importing data + libraries

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
library(ggthemes)
library(ggvis)

## 
## Attaching package: 'ggvis'
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## The following object is masked from 'package:ggplot2':
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library(reshape2)

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## Attaching package: 'reshape2'
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## The following object is masked from 'package:tidyr':
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library(knitr)
library(shiny)
library(reticulate)
library(formatR)
library(maps)

## 
## Attaching package: 'maps'
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## The following object is masked from 'package:purrr':
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##     map

library(mapdata)
library(dplyr)
food_ind <- read_csv("WLD_RTFP_country_2023-09-12.csv")

## Rows: 4773 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): country, ISO3
## dbl  (5): Open, High, Low, Close, Inflation
## date (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

str(food_ind)

## spc_tbl_ [4,773 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Open     : num [1:4773] 0.53 0.53 0.53 0.53 0.56 0.59 0.59 0.6 0.6 0.63 ...
##  $ High     : num [1:4773] 0.54 0.54 0.54 0.55 0.57 0.6 0.6 0.6 0.62 0.64 ...
##  $ Low      : num [1:4773] 0.53 0.53 0.53 0.53 0.55 0.58 0.59 0.59 0.59 0.62 ...
##  $ Close    : num [1:4773] 0.53 0.53 0.53 0.55 0.57 0.59 0.59 0.59 0.62 0.63 ...
##  $ Inflation: num [1:4773] NA NA NA NA NA NA NA NA NA NA ...
##  $ country  : chr [1:4773] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ ISO3     : chr [1:4773] "AFG" "AFG" "AFG" "AFG" ...
##  $ date     : Date[1:4773], format: "2007-01-01" "2007-02-01" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Open = col_double(),
##   ..   High = col_double(),
##   ..   Low = col_double(),
##   ..   Close = col_double(),
##   ..   Inflation = col_double(),
##   ..   country = col_character(),
##   ..   ISO3 = col_character(),
##   ..   date = col_date(format = "")
##   .. )
##  - attr(*, "problems")=<externalptr>

Trying to see the trends for 1 country only, Afghanistan

#creating a subset of Afghanistan 

afg_food_afghanistan <- food_ind[food_ind$country == "Afghanistan",]

# Convert the 'date' column to a Date object
afg_food_afghanistan$date <- as.Date(afg_food_afghanistan$date)

# Create the scatter plot using ggplot2
ggplot(afg_food_afghanistan, aes(x = date)) +
  geom_smooth(aes(y = Open, color = "Open"), alpha = 0.5, method = "loess") +
  geom_smooth(aes(y = High, color = "High"), alpha = 0.5, method = "loess") +
  geom_smooth(aes(y = Low, color = "Low"), alpha = 0.5, method = "loess") +
  geom_smooth(aes(y = Close, color = "Close"), alpha = 0.5, method = "loess") +
  scale_color_manual(values = c("Open" = "blue", "High" = "green", "Low" = "red", "Close" = "purple")) +
  labs(title = "Variation of Food Price Inflation in Afghanistan",
       y = "Price",
       x = "Date",
       color = "Price Type") +
  theme_minimal() +
  theme(legend.position = "top") +
  guides(color = guide_legend(title = "Price Type"))

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Cleaning the dataset

food_ind_clean <- food_ind[!is.na(food_ind$Close), ]

All countries compared on one single graph and listing down the top 10 countries which saw the most change in past years

food_ind_clean$date <- as.Date(food_ind_clean$date)

# Calculate the change in 'Close' price for each country
food_ind_clean <- food_ind_clean %>%
  group_by(country) %>%
  mutate(change = Close - lag(Close))

# Create the scatter plot for 'Close' column with a line connecting points
ggplot(food_ind_clean, aes(x = date, y = Close, color = change, group = country)) +
  geom_point(aes(size = abs(change)), alpha = 0.5) +
  geom_line() +  # Add a line connecting points
  scale_color_gradient(low = "blue", high = "red") +
  scale_size_continuous(range = c(2, 6)) +
  labs(title = "Variation of Food Price Inflation Across Countries",
       y = "Close Price",
       x = "Date",
       color = "Change in Close Price") +
  theme_minimal() +
  theme(legend.position = "top") +
  guides(color = guide_colorbar(title = "Change in Price"))

## Warning: Removed 25 rows containing missing values (`geom_point()`).

country_change <- food_ind %>%
  group_by(country) %>%
  summarize(total_change = sum(Close - lag(Close, default = first(Close))))

# Select the top 10 countries with the highest total change
top_countries <- country_change %>%
  arrange(desc(total_change)) %>%
  head(10)

# Print the names of the top 10 countries
cat("Top 10 Countries with the Most Change in Close Price:\n")

## Top 10 Countries with the Most Change in Close Price:

cat(top_countries$country, sep = "\n")

## Sudan
## Lebanon
## Syrian Arab Republic
## South Sudan
## Haiti
## Yemen, Rep.
## Myanmar
## Nigeria
## Burundi
## Liberia

Generating heatmap of the world map using the geom_polygon to visualize the above top 10 countries.

doing this for past 5 years as per above graph they are the most releveant ones

# Filter the dataset to include data from 2017 to 2023
start_date <- as.Date("2017-01-01")
end_date <- as.Date("2023-12-31")

filtered_data <- food_ind_clean %>%
  filter(date >= start_date & date <= end_date)

# Calculate the change in 'Close' column for each country
filtered_data <- filtered_data %>%
  arrange(country, date) %>%
  group_by(country) %>%
  mutate(Change = Close - lag(Close, default = first(Close)))

# Downloading worldmap dataset 
world_map <- map_data("world")

# Merging dataset 
merged_data <- merge(world_map, filtered_data, by.x = "region", by.y = "country", all.x = TRUE)

# Creating the heatmap
ggplot(merged_data, aes(x = long, y = lat, group = group, fill = Change)) +
  geom_polygon() +
  scale_fill_gradient(low = "maroon", high = "yellow", name = "Change in Price") +
  coord_fixed(ratio = 1.25) +
  labs(title = "Change in  Price Across Countries (2017-2023)",
       fill = "Change in Close Price") +
  theme_void()

Challenge Week 3 - Food Inflation

Nasa

2023-09-18

About the Data

Dataset is available at:

importing data + libraries

Trying to see the trends for 1 country only, Afghanistan

Cleaning the dataset

All countries compared on one single graph and listing down the top 10 countries which saw the most change in past years

Generating heatmap of the world map using the geom_polygon to visualize the above top 10 countries.

doing this for past 5 years as per above graph they are the most releveant ones

This is consistent with the top 10 countries listed as the heatmap is only highlighting those regions/countries