R Programming Code

### Nigeria's Inflation Project

##Installing packages needed for project

#tidyverse for data importation & wrangling
install.packages("tidyverse")
library(tidyverse)

#ggplot for data visualisation
install.packages("ggplot2")
library(ggplot2)

#lubridate for date manipulation
install.packages("lubridate")
library(lubridate)

#dplyr for grammar of data manipulation
install.packages("dplyr")
library(dplyr)

#lmtest for linear regression
install.packages("lmtest")
library(lmtest)

#car for influence analysis and regression model valuation
install.packages("car")
library(car)

#Displaying working directory
getwd()

#Setting working directory
setwd("/Users/Wale/Downloads/Projects/Own Path/Inflation")

#Importing data (csv files)
Inflation_rate <- read.csv("inflation_rate.csv")
Interest_rate <- read.csv("interest_rate.csv")
Fx_rate <- read.csv("fx_rate.csv")
Money_supply <- read.csv("money_supply.csv")
Fx_supply <- read.csv("Fx_Supply.csv")

#Inspecting imported data
View(Inflation_rate)
View(Interest_rate)
View(Fx_rate)
View(Money_supply)
View(Fx_supply)

str(Inflation_rate)
str(Interest_rate)
str(Fx_rate)
str(Money_supply)
str(Fx_supply)

##Removing irrelevant columns
#Inflation rate table
Inflation_data <- Inflation_rate %>% 
  select(-c(All_Items_12_Months_Avg_Change, Food_12_Months_Avg_Change, All_Items_Less_Farm_Produce_Year_On_Change, All_Items_Less_Farm_Produce_12_Months_Avg_Change,
            All_Items_Less_Farm_Produce_and_Energy_Year_on_Change, All_Items_Less_Farm_Produce_and_Energy_12_Months_Avg_Change))
#Interest rate table
Interest_rate_data <- Interest_rate %>% 
  select(-c(InterBankCallRate, MRR, TreasuryBill, SavingsDeposit, OneMonthDeposit, ThreeMonthsDeposit, SixMonthsDeposit, TwelveMonthsDeposit,
            PrimeLending, MaxLending))
#Fx rate table
Fx_rate_data <- Fx_rate %>% 
  select(-c(DAS..USD., IFEM..USD., GB.Pounds, EURO, CFA.Franc))
#Fx supply table
Fx_suppy_data <- Fx_supply %>% 
  select(-c(Date, Level.1.Category, Unit))
#Money supply table
Money_supply_data <- Money_supply %>% 
  select(-c(Narrow.Money, Money.Supply.M3, Money.Supply.M2, Net.Foreign.Assets, Net.Domestic.Credit, Credit.to.Government, 
            Credit.to.Private.Sector, Base.Money, Bank.Reserves, Currency.Outside.Banks, Demand.Deposits, Quasi.Money, Net.Domestic.Assets,
            Credit.To.GovernmentFed, Mirror.Accounts, Other.Assets.Net, Money.Supply.M2, CBN.Bills, Special.Intervention.Reserves))

View(Inflation_data)
View(Interest_rate_data)
View(Fx_rate_data)
View(Fx_suppy_data)
View(Money_supply_data)

##Renaming columns in respective data frames
#Inflation table
Inflation_data <- rename(Inflation_data,
                         all_inflation = "All_Items_Year_On_Change",
                         food_inflation = "Food_Year_on_Change")

#Interest rate data frame
Interest_rate_data <- rename(Interest_rate_data,
                             Baseline_interest_rate = "MPR")

#Fx rate data frame
Fx_rate_data <- rename(Fx_rate_data,
                       BDC_Dollar_Rate = "BDC..USD.")

#Money supply
Money_supply_data <- rename(Money_supply_data,
                            Currency_in_circulation = "Currency.in.Circulation")

##Renaming month description from numeric to month names
month_mapping <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")

Inflation_data$Month[Inflation_data$Month == 1] <- "January"
Inflation_data$Month <- month_mapping[Inflation_data$Month]
Interest_rate_data$Month <- month_mapping[Interest_rate_data$Month]
Fx_rate_data$Month <- month_mapping[Fx_rate_data$Month]
Money_supply_data$Month <- month_mapping[Money_supply_data$Month]


##Converting data type for month column
Inflation_data <- mutate(Inflation_data, Month = as.character(Month))
Interest_rate_data <- mutate(Interest_rate_data, Month = as.character(Month))
Fx_rate_data <- mutate(Fx_rate_data, Month = as.character(Month))
Money_supply_data <- mutate(Money_supply_data, Month = as.character(Month))

str(Inflation_data)
str(Interest_rate_data)
str(Fx_rate_data)
str(Money_supply_data)

View(Inflation_data)
View(Interest_rate_data)
View(Fx_rate_data)
View(Fx_suppy_data)
View(Money_supply_data)

#Sorting data frames in ascending order
Interest_rate_data <- Interest_rate_data %>% 
  arrange(Year)

Money_supply_data <- Money_supply_data %>% 
  arrange(Year)

##Merging the data frames
#Using left join. Left join keeps all rows from original data frames and fills in missing values
Combined_data <- Inflation_data %>% 
  left_join(Interest_rate_data, by = c("Year", "Month")) %>% 
  left_join(Fx_rate_data, by = c("Year", "Month")) %>% 
  left_join(Fx_suppy_data, by = c("Year", "Month")) %>% 
  left_join(Money_supply_data, by = c("Year", "Month"))

View(Combined_data)
str(Combined_data)


##Handling missing values (NA)
#Replacing NA with a specific value (0)

Combined_data$Baseline_interest_rate <- replace(Combined_data$Baseline_interest_rate, is.na(Combined_data$Baseline_interest_rate), 0.00)

Combined_data$BDC_Dollar_Rate <- replace(Combined_data$BDC_Dollar_Rate, is.na(Combined_data$BDC_Dollar_Rate), 0.00)

Combined_data$Fx_Supply <- replace(Combined_data$Fx_Supply, is.na(Combined_data$Fx_Supply), 0.00)

View(Combined_data)


#Renaming columns in Combined_data
Combined_data <- rename(Combined_data,
                        Inflation = "all_inflation",
                        Food_inflation = "food_inflation",
                        Interest_rate = "Baseline_interest_rate")

View(Combined_data)
str(Combined_data)

##Getting average data for each Year
Average_combined_data <- Combined_data %>% 
  group_by(Year) %>% 
  summarize(Inflation = mean(Inflation),
            Food_inflation = mean(Food_inflation),
            Interest_rate = mean(Interest_rate),
            BDC_Dollar_Rate = mean(BDC_Dollar_Rate),
            Fx_Supply = mean(Fx_Supply),
            Currency_in_circulation = mean(Currency_in_circulation))


View(Average_combined_data)

#Running a linear regression on inflation, food_inflation, interestrate, bdc dollar rate and fx supply.
Regression_model <- lm(Inflation ~ Food_inflation + Interest_rate + BDC_Dollar_Rate + Fx_Supply + Currency_in_circulation, 
                       data = Combined_data)

Regression_model_average <- lm(Inflation ~ Food_inflation + Interest_rate + BDC_Dollar_Rate + Fx_Supply, data = Average_combined_data)

summary(Regression_model)
plot(Regression_model)
coeftest(Regression_model)

summary(Regression_model_average)


#Combined data with missing values
Combined_data_original <- Combined_data

Combined_data_original$Interest_rate <- replace(Combined_data_original$Interest_rate, Combined_data_original$Interest_rate == 0.00, NA)

Combined_data_original$BDC_Dollar_Rate <- replace(Combined_data_original$BDC_Dollar_Rate, Combined_data_original$BDC_Dollar_Rate == 0.00, NA)

Combined_data_original$Fx_Supply <- replace(Combined_data_original$Fx_Supply, Combined_data_original$Fx_Supply == 0.00, NA)

View(Combined_data_original)

##Getting average data for each Year in combined_data_original
Average_combined_data_original <- Combined_data_original %>% 
  group_by(Year) %>% 
  summarize(Inflation = mean(Inflation),
            Food_inflation = mean(Food_inflation),
            Interest_rate = mean(Interest_rate),
            BDC_Dollar_Rate = mean(BDC_Dollar_Rate),
            Fx_Supply = mean(Fx_Supply))

##Visualising data
ggplot(data = Average_combined_data_original, aes(x = Year)) + 
  geom_line(aes(y = Inflation, color = "Inflation"), size = 1.5) +
  geom_line(aes(y = Interest_rate, color = "Interest Rate"), size = 1.5) +
  labs(title = "Inflation and Interest Rate Trend",
       x = "Year",
       y = "Value") + 
  scale_color_manual(values = c("Inflation" = "blue", "Interest Rate" = "red")) +
  theme_minimal()

# Time Series Plot for Inflation
ggplot(Combined_data, aes(x = Year, y = Inflation)) +
  geom_line() +
  labs(title = "Time Series Plot of Inflation",
       x = "Year",
       y = "Inflation")

# Scatter Plot: Inflation vs. Food_inflation
ggplot(Combined_data, aes(x = Food_inflation, y = Inflation)) +
  geom_point() +
  labs(title = "Scatter Plot: Inflation vs. Food_inflation",
       x = "Food_inflation",
       y = "Inflation")

# Scatter Plot: Inflation vs. Interest_rate
ggplot(Combined_data, aes(x = Interest_rate, y = Inflation)) +
  geom_point() +
  labs(title = "Scatter Plot: Inflation vs. Interest_rate",
       x = "Interest_rate",
       y = "Inflation")

# Scatter Plot: Inflation vs. Fx_Supply
ggplot(Combined_data, aes(x = Fx_Supply, y = Inflation)) +
  geom_point() +
  labs(title = "Scatter Plot: Inflation vs. Fx_Supply",
       x = "Fx_Supply",
       y = "Inflation")

# Histogram for Inflation
ggplot(Combined_data, aes(x = Inflation)) +
  geom_histogram(binwidth = 1, fill = "blue", color = "black") +
  labs(title = "Histogram of Inflation",
       x = "Year",
       y = "Inflation")

# Predicted vs. Actual Plot
predicted_values <- predict(Regression_model)
actual_values <- Combined_data$Inflation
plot(predicted_values, actual_values, pch = 16, col = "blue",
     xlab = "Predicted Inflation",
     ylab = "Actual Inflation",
     main = "Predicted vs. Actual Plot")


##Exporting data frame as csv file
write.csv(Combined_data, file = "Combined_data.csv", row.names = TRUE)