Tidy Federal Funds Rate information

Introduction

I was trying to analyze the ‘FedFundsRate.csv’ dataset. This dataset summarizes the fluctuation in the federal funds rate over the years from 1954 to 2017 and how it impacts the unemployment rate and Inflation rate. We first start by importing the necessary libraries and setting the working directory to point to the location where the spreadsheet is located.

Code

library(tidyverse)
library(readxl)
library(DescTools)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
setwd("D:/MyDocs/Class Slides/DACSS601/601_Spring_2023/posts/_data")

Read in data

We first read in the data using the read_csv() command. We can first start by removing any unwanted columns from our dataset. At first glance we can notice that the Federal Funds Upper Target and Federal Funds Lower Target columns contains lots of blank values. Only 103 instances are present for each. Also Federal Funds Target Rate can be ignored because we have the Effective Federal Funds Rate column which gives us the information we require. Thus we can remove these 3 columns as we read in the csv file.

Code

setwd("D:/MyDocs/Class Slides/DACSS601/601_Spring_2023/posts/_data")
dataframe <- read_csv("FedFundsRate.csv") %>%
  select(-"Federal Funds Upper Target", -"Federal Funds Lower Target", -"Federal Funds Target Rate")

Tidy Data

We do not need to necessarily pivot any columns here because the chosen format is already suitable for representing the data. We however need to deal with unspecified data values(N/A values) in the dataset. From the remaining columns that we are left with, we first remove all the rows from dataframe where Effective Federal Funds Rate is not specified. On doing this, we notice that only rows with “Day” column set to 1 are remaining. Thus, we can remove the “Day” column from the dataframe. We then go through the remaining dataframe values and set the N/A values in Inflation Rate and Real GDP (Percent Change) columns to 0.

A dataframe containing information about the Effective Federal Funds Rate and the change in Unemployment Rate and Inflation Rate over the years. We group the rows by Year and output the mean of the above three columns over all 12 months. This gives us a nice year-by-year analysis of the readings.

Code

dataframe <- dataframe[!is.na(dataframe$`Effective Federal Funds Rate`), ] %>%
  select(-"Day")

dataframe$`Inflation Rate`[is.na(dataframe$`Inflation Rate`)] <- 0
dataframe$Month <- month(dataframe$Month, label = TRUE, abbr = FALSE)

Error in month(dataframe$Month, label = TRUE, abbr = FALSE): could not find function "month"

Code

print(dataframe)

# A tibble: 752 × 6
    Year Month `Effective Federal Funds Rate` Real GDP (Percen…¹ Unemp…² Infla…³
   <dbl> <dbl>                          <dbl>              <dbl>   <dbl>   <dbl>
 1  1954     7                           0.8                 4.6     5.8       0
 2  1954     8                           1.22               NA       6         0
 3  1954     9                           1.06               NA       6.1       0
 4  1954    10                           0.85                8       5.7       0
 5  1954    11                           0.83               NA       5.3       0
 6  1954    12                           1.28               NA       5         0
 7  1955     1                           1.39               11.9     4.9       0
 8  1955     2                           1.29               NA       4.7       0
 9  1955     3                           1.35               NA       4.6       0
10  1955     4                           1.43                6.7     4.7       0
# … with 742 more rows, and abbreviated variable names
#   ¹​`Real GDP (Percent Change)`, ²​`Unemployment Rate`, ³​`Inflation Rate`

Code

funds_dataframe <- dataframe %>%
  select(-"Real GDP (Percent Change)") %>%
  group_by(Year) %>%
  select("Effective Federal Funds Rate", "Unemployment Rate", "Inflation Rate") %>%
  summarise(`Effective Federal Funds Rate` = mean(`Effective Federal Funds Rate`, na.rm=TRUE),
            `Unemployment Rate` = mean(`Unemployment Rate`, na.rm=TRUE),
            `Inflation Rate` = mean(`Inflation Rate`, na.rm=TRUE))
print(funds_dataframe)

# A tibble: 64 × 4
    Year `Effective Federal Funds Rate` `Unemployment Rate` `Inflation Rate`
   <dbl>                          <dbl>               <dbl>            <dbl>
 1  1954                           1.01                5.65             0   
 2  1955                           1.78                4.37             0   
 3  1956                           2.73                4.12             0   
 4  1957                           3.10                4.3              0   
 5  1958                           1.57                6.84             2.28
 6  1959                           3.30                5.45             1.99
 7  1960                           3.22                5.54             1.52
 8  1961                           1.96                6.69             1.12
 9  1962                           2.71                5.57             1.38
10  1963                           3.18                5.64             1.28
# … with 54 more rows

Identify variables that need to be mutated

Real GDP (Percent Change) is calculated every 3 months so we create a dataframe for GDP analysis. Using the lubridate library’s month() function, the month number is changed to the corresponding month name. The month name and the year are combined into a single column using the paste() function along with sep=” ” as an argument.The GDP values are in the other column of the dataframe. This gives us a clean quarterly analysis of GDP over the years.

Code

gdp_dataframe <- dataframe[!is.na(dataframe$`Real GDP (Percent Change)`), ] %>%
  mutate(`Month Year` = paste(Month, Year, sep = " ")) %>%
  select("Month Year", "Real GDP (Percent Change)")
print(gdp_dataframe)

# A tibble: 250 × 2
   `Month Year` `Real GDP (Percent Change)`
   <chr>                              <dbl>
 1 7 1954                               4.6
 2 10 1954                              8  
 3 1 1955                              11.9
 4 4 1955                               6.7
 5 7 1955                               5.5
 6 10 1955                              2.4
 7 1 1956                              -1.5
 8 4 1956                               3.4
 9 7 1956                              -0.3
10 10 1956                              6.7
# … with 240 more rows