Challenge 6 - Abby Balint

challenge_6

abby_balint

fed_rate

Visualizing Time and Relationships

Author

Abby Balint

Published

October 25, 2022

library(tidyverse)
library(ggplot2)
library(lubridate)
library(hrbrthemes)
library(treemap)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Briefly describe the data

Reading in the Fed Fund Rate dataset.

This data is likely government data for the federal funds rate which determines the rate that banks borrow money from each other at. This data set contains to variables ranging from date, target/upper target/lower target rates, actual rates, as well as GDP, unemployment, and inflation rates. The dataset contains 904 rows initially, beginning in July of 1954 and ending in March of 2017. The dataset is not complete as several of the target rates and upper/lower target rates are not included. This may be because they were not reported during earlier years, or standardly reported every month in general.

My goal here is to look at factors like unemployment and inflation since the year 2000 as well as generate some charts for analysis.

rates <- read_csv("_data/FedFundsRate.csv")
head(rates,2)

# A tibble: 2 × 10
   Year Month   Day Federal Fu…¹ Feder…² Feder…³ Effec…⁴ Real …⁵ Unemp…⁶ Infla…⁷
  <dbl> <dbl> <dbl>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1  1954     7     1           NA      NA      NA    0.8      4.6     5.8      NA
2  1954     8     1           NA      NA      NA    1.22    NA       6        NA
# … with abbreviated variable names ¹`Federal Funds Target Rate`,
#   ²`Federal Funds Upper Target`, ³`Federal Funds Lower Target`,
#   ⁴`Effective Federal Funds Rate`, ⁵`Real GDP (Percent Change)`,
#   ⁶`Unemployment Rate`, ⁷`Inflation Rate`

Tidying the Data

This data set is already pretty tidy since the rates are in a reportable format, however I wanted to use this opportunity to practice mutating month/day/year columns into a date. I used the make_date lubridate function to create a new variable called Date. I filtered to the year 2000 or later for my graphing purposes. I also filtered out some blank values from some of the values I was using to chart so that my line graphs wouldn’t have any breaks. There isn’t much to pivot/mutate/recode here as most of these variables are just straightforward numerical.

rates_tidy <- rates %>%
  mutate(Date = make_date(`Year`, `Month`, `Day`)) %>%
  mutate(`Employment Rate` = (100 - `Unemployment Rate`)) %>%
  filter(`Year` >= 2000) %>%
    drop_na(`Inflation Rate`) %>%
  drop_na(`Unemployment Rate`) %>%
  drop_na(`Real GDP (Percent Change)`)
summary(rates_tidy)

      Year          Month            Day    Federal Funds Target Rate
 Min.   :2000   Min.   : 1.00   Min.   :1   Min.   :1.000            
 1st Qu.:2004   1st Qu.: 3.25   1st Qu.:1   1st Qu.:1.750            
 Median :2008   Median : 5.50   Median :1   Median :3.125            
 Mean   :2008   Mean   : 5.50   Mean   :1   Mean   :3.382            
 3rd Qu.:2012   3rd Qu.: 7.75   3rd Qu.:1   3rd Qu.:5.250            
 Max.   :2016   Max.   :10.00   Max.   :1   Max.   :6.500            
                                            NA's   :32               
 Federal Funds Upper Target Federal Funds Lower Target
 Min.   :0.2500             Min.   :0.00000           
 1st Qu.:0.2500             1st Qu.:0.00000           
 Median :0.2500             Median :0.00000           
 Mean   :0.2812             Mean   :0.03125           
 3rd Qu.:0.2500             3rd Qu.:0.00000           
 Max.   :0.5000             Max.   :0.25000           
 NA's   :36                 NA's   :36                
 Effective Federal Funds Rate Real GDP (Percent Change) Unemployment Rate
 Min.   :0.070                Min.   :-8.200            Min.   : 3.800   
 1st Qu.:0.140                1st Qu.: 0.800            1st Qu.: 4.900   
 Median :1.000                Median : 2.100            Median : 5.700   
 Mean   :1.830                Mean   : 1.872            Mean   : 6.212   
 3rd Qu.:3.388                3rd Qu.: 3.250            3rd Qu.: 7.650   
 Max.   :6.540                Max.   : 7.800            Max.   :10.000   
                                                                         
 Inflation Rate       Date            Employment Rate
 Min.   :0.600   Min.   :2000-01-01   Min.   :90.00  
 1st Qu.:1.700   1st Qu.:2004-03-09   1st Qu.:92.35  
 Median :2.100   Median :2008-05-16   Median :94.30  
 Mean   :1.987   Mean   :2008-05-16   Mean   :93.79  
 3rd Qu.:2.300   3rd Qu.:2012-07-24   3rd Qu.:95.10  
 Max.   :2.700   Max.   :2016-10-01   Max.   :96.20

Time Dependent Visualization

In this visualization, I generated a line graph for the inflation rate since 2000. I used the date variable I generated above to create this chart. I also implemented a color scheme and played around with the line thickness and type.

rates_tidy %>%
  ggplot(aes(x=`Date`, y=`Inflation Rate`)) +
  geom_line(color="#00658E", size=1, alpha=6, linetype=1) +
  ggtitle("Change in Inflation Rate Since Year 2000") +
  theme(panel.background = element_rect(fill="lightblue"))

Visualizing Part-Whole Relationships

In this visualization, I was looking for something that I could create a part-whole chart for. Since there is not many categorical variables in this dataset, I used year. The below tree map makes it easy to see which years had some of the highest unemployment rates since the year 2000.

rates_tidy %>%
  treemap(index=c("Year"), vSize="Unemployment Rate", title="Unemployment Rate Comparison, 2000-2017")