Challenge 6

Visualizing Time and Relationships

library(tidyverse)
library(ggplot2)
library(lubridate)

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

Read in data

library(readr)
FedFundsRate <- read_csv("_data/FedFundsRate.csv")

FedFundsRate

# A tibble: 904 × 10
    Year Month   Day Federal F…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
 3  1954     9     1          NA      NA      NA    1.06    NA       6.1      NA
 4  1954    10     1          NA      NA      NA    0.85     8       5.7      NA
 5  1954    11     1          NA      NA      NA    0.83    NA       5.3      NA
 6  1954    12     1          NA      NA      NA    1.28    NA       5        NA
 7  1955     1     1          NA      NA      NA    1.39    11.9     4.9      NA
 8  1955     2     1          NA      NA      NA    1.29    NA       4.7      NA
 9  1955     3     1          NA      NA      NA    1.35    NA       4.6      NA
10  1955     4     1          NA      NA      NA    1.43     6.7     4.7      NA
# … with 894 more rows, and 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`
# ℹ Use `print(n = ...)` to see more rows

Briefly describe the data

Tidy Data (as needed)

First I will rename the columns and add a new date column.

#rename columns
FedFundsRate <- rename(FedFundsRate, 
                       "Target Rate" = "Federal Funds Target Rate",
                       "Upper Target" = "Federal Funds Upper Target",
                       "Lower Target" = "Federal Funds Lower Target",
                       "Effective Rate" = "Effective Federal Funds Rate",
                       "Real GDP Δ" = "Real GDP (Percent Change)",
                       "Unemployment" = "Unemployment Rate",
                       "Inflation" = "Inflation Rate")

#mutate to create a new date column
fed_rates_tidy <- FedFundsRate%>%
  mutate(Date = str_c(Year, Month, Day, sep = "-"),
         Date = ymd(Date))

#remove excess date columns
fed_rates_tidy <- fed_rates_tidy%>%
  select(-Year, -Month, -Day)

#move new date column to the front
fed_rates_tidy <- fed_rates_tidy%>%
  relocate(Date)

fed_rates_tidy

# A tibble: 904 × 8
   Date       `Target Rate` Upper Targ…¹ Lower…² Effec…³ Real …⁴ Unemp…⁵ Infla…⁶
   <date>             <dbl>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1 1954-07-01            NA           NA      NA    0.8      4.6     5.8      NA
 2 1954-08-01            NA           NA      NA    1.22    NA       6        NA
 3 1954-09-01            NA           NA      NA    1.06    NA       6.1      NA
 4 1954-10-01            NA           NA      NA    0.85     8       5.7      NA
 5 1954-11-01            NA           NA      NA    0.83    NA       5.3      NA
 6 1954-12-01            NA           NA      NA    1.28    NA       5        NA
 7 1955-01-01            NA           NA      NA    1.39    11.9     4.9      NA
 8 1955-02-01            NA           NA      NA    1.29    NA       4.7      NA
 9 1955-03-01            NA           NA      NA    1.35    NA       4.6      NA
10 1955-04-01            NA           NA      NA    1.43     6.7     4.7      NA
# … with 894 more rows, and abbreviated variable names ¹​`Upper Target`,
#   ²​`Lower Target`, ³​`Effective Rate`, ⁴​`Real GDP Δ`, ⁵​Unemployment,
#   ⁶​Inflation
# ℹ Use `print(n = ...)` to see more rows

Time Dependent Visualization

Plotting unemployment and inflation over time. I chose a line chart because I felt it most accurately displays change over time.

ggplot(fed_rates_tidy, aes(x=Date, y=Unemployment, group = 1))+
  geom_line()+
  xlab("")+
  ggtitle("Federal Unemployment Rate",
          subtitle = "1954 - 2017")+
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5))