Challenge 6 Submission

Visualizing Time and Relationships

library(tidyverse)
library(ggplot2)

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

Read in data

The data that I chose to read in was the RedFundsRate.csv

• fed_rate ⭐⭐

fed_rate <- read_csv("_data/FedFundsRate.csv")

fed_rate

# A tibble: 904 × 10
    Year Month   Day `Federal Funds Target Rate` `Federal Funds Upper Target`
   <dbl> <dbl> <dbl>                       <dbl>                        <dbl>
 1  1954     7     1                          NA                           NA
 2  1954     8     1                          NA                           NA
 3  1954     9     1                          NA                           NA
 4  1954    10     1                          NA                           NA
 5  1954    11     1                          NA                           NA
 6  1954    12     1                          NA                           NA
 7  1955     1     1                          NA                           NA
 8  1955     2     1                          NA                           NA
 9  1955     3     1                          NA                           NA
10  1955     4     1                          NA                           NA
# ℹ 894 more rows
# ℹ 5 more variables: `Federal Funds Lower Target` <dbl>,
#   `Effective Federal Funds Rate` <dbl>, `Real GDP (Percent Change)` <dbl>,
#   `Unemployment Rate` <dbl>, `Inflation Rate` <dbl>

Briefly describe the data

Tidy Data (as needed)

The only variable I needed to mutate was the Year, Month and Day into a single date object. I first combined them into one column, then used the as.Date function.

date_col <- paste(fed_rate$Year, fed_rate$Month, fed_rate$Day)

date_col <- as.Date(date_col, format = "%Y %m %d")

head(date_col)

[1] "1954-07-01" "1954-08-01" "1954-09-01" "1954-10-01" "1954-11-01"
[6] "1954-12-01"

fed_rate <- fed_rate[-c(1, 2, 3)]

fed_rate <- fed_rate %>% add_column(`Date`=date_col, .after=0)

head(fed_rate)

# A tibble: 6 × 8
  Date       `Federal Funds Target Rate` `Federal Funds Upper Target`
  <date>                           <dbl>                        <dbl>
1 1954-07-01                          NA                           NA
2 1954-08-01                          NA                           NA
3 1954-09-01                          NA                           NA
4 1954-10-01                          NA                           NA
5 1954-11-01                          NA                           NA
6 1954-12-01                          NA                           NA
# ℹ 5 more variables: `Federal Funds Lower Target` <dbl>,
#   `Effective Federal Funds Rate` <dbl>, `Real GDP (Percent Change)` <dbl>,
#   `Unemployment Rate` <dbl>, `Inflation Rate` <dbl>

The variables that I mutated were most of the rates. This included the Effective Fed Funds Rate, Unemplyoment Rate, and Inflation Rate. I mutated these to allow us to compare how they changed through the years and graphing them a bit simpler.

These new variables were created by the diff command, and can be seen in the last 3 columns.

effective_fed_fund_change <- diff(fed_rate$`Effective Federal Funds Rate`)
effective_fed_fund_change <- append(effective_fed_fund_change, NA, after=0)

fed_rate['Effective Fed Funds Rate Change'] <- effective_fed_fund_change

unemployment_change <- diff(fed_rate$`Unemployment Rate`)
unemployment_change <- append(unemployment_change, NA, after=0)

fed_rate['Unemployment Rate Change'] <- unemployment_change

inflation_rate_change <- diff(fed_rate$`Inflation Rate`)
inflation_rate_change <- append(inflation_rate_change, NA, after=0)

fed_rate['Inflation Rate Change'] <- inflation_rate_change

head(fed_rate)

# A tibble: 6 × 11
  Date       `Federal Funds Target Rate` `Federal Funds Upper Target`
  <date>                           <dbl>                        <dbl>
1 1954-07-01                          NA                           NA
2 1954-08-01                          NA                           NA
3 1954-09-01                          NA                           NA
4 1954-10-01                          NA                           NA
5 1954-11-01                          NA                           NA
6 1954-12-01                          NA                           NA
# ℹ 8 more variables: `Federal Funds Lower Target` <dbl>,
#   `Effective Federal Funds Rate` <dbl>, `Real GDP (Percent Change)` <dbl>,
#   `Unemployment Rate` <dbl>, `Inflation Rate` <dbl>,
#   `Effective Fed Funds Rate Change` <dbl>, `Unemployment Rate Change` <dbl>,
#   `Inflation Rate Change` <dbl>

Time Dependent Visualization

For the time dependent I chose to graph the inflation rate.

ggplot(fed_rate, aes(x=`Date`, y=`Inflation Rate`)) +
  geom_line()

I chose to do a line graph because I believe it is the best way to graph a time-dependent variable. It offers a good visualization of how the inflation rate from year to year is fairly constant, aside from a few hiccups.

I chose to not include the points as I thought they made the graph too noisey and took away from the finer details.

Visualizing Part-Whole Relationships

For the Part-Whole Relationship Visualization, I chose to graph inflation rate change over the samples given.

#rates <- fed_rate %>% select(`Effective Federal Funds Rate`, `Unemployment Rate`, `Inflation Rate`)

#fed_rate$`Inflation Rate Change`

ggplot(fed_rate, aes(x=`Date`, y=`Inflation Rate Change`)) +
  geom_line()

I chose a line graph because it made the most logical sense on how to graph a variable where we are trying to see the change over a period of time. It also allows the viewer to see a nice progression of how the inflation rate change stays fairly constant over the years.

Similarly I omitted the dots because they were too large for how many points I had.