library(tidyverse)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 6
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_households
abc_poll
Visualizing Time and Relationships
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- fed_rate ⭐⭐
fed_data <- read_csv("_data/FedFundsRate.csv")
fed_data# 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`Briefly describe the data
summary(fed_data) Year Month Day Federal Funds Target Rate
Min. :1954 Min. : 1.000 Min. : 1.000 Min. : 1.000
1st Qu.:1973 1st Qu.: 4.000 1st Qu.: 1.000 1st Qu.: 3.750
Median :1988 Median : 7.000 Median : 1.000 Median : 5.500
Mean :1987 Mean : 6.598 Mean : 3.598 Mean : 5.658
3rd Qu.:2001 3rd Qu.:10.000 3rd Qu.: 1.000 3rd Qu.: 7.750
Max. :2017 Max. :12.000 Max. :31.000 Max. :11.500
NA's :442
Federal Funds Upper Target Federal Funds Lower Target
Min. :0.2500 Min. :0.0000
1st Qu.:0.2500 1st Qu.:0.0000
Median :0.2500 Median :0.0000
Mean :0.3083 Mean :0.0583
3rd Qu.:0.2500 3rd Qu.:0.0000
Max. :1.0000 Max. :0.7500
NA's :801 NA's :801
Effective Federal Funds Rate Real GDP (Percent Change) Unemployment Rate
Min. : 0.070 Min. :-10.000 Min. : 3.400
1st Qu.: 2.428 1st Qu.: 1.400 1st Qu.: 4.900
Median : 4.700 Median : 3.100 Median : 5.700
Mean : 4.911 Mean : 3.138 Mean : 5.979
3rd Qu.: 6.580 3rd Qu.: 4.875 3rd Qu.: 7.000
Max. :19.100 Max. : 16.500 Max. :10.800
NA's :152 NA's :654 NA's :152
Inflation Rate
Min. : 0.600
1st Qu.: 2.000
Median : 2.800
Mean : 3.733
3rd Qu.: 4.700
Max. :13.600
NA's :194 This dataset offers information on federal fund rates from 1954 to 2017. It covers the exact day, month, and year of these rates, as well as the funds’ upper and lower targets, unemployment rate, GDP, and inflation rate.
Tidy Data (as needed)
To clean up the data, I’m merging the day, month, and year into one and structuring it for simpler analysis.
fed_data$Date <- as.Date(with(fed_data,paste(Day,Month,Year,sep="-")),"%d-%m-%Y")
fed_data# A tibble: 904 × 11
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, 1 more variable: Date <date>, 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`Time Dependent Visualization
select(fed_data, c('Date','Unemployment Rate'))# A tibble: 904 × 2
Date `Unemployment Rate`
<date> <dbl>
1 1954-07-01 5.8
2 1954-08-01 6
3 1954-09-01 6.1
4 1954-10-01 5.7
5 1954-11-01 5.3
6 1954-12-01 5
7 1955-01-01 4.9
8 1955-02-01 4.7
9 1955-03-01 4.6
10 1955-04-01 4.7
# … with 894 more rowsggplot(fed_data, aes(x=Date, y=fed_data$`Unemployment Rate`)) +
geom_line() +
xlab("Date") +
ylab("Unemployment Rate") +
ggtitle("Date vs Unemployment Rate")
data_filled <- fed_data %>% fill(`Unemployment Rate`, .direction = 'updown')
ggplot(data_filled, aes(x=Date, y=data_filled$`Unemployment Rate`)) +
geom_line() +
xlab("Date") +
ylab("Effective Federal Funds Rate") +
ggtitle("Date vs Effective Federal Funds Rate")
Visualizing Part-Whole Relationships
I am visualizing the rate of unemployment over the years from 2000 to 2017 to get a clear idea of the recent trends.
data_filled <- fed_data %>% fill(`Unemployment Rate`, .direction = 'updown')
data_filled %>%
filter(Year > 1999) %>%
ggplot(aes(x = Date, y = `Unemployment Rate`)) +
geom_line() +
xlab("Date") +
ylab("Unemployment Rate") +
ggtitle("Unemployment Rate from 2000 to 2017")