library(tidyverse)
library(ggplot2)
library(treemap)
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
Challenge Overview
Today’s challenge is to:
- read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
- tidy data (as needed, including sanity checks)
- mutate variables as needed (including sanity checks)
- create at least one graph including time (evolution)
- try to make them “publication” ready (optional)
- Explain why you choose the specific graph type
- Create at least one graph depicting part-whole or flow relationships
- try to make them “publication” ready (optional)
- Explain why you choose the specific graph type
R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.
(be sure to only include the category tags for the data you use!)
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- debt ⭐
- fed_rate ⭐⭐
- abc_poll ⭐⭐⭐
- usa_hh ⭐⭐⭐
- hotel_bookings ⭐⭐⭐⭐
- AB_NYC ⭐⭐⭐⭐⭐
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`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 Briefly describe the data
This dataset contains information about federal fund rates from years 1954 to 2017. It includes the exact day, month and year of these rates along with these upper and lower target of the funds, unemployment rate, GDP, and inflation rate.
Tidy Data (as needed)
Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.
To tidy data, I am combining the day, month, year into one and formatting them for easier 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`Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?
Document your work here.
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 for a less cluttered graph.
data_filtered <- data_filled[data_filled$Year>1999,]
head(data_filtered)# A tibble: 6 × 11
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 2000 1 1 5.5 NA NA 5.45 1.2 4 2
2 2000 2 1 5.5 NA NA 5.73 NA 4.1 2.2
3 2000 2 2 5.75 NA NA NA NA 4 NA
4 2000 3 1 5.75 NA NA 5.85 NA 4 2.4
5 2000 3 21 6 NA NA NA NA 3.8 NA
6 2000 4 1 6 NA NA 6.02 7.8 3.8 2.3
# … with 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`data_filtered %>%
treemap(index=c("Year"), vSize="Unemployment Rate", title="Unemployment Rate Comparison, 2000-2017", palette="RdYlBu")