Challenge 4

More data wrangling: pivoting

Code

library(tidyverse)
library(stringr)
library(skimr)

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

Read in data

I’ll be reading in the ‘FedFundsRate.csv’ dataset.

Code

fed_funds_rate<-read_csv("_data/FedFundsRate.csv",
                        show_col_types = FALSE)

Briefly describe the data

Generating an overview of the data.

Code

skim(fed_funds_rate)

Data summary

Name	fed_funds_rate
Number of rows	904
Number of columns	10
_______________________
Column type frequency:
numeric	10
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1.00	1986.68	17.17	1954.00	1973.00	1987.50	2001.00	2017.00	▅▆▇▇▆
Month	0	1.00	6.60	3.47	1.00	4.00	7.00	10.00	12.00	▇▅▅▅▇
Day	0	1.00	3.60	6.79	1.00	1.00	1.00	1.00	31.00	▇▁▁▁▁
Federal Funds Target Rate	442	0.51	5.66	2.55	1.00	3.75	5.50	7.75	11.50	▅▅▇▅▂
Federal Funds Upper Target	801	0.11	0.31	0.14	0.25	0.25	0.25	0.25	1.00	▇▁▁▁▁
Federal Funds Lower Target	801	0.11	0.06	0.14	0.00	0.00	0.00	0.00	0.75	▇▁▁▁▁
Effective Federal Funds Rate	152	0.83	4.91	3.61	0.07	2.43	4.70	6.58	19.10	▇▇▃▁▁
Real GDP (Percent Change)	654	0.28	3.14	3.60	-10.00	1.40	3.10	4.88	16.50	▁▂▇▂▁
Unemployment Rate	152	0.83	5.98	1.57	3.40	4.90	5.70	7.00	10.80	▅▇▅▁▁
Inflation Rate	194	0.79	3.73	2.57	0.60	2.00	2.80	4.70	13.60	▇▃▁▁▁

There are 904 rows and 10 columns in this dataset, all of which are numeric. It includes information on federal fund targets, unemployment rate, real GDP, and inflation rate between the years of 1954 and 2017. There seem to be a lot of missing values.

The ‘Federal Funds Target Rate’ varies between 1 and 11.5 percent, and the ‘Effective Federal Funds Target Rate’ varies between 0.07 and 19.1 percent. ‘Real GDP (Percent Change)’ lies between -10 and 16.5 percent, ‘Unemployment Rate’ between 3.4 and 10.8 percent, and ‘Inflation Rate’ between 0.6 and 13.6 percent.

Tidy Data and Mutate Variables

I think that converting the missing values to a numeric value like ‘0.0’ would hold some weight and not make much sense, since some of the existing column values are ‘0.0’. The columns ‘Year’, ‘Month’, and ‘Day’ can be combined to give a comprehensive date for each row.

Code

fed_funds_rate$Date <- str_c(fed_funds_rate$Year,"-", fed_funds_rate$Month,"-",fed_funds_rate$Day)

str(fed_funds_rate$Date) # the data type is character

 chr [1:904] "1954-7-1" "1954-8-1" "1954-9-1" "1954-10-1" "1954-11-1" ...

Code

Dates <- as.Date(fed_funds_rate$Date)

fed_funds_rate$Dates <- as.Date(fed_funds_rate$Date, format="%Y-%m-%d")

str(fed_funds_rate$Dates) # the data type is date

 Date[1:904], format: "1954-07-01" "1954-08-01" "1954-09-01" "1954-10-01" "1954-11-01" ...

Code

fed_funds_rate_final <- fed_funds_rate %>%
  select(-Date, -Year, -Month, -Day)

After combining the three columns (‘Year’,‘Month’,‘Day’) into a new column ‘Date’, its data type was still ‘character’. I used the as.Date() function to convert the values to the date type and stored them in a new column, ‘Dates’. I removed the ‘Year’,‘Month’,‘Day’, and (old) ‘Date’ columns as well. I’m also reordering the columns such that ‘Dates’ is the first one.

Code

fed_funds_rate_final <- fed_funds_rate_final[, c(8,1,2,3,4,5,6,7)]

Besides this, I think that the dataset is tidy enough to work with.