Challenge 4 Instructions

More data wrangling: pivoting

Code

library(tidyverse)
library(readr)
library(lubridate)
library(summarytools)

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

Read in data

Code

fedfundrate <- read_csv("_data/FedFundsRate.csv",
                        show_col_types = FALSE, col_names = c("Year", "Month", "Day", "federal_funds_target_rate", "federal_funds_upper_target", "federal_funds_lower_target", "effective_federal_funds_rate", "real_GDP_percent_change", "unemployment_rate", "inflation_rate"),
                        skip=1
)
fedfundrate

ABCDEFGHIJ0123456789

Year <dbl>	Month <dbl>	Day <dbl>	federal_funds_target_rate <dbl>	federal_funds_upper_target <dbl>	federal_funds_lower_target <dbl>	effective_federal_funds_rate <dbl>
1954	7	1	NA	NA	NA	0.80
1954	8	1	NA	NA	NA	1.22
1954	9	1	NA	NA	NA	1.06
1954	10	1	NA	NA	NA	0.85
1954	11	1	NA	NA	NA	0.83
1954	12	1	NA	NA	NA	1.28
1955	1	1	NA	NA	NA	1.39
1955	2	1	NA	NA	NA	1.29
1955	3	1	NA	NA	NA	1.35
1955	4	1	NA	NA	NA	1.43

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1-10 of 904 rows | 1-7 of 10 columns

Briefly describe the data

This data has been collected monthly since July 1st of 1954 and ended on march 16, 2017. There is a year-month-day combination that looks at 4 different Federal Funds Rates, while the other variables are indicators that effect the federal fund rate (inflation, unemployment and GDP changes).

Tidy Data (as needed)

Code

colnames(fedfundrate)

 [1] "Year"                         "Month"                       
 [3] "Day"                          "federal_funds_target_rate"   
 [5] "federal_funds_upper_target"   "federal_funds_lower_target"  
 [7] "effective_federal_funds_rate" "real_GDP_percent_change"     
 [9] "unemployment_rate"            "inflation_rate"              

Code

print(summarytools::dfSummary(fedfundrate,
                        varnumbers = FALSE,
                        plain.ascii  = FALSE, 
                        style        = "grid", 
                        graph.magnif = 0.70, 
                        valid.col    = FALSE),
      method = 'render',
      table.classes = 'table-condensed')

Data Frame Summary

fedfundrate

Dimensions: 904 x 10
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
Year [numeric]	Mean (sd) : 1986.7 (17.2) min ≤ med ≤ max: 1954 ≤ 1987.5 ≤ 2017 IQR (CV) : 28 (0)	64 distinct values		0 (0.0%)
Month [numeric]	Mean (sd) : 6.6 (3.5) min ≤ med ≤ max: 1 ≤ 7 ≤ 12 IQR (CV) : 6 (0.5)	12 distinct values		0 (0.0%)
Day [numeric]	Mean (sd) : 3.6 (6.8) min ≤ med ≤ max: 1 ≤ 1 ≤ 31 IQR (CV) : 0 (1.9)	29 distinct values		0 (0.0%)
federal_funds_target_rate [numeric]	Mean (sd) : 5.7 (2.6) min ≤ med ≤ max: 1 ≤ 5.5 ≤ 11.5 IQR (CV) : 4 (0.5)	63 distinct values		442 (48.9%)
federal_funds_upper_target [numeric]	Mean (sd) : 0.3 (0.1) min ≤ med ≤ max: 0.2 ≤ 0.2 ≤ 1 IQR (CV) : 0 (0.5)	4 distinct values		801 (88.6%)
federal_funds_lower_target [numeric]	Mean (sd) : 0.1 (0.1) min ≤ med ≤ max: 0 ≤ 0 ≤ 0.8 IQR (CV) : 0 (2.4)	4 distinct values		801 (88.6%)
effective_federal_funds_rate [numeric]	Mean (sd) : 4.9 (3.6) min ≤ med ≤ max: 0.1 ≤ 4.7 ≤ 19.1 IQR (CV) : 4.2 (0.7)	466 distinct values		152 (16.8%)
real_GDP_percent_change [numeric]	Mean (sd) : 3.1 (3.6) min ≤ med ≤ max: -10 ≤ 3.1 ≤ 16.5 IQR (CV) : 3.5 (1.1)	113 distinct values		654 (72.3%)
unemployment_rate [numeric]	Mean (sd) : 6 (1.6) min ≤ med ≤ max: 3.4 ≤ 5.7 ≤ 10.8 IQR (CV) : 2.1 (0.3)	71 distinct values		152 (16.8%)
inflation_rate [numeric]	Mean (sd) : 3.7 (2.6) min ≤ med ≤ max: 0.6 ≤ 2.8 ≤ 13.6 IQR (CV) : 2.7 (0.7)	106 distinct values		194 (21.5%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-08-28

Description of the variables

I think that the summary tools function is really great because it captures the stats/values within each variable. Additionally it displays a graph of the data from each observation. One thing that I think is interesting to point out is that

Identify variables that need to be mutated

Code

ffr_long <- fedfundrate %>% 
  pivot_longer(cols = c( "federal_funds_target_rate", "federal_funds_upper_target", "federal_funds_lower_target", "effective_federal_funds_rate", "real_GDP_percent_change", "unemployment_rate", "inflation_rate"),
               names_to = "ffr_type",
               values_to = "value",
               values_drop_na = TRUE)

ffr_long

ABCDEFGHIJ0123456789

Year <dbl>	Month <dbl>	Day <dbl>	ffr_type <chr>	value <dbl>
1954	7	1	effective_federal_funds_rate	0.8000
1954	7	1	real_GDP_percent_change	4.6000
1954	7	1	unemployment_rate	5.8000
1954	8	1	effective_federal_funds_rate	1.2200
1954	8	1	unemployment_rate	6.0000
1954	9	1	effective_federal_funds_rate	1.0600
1954	9	1	unemployment_rate	6.1000
1954	10	1	effective_federal_funds_rate	0.8500
1954	10	1	real_GDP_percent_change	8.0000
1954	10	1	unemployment_rate	5.7000

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1-10 of 3,132 rows

I think that using the function pivot_longer() is a great way to tidy the data and be able to get a better understanding/read of the data.