Summary
The above visualization displays the relationship between the Dow Jones
Industrial Average (DJIA), 10 Year Treasury Yield (GS10) and Federal
Funds Rate between 09/2016 and 09/2025. The chart demonstrates that
although all three indexes are somewhat correlated with each other,
especially during periods of marked decreases such as during the Covid
recession, the chart supports the notion that GS10 is a precursor of
federal policy surrounding interest rate hikes and cuts. Between
2015-2019, the GS10 outperformed the DJIA and the Federal Funds Rate.
However, beginning in 2019, the GS10 illustrated a marked decrease which
was not reflected in interest rates cuts and DJIA decrease until
approximately six months (Federal Funds Rate) to a year later (DJIA).
Likewise, the GS10 demonstrated noticeable increases in 2021, about a
year after the Covid recession, while while the Federal Funds Rate did
not begin it’s similar upward trajectory until two years after the Covid
recession. As such the GS10 in this context can be viewed as a precursor
to Federal Funds Rate policy. On a side note, the DJIA appears to be a
lagging indicator of economic health and driven by sentiment, as the
DJIA did not react to the Covid recession until the recession was
underway, and the DJIA immediately recovered after the Covid recession
and trended upward during the entire ten year period, as opposed to the
other two indices.
Part 1: Import data from FRED
Dow Jones Industrial Average Data (DJIA)
Data was imported from FRED via API. The DJIA data was limited to 10
years of data, with 2,609 rows of data between dates 2015-09-18 and
2025-09-17. There are two columns in the data set, ‘observation_date’
which is date of the observations and ‘DJIA’ which is where the DJIA
closed on that particular day. Of note, DJIA is recorded on a semi-daily
basis, meaning on days the market is open but excluding holidays.
10-Year Treasury Yield Data (GS10)
Twenty-five years of the GS10 data was obtainable via the API, but in a
different format than the DJIA. The GS10 is reported monthly, as opposed
to the DJIA which is reported daily. There were 300 rows and two columns
in the GS10 dataset, ‘observation_date’ and the actual point measurement
‘GS10’. The dataset contained observations for dates between 2000-09-01
and 2025-08-01.
Federal Funds Rate
Twenty-five years of the Federal Funds Rate was obtainable via the API,
and, again, in a different format than the DJIA. The Federal Funds Rate
is reported monthly, again at odds with the DJIA data set which is
reported daily. There are 300 rows in the Federal Funds Rate data set,
with observations available between 2000-09-01 and 2025-08-01.
Part 2: Data Inspection
Data was inspected for NA/missing values. DJIA was determined to have
NA/missing observation values for 95 observations, while GS10 and
Federal Funds Rate data was determined to have no NA/missing values.
NA/Missing values for the DJIA appeared to be approximately distributed
evenly throughout the months and years. It was also determined that many
(72) of the NA/missing values (95) were holidays. Since the NA/missing
values were mostly holidays and the remaining 23 NA/missing values were
approximately distributed evenly through the data, a decision was made
to drop the NA/missing values from the dataset. After dropping the rows
with the missing observations, the DJIA data set contained 2514
rows.
Part 3: Wrangling
Since the DJIA dates are reported daily and the GS10 and Federal Funds
Rate are reported monthly, the DJIA dates were made a monthly average to
allow the dates between the three datasets to be
comparable/standardized. The three datasets were then joined into a
single dataframe. Additionally, since the DJIA, GS10 and Federal Funds
Rate are measured on different scales, meaning one is a percentage and
the other two are point based measurements, each series was standardized
using max-min scaling in order for the scores to be compared on the same
chart.
Part 4: Plotting and Write Up
Finally, the datasets were plotted. After much experimentation a final
chart was decided on. Please see below.
The above visualization displays the relationship between the Dow Jones Industrial Average (DJIA), 10 Year Treasury Yield (GS10) and Federal Funds Rate between 09/2016 and 09/2025. The chart demonstrates that although all three indexes are somewhat correlated with each other, especially during periods of marked decreases such as during the Covid recession, the chart supports the notion that GS10 is a precursor of federal policy surrounding interest rate hikes and cuts. Between 2015-2019, the GS10 outperformed the DJIA and the Federal Funds Rate. However, beginning in 2019, the GS10 illustrated a marked decrease which was not reflected in interest rates cuts and DJIA decrease until approximately six months (Federal Funds Rate) to a year later (DJIA). Likewise, the GS10 demonstrated noticeable increases in 2021, about a year after the Covid recession, while while the Federal Funds Rate did not begin it’s similar upward trajectory until two years after the Covid recession. As such the GS10 in this context can be viewed as a precursor to Federal Funds Rate policy. On a side note, the DJIA appears to be a lagging indicator of economic health and driven by sentiment, as the DJIA did not react to the Covid recession until the recession was underway, and the DJIA immediately recovered after the Covid recession and trended upward during the entire ten year period, as opposed to the other two indices.
# read CSV from Fred
read_fred_csv <- function(url) {
con <- url(url, open = "r")
on.exit(try(close(con), silent = TRUE))
dat <- read.csv(con, stringsAsFactors = FALSE)
dat
}
# Last 25 years
today <- Sys.Date()
start_month<- as.Date(format(today, "%Y-%m-01"))
start_25y <- as.Date(format(seq(start_month, length = 2, by = "-300 months")[2], "%Y-%m-01"))
obs_start <- format(start_25y, "%Y-%m-%d")
obs_end <- format(today, "%Y-%m-%d")
# DJIA
djia_url <- paste0(
"https://fred.stlouisfed.org/graph/fredgraph.csv?id=DJIA",
"&cosd=", obs_start, "&coed=", obs_end
)
djia_raw <- read_fred_csv(djia_url)
# Fix date column
djia_raw$DATE <- as.Date(djia_raw$observation_date)
names(djia_raw)[2] <- "DJIA"
# Show first and last dates
cat("First available date:", min(djia_raw$DATE, na.rm = TRUE), "\n")## First available date: 16839cat("Last available date :", max(djia_raw$DATE, na.rm = TRUE), "\n")## Last available date : 20490# set date to date type
djia_raw$DATE <- as.Date(djia_raw$observation_date)
str(djia_raw$DATE)## Date[1:2610], format: "2016-02-08" "2016-02-09" "2016-02-10" "2016-02-11" "2016-02-12" ...# format first and last dates
first_date <- min(djia_raw$DATE, na.rm = TRUE)
last_date <- max(djia_raw$DATE, na.rm = TRUE)
cat("First available date:", format(first_date, "%Y-%m-%d"), "\n")## First available date: 2016-02-08cat("Last available date :", format(last_date, "%Y-%m-%d"), "\n")## Last available date : 2026-02-06head(djia_raw)## observation_date DJIA DATE
## 1 2016-02-08 16027.05 2016-02-08
## 2 2016-02-09 16014.38 2016-02-09
## 3 2016-02-10 15914.74 2016-02-10
## 4 2016-02-11 15660.18 2016-02-11
## 5 2016-02-12 15973.84 2016-02-12
## 6 2016-02-15 NA 2016-02-15# 10-Year Treasury Yield
gs10_url <- paste0(
"https://fred.stlouisfed.org/graph/fredgraph.csv?id=GS10",
"&cosd=", obs_start, "&coed=", obs_end
)
gs10_raw <- read_fred_csv(gs10_url)
gs10_raw$DATE <- as.Date(gs10_raw$observation_date)
names(gs10_raw)[2] <- "GS10"
gs10_raw## observation_date GS10 DATE
## 1 2001-02-01 5.10 2001-02-01
## 2 2001-03-01 4.89 2001-03-01
## 3 2001-04-01 5.14 2001-04-01
## 4 2001-05-01 5.39 2001-05-01
## 5 2001-06-01 5.28 2001-06-01
## 6 2001-07-01 5.24 2001-07-01
## 7 2001-08-01 4.97 2001-08-01
## 8 2001-09-01 4.73 2001-09-01
## 9 2001-10-01 4.57 2001-10-01
## 10 2001-11-01 4.65 2001-11-01
## 11 2001-12-01 5.09 2001-12-01
## 12 2002-01-01 5.04 2002-01-01
## 13 2002-02-01 4.91 2002-02-01
## 14 2002-03-01 5.28 2002-03-01
## 15 2002-04-01 5.21 2002-04-01
## 16 2002-05-01 5.16 2002-05-01
## 17 2002-06-01 4.93 2002-06-01
## 18 2002-07-01 4.65 2002-07-01
## 19 2002-08-01 4.26 2002-08-01
## 20 2002-09-01 3.87 2002-09-01
## 21 2002-10-01 3.94 2002-10-01
## 22 2002-11-01 4.05 2002-11-01
## 23 2002-12-01 4.03 2002-12-01
## 24 2003-01-01 4.05 2003-01-01
## 25 2003-02-01 3.90 2003-02-01
## 26 2003-03-01 3.81 2003-03-01
## 27 2003-04-01 3.96 2003-04-01
## 28 2003-05-01 3.57 2003-05-01
## 29 2003-06-01 3.33 2003-06-01
## 30 2003-07-01 3.98 2003-07-01
## 31 2003-08-01 4.45 2003-08-01
## 32 2003-09-01 4.27 2003-09-01
## 33 2003-10-01 4.29 2003-10-01
## 34 2003-11-01 4.30 2003-11-01
## 35 2003-12-01 4.27 2003-12-01
## 36 2004-01-01 4.15 2004-01-01
## 37 2004-02-01 4.08 2004-02-01
## 38 2004-03-01 3.83 2004-03-01
## 39 2004-04-01 4.35 2004-04-01
## 40 2004-05-01 4.72 2004-05-01
## 41 2004-06-01 4.73 2004-06-01
## 42 2004-07-01 4.50 2004-07-01
## 43 2004-08-01 4.28 2004-08-01
## 44 2004-09-01 4.13 2004-09-01
## 45 2004-10-01 4.10 2004-10-01
## 46 2004-11-01 4.19 2004-11-01
## 47 2004-12-01 4.23 2004-12-01
## 48 2005-01-01 4.22 2005-01-01
## 49 2005-02-01 4.17 2005-02-01
## 50 2005-03-01 4.50 2005-03-01
## 51 2005-04-01 4.34 2005-04-01
## 52 2005-05-01 4.14 2005-05-01
## 53 2005-06-01 4.00 2005-06-01
## 54 2005-07-01 4.18 2005-07-01
## 55 2005-08-01 4.26 2005-08-01
## 56 2005-09-01 4.20 2005-09-01
## 57 2005-10-01 4.46 2005-10-01
## 58 2005-11-01 4.54 2005-11-01
## 59 2005-12-01 4.47 2005-12-01
## 60 2006-01-01 4.42 2006-01-01
## 61 2006-02-01 4.57 2006-02-01
## 62 2006-03-01 4.72 2006-03-01
## 63 2006-04-01 4.99 2006-04-01
## 64 2006-05-01 5.11 2006-05-01
## 65 2006-06-01 5.11 2006-06-01
## 66 2006-07-01 5.09 2006-07-01
## 67 2006-08-01 4.88 2006-08-01
## 68 2006-09-01 4.72 2006-09-01
## 69 2006-10-01 4.73 2006-10-01
## 70 2006-11-01 4.60 2006-11-01
## 71 2006-12-01 4.56 2006-12-01
## 72 2007-01-01 4.76 2007-01-01
## 73 2007-02-01 4.72 2007-02-01
## 74 2007-03-01 4.56 2007-03-01
## 75 2007-04-01 4.69 2007-04-01
## 76 2007-05-01 4.75 2007-05-01
## 77 2007-06-01 5.10 2007-06-01
## 78 2007-07-01 5.00 2007-07-01
## 79 2007-08-01 4.67 2007-08-01
## 80 2007-09-01 4.52 2007-09-01
## 81 2007-10-01 4.53 2007-10-01
## 82 2007-11-01 4.15 2007-11-01
## 83 2007-12-01 4.10 2007-12-01
## 84 2008-01-01 3.74 2008-01-01
## 85 2008-02-01 3.74 2008-02-01
## 86 2008-03-01 3.51 2008-03-01
## 87 2008-04-01 3.68 2008-04-01
## 88 2008-05-01 3.88 2008-05-01
## 89 2008-06-01 4.10 2008-06-01
## 90 2008-07-01 4.01 2008-07-01
## 91 2008-08-01 3.89 2008-08-01
## 92 2008-09-01 3.69 2008-09-01
## 93 2008-10-01 3.81 2008-10-01
## 94 2008-11-01 3.53 2008-11-01
## 95 2008-12-01 2.42 2008-12-01
## 96 2009-01-01 2.52 2009-01-01
## 97 2009-02-01 2.87 2009-02-01
## 98 2009-03-01 2.82 2009-03-01
## 99 2009-04-01 2.93 2009-04-01
## 100 2009-05-01 3.29 2009-05-01
## 101 2009-06-01 3.72 2009-06-01
## 102 2009-07-01 3.56 2009-07-01
## 103 2009-08-01 3.59 2009-08-01
## 104 2009-09-01 3.40 2009-09-01
## 105 2009-10-01 3.39 2009-10-01
## 106 2009-11-01 3.40 2009-11-01
## 107 2009-12-01 3.59 2009-12-01
## 108 2010-01-01 3.73 2010-01-01
## 109 2010-02-01 3.69 2010-02-01
## 110 2010-03-01 3.73 2010-03-01
## 111 2010-04-01 3.85 2010-04-01
## 112 2010-05-01 3.42 2010-05-01
## 113 2010-06-01 3.20 2010-06-01
## 114 2010-07-01 3.01 2010-07-01
## 115 2010-08-01 2.70 2010-08-01
## 116 2010-09-01 2.65 2010-09-01
## 117 2010-10-01 2.54 2010-10-01
## 118 2010-11-01 2.76 2010-11-01
## 119 2010-12-01 3.29 2010-12-01
## 120 2011-01-01 3.39 2011-01-01
## 121 2011-02-01 3.58 2011-02-01
## 122 2011-03-01 3.41 2011-03-01
## 123 2011-04-01 3.46 2011-04-01
## 124 2011-05-01 3.17 2011-05-01
## 125 2011-06-01 3.00 2011-06-01
## 126 2011-07-01 3.00 2011-07-01
## 127 2011-08-01 2.30 2011-08-01
## 128 2011-09-01 1.98 2011-09-01
## 129 2011-10-01 2.15 2011-10-01
## 130 2011-11-01 2.01 2011-11-01
## 131 2011-12-01 1.98 2011-12-01
## 132 2012-01-01 1.97 2012-01-01
## 133 2012-02-01 1.97 2012-02-01
## 134 2012-03-01 2.17 2012-03-01
## 135 2012-04-01 2.05 2012-04-01
## 136 2012-05-01 1.80 2012-05-01
## 137 2012-06-01 1.62 2012-06-01
## 138 2012-07-01 1.53 2012-07-01
## 139 2012-08-01 1.68 2012-08-01
## 140 2012-09-01 1.72 2012-09-01
## 141 2012-10-01 1.75 2012-10-01
## 142 2012-11-01 1.65 2012-11-01
## 143 2012-12-01 1.72 2012-12-01
## 144 2013-01-01 1.91 2013-01-01
## 145 2013-02-01 1.98 2013-02-01
## 146 2013-03-01 1.96 2013-03-01
## 147 2013-04-01 1.76 2013-04-01
## 148 2013-05-01 1.93 2013-05-01
## 149 2013-06-01 2.30 2013-06-01
## 150 2013-07-01 2.58 2013-07-01
## 151 2013-08-01 2.74 2013-08-01
## 152 2013-09-01 2.81 2013-09-01
## 153 2013-10-01 2.62 2013-10-01
## 154 2013-11-01 2.72 2013-11-01
## 155 2013-12-01 2.90 2013-12-01
## 156 2014-01-01 2.86 2014-01-01
## 157 2014-02-01 2.71 2014-02-01
## 158 2014-03-01 2.72 2014-03-01
## 159 2014-04-01 2.71 2014-04-01
## 160 2014-05-01 2.56 2014-05-01
## 161 2014-06-01 2.60 2014-06-01
## 162 2014-07-01 2.54 2014-07-01
## 163 2014-08-01 2.42 2014-08-01
## 164 2014-09-01 2.53 2014-09-01
## 165 2014-10-01 2.30 2014-10-01
## 166 2014-11-01 2.33 2014-11-01
## 167 2014-12-01 2.21 2014-12-01
## 168 2015-01-01 1.88 2015-01-01
## 169 2015-02-01 1.98 2015-02-01
## 170 2015-03-01 2.04 2015-03-01
## 171 2015-04-01 1.94 2015-04-01
## 172 2015-05-01 2.20 2015-05-01
## 173 2015-06-01 2.36 2015-06-01
## 174 2015-07-01 2.32 2015-07-01
## 175 2015-08-01 2.17 2015-08-01
## 176 2015-09-01 2.17 2015-09-01
## 177 2015-10-01 2.07 2015-10-01
## 178 2015-11-01 2.26 2015-11-01
## 179 2015-12-01 2.24 2015-12-01
## 180 2016-01-01 2.09 2016-01-01
## 181 2016-02-01 1.78 2016-02-01
## 182 2016-03-01 1.89 2016-03-01
## 183 2016-04-01 1.81 2016-04-01
## 184 2016-05-01 1.81 2016-05-01
## 185 2016-06-01 1.64 2016-06-01
## 186 2016-07-01 1.50 2016-07-01
## 187 2016-08-01 1.56 2016-08-01
## 188 2016-09-01 1.63 2016-09-01
## 189 2016-10-01 1.76 2016-10-01
## 190 2016-11-01 2.14 2016-11-01
## 191 2016-12-01 2.49 2016-12-01
## 192 2017-01-01 2.43 2017-01-01
## 193 2017-02-01 2.42 2017-02-01
## 194 2017-03-01 2.48 2017-03-01
## 195 2017-04-01 2.30 2017-04-01
## 196 2017-05-01 2.30 2017-05-01
## 197 2017-06-01 2.19 2017-06-01
## 198 2017-07-01 2.32 2017-07-01
## 199 2017-08-01 2.21 2017-08-01
## 200 2017-09-01 2.20 2017-09-01
## 201 2017-10-01 2.36 2017-10-01
## 202 2017-11-01 2.35 2017-11-01
## 203 2017-12-01 2.40 2017-12-01
## 204 2018-01-01 2.58 2018-01-01
## 205 2018-02-01 2.86 2018-02-01
## 206 2018-03-01 2.84 2018-03-01
## 207 2018-04-01 2.87 2018-04-01
## 208 2018-05-01 2.98 2018-05-01
## 209 2018-06-01 2.91 2018-06-01
## 210 2018-07-01 2.89 2018-07-01
## 211 2018-08-01 2.89 2018-08-01
## 212 2018-09-01 3.00 2018-09-01
## 213 2018-10-01 3.15 2018-10-01
## 214 2018-11-01 3.12 2018-11-01
## 215 2018-12-01 2.83 2018-12-01
## 216 2019-01-01 2.71 2019-01-01
## 217 2019-02-01 2.68 2019-02-01
## 218 2019-03-01 2.57 2019-03-01
## 219 2019-04-01 2.53 2019-04-01
## 220 2019-05-01 2.40 2019-05-01
## 221 2019-06-01 2.07 2019-06-01
## 222 2019-07-01 2.06 2019-07-01
## 223 2019-08-01 1.63 2019-08-01
## 224 2019-09-01 1.70 2019-09-01
## 225 2019-10-01 1.71 2019-10-01
## 226 2019-11-01 1.81 2019-11-01
## 227 2019-12-01 1.86 2019-12-01
## 228 2020-01-01 1.76 2020-01-01
## 229 2020-02-01 1.50 2020-02-01
## 230 2020-03-01 0.87 2020-03-01
## 231 2020-04-01 0.66 2020-04-01
## 232 2020-05-01 0.67 2020-05-01
## 233 2020-06-01 0.73 2020-06-01
## 234 2020-07-01 0.62 2020-07-01
## 235 2020-08-01 0.65 2020-08-01
## 236 2020-09-01 0.68 2020-09-01
## 237 2020-10-01 0.79 2020-10-01
## 238 2020-11-01 0.87 2020-11-01
## 239 2020-12-01 0.93 2020-12-01
## 240 2021-01-01 1.08 2021-01-01
## 241 2021-02-01 1.26 2021-02-01
## 242 2021-03-01 1.61 2021-03-01
## 243 2021-04-01 1.64 2021-04-01
## 244 2021-05-01 1.62 2021-05-01
## 245 2021-06-01 1.52 2021-06-01
## 246 2021-07-01 1.32 2021-07-01
## 247 2021-08-01 1.28 2021-08-01
## 248 2021-09-01 1.37 2021-09-01
## 249 2021-10-01 1.58 2021-10-01
## 250 2021-11-01 1.56 2021-11-01
## 251 2021-12-01 1.47 2021-12-01
## 252 2022-01-01 1.76 2022-01-01
## 253 2022-02-01 1.93 2022-02-01
## 254 2022-03-01 2.13 2022-03-01
## 255 2022-04-01 2.75 2022-04-01
## 256 2022-05-01 2.90 2022-05-01
## 257 2022-06-01 3.14 2022-06-01
## 258 2022-07-01 2.90 2022-07-01
## 259 2022-08-01 2.90 2022-08-01
## 260 2022-09-01 3.52 2022-09-01
## 261 2022-10-01 3.98 2022-10-01
## 262 2022-11-01 3.89 2022-11-01
## 263 2022-12-01 3.62 2022-12-01
## 264 2023-01-01 3.53 2023-01-01
## 265 2023-02-01 3.75 2023-02-01
## 266 2023-03-01 3.66 2023-03-01
## 267 2023-04-01 3.46 2023-04-01
## 268 2023-05-01 3.57 2023-05-01
## 269 2023-06-01 3.75 2023-06-01
## 270 2023-07-01 3.90 2023-07-01
## 271 2023-08-01 4.17 2023-08-01
## 272 2023-09-01 4.38 2023-09-01
## 273 2023-10-01 4.80 2023-10-01
## 274 2023-11-01 4.50 2023-11-01
## 275 2023-12-01 4.02 2023-12-01
## 276 2024-01-01 4.06 2024-01-01
## 277 2024-02-01 4.21 2024-02-01
## 278 2024-03-01 4.21 2024-03-01
## 279 2024-04-01 4.54 2024-04-01
## 280 2024-05-01 4.48 2024-05-01
## 281 2024-06-01 4.31 2024-06-01
## 282 2024-07-01 4.25 2024-07-01
## 283 2024-08-01 3.87 2024-08-01
## 284 2024-09-01 3.72 2024-09-01
## 285 2024-10-01 4.10 2024-10-01
## 286 2024-11-01 4.36 2024-11-01
## 287 2024-12-01 4.39 2024-12-01
## 288 2025-01-01 4.63 2025-01-01
## 289 2025-02-01 4.45 2025-02-01
## 290 2025-03-01 4.28 2025-03-01
## 291 2025-04-01 4.28 2025-04-01
## 292 2025-05-01 4.42 2025-05-01
## 293 2025-06-01 4.38 2025-06-01
## 294 2025-07-01 4.39 2025-07-01
## 295 2025-08-01 4.26 2025-08-01
## 296 2025-09-01 4.12 2025-09-01
## 297 2025-10-01 4.06 2025-10-01
## 298 2025-11-01 4.09 2025-11-01
## 299 2025-12-01 4.14 2025-12-01
## 300 2026-01-01 4.21 2026-01-01# --- Show available date range for GS10 ---
first_gs10 <- min(gs10_raw$DATE, na.rm = TRUE)
last_gs10 <- max(gs10_raw$DATE, na.rm = TRUE)
cat("GS10 first available date:", format(first_gs10, "%Y-%m-%d"), "\n")## GS10 first available date: 2001-02-01cat("GS10 last available date :", format(last_gs10, "%Y-%m-%d"), "\n")## GS10 last available date : 2026-01-01gs10_monthly <- data.frame(DATE = gs10_raw$DATE, GS10 = gs10_raw$GS10)# Federal Funds Rate
effr_url <- paste0(
"https://fred.stlouisfed.org/graph/fredgraph.csv?id=FEDFUNDS",
"&cosd=", obs_start, "&coed=", obs_end
)
effr_raw <- read_fred_csv(effr_url)
effr_raw$DATE <- as.Date(effr_raw$observation_date)
names(effr_raw)[2] <- "FEDFUNDS"
effr_raw ## observation_date FEDFUNDS DATE
## 1 2001-02-01 5.49 2001-02-01
## 2 2001-03-01 5.31 2001-03-01
## 3 2001-04-01 4.80 2001-04-01
## 4 2001-05-01 4.21 2001-05-01
## 5 2001-06-01 3.97 2001-06-01
## 6 2001-07-01 3.77 2001-07-01
## 7 2001-08-01 3.65 2001-08-01
## 8 2001-09-01 3.07 2001-09-01
## 9 2001-10-01 2.49 2001-10-01
## 10 2001-11-01 2.09 2001-11-01
## 11 2001-12-01 1.82 2001-12-01
## 12 2002-01-01 1.73 2002-01-01
## 13 2002-02-01 1.74 2002-02-01
## 14 2002-03-01 1.73 2002-03-01
## 15 2002-04-01 1.75 2002-04-01
## 16 2002-05-01 1.75 2002-05-01
## 17 2002-06-01 1.75 2002-06-01
## 18 2002-07-01 1.73 2002-07-01
## 19 2002-08-01 1.74 2002-08-01
## 20 2002-09-01 1.75 2002-09-01
## 21 2002-10-01 1.75 2002-10-01
## 22 2002-11-01 1.34 2002-11-01
## 23 2002-12-01 1.24 2002-12-01
## 24 2003-01-01 1.24 2003-01-01
## 25 2003-02-01 1.26 2003-02-01
## 26 2003-03-01 1.25 2003-03-01
## 27 2003-04-01 1.26 2003-04-01
## 28 2003-05-01 1.26 2003-05-01
## 29 2003-06-01 1.22 2003-06-01
## 30 2003-07-01 1.01 2003-07-01
## 31 2003-08-01 1.03 2003-08-01
## 32 2003-09-01 1.01 2003-09-01
## 33 2003-10-01 1.01 2003-10-01
## 34 2003-11-01 1.00 2003-11-01
## 35 2003-12-01 0.98 2003-12-01
## 36 2004-01-01 1.00 2004-01-01
## 37 2004-02-01 1.01 2004-02-01
## 38 2004-03-01 1.00 2004-03-01
## 39 2004-04-01 1.00 2004-04-01
## 40 2004-05-01 1.00 2004-05-01
## 41 2004-06-01 1.03 2004-06-01
## 42 2004-07-01 1.26 2004-07-01
## 43 2004-08-01 1.43 2004-08-01
## 44 2004-09-01 1.61 2004-09-01
## 45 2004-10-01 1.76 2004-10-01
## 46 2004-11-01 1.93 2004-11-01
## 47 2004-12-01 2.16 2004-12-01
## 48 2005-01-01 2.28 2005-01-01
## 49 2005-02-01 2.50 2005-02-01
## 50 2005-03-01 2.63 2005-03-01
## 51 2005-04-01 2.79 2005-04-01
## 52 2005-05-01 3.00 2005-05-01
## 53 2005-06-01 3.04 2005-06-01
## 54 2005-07-01 3.26 2005-07-01
## 55 2005-08-01 3.50 2005-08-01
## 56 2005-09-01 3.62 2005-09-01
## 57 2005-10-01 3.78 2005-10-01
## 58 2005-11-01 4.00 2005-11-01
## 59 2005-12-01 4.16 2005-12-01
## 60 2006-01-01 4.29 2006-01-01
## 61 2006-02-01 4.49 2006-02-01
## 62 2006-03-01 4.59 2006-03-01
## 63 2006-04-01 4.79 2006-04-01
## 64 2006-05-01 4.94 2006-05-01
## 65 2006-06-01 4.99 2006-06-01
## 66 2006-07-01 5.24 2006-07-01
## 67 2006-08-01 5.25 2006-08-01
## 68 2006-09-01 5.25 2006-09-01
## 69 2006-10-01 5.25 2006-10-01
## 70 2006-11-01 5.25 2006-11-01
## 71 2006-12-01 5.24 2006-12-01
## 72 2007-01-01 5.25 2007-01-01
## 73 2007-02-01 5.26 2007-02-01
## 74 2007-03-01 5.26 2007-03-01
## 75 2007-04-01 5.25 2007-04-01
## 76 2007-05-01 5.25 2007-05-01
## 77 2007-06-01 5.25 2007-06-01
## 78 2007-07-01 5.26 2007-07-01
## 79 2007-08-01 5.02 2007-08-01
## 80 2007-09-01 4.94 2007-09-01
## 81 2007-10-01 4.76 2007-10-01
## 82 2007-11-01 4.49 2007-11-01
## 83 2007-12-01 4.24 2007-12-01
## 84 2008-01-01 3.94 2008-01-01
## 85 2008-02-01 2.98 2008-02-01
## 86 2008-03-01 2.61 2008-03-01
## 87 2008-04-01 2.28 2008-04-01
## 88 2008-05-01 1.98 2008-05-01
## 89 2008-06-01 2.00 2008-06-01
## 90 2008-07-01 2.01 2008-07-01
## 91 2008-08-01 2.00 2008-08-01
## 92 2008-09-01 1.81 2008-09-01
## 93 2008-10-01 0.97 2008-10-01
## 94 2008-11-01 0.39 2008-11-01
## 95 2008-12-01 0.16 2008-12-01
## 96 2009-01-01 0.15 2009-01-01
## 97 2009-02-01 0.22 2009-02-01
## 98 2009-03-01 0.18 2009-03-01
## 99 2009-04-01 0.15 2009-04-01
## 100 2009-05-01 0.18 2009-05-01
## 101 2009-06-01 0.21 2009-06-01
## 102 2009-07-01 0.16 2009-07-01
## 103 2009-08-01 0.16 2009-08-01
## 104 2009-09-01 0.15 2009-09-01
## 105 2009-10-01 0.12 2009-10-01
## 106 2009-11-01 0.12 2009-11-01
## 107 2009-12-01 0.12 2009-12-01
## 108 2010-01-01 0.11 2010-01-01
## 109 2010-02-01 0.13 2010-02-01
## 110 2010-03-01 0.16 2010-03-01
## 111 2010-04-01 0.20 2010-04-01
## 112 2010-05-01 0.20 2010-05-01
## 113 2010-06-01 0.18 2010-06-01
## 114 2010-07-01 0.18 2010-07-01
## 115 2010-08-01 0.19 2010-08-01
## 116 2010-09-01 0.19 2010-09-01
## 117 2010-10-01 0.19 2010-10-01
## 118 2010-11-01 0.19 2010-11-01
## 119 2010-12-01 0.18 2010-12-01
## 120 2011-01-01 0.17 2011-01-01
## 121 2011-02-01 0.16 2011-02-01
## 122 2011-03-01 0.14 2011-03-01
## 123 2011-04-01 0.10 2011-04-01
## 124 2011-05-01 0.09 2011-05-01
## 125 2011-06-01 0.09 2011-06-01
## 126 2011-07-01 0.07 2011-07-01
## 127 2011-08-01 0.10 2011-08-01
## 128 2011-09-01 0.08 2011-09-01
## 129 2011-10-01 0.07 2011-10-01
## 130 2011-11-01 0.08 2011-11-01
## 131 2011-12-01 0.07 2011-12-01
## 132 2012-01-01 0.08 2012-01-01
## 133 2012-02-01 0.10 2012-02-01
## 134 2012-03-01 0.13 2012-03-01
## 135 2012-04-01 0.14 2012-04-01
## 136 2012-05-01 0.16 2012-05-01
## 137 2012-06-01 0.16 2012-06-01
## 138 2012-07-01 0.16 2012-07-01
## 139 2012-08-01 0.13 2012-08-01
## 140 2012-09-01 0.14 2012-09-01
## 141 2012-10-01 0.16 2012-10-01
## 142 2012-11-01 0.16 2012-11-01
## 143 2012-12-01 0.16 2012-12-01
## 144 2013-01-01 0.14 2013-01-01
## 145 2013-02-01 0.15 2013-02-01
## 146 2013-03-01 0.14 2013-03-01
## 147 2013-04-01 0.15 2013-04-01
## 148 2013-05-01 0.11 2013-05-01
## 149 2013-06-01 0.09 2013-06-01
## 150 2013-07-01 0.09 2013-07-01
## 151 2013-08-01 0.08 2013-08-01
## 152 2013-09-01 0.08 2013-09-01
## 153 2013-10-01 0.09 2013-10-01
## 154 2013-11-01 0.08 2013-11-01
## 155 2013-12-01 0.09 2013-12-01
## 156 2014-01-01 0.07 2014-01-01
## 157 2014-02-01 0.07 2014-02-01
## 158 2014-03-01 0.08 2014-03-01
## 159 2014-04-01 0.09 2014-04-01
## 160 2014-05-01 0.09 2014-05-01
## 161 2014-06-01 0.10 2014-06-01
## 162 2014-07-01 0.09 2014-07-01
## 163 2014-08-01 0.09 2014-08-01
## 164 2014-09-01 0.09 2014-09-01
## 165 2014-10-01 0.09 2014-10-01
## 166 2014-11-01 0.09 2014-11-01
## 167 2014-12-01 0.12 2014-12-01
## 168 2015-01-01 0.11 2015-01-01
## 169 2015-02-01 0.11 2015-02-01
## 170 2015-03-01 0.11 2015-03-01
## 171 2015-04-01 0.12 2015-04-01
## 172 2015-05-01 0.12 2015-05-01
## 173 2015-06-01 0.13 2015-06-01
## 174 2015-07-01 0.13 2015-07-01
## 175 2015-08-01 0.14 2015-08-01
## 176 2015-09-01 0.14 2015-09-01
## 177 2015-10-01 0.12 2015-10-01
## 178 2015-11-01 0.12 2015-11-01
## 179 2015-12-01 0.24 2015-12-01
## 180 2016-01-01 0.34 2016-01-01
## 181 2016-02-01 0.38 2016-02-01
## 182 2016-03-01 0.36 2016-03-01
## 183 2016-04-01 0.37 2016-04-01
## 184 2016-05-01 0.37 2016-05-01
## 185 2016-06-01 0.38 2016-06-01
## 186 2016-07-01 0.39 2016-07-01
## 187 2016-08-01 0.40 2016-08-01
## 188 2016-09-01 0.40 2016-09-01
## 189 2016-10-01 0.40 2016-10-01
## 190 2016-11-01 0.41 2016-11-01
## 191 2016-12-01 0.54 2016-12-01
## 192 2017-01-01 0.65 2017-01-01
## 193 2017-02-01 0.66 2017-02-01
## 194 2017-03-01 0.79 2017-03-01
## 195 2017-04-01 0.90 2017-04-01
## 196 2017-05-01 0.91 2017-05-01
## 197 2017-06-01 1.04 2017-06-01
## 198 2017-07-01 1.15 2017-07-01
## 199 2017-08-01 1.16 2017-08-01
## 200 2017-09-01 1.15 2017-09-01
## 201 2017-10-01 1.15 2017-10-01
## 202 2017-11-01 1.16 2017-11-01
## 203 2017-12-01 1.30 2017-12-01
## 204 2018-01-01 1.41 2018-01-01
## 205 2018-02-01 1.42 2018-02-01
## 206 2018-03-01 1.51 2018-03-01
## 207 2018-04-01 1.69 2018-04-01
## 208 2018-05-01 1.70 2018-05-01
## 209 2018-06-01 1.82 2018-06-01
## 210 2018-07-01 1.91 2018-07-01
## 211 2018-08-01 1.91 2018-08-01
## 212 2018-09-01 1.95 2018-09-01
## 213 2018-10-01 2.19 2018-10-01
## 214 2018-11-01 2.20 2018-11-01
## 215 2018-12-01 2.27 2018-12-01
## 216 2019-01-01 2.40 2019-01-01
## 217 2019-02-01 2.40 2019-02-01
## 218 2019-03-01 2.41 2019-03-01
## 219 2019-04-01 2.42 2019-04-01
## 220 2019-05-01 2.39 2019-05-01
## 221 2019-06-01 2.38 2019-06-01
## 222 2019-07-01 2.40 2019-07-01
## 223 2019-08-01 2.13 2019-08-01
## 224 2019-09-01 2.04 2019-09-01
## 225 2019-10-01 1.83 2019-10-01
## 226 2019-11-01 1.55 2019-11-01
## 227 2019-12-01 1.55 2019-12-01
## 228 2020-01-01 1.55 2020-01-01
## 229 2020-02-01 1.58 2020-02-01
## 230 2020-03-01 0.65 2020-03-01
## 231 2020-04-01 0.05 2020-04-01
## 232 2020-05-01 0.05 2020-05-01
## 233 2020-06-01 0.08 2020-06-01
## 234 2020-07-01 0.09 2020-07-01
## 235 2020-08-01 0.10 2020-08-01
## 236 2020-09-01 0.09 2020-09-01
## 237 2020-10-01 0.09 2020-10-01
## 238 2020-11-01 0.09 2020-11-01
## 239 2020-12-01 0.09 2020-12-01
## 240 2021-01-01 0.09 2021-01-01
## 241 2021-02-01 0.08 2021-02-01
## 242 2021-03-01 0.07 2021-03-01
## 243 2021-04-01 0.07 2021-04-01
## 244 2021-05-01 0.06 2021-05-01
## 245 2021-06-01 0.08 2021-06-01
## 246 2021-07-01 0.10 2021-07-01
## 247 2021-08-01 0.09 2021-08-01
## 248 2021-09-01 0.08 2021-09-01
## 249 2021-10-01 0.08 2021-10-01
## 250 2021-11-01 0.08 2021-11-01
## 251 2021-12-01 0.08 2021-12-01
## 252 2022-01-01 0.08 2022-01-01
## 253 2022-02-01 0.08 2022-02-01
## 254 2022-03-01 0.20 2022-03-01
## 255 2022-04-01 0.33 2022-04-01
## 256 2022-05-01 0.77 2022-05-01
## 257 2022-06-01 1.21 2022-06-01
## 258 2022-07-01 1.68 2022-07-01
## 259 2022-08-01 2.33 2022-08-01
## 260 2022-09-01 2.56 2022-09-01
## 261 2022-10-01 3.08 2022-10-01
## 262 2022-11-01 3.78 2022-11-01
## 263 2022-12-01 4.10 2022-12-01
## 264 2023-01-01 4.33 2023-01-01
## 265 2023-02-01 4.57 2023-02-01
## 266 2023-03-01 4.65 2023-03-01
## 267 2023-04-01 4.83 2023-04-01
## 268 2023-05-01 5.06 2023-05-01
## 269 2023-06-01 5.08 2023-06-01
## 270 2023-07-01 5.12 2023-07-01
## 271 2023-08-01 5.33 2023-08-01
## 272 2023-09-01 5.33 2023-09-01
## 273 2023-10-01 5.33 2023-10-01
## 274 2023-11-01 5.33 2023-11-01
## 275 2023-12-01 5.33 2023-12-01
## 276 2024-01-01 5.33 2024-01-01
## 277 2024-02-01 5.33 2024-02-01
## 278 2024-03-01 5.33 2024-03-01
## 279 2024-04-01 5.33 2024-04-01
## 280 2024-05-01 5.33 2024-05-01
## 281 2024-06-01 5.33 2024-06-01
## 282 2024-07-01 5.33 2024-07-01
## 283 2024-08-01 5.33 2024-08-01
## 284 2024-09-01 5.13 2024-09-01
## 285 2024-10-01 4.83 2024-10-01
## 286 2024-11-01 4.64 2024-11-01
## 287 2024-12-01 4.48 2024-12-01
## 288 2025-01-01 4.33 2025-01-01
## 289 2025-02-01 4.33 2025-02-01
## 290 2025-03-01 4.33 2025-03-01
## 291 2025-04-01 4.33 2025-04-01
## 292 2025-05-01 4.33 2025-05-01
## 293 2025-06-01 4.33 2025-06-01
## 294 2025-07-01 4.33 2025-07-01
## 295 2025-08-01 4.33 2025-08-01
## 296 2025-09-01 4.22 2025-09-01
## 297 2025-10-01 4.09 2025-10-01
## 298 2025-11-01 3.88 2025-11-01
## 299 2025-12-01 3.72 2025-12-01
## 300 2026-01-01 3.64 2026-01-01# Show available date range
first_effr <- min(effr_raw$DATE, na.rm = TRUE)
last_effr <- max(effr_raw$DATE, na.rm = TRUE)
cat("FEDFUNDS first available date:", format(first_effr, "%Y-%m-%d"), "\n")## FEDFUNDS first available date: 2001-02-01cat("FEDFUNDS last available date :", format(last_effr, "%Y-%m-%d"), "\n")## FEDFUNDS last available date : 2026-01-01effr_monthly <- data.frame(DATE = effr_raw$DATE, FEDFUNDS = effr_raw$FEDFUNDS)# Looking for NA's
check_missing <- function(df, name) {
total <- nrow(df)
nas <- sum(is.na(df[, 2]))
cat("\n", name, "\n",
"Rows:", total, "\n",
"Missing values:", nas, "\n",
"Date range:", format(min(df$DATE), "%Y-%m"), "to", format(max(df$DATE), "%Y-%m"), "\n")
if (nas > 0) {
missing_idx <- which(is.na(df[, 2]))
cat("First few missing dates:\n")
print(head(df$DATE[missing_idx], 10))
}
}
# Run for each source
check_missing(djia_raw, "Dow Jones INdustrial Average (SP500)")##
## Dow Jones INdustrial Average (SP500)
## Rows: 2610
## Missing values: 95
## Date range: 2016-02 to 2026-02
## First few missing dates:
## [1] "2016-02-15" "2016-03-25" "2016-05-30" "2016-07-04" "2016-09-05"
## [6] "2016-11-24" "2016-12-26" "2017-01-02" "2017-01-16" "2017-02-20"check_missing(gs10_raw, "10-Year Treasury Yield (GS10)")##
## 10-Year Treasury Yield (GS10)
## Rows: 300
## Missing values: 0
## Date range: 2001-02 to 2026-01check_missing(effr_raw, "Fed Funds Rate (FEDFUNDS)")##
## Fed Funds Rate (FEDFUNDS)
## Rows: 300
## Missing values: 0
## Date range: 2001-02 to 2026-01#MIssing values/NA
# Extract year/ month
djia_raw$Year <- format(djia_raw$DATE, "%Y")
djia_raw$Month <- format(djia_raw$DATE, "%m")
# Count NAs per month/year
na_table <- aggregate(djia_raw$DJIA,
by = list(Year = djia_raw$Year, Month = djia_raw$Month),
FUN = function(x) sum(is.na(x)))
# filter to months with NAs
names(na_table)[3] <- "NA_Count"
na_table_with_na <- na_table[na_table$NA_Count > 0, ]
# Yes/No flag
na_table_with_na$Has_Missing <- "Yes"
na_table_with_na## Year Month NA_Count Has_Missing
## 1 2017 01 2 Yes
## 2 2018 01 2 Yes
## 3 2019 01 2 Yes
## 4 2020 01 2 Yes
## 5 2021 01 2 Yes
## 6 2022 01 1 Yes
## 7 2023 01 2 Yes
## 8 2024 01 2 Yes
## 9 2025 01 3 Yes
## 10 2026 01 2 Yes
## 11 2016 02 1 Yes
## 12 2017 02 1 Yes
## 13 2018 02 1 Yes
## 14 2019 02 1 Yes
## 15 2020 02 1 Yes
## 16 2021 02 1 Yes
## 17 2022 02 1 Yes
## 18 2023 02 1 Yes
## 19 2024 02 1 Yes
## 20 2025 02 1 Yes
## 22 2016 03 1 Yes
## 24 2018 03 1 Yes
## 30 2024 03 1 Yes
## 33 2017 04 1 Yes
## 35 2019 04 1 Yes
## 36 2020 04 1 Yes
## 37 2021 04 1 Yes
## 38 2022 04 1 Yes
## 39 2023 04 1 Yes
## 41 2025 04 1 Yes
## 42 2016 05 1 Yes
## 43 2017 05 1 Yes
## 44 2018 05 1 Yes
## 45 2019 05 1 Yes
## 46 2020 05 1 Yes
## 47 2021 05 1 Yes
## 48 2022 05 1 Yes
## 49 2023 05 1 Yes
## 50 2024 05 1 Yes
## 51 2025 05 1 Yes
## 58 2022 06 1 Yes
## 59 2023 06 1 Yes
## 60 2024 06 1 Yes
## 61 2025 06 1 Yes
## 62 2016 07 1 Yes
## 63 2017 07 1 Yes
## 64 2018 07 1 Yes
## 65 2019 07 1 Yes
## 66 2020 07 1 Yes
## 67 2021 07 1 Yes
## 68 2022 07 1 Yes
## 69 2023 07 1 Yes
## 70 2024 07 1 Yes
## 71 2025 07 1 Yes
## 82 2016 09 1 Yes
## 83 2017 09 1 Yes
## 84 2018 09 1 Yes
## 85 2019 09 1 Yes
## 86 2020 09 1 Yes
## 87 2021 09 1 Yes
## 88 2022 09 1 Yes
## 89 2023 09 1 Yes
## 90 2024 09 1 Yes
## 91 2025 09 1 Yes
## 102 2016 11 1 Yes
## 103 2017 11 1 Yes
## 104 2018 11 1 Yes
## 105 2019 11 1 Yes
## 106 2020 11 1 Yes
## 107 2021 11 1 Yes
## 108 2022 11 1 Yes
## 109 2023 11 1 Yes
## 110 2024 11 1 Yes
## 111 2025 11 1 Yes
## 112 2016 12 1 Yes
## 113 2017 12 1 Yes
## 114 2018 12 2 Yes
## 115 2019 12 1 Yes
## 116 2020 12 1 Yes
## 117 2021 12 1 Yes
## 118 2022 12 1 Yes
## 119 2023 12 1 Yes
## 120 2024 12 1 Yes
## 121 2025 12 1 Yes#NA?missing values holidays or weekend??
na_dates <- djia_raw$DATE[is.na(djia_raw$DJIA)]
# Check weekdays
weekday_check <- weekdays(na_dates) # e.g., "Saturday", "Sunday"
# check holidays
is_holiday <- function(d) {
y <- as.integer(format(d, "%Y"))
m <- as.integer(format(d, "%m"))
day <- as.integer(format(d, "%d"))
if ((m == 1 && day == 1) ||
(m == 7 && day == 4) ||
(m == 12 && day == 25)) {
return(TRUE)
}
if (m == 1 && weekdays(d) == "Monday" && day >= 15 && day <= 21) return(TRUE)
if (m == 2 && weekdays(d) == "Monday" && day >= 15 && day <= 21) return(TRUE)
if (m == 5 && weekdays(d) == "Monday" && day >= 25) return(TRUE)
if (m == 9 && weekdays(d) == "Monday" && day <= 7) return(TRUE)
if (m == 11 && weekdays(d) == "Thursday" && day >= 22 && day <= 28) return(TRUE)
return(FALSE)
}
# table
na_info <- data.frame(
Date = na_dates,
Weekday = weekday_check,
Weekend = weekday_check %in% c("Saturday", "Sunday"),
Holiday = sapply(na_dates, is_holiday),
stringsAsFactors = FALSE
)
#results
head(na_info, 100)## Date Weekday Weekend Holiday
## 1 2016-02-15 Monday FALSE TRUE
## 2 2016-03-25 Friday FALSE FALSE
## 3 2016-05-30 Monday FALSE TRUE
## 4 2016-07-04 Monday FALSE TRUE
## 5 2016-09-05 Monday FALSE TRUE
## 6 2016-11-24 Thursday FALSE TRUE
## 7 2016-12-26 Monday FALSE FALSE
## 8 2017-01-02 Monday FALSE FALSE
## 9 2017-01-16 Monday FALSE TRUE
## 10 2017-02-20 Monday FALSE TRUE
## 11 2017-04-14 Friday FALSE FALSE
## 12 2017-05-29 Monday FALSE TRUE
## 13 2017-07-04 Tuesday FALSE TRUE
## 14 2017-09-04 Monday FALSE TRUE
## 15 2017-11-23 Thursday FALSE TRUE
## 16 2017-12-25 Monday FALSE TRUE
## 17 2018-01-01 Monday FALSE TRUE
## 18 2018-01-15 Monday FALSE TRUE
## 19 2018-02-19 Monday FALSE TRUE
## 20 2018-03-30 Friday FALSE FALSE
## 21 2018-05-28 Monday FALSE TRUE
## 22 2018-07-04 Wednesday FALSE TRUE
## 23 2018-09-03 Monday FALSE TRUE
## 24 2018-11-22 Thursday FALSE TRUE
## 25 2018-12-05 Wednesday FALSE FALSE
## 26 2018-12-25 Tuesday FALSE TRUE
## 27 2019-01-01 Tuesday FALSE TRUE
## 28 2019-01-21 Monday FALSE TRUE
## 29 2019-02-18 Monday FALSE TRUE
## 30 2019-04-19 Friday FALSE FALSE
## 31 2019-05-27 Monday FALSE TRUE
## 32 2019-07-04 Thursday FALSE TRUE
## 33 2019-09-02 Monday FALSE TRUE
## 34 2019-11-28 Thursday FALSE TRUE
## 35 2019-12-25 Wednesday FALSE TRUE
## 36 2020-01-01 Wednesday FALSE TRUE
## 37 2020-01-20 Monday FALSE TRUE
## 38 2020-02-17 Monday FALSE TRUE
## 39 2020-04-10 Friday FALSE FALSE
## 40 2020-05-25 Monday FALSE TRUE
## 41 2020-07-03 Friday FALSE FALSE
## 42 2020-09-07 Monday FALSE TRUE
## 43 2020-11-26 Thursday FALSE TRUE
## 44 2020-12-25 Friday FALSE TRUE
## 45 2021-01-01 Friday FALSE TRUE
## 46 2021-01-18 Monday FALSE TRUE
## 47 2021-02-15 Monday FALSE TRUE
## 48 2021-04-02 Friday FALSE FALSE
## 49 2021-05-31 Monday FALSE TRUE
## 50 2021-07-05 Monday FALSE FALSE
## 51 2021-09-06 Monday FALSE TRUE
## 52 2021-11-25 Thursday FALSE TRUE
## 53 2021-12-24 Friday FALSE FALSE
## 54 2022-01-17 Monday FALSE TRUE
## 55 2022-02-21 Monday FALSE TRUE
## 56 2022-04-15 Friday FALSE FALSE
## 57 2022-05-30 Monday FALSE TRUE
## 58 2022-06-20 Monday FALSE FALSE
## 59 2022-07-04 Monday FALSE TRUE
## 60 2022-09-05 Monday FALSE TRUE
## 61 2022-11-24 Thursday FALSE TRUE
## 62 2022-12-26 Monday FALSE FALSE
## 63 2023-01-02 Monday FALSE FALSE
## 64 2023-01-16 Monday FALSE TRUE
## 65 2023-02-20 Monday FALSE TRUE
## 66 2023-04-07 Friday FALSE FALSE
## 67 2023-05-29 Monday FALSE TRUE
## 68 2023-06-19 Monday FALSE FALSE
## 69 2023-07-04 Tuesday FALSE TRUE
## 70 2023-09-04 Monday FALSE TRUE
## 71 2023-11-23 Thursday FALSE TRUE
## 72 2023-12-25 Monday FALSE TRUE
## 73 2024-01-01 Monday FALSE TRUE
## 74 2024-01-15 Monday FALSE TRUE
## 75 2024-02-19 Monday FALSE TRUE
## 76 2024-03-29 Friday FALSE FALSE
## 77 2024-05-27 Monday FALSE TRUE
## 78 2024-06-19 Wednesday FALSE FALSE
## 79 2024-07-04 Thursday FALSE TRUE
## 80 2024-09-02 Monday FALSE TRUE
## 81 2024-11-28 Thursday FALSE TRUE
## 82 2024-12-25 Wednesday FALSE TRUE
## 83 2025-01-01 Wednesday FALSE TRUE
## 84 2025-01-09 Thursday FALSE FALSE
## 85 2025-01-20 Monday FALSE TRUE
## 86 2025-02-17 Monday FALSE TRUE
## 87 2025-04-18 Friday FALSE FALSE
## 88 2025-05-26 Monday FALSE TRUE
## 89 2025-06-19 Thursday FALSE FALSE
## 90 2025-07-04 Friday FALSE TRUE
## 91 2025-09-01 Monday FALSE TRUE
## 92 2025-11-27 Thursday FALSE TRUE
## 93 2025-12-25 Thursday FALSE TRUE
## 94 2026-01-01 Thursday FALSE TRUE
## 95 2026-01-19 Monday FALSE TRUEtable(na_info$Holiday)##
## FALSE TRUE
## 23 72## drop missing values/na
na_dates <- djia_raw$DATE[is.na(djia_raw$DJIA)]
# Remove NA
djia_clean <- djia_raw[!is.na(djia_raw$DJIA), ]
# results
cat("Original rows :", nrow(djia_raw), "\n")## Original rows : 2610cat("Dropped rows :", length(na_dates), "\n")## Dropped rows : 95cat("Remaining rows:", nrow(djia_clean), "\n")## Remaining rows: 2515#table of number of obs after dropping NA/missing values
row_counts <- data.frame(
Dataset = c("Original DJIA", "Cleaned DJIA"),
Row_Count = c(nrow(djia_raw), nrow(djia_clean))
)
print(row_counts)## Dataset Row_Count
## 1 Original DJIA 2610
## 2 Cleaned DJIA 2515compare_summary <- data.frame(
Dataset = c("DJIA (original)", "DJIA (cleaned)",
"10-Year Treasury (GS10)", "Fed Funds Rate (FEDFUNDS)"),
Rows = c(nrow(djia_raw), nrow(djia_clean),
nrow(gs10_raw), nrow(effr_raw)),
NA_Count = c(sum(is.na(djia_raw$DJIA)),
sum(is.na(djia_clean$DJIA)),
sum(is.na(gs10_raw$GS10)),
sum(is.na(effr_raw$FEDFUNDS)))
)
print(compare_summary, row.names = FALSE)## Dataset Rows NA_Count
## DJIA (original) 2610 95
## DJIA (cleaned) 2515 0
## 10-Year Treasury (GS10) 300 0
## Fed Funds Rate (FEDFUNDS) 300 0## Collapse DJIA to one row per month (monthly average)
djia_monthly <- aggregate(DJIA ~ format(DATE, "%Y-%m"), data = djia_raw, FUN = mean, na.rm = TRUE)
# Fix column names
names(djia_monthly)[1] <- "YearMonth"
djia_monthly$DATE <- as.Date(paste0(djia_monthly$YearMonth, "-01"))
djia_monthly <- djia_monthly[, c("DATE", "DJIA")]
djia_monthly## DATE DJIA
## 1 2016-02-01 16295.80
## 2 2016-03-01 17302.14
## 3 2016-04-01 17844.37
## 4 2016-05-01 17692.32
## 5 2016-06-01 17754.87
## 6 2016-07-01 18341.18
## 7 2016-08-01 18495.19
## 8 2016-09-01 18267.40
## 9 2016-10-01 18184.55
## 10 2016-11-01 18697.33
## 11 2016-12-01 19712.42
## 12 2017-01-01 19908.15
## 13 2017-02-01 20424.14
## 14 2017-03-01 20823.06
## 15 2017-04-01 20684.69
## 16 2017-05-01 20936.81
## 17 2017-06-01 21317.80
## 18 2017-07-01 21581.25
## 19 2017-08-01 21914.08
## 20 2017-09-01 22173.41
## 21 2017-10-01 23036.24
## 22 2017-11-01 23557.93
## 23 2017-12-01 24545.38
## 24 2018-01-01 25804.02
## 25 2018-02-01 24981.55
## 26 2018-03-01 24582.17
## 27 2018-04-01 24304.21
## 28 2018-05-01 24572.53
## 29 2018-06-01 24790.11
## 30 2018-07-01 24978.23
## 31 2018-08-01 25629.99
## 32 2018-09-01 26232.67
## 33 2018-10-01 25609.34
## 34 2018-11-01 25258.68
## 35 2018-12-01 23805.55
## 36 2019-01-01 24157.80
## 37 2019-02-01 25605.53
## 38 2019-03-01 25722.62
## 39 2019-04-01 26401.58
## 40 2019-05-01 25744.79
## 41 2019-06-01 26160.10
## 42 2019-07-01 27089.19
## 43 2019-08-01 26058.22
## 44 2019-09-01 26900.21
## 45 2019-10-01 26736.80
## 46 2019-11-01 27797.05
## 47 2019-12-01 28167.01
## 48 2020-01-01 28879.99
## 49 2020-02-01 28519.73
## 50 2020-03-01 22637.42
## 51 2020-04-01 23293.90
## 52 2020-05-01 24271.02
## 53 2020-06-01 26062.27
## 54 2020-07-01 26385.83
## 55 2020-08-01 27821.37
## 56 2020-09-01 27733.40
## 57 2020-10-01 28005.10
## 58 2020-11-01 29124.04
## 59 2020-12-01 30148.58
## 60 2021-01-01 30821.35
## 61 2021-02-01 31283.91
## 62 2021-03-01 32373.29
## 63 2021-04-01 33803.29
## 64 2021-05-01 34270.31
## 65 2021-06-01 34289.91
## 66 2021-07-01 34798.80
## 67 2021-08-01 35243.97
## 68 2021-09-01 34688.42
## 69 2021-10-01 35055.52
## 70 2021-11-01 35848.57
## 71 2021-12-01 35641.33
## 72 2022-01-01 35456.15
## 73 2022-02-01 34648.48
## 74 2022-03-01 34029.74
## 75 2022-04-01 34314.99
## 76 2022-05-01 32379.46
## 77 2022-06-01 31446.71
## 78 2022-07-01 31535.32
## 79 2022-08-01 33009.56
## 80 2022-09-01 30649.56
## 81 2022-10-01 30570.68
## 82 2022-11-01 33417.96
## 83 2022-12-01 33482.26
## 84 2023-01-01 33656.00
## 85 2023-02-01 33648.26
## 86 2023-03-01 32483.48
## 87 2023-04-01 33731.28
## 88 2023-05-01 33316.80
## 89 2023-06-01 33904.98
## 90 2023-07-01 34777.20
## 91 2023-08-01 34880.66
## 92 2023-09-01 34318.89
## 93 2023-10-01 33319.49
## 94 2023-11-01 34704.50
## 95 2023-12-01 36947.93
## 96 2024-01-01 37763.95
## 97 2024-02-01 38720.52
## 98 2024-03-01 39105.52
## 99 2024-04-01 38401.22
## 100 2024-05-01 39129.39
## 101 2024-06-01 38903.73
## 102 2024-07-01 40086.03
## 103 2024-08-01 40310.81
## 104 2024-09-01 41490.88
## 105 2024-10-01 42494.20
## 106 2024-11-01 43716.93
## 107 2024-12-01 43655.57
## 108 2025-01-01 43524.35
## 109 2025-02-01 44209.31
## 110 2025-03-01 42092.13
## 111 2025-04-01 39876.33
## 112 2025-05-01 41864.25
## 113 2025-06-01 42752.94
## 114 2025-07-01 44499.74
## 115 2025-08-01 44764.85
## 116 2025-09-01 45908.10
## 117 2025-10-01 46709.67
## 118 2025-11-01 47016.37
## 119 2025-12-01 48118.87
## 120 2026-01-01 49138.27
## 121 2026-02-01 49434.87# line plot
plot(djia_monthly$DATE, djia_monthly$DJIA,
type = "l", lwd = 2, col = "blue",
xlab = "Year",
ylab = "DJIA (Monthly Avg)",
main = "DJIA Monthly Average (Past 10 Years)")
grid(nx = NA, ny = NULL, lty = "dotted")
# --- Restrict datasets across common date range ---
common_start <- max(min(djia_monthly$DATE, na.rm = TRUE),
min(gs10_monthly$DATE, na.rm = TRUE),
min(effr_monthly$DATE, na.rm = TRUE))
common_end <- min(max(djia_monthly$DATE, na.rm = TRUE),
max(gs10_monthly$DATE, na.rm = TRUE),
max(effr_monthly$DATE, na.rm = TRUE))
cat("Restricting all datasets to:", common_start, "to", common_end, "\n")## Restricting all datasets to: 16832 to 20454# restrict each dataset
djia_monthly <- subset(djia_monthly, DATE >= common_start & DATE <= common_end)
gs10_monthly <- subset(gs10_monthly, DATE >= common_start & DATE <= common_end)
effr_monthly <- subset(effr_monthly, DATE >= common_start & DATE <= common_end)
# --- Merge all three on DATE ---
merged_data <- merge(djia_monthly, gs10_monthly, by = "DATE", all = FALSE)
merged_data <- merge(merged_data, effr_monthly, by = "DATE", all = FALSE)
merged_data <- merged_data[order(merged_data$DATE), ]
# --- Min-Max Scaling Function (0–1) ---
minmax_scale <- function(x) (x - min(x, na.rm = TRUE)) /
(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
merged_data$DJIA_mm <- minmax_scale(merged_data$DJIA)
merged_data$GS10_mm <- minmax_scale(merged_data$GS10)
merged_data$FEDFUNDS_mm <- minmax_scale(merged_data$FEDFUNDS)
# --- Plot setup (suppress default x-axis for custom labels) ---
plot(merged_data$DATE, merged_data$DJIA_mm, type = "n",
xaxt = "n", # suppress default axis
xlab = "Year", ylab = "Min–Max Scaled (0–1)",
main = "Trifecta of Economic Indicators (Min–Max Scaled)")
# --- Custom x-axis with every year ---
years <- seq(from = as.integer(format(min(merged_data$DATE), "%Y")),
to = as.integer(format(max(merged_data$DATE), "%Y")),
by = 1)
axis(1, at = as.Date(paste0(years, "-01-01")), labels = years)
# --- Add shaded COVID recession (Feb–Apr 2020) ---
rect(as.Date("2020-02-01"), 0, as.Date("2020-04-30"), 1,
col = rgb(0.8, 0.8, 0.8, 0.5), border = NA)
# --- Add label for shaded region ---
text(as.Date("2020-06-01"), 0.9, "COVID recession", cex = 0.8, col = "black", adj = 0)
# --- Add the three lines ---
lines(merged_data$DATE, merged_data$DJIA_mm, lwd = 2, col = "orange", lty = 2)
lines(merged_data$DATE, merged_data$GS10_mm, lwd = 2, col = "black", lty = 3)
lines(merged_data$DATE, merged_data$FEDFUNDS_mm, lwd = 2, col = "lightblue", lty = 4)
# --- Legend moved to top left ---
legend("topleft",
legend = c("DJIA (Min–Max)", "10Y Treasury Yield (Min–Max)",
"Fed Funds Rate (Min–Max)"),
col = c("orange", "black", "lightblue"),
lwd = c(2, 2, 2), lty = c(2, 3, 4),
bty = "n", cex = 0.8)
# --- Grid ---
grid(nx = NA, ny = NULL, lty = "dotted")