DSC607 Module 1 Programming Assignment
Brett Orzechowski
5/17/2020
Select a Data File
Select a data file that has not already been preprocessed from the R data repository or of your own choosing:
Local Area Unemployment Statistics: Beginning 1976
The Local Area Unemployment Statistics program estimates labor force statistics (labor force, employed, unemployment, unemployment rate) for New York State civilian labor force aged 16 and up. Areas covered include, New York State, New York City, Balance of State, Metropolitan Statistical Areas, Counties, Labor Market Regions, Workforce Investment Board Areas, and cities and towns with populations of 25,000 or more. Data are not seasonally adjusted. Civilian labor force data do not include military, prison inmate, or other institutional populations.
However, for a 30-year snapshot, I removed data up until 1990, so the analyzed data is from 1990 to the last available data set for 2020, which is March.
Dataset
Local_Area_Unemployment_Statistics__Beginning_1976.csv Just the city of Albany 364 rows – monthly data with row one header Imported xls dataset, no missing cells Source: New York State Open Data Portal: https://data.ny.gov/
Import Data as a Data Frame into R Environment
df <- read.csv("C:/Users/bjorzech/Desktop/DSC607_Albany_City_Labor1990.csv",stringsAsFactors = FALSE)
(df)## Area Year Month Labor.Force Employed Unemployed Unemployment.Rate
## 1 Albany City 2020 3 46800 44700 2100 4.4
## 2 Albany City 2020 2 47100 45200 1900 4.1
## 3 Albany City 2020 1 47100 45000 2100 4.5
## 4 Albany City 2019 12 46600 44600 2000 4.3
## 5 Albany City 2019 11 47000 45000 2000 4.3
## 6 Albany City 2019 10 47100 45000 2100 4.4
## 7 Albany City 2019 9 46700 44600 2100 4.5
## 8 Albany City 2019 8 46700 44500 2300 4.9
## 9 Albany City 2019 7 46900 44500 2300 5.0
## 10 Albany City 2019 6 46900 44700 2200 4.7
## 11 Albany City 2019 5 46200 44300 1900 4.1
## 12 Albany City 2019 4 46000 44300 1800 3.8
## 13 Albany City 2019 3 46200 44200 1900 4.2
## 14 Albany City 2019 2 46100 44100 2000 4.4
## 15 Albany City 2019 1 46200 44000 2200 4.7
## 16 Albany City 2018 12 45900 44100 1800 3.9
## 17 Albany City 2018 11 46400 44700 1700 3.6
## 18 Albany City 2018 10 46800 45000 1800 3.8
## 19 Albany City 2018 9 46500 44600 1900 4.0
## 20 Albany City 2018 8 46800 44600 2200 4.6
## 21 Albany City 2018 7 47400 45100 2300 4.8
## 22 Albany City 2018 6 47600 45300 2300 4.9
## 23 Albany City 2018 5 46600 44800 1900 4.0
## 24 Albany City 2018 4 46300 44400 2000 4.2
## 25 Albany City 2018 3 46200 44100 2100 4.5
## 26 Albany City 2018 2 46000 43800 2300 5.0
## 27 Albany City 2018 1 45700 43300 2400 5.3
## 28 Albany City 2017 12 45900 43800 2100 4.6
## 29 Albany City 2017 11 46500 44200 2300 4.9
## 30 Albany City 2017 10 47000 44700 2300 4.9
## 31 Albany City 2017 9 47200 44800 2400 5.2
## 32 Albany City 2017 8 47400 44700 2700 5.6
## 33 Albany City 2017 7 47700 45000 2700 5.6
## 34 Albany City 2017 6 47800 45200 2600 5.5
## 35 Albany City 2017 5 46900 44600 2200 4.8
## 36 Albany City 2017 4 47000 44900 2100 4.5
## 37 Albany City 2017 3 46700 44600 2100 4.5
## 38 Albany City 2017 2 46300 43900 2400 5.1
## 39 Albany City 2017 1 46200 43800 2400 5.3
## 40 Albany City 2016 12 46200 44100 2100 4.5
## 41 Albany City 2016 11 46500 44500 2100 4.5
## 42 Albany City 2016 10 47000 44800 2300 4.8
## 43 Albany City 2016 9 46800 44400 2400 5.1
## 44 Albany City 2016 8 47300 44800 2500 5.3
## 45 Albany City 2016 7 47500 45000 2500 5.3
## 46 Albany City 2016 6 47600 45200 2400 5.1
## 47 Albany City 2016 5 46900 44900 2000 4.3
## 48 Albany City 2016 4 46900 44800 2100 4.4
## 49 Albany City 2016 3 46900 44800 2100 4.4
## 50 Albany City 2016 2 46900 44700 2200 4.8
## 51 Albany City 2016 1 46900 44500 2400 5.0
## 52 Albany City 2015 12 46700 44600 2100 4.5
## 53 Albany City 2015 11 46900 44700 2200 4.6
## 54 Albany City 2015 10 47200 44900 2300 4.8
## 55 Albany City 2015 9 46700 44400 2300 4.8
## 56 Albany City 2015 8 47300 44800 2500 5.4
## 57 Albany City 2015 7 47600 44900 2800 5.8
## 58 Albany City 2015 6 48100 45300 2800 5.7
## 59 Albany City 2015 5 47500 45000 2500 5.3
## 60 Albany City 2015 4 47000 44700 2300 4.9
## 61 Albany City 2015 3 46600 44400 2200 4.7
## 62 Albany City 2015 2 46600 44100 2500 5.3
## 63 Albany City 2015 1 46800 44100 2700 5.8
## 64 Albany City 2014 12 46200 44000 2300 4.9
## 65 Albany City 2014 11 46800 44300 2500 5.3
## 66 Albany City 2014 10 47100 44600 2500 5.3
## 67 Albany City 2014 9 46600 44000 2700 5.7
## 68 Albany City 2014 8 47300 44200 3000 6.4
## 69 Albany City 2014 7 47600 44500 3100 6.5
## 70 Albany City 2014 6 47900 45000 2900 6.1
## 71 Albany City 2014 5 47100 44500 2600 5.6
## 72 Albany City 2014 4 46700 44300 2400 5.1
## 73 Albany City 2014 3 47000 44200 2700 5.8
## 74 Albany City 2014 2 46900 43900 3000 6.4
## 75 Albany City 2014 1 47000 43900 3100 6.6
## 76 Albany City 2013 12 47100 44400 2800 5.9
## 77 Albany City 2013 11 47600 44700 2900 6.1
## 78 Albany City 2013 10 47800 44600 3200 6.7
## 79 Albany City 2013 9 48200 44800 3400 7.0
## 80 Albany City 2013 8 48800 45200 3600 7.4
## 81 Albany City 2013 7 49100 45400 3700 7.6
## 82 Albany City 2013 6 49600 45700 3900 7.8
## 83 Albany City 2013 5 48700 45300 3400 7.1
## 84 Albany City 2013 4 48100 45000 3100 6.5
## 85 Albany City 2013 3 47900 44500 3300 7.0
## 86 Albany City 2013 2 48000 44400 3600 7.6
## 87 Albany City 2013 1 48300 44200 4100 8.5
## 88 Albany City 2012 12 48100 44500 3600 7.4
## 89 Albany City 2012 11 48700 45200 3500 7.2
## 90 Albany City 2012 10 49100 45100 3900 8.0
## 91 Albany City 2012 9 48600 44600 4000 8.2
## 92 Albany City 2012 8 48900 44500 4400 8.9
## 93 Albany City 2012 7 49100 44500 4600 9.4
## 94 Albany City 2012 6 49500 44900 4600 9.2
## 95 Albany City 2012 5 48700 44500 4100 8.5
## 96 Albany City 2012 4 48200 44300 3800 8.0
## 97 Albany City 2012 3 47800 43800 4000 8.3
## 98 Albany City 2012 2 47800 43800 4100 8.5
## 99 Albany City 2012 1 47700 43500 4200 8.8
## 100 Albany City 2011 12 47400 43700 3700 7.9
## 101 Albany City 2011 11 47800 44100 3700 7.7
## 102 Albany City 2011 10 47900 44100 3800 7.9
## 103 Albany City 2011 9 47800 43700 4000 8.5
## 104 Albany City 2011 8 48200 44000 4200 8.7
## 105 Albany City 2011 7 48300 43900 4300 9.0
## 106 Albany City 2011 6 48200 44000 4200 8.8
## 107 Albany City 2011 5 47600 43900 3700 7.7
## 108 Albany City 2011 4 47600 44000 3600 7.5
## 109 Albany City 2011 3 47400 43800 3600 7.7
## 110 Albany City 2011 2 47400 43600 3800 8.0
## 111 Albany City 2011 1 47600 43400 4100 8.7
## 112 Albany City 2010 12 47700 43900 3800 8.0
## 113 Albany City 2010 11 48200 44200 4000 8.2
## 114 Albany City 2010 10 48300 44400 4000 8.2
## 115 Albany City 2010 9 48700 44600 4100 8.4
## 116 Albany City 2010 8 49400 45200 4200 8.5
## 117 Albany City 2010 7 49500 45200 4300 8.7
## 118 Albany City 2010 6 49700 45500 4200 8.5
## 119 Albany City 2010 5 49100 45200 3900 7.9
## 120 Albany City 2010 4 49100 45300 3700 7.6
## 121 Albany City 2010 3 48300 44600 3700 7.7
## 122 Albany City 2010 2 48400 44400 4000 8.2
## 123 Albany City 2010 1 48500 44200 4300 8.9
## 124 Albany City 2009 12 45700 42000 3700 8.1
## 125 Albany City 2009 11 46600 42800 3800 8.1
## 126 Albany City 2009 10 46700 42800 4000 8.5
## 127 Albany City 2009 9 46900 42800 4100 8.7
## 128 Albany City 2009 8 47800 43700 4100 8.6
## 129 Albany City 2009 7 48000 43900 4100 8.5
## 130 Albany City 2009 6 48300 44200 4100 8.4
## 131 Albany City 2009 5 47200 43600 3600 7.6
## 132 Albany City 2009 4 47100 43800 3300 7.0
## 133 Albany City 2009 3 46600 43300 3300 7.1
## 134 Albany City 2009 2 46900 43400 3400 7.3
## 135 Albany City 2009 1 46900 43400 3400 7.3
## 136 Albany City 2008 12 46800 44000 2800 6.0
## 137 Albany City 2008 11 47300 44600 2700 5.7
## 138 Albany City 2008 10 47600 44800 2800 5.8
## 139 Albany City 2008 9 47300 44500 2900 6.1
## 140 Albany City 2008 8 48100 45100 3000 6.1
## 141 Albany City 2008 7 48200 45300 2900 6.1
## 142 Albany City 2008 6 48200 45300 2800 5.9
## 143 Albany City 2008 5 47300 44700 2600 5.5
## 144 Albany City 2008 4 46700 44400 2200 4.8
## 145 Albany City 2008 3 46400 44000 2400 5.2
## 146 Albany City 2008 2 46300 43900 2400 5.3
## 147 Albany City 2008 1 46500 43900 2600 5.6
## 148 Albany City 2007 12 46600 44500 2100 4.6
## 149 Albany City 2007 11 47000 44900 2100 4.5
## 150 Albany City 2007 10 46700 44600 2100 4.5
## 151 Albany City 2007 9 46700 44500 2200 4.7
## 152 Albany City 2007 8 47200 44900 2300 4.9
## 153 Albany City 2007 7 47500 45100 2400 5.1
## 154 Albany City 2007 6 47500 45200 2300 4.8
## 155 Albany City 2007 5 46400 44400 2000 4.2
## 156 Albany City 2007 4 46100 44200 1900 4.1
## 157 Albany City 2007 3 46300 44400 1900 4.1
## 158 Albany City 2007 2 46300 44200 2100 4.5
## 159 Albany City 2007 1 46600 44300 2300 4.9
## 160 Albany City 2006 12 46800 45000 1800 3.8
## 161 Albany City 2006 11 47000 45100 2000 4.2
## 162 Albany City 2006 10 47100 45200 1900 4.1
## 163 Albany City 2006 9 46900 44700 2200 4.7
## 164 Albany City 2006 8 47800 45500 2300 4.8
## 165 Albany City 2006 7 48100 45600 2500 5.1
## 166 Albany City 2006 6 48000 45600 2400 5.0
## 167 Albany City 2006 5 47100 45000 2100 4.5
## 168 Albany City 2006 4 47000 44900 2100 4.5
## 169 Albany City 2006 3 46900 44700 2100 4.6
## 170 Albany City 2006 2 46700 44600 2200 4.6
## 171 Albany City 2006 1 46600 44400 2200 4.8
## 172 Albany City 2005 12 46600 44700 1900 4.0
## 173 Albany City 2005 11 46900 44800 2100 4.5
## 174 Albany City 2005 10 47200 45100 2000 4.3
## 175 Albany City 2005 9 47000 44700 2300 4.8
## 176 Albany City 2005 8 47900 45700 2200 4.5
## 177 Albany City 2005 7 48000 45600 2400 5.0
## 178 Albany City 2005 6 47500 45200 2300 4.8
## 179 Albany City 2005 5 46700 44600 2100 4.5
## 180 Albany City 2005 4 46400 44400 2000 4.2
## 181 Albany City 2005 3 45900 44000 2000 4.3
## 182 Albany City 2005 2 46000 43900 2100 4.6
## 183 Albany City 2005 1 46300 44000 2300 5.0
## 184 Albany City 2004 12 46700 44600 2100 4.4
## 185 Albany City 2004 11 46900 44800 2100 4.5
## 186 Albany City 2004 10 46800 44700 2100 4.4
## 187 Albany City 2004 9 46600 44500 2200 4.6
## 188 Albany City 2004 8 47700 45400 2300 4.7
## 189 Albany City 2004 7 48000 45500 2500 5.2
## 190 Albany City 2004 6 47600 45100 2500 5.2
## 191 Albany City 2004 5 46800 44600 2200 4.8
## 192 Albany City 2004 4 46500 44300 2200 4.8
## 193 Albany City 2004 3 46400 43900 2500 5.4
## 194 Albany City 2004 2 46600 44000 2500 5.4
## 195 Albany City 2004 1 46700 44000 2700 5.7
## 196 Albany City 2003 12 46600 44300 2200 4.8
## 197 Albany City 2003 11 46800 44500 2300 4.9
## 198 Albany City 2003 10 46700 44400 2300 4.9
## 199 Albany City 2003 9 46500 44100 2400 5.1
## 200 Albany City 2003 8 47500 45100 2400 5.0
## 201 Albany City 2003 7 47600 45000 2500 5.3
## 202 Albany City 2003 6 47500 45000 2500 5.3
## 203 Albany City 2003 5 46700 44500 2200 4.7
## 204 Albany City 2003 4 46500 44500 2000 4.3
## 205 Albany City 2003 3 46200 44200 2100 4.4
## 206 Albany City 2003 2 46500 44300 2200 4.8
## 207 Albany City 2003 1 46400 44100 2400 5.1
## 208 Albany City 2002 12 46400 44500 2000 4.2
## 209 Albany City 2002 11 46600 44600 2000 4.3
## 210 Albany City 2002 10 46900 45000 1900 4.0
## 211 Albany City 2002 9 47100 45100 2000 4.2
## 212 Albany City 2002 8 47800 45600 2100 4.5
## 213 Albany City 2002 7 47600 45300 2300 4.8
## 214 Albany City 2002 6 47500 45200 2200 4.7
## 215 Albany City 2002 5 46700 44600 2100 4.5
## 216 Albany City 2002 4 46600 44500 2100 4.5
## 217 Albany City 2002 3 46200 44100 2100 4.6
## 218 Albany City 2002 2 46300 44000 2200 4.8
## 219 Albany City 2002 1 45500 43300 2200 4.9
## 220 Albany City 2001 12 46300 44400 1900 4.0
## 221 Albany City 2001 11 46300 44500 1800 4.0
## 222 Albany City 2001 10 46300 44500 1700 3.8
## 223 Albany City 2001 9 46000 44200 1800 3.9
## 224 Albany City 2001 8 46600 44800 1800 4.0
## 225 Albany City 2001 7 46900 45000 1900 4.0
## 226 Albany City 2001 6 46700 44900 1800 3.8
## 227 Albany City 2001 5 45800 44300 1600 3.4
## 228 Albany City 2001 4 45600 44200 1500 3.2
## 229 Albany City 2001 3 45700 44100 1600 3.4
## 230 Albany City 2001 2 45700 43900 1700 3.8
## 231 Albany City 2001 1 45800 44000 1900 4.0
## 232 Albany City 2000 12 46000 44500 1500 3.3
## 233 Albany City 2000 11 45800 44200 1600 3.4
## 234 Albany City 2000 10 45800 44200 1600 3.5
## 235 Albany City 2000 9 45500 43800 1800 3.9
## 236 Albany City 2000 8 46500 44700 1800 3.9
## 237 Albany City 2000 7 46500 44700 1900 4.0
## 238 Albany City 2000 6 46700 45000 1700 3.7
## 239 Albany City 2000 5 45600 44000 1600 3.6
## 240 Albany City 2000 4 45700 44200 1500 3.4
## 241 Albany City 2000 3 45700 43900 1800 3.9
## 242 Albany City 2000 2 45900 44000 1900 4.1
## 243 Albany City 2000 1 45900 44000 1900 4.1
## 244 Albany City 1999 12 53200 51200 2000 3.7
## 245 Albany City 1999 11 53400 51400 2000 3.7
## 246 Albany City 1999 10 53300 51200 2100 4.0
## 247 Albany City 1999 9 53400 51000 2300 4.4
## 248 Albany City 1999 8 53800 51800 2000 3.7
## 249 Albany City 1999 7 54000 51900 2200 4.0
## 250 Albany City 1999 6 54300 52200 2100 3.8
## 251 Albany City 1999 5 53400 51400 2000 3.7
## 252 Albany City 1999 4 53200 51200 2000 3.8
## 253 Albany City 1999 3 53200 50900 2200 4.2
## 254 Albany City 1999 2 53300 50900 2400 4.5
## 255 Albany City 1999 1 53400 51000 2400 4.4
## 256 Albany City 1998 12 53600 51700 1900 3.5
## 257 Albany City 1998 11 53800 51900 1900 3.5
## 258 Albany City 1998 10 53600 51500 2100 3.9
## 259 Albany City 1998 9 53600 51400 2200 4.2
## 260 Albany City 1998 8 54300 52300 2000 3.7
## 261 Albany City 1998 7 54600 52500 2100 3.8
## 262 Albany City 1998 6 54700 52400 2200 4.1
## 263 Albany City 1998 5 54000 51700 2300 4.3
## 264 Albany City 1998 4 53500 51400 2100 3.9
## 265 Albany City 1998 3 53500 50900 2600 4.9
## 266 Albany City 1998 2 53800 51000 2900 5.3
## 267 Albany City 1998 1 53600 50700 2900 5.3
## 268 Albany City 1997 12 53900 51500 2400 4.4
## 269 Albany City 1997 11 54100 51700 2400 4.4
## 270 Albany City 1997 10 53800 51300 2500 4.7
## 271 Albany City 1997 9 53700 51000 2700 5.0
## 272 Albany City 1997 8 54800 52300 2500 4.5
## 273 Albany City 1997 7 54800 52300 2500 4.6
## 274 Albany City 1997 6 54500 52000 2500 4.5
## 275 Albany City 1997 5 53700 51400 2300 4.3
## 276 Albany City 1997 4 53300 51100 2200 4.2
## 277 Albany City 1997 3 53500 50900 2600 4.8
## 278 Albany City 1997 2 53100 50300 2800 5.3
## 279 Albany City 1997 1 52600 49700 2800 5.4
## 280 Albany City 1996 12 52800 50500 2400 4.5
## 281 Albany City 1996 11 53300 50800 2500 4.7
## 282 Albany City 1996 10 53200 50600 2600 4.8
## 283 Albany City 1996 9 53100 50200 2800 5.4
## 284 Albany City 1996 8 53800 51100 2700 4.9
## 285 Albany City 1996 7 53900 51000 3000 5.5
## 286 Albany City 1996 6 53700 50800 2900 5.4
## 287 Albany City 1996 5 52900 50000 2900 5.4
## 288 Albany City 1996 4 52600 50100 2500 4.8
## 289 Albany City 1996 3 52800 49900 2900 5.5
## 290 Albany City 1996 2 52700 49700 3000 5.7
## 291 Albany City 1996 1 52400 49200 3200 6.1
## 292 Albany City 1995 12 52700 49800 2900 5.4
## 293 Albany City 1995 11 52900 50100 2800 5.4
## 294 Albany City 1995 10 53200 50100 3100 5.9
## 295 Albany City 1995 9 53200 49700 3500 6.6
## 296 Albany City 1995 8 54200 50800 3300 6.1
## 297 Albany City 1995 7 54500 51300 3300 6.0
## 298 Albany City 1995 6 54200 51000 3200 6.0
## 299 Albany City 1995 5 53500 50400 3100 5.9
## 300 Albany City 1995 4 53700 50800 2900 5.5
## 301 Albany City 1995 3 53600 50600 3000 5.6
## 302 Albany City 1995 2 53800 50700 3100 5.7
## 303 Albany City 1995 1 53600 50400 3200 6.0
## 304 Albany City 1994 12 53000 50300 2700 5.0
## 305 Albany City 1994 11 53300 50500 2800 5.2
## 306 Albany City 1994 10 53400 50500 2900 5.4
## 307 Albany City 1994 9 52900 49900 3100 5.8
## 308 Albany City 1994 8 54200 51300 3000 5.4
## 309 Albany City 1994 7 54400 51200 3200 5.9
## 310 Albany City 1994 6 53600 50700 3000 5.5
## 311 Albany City 1994 5 52900 50000 3000 5.6
## 312 Albany City 1994 4 52400 49500 3000 5.7
## 313 Albany City 1994 3 52600 49400 3300 6.2
## 314 Albany City 1994 2 53300 49700 3500 6.6
## 315 Albany City 1994 1 53000 49400 3500 6.6
## 316 Albany City 1993 12 53000 50000 3000 5.6
## 317 Albany City 1993 11 53000 50100 2900 5.5
## 318 Albany City 1993 10 53100 50000 3100 5.9
## 319 Albany City 1993 9 52600 49400 3200 6.1
## 320 Albany City 1993 8 54200 51100 3100 5.7
## 321 Albany City 1993 7 54400 51300 3100 5.6
## 322 Albany City 1993 6 54200 51100 3100 5.7
## 323 Albany City 1993 5 53200 50100 3100 5.9
## 324 Albany City 1993 4 52200 49300 2800 5.4
## 325 Albany City 1993 3 52500 49400 3100 5.9
## 326 Albany City 1993 2 52400 49000 3500 6.6
## 327 Albany City 1993 1 52400 48700 3700 7.0
## 328 Albany City 1992 12 53200 49700 3400 6.4
## 329 Albany City 1992 11 53300 49800 3500 6.5
## 330 Albany City 1992 10 53300 49900 3500 6.5
## 331 Albany City 1992 9 53100 49400 3700 7.0
## 332 Albany City 1992 8 54600 51000 3600 6.5
## 333 Albany City 1992 7 54800 51000 3800 6.8
## 334 Albany City 1992 6 54200 50400 3800 6.9
## 335 Albany City 1992 5 52800 49200 3600 6.8
## 336 Albany City 1992 4 52500 49000 3500 6.6
## 337 Albany City 1992 3 52700 48800 3900 7.3
## 338 Albany City 1992 2 52700 48500 4200 8.0
## 339 Albany City 1992 1 52700 48400 4300 8.2
## 340 Albany City 1991 12 52700 49000 3700 7.1
## 341 Albany City 1991 11 53400 49600 3800 7.1
## 342 Albany City 1991 10 53400 49900 3500 6.6
## 343 Albany City 1991 9 53300 49700 3700 6.9
## 344 Albany City 1991 8 54600 51000 3500 6.5
## 345 Albany City 1991 7 55000 51400 3600 6.5
## 346 Albany City 1991 6 54800 51200 3600 6.5
## 347 Albany City 1991 5 53200 49800 3400 6.3
## 348 Albany City 1991 4 53600 50200 3400 6.3
## 349 Albany City 1991 3 53400 49900 3500 6.6
## 350 Albany City 1991 2 53300 49800 3500 6.6
## 351 Albany City 1991 1 53200 49900 3300 6.2
## 352 Albany City 1990 12 53200 50600 2600 5.0
## 353 Albany City 1990 11 53200 50700 2500 4.7
## 354 Albany City 1990 10 53300 51100 2200 4.2
## 355 Albany City 1990 9 53100 50800 2300 4.3
## 356 Albany City 1990 8 54300 52300 2100 3.8
## 357 Albany City 1990 7 54700 52500 2100 3.9
## 358 Albany City 1990 6 54300 52400 1900 3.5
## 359 Albany City 1990 5 53200 51200 2000 3.7
## 360 Albany City 1990 4 52600 50700 1900 3.5
## 361 Albany City 1990 3 52600 50400 2200 4.3
## 362 Albany City 1990 2 52500 50000 2400 4.7
## 363 Albany City 1990 1 52400 49900 2400 4.6Generate basic information about the data set or summary statistics. Here are a few of the many functions available in R for this purpose: summary(); str(); head().
Summary
summary(df)## Area Year Month Labor.Force
## Length:363 Min. :1990 Min. : 1.000 Min. :45500
## Class :character 1st Qu.:1997 1st Qu.: 3.000 1st Qu.:46700
## Mode :character Median :2005 Median : 6.000 Median :47600
## Mean :2005 Mean : 6.463 Mean :49197
## 3rd Qu.:2012 3rd Qu.: 9.000 3rd Qu.:53000
## Max. :2020 Max. :12.000 Max. :55000
## Employed Unemployed Unemployment.Rate
## Min. :42000 Min. :1500 Min. :3.200
## 1st Qu.:44300 1st Qu.:2100 1st Qu.:4.400
## Median :44900 Median :2500 Median :5.000
## Mean :46507 Mean :2690 Mean :5.454
## 3rd Qu.:49900 3rd Qu.:3200 3rd Qu.:6.200
## Max. :52500 Max. :4600 Max. :9.400Structure
str(df)## 'data.frame': 363 obs. of 7 variables:
## $ Area : chr "Albany City" "Albany City" "Albany City" "Albany City" ...
## $ Year : int 2020 2020 2020 2019 2019 2019 2019 2019 2019 2019 ...
## $ Month : int 3 2 1 12 11 10 9 8 7 6 ...
## $ Labor.Force : int 46800 47100 47100 46600 47000 47100 46700 46700 46900 46900 ...
## $ Employed : int 44700 45200 45000 44600 45000 45000 44600 44500 44500 44700 ...
## $ Unemployed : int 2100 1900 2100 2000 2000 2100 2100 2300 2300 2200 ...
## $ Unemployment.Rate: num 4.4 4.1 4.5 4.3 4.3 4.4 4.5 4.9 5 4.7 ...Head
head(df)## Area Year Month Labor.Force Employed Unemployed Unemployment.Rate
## 1 Albany City 2020 3 46800 44700 2100 4.4
## 2 Albany City 2020 2 47100 45200 1900 4.1
## 3 Albany City 2020 1 47100 45000 2100 4.5
## 4 Albany City 2019 12 46600 44600 2000 4.3
## 5 Albany City 2019 11 47000 45000 2000 4.3
## 6 Albany City 2019 10 47100 45000 2100 4.4Perform at Least Two Data Preparation Tasks
Aggregate
aggregate(Unemployment.Rate~Labor.Force, df, mean)## Labor.Force Unemployment.Rate
## 1 45500 4.400000
## 2 45600 3.400000
## 3 45700 4.650000
## 4 45800 3.575000
## 5 45900 4.200000
## 6 46000 4.120000
## 7 46100 4.250000
## 8 46200 4.577778
## 9 46300 4.480000
## 10 46400 4.557143
## 11 46500 4.590000
## 12 46600 5.005882
## 13 46700 4.821053
## 14 46800 4.809091
## 15 46900 5.157895
## 16 47000 4.890909
## 17 47100 4.960000
## 18 47200 5.360000
## 19 47300 5.733333
## 20 47400 6.800000
## 21 47500 5.037500
## 22 47600 6.116667
## 23 47700 6.775000
## 24 47800 7.011111
## 25 47900 6.375000
## 26 48000 6.260000
## 27 48100 6.160000
## 28 48200 7.528571
## 29 48300 8.360000
## 30 48400 8.200000
## 31 48500 8.900000
## 32 48600 8.200000
## 33 48700 7.800000
## 34 48800 7.400000
## 35 48900 8.900000
## 36 49100 8.100000
## 37 49400 8.500000
## 38 49500 8.950000
## 39 49600 7.800000
## 40 49700 8.500000
## 41 52200 5.400000
## 42 52400 6.000000
## 43 52500 5.733333
## 44 52600 5.050000
## 45 52700 6.950000
## 46 52800 5.600000
## 47 52900 5.550000
## 48 53000 5.675000
## 49 53100 5.580000
## 50 53200 5.169231
## 51 53300 5.445455
## 52 53400 5.237500
## 53 53500 4.875000
## 54 53600 5.037500
## 55 53700 5.050000
## 56 53800 4.633333
## 57 53900 4.950000
## 58 54000 4.150000
## 59 54100 4.400000
## 60 54200 5.966667
## 61 54300 3.700000
## 62 54400 5.750000
## 63 54500 5.250000
## 64 54600 5.600000
## 65 54700 4.000000
## 66 54800 5.600000
## 67 55000 6.500000Why It Would Be Necessary or Useful: When aggregating data, replacing groups of observations with summary statistics based on those observations offers a more holistic view of longitudinal data. If the user wishes to dive into more granular findings, the aggregate data offers a staring point.
Scale
scale(df$Unemployed)## [,1]
## [1,] -0.81215571
## [2,] -1.08729572
## [3,] -0.81215571
## [4,] -0.94972572
## [5,] -0.94972572
## [6,] -0.81215571
## [7,] -0.81215571
## [8,] -0.53701570
## [9,] -0.53701570
## [10,] -0.67458571
## [11,] -1.08729572
## [12,] -1.22486573
## [13,] -1.08729572
## [14,] -0.94972572
## [15,] -0.67458571
## [16,] -1.22486573
## [17,] -1.36243574
## [18,] -1.22486573
## [19,] -1.08729572
## [20,] -0.67458571
## [21,] -0.53701570
## [22,] -0.53701570
## [23,] -1.08729572
## [24,] -0.94972572
## [25,] -0.81215571
## [26,] -0.53701570
## [27,] -0.39944569
## [28,] -0.81215571
## [29,] -0.53701570
## [30,] -0.53701570
## [31,] -0.39944569
## [32,] 0.01326433
## [33,] 0.01326433
## [34,] -0.12430568
## [35,] -0.67458571
## [36,] -0.81215571
## [37,] -0.81215571
## [38,] -0.39944569
## [39,] -0.39944569
## [40,] -0.81215571
## [41,] -0.81215571
## [42,] -0.53701570
## [43,] -0.39944569
## [44,] -0.26187569
## [45,] -0.26187569
## [46,] -0.39944569
## [47,] -0.94972572
## [48,] -0.81215571
## [49,] -0.81215571
## [50,] -0.67458571
## [51,] -0.39944569
## [52,] -0.81215571
## [53,] -0.67458571
## [54,] -0.53701570
## [55,] -0.53701570
## [56,] -0.26187569
## [57,] 0.15083433
## [58,] 0.15083433
## [59,] -0.26187569
## [60,] -0.53701570
## [61,] -0.67458571
## [62,] -0.26187569
## [63,] 0.01326433
## [64,] -0.53701570
## [65,] -0.26187569
## [66,] -0.26187569
## [67,] 0.01326433
## [68,] 0.42597434
## [69,] 0.56354435
## [70,] 0.28840434
## [71,] -0.12430568
## [72,] -0.39944569
## [73,] 0.01326433
## [74,] 0.42597434
## [75,] 0.56354435
## [76,] 0.15083433
## [77,] 0.28840434
## [78,] 0.70111436
## [79,] 0.97625437
## [80,] 1.25139438
## [81,] 1.38896439
## [82,] 1.66410440
## [83,] 0.97625437
## [84,] 0.56354435
## [85,] 0.83868436
## [86,] 1.25139438
## [87,] 1.93924441
## [88,] 1.25139438
## [89,] 1.11382438
## [90,] 1.66410440
## [91,] 1.80167441
## [92,] 2.35195443
## [93,] 2.62709444
## [94,] 2.62709444
## [95,] 1.93924441
## [96,] 1.52653439
## [97,] 1.80167441
## [98,] 1.93924441
## [99,] 2.07681442
## [100,] 1.38896439
## [101,] 1.38896439
## [102,] 1.52653439
## [103,] 1.80167441
## [104,] 2.07681442
## [105,] 2.21438443
## [106,] 2.07681442
## [107,] 1.38896439
## [108,] 1.25139438
## [109,] 1.25139438
## [110,] 1.52653439
## [111,] 1.93924441
## [112,] 1.52653439
## [113,] 1.80167441
## [114,] 1.80167441
## [115,] 1.93924441
## [116,] 2.07681442
## [117,] 2.21438443
## [118,] 2.07681442
## [119,] 1.66410440
## [120,] 1.38896439
## [121,] 1.38896439
## [122,] 1.80167441
## [123,] 2.21438443
## [124,] 1.38896439
## [125,] 1.52653439
## [126,] 1.80167441
## [127,] 1.93924441
## [128,] 1.93924441
## [129,] 1.93924441
## [130,] 1.93924441
## [131,] 1.25139438
## [132,] 0.83868436
## [133,] 0.83868436
## [134,] 0.97625437
## [135,] 0.97625437
## [136,] 0.15083433
## [137,] 0.01326433
## [138,] 0.15083433
## [139,] 0.28840434
## [140,] 0.42597434
## [141,] 0.28840434
## [142,] 0.15083433
## [143,] -0.12430568
## [144,] -0.67458571
## [145,] -0.39944569
## [146,] -0.39944569
## [147,] -0.12430568
## [148,] -0.81215571
## [149,] -0.81215571
## [150,] -0.81215571
## [151,] -0.67458571
## [152,] -0.53701570
## [153,] -0.39944569
## [154,] -0.53701570
## [155,] -0.94972572
## [156,] -1.08729572
## [157,] -1.08729572
## [158,] -0.81215571
## [159,] -0.53701570
## [160,] -1.22486573
## [161,] -0.94972572
## [162,] -1.08729572
## [163,] -0.67458571
## [164,] -0.53701570
## [165,] -0.26187569
## [166,] -0.39944569
## [167,] -0.81215571
## [168,] -0.81215571
## [169,] -0.81215571
## [170,] -0.67458571
## [171,] -0.67458571
## [172,] -1.08729572
## [173,] -0.81215571
## [174,] -0.94972572
## [175,] -0.53701570
## [176,] -0.67458571
## [177,] -0.39944569
## [178,] -0.53701570
## [179,] -0.81215571
## [180,] -0.94972572
## [181,] -0.94972572
## [182,] -0.81215571
## [183,] -0.53701570
## [184,] -0.81215571
## [185,] -0.81215571
## [186,] -0.81215571
## [187,] -0.67458571
## [188,] -0.53701570
## [189,] -0.26187569
## [190,] -0.26187569
## [191,] -0.67458571
## [192,] -0.67458571
## [193,] -0.26187569
## [194,] -0.26187569
## [195,] 0.01326433
## [196,] -0.67458571
## [197,] -0.53701570
## [198,] -0.53701570
## [199,] -0.39944569
## [200,] -0.39944569
## [201,] -0.26187569
## [202,] -0.26187569
## [203,] -0.67458571
## [204,] -0.94972572
## [205,] -0.81215571
## [206,] -0.67458571
## [207,] -0.39944569
## [208,] -0.94972572
## [209,] -0.94972572
## [210,] -1.08729572
## [211,] -0.94972572
## [212,] -0.81215571
## [213,] -0.53701570
## [214,] -0.67458571
## [215,] -0.81215571
## [216,] -0.81215571
## [217,] -0.81215571
## [218,] -0.67458571
## [219,] -0.67458571
## [220,] -1.08729572
## [221,] -1.22486573
## [222,] -1.36243574
## [223,] -1.22486573
## [224,] -1.22486573
## [225,] -1.08729572
## [226,] -1.22486573
## [227,] -1.50000574
## [228,] -1.63757575
## [229,] -1.50000574
## [230,] -1.36243574
## [231,] -1.08729572
## [232,] -1.63757575
## [233,] -1.50000574
## [234,] -1.50000574
## [235,] -1.22486573
## [236,] -1.22486573
## [237,] -1.08729572
## [238,] -1.36243574
## [239,] -1.50000574
## [240,] -1.63757575
## [241,] -1.22486573
## [242,] -1.08729572
## [243,] -1.08729572
## [244,] -0.94972572
## [245,] -0.94972572
## [246,] -0.81215571
## [247,] -0.53701570
## [248,] -0.94972572
## [249,] -0.67458571
## [250,] -0.81215571
## [251,] -0.94972572
## [252,] -0.94972572
## [253,] -0.67458571
## [254,] -0.39944569
## [255,] -0.39944569
## [256,] -1.08729572
## [257,] -1.08729572
## [258,] -0.81215571
## [259,] -0.67458571
## [260,] -0.94972572
## [261,] -0.81215571
## [262,] -0.67458571
## [263,] -0.53701570
## [264,] -0.81215571
## [265,] -0.12430568
## [266,] 0.28840434
## [267,] 0.28840434
## [268,] -0.39944569
## [269,] -0.39944569
## [270,] -0.26187569
## [271,] 0.01326433
## [272,] -0.26187569
## [273,] -0.26187569
## [274,] -0.26187569
## [275,] -0.53701570
## [276,] -0.67458571
## [277,] -0.12430568
## [278,] 0.15083433
## [279,] 0.15083433
## [280,] -0.39944569
## [281,] -0.26187569
## [282,] -0.12430568
## [283,] 0.15083433
## [284,] 0.01326433
## [285,] 0.42597434
## [286,] 0.28840434
## [287,] 0.28840434
## [288,] -0.26187569
## [289,] 0.28840434
## [290,] 0.42597434
## [291,] 0.70111436
## [292,] 0.28840434
## [293,] 0.15083433
## [294,] 0.56354435
## [295,] 1.11382438
## [296,] 0.83868436
## [297,] 0.83868436
## [298,] 0.70111436
## [299,] 0.56354435
## [300,] 0.28840434
## [301,] 0.42597434
## [302,] 0.56354435
## [303,] 0.70111436
## [304,] 0.01326433
## [305,] 0.15083433
## [306,] 0.28840434
## [307,] 0.56354435
## [308,] 0.42597434
## [309,] 0.70111436
## [310,] 0.42597434
## [311,] 0.42597434
## [312,] 0.42597434
## [313,] 0.83868436
## [314,] 1.11382438
## [315,] 1.11382438
## [316,] 0.42597434
## [317,] 0.28840434
## [318,] 0.56354435
## [319,] 0.70111436
## [320,] 0.56354435
## [321,] 0.56354435
## [322,] 0.56354435
## [323,] 0.56354435
## [324,] 0.15083433
## [325,] 0.56354435
## [326,] 1.11382438
## [327,] 1.38896439
## [328,] 0.97625437
## [329,] 1.11382438
## [330,] 1.11382438
## [331,] 1.38896439
## [332,] 1.25139438
## [333,] 1.52653439
## [334,] 1.52653439
## [335,] 1.25139438
## [336,] 1.11382438
## [337,] 1.66410440
## [338,] 2.07681442
## [339,] 2.21438443
## [340,] 1.38896439
## [341,] 1.52653439
## [342,] 1.11382438
## [343,] 1.38896439
## [344,] 1.11382438
## [345,] 1.25139438
## [346,] 1.25139438
## [347,] 0.97625437
## [348,] 0.97625437
## [349,] 1.11382438
## [350,] 1.11382438
## [351,] 0.83868436
## [352,] -0.12430568
## [353,] -0.26187569
## [354,] -0.67458571
## [355,] -0.53701570
## [356,] -0.81215571
## [357,] -0.81215571
## [358,] -1.08729572
## [359,] -0.94972572
## [360,] -1.08729572
## [361,] -0.67458571
## [362,] -0.39944569
## [363,] -0.39944569
## attr(,"scaled:center")
## [1] 2690.358
## attr(,"scaled:scale")
## [1] 726.9026Why It Would Be Necessary or Useful: The scale function is used to determine standardized values for each element in a data set. This can be used in clustering and regression analysis. When you standardize the data, it’s important to create standard deviations from the mean. Overall, z-scores offer a “truer” analytical view simply because of too much reliance on the simple mean. Standard deviations also allow stronger clustering models with much larger datasets than the one provided here.
Use Any Three Plots
Bar
barplot(df$Unemployment.Rate, main = "Albany Unemployment Rate (1990-2020)", xlab = "Year", ylab = "%", las=1)
Briefly describe what each visualization reveals and how it may be used to conduct additional analyses. Over a 30-year period, bar charts are an effective method of visualizing spikes in performance, especially with employment or any economic data in a longitudinal fashion. Here, save for one spike 10 years ago, the Albany employment data appeared steady for an extended period as time, as most employment data follow 10-year periods due to recessionary factors.
Boxplot
boxplot(df$Employed, main="Albany Employment Level (1990-2020)", ylim = c(42000, 52000), ylab = "Level", probs = c(0, 0.25, 0.5, 0.75, 1)) 
Briefly describe what each visualization reveals and how it may be used to conduct additional analyses. Boxplots offer the end user some guidance in terms of understanding how the mean is often misinterpreted as a sliver of insight while often it shows some variablity. In a box-and-whisker setting, the upper and lower bounds offer a “truer” depiction of volatility, especially with employment data.
Scatter Plot
plot(df$Year, df$Unemployment.Rate, main="Albany: Month and Unemployment Rate Relationship (1990-2020)", xlab = "Year", ylab = "Unemployment Rate", las=1, cex=0.5, pch=8, col=4)
abline(lm(df$Unemployment.Rate~df$Year))
abline(lm(df$Unemployment.Rate~df$Year), col=6)
lines(smooth.spline(df$Unemployment.Rate~df$Year))
lines(smooth.spline(df$Unemployment.Rate~df$Year), lwd=5)
Briefly describe what each visualization reveals and how it may be used to conduct additional analyses. With 30 years of employment data, a scatter plot offers longitudinal evidence of performance, but also ensures the end-user understands seasonality in the more commonly used 12-month period each year. This offers the end-user two views – longitudinal and monthly. These indicators are often used as benchmarks to gauge economic performance.
Run a (Generalized) Linear Model
model1 <- glm(df$Year~df$Unemployment.Rate)
model1##
## Call: glm(formula = df$Year ~ df$Unemployment.Rate)
##
## Coefficients:
## (Intercept) df$Unemployment.Rate
## 2000.7318 0.7144
##
## Degrees of Freedom: 362 Total (i.e. Null); 361 Residual
## Null Deviance: 27680
## Residual Deviance: 27310 AIC: 2605GENERALIZED LINEAR MODEL
The general linear model (GLM) was run to assess whether unemployment rate (indepedent variable) inpacts the yearly performance (dependent). In this GLM, the left variable of the ~ is the dependent in a binary capacity. With a coefficient of .714, it’s fair to assess that a strong relationship exists between the two variables. With further GLM developing to assess “fit” around the trendline, a more robust picture will appear.