data census gov demographic per state additions
Janis Corona
7/19/2020
This script pulls together other tables created from related scripts for extracting zillow two bedroom home values for May 2020, current July 2020 number of jobs available and hourly and annual average pay per job compared to licensed massage therapists (LMTs) in each of 50 of the US states not including the new District of Columbia state added this year 2020. It also pulls in the top 3 populated city per state that makes those average pay values according to the advertised pay per job and city from Indeed. All files are in the github repository located at https://github.com/JanJanJan2018/LMT-State-Licensing-Database and the census data that we start adding to this table is pulled from 2018 data from Data.census.gov demographics to add per state to state licensing requirements excel file: https://data.census.gov/cedsci/table?q=median%20pay%20by%20region%20and%20race&tid=ACSDP1Y2018.DP05&vintage=2018&cid=DP05_0001E&g=0100000US.04000.001&hidePreview=false
library(ggplot2)
library(grid)
library(gridExtra)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)data <- read.csv('ACSDP1Y2018.DP05_data_with_overlays_2020-07-07T154725.csv',
sep=',',
header=TRUE, na.strings = c('',' ','NA'))
meta <- read.csv('ACSDP1Y2018.DP05_metadata_2020-07-07T154725.csv',header=TRUE,
sep=',',na.strings=c('',' ','NA'))head(data)## GEO_ID NAME DP05_0001E
## 1 id Geographic Area Name Estimate!!SEX AND AGE!!Total population
## 2 0400000US17 Illinois 12741080
## 3 0400000US19 Iowa 3156145
## 4 0400000US29 Missouri 6126452
## 5 0400000US32 Nevada 3034392
## 6 0400000US42 Pennsylvania 12807060
## DP05_0001M
## 1 Margin of Error!!SEX AND AGE!!Total population
## 2 *****
## 3 *****
## 4 *****
## 5 *****
## 6 *****
## DP05_0001PE
## 1 Percent Estimate!!SEX AND AGE!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0001PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0002E
## 1 Estimate!!SEX AND AGE!!Total population!!Male
## 2 6266062
## 3 1564888
## 4 3003165
## 5 1522374
## 6 6271620
## DP05_0002M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Male
## 2 4378
## 3 3527
## 4 4798
## 5 2263
## 6 4457
## DP05_0002PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Male
## 2 49.2
## 3 49.6
## 4 49.0
## 5 50.2
## 6 49.0
## DP05_0002PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Male
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0003E
## 1 Estimate!!SEX AND AGE!!Total population!!Female
## 2 6475018
## 3 1591257
## 4 3123287
## 5 1512018
## 6 6535440
## DP05_0003M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Female
## 2 4378
## 3 3527
## 4 4798
## 5 2263
## 6 4457
## DP05_0003PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Female
## 2 50.8
## 3 50.4
## 4 51.0
## 5 49.8
## 6 51.0
## DP05_0003PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Female
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0004E
## 1 Estimate!!SEX AND AGE!!Total population!!Sex ratio (males per 100 females)
## 2 96.8
## 3 98.3
## 4 96.2
## 5 100.7
## 6 96.0
## DP05_0004M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Sex ratio (males per 100 females)
## 2 0.1
## 3 0.4
## 4 0.3
## 5 0.3
## 6 0.1
## DP05_0004PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0004PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0005E
## 1 Estimate!!SEX AND AGE!!Total population!!Under 5 years
## 2 759456
## 3 197883
## 4 370408
## 5 184539
## 6 700416
## DP05_0005M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Under 5 years
## 2 2046
## 3 2271
## 4 2583
## 5 1173
## 6 2083
## DP05_0005PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Under 5 years
## 2 6.0
## 3 6.3
## 4 6.0
## 5 6.1
## 6 5.5
## DP05_0005PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Under 5 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0006E
## 1 Estimate!!SEX AND AGE!!Total population!!5 to 9 years
## 2 762237
## 3 197454
## 4 373737
## 5 188880
## 6 708163
## DP05_0006M
## 1 Margin of Error!!SEX AND AGE!!Total population!!5 to 9 years
## 2 10975
## 3 4889
## 4 8163
## 5 5734
## 6 9762
## DP05_0006PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!5 to 9 years
## 2 6.0
## 3 6.3
## 4 6.1
## 5 6.2
## 6 5.5
## DP05_0006PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!5 to 9 years
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0007E
## 1 Estimate!!SEX AND AGE!!Total population!!10 to 14 years
## 2 838140
## 3 215222
## 4 401199
## 5 201625
## 6 775827
## DP05_0007M
## 1 Margin of Error!!SEX AND AGE!!Total population!!10 to 14 years
## 2 10161
## 3 5215
## 4 7657
## 5 5704
## 6 9517
## DP05_0007PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!10 to 14 years
## 2 6.6
## 3 6.8
## 4 6.5
## 5 6.6
## 6 6.1
## DP05_0007PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!10 to 14 years
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0008E
## 1 Estimate!!SEX AND AGE!!Total population!!15 to 19 years
## 2 835845
## 3 220659
## 4 391923
## 5 182398
## 6 815621
## DP05_0008M
## 1 Margin of Error!!SEX AND AGE!!Total population!!15 to 19 years
## 2 4854
## 3 4122
## 4 4812
## 5 2209
## 6 4096
## DP05_0008PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!15 to 19 years
## 2 6.6
## 3 7.0
## 4 6.4
## 5 6.0
## 6 6.4
## DP05_0008PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!15 to 19 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0009E
## 1 Estimate!!SEX AND AGE!!Total population!!20 to 24 years
## 2 839026
## 3 220135
## 4 406398
## 5 180376
## 6 812406
## DP05_0009M
## 1 Margin of Error!!SEX AND AGE!!Total population!!20 to 24 years
## 2 5924
## 3 3995
## 4 5343
## 5 2061
## 6 5651
## DP05_0009PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!20 to 24 years
## 2 6.6
## 3 7.0
## 4 6.6
## 5 5.9
## 6 6.3
## DP05_0009PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!20 to 24 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0010E
## 1 Estimate!!SEX AND AGE!!Total population!!25 to 34 years
## 2 1765517
## 3 394507
## 4 819132
## 5 442713
## 6 1695073
## DP05_0010M
## 1 Margin of Error!!SEX AND AGE!!Total population!!25 to 34 years
## 2 5311
## 3 4452
## 4 5822
## 5 2829
## 6 5655
## DP05_0010PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!25 to 34 years
## 2 13.9
## 3 12.5
## 4 13.4
## 5 14.6
## 6 13.2
## DP05_0010PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!25 to 34 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0011E
## 1 Estimate!!SEX AND AGE!!Total population!!35 to 44 years
## 2 1644390
## 3 383617
## 4 742343
## 5 405128
## 6 1496113
## DP05_0011M
## 1 Margin of Error!!SEX AND AGE!!Total population!!35 to 44 years
## 2 5142
## 3 3741
## 4 3976
## 5 2750
## 6 4296
## DP05_0011PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!35 to 44 years
## 2 12.9
## 3 12.2
## 4 12.1
## 5 13.4
## 6 11.7
## DP05_0011PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!35 to 44 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0012E
## 1 Estimate!!SEX AND AGE!!Total population!!45 to 54 years
## 2 1636054
## 3 369396
## 4 753354
## 5 396167
## 6 1659541
## DP05_0012M
## 1 Margin of Error!!SEX AND AGE!!Total population!!45 to 54 years
## 2 4871
## 3 3161
## 4 3315
## 5 2674
## 6 4842
## DP05_0012PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!45 to 54 years
## 2 12.8
## 3 11.7
## 4 12.3
## 5 13.1
## 6 13.0
## DP05_0012PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!45 to 54 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0013E
## 1 Estimate!!SEX AND AGE!!Total population!!55 to 59 years
## 2 855616
## 3 213657
## 4 419591
## 5 191059
## 6 916749
## DP05_0013M
## 1 Margin of Error!!SEX AND AGE!!Total population!!55 to 59 years
## 2 9963
## 3 4454
## 4 7243
## 5 4897
## 6 9701
## DP05_0013PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!55 to 59 years
## 2 6.7
## 3 6.8
## 4 6.8
## 5 6.3
## 6 7.2
## DP05_0013PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!55 to 59 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0014E
## 1 Estimate!!SEX AND AGE!!Total population!!60 to 64 years
## 2 814251
## 3 205797
## 4 413293
## 5 186387
## 6 894782
## DP05_0014M
## 1 Margin of Error!!SEX AND AGE!!Total population!!60 to 64 years
## 2 9868
## 3 4119
## 4 7024
## 5 5137
## 6 9663
## DP05_0014PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!60 to 64 years
## 2 6.4
## 3 6.5
## 4 6.7
## 5 6.1
## 6 7.0
## DP05_0014PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!60 to 64 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0015E
## 1 Estimate!!SEX AND AGE!!Total population!!65 to 74 years
## 2 1143329
## 3 299333
## 4 596623
## 5 291996
## 6 1311159
## DP05_0015M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 to 74 years
## 2 2976
## 3 1873
## 4 2812
## 5 1221
## 6 3293
## DP05_0015PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 to 74 years
## 2 9.0
## 3 9.5
## 4 9.7
## 5 9.6
## 6 10.2
## DP05_0015PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 to 74 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0016E
## 1 Estimate!!SEX AND AGE!!Total population!!75 to 84 years
## 2 587651
## 3 162869
## 4 313646
## 5 138844
## 6 707570
## DP05_0016M
## 1 Margin of Error!!SEX AND AGE!!Total population!!75 to 84 years
## 2 6188
## 3 3158
## 4 5352
## 5 3225
## 6 7958
## DP05_0016PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!75 to 84 years
## 2 4.6
## 3 5.2
## 4 5.1
## 5 4.6
## 6 5.5
## DP05_0016PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!75 to 84 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0017E
## 1 Estimate!!SEX AND AGE!!Total population!!85 years and over
## 2 259568
## 3 75616
## 4 124805
## 5 44280
## 6 313640
## DP05_0017M
## 1 Margin of Error!!SEX AND AGE!!Total population!!85 years and over
## 2 5554
## 3 3074
## 4 5244
## 5 3193
## 6 7963
## DP05_0017PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!85 years and over
## 2 2.0
## 3 2.4
## 4 2.0
## 5 1.5
## 6 2.4
## DP05_0017PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!85 years and over
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0018E
## 1 Estimate!!SEX AND AGE!!Total population!!Median age (years)
## 2 38.3
## 3 38.1
## 4 38.8
## 5 38.2
## 6 40.8
## DP05_0018M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Median age (years)
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0018PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Median age (years)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0018PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Median age (years)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0019E
## 1 Estimate!!SEX AND AGE!!Total population!!Under 18 years
## 2 2855802
## 3 733389
## 4 1377726
## 5 689220
## 6 2647617
## DP05_0019M
## 1 Margin of Error!!SEX AND AGE!!Total population!!Under 18 years
## 2 2274
## 3 2389
## 4 3160
## 5 913
## 6 1899
## DP05_0019PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!Under 18 years
## 2 22.4
## 3 23.2
## 4 22.5
## 5 22.7
## 6 20.7
## DP05_0019PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!Under 18 years
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0020E
## 1 Estimate!!SEX AND AGE!!Total population!!16 years and over
## 2 10216304
## 3 2506580
## 4 4907003
## 5 2420990
## 6 10470798
## DP05_0020M
## 1 Margin of Error!!SEX AND AGE!!Total population!!16 years and over
## 2 5459
## 3 3373
## 4 4497
## 5 3306
## 6 5368
## DP05_0020PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!16 years and over
## 2 80.2
## 3 79.4
## 4 80.1
## 5 79.8
## 6 81.8
## DP05_0020PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!16 years and over
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0021E
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over
## 2 9885278
## 3 2422756
## 4 4748726
## 5 2345172
## 6 10159443
## DP05_0021M
## 1 Margin of Error!!SEX AND AGE!!Total population!!18 years and over
## 2 2274
## 3 2389
## 4 3160
## 5 913
## 6 1899
## DP05_0021PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!18 years and over
## 2 77.6
## 3 76.8
## 4 77.5
## 5 77.3
## 6 79.3
## DP05_0021PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!18 years and over
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0022E
## 1 Estimate!!SEX AND AGE!!Total population!!21 years and over
## 2 9379230
## 3 2277260
## 4 4505646
## 5 2236266
## 6 9635621
## DP05_0022M
## 1 Margin of Error!!SEX AND AGE!!Total population!!21 years and over
## 2 8440
## 3 5679
## 4 6850
## 5 4122
## 6 7363
## DP05_0022PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!21 years and over
## 2 73.6
## 3 72.2
## 4 73.5
## 5 73.7
## 6 75.2
## DP05_0022PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!21 years and over
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0023E
## 1 Estimate!!SEX AND AGE!!Total population!!62 years and over
## 2 2459839
## 3 661366
## 4 1284321
## 5 589051
## 6 2861918
## DP05_0023M
## 1 Margin of Error!!SEX AND AGE!!Total population!!62 years and over
## 2 9329
## 3 4411
## 4 6208
## 5 4630
## 6 9666
## DP05_0023PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!62 years and over
## 2 19.3
## 3 21.0
## 4 21.0
## 5 19.4
## 6 22.3
## DP05_0023PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!62 years and over
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0024E
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over
## 2 1990548
## 3 537818
## 4 1035074
## 5 475120
## 6 2332369
## DP05_0024M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 years and over
## 2 2970
## 3 2027
## 4 3446
## 5 1480
## 6 2802
## DP05_0024PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 years and over
## 2 15.6
## 3 17.0
## 4 16.9
## 5 15.7
## 6 18.2
## DP05_0024PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 years and over
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0025E
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over
## 2 9885278
## 3 2422756
## 4 4748726
## 5 2345172
## 6 10159443
## DP05_0025M
## 1 Margin of Error!!SEX AND AGE!!Total population!!18 years and over
## 2 2274
## 3 2389
## 4 3160
## 5 913
## 6 1899
## DP05_0025PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!18 years and over
## 2 9885278
## 3 2422756
## 4 4748726
## 5 2345172
## 6 10159443
## DP05_0025PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!18 years and over
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0026E
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over!!Male
## 2 4801941
## 3 1188114
## 4 2298091
## 5 1169070
## 6 4919474
## DP05_0026M
## 1 Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Male
## 2 3028
## 3 2543
## 4 3571
## 5 1337
## 6 3260
## DP05_0026PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!18 years and over!!Male
## 2 48.6
## 3 49.0
## 4 48.4
## 5 49.9
## 6 48.4
## DP05_0026PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Male
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0027E
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over!!Female
## 2 5083337
## 3 1234642
## 4 2450635
## 5 1176102
## 6 5239969
## DP05_0027M
## 1 Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Female
## 2 2781
## 3 2254
## 4 3289
## 5 1437
## 6 3023
## DP05_0027PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!18 years and over!!Female
## 2 51.4
## 3 51.0
## 4 51.6
## 5 50.1
## 6 51.6
## DP05_0027PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Female
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0028E
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over!!Sex ratio (males per 100 females)
## 2 94.5
## 3 96.2
## 4 93.8
## 5 99.4
## 6 93.9
## DP05_0028M
## 1 Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Sex ratio (males per 100 females)
## 2 0.1
## 3 0.3
## 4 0.2
## 5 0.2
## 6 0.1
## DP05_0028PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!18 years and over!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0028PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!18 years and over!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0029E
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over
## 2 1990548
## 3 537818
## 4 1035074
## 5 475120
## 6 2332369
## DP05_0029M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 years and over
## 2 2970
## 3 2027
## 4 3446
## 5 1480
## 6 2802
## DP05_0029PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 years and over
## 2 1990548
## 3 537818
## 4 1035074
## 5 475120
## 6 2332369
## DP05_0029PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 years and over
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0030E
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over!!Male
## 2 869736
## 3 239515
## 4 456820
## 5 223916
## 6 1020825
## DP05_0030M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Male
## 2 1783
## 3 1176
## 4 2154
## 5 1014
## 6 1549
## DP05_0030PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 years and over!!Male
## 2 43.7
## 3 44.5
## 4 44.1
## 5 47.1
## 6 43.8
## DP05_0030PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Male
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0031E
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over!!Female
## 2 1120812
## 3 298303
## 4 578254
## 5 251204
## 6 1311544
## DP05_0031M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Female
## 2 2090
## 3 1394
## 4 2434
## 5 930
## 6 2017
## DP05_0031PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 years and over!!Female
## 2 56.3
## 3 55.5
## 4 55.9
## 5 52.9
## 6 56.2
## DP05_0031PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Female
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0032E
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over!!Sex ratio (males per 100 females)
## 2 77.6
## 3 80.3
## 4 79.0
## 5 89.1
## 6 77.8
## DP05_0032M
## 1 Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Sex ratio (males per 100 females)
## 2 0.2
## 3 0.5
## 4 0.5
## 5 0.5
## 6 0.1
## DP05_0032PE
## 1 Percent Estimate!!SEX AND AGE!!Total population!!65 years and over!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0032PM
## 1 Percent Margin of Error!!SEX AND AGE!!Total population!!65 years and over!!Sex ratio (males per 100 females)
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0033E DP05_0033M
## 1 Estimate!!RACE!!Total population Margin of Error!!RACE!!Total population
## 2 12741080 *****
## 3 3156145 *****
## 4 6126452 *****
## 5 3034392 *****
## 6 12807060 *****
## DP05_0033PE
## 1 Percent Estimate!!RACE!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0033PM
## 1 Percent Margin of Error!!RACE!!Total population
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0034E
## 1 Estimate!!RACE!!Total population!!One race
## 2 12400103
## 3 3086877
## 4 5952588
## 5 2878562
## 6 12473515
## DP05_0034M
## 1 Margin of Error!!RACE!!Total population!!One race
## 2 12577
## 3 5586
## 4 7671
## 5 9895
## 6 13725
## DP05_0034PE
## 1 Percent Estimate!!RACE!!Total population!!One race
## 2 97.3
## 3 97.8
## 4 97.2
## 5 94.9
## 6 97.4
## DP05_0034PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0035E
## 1 Estimate!!RACE!!Total population!!Two or more races
## 2 340977
## 3 69268
## 4 173864
## 5 155830
## 6 333545
## DP05_0035M
## 1 Margin of Error!!RACE!!Total population!!Two or more races
## 2 12577
## 3 5586
## 4 7671
## 5 9895
## 6 13725
## DP05_0035PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races
## 2 2.7
## 3 2.2
## 4 2.8
## 5 5.1
## 6 2.6
## DP05_0035PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0036E
## 1 Estimate!!RACE!!Total population!!One race
## 2 12400103
## 3 3086877
## 4 5952588
## 5 2878562
## 6 12473515
## DP05_0036M
## 1 Margin of Error!!RACE!!Total population!!One race
## 2 12577
## 3 5586
## 4 7671
## 5 9895
## 6 13725
## DP05_0036PE
## 1 Percent Estimate!!RACE!!Total population!!One race
## 2 97.3
## 3 97.8
## 4 97.2
## 5 94.9
## 6 97.4
## DP05_0036PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0037E
## 1 Estimate!!RACE!!Total population!!One race!!White
## 2 9135145
## 3 2846099
## 4 5035197
## 5 1924976
## 6 10256084
## DP05_0037M
## 1 Margin of Error!!RACE!!Total population!!One race!!White
## 2 26772
## 3 5629
## 4 7994
## 5 16699
## 6 17296
## DP05_0037PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!White
## 2 71.7
## 3 90.2
## 4 82.2
## 5 63.4
## 6 80.1
## DP05_0037PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!White
## 2 0.2
## 3 0.2
## 4 0.1
## 5 0.6
## 6 0.1
## DP05_0038E
## 1 Estimate!!RACE!!Total population!!One race!!Black or African American
## 2 1793079
## 3 115137
## 4 704327
## 5 280385
## 6 1428406
## DP05_0038M
## 1 Margin of Error!!RACE!!Total population!!One race!!Black or African American
## 2 10015
## 3 4782
## 4 6416
## 5 7032
## 6 12545
## DP05_0038PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Black or African American
## 2 14.1
## 3 3.6
## 4 11.5
## 5 9.2
## 6 11.2
## DP05_0038PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Black or African American
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0039E
## 1 Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native
## 2 35846
## 3 11494
## 4 27705
## 5 44666
## 6 21418
## DP05_0039M
## 1 Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native
## 2 4591
## 3 1274
## 4 3183
## 5 3794
## 6 2246
## DP05_0039PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native
## 2 0.3
## 3 0.4
## 4 0.5
## 5 1.5
## 6 0.2
## DP05_0039PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0040E
## 1 Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Cherokee tribal grouping
## 2 3055
## 3 603
## 4 8373
## 5 2626
## 6 2569
## DP05_0040M
## 1 Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Cherokee tribal grouping
## 2 1023
## 3 412
## 4 1600
## 5 1119
## 6 1058
## DP05_0040PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Cherokee tribal grouping
## 2 0.0
## 3 0.0
## 4 0.1
## 5 0.1
## 6 0.0
## DP05_0040PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Cherokee tribal grouping
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0041E
## 1 Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Chippewa tribal grouping
## 2 2651
## 3 153
## 4 462
## 5 512
## 6 351
## DP05_0041M
## 1 Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Chippewa tribal grouping
## 2 1224
## 3 142
## 4 359
## 5 505
## 6 267
## DP05_0041PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Chippewa tribal grouping
## 2 0.0
## 3 0.0
## 4 0.0
## 5 0.0
## 6 0.0
## DP05_0041PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Chippewa tribal grouping
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0042E
## 1 Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Navajo tribal grouping
## 2 135
## 3 264
## 4 1210
## 5 2425
## 6 302
## DP05_0042M
## 1 Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Navajo tribal grouping
## 2 212
## 3 218
## 4 790
## 5 1174
## 6 372
## DP05_0042PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Navajo tribal grouping
## 2 0.0
## 3 0.0
## 4 0.0
## 5 0.1
## 6 0.0
## DP05_0042PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Navajo tribal grouping
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0043E
## 1 Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Sioux tribal grouping
## 2 222
## 3 1964
## 4 653
## 5 857
## 6 447
## DP05_0043M
## 1 Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Sioux tribal grouping
## 2 174
## 3 681
## 4 330
## 5 451
## 6 425
## DP05_0043PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Sioux tribal grouping
## 2 0.0
## 3 0.1
## 4 0.0
## 5 0.0
## 6 0.0
## DP05_0043PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!American Indian and Alaska Native!!Sioux tribal grouping
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0044E
## 1 Estimate!!RACE!!Total population!!One race!!Asian
## 2 717938
## 3 79971
## 4 121417
## 5 250137
## 6 455027
## DP05_0044M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian
## 2 6617
## 3 3082
## 4 3689
## 5 5954
## 6 6288
## DP05_0044PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian
## 2 5.6
## 3 2.5
## 4 2.0
## 5 8.2
## 6 3.6
## DP05_0044PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0045E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Asian Indian
## 2 256122
## 3 12415
## 4 30435
## 5 11123
## 6 143539
## DP05_0045M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Asian Indian
## 2 10098
## 3 2942
## 4 3766
## 5 2466
## 6 8217
## DP05_0045PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Asian Indian
## 2 2.0
## 3 0.4
## 4 0.5
## 5 0.4
## 6 1.1
## DP05_0045PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Asian Indian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0046E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Chinese
## 2 132447
## 3 13121
## 4 29621
## 5 39196
## 6 119817
## DP05_0046M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Chinese
## 2 8810
## 3 1977
## 4 3299
## 5 5103
## 6 7260
## DP05_0046PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Chinese
## 2 1.0
## 3 0.4
## 4 0.5
## 5 1.3
## 6 0.9
## DP05_0046PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Chinese
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0047E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Filipino
## 2 124649
## 3 5029
## 4 11302
## 5 129739
## 6 25128
## DP05_0047M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Filipino
## 2 8120
## 3 1225
## 4 2102
## 5 7539
## 6 3284
## DP05_0047PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Filipino
## 2 1.0
## 3 0.2
## 4 0.2
## 5 4.3
## 6 0.2
## DP05_0047PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Filipino
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0048E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Japanese
## 2 17958
## 3 1494
## 4 2388
## 5 11514
## 6 5056
## DP05_0048M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Japanese
## 2 2295
## 3 775
## 4 1159
## 5 2257
## 6 1189
## DP05_0048PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Japanese
## 2 0.1
## 3 0.0
## 4 0.0
## 5 0.4
## 6 0.0
## DP05_0048PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Japanese
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0049E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Korean
## 2 56597
## 3 6717
## 4 11526
## 5 11750
## 6 39144
## DP05_0049M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Korean
## 2 5468
## 3 1890
## 4 1973
## 5 2453
## 6 4971
## DP05_0049PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Korean
## 2 0.4
## 3 0.2
## 4 0.2
## 5 0.4
## 6 0.3
## DP05_0049PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Korean
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0050E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Vietnamese
## 2 35367
## 3 11705
## 4 17208
## 5 17744
## 6 40920
## DP05_0050M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Vietnamese
## 2 6566
## 3 2817
## 4 4212
## 5 4766
## 6 4670
## DP05_0050PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Vietnamese
## 2 0.3
## 3 0.4
## 4 0.3
## 5 0.6
## 6 0.3
## DP05_0050PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Vietnamese
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0051E
## 1 Estimate!!RACE!!Total population!!One race!!Asian!!Other Asian
## 2 94798
## 3 29490
## 4 18937
## 5 29071
## 6 81423
## DP05_0051M
## 1 Margin of Error!!RACE!!Total population!!One race!!Asian!!Other Asian
## 2 8476
## 3 3525
## 4 3013
## 5 4901
## 6 6597
## DP05_0051PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Asian!!Other Asian
## 2 0.7
## 3 0.9
## 4 0.3
## 5 1.0
## 6 0.6
## DP05_0051PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Asian!!Other Asian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0052E
## 1 Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander
## 2 5317
## 3 3463
## 4 7385
## 5 19612
## 6 5008
## DP05_0052M
## 1 Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander
## 2 1835
## 3 1362
## 4 1417
## 5 1707
## 6 1684
## DP05_0052PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander
## 2 0.0
## 3 0.1
## 4 0.1
## 5 0.6
## 6 0.0
## DP05_0052PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0053E
## 1 Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Native Hawaiian
## 2 1927
## 3 541
## 4 1844
## 5 6472
## 6 935
## DP05_0053M
## 1 Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Native Hawaiian
## 2 757
## 3 474
## 4 1238
## 5 1757
## 6 498
## DP05_0053PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Native Hawaiian
## 2 0.0
## 3 0.0
## 4 0.0
## 5 0.2
## 6 0.0
## DP05_0053PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Native Hawaiian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0054E
## 1 Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Guamanian or Chamorro
## 2 789
## 3 561
## 4 226
## 5 5668
## 6 746
## DP05_0054M
## 1 Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Guamanian or Chamorro
## 2 461
## 3 469
## 4 247
## 5 2108
## 6 528
## DP05_0054PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Guamanian or Chamorro
## 2 0.0
## 3 0.0
## 4 0.0
## 5 0.2
## 6 0.0
## DP05_0054PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Guamanian or Chamorro
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0055E
## 1 Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Samoan
## 2 710
## 3 348
## 4 2729
## 5 3528
## 6 766
## DP05_0055M
## 1 Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Samoan
## 2 681
## 3 473
## 4 828
## 5 1359
## 6 483
## DP05_0055PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Samoan
## 2 0.0
## 3 0.0
## 4 0.0
## 5 0.1
## 6 0.0
## DP05_0055PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Samoan
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0056E
## 1 Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Other Pacific Islander
## 2 1891
## 3 2013
## 4 2586
## 5 3944
## 6 2561
## DP05_0056M
## 1 Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Other Pacific Islander
## 2 1640
## 3 1109
## 4 660
## 5 1984
## 6 1515
## DP05_0056PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Other Pacific Islander
## 2 0.0
## 3 0.1
## 4 0.0
## 5 0.1
## 6 0.0
## DP05_0056PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Other Pacific Islander
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0057E
## 1 Estimate!!RACE!!Total population!!One race!!Some other race
## 2 712778
## 3 30713
## 4 56557
## 5 358786
## 6 307572
## DP05_0057M
## 1 Margin of Error!!RACE!!Total population!!One race!!Some other race
## 2 25545
## 3 5286
## 4 6735
## 5 16689
## 6 16186
## DP05_0057PE
## 1 Percent Estimate!!RACE!!Total population!!One race!!Some other race
## 2 5.6
## 3 1.0
## 4 0.9
## 5 11.8
## 6 2.4
## DP05_0057PM
## 1 Percent Margin of Error!!RACE!!Total population!!One race!!Some other race
## 2 0.2
## 3 0.2
## 4 0.1
## 5 0.5
## 6 0.1
## DP05_0058E
## 1 Estimate!!RACE!!Total population!!Two or more races
## 2 340977
## 3 69268
## 4 173864
## 5 155830
## 6 333545
## DP05_0058M
## 1 Margin of Error!!RACE!!Total population!!Two or more races
## 2 12577
## 3 5586
## 4 7671
## 5 9895
## 6 13725
## DP05_0058PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races
## 2 2.7
## 3 2.2
## 4 2.8
## 5 5.1
## 6 2.6
## DP05_0058PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0059E
## 1 Estimate!!RACE!!Total population!!Two or more races!!White and Black or African American
## 2 116872
## 3 30402
## 4 75345
## 5 32463
## 6 148313
## DP05_0059M
## 1 Margin of Error!!RACE!!Total population!!Two or more races!!White and Black or African American
## 2 7015
## 3 3934
## 4 5750
## 5 5046
## 6 9404
## DP05_0059PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races!!White and Black or African American
## 2 0.9
## 3 1.0
## 4 1.2
## 5 1.1
## 6 1.2
## DP05_0059PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races!!White and Black or African American
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0060E
## 1 Estimate!!RACE!!Total population!!Two or more races!!White and American Indian and Alaska Native
## 2 35817
## 3 9285
## 4 39150
## 5 14754
## 6 32795
## DP05_0060M
## 1 Margin of Error!!RACE!!Total population!!Two or more races!!White and American Indian and Alaska Native
## 2 3506
## 3 1612
## 4 3567
## 5 2162
## 6 2462
## DP05_0060PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races!!White and American Indian and Alaska Native
## 2 0.3
## 3 0.3
## 4 0.6
## 5 0.5
## 6 0.3
## DP05_0060PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races!!White and American Indian and Alaska Native
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0061E
## 1 Estimate!!RACE!!Total population!!Two or more races!!White and Asian
## 2 82982
## 3 13266
## 4 29126
## 5 38991
## 6 55627
## DP05_0061M
## 1 Margin of Error!!RACE!!Total population!!Two or more races!!White and Asian
## 2 7432
## 3 2302
## 4 3547
## 5 5565
## 6 5231
## DP05_0061PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races!!White and Asian
## 2 0.7
## 3 0.4
## 4 0.5
## 5 1.3
## 6 0.4
## DP05_0061PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races!!White and Asian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0062E
## 1 Estimate!!RACE!!Total population!!Two or more races!!Black or African American and American Indian and Alaska Native
## 2 8368
## 3 1951
## 4 4404
## 5 5393
## 6 11223
## DP05_0062M
## 1 Margin of Error!!RACE!!Total population!!Two or more races!!Black or African American and American Indian and Alaska Native
## 2 2255
## 3 1063
## 4 1478
## 5 2120
## 6 2142
## DP05_0062PE
## 1 Percent Estimate!!RACE!!Total population!!Two or more races!!Black or African American and American Indian and Alaska Native
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0062PM
## 1 Percent Margin of Error!!RACE!!Total population!!Two or more races!!Black or African American and American Indian and Alaska Native
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0063E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0063M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population
## 2 *****
## 3 *****
## 4 *****
## 5 *****
## 6 *****
## DP05_0063PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0063PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0064E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!White
## 2 9431680
## 3 2909568
## 4 5196815
## 5 2050048
## 6 10543254
## DP05_0064M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!White
## 2 27696
## 3 7192
## 4 10284
## 5 19415
## 6 19389
## DP05_0064PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!White
## 2 74.0
## 3 92.2
## 4 84.8
## 5 67.6
## 6 82.3
## DP05_0064PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!White
## 2 0.2
## 3 0.2
## 4 0.2
## 5 0.6
## 6 0.2
## DP05_0065E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!Black or African American
## 2 1952694
## 3 151562
## 4 794984
## 5 337660
## 6 1632035
## DP05_0065M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Black or African American
## 2 6946
## 3 2986
## 4 4485
## 5 5928
## 6 9570
## DP05_0065PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!Black or African American
## 2 15.3
## 3 4.8
## 4 13.0
## 5 11.1
## 6 12.7
## DP05_0065PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Black or African American
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0066E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!American Indian and Alaska Native
## 2 100555
## 3 27116
## 4 79954
## 5 71817
## 6 89728
## DP05_0066M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!American Indian and Alaska Native
## 2 6939
## 3 2729
## 4 4579
## 5 4490
## 6 5118
## DP05_0066PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!American Indian and Alaska Native
## 2 0.8
## 3 0.9
## 4 1.3
## 5 2.4
## 6 0.7
## DP05_0066PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!American Indian and Alaska Native
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0067E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!Asian
## 2 831487
## 3 98900
## 4 159166
## 5 318010
## 6 536235
## DP05_0067M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Asian
## 2 4879
## 3 2688
## 4 2654
## 5 4322
## 6 4513
## DP05_0067PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!Asian
## 2 6.5
## 3 3.1
## 4 2.6
## 5 10.5
## 6 4.2
## DP05_0067PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Asian
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0068E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!Native Hawaiian and Other Pacific Islander
## 2 16444
## 3 6579
## 4 13760
## 5 41746
## 6 18764
## DP05_0068M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Native Hawaiian and Other Pacific Islander
## 2 3163
## 3 1746
## 4 2328
## 5 3352
## 6 3667
## DP05_0068PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!Native Hawaiian and Other Pacific Islander
## 2 0.1
## 3 0.2
## 4 0.2
## 5 1.4
## 6 0.1
## DP05_0068PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Native Hawaiian and Other Pacific Islander
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0069E
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!Some other race
## 2 776129
## 3 37007
## 4 66685
## 5 390057
## 6 355417
## DP05_0069M
## 1 Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Some other race
## 2 26600
## 3 5555
## 4 7078
## 5 15973
## 6 16623
## DP05_0069PE
## 1 Percent Estimate!!Race alone or in combination with one or more other races!!Total population!!Some other race
## 2 6.1
## 3 1.2
## 4 1.1
## 5 12.9
## 6 2.8
## DP05_0069PM
## 1 Percent Margin of Error!!Race alone or in combination with one or more other races!!Total population!!Some other race
## 2 0.2
## 3 0.2
## 4 0.1
## 5 0.5
## 6 0.1
## DP05_0070E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0070M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population
## 2 *****
## 3 *****
## 4 *****
## 5 *****
## 6 *****
## DP05_0070PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population
## 2 12741080
## 3 3156145
## 4 6126452
## 5 3034392
## 6 12807060
## DP05_0070PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0071E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 2 2208868
## 3 191473
## 4 253474
## 5 881145
## 6 974763
## DP05_0071M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 2 1158
## 3 901
## 4 2044
## 5 *****
## 6 1237
## DP05_0071PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 2 17.3
## 3 6.1
## 4 4.1
## 5 29.0
## 6 7.6
## DP05_0071PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 2 0.1
## 3 0.1
## 4 0.1
## 5 *****
## 6 0.1
## DP05_0072E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Mexican
## 2 1744028
## 3 155179
## 4 179787
## 5 668365
## 6 158286
## DP05_0072M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Mexican
## 2 18280
## 3 4714
## 4 5745
## 5 11586
## 6 10718
## DP05_0072PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Mexican
## 2 13.7
## 3 4.9
## 4 2.9
## 5 22.0
## 6 1.2
## DP05_0072PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Mexican
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.4
## 6 0.1
## DP05_0073E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Puerto Rican
## 2 206543
## 3 7792
## 4 16816
## 5 25740
## 6 477312
## DP05_0073M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Puerto Rican
## 2 13313
## 3 2075
## 4 3020
## 5 3589
## 6 13697
## DP05_0073PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Puerto Rican
## 2 1.6
## 3 0.2
## 4 0.3
## 5 0.8
## 6 3.7
## DP05_0073PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Puerto Rican
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0074E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Cuban
## 2 27617
## 3 1714
## 4 8220
## 5 35071
## 6 24185
## DP05_0074M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Cuban
## 2 3919
## 3 696
## 4 2577
## 5 5849
## 6 3640
## DP05_0074PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Cuban
## 2 0.2
## 3 0.1
## 4 0.1
## 5 1.2
## 6 0.2
## DP05_0074PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Cuban
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0075E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Other Hispanic or Latino
## 2 230680
## 3 26788
## 4 48651
## 5 151969
## 6 314980
## DP05_0075M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Other Hispanic or Latino
## 2 14103
## 3 4189
## 4 5487
## 5 10271
## 6 15287
## DP05_0075PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Other Hispanic or Latino
## 2 1.8
## 3 0.8
## 4 0.8
## 5 5.0
## 6 2.5
## DP05_0075PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Other Hispanic or Latino
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0076E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino
## 2 10532212
## 3 2964672
## 4 5872978
## 5 2153247
## 6 11832297
## DP05_0076M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino
## 2 1158
## 3 901
## 4 2044
## 5 *****
## 6 1237
## DP05_0076PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino
## 2 82.7
## 3 93.9
## 4 95.9
## 5 71.0
## 6 92.4
## DP05_0076PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino
## 2 0.1
## 3 0.1
## 4 0.1
## 5 *****
## 6 0.1
## DP05_0077E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone
## 2 7760732
## 3 2695583
## 4 4857174
## 5 1469593
## 6 9723288
## DP05_0077M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone
## 2 3669
## 3 2165
## 4 2076
## 5 1806
## 6 3360
## DP05_0077PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone
## 2 60.9
## 3 85.4
## 4 79.3
## 5 48.4
## 6 75.9
## DP05_0077PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0078E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone
## 2 1759316
## 3 113562
## 4 698641
## 5 266440
## 6 1352804
## DP05_0078M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone
## 2 8262
## 3 4619
## 4 6145
## 5 5865
## 6 9315
## DP05_0078PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone
## 2 13.8
## 3 3.6
## 4 11.4
## 5 8.8
## 6 10.6
## DP05_0078PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0079E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!American Indian and Alaska Native alone
## 2 17215
## 3 9710
## 4 23149
## 5 30185
## 6 14367
## DP05_0079M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!American Indian and Alaska Native alone
## 2 2351
## 3 951
## 4 2507
## 5 2060
## 6 1986
## DP05_0079PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!American Indian and Alaska Native alone
## 2 0.1
## 3 0.3
## 4 0.4
## 5 1.0
## 6 0.1
## DP05_0079PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!American Indian and Alaska Native alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0080E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone
## 2 711970
## 3 79643
## 4 120440
## 5 244471
## 6 450077
## DP05_0080M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone
## 2 6521
## 3 3042
## 4 3635
## 5 5630
## 6 5816
## DP05_0080PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone
## 2 5.6
## 3 2.5
## 4 2.0
## 5 8.1
## 6 3.5
## DP05_0080PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0081E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Native Hawaiian and Other Pacific Islander alone
## 2 2887
## 3 2945
## 4 7095
## 5 18249
## 6 3557
## DP05_0081M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Native Hawaiian and Other Pacific Islander alone
## 2 697
## 3 1388
## 4 1376
## 5 1393
## 6 1479
## DP05_0081PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Native Hawaiian and Other Pacific Islander alone
## 2 0.0
## 3 0.1
## 4 0.1
## 5 0.6
## 6 0.0
## DP05_0081PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Native Hawaiian and Other Pacific Islander alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0082E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Some other race alone
## 2 24310
## 3 4000
## 4 9832
## 5 11810
## 6 21357
## DP05_0082M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Some other race alone
## 2 3879
## 3 1973
## 4 2191
## 5 3634
## 6 3645
## DP05_0082PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Some other race alone
## 2 0.2
## 3 0.1
## 4 0.2
## 5 0.4
## 6 0.2
## DP05_0082PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Some other race alone
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0083E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races
## 2 255782
## 3 59229
## 4 156647
## 5 112499
## 6 266847
## DP05_0083M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races
## 2 10111
## 3 4832
## 4 6711
## 5 8253
## 6 11020
## DP05_0083PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races
## 2 2.0
## 3 1.9
## 4 2.6
## 5 3.7
## 6 2.1
## DP05_0083PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0084E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races including Some other race
## 2 11405
## 3 493
## 4 2330
## 5 3740
## 6 11356
## DP05_0084M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races including Some other race
## 2 2606
## 3 321
## 4 1060
## 5 1741
## 6 2873
## DP05_0084PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races including Some other race
## 2 0.1
## 3 0.0
## 4 0.0
## 5 0.1
## 6 0.1
## DP05_0084PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races including Some other race
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.1
## 6 0.1
## DP05_0085E
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races excluding Some other race, and Three or more races
## 2 244377
## 3 58736
## 4 154317
## 5 108759
## 6 255491
## DP05_0085M
## 1 Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races excluding Some other race, and Three or more races
## 2 10212
## 3 4738
## 4 6664
## 5 8217
## 6 10346
## DP05_0085PE
## 1 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races excluding Some other race, and Three or more races
## 2 1.9
## 3 1.9
## 4 2.5
## 5 3.6
## 6 2.0
## DP05_0085PM
## 1 Percent Margin of Error!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races excluding Some other race, and Three or more races
## 2 0.1
## 3 0.2
## 4 0.1
## 5 0.3
## 6 0.1
## DP05_0086E DP05_0086M
## 1 Estimate!!Total housing units Margin of Error!!Total housing units
## 2 5376176 518
## 3 1409568 354
## 4 2806296 623
## 5 1268717 151
## 6 5713136 439
## DP05_0086PE
## 1 Percent Estimate!!Total housing units
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0086PM
## 1 Percent Margin of Error!!Total housing units
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0087E
## 1 Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population
## 2 9074766
## 3 2327219
## 4 4633370
## 5 2075166
## 6 9781212
## DP05_0087M
## 1 Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population
## 2 18777
## 3 6259
## 4 7810
## 5 9912
## 6 13540
## DP05_0087PE
## 1 Percent Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population
## 2 9074766
## 3 2327219
## 4 4633370
## 5 2075166
## 6 9781212
## DP05_0087PM
## 1 Percent Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population
## 2 (X)
## 3 (X)
## 4 (X)
## 5 (X)
## 6 (X)
## DP05_0088E
## 1 Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Male
## 2 4385340
## 3 1139250
## 4 2240784
## 5 1036350
## 6 4726343
## DP05_0088M
## 1 Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Male
## 2 12630
## 3 3838
## 4 5295
## 5 6057
## 6 8924
## DP05_0088PE
## 1 Percent Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Male
## 2 48.3
## 3 49.0
## 4 48.4
## 5 49.9
## 6 48.3
## DP05_0088PM
## 1 Percent Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Male
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1
## DP05_0089E
## 1 Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Female
## 2 4689426
## 3 1187969
## 4 2392586
## 5 1038816
## 6 5054869
## DP05_0089M
## 1 Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Female
## 2 12019
## 3 4084
## 4 5182
## 5 5752
## 6 8755
## DP05_0089PE
## 1 Percent Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Female
## 2 51.7
## 3 51.0
## 4 51.6
## 5 50.1
## 6 51.7
## DP05_0089PM
## 1 Percent Margin of Error!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population!!Female
## 2 0.1
## 3 0.1
## 4 0.1
## 5 0.2
## 6 0.1meta <- meta[,-3]
head(meta)## GEO_ID id
## 1 NAME Geographic Area Name
## 2 DP05_0001E Estimate!!SEX AND AGE!!Total population
## 3 DP05_0001M Margin of Error!!SEX AND AGE!!Total population
## 4 DP05_0001PE Percent Estimate!!SEX AND AGE!!Total population
## 5 DP05_0001PM Percent Margin of Error!!SEX AND AGE!!Total population
## 6 DP05_0002E Estimate!!SEX AND AGE!!Total population!!Maledf <- data[-c(1,21,40),-1]
row.names(df) <- NULL
df$NAME <- as.character(paste(df$NAME))metanames <- meta$GEO_ID
dfnames <- as.factor(colnames(df))length(metanames)==length(dfnames)## [1] TRUEg <- metanames==dfnames
sum(g==TRUE)## [1] 357meta$id <- as.character(paste(meta$id))
err <- grep('Error',meta$id)
DF <- df[,-err]head(DF)## NAME DP05_0001E DP05_0001PE DP05_0002E DP05_0002PE DP05_0003E
## 1 Illinois 12741080 12741080 6266062 49.2 6475018
## 2 Iowa 3156145 3156145 1564888 49.6 1591257
## 3 Missouri 6126452 6126452 3003165 49.0 3123287
## 4 Nevada 3034392 3034392 1522374 50.2 1512018
## 5 Pennsylvania 12807060 12807060 6271620 49.0 6535440
## 6 Utah 3161105 3161105 1592457 50.4 1568648
## DP05_0003PE DP05_0004E DP05_0004PE DP05_0005E DP05_0005PE DP05_0006E
## 1 50.8 96.8 (X) 759456 6.0 762237
## 2 50.4 98.3 (X) 197883 6.3 197454
## 3 51.0 96.2 (X) 370408 6.0 373737
## 4 49.8 100.7 (X) 184539 6.1 188880
## 5 51.0 96.0 (X) 700416 5.5 708163
## 6 49.6 101.5 (X) 250194 7.9 257110
## DP05_0006PE DP05_0007E DP05_0007PE DP05_0008E DP05_0008PE DP05_0009E
## 1 6.0 838140 6.6 835845 6.6 839026
## 2 6.3 215222 6.8 220659 7.0 220135
## 3 6.1 401199 6.5 391923 6.4 406398
## 4 6.2 201625 6.6 182398 6.0 180376
## 5 5.5 775827 6.1 815621 6.4 812406
## 6 8.1 270952 8.6 250173 7.9 260774
## DP05_0009PE DP05_0010E DP05_0010PE DP05_0011E DP05_0011PE DP05_0012E
## 1 6.6 1765517 13.9 1644390 12.9 1636054
## 2 7.0 394507 12.5 383617 12.2 369396
## 3 6.6 819132 13.4 742343 12.1 753354
## 4 5.9 442713 14.6 405128 13.4 396167
## 5 6.3 1695073 13.2 1496113 11.7 1659541
## 6 8.2 463344 14.7 437953 13.9 319031
## DP05_0012PE DP05_0013E DP05_0013PE DP05_0014E DP05_0014PE DP05_0015E
## 1 12.8 855616 6.7 814251 6.4 1143329
## 2 11.7 213657 6.8 205797 6.5 299333
## 3 12.3 419591 6.8 413293 6.7 596623
## 4 13.1 191059 6.3 186387 6.1 291996
## 5 13.0 916749 7.2 894782 7.0 1311159
## 6 10.1 152425 4.8 147852 4.7 212206
## DP05_0015PE DP05_0016E DP05_0016PE DP05_0017E DP05_0017PE DP05_0018E
## 1 9.0 587651 4.6 259568 2.0 38.3
## 2 9.5 162869 5.2 75616 2.4 38.1
## 3 9.7 313646 5.1 124805 2.0 38.8
## 4 9.6 138844 4.6 44280 1.5 38.2
## 5 10.2 707570 5.5 313640 2.4 40.8
## 6 6.7 103828 3.3 35263 1.1 31.0
## DP05_0018PE DP05_0019E DP05_0019PE DP05_0020E DP05_0020PE DP05_0021E
## 1 (X) 2855802 22.4 10216304 80.2 9885278
## 2 (X) 733389 23.2 2506580 79.4 2422756
## 3 (X) 1377726 22.5 4907003 80.1 4748726
## 4 (X) 689220 22.7 2420990 79.8 2345172
## 5 (X) 2647617 20.7 10470798 81.8 10159443
## 6 (X) 931831 29.5 2329530 73.7 2229274
## DP05_0021PE DP05_0022E DP05_0022PE DP05_0023E DP05_0023PE DP05_0024E
## 1 77.6 9379230 73.6 2459839 19.3 1990548
## 2 76.8 2277260 72.2 661366 21.0 537818
## 3 77.5 4505646 73.5 1284321 21.0 1035074
## 4 77.3 2236266 73.7 589051 19.4 475120
## 5 79.3 9635621 75.2 2861918 22.3 2332369
## 6 70.5 2087145 66.0 439430 13.9 351297
## DP05_0024PE DP05_0025E DP05_0025PE DP05_0026E DP05_0026PE DP05_0027E
## 1 15.6 9885278 9885278 4801941 48.6 5083337
## 2 17.0 2422756 2422756 1188114 49.0 1234642
## 3 16.9 4748726 4748726 2298091 48.4 2450635
## 4 15.7 2345172 2345172 1169070 49.9 1176102
## 5 18.2 10159443 10159443 4919474 48.4 5239969
## 6 11.1 2229274 2229274 1113904 50.0 1115370
## DP05_0027PE DP05_0028E DP05_0028PE DP05_0029E DP05_0029PE DP05_0030E
## 1 51.4 94.5 (X) 1990548 1990548 869736
## 2 51.0 96.2 (X) 537818 537818 239515
## 3 51.6 93.8 (X) 1035074 1035074 456820
## 4 50.1 99.4 (X) 475120 475120 223916
## 5 51.6 93.9 (X) 2332369 2332369 1020825
## 6 50.0 99.9 (X) 351297 351297 164068
## DP05_0030PE DP05_0031E DP05_0031PE DP05_0032E DP05_0032PE DP05_0033E
## 1 43.7 1120812 56.3 77.6 (X) 12741080
## 2 44.5 298303 55.5 80.3 (X) 3156145
## 3 44.1 578254 55.9 79.0 (X) 6126452
## 4 47.1 251204 52.9 89.1 (X) 3034392
## 5 43.8 1311544 56.2 77.8 (X) 12807060
## 6 46.7 187229 53.3 87.6 (X) 3161105
## DP05_0033PE DP05_0034E DP05_0034PE DP05_0035E DP05_0035PE DP05_0036E
## 1 12741080 12400103 97.3 340977 2.7 12400103
## 2 3156145 3086877 97.8 69268 2.2 3086877
## 3 6126452 5952588 97.2 173864 2.8 5952588
## 4 3034392 2878562 94.9 155830 5.1 2878562
## 5 12807060 12473515 97.4 333545 2.6 12473515
## 6 3161105 3059954 96.8 101151 3.2 3059954
## DP05_0036PE DP05_0037E DP05_0037PE DP05_0038E DP05_0038PE DP05_0039E
## 1 97.3 9135145 71.7 1793079 14.1 35846
## 2 97.8 2846099 90.2 115137 3.6 11494
## 3 97.2 5035197 82.2 704327 11.5 27705
## 4 94.9 1924976 63.4 280385 9.2 44666
## 5 97.4 10256084 80.1 1428406 11.2 21418
## 6 96.8 2708195 85.7 40365 1.3 34678
## DP05_0039PE DP05_0040E DP05_0040PE DP05_0041E DP05_0041PE DP05_0042E
## 1 0.3 3055 0.0 2651 0.0 135
## 2 0.4 603 0.0 153 0.0 264
## 3 0.5 8373 0.1 462 0.0 1210
## 4 1.5 2626 0.1 512 0.0 2425
## 5 0.2 2569 0.0 351 0.0 302
## 6 1.1 709 0.0 0 0.0 15970
## DP05_0042PE DP05_0043E DP05_0043PE DP05_0044E DP05_0044PE DP05_0045E
## 1 0.0 222 0.0 717938 5.6 256122
## 2 0.0 1964 0.1 79971 2.5 12415
## 3 0.0 653 0.0 121417 2.0 30435
## 4 0.1 857 0.0 250137 8.2 11123
## 5 0.0 447 0.0 455027 3.6 143539
## 6 0.5 2265 0.1 75898 2.4 12079
## DP05_0045PE DP05_0046E DP05_0046PE DP05_0047E DP05_0047PE DP05_0048E
## 1 2.0 132447 1.0 124649 1.0 17958
## 2 0.4 13121 0.4 5029 0.2 1494
## 3 0.5 29621 0.5 11302 0.2 2388
## 4 0.4 39196 1.3 129739 4.3 11514
## 5 1.1 119817 0.9 25128 0.2 5056
## 6 0.4 14285 0.5 8034 0.3 6208
## DP05_0048PE DP05_0049E DP05_0049PE DP05_0050E DP05_0050PE DP05_0051E
## 1 0.1 56597 0.4 35367 0.3 94798
## 2 0.0 6717 0.2 11705 0.4 29490
## 3 0.0 11526 0.2 17208 0.3 18937
## 4 0.4 11750 0.4 17744 0.6 29071
## 5 0.0 39144 0.3 40920 0.3 81423
## 6 0.2 6756 0.2 9663 0.3 18873
## DP05_0051PE DP05_0052E DP05_0052PE DP05_0053E DP05_0053PE DP05_0054E
## 1 0.7 5317 0.0 1927 0.0 789
## 2 0.9 3463 0.1 541 0.0 561
## 3 0.3 7385 0.1 1844 0.0 226
## 4 1.0 19612 0.6 6472 0.2 5668
## 5 0.6 5008 0.0 935 0.0 746
## 6 0.6 29362 0.9 2090 0.1 836
## DP05_0054PE DP05_0055E DP05_0055PE DP05_0056E DP05_0056PE DP05_0057E
## 1 0.0 710 0.0 1891 0.0 712778
## 2 0.0 348 0.0 2013 0.1 30713
## 3 0.0 2729 0.0 2586 0.0 56557
## 4 0.2 3528 0.1 3944 0.1 358786
## 5 0.0 766 0.0 2561 0.0 307572
## 6 0.0 8386 0.3 18050 0.6 171456
## DP05_0057PE DP05_0058E DP05_0058PE DP05_0059E DP05_0059PE DP05_0060E
## 1 5.6 340977 2.7 116872 0.9 35817
## 2 1.0 69268 2.2 30402 1.0 9285
## 3 0.9 173864 2.8 75345 1.2 39150
## 4 11.8 155830 5.1 32463 1.1 14754
## 5 2.4 333545 2.6 148313 1.2 32795
## 6 5.4 101151 3.2 12469 0.4 17190
## DP05_0060PE DP05_0061E DP05_0061PE DP05_0062E DP05_0062PE DP05_0063E
## 1 0.3 82982 0.7 8368 0.1 12741080
## 2 0.3 13266 0.4 1951 0.1 3156145
## 3 0.6 29126 0.5 4404 0.1 6126452
## 4 0.5 38991 1.3 5393 0.2 3034392
## 5 0.3 55627 0.4 11223 0.1 12807060
## 6 0.5 29698 0.9 988 0.0 3161105
## DP05_0063PE DP05_0064E DP05_0064PE DP05_0065E DP05_0065PE DP05_0066E
## 1 12741080 9431680 74.0 1952694 15.3 100555
## 2 3156145 2909568 92.2 151562 4.8 27116
## 3 6126452 5196815 84.8 794984 13.0 79954
## 4 3034392 2050048 67.6 337660 11.1 71817
## 5 12807060 10543254 82.3 1632035 12.7 89728
## 6 3161105 2800377 88.6 58241 1.8 56454
## DP05_0066PE DP05_0067E DP05_0067PE DP05_0068E DP05_0068PE DP05_0069E
## 1 0.8 831487 6.5 16444 0.1 776129
## 2 0.9 98900 3.1 6579 0.2 37007
## 3 1.3 159166 2.6 13760 0.2 66685
## 4 2.4 318010 10.5 41746 1.4 390057
## 5 0.7 536235 4.2 18764 0.1 355417
## 6 1.8 115638 3.7 47609 1.5 191869
## DP05_0069PE DP05_0070E DP05_0070PE DP05_0071E DP05_0071PE DP05_0072E
## 1 6.1 12741080 12741080 2208868 17.3 1744028
## 2 1.2 3156145 3156145 191473 6.1 155179
## 3 1.1 6126452 6126452 253474 4.1 179787
## 4 12.9 3034392 3034392 881145 29.0 668365
## 5 2.8 12807060 12807060 974763 7.6 158286
## 6 6.1 3161105 3161105 450220 14.2 313226
## DP05_0072PE DP05_0073E DP05_0073PE DP05_0074E DP05_0074PE DP05_0075E
## 1 13.7 206543 1.6 27617 0.2 230680
## 2 4.9 7792 0.2 1714 0.1 26788
## 3 2.9 16816 0.3 8220 0.1 48651
## 4 22.0 25740 0.8 35071 1.2 151969
## 5 1.2 477312 3.7 24185 0.2 314980
## 6 9.9 12566 0.4 2766 0.1 121662
## DP05_0075PE DP05_0076E DP05_0076PE DP05_0077E DP05_0077PE DP05_0078E
## 1 1.8 10532212 82.7 7760732 60.9 1759316
## 2 0.8 2964672 93.9 2695583 85.4 113562
## 3 0.8 5872978 95.9 4857174 79.3 698641
## 4 5.0 2153247 71.0 1469593 48.4 266440
## 5 2.5 11832297 92.4 9723288 75.9 1352804
## 6 3.8 2710885 85.8 2459891 77.8 37546
## DP05_0078PE DP05_0079E DP05_0079PE DP05_0080E DP05_0080PE DP05_0081E
## 1 13.8 17215 0.1 711970 5.6 2887
## 2 3.6 9710 0.3 79643 2.5 2945
## 3 11.4 23149 0.4 120440 2.0 7095
## 4 8.8 30185 1.0 244471 8.1 18249
## 5 10.6 14367 0.1 450077 3.5 3557
## 6 1.2 27959 0.9 74784 2.4 28810
## DP05_0081PE DP05_0082E DP05_0082PE DP05_0083E DP05_0083PE DP05_0084E
## 1 0.0 24310 0.2 255782 2.0 11405
## 2 0.1 4000 0.1 59229 1.9 493
## 3 0.1 9832 0.2 156647 2.6 2330
## 4 0.6 11810 0.4 112499 3.7 3740
## 5 0.0 21357 0.2 266847 2.1 11356
## 6 0.9 6356 0.2 75539 2.4 2925
## DP05_0084PE DP05_0085E DP05_0085PE DP05_0086E DP05_0086PE DP05_0087E
## 1 0.1 244377 1.9 5376176 (X) 9074766
## 2 0.0 58736 1.9 1409568 (X) 2327219
## 3 0.0 154317 2.5 2806296 (X) 4633370
## 4 0.1 108759 3.6 1268717 (X) 2075166
## 5 0.1 255491 2.0 5713136 (X) 9781212
## 6 0.1 72614 2.3 1108739 (X) 2087341
## DP05_0087PE DP05_0088E DP05_0088PE DP05_0089E DP05_0089PE
## 1 9074766 4385340 48.3 4689426 51.7
## 2 2327219 1139250 49.0 1187969 51.0
## 3 4633370 2240784 48.4 2392586 51.6
## 4 2075166 1036350 49.9 1038816 50.1
## 5 9781212 4726343 48.3 5054869 51.7
## 6 2087341 1041497 49.9 1045844 50.1estimate <- grep('Estimate',meta$id)
Estimate <- meta[estimate,]
head(Estimate)## GEO_ID id
## 2 DP05_0001E Estimate!!SEX AND AGE!!Total population
## 4 DP05_0001PE Percent Estimate!!SEX AND AGE!!Total population
## 6 DP05_0002E Estimate!!SEX AND AGE!!Total population!!Male
## 8 DP05_0002PE Percent Estimate!!SEX AND AGE!!Total population!!Male
## 10 DP05_0003E Estimate!!SEX AND AGE!!Total population!!Female
## 12 DP05_0003PE Percent Estimate!!SEX AND AGE!!Total population!!Femaleage <- Estimate[grep('AGE',Estimate$id),]
head(age)## GEO_ID id
## 2 DP05_0001E Estimate!!SEX AND AGE!!Total population
## 4 DP05_0001PE Percent Estimate!!SEX AND AGE!!Total population
## 6 DP05_0002E Estimate!!SEX AND AGE!!Total population!!Male
## 8 DP05_0002PE Percent Estimate!!SEX AND AGE!!Total population!!Male
## 10 DP05_0003E Estimate!!SEX AND AGE!!Total population!!Female
## 12 DP05_0003PE Percent Estimate!!SEX AND AGE!!Total population!!Femalerace <- Estimate[grep('RACE',Estimate$id),]
head(race)## GEO_ID id
## 130 DP05_0033E Estimate!!RACE!!Total population
## 132 DP05_0033PE Percent Estimate!!RACE!!Total population
## 134 DP05_0034E Estimate!!RACE!!Total population!!One race
## 136 DP05_0034PE Percent Estimate!!RACE!!Total population!!One race
## 138 DP05_0035E Estimate!!RACE!!Total population!!Two or more races
## 140 DP05_0035PE Percent Estimate!!RACE!!Total population!!Two or more racespercentRace <- race[grep('Percent',race$id),]
percentRace$id <- gsub('Percent Estimate!!RACE!!','',percentRace$id)
percentRace## GEO_ID
## 132 DP05_0033PE
## 136 DP05_0034PE
## 140 DP05_0035PE
## 144 DP05_0036PE
## 148 DP05_0037PE
## 152 DP05_0038PE
## 156 DP05_0039PE
## 160 DP05_0040PE
## 164 DP05_0041PE
## 168 DP05_0042PE
## 172 DP05_0043PE
## 176 DP05_0044PE
## 180 DP05_0045PE
## 184 DP05_0046PE
## 188 DP05_0047PE
## 192 DP05_0048PE
## 196 DP05_0049PE
## 200 DP05_0050PE
## 204 DP05_0051PE
## 208 DP05_0052PE
## 212 DP05_0053PE
## 216 DP05_0054PE
## 220 DP05_0055PE
## 224 DP05_0056PE
## 228 DP05_0057PE
## 232 DP05_0058PE
## 236 DP05_0059PE
## 240 DP05_0060PE
## 244 DP05_0061PE
## 248 DP05_0062PE
## 280 DP05_0070PE
## 284 DP05_0071PE
## 288 DP05_0072PE
## 292 DP05_0073PE
## 296 DP05_0074PE
## 300 DP05_0075PE
## 304 DP05_0076PE
## 308 DP05_0077PE
## 312 DP05_0078PE
## 316 DP05_0079PE
## 320 DP05_0080PE
## 324 DP05_0081PE
## 328 DP05_0082PE
## 332 DP05_0083PE
## 336 DP05_0084PE
## 340 DP05_0085PE
## id
## 132 Total population
## 136 Total population!!One race
## 140 Total population!!Two or more races
## 144 Total population!!One race
## 148 Total population!!One race!!White
## 152 Total population!!One race!!Black or African American
## 156 Total population!!One race!!American Indian and Alaska Native
## 160 Total population!!One race!!American Indian and Alaska Native!!Cherokee tribal grouping
## 164 Total population!!One race!!American Indian and Alaska Native!!Chippewa tribal grouping
## 168 Total population!!One race!!American Indian and Alaska Native!!Navajo tribal grouping
## 172 Total population!!One race!!American Indian and Alaska Native!!Sioux tribal grouping
## 176 Total population!!One race!!Asian
## 180 Total population!!One race!!Asian!!Asian Indian
## 184 Total population!!One race!!Asian!!Chinese
## 188 Total population!!One race!!Asian!!Filipino
## 192 Total population!!One race!!Asian!!Japanese
## 196 Total population!!One race!!Asian!!Korean
## 200 Total population!!One race!!Asian!!Vietnamese
## 204 Total population!!One race!!Asian!!Other Asian
## 208 Total population!!One race!!Native Hawaiian and Other Pacific Islander
## 212 Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Native Hawaiian
## 216 Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Guamanian or Chamorro
## 220 Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Samoan
## 224 Total population!!One race!!Native Hawaiian and Other Pacific Islander!!Other Pacific Islander
## 228 Total population!!One race!!Some other race
## 232 Total population!!Two or more races
## 236 Total population!!Two or more races!!White and Black or African American
## 240 Total population!!Two or more races!!White and American Indian and Alaska Native
## 244 Total population!!Two or more races!!White and Asian
## 248 Total population!!Two or more races!!Black or African American and American Indian and Alaska Native
## 280 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population
## 284 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 288 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Mexican
## 292 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Puerto Rican
## 296 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Cuban
## 300 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)!!Other Hispanic or Latino
## 304 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino
## 308 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone
## 312 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone
## 316 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!American Indian and Alaska Native alone
## 320 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone
## 324 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Native Hawaiian and Other Pacific Islander alone
## 328 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Some other race alone
## 332 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races
## 336 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races including Some other race
## 340 Percent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Two or more races!!Two races excluding Some other race, and Three or more racesLets get the white, black, asian, native american indian, two or more total, pacific islander, and latino groups.
totalPop <- percentRace[1,]
black <- percentRace[6,]
white <- percentRace[5,]
twoOrMore <- percentRace[3,]
AmericanIndian <- percentRace[7,]
Asian <- percentRace[12,]
pacificIslander <- percentRace[20,]
LatinoHispanic <- percentRace[32,]
totalPop$id <- 'total_state_population'
black$id <- 'percent_black'
white$id <- 'percent_white'
twoOrMore$id <- 'percent_two_or_more'
AmericanIndian$id <- 'percent_Native_American'
Asian$id <- 'percent_Asian'
pacificIslander$id <- 'percent_Pacific_Islander'
LatinoHispanic$id <- 'percent_Latino'
EthnicBackground <- rbind(totalPop,black,white,twoOrMore,AmericanIndian,Asian,pacificIslander,LatinoHispanic)
EthnicBackground## GEO_ID id
## 132 DP05_0033PE total_state_population
## 152 DP05_0038PE percent_black
## 148 DP05_0037PE percent_white
## 140 DP05_0035PE percent_two_or_more
## 156 DP05_0039PE percent_Native_American
## 176 DP05_0044PE percent_Asian
## 208 DP05_0052PE percent_Pacific_Islander
## 284 DP05_0071PE percent_LatinoIDs <- EthnicBackground$GEO_ID
class(IDs)## [1] "factor"IDs <- as.character(paste(IDs))DF2 <- DF[,c('NAME',IDs)]
DF2## NAME DP05_0033PE DP05_0038PE DP05_0037PE DP05_0035PE DP05_0039PE
## 1 Illinois 12741080 14.1 71.7 2.7 0.3
## 2 Iowa 3156145 3.6 90.2 2.2 0.4
## 3 Missouri 6126452 11.5 82.2 2.8 0.5
## 4 Nevada 3034392 9.2 63.4 5.1 1.5
## 5 Pennsylvania 12807060 11.2 80.1 2.6 0.2
## 6 Utah 3161105 1.3 85.7 3.2 1.1
## 7 West Virginia 1805832 3.8 93.0 1.9 0.1
## 8 Colorado 5695564 4.2 84.1 4.0 1.0
## 9 Georgia 10519475 31.6 58.3 2.7 0.3
## 10 Idaho 1754208 0.7 89.9 3.0 1.3
## 11 Maryland 6042718 30.0 54.7 3.7 0.2
## 12 New Jersey 8908520 13.6 66.9 2.8 0.2
## 13 New Mexico 2095428 2.2 76.4 3.2 9.6
## 14 Ohio 11689442 12.4 81.0 3.1 0.2
## 15 Alaska 737438 3.4 64.4 8.4 15.1
## 16 Florida 21299325 16.0 74.6 2.9 0.3
## 17 Hawaii 1420491 2.0 24.3 24.3 0.2
## 18 Massachusetts 6902149 7.8 77.3 3.4 0.2
## 19 South Carolina 5084127 26.6 67.0 2.4 0.5
## 20 Maine 1338404 1.4 94.3 2.2 0.6
## 21 North Carolina 10383620 21.4 68.4 2.9 1.2
## 22 South Dakota 882235 2.2 84.0 2.7 8.8
## 23 Alabama 4887871 26.7 67.7 2.1 0.5
## 24 Indiana 6691878 9.5 82.8 2.7 0.2
## 25 Kentucky 4468402 7.9 86.7 2.4 0.2
## 26 Louisiana 4659978 32.4 61.7 2.3 0.6
## 27 Tennessee 6770010 16.8 77.3 2.2 0.3
## 28 Vermont 626299 1.2 94.1 2.0 0.3
## 29 Washington 7535591 3.9 74.8 6.0 1.3
## 30 Michigan 9995915 13.8 78.3 2.9 0.5
## 31 Nebraska 1929268 4.7 86.4 3.1 1.0
## 32 New Hampshire 1356458 1.7 92.4 2.2 0.3
## 33 Oklahoma 3943079 7.3 72.2 7.7 7.8
## 34 Oregon 4190713 2.0 83.9 4.7 1.2
## 35 Rhode Island 1057315 6.7 80.7 3.1 0.4
## 36 Wisconsin 5813568 6.4 85.3 2.5 0.9
## 37 Wyoming 577737 0.6 91.5 2.4 2.8
## 38 Arkansas 3013825 15.2 76.5 2.9 0.7
## 39 California 39557045 5.8 59.5 5.1 0.8
## 40 Delaware 967171 22.5 68.2 2.7 0.5
## 41 Kansas 2911510 5.9 84.0 3.7 0.9
## 42 Minnesota 5611179 6.6 82.5 3.1 1.1
## 43 Mississippi 2986530 38.0 58.1 1.5 0.4
## 44 Montana 1062305 0.5 88.6 3.1 6.4
## 45 Virginia 8517685 19.2 67.4 4.1 0.3
## 46 Arizona 7171646 4.7 78.0 4.0 4.6
## 47 Connecticut 3572665 11.0 75.2 3.4 0.3
## 48 New York 19542209 15.7 63.3 3.3 0.4
## 49 North Dakota 760077 3.4 85.7 2.3 5.4
## 50 Texas 28701845 12.3 73.5 2.7 0.5
## DP05_0044PE DP05_0052PE DP05_0071PE
## 1 5.6 0.0 17.3
## 2 2.5 0.1 6.1
## 3 2.0 0.1 4.1
## 4 8.2 0.6 29.0
## 5 3.6 0.0 7.6
## 6 2.4 0.9 14.2
## 7 0.7 0.0 1.4
## 8 3.2 0.1 21.7
## 9 4.1 0.1 9.7
## 10 1.5 0.2 12.7
## 11 6.3 0.1 10.4
## 12 9.7 0.0 20.6
## 13 1.6 0.1 49.1
## 14 2.3 0.0 3.9
## 15 6.3 1.1 7.2
## 16 2.8 0.1 26.1
## 17 37.6 10.2 10.7
## 18 6.8 0.0 12.3
## 19 1.6 0.1 5.8
## 20 1.2 0.0 1.7
## 21 3.0 0.1 9.6
## 22 1.7 0.0 3.9
## 23 1.3 0.0 4.3
## 24 2.3 0.1 7.1
## 25 1.5 0.1 3.6
## 26 1.6 0.0 5.1
## 27 1.8 0.1 5.5
## 28 1.9 0.0 2.0
## 29 8.8 0.7 12.9
## 30 3.3 0.0 5.2
## 31 2.4 0.1 11.1
## 32 2.7 0.0 3.9
## 33 2.1 0.1 10.9
## 34 4.6 0.4 13.3
## 35 3.4 0.1 15.9
## 36 2.8 0.0 6.9
## 37 1.0 0.2 10.0
## 38 1.6 0.3 7.6
## 39 14.7 0.4 39.3
## 40 4.0 0.1 9.5
## 41 2.8 0.1 12.0
## 42 4.9 0.0 5.5
## 43 0.9 0.0 2.9
## 44 0.8 0.1 3.9
## 45 6.5 0.1 9.5
## 46 3.3 0.2 31.6
## 47 4.6 0.0 16.5
## 48 8.5 0.0 19.2
## 49 1.8 0.0 3.6
## 50 5.0 0.1 39.6colnames(DF2) <- c('state',EthnicBackground$id)
DF2## state total_state_population percent_black percent_white
## 1 Illinois 12741080 14.1 71.7
## 2 Iowa 3156145 3.6 90.2
## 3 Missouri 6126452 11.5 82.2
## 4 Nevada 3034392 9.2 63.4
## 5 Pennsylvania 12807060 11.2 80.1
## 6 Utah 3161105 1.3 85.7
## 7 West Virginia 1805832 3.8 93.0
## 8 Colorado 5695564 4.2 84.1
## 9 Georgia 10519475 31.6 58.3
## 10 Idaho 1754208 0.7 89.9
## 11 Maryland 6042718 30.0 54.7
## 12 New Jersey 8908520 13.6 66.9
## 13 New Mexico 2095428 2.2 76.4
## 14 Ohio 11689442 12.4 81.0
## 15 Alaska 737438 3.4 64.4
## 16 Florida 21299325 16.0 74.6
## 17 Hawaii 1420491 2.0 24.3
## 18 Massachusetts 6902149 7.8 77.3
## 19 South Carolina 5084127 26.6 67.0
## 20 Maine 1338404 1.4 94.3
## 21 North Carolina 10383620 21.4 68.4
## 22 South Dakota 882235 2.2 84.0
## 23 Alabama 4887871 26.7 67.7
## 24 Indiana 6691878 9.5 82.8
## 25 Kentucky 4468402 7.9 86.7
## 26 Louisiana 4659978 32.4 61.7
## 27 Tennessee 6770010 16.8 77.3
## 28 Vermont 626299 1.2 94.1
## 29 Washington 7535591 3.9 74.8
## 30 Michigan 9995915 13.8 78.3
## 31 Nebraska 1929268 4.7 86.4
## 32 New Hampshire 1356458 1.7 92.4
## 33 Oklahoma 3943079 7.3 72.2
## 34 Oregon 4190713 2.0 83.9
## 35 Rhode Island 1057315 6.7 80.7
## 36 Wisconsin 5813568 6.4 85.3
## 37 Wyoming 577737 0.6 91.5
## 38 Arkansas 3013825 15.2 76.5
## 39 California 39557045 5.8 59.5
## 40 Delaware 967171 22.5 68.2
## 41 Kansas 2911510 5.9 84.0
## 42 Minnesota 5611179 6.6 82.5
## 43 Mississippi 2986530 38.0 58.1
## 44 Montana 1062305 0.5 88.6
## 45 Virginia 8517685 19.2 67.4
## 46 Arizona 7171646 4.7 78.0
## 47 Connecticut 3572665 11.0 75.2
## 48 New York 19542209 15.7 63.3
## 49 North Dakota 760077 3.4 85.7
## 50 Texas 28701845 12.3 73.5
## percent_two_or_more percent_Native_American percent_Asian
## 1 2.7 0.3 5.6
## 2 2.2 0.4 2.5
## 3 2.8 0.5 2.0
## 4 5.1 1.5 8.2
## 5 2.6 0.2 3.6
## 6 3.2 1.1 2.4
## 7 1.9 0.1 0.7
## 8 4.0 1.0 3.2
## 9 2.7 0.3 4.1
## 10 3.0 1.3 1.5
## 11 3.7 0.2 6.3
## 12 2.8 0.2 9.7
## 13 3.2 9.6 1.6
## 14 3.1 0.2 2.3
## 15 8.4 15.1 6.3
## 16 2.9 0.3 2.8
## 17 24.3 0.2 37.6
## 18 3.4 0.2 6.8
## 19 2.4 0.5 1.6
## 20 2.2 0.6 1.2
## 21 2.9 1.2 3.0
## 22 2.7 8.8 1.7
## 23 2.1 0.5 1.3
## 24 2.7 0.2 2.3
## 25 2.4 0.2 1.5
## 26 2.3 0.6 1.6
## 27 2.2 0.3 1.8
## 28 2.0 0.3 1.9
## 29 6.0 1.3 8.8
## 30 2.9 0.5 3.3
## 31 3.1 1.0 2.4
## 32 2.2 0.3 2.7
## 33 7.7 7.8 2.1
## 34 4.7 1.2 4.6
## 35 3.1 0.4 3.4
## 36 2.5 0.9 2.8
## 37 2.4 2.8 1.0
## 38 2.9 0.7 1.6
## 39 5.1 0.8 14.7
## 40 2.7 0.5 4.0
## 41 3.7 0.9 2.8
## 42 3.1 1.1 4.9
## 43 1.5 0.4 0.9
## 44 3.1 6.4 0.8
## 45 4.1 0.3 6.5
## 46 4.0 4.6 3.3
## 47 3.4 0.3 4.6
## 48 3.3 0.4 8.5
## 49 2.3 5.4 1.8
## 50 2.7 0.5 5.0
## percent_Pacific_Islander percent_Latino
## 1 0.0 17.3
## 2 0.1 6.1
## 3 0.1 4.1
## 4 0.6 29.0
## 5 0.0 7.6
## 6 0.9 14.2
## 7 0.0 1.4
## 8 0.1 21.7
## 9 0.1 9.7
## 10 0.2 12.7
## 11 0.1 10.4
## 12 0.0 20.6
## 13 0.1 49.1
## 14 0.0 3.9
## 15 1.1 7.2
## 16 0.1 26.1
## 17 10.2 10.7
## 18 0.0 12.3
## 19 0.1 5.8
## 20 0.0 1.7
## 21 0.1 9.6
## 22 0.0 3.9
## 23 0.0 4.3
## 24 0.1 7.1
## 25 0.1 3.6
## 26 0.0 5.1
## 27 0.1 5.5
## 28 0.0 2.0
## 29 0.7 12.9
## 30 0.0 5.2
## 31 0.1 11.1
## 32 0.0 3.9
## 33 0.1 10.9
## 34 0.4 13.3
## 35 0.1 15.9
## 36 0.0 6.9
## 37 0.2 10.0
## 38 0.3 7.6
## 39 0.4 39.3
## 40 0.1 9.5
## 41 0.1 12.0
## 42 0.0 5.5
## 43 0.0 2.9
## 44 0.1 3.9
## 45 0.1 9.5
## 46 0.2 31.6
## 47 0.0 16.5
## 48 0.0 19.2
## 49 0.0 3.6
## 50 0.1 39.6Ethnicities <- DF2[order(DF2$state),]
row.names(Ethnicities) <- NULL
Ethnicities## state total_state_population percent_black percent_white
## 1 Alabama 4887871 26.7 67.7
## 2 Alaska 737438 3.4 64.4
## 3 Arizona 7171646 4.7 78.0
## 4 Arkansas 3013825 15.2 76.5
## 5 California 39557045 5.8 59.5
## 6 Colorado 5695564 4.2 84.1
## 7 Connecticut 3572665 11.0 75.2
## 8 Delaware 967171 22.5 68.2
## 9 Florida 21299325 16.0 74.6
## 10 Georgia 10519475 31.6 58.3
## 11 Hawaii 1420491 2.0 24.3
## 12 Idaho 1754208 0.7 89.9
## 13 Illinois 12741080 14.1 71.7
## 14 Indiana 6691878 9.5 82.8
## 15 Iowa 3156145 3.6 90.2
## 16 Kansas 2911510 5.9 84.0
## 17 Kentucky 4468402 7.9 86.7
## 18 Louisiana 4659978 32.4 61.7
## 19 Maine 1338404 1.4 94.3
## 20 Maryland 6042718 30.0 54.7
## 21 Massachusetts 6902149 7.8 77.3
## 22 Michigan 9995915 13.8 78.3
## 23 Minnesota 5611179 6.6 82.5
## 24 Mississippi 2986530 38.0 58.1
## 25 Missouri 6126452 11.5 82.2
## 26 Montana 1062305 0.5 88.6
## 27 Nebraska 1929268 4.7 86.4
## 28 Nevada 3034392 9.2 63.4
## 29 New Hampshire 1356458 1.7 92.4
## 30 New Jersey 8908520 13.6 66.9
## 31 New Mexico 2095428 2.2 76.4
## 32 New York 19542209 15.7 63.3
## 33 North Carolina 10383620 21.4 68.4
## 34 North Dakota 760077 3.4 85.7
## 35 Ohio 11689442 12.4 81.0
## 36 Oklahoma 3943079 7.3 72.2
## 37 Oregon 4190713 2.0 83.9
## 38 Pennsylvania 12807060 11.2 80.1
## 39 Rhode Island 1057315 6.7 80.7
## 40 South Carolina 5084127 26.6 67.0
## 41 South Dakota 882235 2.2 84.0
## 42 Tennessee 6770010 16.8 77.3
## 43 Texas 28701845 12.3 73.5
## 44 Utah 3161105 1.3 85.7
## 45 Vermont 626299 1.2 94.1
## 46 Virginia 8517685 19.2 67.4
## 47 Washington 7535591 3.9 74.8
## 48 West Virginia 1805832 3.8 93.0
## 49 Wisconsin 5813568 6.4 85.3
## 50 Wyoming 577737 0.6 91.5
## percent_two_or_more percent_Native_American percent_Asian
## 1 2.1 0.5 1.3
## 2 8.4 15.1 6.3
## 3 4.0 4.6 3.3
## 4 2.9 0.7 1.6
## 5 5.1 0.8 14.7
## 6 4.0 1.0 3.2
## 7 3.4 0.3 4.6
## 8 2.7 0.5 4.0
## 9 2.9 0.3 2.8
## 10 2.7 0.3 4.1
## 11 24.3 0.2 37.6
## 12 3.0 1.3 1.5
## 13 2.7 0.3 5.6
## 14 2.7 0.2 2.3
## 15 2.2 0.4 2.5
## 16 3.7 0.9 2.8
## 17 2.4 0.2 1.5
## 18 2.3 0.6 1.6
## 19 2.2 0.6 1.2
## 20 3.7 0.2 6.3
## 21 3.4 0.2 6.8
## 22 2.9 0.5 3.3
## 23 3.1 1.1 4.9
## 24 1.5 0.4 0.9
## 25 2.8 0.5 2.0
## 26 3.1 6.4 0.8
## 27 3.1 1.0 2.4
## 28 5.1 1.5 8.2
## 29 2.2 0.3 2.7
## 30 2.8 0.2 9.7
## 31 3.2 9.6 1.6
## 32 3.3 0.4 8.5
## 33 2.9 1.2 3.0
## 34 2.3 5.4 1.8
## 35 3.1 0.2 2.3
## 36 7.7 7.8 2.1
## 37 4.7 1.2 4.6
## 38 2.6 0.2 3.6
## 39 3.1 0.4 3.4
## 40 2.4 0.5 1.6
## 41 2.7 8.8 1.7
## 42 2.2 0.3 1.8
## 43 2.7 0.5 5.0
## 44 3.2 1.1 2.4
## 45 2.0 0.3 1.9
## 46 4.1 0.3 6.5
## 47 6.0 1.3 8.8
## 48 1.9 0.1 0.7
## 49 2.5 0.9 2.8
## 50 2.4 2.8 1.0
## percent_Pacific_Islander percent_Latino
## 1 0.0 4.3
## 2 1.1 7.2
## 3 0.2 31.6
## 4 0.3 7.6
## 5 0.4 39.3
## 6 0.1 21.7
## 7 0.0 16.5
## 8 0.1 9.5
## 9 0.1 26.1
## 10 0.1 9.7
## 11 10.2 10.7
## 12 0.2 12.7
## 13 0.0 17.3
## 14 0.1 7.1
## 15 0.1 6.1
## 16 0.1 12.0
## 17 0.1 3.6
## 18 0.0 5.1
## 19 0.0 1.7
## 20 0.1 10.4
## 21 0.0 12.3
## 22 0.0 5.2
## 23 0.0 5.5
## 24 0.0 2.9
## 25 0.1 4.1
## 26 0.1 3.9
## 27 0.1 11.1
## 28 0.6 29.0
## 29 0.0 3.9
## 30 0.0 20.6
## 31 0.1 49.1
## 32 0.0 19.2
## 33 0.1 9.6
## 34 0.0 3.6
## 35 0.0 3.9
## 36 0.1 10.9
## 37 0.4 13.3
## 38 0.0 7.6
## 39 0.1 15.9
## 40 0.1 5.8
## 41 0.0 3.9
## 42 0.1 5.5
## 43 0.1 39.6
## 44 0.9 14.2
## 45 0.0 2.0
## 46 0.1 9.5
## 47 0.7 12.9
## 48 0.0 1.4
## 49 0.0 6.9
## 50 0.2 10.0write.csv(Ethnicities, 'race_distribution_2018.csv',row.names=FALSE)Merge the state licensing excel file with this csv file to add in the demographic race data per state.
stateREQs <- read.csv('stateLicensingRequirements.csv', sep=',', header=TRUE, na.strings=c('',' ','NA'))colnames(stateREQs)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_medianJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyMedianPayRangeAdvertised_Indeed"
## [34] "LMT_AnualMedianPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityMedianHomeValue"
## [36] "median2018IncomeByState"stateREQs <- stateREQs[,1:36]
colnames(stateREQs)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_medianJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyMedianPayRangeAdvertised_Indeed"
## [34] "LMT_AnualMedianPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityMedianHomeValue"
## [36] "median2018IncomeByState"Ethnicities$state <- as.factor(Ethnicities$state)StateLicensing <- merge(stateREQs,Ethnicities, by.x='state',by.y='state')
dim(StateLicensing)## [1] 50 44colnames(StateLicensing)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_medianJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyMedianPayRangeAdvertised_Indeed"
## [34] "LMT_AnualMedianPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityMedianHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"We added the demographics to the state licensing requirements and facts csv version of the excel version.
Now lets add in the fields from our Indeed web scrape to update the columns 32-34 of the StateLicensing table.We added them in manually before, but now that a table was made with the copy of the indeed web scrape script that uses the modified writeIndeedJobData5Pages() using the original getIndeedJobData5Pages(). We can update the table on the fly as needed as long as the Indeed site doesn’t change its layout. This file is the statesRates.csv file made in the ‘copy-indeed-webscrape-function-altered.Rmd’ script.
statesRates <- read.csv('statesRates.csv', sep=',',header=TRUE, na.strings=c('',' ','NA'),
stringsAsFactors = FALSE)
head(statesRates)## state jobsListed MinHourlySalary MaxHourlySalary MinAnnualSalary
## 1 AK 39 15.00000 15.00000 40000.00
## 2 AL 88 14.68750 53.93750 25000.00
## 3 AR 73 NA NA 35000.00
## 4 AZ 212 14.61792 76.93396 39622.64
## 5 CA 223 14.36099 85.91928 35640.52
## 6 CO 241 11.34191 68.46473 25000.00
## MaxAnnualSalary avgHourly avgAnualSalary
## 1 120000.00 15.00000 80000.00
## 2 50000.00 34.31250 37500.00
## 3 50000.00 NA 42500.00
## 4 100070.75 45.77594 69846.70
## 5 97261.44 50.14013 66450.98
## 6 62000.00 39.90332 43500.00statesOrdered <- statesRates[order(statesRates$state),]
head(statesOrdered)## state jobsListed MinHourlySalary MaxHourlySalary MinAnnualSalary
## 1 AK 39 15.00000 15.00000 40000.00
## 2 AL 88 14.68750 53.93750 25000.00
## 3 AR 73 NA NA 35000.00
## 4 AZ 212 14.61792 76.93396 39622.64
## 5 CA 223 14.36099 85.91928 35640.52
## 6 CO 241 11.34191 68.46473 25000.00
## MaxAnnualSalary avgHourly avgAnualSalary
## 1 120000.00 15.00000 80000.00
## 2 50000.00 34.31250 37500.00
## 3 50000.00 NA 42500.00
## 4 100070.75 45.77594 69846.70
## 5 97261.44 50.14013 66450.98
## 6 62000.00 39.90332 43500.00When ordering by the state abbreviations, the order isn’t the same as by the state spelled out. So lets compare the two and adjust changes.
statesOrdered$state## [1] "AK" "AL" "AR" "AZ" "CA" "CO" "CT" "DE" "FL" "GA" "HI" "IA" "ID" "IL" "IN"
## [16] "KS" "KY" "LA" "MA" "MD" "ME" "MI" "MN" "MO" "MS" "MT" "NC" "ND" "NE" "NH"
## [31] "NJ" "NM" "NV" "NY" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT" "VA"
## [46] "VT" "WA" "WI" "WV" "WY" NAThe statesOrdered has an NA, lets remove it.
statesOrdered <- statesOrdered[1:50,]states <- c("Alaska" ,"Alabama" , "Arkansas" , "Arizona" , "California",
"Colorado" , "Connecticut" , "Delaware" , "Florida" , "Georgia" ,
"Hawaii" , "Iowa" , "Idaho" , "Illinois" , "Indiana" ,
"Kansas" , "Kentucky", "Louisiana" , "Massachusetts", "Maryland" , "Maine",
"Michigan" , "Minnesota" , "Missouri" , "Mississippi" ,
"Montana" , "North Carolina","North Dakota" ,"Nebraska" , "New Hampshire" , "New Jersey", "New Mexico" , "Nevada" ,
"New York" , "Ohio" ,
"Oklahoma" , "Oregon" , "Pennsylvania" ,"Rhode Island", "South Carolina",
"South Dakota" , "Tennessee" , "Texas" ,"Utah" , "Virginia", "Vermont",
"Washington" ,"Wisconsin" , "West Virginia" , "Wyoming" )
states## [1] "Alaska" "Alabama" "Arkansas" "Arizona"
## [5] "California" "Colorado" "Connecticut" "Delaware"
## [9] "Florida" "Georgia" "Hawaii" "Iowa"
## [13] "Idaho" "Illinois" "Indiana" "Kansas"
## [17] "Kentucky" "Louisiana" "Massachusetts" "Maryland"
## [21] "Maine" "Michigan" "Minnesota" "Missouri"
## [25] "Mississippi" "Montana" "North Carolina" "North Dakota"
## [29] "Nebraska" "New Hampshire" "New Jersey" "New Mexico"
## [33] "Nevada" "New York" "Ohio" "Oklahoma"
## [37] "Oregon" "Pennsylvania" "Rhode Island" "South Carolina"
## [41] "South Dakota" "Tennessee" "Texas" "Utah"
## [45] "Virginia" "Vermont" "Washington" "Wisconsin"
## [49] "West Virginia" "Wyoming"statesOrdered$stateName <- states
statesOrdered <- statesOrdered[,c(1,9,2:8)]
statesOrdered <- statesOrdered[order(statesOrdered$stateName),]
head(statesOrdered)## state stateName jobsListed MinHourlySalary MaxHourlySalary MinAnnualSalary
## 2 AL Alabama 88 14.68750 53.93750 25000.00
## 1 AK Alaska 39 15.00000 15.00000 40000.00
## 4 AZ Arizona 212 14.61792 76.93396 39622.64
## 3 AR Arkansas 73 NA NA 35000.00
## 5 CA California 223 14.36099 85.91928 35640.52
## 6 CO Colorado 241 11.34191 68.46473 25000.00
## MaxAnnualSalary avgHourly avgAnualSalary
## 2 50000.00 34.31250 37500.00
## 1 120000.00 15.00000 80000.00
## 4 100070.75 45.77594 69846.70
## 3 50000.00 NA 42500.00
## 5 97261.44 50.14013 66450.98
## 6 62000.00 39.90332 43500.00Lets make sure the stateName of the statesOrdered and the state of the StateLicensing tables are identical before updating the table with the latest job information from Indeed.
as.factor(statesOrdered$stateName)==StateLicensing$state## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUEThat checks out, so we can now update the information with the latest data.
StateLicensing$LMT_AnualMedianPayAdvertised_Indeed <- statesOrdered$avgAnualSalary
StateLicensing$LMT_HourlyMedianPayRangeAdvertised_Indeed <- statesOrdered$avgHourly
StateLicensing$LMT_medianJobsListed_IndeedFirst5pages <- statesOrdered$jobsListedcolnames(StateLicensing)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_medianJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyMedianPayRangeAdvertised_Indeed"
## [34] "LMT_AnualMedianPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityMedianHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"We should change the names of those columns to say ‘avg’ instead of ‘median’.
colnames(StateLicensing)[32:34] <- gsub('[mM]edian','Avg',colnames(StateLicensing)[32:34])
colnames(StateLicensing)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_AvgJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyAvgPayRangeAdvertised_Indeed"
## [34] "LMT_AnualAvgPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityMedianHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"The Zillow data was also added manually from the find3zillowCitiesFunctionMedian2BRHomeValues.Rmd file that used a downloaded Zillow dataset on 2 bedroom zillow home value index value per city. The file is ‘updatedZillow2BR.csv’ made from our copy-find3zillowCitiesFunctionMean2BRHomeValues.Rmd script.
zillowData <- read.csv('updatedZillow2BR-10.csv',sep=',', header=TRUE, na.strings=c('',' ','NA'))head(zillowData)## State X6.30.2020
## 1 AK 244501.7
## 2 AL 104537.1
## 3 AR 114929.8
## 4 AZ 240336.3
## 5 CA 669171.6
## 6 CO 327365.4zillowData$State## [1] AK AL AR AZ CA CO CT DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS
## [26] MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
## 50 Levels: AK AL AR AZ CA CO CT DE FL GA HI IA ID IL IN KS KY LA MA MD ... WYzillowData$stateName <- states
zillowData## State X6.30.2020 stateName
## 1 AK 244501.67 Alaska
## 2 AL 104537.10 Alabama
## 3 AR 114929.76 Arkansas
## 4 AZ 240336.29 Arizona
## 5 CA 669171.59 California
## 6 CO 327365.43 Colorado
## 7 CT 212252.40 Connecticut
## 8 DE 215976.06 Delaware
## 9 FL 215245.23 Florida
## 10 GA 192060.49 Georgia
## 11 HI 574836.56 Hawaii
## 12 IA 129324.62 Iowa
## 13 ID 238004.33 Idaho
## 14 IL 202689.33 Illinois
## 15 IN 125366.69 Indiana
## 16 KS 152226.63 Kansas
## 17 KY 193124.32 Kentucky
## 18 LA 141609.86 Louisiana
## 19 MA 537437.75 Massachusetts
## 20 MD 207428.66 Maryland
## 21 ME 301941.27 Maine
## 22 MI 107764.58 Michigan
## 23 MN 252568.04 Minnesota
## 24 MO 143279.22 Missouri
## 25 MS 81255.42 Mississippi
## 26 MT 240545.87 Montana
## 27 NC 166565.93 North Carolina
## 28 ND 160193.50 North Dakota
## 29 NE 192748.95 Nebraska
## 30 NH 227562.85 New Hampshire
## 31 NJ 259979.79 New Jersey
## 32 NM 180789.56 New Mexico
## 33 NV 264014.69 Nevada
## 34 NY 637718.65 New York
## 35 OH 102083.29 Ohio
## 36 OK 109105.35 Oklahoma
## 37 OR 364129.81 Oregon
## 38 PA 172920.62 Pennsylvania
## 39 RI 252537.90 Rhode Island
## 40 SC 181933.17 South Carolina
## 41 SD 164059.75 South Dakota
## 42 TN 177130.29 Tennessee
## 43 TX 195904.48 Texas
## 44 UT 319213.45 Utah
## 45 VA 252271.37 Virginia
## 46 VT 193815.55 Vermont
## 47 WA 417702.86 Washington
## 48 WI 170618.96 Wisconsin
## 49 WV 82370.00 West Virginia
## 50 WY 240742.71 WyomingzOrdered <- zillowData[order(zillowData$stateName),]
zOrdered## State X6.30.2020 stateName
## 2 AL 104537.10 Alabama
## 1 AK 244501.67 Alaska
## 4 AZ 240336.29 Arizona
## 3 AR 114929.76 Arkansas
## 5 CA 669171.59 California
## 6 CO 327365.43 Colorado
## 7 CT 212252.40 Connecticut
## 8 DE 215976.06 Delaware
## 9 FL 215245.23 Florida
## 10 GA 192060.49 Georgia
## 11 HI 574836.56 Hawaii
## 13 ID 238004.33 Idaho
## 14 IL 202689.33 Illinois
## 15 IN 125366.69 Indiana
## 12 IA 129324.62 Iowa
## 16 KS 152226.63 Kansas
## 17 KY 193124.32 Kentucky
## 18 LA 141609.86 Louisiana
## 21 ME 301941.27 Maine
## 20 MD 207428.66 Maryland
## 19 MA 537437.75 Massachusetts
## 22 MI 107764.58 Michigan
## 23 MN 252568.04 Minnesota
## 25 MS 81255.42 Mississippi
## 24 MO 143279.22 Missouri
## 26 MT 240545.87 Montana
## 29 NE 192748.95 Nebraska
## 33 NV 264014.69 Nevada
## 30 NH 227562.85 New Hampshire
## 31 NJ 259979.79 New Jersey
## 32 NM 180789.56 New Mexico
## 34 NY 637718.65 New York
## 27 NC 166565.93 North Carolina
## 28 ND 160193.50 North Dakota
## 35 OH 102083.29 Ohio
## 36 OK 109105.35 Oklahoma
## 37 OR 364129.81 Oregon
## 38 PA 172920.62 Pennsylvania
## 39 RI 252537.90 Rhode Island
## 40 SC 181933.17 South Carolina
## 41 SD 164059.75 South Dakota
## 42 TN 177130.29 Tennessee
## 43 TX 195904.48 Texas
## 44 UT 319213.45 Utah
## 46 VT 193815.55 Vermont
## 45 VA 252271.37 Virginia
## 47 WA 417702.86 Washington
## 49 WV 82370.00 West Virginia
## 48 WI 170618.96 Wisconsin
## 50 WY 240742.71 WyomingzillowOrdered <- zOrdered[,2:3]
colnames(zillowOrdered)[1] <- 'zillow2BR_2020May_10cities'
zillowOrdered## zillow2BR_2020May_10cities stateName
## 2 104537.10 Alabama
## 1 244501.67 Alaska
## 4 240336.29 Arizona
## 3 114929.76 Arkansas
## 5 669171.59 California
## 6 327365.43 Colorado
## 7 212252.40 Connecticut
## 8 215976.06 Delaware
## 9 215245.23 Florida
## 10 192060.49 Georgia
## 11 574836.56 Hawaii
## 13 238004.33 Idaho
## 14 202689.33 Illinois
## 15 125366.69 Indiana
## 12 129324.62 Iowa
## 16 152226.63 Kansas
## 17 193124.32 Kentucky
## 18 141609.86 Louisiana
## 21 301941.27 Maine
## 20 207428.66 Maryland
## 19 537437.75 Massachusetts
## 22 107764.58 Michigan
## 23 252568.04 Minnesota
## 25 81255.42 Mississippi
## 24 143279.22 Missouri
## 26 240545.87 Montana
## 29 192748.95 Nebraska
## 33 264014.69 Nevada
## 30 227562.85 New Hampshire
## 31 259979.79 New Jersey
## 32 180789.56 New Mexico
## 34 637718.65 New York
## 27 166565.93 North Carolina
## 28 160193.50 North Dakota
## 35 102083.29 Ohio
## 36 109105.35 Oklahoma
## 37 364129.81 Oregon
## 38 172920.62 Pennsylvania
## 39 252537.90 Rhode Island
## 40 181933.17 South Carolina
## 41 164059.75 South Dakota
## 42 177130.29 Tennessee
## 43 195904.48 Texas
## 44 319213.45 Utah
## 46 193815.55 Vermont
## 45 252271.37 Virginia
## 47 417702.86 Washington
## 49 82370.00 West Virginia
## 48 170618.96 Wisconsin
## 50 240742.71 WyomingStateLicensing$state==zillowOrdered$stateName## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUEEverything checks out so we can now update our zillow 2 bedroom median home values per state to the mean values per state instead. Some of the top 3 cities of population weren’t in the Zillow data, such as AL.
StateLicensing$Zillow_2BR_3cityMedianHomeValue <- zillowOrdered$zillow2BR_2020May
colnames(StateLicensing)[35] <- 'Zillow_2BR_3cityAverageHomeValue'
colnames(StateLicensing)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_AvgJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyAvgPayRangeAdvertised_Indeed"
## [34] "LMT_AnualAvgPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityAverageHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"Now, we can write our table out to csv. We manually put in the 2018 median household income per state from data.census.gov, but we can update that later or as needed when more relevant data is available. That was two years ago, and there should be 2020 data soon because of the 2020 census.
Because this will be the same table but with added and updated columns, we should name it something else to keep separate from the original table with fewer columns.
write.csv(StateLicensing,'stateLicensingDemographicsAddedAndUpdated.csv',row.names=FALSE)Note that the original csv file stateLicensingRequirements.csv has to be manually input for the changes, then use this script to add the demographics, updated Zillow 2BR home values, and update the average Indeed pay rates and job listings. Don’t make changes in the file just written out or else they will be lost if modifying the state by state licensing requirements of the first variables.
Now, lets add in the data on the alternate jobs available for a massage therapist looking to move to another state and work as a massage therapist, but needing alternate work to pay bills while waiting for license approval.
I have gathered the information on job title for the first five web pages of job listings on Indeed for similar jobs to a massage therapist or that might not need any license to work as. These jobs are: nanny, server, personal trainer, security, data science, house cleaner, warehouse worker, and cashier. The data scientist job is more relevant to me specifically, but some people could possibly have experience as a coder or computer programmer and statistical analyst to land a job in data science without the intense and rigorous education required in machine learning that I went through. It is not likely, but some people do make it to similar jobs with just a couple months to years of proving they are qualified as computer programmers and coders. So, we will assume that is the case, and that companies don’t want to pay higher salaries to arrogant top educated and possibly difficult to work with graduates and would rather pay motivated and slightly proven but coachable males and females from team building backgrounds or sports competitive athletes used to the heirarchical structure of ‘yes sir and mam’ to their directors at lower wages than a professional, skilled, and educated in the background theory that could possibly question the director’s motives, authority, and decisions, and possibly leak the lack of sound leadership within the company to something as viral as a social media site.
So, without further a do, lets start, shall we?
We will import the data sets individually for each of the 50 states’ counts, and salaries be it hourly, annual, or both from our top three populated cities of each state.
house cleaner:
houseCleaner <- read.csv('./Alternate Jobs Each State Indeed/statesRates-house cleaner.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
houseCleaner$stateName <- states
houseCleaner <- houseCleaner[,c(1,10,2:9)]
head(houseCleaner)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 4 house cleaner 10.000000 14.000000
## 2 AL Alabama 72 house cleaner 8.738971 9.856618
## 3 AR Arkansas 36 house cleaner 12.636364 15.000000
## 4 AZ Arizona 171 house cleaner 10.556433 23.935673
## 5 CA California 216 house cleaner 11.579630 29.375000
## 6 CO Colorado 217 house cleaner 12.000000 23.525346
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 12.000000 NA
## 2 17000 20000 9.297794 18500
## 3 NA NA 13.818182 NA
## 4 NA NA 17.246053 NA
## 5 40600 40600 20.477315 40600
## 6 20000 30000 17.762673 25000nanny:
nanny <- read.csv('./Alternate Jobs Each State Indeed/statesRates-nanny.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
nanny$stateName <- states
nanny <- nanny[,c(1,10,2:9)]
head(nanny)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 23 nanny 10.347826 15.30435
## 2 AL Alabama 42 nanny 10.666667 16.38095
## 3 AR Arkansas 81 nanny 9.308642 16.88889
## 4 AZ Arizona 182 nanny 10.000000 25.00000
## 5 CA California 199 nanny 10.000000 40.85427
## 6 CO Colorado 157 nanny 10.000000 25.89172
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 12.82609 NA
## 2 NA NA 13.52381 NA
## 3 NA NA 13.09877 NA
## 4 33000 35000 17.50000 34000
## 5 65000 110000 25.42714 87500
## 6 NA NA 17.94586 NAcashier:
cashier <- read.csv('./Alternate Jobs Each State Indeed/statesRates-cashier.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
cashier$stateName <- states
cashier <- cashier[,c(1,10,2:9)]
head(cashier)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 103 cashier 10.269663 15.19101
## 2 AL Alabama 183 cashier 7.959016 13.42077
## 3 AR Arkansas 190 cashier 8.559211 12.07895
## 4 AZ Arizona 217 cashier 10.751152 15.83871
## 5 CA California 227 cashier 13.004405 20.00000
## 6 CO Colorado 212 cashier 10.306604 18.63679
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 12.73034 NA
## 2 NA NA 10.68989 NA
## 3 NA NA 10.31908 NA
## 4 NA NA 13.29493 NA
## 5 NA NA 16.50220 NA
## 6 NA NA 14.47170 NApersonal trainer:
personalTrainer <- read.csv('./Alternate Jobs Each State Indeed/statesRates-personal trainer.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
personalTrainer$stateName <- states
personalTrainer <- personalTrainer[,c(1,10,2:9)]
head(personalTrainer)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 47 personal trainer NA NA
## 2 AL Alabama 92 personal trainer 8.650602 51.74699
## 3 AR Arkansas 72 personal trainer 15.888889 27.55556
## 4 AZ Arizona 168 personal trainer 9.517857 35.86310
## 5 CA California 190 personal trainer 13.178947 50.02105
## 6 CO Colorado 196 personal trainer 12.336735 43.07653
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 35000.00 60000.00 NA 47500.00
## 2 40000.00 50000.00 30.19880 45000.00
## 3 50000.00 50000.00 21.72222 50000.00
## 4 31882.84 83681.57 22.69048 57782.20
## 5 31210.53 90894.74 31.60000 61052.63
## 6 49222.36 66696.85 27.70663 57959.61security:
security <- read.csv('./Alternate Jobs Each State Indeed/statesRates-security.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
security$stateName <- states
security <- security[,c(1,10,2:9)]
head(security)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 169 security 13.709467 20.00592
## 2 AL Alabama 212 security 7.818396 14.27358
## 3 AR Arkansas 199 security 9.276382 12.86809
## 4 AZ Arizona 221 security 12.000000 26.16109
## 5 CA California 230 security 13.856087 25.96522
## 6 CO Colorado 234 security 12.316239 24.99897
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 65119.07 150000.0 16.85769 107559.53
## 2 23170.14 150000.0 11.04599 86585.07
## 3 25658.97 204271.4 11.07224 114965.17
## 4 29674.76 73751.1 19.08054 51712.93
## 5 55979.92 107132.8 19.91065 81556.37
## 6 36821.15 136095.5 18.65761 86458.35server <- read.csv('./Alternate Jobs Each State Indeed/statesRates-server.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
server$stateName <- states
server <- server[,c(1,10,2:9)]
head(server)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 73 server 10.026849 10.52000
## 2 AL Alabama 220 server 8.643182 25.24545
## 3 AR Arkansas 197 server 8.000000 18.47619
## 4 AZ Arizona 221 server 9.000000 22.81448
## 5 CA California 224 server 12.343750 29.91071
## 6 CO Colorado 225 server 9.077200 32.33333
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 10.27342 NA
## 2 NA NA 16.94432 NA
## 3 NA NA 13.23810 NA
## 4 NA NA 15.90724 NA
## 5 55000 75000 21.12723 65000
## 6 NA NA 20.70527 NAwarehouse worker:
warehouseWorker <- read.csv('./Alternate Jobs Each State Indeed/statesRates-warehouse worker.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
warehouseWorker$stateName <- states
warehouseWorker <- warehouseWorker[,c(1,10,2:9)]
head(warehouseWorker)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 206 warehouse worker 11.907767 32.19903
## 2 AL Alabama 219 warehouse worker 9.221461 17.84932
## 3 AR Arkansas 212 warehouse worker 9.702830 21.47830
## 4 AZ Arizona 235 warehouse worker 11.405319 27.69255
## 5 CA California 243 warehouse worker 13.012346 32.50198
## 6 CO Colorado 250 warehouse worker 11.672000 22.19608
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 52697.00 61420.00 22.05340 57058.50
## 2 50000.00 55000.00 13.53539 52500.00
## 3 NA NA 15.59057 NA
## 4 45000.00 55000.00 19.54894 50000.00
## 5 37556.68 60359.91 22.75716 48958.29
## 6 25519.46 48776.00 16.93404 37147.73data scientist:
dataScientist <- read.csv('./Alternate Jobs Each State Indeed/statesRates-data scientist.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
dataScientist$stateName <- states
dataScientist <- dataScientist[,c(1,10,2:9)]
head(dataScientist)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 37 data scientist NA NA
## 2 AL Alabama 122 data scientist NA NA
## 3 AR Arkansas 70 data scientist NA NA
## 4 AZ Arizona 183 data scientist 45.00000 50.00000
## 5 CA California 231 data scientist 12.79545 35.02597
## 6 CO Colorado 194 data scientist 16.00000 18.00000
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 89345.30 162810.8 NA 126078.1
## 2 102923.16 160000.0 NA 131461.6
## 3 88068.19 160000.0 NA 124034.1
## 4 75991.25 130854.2 47.50000 103422.7
## 5 81558.44 159653.7 23.91071 120606.1
## 6 91666.98 160000.0 17.00000 125833.5tutor:
tutor <- read.csv('./Alternate Jobs Each State Indeed/statesRates-tutor.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
tutor$stateName <- states
tutor <- tutor[,c(1,10,2:9)]
head(tutor)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 142 tutor 11.84109 29.10891
## 2 AL Alabama 201 tutor 11.34109 25.00000
## 3 AR Arkansas 112 tutor 10.53571 14.10714
## 4 AZ Arizona 214 tutor 11.65421 43.73832
## 5 CA California 217 tutor 12.66359 71.70507
## 6 CO Colorado 207 tutor 12.85507 37.14976
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 20.47500 NA
## 2 21623.00 38100 18.17055 29861.5
## 3 NA NA 12.32143 NA
## 4 40979.59 58000 27.69626 49489.8
## 5 50000.00 50000 42.18433 50000.0
## 6 25000.00 36750 25.00242 30875.0clerical:
clerical <- read.csv('./Alternate Jobs Each State Indeed/statesRates-clerical.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
clerical$stateName <- states
clerical <- clerical[,c(1,10,2:9)]
head(clerical)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 211 clerical 10.625592 30.51005
## 2 AL Alabama 223 clerical 8.336323 19.00897
## 3 AR Arkansas 222 clerical 10.084459 19.32432
## 4 AZ Arizona 226 clerical 11.340708 27.77336
## 5 CA California 225 clerical 12.986667 36.22613
## 6 CO Colorado 243 clerical 11.349794 32.10469
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 33277.43 79972.38 20.56782 56624.91
## 2 25087.93 62713.78 13.67265 43900.86
## 3 23959.70 73183.76 14.70439 48571.73
## 4 30937.42 64084.62 19.55704 47511.02
## 5 38617.78 81268.33 24.60640 59943.06
## 6 22628.77 64479.38 21.72724 43554.08teacher:
teacher <- read.csv('./Alternate Jobs Each State Indeed/statesRates-teacher.csv', header=TRUE,
na.strings=c('',' ','NA'), sep=',')
teacher$stateName <- states
teacher <- teacher[,c(1,10,2:9)]
head(teacher)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 194 teacher 13.865979 34.71134
## 2 AL Alabama 213 teacher 7.771127 31.05634
## 3 AR Arkansas 178 teacher 11.280899 24.86517
## 4 AZ Arizona 245 teacher 12.624490 55.10204
## 5 CA California 236 teacher 13.038136 63.00847
## 6 CO Colorado 247 teacher 12.631579 27.12551
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 48329.08 80488.00 24.28866 64408.54
## 2 45346.45 59773.06 19.41373 52559.76
## 3 34494.17 53737.70 18.07303 44115.93
## 4 20194.35 64451.22 33.86327 42322.79
## 5 26761.02 78625.00 38.02331 52693.01
## 6 20485.83 83157.89 19.87854 51821.86Lets get only the columns needed.
dataScientist2 <- dataScientist[,c(2,3,10)]
colnames(dataScientist2)[2:3] <- paste('dataScientist',colnames(dataScientist2)[2:3],sep='_')
dataScientist2$dataScientist_avgHourly <- (dataScientist2$dataScientist_avgAnualSalary/52)/40
dataScientist2 <- dataScientist2[,c(1,2,4,3)]
head(dataScientist2)## stateName dataScientist_jobsListed dataScientist_avgHourly
## 1 Alaska 37 60.61445
## 2 Alabama 122 63.20268
## 3 Arkansas 70 59.63178
## 4 Arizona 183 49.72246
## 5 California 231 57.98368
## 6 Colorado 194 60.49687
## dataScientist_avgAnualSalary
## 1 126078.1
## 2 131461.6
## 3 124034.1
## 4 103422.7
## 5 120606.1
## 6 125833.5warehouseWorker2 <- warehouseWorker[,c(2,3,9)]
colnames(warehouseWorker2)[2:3] <- paste('warehouse',colnames(warehouseWorker2)[2:3],
sep='_')
warehouseWorker2$warehouse_avgAnnualSalary <- warehouseWorker2$warehouse_avgHourly*52*40
head(warehouseWorker2)## stateName warehouse_jobsListed warehouse_avgHourly warehouse_avgAnnualSalary
## 1 Alaska 206 22.05340 45871.07
## 2 Alabama 219 13.53539 28153.61
## 3 Arkansas 212 15.59057 32428.38
## 4 Arizona 235 19.54894 40661.79
## 5 California 243 22.75716 47334.89
## 6 Colorado 250 16.93404 35222.80nanny2 <- nanny[,c(2,3,9)]
colnames(nanny2)[2:3] <- paste('nanny',colnames(nanny2)[2:3],
sep='_')
nanny2$nanny_avgAnnualSalary <- nanny2$nanny_avgHourly*52*40
head(nanny2)## stateName nanny_jobsListed nanny_avgHourly nanny_avgAnnualSalary
## 1 Alaska 23 12.82609 26678.26
## 2 Alabama 42 13.52381 28129.52
## 3 Arkansas 81 13.09877 27245.43
## 4 Arizona 182 17.50000 36400.00
## 5 California 199 25.42714 52888.44
## 6 Colorado 157 17.94586 37327.39personalTrainer2 <- personalTrainer[,c(2,3,9)]
colnames(personalTrainer2)[2:3] <- paste('personalTrainer',colnames(personalTrainer2)[2:3],
sep='_')
personalTrainer2$personalTrainer_avgAnnualSalary <-
personalTrainer2$personalTrainer_avgHourly*52*40
head(personalTrainer2)## stateName personalTrainer_jobsListed personalTrainer_avgHourly
## 1 Alaska 47 NA
## 2 Alabama 92 30.19880
## 3 Arkansas 72 21.72222
## 4 Arizona 168 22.69048
## 5 California 190 31.60000
## 6 Colorado 196 27.70663
## personalTrainer_avgAnnualSalary
## 1 NA
## 2 62813.49
## 3 45182.22
## 4 47196.19
## 5 65728.00
## 6 57629.80security2 <- security[,c(2,3,9)]
colnames(security2)[2:3] <- paste('security',colnames(security2)[2:3],
sep='_')
security2$security_avgAnnualSalary <- security2$security_avgHourly*52*40
head(security2)## stateName security_jobsListed security_avgHourly security_avgAnnualSalary
## 1 Alaska 169 16.85769 35064.00
## 2 Alabama 212 11.04599 22975.66
## 3 Arkansas 199 11.07224 23030.25
## 4 Arizona 221 19.08054 39687.53
## 5 California 230 19.91065 41414.16
## 6 Colorado 234 18.65761 38807.82houseCleaner2 <- houseCleaner[,c(2,3,9)]
colnames(houseCleaner2)[2:3] <- paste('houseCleaner',colnames(houseCleaner2)[2:3],
sep='_')
houseCleaner2$houseCleaner_avgAnnualSalary <- houseCleaner2$houseCleaner_avgHourly*52*40
head(houseCleaner2)## stateName houseCleaner_jobsListed houseCleaner_avgHourly
## 1 Alaska 4 12.000000
## 2 Alabama 72 9.297794
## 3 Arkansas 36 13.818182
## 4 Arizona 171 17.246053
## 5 California 216 20.477315
## 6 Colorado 217 17.762673
## houseCleaner_avgAnnualSalary
## 1 24960.00
## 2 19339.41
## 3 28741.82
## 4 35871.79
## 5 42592.81
## 6 36946.36server2 <- server[,c(2,3,9)]
colnames(server2)[2:3] <- paste('server',colnames(server2)[2:3],
sep='_')
server2$server_avgAnnualSalary <- server2$server_avgHourly*52*40
head(server2)## stateName server_jobsListed server_avgHourly server_avgAnnualSalary
## 1 Alaska 73 10.27342 21368.72
## 2 Alabama 220 16.94432 35244.18
## 3 Arkansas 197 13.23810 27535.24
## 4 Arizona 221 15.90724 33087.06
## 5 California 224 21.12723 43944.64
## 6 Colorado 225 20.70527 43066.95cashier2 <- cashier[,c(2,3,9)]
colnames(cashier2)[2:3] <- paste('cashier',colnames(cashier2)[2:3],
sep='_')
cashier2$cashier_avgAnnualSalary <- cashier2$cashier_avgHourly*52*40
head(cashier2)## stateName cashier_jobsListed cashier_avgHourly cashier_avgAnnualSalary
## 1 Alaska 103 12.73034 26479.10
## 2 Alabama 183 10.68989 22234.97
## 3 Arkansas 190 10.31908 21463.68
## 4 Arizona 217 13.29493 27653.46
## 5 California 227 16.50220 34324.58
## 6 Colorado 212 14.47170 30101.13tutor2 <- tutor[,c(2,3,9)]
colnames(tutor2)[2:3] <- paste('tutor',colnames(tutor2)[2:3],
sep='_')
tutor2$tutor_avgAnnualSalary <- tutor2$tutor_avgHourly*52*40
head(tutor2)## stateName tutor_jobsListed tutor_avgHourly tutor_avgAnnualSalary
## 1 Alaska 142 20.47500 42588.00
## 2 Alabama 201 18.17055 37794.74
## 3 Arkansas 112 12.32143 25628.57
## 4 Arizona 214 27.69626 57608.22
## 5 California 217 42.18433 87743.41
## 6 Colorado 207 25.00242 52005.02clerical2 <- clerical[,c(2,3,9)]
colnames(clerical2)[2:3] <- paste('clerical',colnames(clerical2)[2:3],
sep='_')
clerical2$clerical_avgAnnualSalary <- clerical2$clerical_avgHourly*52*40
head(clerical2)## stateName clerical_jobsListed clerical_avgHourly clerical_avgAnnualSalary
## 1 Alaska 211 20.56782 42781.07
## 2 Alabama 223 13.67265 28439.10
## 3 Arkansas 222 14.70439 30585.14
## 4 Arizona 226 19.55704 40678.63
## 5 California 225 24.60640 51181.31
## 6 Colorado 243 21.72724 45192.67teacher2 <- teacher[,c(2,3,10)]
colnames(teacher2)[2:3] <- paste('teacher',colnames(teacher2)[2:3],sep='_')
teacher2$teacher_avgHourly <- (teacher2$teacher_avgAnualSalary/52)/40
teacher2 <- teacher2[,c(1,2,4,3)]
head(teacher2)## stateName teacher_jobsListed teacher_avgHourly teacher_avgAnualSalary
## 1 Alaska 194 30.96564 64408.54
## 2 Alabama 213 25.26911 52559.76
## 3 Arkansas 178 21.20958 44115.93
## 4 Arizona 245 20.34749 42322.79
## 5 California 236 25.33318 52693.01
## 6 Colorado 247 24.91436 51821.86Lets add these new columns to our data table StateLicensing one at a time.
slr <- merge(StateLicensing,cashier2, by.x='state', by.y='stateName')
slr <- merge(slr, server2, by.x='state', by.y='stateName')
slr <- merge(slr, personalTrainer2, by.x='state', by.y='stateName')
slr <- merge(slr, houseCleaner2, by.x='state', by.y='stateName')
slr <- merge(slr, warehouseWorker2, by.x='state', by.y='stateName')
slr <- merge(slr, security2, by.x='state', by.y='stateName')
slr <- merge(slr, nanny2, by.x='state', by.y='stateName')
slr <- merge(slr, clerical2, by.x='state', by.y='stateName')
slr <- merge(slr, tutor2, by.x='state', by.y='stateName')
slr <- merge(slr, teacher2, by.x='state', by.y='stateName')
slr <- merge(slr, dataScientist2, by.x='state', by.y='stateName')
colnames(slr)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_AvgJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyAvgPayRangeAdvertised_Indeed"
## [34] "LMT_AnualAvgPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityAverageHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"
## [45] "cashier_jobsListed"
## [46] "cashier_avgHourly"
## [47] "cashier_avgAnnualSalary"
## [48] "server_jobsListed"
## [49] "server_avgHourly"
## [50] "server_avgAnnualSalary"
## [51] "personalTrainer_jobsListed"
## [52] "personalTrainer_avgHourly"
## [53] "personalTrainer_avgAnnualSalary"
## [54] "houseCleaner_jobsListed"
## [55] "houseCleaner_avgHourly"
## [56] "houseCleaner_avgAnnualSalary"
## [57] "warehouse_jobsListed"
## [58] "warehouse_avgHourly"
## [59] "warehouse_avgAnnualSalary"
## [60] "security_jobsListed"
## [61] "security_avgHourly"
## [62] "security_avgAnnualSalary"
## [63] "nanny_jobsListed"
## [64] "nanny_avgHourly"
## [65] "nanny_avgAnnualSalary"
## [66] "clerical_jobsListed"
## [67] "clerical_avgHourly"
## [68] "clerical_avgAnnualSalary"
## [69] "tutor_jobsListed"
## [70] "tutor_avgHourly"
## [71] "tutor_avgAnnualSalary"
## [72] "teacher_jobsListed"
## [73] "teacher_avgHourly"
## [74] "teacher_avgAnualSalary"
## [75] "dataScientist_jobsListed"
## [76] "dataScientist_avgHourly"
## [77] "dataScientist_avgAnualSalary"Lets now write this data table out to csv.
write.csv(slr,'stateLicensingDemographicsAddedAndUpdated.csv', row.names=FALSE)Lets plot some of this data to get a good sense of what the job outlook is like compared to massage therapy for other professions.
numberOfJobs <- slr[,c(1,32,45,48,51,54,57,60,63,66,69,72,75)]
numberOfJobs## state LMT_AvgJobsListed_IndeedFirst5pages cashier_jobsListed
## 1 Alabama 88 183
## 2 Alaska 39 103
## 3 Arizona 212 217
## 4 Arkansas 73 190
## 5 California 223 227
## 6 Colorado 241 212
## 7 Connecticut 203 204
## 8 Delaware 159 203
## 9 Florida 222 225
## 10 Georgia 105 180
## 11 Hawaii 99 136
## 12 Idaho 203 200
## 13 Illinois 226 221
## 14 Indiana 109 211
## 15 Iowa 152 212
## 16 Kansas 179 213
## 17 Kentucky 123 214
## 18 Louisiana 110 204
## 19 Maine 73 139
## 20 Maryland 237 209
## 21 Massachusetts 229 221
## 22 Michigan 237 232
## 23 Minnesota 187 210
## 24 Mississippi 93 204
## 25 Missouri 190 214
## 26 Montana 46 196
## 27 Nebraska 169 195
## 28 Nevada 230 208
## 29 New Hampshire 139 187
## 30 New Jersey 236 232
## 31 New Mexico 162 184
## 32 New York 196 209
## 33 North Carolina 203 217
## 34 North Dakota 46 211
## 35 Ohio 235 227
## 36 Oklahoma 192 211
## 37 Oregon 171 181
## 38 Pennsylvania 227 226
## 39 Rhode Island 215 211
## 40 South Carolina 176 215
## 41 South Dakota 56 134
## 42 Tennessee 174 214
## 43 Texas 234 224
## 44 Utah 224 222
## 45 Vermont 70 187
## 46 Virginia 177 209
## 47 Washington 218 200
## 48 West Virginia 61 178
## 49 Wisconsin 166 197
## 50 Wyoming 35 175
## server_jobsListed personalTrainer_jobsListed houseCleaner_jobsListed
## 1 220 92 72
## 2 73 47 4
## 3 221 168 171
## 4 197 72 36
## 5 224 190 216
## 6 225 196 217
## 7 217 211 127
## 8 201 157 284
## 9 224 200 194
## 10 200 103 155
## 11 144 38 27
## 12 211 87 198
## 13 233 206 195
## 14 208 96 84
## 15 202 82 106
## 16 227 172 168
## 17 195 67 85
## 18 220 87 85
## 19 119 28 113
## 20 227 229 224
## 21 215 207 178
## 22 236 175 186
## 23 230 150 153
## 24 198 82 82
## 25 227 147 152
## 26 189 18 17
## 27 229 146 142
## 28 215 187 198
## 29 216 183 206
## 30 240 237 227
## 31 209 102 70
## 32 215 135 175
## 33 227 164 198
## 34 199 27 125
## 35 227 194 207
## 36 225 133 76
## 37 188 123 128
## 38 228 211 186
## 39 228 192 199
## 40 230 184 204
## 41 155 33 36
## 42 218 152 192
## 43 229 199 205
## 44 220 162 199
## 45 164 4 89
## 46 241 198 202
## 47 204 133 178
## 48 206 52 27
## 49 222 141 89
## 50 147 17 19
## warehouse_jobsListed security_jobsListed nanny_jobsListed
## 1 219 212 42
## 2 206 169 23
## 3 235 221 182
## 4 212 199 81
## 5 243 230 199
## 6 250 234 157
## 7 233 223 172
## 8 221 222 133
## 9 233 228 173
## 10 202 220 104
## 11 219 216 48
## 12 234 215 102
## 13 242 232 195
## 14 233 217 72
## 15 227 209 42
## 16 233 222 104
## 17 240 218 87
## 18 224 216 28
## 19 143 134 23
## 20 240 228 164
## 21 244 232 160
## 22 246 240 91
## 23 233 225 133
## 24 223 206 62
## 25 225 210 119
## 26 199 197 13
## 27 236 222 97
## 28 228 226 132
## 29 240 221 101
## 30 230 238 211
## 31 224 236 62
## 32 232 222 102
## 33 229 210 150
## 34 222 206 19
## 35 253 245 117
## 36 227 213 97
## 37 236 225 62
## 38 232 230 161
## 39 253 225 162
## 40 239 201 77
## 41 220 153 13
## 42 230 223 113
## 43 224 234 166
## 44 254 227 127
## 45 233 194 32
## 46 229 229 166
## 47 238 227 126
## 48 196 214 22
## 49 234 225 94
## 50 164 152 14
## clerical_jobsListed tutor_jobsListed teacher_jobsListed
## 1 223 201 213
## 2 211 142 194
## 3 226 214 245
## 4 222 112 178
## 5 225 217 236
## 6 243 207 247
## 7 233 204 239
## 8 219 192 242
## 9 236 220 225
## 10 224 164 217
## 11 230 167 231
## 12 224 209 219
## 13 228 222 242
## 14 229 181 226
## 15 236 171 190
## 16 231 207 239
## 17 218 200 203
## 18 227 200 219
## 19 165 118 150
## 20 234 225 249
## 21 228 212 231
## 22 235 211 247
## 23 233 225 225
## 24 222 186 216
## 25 234 213 222
## 26 232 125 174
## 27 233 212 230
## 28 230 219 230
## 29 224 190 239
## 30 233 224 242
## 31 222 178 218
## 32 226 209 230
## 33 223 205 233
## 34 223 173 193
## 35 226 215 246
## 36 224 202 222
## 37 230 180 223
## 38 228 203 235
## 39 243 217 238
## 40 231 216 222
## 41 215 72 164
## 42 238 198 232
## 43 227 223 234
## 44 226 203 216
## 45 219 111 209
## 46 234 204 223
## 47 237 193 231
## 48 222 75 209
## 49 238 215 238
## 50 198 123 160
## dataScientist_jobsListed
## 1 122
## 2 37
## 3 183
## 4 70
## 5 231
## 6 194
## 7 226
## 8 161
## 9 174
## 10 95
## 11 117
## 12 94
## 13 207
## 14 91
## 15 105
## 16 133
## 17 96
## 18 38
## 19 36
## 20 239
## 21 214
## 22 152
## 23 146
## 24 27
## 25 148
## 26 38
## 27 74
## 28 49
## 29 159
## 30 234
## 31 161
## 32 144
## 33 167
## 34 20
## 35 205
## 36 98
## 37 96
## 38 182
## 39 179
## 40 78
## 41 29
## 42 152
## 43 207
## 44 175
## 45 35
## 46 219
## 47 178
## 48 37
## 49 142
## 50 31gg <- ggplot(numberOfJobs, aes(x=numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages))
gg <- gg + geom_histogram(binwidth=2, colour="black",
aes(y=..density.., fill=..count..))
gg <- gg + stat_function(fun=dnorm,
color="red",
args=list(mean=mean(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages),
sd=sd(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages)))
gg
avg <- mean(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages)
m <- min(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages)
M <- max(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages)
ggplot(data = numberOfJobs, aes(y=numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages, x=numberOfJobs$state)) +
geom_bar(stat='identity', position=position_dodge())+
scale_y_continuous(breaks=c(m,avg,M),labels=c(m,avg,M))+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=mean(numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages), linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('LMT Average Job Listings in the US Top 3 Cities of each State')+
ylab(NULL)+
xlab(NULL)
Lets look at those states whose job listings on Indeed are more than the US average job listings for licensed massage therapists.
moreThanAvgLMT <- subset(numberOfJobs, numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages>avg)
moreThanAvgLMT$state## [1] Arizona California Colorado Connecticut Florida
## [6] Idaho Illinois Kansas Maryland Massachusetts
## [11] Michigan Minnesota Missouri Nebraska Nevada
## [16] New Jersey New Mexico New York North Carolina Ohio
## [21] Oklahoma Oregon Pennsylvania Rhode Island South Carolina
## [26] Tennessee Texas Utah Virginia Washington
## [31] Wisconsin
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingThe above list of 31 states look like heavily populated states, such as CA, CO, NY, and TX.
Now, lets see which states have fewer than the US average number of job listings for LMT on Indeed.
lessThanAvgLMT <- subset(numberOfJobs, numberOfJobs$LMT_AvgJobsListed_IndeedFirst5pages<avg)
lessThanAvgLMT$state## [1] Alabama Alaska Arkansas Delaware Georgia
## [6] Hawaii Indiana Iowa Kentucky Louisiana
## [11] Maine Mississippi Montana New Hampshire North Dakota
## [16] South Dakota Vermont West Virginia Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingFrom the above, Alaska and many southern and sierra mountain range north mid-western states as well as Hawaii don’t have as many job listings for massage therapists as the more city or urban states do. Surprising Georgia is on this list, as that state has a large somewhat urban population and high pay for LMTs on average compared to other states. Lets look at the pay visually for the 50 states to see this claim just made as true or false.
payRates <- slr[,c(1,33,46,49,52,55,58,61,64,67,70,73,76)]
payRates## state LMT_HourlyAvgPayRangeAdvertised_Indeed cashier_avgHourly
## 1 Alabama 34.31250 10.68989
## 2 Alaska 15.00000 12.73034
## 3 Arizona 45.77594 13.29493
## 4 Arkansas NA 10.31908
## 5 California 50.14013 16.50220
## 6 Colorado 39.90332 14.47170
## 7 Connecticut 40.91872 13.45760
## 8 Delaware 32.42857 12.17488
## 9 Florida 35.71622 10.75333
## 10 Georgia 69.00000 10.25000
## 11 Hawaii 37.00000 14.36029
## 12 Idaho 37.50000 10.29250
## 13 Illinois 36.60398 12.74717
## 14 Indiana 39.93578 10.77310
## 15 Iowa 27.45066 11.07193
## 16 Kansas 35.00000 11.10681
## 17 Kentucky 34.44118 10.93049
## 18 Louisiana 37.50000 10.16115
## 19 Maine 37.57534 13.50000
## 20 Maryland 52.82278 13.82775
## 21 Massachusetts 35.38428 16.37783
## 22 Michigan 46.81435 12.71552
## 23 Minnesota 41.74599 12.95000
## 24 Mississippi 44.00000 10.01532
## 25 Missouri 30.24737 11.12652
## 26 Montana 9.00000 11.60630
## 27 Nebraska 45.81679 11.10513
## 28 Nevada 66.72609 14.41346
## 29 New Hampshire 24.79856 11.74398
## 30 New Jersey 61.79025 12.26810
## 31 New Mexico 39.39815 11.47011
## 32 New York 53.36709 15.12440
## 33 North Carolina 48.08867 11.09389
## 34 North Dakota 24.00000 11.75118
## 35 Ohio 36.28298 11.92225
## 36 Oklahoma 49.03385 10.67654
## 37 Oregon 38.45322 14.31077
## 38 Pennsylvania 39.76846 11.38350
## 39 Rhode Island 42.62500 14.25000
## 40 South Carolina 43.51136 11.34128
## 41 South Dakota 26.25000 10.70780
## 42 Tennessee 33.76724 15.01227
## 43 Texas 38.29487 12.91629
## 44 Utah 40.96652 12.56892
## 45 Vermont 20.50000 12.41711
## 46 Virginia 36.86441 10.96950
## 47 Washington 45.15138 14.84650
## 48 West Virginia 21.94262 9.95927
## 49 Wisconsin 43.26105 11.04721
## 50 Wyoming 41.15385 11.88977
## server_avgHourly personalTrainer_avgHourly houseCleaner_avgHourly
## 1 16.94432 30.19880 9.297794
## 2 10.27342 NA 12.000000
## 3 15.90724 22.69048 17.246053
## 4 13.23810 21.72222 13.818182
## 5 21.12723 31.60000 20.477315
## 6 20.70527 27.70663 17.762673
## 7 20.16359 45.78673 16.081693
## 8 13.61692 46.75725 13.996479
## 9 21.64286 30.85095 16.649485
## 10 13.93250 25.36364 16.848387
## 11 18.37500 NA 14.148148
## 12 17.02547 21.50000 15.500000
## 13 18.20352 43.00000 18.730769
## 14 15.66526 33.81034 15.000000
## 15 13.69059 24.69512 12.580189
## 16 22.12930 13.89680 15.099702
## 17 15.85897 22.50000 14.250000
## 18 11.32823 22.10920 13.772727
## 19 17.87395 NA 15.825221
## 20 16.23128 40.39847 18.959821
## 21 17.10930 34.84300 17.127107
## 22 19.99153 31.87405 14.908602
## 23 14.79209 37.79433 15.550000
## 24 10.10038 23.67073 11.274390
## 25 14.77313 15.14966 14.902303
## 26 10.83862 NA 13.096154
## 27 23.50218 28.44178 11.696303
## 28 15.39977 44.48930 18.378788
## 29 12.00000 29.95902 16.327670
## 30 17.15417 54.89662 20.500000
## 31 12.62500 24.88235 14.000000
## 32 15.78791 27.10000 17.942857
## 33 13.94934 28.55183 14.888889
## 34 13.83920 NA 12.901408
## 35 24.68998 29.37113 14.995169
## 36 14.25269 19.61170 13.032895
## 37 14.66822 30.24390 18.128125
## 38 13.18695 36.90284 19.354839
## 39 22.55702 22.50000 17.500000
## 40 15.60978 41.95652 13.691176
## 41 10.28085 12.50000 12.781250
## 42 12.90138 20.52961 14.997396
## 43 15.86845 39.51256 17.951220
## 44 16.73750 33.97685 16.728643
## 45 14.79268 20.50000 13.000000
## 46 14.12500 23.32071 14.236386
## 47 17.79289 52.67669 17.491573
## 48 13.99393 15.50000 13.000000
## 49 14.16385 28.43972 14.480519
## 50 11.09789 NA 17.250000
## warehouse_avgHourly security_avgHourly nanny_avgHourly clerical_avgHourly
## 1 13.53539 11.04599 13.52381 13.67265
## 2 22.05340 16.85769 12.82609 20.56782
## 3 19.54894 19.08054 17.50000 19.55704
## 4 15.59057 11.07224 13.09877 14.70439
## 5 22.75716 19.91065 25.42714 24.60640
## 6 16.93404 18.65761 17.94586 21.72724
## 7 16.90558 17.17265 27.50000 22.07189
## 8 16.25572 13.94595 16.89474 28.58333
## 9 16.82897 17.14105 19.43642 17.81222
## 10 16.55272 19.70227 15.58173 18.71873
## 11 18.30502 18.73727 12.50000 19.37413
## 12 15.36970 11.41558 22.25000 15.33482
## 13 18.54959 16.25216 22.38462 18.52632
## 14 16.55472 13.40783 15.07639 15.61869
## 15 17.93797 16.47557 14.91667 16.50000
## 16 17.74560 17.59349 13.77885 17.45134
## 17 17.43719 14.96385 15.30172 15.20050
## 18 15.53125 14.76903 13.00000 15.05121
## 19 17.84965 14.76119 15.00000 20.17212
## 20 17.19008 18.58303 20.17073 27.50000
## 21 21.00494 16.65248 21.48438 22.15570
## 22 19.34045 19.51150 15.00000 18.11202
## 23 17.04024 16.12196 19.69549 21.06867
## 24 14.70188 11.50728 11.38710 15.71284
## 25 16.28622 15.50419 14.93277 18.85609
## 26 17.73500 14.30015 11.53846 19.13897
## 27 16.47742 19.13288 16.03093 17.80687
## 28 17.94079 15.99558 13.58712 18.51300
## 29 14.32500 20.23077 17.34653 20.00223
## 30 16.87174 18.72269 25.00000 32.59442
## 31 16.81938 16.03072 12.80645 14.87838
## 32 17.79267 16.79971 21.29510 33.63274
## 33 18.41157 17.46190 20.22000 16.85233
## 34 18.03914 15.35922 9.00000 18.55605
## 35 18.40326 13.38776 17.28632 17.00177
## 36 16.84573 18.42723 13.51546 14.47154
## 37 18.45729 18.34527 20.38710 19.82030
## 38 17.94224 17.72826 16.57453 17.38070
## 39 17.80213 17.75000 18.50000 20.26778
## 40 16.08368 14.17164 13.60390 17.28139
## 41 16.80909 17.67020 12.50000 13.71523
## 42 15.53950 14.07063 15.08850 15.98101
## 43 15.85268 18.76361 16.61747 18.98899
## 44 17.03445 18.05507 15.29528 18.78277
## 45 17.83906 16.84304 15.50000 15.47774
## 46 17.87773 16.36358 16.50000 15.45139
## 47 21.63210 19.30753 24.16667 23.29329
## 48 14.98406 11.31484 11.02273 28.92399
## 49 28.55556 15.06667 16.14894 20.75840
## 50 16.02293 18.92308 NA 14.15566
## tutor_avgHourly teacher_avgHourly dataScientist_avgHourly
## 1 18.17055 25.26911 63.20268
## 2 20.47500 30.96564 60.61445
## 3 27.69626 20.34749 49.72246
## 4 12.32143 21.20958 59.63178
## 5 42.18433 25.33318 57.98368
## 6 25.00242 24.91436 60.49687
## 7 45.77819 29.89668 55.92059
## 8 21.69253 26.97374 59.37948
## 9 26.42118 23.12273 60.19286
## 10 15.99695 29.56272 57.69231
## 11 21.94000 24.57585 55.32177
## 12 20.00000 24.75018 64.90385
## 13 22.79505 23.78119 55.06044
## 14 17.71823 24.19220 58.78250
## 15 22.99107 20.00285 64.90385
## 16 18.47464 26.42352 54.00481
## 17 19.10638 24.82924 48.92048
## 18 22.34500 22.15002 65.08823
## 19 23.69492 27.46056 64.90385
## 20 36.61667 31.59383 56.39881
## 21 38.85613 23.99374 62.15178
## 22 17.58175 25.71203 64.90385
## 23 26.75000 23.65966 64.90385
## 24 16.99194 28.94959 64.90385
## 25 27.08451 24.69061 52.36881
## 26 NA 25.85343 64.90385
## 27 25.10345 19.71154 64.90385
## 28 19.71804 25.86531 64.90385
## 29 23.34211 27.54287 60.42876
## 30 59.07366 30.25475 80.39789
## 31 20.00000 31.96516 59.07340
## 32 43.22344 22.22748 68.92841
## 33 19.85000 18.39623 49.98561
## 34 21.50000 28.36887 64.90385
## 35 19.82326 18.24373 59.84002
## 36 29.20891 18.39014 64.90385
## 37 17.48348 28.09377 64.90385
## 38 25.16872 19.01989 48.43305
## 39 22.67512 22.96835 64.90385
## 40 19.82870 22.48870 60.58296
## 41 NA 28.51522 64.90385
## 42 19.17929 23.95844 61.85611
## 43 22.69507 23.38367 61.77597
## 44 19.47660 20.07991 55.21154
## 45 16.00000 26.55992 64.90385
## 46 20.01471 13.69036 56.49038
## 47 26.33420 30.24264 74.37227
## 48 NA 26.72927 64.90385
## 49 18.42326 24.39003 63.19705
## 50 10.41774 30.64207 60.76752avgPay <- round(mean(payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed,na.rm=TRUE),2)
mPay <- min(payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed,na.rm=TRUE)
MPay <- max(payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed,na.rm=TRUE)
ggplot(data = numberOfJobs, aes(y=payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed, x=payRates$state)) +
geom_bar(stat='identity', position=position_dodge(), na.rm=TRUE)+
scale_y_continuous(breaks=c(mPay,avgPay,MPay),labels=c(mPay,avgPay,MPay))+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=mean(payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed, na.rm=TRUE), linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('LMT Average Hourly Pay in the US Top 3 Cities of each State')+
ylab(NULL)+
xlab(NULL)
Lets see what states are paying their massage therapists more than the US average. Visually, above we can see Georgia does, and that GA is also in the list of the states with less than the average number of LMT job listings in the US per state. This logically indicates there is a demand for massage therapists in these states, and that the businesses offering massage therapy need LMTs and are willing to offer them more pay.
morePayThanAvgLMT <- subset(payRates, payRates$LMT_HourlyAvgPayRangeAdvertised_Indeed > avgPay)
morePayThanAvgLMT$state## [1] Arizona California Colorado Connecticut Georgia
## [6] Indiana Maryland Michigan Minnesota Mississippi
## [11] Nebraska Nevada New Jersey New Mexico New York
## [16] North Carolina Oklahoma Pennsylvania Rhode Island South Carolina
## [21] Utah Washington Wisconsin Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingLets see which states offer more than the national LMT average hourly pay and are also in the list of states with less than the national average number of LMT job listings.
fewListings <- lessThanAvgLMT$state
morePayAdvertised <- morePayThanAvgLMT$state
demandStatesLMT <- fewListings %in% morePayAdvertised
demanded <- fewListings[demandStatesLMT]
demanded## [1] Georgia Indiana Mississippi Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingThe above states have a demand for massage therapists because they have fewer listings advertised on Indeed, but are willing to pay more than the national average for massage therapists. Those states are Georgia, Indiana, Mississippi, and Wyoming.
Using this same logic, it is fair to say those states that have a higher than average demand for massage therapists and also more pay than average for massage therapists must also be states where the cost of living is higher than normal. Lets look at those states and compare their home values for a two bedroom Zillow listed price from our larger data table called slr.
moreListings <- moreThanAvgLMT$state
highCostStates <- moreListings %in% morePayAdvertised
highLivingCost <- moreListings[highCostStates]
highLivingCost## [1] Arizona California Colorado Connecticut Maryland
## [6] Michigan Minnesota Nebraska Nevada New Jersey
## [11] New Mexico New York North Carolina Oklahoma Pennsylvania
## [16] Rhode Island South Carolina Utah Washington Wisconsin
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingThere are 20 states that are listed as possible high cost of living states because we are making the assumption that those states with higher than average pay for LMTs and higher than average job listings for LMT are in high cost of living states. We can verify this by looking at the two bedroom homes for sale in all states and get the average to compare to these 20 states.
avgHomePrice <- round(mean(slr$Zillow_2BR_3cityAverageHomeValue),2)
mHomePrice <- round(min(slr$Zillow_2BR_3cityAverageHomeValue),2)
MHomePrice <- round(max(slr$Zillow_2BR_3cityAverageHomeValue),2)
ggplot(data = numberOfJobs, aes(y=slr$Zillow_2BR_3cityAverageHomeValue, x=slr$state)) +
geom_bar(stat='identity', position=position_dodge())+
scale_y_continuous(breaks=c(mHomePrice,avgHomePrice,MHomePrice),
labels=c(mHomePrice,avgHomePrice,MHomePrice))+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=avgHomePrice, linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('Zillow 2 Bedroom Home Value in the US Top 3 Cities of each State')+
ylab(NULL)+
xlab(NULL)
Lets see exactly which states have higher priced homes than the national average.
expensiveHomes <- subset(slr,slr$Zillow_2BR_3cityAverageHomeValue>avgHomePrice)
e <- expensiveHomes$state
e## [1] Alaska Arizona California Colorado Hawaii
## [6] Idaho Maine Massachusetts Minnesota Montana
## [11] Nevada New Jersey New York Oregon Rhode Island
## [16] Utah Virginia Washington Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingThere are 22 states with higher priced homes than the national average from the list produced above.
Lets see if those states are also in the list of states with high living costs.
expensive <- expensiveHomes$state %in% highLivingCost
expensiveHomes <- e[expensive]
expensiveHomes## [1] Arizona California Colorado Minnesota Nevada
## [6] New Jersey New York Rhode Island Utah Washington
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingThere are 10 states with expensive homes compared to the national average home price, and that also have higher pay advertised for LMTs and more advertised job listings for LMTs in the three most populated cities in each state according to the first five web pages of Indeed.
Lets see which states have lower priced homes than the national average but also have higher pay advertised for LMTs and more job listings advertised for LMTs.
inexpensiveHomes <- subset(slr,slr$Zillow_2BR_3cityAverageHomeValue < avgHomePrice)
inexpensive <- inexpensiveHomes$state
notExpensive <- inexpensive %in% highLivingCost
affordable <- inexpensive[notExpensive]
affordable## [1] Connecticut Maryland Michigan Nebraska New Mexico
## [6] North Carolina Oklahoma Pennsylvania South Carolina Wisconsin
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingWe have a couple lists we generated, one for the higher priced states that also pay more for LMTs and have more jobs available called ‘expensiveHomes’, and another list that is the list of states that aren’t as expensive but also pay more for LMTs and have more jobs available for LMTs called ‘affordable’ that we can compare other factors in our large dataset called slr. We also have a third list called ‘demanded’ that lists those states that don’t have as many jobs available as the national average for LMTs but pays more than the national average. Lets see if those states are in our list of states with a higher than national average home according to Zillow’s two bedroom home values that was named ‘e’ for expensive homes by state.
demanded## [1] Georgia Indiana Mississippi Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... Wyomingdemanded %in% e## [1] FALSE FALSE FALSE TRUEWe can see none of the demand for LMT states are in the list of states with expensive homes or homes with higher than average two bedroom home values.
So, we actually have four lists: e is the list of states with expensive homes, demanded is our list of states with demand has influenced LMT pay to higher for LMTs than the national average, expensiveHomes is our list of states with high cost of living based on higher home values and that also pay more and have more jobs available for LMTs, and our 4th list is the affordable list of lower cost of living by home price less than national average but also that has higher pay and more jobs available for LMTs than the national average.
Lets look at the demographics of the states that are in our affordable list and compare to the demographics or our expensiveHomes list to get an idea of the diversity.
demographics <- subset(slr, slr$state %in% affordable)
demographics2 <- demographics[,c(1,37:44)]
demographics2## state total_state_population percent_black percent_white
## 7 Connecticut 3572665 11.0 75.2
## 20 Maryland 6042718 30.0 54.7
## 22 Michigan 9995915 13.8 78.3
## 27 Nebraska 1929268 4.7 86.4
## 31 New Mexico 2095428 2.2 76.4
## 33 North Carolina 10383620 21.4 68.4
## 36 Oklahoma 3943079 7.3 72.2
## 38 Pennsylvania 12807060 11.2 80.1
## 40 South Carolina 5084127 26.6 67.0
## 49 Wisconsin 5813568 6.4 85.3
## percent_two_or_more percent_Native_American percent_Asian
## 7 3.4 0.3 4.6
## 20 3.7 0.2 6.3
## 22 2.9 0.5 3.3
## 27 3.1 1.0 2.4
## 31 3.2 9.6 1.6
## 33 2.9 1.2 3.0
## 36 7.7 7.8 2.1
## 38 2.6 0.2 3.6
## 40 2.4 0.5 1.6
## 49 2.5 0.9 2.8
## percent_Pacific_Islander percent_Latino
## 7 0.0 16.5
## 20 0.1 10.4
## 22 0.0 5.2
## 27 0.1 11.1
## 31 0.1 49.1
## 33 0.1 9.6
## 36 0.1 10.9
## 38 0.0 7.6
## 40 0.1 5.8
## 49 0.0 6.9Lets look at this above chart of affordable states for LMTs compared to the demographics of CA.
CA_demog <- subset(slr, slr$state=='California')
CA_demog2 <- CA_demog[,c(1,37:44)]
CA_demog2## state total_state_population percent_black percent_white
## 5 California 39557045 5.8 59.5
## percent_two_or_more percent_Native_American percent_Asian
## 5 5.1 0.8 14.7
## percent_Pacific_Islander percent_Latino
## 5 0.4 39.3When comparing demographics of percent race to population, it could be similar in fashion to looking for an alternate planet similar to Earth in a vast universe. But we can see the state with a closer distribution of the population by Asian, Latino, Black, and two or more races is New York. The other states that are affordable in our list of 10 when compared to CA, a high cost of living state, don’t really compare similarly as far as race distribution goes. Lets look at how a few of our alternate jobs from these 10 states compare to CA next.
altjobs_aff <- demographics[,c(1,32,45,48,51,54,57,60,63,66,69,72,75)]
CA_altjobs <- CA_demog[,c(1,32,45,48,51,54,57,60,63,66,69,72,75)]
altjobs_aff## state LMT_AvgJobsListed_IndeedFirst5pages cashier_jobsListed
## 7 Connecticut 203 204
## 20 Maryland 237 209
## 22 Michigan 237 232
## 27 Nebraska 169 195
## 31 New Mexico 162 184
## 33 North Carolina 203 217
## 36 Oklahoma 192 211
## 38 Pennsylvania 227 226
## 40 South Carolina 176 215
## 49 Wisconsin 166 197
## server_jobsListed personalTrainer_jobsListed houseCleaner_jobsListed
## 7 217 211 127
## 20 227 229 224
## 22 236 175 186
## 27 229 146 142
## 31 209 102 70
## 33 227 164 198
## 36 225 133 76
## 38 228 211 186
## 40 230 184 204
## 49 222 141 89
## warehouse_jobsListed security_jobsListed nanny_jobsListed
## 7 233 223 172
## 20 240 228 164
## 22 246 240 91
## 27 236 222 97
## 31 224 236 62
## 33 229 210 150
## 36 227 213 97
## 38 232 230 161
## 40 239 201 77
## 49 234 225 94
## clerical_jobsListed tutor_jobsListed teacher_jobsListed
## 7 233 204 239
## 20 234 225 249
## 22 235 211 247
## 27 233 212 230
## 31 222 178 218
## 33 223 205 233
## 36 224 202 222
## 38 228 203 235
## 40 231 216 222
## 49 238 215 238
## dataScientist_jobsListed
## 7 226
## 20 239
## 22 152
## 27 74
## 31 161
## 33 167
## 36 98
## 38 182
## 40 78
## 49 142There are less nanny, personal training, house cleaning, and data science jobs in the affordable states than in CA. While there are more cashier, server, and teaching jobs in the affordable states than in CA. The number of tutoring, clerical, warehouse, and security jobs are similar in available jobs.
CA_altjobs## state LMT_AvgJobsListed_IndeedFirst5pages cashier_jobsListed
## 5 California 223 227
## server_jobsListed personalTrainer_jobsListed houseCleaner_jobsListed
## 5 224 190 216
## warehouse_jobsListed security_jobsListed nanny_jobsListed clerical_jobsListed
## 5 243 230 199 225
## tutor_jobsListed teacher_jobsListed dataScientist_jobsListed
## 5 217 236 231Now, lets compare CA to our list of expensive states for demographics and alternate jobs available. CA is in our list of expensive states, so we don’t have to do separate table chart comparisons.
expensiveDemog <- subset(slr, slr$state %in% expensiveHomes)
expensiveDemog2 <- expensiveDemog[,c(1,37:44)]
expensiveDemog2## state total_state_population percent_black percent_white
## 3 Arizona 7171646 4.7 78.0
## 5 California 39557045 5.8 59.5
## 6 Colorado 5695564 4.2 84.1
## 23 Minnesota 5611179 6.6 82.5
## 28 Nevada 3034392 9.2 63.4
## 30 New Jersey 8908520 13.6 66.9
## 32 New York 19542209 15.7 63.3
## 39 Rhode Island 1057315 6.7 80.7
## 44 Utah 3161105 1.3 85.7
## 47 Washington 7535591 3.9 74.8
## percent_two_or_more percent_Native_American percent_Asian
## 3 4.0 4.6 3.3
## 5 5.1 0.8 14.7
## 6 4.0 1.0 3.2
## 23 3.1 1.1 4.9
## 28 5.1 1.5 8.2
## 30 2.8 0.2 9.7
## 32 3.3 0.4 8.5
## 39 3.1 0.4 3.4
## 44 3.2 1.1 2.4
## 47 6.0 1.3 8.8
## percent_Pacific_Islander percent_Latino
## 3 0.2 31.6
## 5 0.4 39.3
## 6 0.1 21.7
## 23 0.0 5.5
## 28 0.6 29.0
## 30 0.0 20.6
## 32 0.0 19.2
## 39 0.1 15.9
## 44 0.9 14.2
## 47 0.7 12.9From the above data, the closest state in comparison to diversity to CA is NV or Nevada. The next state would be Colorado or Washington.
Now, we will compare the alternate jobs available in our list of expensive states that includes CA.
altJobs_expStates <- expensiveDemog[,c(1,32,45,48,51,54,57,60,63,66,69,72,75)]
altJobs_expStates## state LMT_AvgJobsListed_IndeedFirst5pages cashier_jobsListed
## 3 Arizona 212 217
## 5 California 223 227
## 6 Colorado 241 212
## 23 Minnesota 187 210
## 28 Nevada 230 208
## 30 New Jersey 236 232
## 32 New York 196 209
## 39 Rhode Island 215 211
## 44 Utah 224 222
## 47 Washington 218 200
## server_jobsListed personalTrainer_jobsListed houseCleaner_jobsListed
## 3 221 168 171
## 5 224 190 216
## 6 225 196 217
## 23 230 150 153
## 28 215 187 198
## 30 240 237 227
## 32 215 135 175
## 39 228 192 199
## 44 220 162 199
## 47 204 133 178
## warehouse_jobsListed security_jobsListed nanny_jobsListed
## 3 235 221 182
## 5 243 230 199
## 6 250 234 157
## 23 233 225 133
## 28 228 226 132
## 30 230 238 211
## 32 232 222 102
## 39 253 225 162
## 44 254 227 127
## 47 238 227 126
## clerical_jobsListed tutor_jobsListed teacher_jobsListed
## 3 226 214 245
## 5 225 217 236
## 6 243 207 247
## 23 233 225 225
## 28 230 219 230
## 30 233 224 242
## 32 226 209 230
## 39 243 217 238
## 44 226 203 216
## 47 237 193 231
## dataScientist_jobsListed
## 3 183
## 5 231
## 6 194
## 23 146
## 28 49
## 30 234
## 32 144
## 39 179
## 44 175
## 47 178For the most part, the expensive states have about the same distribution of available jobs alternative to LMT work, except some states have noticeably less nanny and personal training jobs. Arizona and Washington have less personal training jobs, while Nevada, Rhode Island, and Washington have less nanny jobs. And there are far less house cleaning jobs in Connecticut than any other state that is expensive. Also, there are not as many data science jobs available in Nevada as any other expensive state.
Lets now plot the number of available jobs by category in CA compared to New Jersey and New York. New Jersey is an expensive state and New York is not so expensive compared to the average home value of the three top populated cities in each state.
NY_altJobs <- subset(altJobs_expStates, altJobs_expStates$state=='New York')
CA_altJobs <- subset(altJobs_expStates, altJobs_expStates$state=='California')
NJ_altJobs <- subset(altJobs_expStates, altJobs_expStates$state=='New Jersey')
NY_tidyJobs <- gather(NY_altJobs,key="jobTitle", value="jobsListed",2:13, na.rm=TRUE)
NY_tidyJobs$jobTitle <- gsub('_.*$','',NY_tidyJobs$jobTitle, perl=TRUE)
CA_tidyJobs <- gather(CA_altJobs,key="jobTitle", value="jobsListed",2:13, na.rm=TRUE)
CA_tidyJobs$jobTitle <- gsub('_.*$','',CA_tidyJobs$jobTitle, perl=TRUE)
NJ_tidyJobs <- gather(NJ_altJobs,key="jobTitle", value="jobsListed",2:13, na.rm=TRUE)
NJ_tidyJobs$jobTitle <- gsub('_.*$','',NJ_tidyJobs$jobTitle, perl=TRUE)ggplot(data = CA_tidyJobs, aes(y=CA_tidyJobs$jobsListed, x=CA_tidyJobs$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA Alternate and LMT Jobs Advertised')+
ylab(NULL)+
xlab(NULL)
There are more warehouse and security jobs available on Indeed than the other listed jobs in CA. In comparison to other job categories available in CA, there are less tutor, nanny, and LMT jobs available than other comparative jobs.
ggplot(data = NY_tidyJobs, aes(y=NY_tidyJobs$jobsListed, x=NY_tidyJobs$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('NY Alternate and LMT Jobs Advertised')+
ylab(NULL)+
xlab(NULL)
New York has more clerical, teaching, and warehouse jobs available than other jobs in NY, but has far less personal training and nanny jobs available than CA did.
ggplot(data = NJ_tidyJobs, aes(y=NJ_tidyJobs$jobsListed, x=NJ_tidyJobs$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('NJ Alternate and LMT Jobs Advertised')+
ylab(NULL)+
xlab(NULL)
AZ has more teacher and warehouse jobs available just like NY than other jobs in the state from our categories of alternate jobs to LMT. There are as many LMT jobs available as there are security, clerical, cashier, and server.
Now, lets compare the pay to each of these states of CA, NY, and NV. To see how LMTs get paid compared to these alternative jobs in those states.
NY_pay <- subset(slr, slr$state=='New York')
NY_pay2 <- NY_pay[,c(1,34,47,50,53,56,59,62,65,68,71,74,77)]
CA_pay <- subset(slr, slr$state=='California')
CA_pay2 <- CA_pay[,c(1,34,47,50,53,56,59,62,65,68,71,74,77)]
AZ_pay <- subset(slr, slr$state=='Arizona')
AZ_pay2 <- AZ_pay[,c(1,34,47,50,53,56,59,62,65,68,71,74,77)]
NY_PAY <- gather(NY_pay2,key='jobTitle',value='AnnualPay',2:13)
NY_PAY$jobTitle <- gsub('_.*$','',NY_PAY$jobTitle)
CA_PAY <- gather(CA_pay2,key='jobTitle',value='AnnualPay',2:13)
CA_PAY$jobTitle <- gsub('_.*$','',CA_PAY$jobTitle)
AZ_PAY <- gather(AZ_pay2, key='jobTitle',value='AnnualPay',2:13)
AZ_PAY$jobTitle <- gsub('_.*$','',AZ_PAY$jobTitle)ggplot(data = CA_PAY, aes(y=CA_PAY$AnnualPay, x=CA_PAY$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=mean(CA_pay$median2018IncomeByState), linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA Alternate and LMT Jobs Advertised Annual Pay with Median CA pay')+
ylab(NULL)+
xlab(NULL)
ggplot(data = NY_PAY, aes(y=NY_PAY$AnnualPay, x=NY_PAY$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=mean(NY_pay$median2018IncomeByState), linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('NY Alternate and LMT Jobs Advertised Annual Pay')+
ylab(NULL)+
xlab(NULL)
ggplot(data = AZ_PAY, aes(y=AZ_PAY$AnnualPay, x=AZ_PAY$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
geom_hline(yintercept=mean(AZ_pay$median2018IncomeByState), linetype="dashed", color = "red")+
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('AZ Alternate and LMT Jobs Advertised Annual Pay')+
ylab(NULL)+
xlab(NULL)
Lets summarize the pay for each comparative job to that of the number of jobs available. In each state, there were more warehouse jobs available than most other jobs in our categories selected, but this role is on the low end of the annual pay scale compared to those other jobs available. In all jobs, data scientist pays the most in CA, AZ, and NY. LMT pays more than clerical in AZ or CA but not in NY. In CA, nannys get paid more than LMTs and so do personal trainers. In NY, security pays more than LMTs but not in CA or AZ. Also, tutors get paid more than teachers in NY and CA, but almost the same but less than in AZ.
Those are interesting findings for those three states. Data hasn’t been analyzed on the actual requirements to get licensed as a massage therapist in each state, because the table hasn’t been filled in. Each state has to be handled separately by finding each requirement from their massage board website of each state, and if not available or not intuitively navigated to from their home page, then emailed and asked about each specific requirements on cost, hours required, continuing education, time to process, distribution of hours, license by endorsement or reciprocity available, etc. It is being added to as time and desire permits on a motivational need. I haven’t found a table of these requirements on any internet search, but did find something close with American Massage Therapy Association’s listing of schools and state requirements that lacked a date updated or sources to validate and the other variables sought.
It would now be interesting to separately use our function from another script to pull in the jobs in our same wellness market of licensed massage therapists. Such as chiropractors, physical therapists, keep the personal trainer data, and add estheticians or skin care or medical spa jobs maybe by just searching for those jobs, as well as resort spas. This would add value to how effective our decision would be to move to a state to earn more, live better, or even open up a business in massage therapy that would do well based on evidence gathered from other top cities in each state and home values alongside the diversity indicated by percent race in population of the state. finding and replacing the job search sought in the scripts that are similar in file name to ‘copy-indeed-webscrape-function-Alt-jobs-dataScientist.Rmd’ in the github repository mentioned at the beginning of this script will get you the information you need in about 10-15 minutes. As it goes through the first five web page job listings on Indeed for 150 cities or the three most populated cities in each of the 50 US states.
Lets look at some other salaries from other professions, that reflect the state of our economy. I have ran the function to pull the wage information and number of jobs listed for nurses, medical doctors, chiropractors, physical therapists, estheticians, medical spa technicians, and personal assistants. Lets see what our data tells us in a state by state comparison.
nurses <- read.csv('./Alternate Jobs Each State Indeed/statesRates-nurse.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
personalAssistants <- read.csv('./Alternate Jobs Each State Indeed/statesRates-personal assistant.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
chiropractor <- read.csv('./Alternate Jobs Each State Indeed/statesRates-chiropractor.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
physicalTherapist <- read.csv('./Alternate Jobs Each State Indeed/statesRates-physical therapist.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
esthetician <- read.csv('./Alternate Jobs Each State Indeed/statesRates-esthetician.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
medicalSpaEsthetician <- read.csv('./Alternate Jobs Each State Indeed/statesRates-medical spa technician.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
medicalDoctor <- read.csv('./Alternate Jobs Each State Indeed/statesRates-medical doctor.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
yogaInstructor <- read.csv('./Alternate Jobs Each State Indeed/statesRates-yoga Instructor.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))
pilatesInstructor <- read.csv('./Alternate Jobs Each State Indeed/statesRates-pilates Instructor.csv', sep=',',
header=TRUE, na.strings=c('',' ','NA'))nurses$stateName <- states
nurses <- nurses[,c(1,10,2:9)]
head(nurses)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 231 nurse 27.35931 78.75758
## 2 AL Alabama 238 nurse 16.07983 36.43277
## 3 AR Arkansas 260 nurse 18.36923 38.13077
## 4 AZ Arizona 248 nurse 20.34677 63.25806
## 5 CA California 234 nurse 21.66667 86.66667
## 6 CO Colorado 254 nurse 19.64567 78.70079
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 44345.73 152497.1 53.05844 98421.42
## 2 50879.47 138256.3 26.25630 94567.88
## 3 45058.82 133460.2 28.25000 89259.51
## 4 34455.23 151675.0 41.80242 93065.11
## 5 69662.00 161812.7 54.16667 115737.33
## 6 46538.89 117747.5 49.17323 82143.21personalAssistants$stateName <- states
personalAssistants <- personalAssistants[,c(1,10,2:9)]
head(personalAssistants)## state stateName jobsListed jobSearched MinHourlySalary
## 1 AK Alaska 107 personal assistant 11.88131
## 2 AL Alabama 179 personal assistant 8.00000
## 3 AR Arkansas 128 personal assistant 9.31250
## 4 AZ Arizona 210 personal assistant 11.00000
## 5 CA California 223 personal assistant 11.33632
## 6 CO Colorado 209 personal assistant 12.26555
## MaxHourlySalary MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 28.91252 31988.57 145588.57 20.39692 88788.57
## 2 19.62570 34018.08 69055.93 13.81285 51537.01
## 3 17.25625 45464.50 78900.69 13.28438 62182.59
## 4 26.03138 37234.52 91642.61 18.51569 64438.57
## 5 33.34081 33221.35 165794.13 22.33857 99507.74
## 6 27.33244 29429.28 122039.61 19.79900 75734.44chiropractor$stateName <- states
chiropractor <- chiropractor[,c(1,10,2:9)]
head(chiropractor)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 36 chiropractor NA NA
## 2 AL Alabama 42 chiropractor 12.25000 77.50000
## 3 AR Arkansas 27 chiropractor 12.47826 13.52174
## 4 AZ Arizona 155 chiropractor 12.00000 34.60993
## 5 CA California 205 chiropractor 12.93659 46.23415
## 6 CO Colorado 197 chiropractor 12.67005 67.56345
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 50000.00 132777.8 NA 91388.89
## 2 50000.00 80000.0 44.87500 65000.00
## 3 50000.00 80000.0 13.00000 65000.00
## 4 37264.52 211903.2 23.30496 124583.87
## 5 45038.85 133287.1 29.58537 89162.96
## 6 45000.00 125076.1 40.11675 85038.07physicalTherapist$stateName <- states
physicalTherapist <- physicalTherapist[,c(1,10,2:9)]
head(physicalTherapist)## state stateName jobsListed jobSearched MinHourlySalary
## 1 AK Alaska 87 physical therapist NA
## 2 AL Alabama 215 physical therapist 35.26718
## 3 AR Arkansas 179 physical therapist 42.00000
## 4 AZ Arizona 231 physical therapist 13.88312
## 5 CA California 215 physical therapist 26.83023
## 6 CO Colorado 215 physical therapist 34.55349
## MaxHourlySalary MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA 45627.00 115000.00 NA 80313.50
## 2 47.10401 62941.00 102606.00 41.18560 82773.50
## 3 60.00000 NA NA 51.00000 NA
## 4 59.67177 60382.17 97547.77 36.77745 78964.97
## 5 89.76726 49542.50 138781.65 58.29874 94162.07
## 6 86.04651 47231.63 107740.00 60.30000 77485.81esthetician$stateName <- states
esthetician <- esthetician[,c(1,10,2:9)]
head(esthetician)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 28 esthetician 20.00000 25.00000
## 2 AL Alabama 88 esthetician 13.93182 39.45455
## 3 AR Arkansas 47 esthetician 15.00000 15.00000
## 4 AZ Arizona 211 esthetician 14.25118 43.83886
## 5 CA California 219 esthetician 13.29224 115.52511
## 6 CO Colorado 186 esthetician 11.93548 60.77957
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA 22.50000 NA
## 2 28000.00 35000.00 26.69318 31500.00
## 3 30000.00 45000.00 15.00000 37500.00
## 4 17500.00 70000.00 29.04502 43750.00
## 5 21155.25 78493.15 64.40868 49824.20
## 6 35155.91 50000.00 36.35753 42577.96medicalSpaEsthetician$stateName <- states
medicalSpaEsthetician <- medicalSpaEsthetician[,c(1,10,2:9)]
head(medicalSpaEsthetician)## state stateName jobsListed jobSearched MinHourlySalary
## 1 AK Alaska 10 medical spa technician 25.00000
## 2 AL Alabama 29 medical spa technician 13.86207
## 3 AR Arkansas 40 medical spa technician 25.00000
## 4 AZ Arizona 170 medical spa technician 14.80588
## 5 CA California 72 medical spa technician 16.87500
## 6 CO Colorado 159 medical spa technician 14.48113
## MaxHourlySalary MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 38 NA NA 31.50000 NA
## 2 38 NA NA 25.93103 NA
## 3 38 45000 75000 31.50000 60000
## 4 38 NA NA 26.40294 NA
## 5 38 NA NA 27.43750 NA
## 6 38 36000 50000 26.24057 43000medicalDoctor$stateName <- states
medicalDoctor <- medicalDoctor[,c(1,10,2:9)]
head(medicalDoctor)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 203 medical doctor 29.908867 45.87931
## 2 AL Alabama 235 medical doctor 8.668085 73.68085
## 3 AR Arkansas 204 medical doctor 11.000000 200.00000
## 4 AZ Arizona 232 medical doctor 11.663793 28.31897
## 5 CA California 233 medical doctor 13.100858 162.87554
## 6 CO Colorado 237 medical doctor 12.000000 38.73325
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 42123.19 457881.8 37.89409 250002.48
## 2 46086.99 414697.9 41.17447 230392.43
## 3 48433.39 374019.6 105.50000 211226.50
## 4 34491.32 182974.1 19.99138 108732.73
## 5 52008.58 129334.8 87.98820 90671.67
## 6 37155.54 192524.9 25.36662 114840.22yogaInstructor$stateName <- states
yogaInstructor <- yogaInstructor[,c(1,10,2:9)]
yogaInstructor$avgAnualSalary <- yogaInstructor$avgHourly*40*52
head(yogaInstructor)## state stateName jobsListed jobSearched MinHourlySalary MaxHourlySalary
## 1 AK Alaska 4 yoga instructor NA NA
## 2 AL Alabama 27 yoga instructor 25.00000 72.60870
## 3 AR Arkansas 20 yoga instructor 10.00000 100.00000
## 4 AZ Arizona 152 yoga instructor 21.21711 93.68421
## 5 CA California 209 yoga instructor 15.00000 100.00000
## 6 CO Colorado 155 yoga instructor 15.01935 100.00000
## MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 NA NA NA NA
## 2 NA NA 48.80435 101513.0
## 3 NA NA 55.00000 114400.0
## 4 NA NA 57.45066 119497.4
## 5 36000 145266 57.50000 119600.0
## 6 NA NA 57.50968 119620.1pilatesInstructor$stateName <- states[2:50]
pilatesInstructor <- pilatesInstructor[,c(1,10,2:9)]
head(pilatesInstructor)## state stateName jobsListed jobSearched MinHourlySalary
## 1 AL Alabama 31 pilates instructor 25.00000
## 2 AR Arkansas 9 pilates instructor 25.00000
## 3 AZ Arizona 111 pilates instructor 12.00000
## 4 CA California 198 pilates instructor 14.77778
## 5 CO Colorado 157 pilates instructor 19.45363
## 6 CT Connecticut 159 pilates instructor 19.64151
## MaxHourlySalary MinAnnualSalary MaxAnnualSalary avgHourly avgAnualSalary
## 1 100 NA NA 62.50000 NA
## 2 100 NA NA 62.50000 NA
## 3 100 NA NA 56.00000 NA
## 4 100 36000 55000 57.38889 45500
## 5 100 NA NA 59.72682 NA
## 6 100 NA NA 59.82075 NALets combine these jobs to the slr dataset.
nurses2 <- nurses[,c(2,3,9,10)]
personalAssistants2 <- personalAssistants[,c(2,3,9,10)]
chiropractor2 <- chiropractor[,c(2,3,9,10)]
physicalTherapist2 <- physicalTherapist[,c(2,3,9,10)]
esthetician2 <- esthetician[,c(2,3,9,10)]
medicalSpaEsthetician2 <- medicalSpaEsthetician[,c(2,3,9,10)]
medicalDoctor2 <- medicalDoctor[,c(2,3,9,10)]
yogaInstructor2 <- yogaInstructor[,c(2,3,9,10)]
pilatesInstructor2 <- pilatesInstructor[,c(2,3,9,10)]nurses2$avgAnualSalary <- ifelse(nurses2$avgAnualSalary,nurses2$avgAnualSalary,
nurses2$avgHourly*40*52)
colnames(nurses2)[2:4] <- paste('nurses',colnames(nurses2)[2:4])
personalAssistants2$avgAnualSalary <- ifelse(personalAssistants2$avgAnualSalary,
personalAssistants2$avgAnualSalary,
personalAssistants2$avgHourly*40*52)
colnames(personalAssistants2)[2:4] <- paste('personalAssistant',colnames(personalAssistants2)[2:4])
chiropractor2$avgAnualSalary <- chiropractor2$avgHourly*40*52
colnames(chiropractor2)[2:4] <- paste('chiropractor',colnames(chiropractor2)[2:4])
physicalTherapist2$avgAnualSalary <- physicalTherapist2$avgHourly*40*52
colnames(physicalTherapist2)[2:4] <- paste('physicalTherapist',colnames(physicalTherapist2)[2:4])
esthetician2$avgAnualSalary <- esthetician2$avgHourly*40*52
colnames(esthetician2)[2:4] <- paste('esthetician',colnames(esthetician2)[2:4])
medicalSpaEsthetician2$avgAnualSalary <- medicalSpaEsthetician2$avgHourly*40*52
colnames(medicalSpaEsthetician2)[2:4] <- paste('medicalSpaEsthetician',colnames(medicalSpaEsthetician2)[2:4])
medicalDoctor2$avgAnualSalary <- ifelse(medicalDoctor2$avgAnualSalary,
medicalDoctor2$avgAnualSalary,
medicalDoctor2$avgHourly*40*52)
colnames(medicalDoctor2)[2:4] <- paste('medicalDoctor',colnames(medicalDoctor2)[2:4])
colnames(yogaInstructor2)[2:4] <- paste('yogaInstructor',colnames(yogaInstructor2)[2:4])
pilatesInstructor2$avgAnualSalary <- pilatesInstructor2$avgHourly*40*52
colnames(pilatesInstructor2)[2:4] <- paste('pilatesInstructor',colnames(pilatesInstructor2)[2:4])Combine these other jobs into a table with the slr data table.
slr2 <- merge(slr, nurses2, by.x='state',by.y='stateName')
slr2 <- merge(slr2,personalAssistants2, by.x='state',by.y='stateName')
slr2 <- merge(slr2, chiropractor2, by.x='state',by.y='stateName')
slr2 <- merge(slr2, physicalTherapist2, by.x='state', by.y='stateName')
slr2 <- merge(slr2, esthetician2, by.x='state', by.y='stateName')
slr2 <- merge(slr2, medicalSpaEsthetician2, by.x='state', by.y='stateName')
slr2 <- merge(slr2, medicalDoctor2, by.x='state',by.y='stateName')
slr2 <- merge(slr2, yogaInstructor2, by.x='state',by.y='stateName')
slr2 <- merge(slr2, pilatesInstructor2, by.x='state', by.y='stateName',all.x=TRUE)write.csv(slr2,'stateLicensingDemographicsAddedAndUpdated.csv', row.names=FALSE)Number of jobs listed and average annual salary in each category by state’s top three populated cities:
jbl <- grep('jobsListed',colnames(slr2))
listedJobsAll <- slr2[,c(1,32,jbl)]
ansy <- grep('avgAn',colnames(slr2))
avgSalaryAll <- slr2[,c(1,34,ansy)]colnames(listedJobsAll) <- gsub('_.*$','',colnames(listedJobsAll),perl=TRUE)
colnames(listedJobsAll) <- gsub(' jobsListed','',colnames(listedJobsAll))
listedJobsAll## state LMT cashier server personalTrainer houseCleaner warehouse
## 1 Alabama 88 183 220 92 72 219
## 2 Alaska 39 103 73 47 4 206
## 3 Arizona 212 217 221 168 171 235
## 4 Arkansas 73 190 197 72 36 212
## 5 California 223 227 224 190 216 243
## 6 Colorado 241 212 225 196 217 250
## 7 Connecticut 203 204 217 211 127 233
## 8 Delaware 159 203 201 157 284 221
## 9 Florida 222 225 224 200 194 233
## 10 Georgia 105 180 200 103 155 202
## 11 Hawaii 99 136 144 38 27 219
## 12 Idaho 203 200 211 87 198 234
## 13 Illinois 226 221 233 206 195 242
## 14 Indiana 109 211 208 96 84 233
## 15 Iowa 152 212 202 82 106 227
## 16 Kansas 179 213 227 172 168 233
## 17 Kentucky 123 214 195 67 85 240
## 18 Louisiana 110 204 220 87 85 224
## 19 Maine 73 139 119 28 113 143
## 20 Maryland 237 209 227 229 224 240
## 21 Massachusetts 229 221 215 207 178 244
## 22 Michigan 237 232 236 175 186 246
## 23 Minnesota 187 210 230 150 153 233
## 24 Mississippi 93 204 198 82 82 223
## 25 Missouri 190 214 227 147 152 225
## 26 Montana 46 196 189 18 17 199
## 27 Nebraska 169 195 229 146 142 236
## 28 Nevada 230 208 215 187 198 228
## 29 New Hampshire 139 187 216 183 206 240
## 30 New Jersey 236 232 240 237 227 230
## 31 New Mexico 162 184 209 102 70 224
## 32 New York 196 209 215 135 175 232
## 33 North Carolina 203 217 227 164 198 229
## 34 North Dakota 46 211 199 27 125 222
## 35 Ohio 235 227 227 194 207 253
## 36 Oklahoma 192 211 225 133 76 227
## 37 Oregon 171 181 188 123 128 236
## 38 Pennsylvania 227 226 228 211 186 232
## 39 Rhode Island 215 211 228 192 199 253
## 40 South Carolina 176 215 230 184 204 239
## 41 South Dakota 56 134 155 33 36 220
## 42 Tennessee 174 214 218 152 192 230
## 43 Texas 234 224 229 199 205 224
## 44 Utah 224 222 220 162 199 254
## 45 Vermont 70 187 164 4 89 233
## 46 Virginia 177 209 241 198 202 229
## 47 Washington 218 200 204 133 178 238
## 48 West Virginia 61 178 206 52 27 196
## 49 Wisconsin 166 197 222 141 89 234
## 50 Wyoming 35 175 147 17 19 164
## security nanny clerical tutor teacher dataScientist nurses personalAssistant
## 1 212 42 223 201 213 122 238 179
## 2 169 23 211 142 194 37 231 107
## 3 221 182 226 214 245 183 248 210
## 4 199 81 222 112 178 70 260 128
## 5 230 199 225 217 236 231 234 223
## 6 234 157 243 207 247 194 254 209
## 7 223 172 233 204 239 226 233 221
## 8 222 133 219 192 242 161 241 181
## 9 228 173 236 220 225 174 245 228
## 10 220 104 224 164 217 95 235 170
## 11 216 48 230 167 231 117 234 177
## 12 215 102 224 209 219 94 247 195
## 13 232 195 228 222 242 207 259 212
## 14 217 72 229 181 226 91 249 170
## 15 209 42 236 171 190 105 249 148
## 16 222 104 231 207 239 133 236 196
## 17 218 87 218 200 203 96 228 183
## 18 216 28 227 200 219 38 249 179
## 19 134 23 165 118 150 36 170 98
## 20 228 164 234 225 249 239 242 233
## 21 232 160 228 212 231 214 247 221
## 22 240 91 235 211 247 152 245 217
## 23 225 133 233 225 225 146 245 196
## 24 206 62 222 186 216 27 234 178
## 25 210 119 234 213 222 148 244 202
## 26 197 13 232 125 174 38 245 118
## 27 222 97 233 212 230 74 254 166
## 28 226 132 230 219 230 49 242 216
## 29 221 101 224 190 239 159 240 192
## 30 238 211 233 224 242 234 230 226
## 31 236 62 222 178 218 161 240 193
## 32 222 102 226 209 230 144 261 210
## 33 210 150 223 205 233 167 243 216
## 34 206 19 223 173 193 20 246 152
## 35 245 117 226 215 246 205 234 221
## 36 213 97 224 202 222 98 238 203
## 37 225 62 230 180 223 96 244 169
## 38 230 161 228 203 235 182 240 228
## 39 225 162 243 217 238 179 239 206
## 40 201 77 231 216 222 78 244 229
## 41 153 13 215 72 164 29 230 128
## 42 223 113 238 198 232 152 253 218
## 43 234 166 227 223 234 207 246 226
## 44 227 127 226 203 216 175 229 230
## 45 194 32 219 111 209 35 239 118
## 46 229 166 234 204 223 219 268 228
## 47 227 126 237 193 231 178 262 198
## 48 214 22 222 75 209 37 236 179
## 49 225 94 238 215 238 142 245 192
## 50 152 14 198 123 160 31 228 93
## chiropractor physicalTherapist esthetician medicalSpaEsthetician
## 1 42 215 88 29
## 2 36 87 28 10
## 3 155 231 211 170
## 4 27 179 47 40
## 5 205 215 219 72
## 6 197 215 186 159
## 7 126 234 195 44
## 8 115 193 144 32
## 9 192 225 184 124
## 10 89 208 74 71
## 11 31 174 68 33
## 12 129 217 72 36
## 13 214 212 221 143
## 14 87 153 94 52
## 15 76 125 42 22
## 16 51 212 164 118
## 17 77 201 62 25
## 18 32 200 118 12
## 19 36 75 57 41
## 20 210 247 200 169
## 21 108 222 199 74
## 22 207 220 147 112
## 23 175 205 171 129
## 24 19 190 195 36
## 25 118 222 170 83
## 26 25 111 8 41
## 27 39 150 57 30
## 28 126 207 184 115
## 29 60 187 153 52
## 30 226 254 247 183
## 31 31 179 52 41
## 32 44 219 202 79
## 33 156 219 195 70
## 34 18 75 22 10
## 35 151 212 201 40
## 36 91 231 117 81
## 37 147 203 91 69
## 38 175 197 181 62
## 39 62 202 191 39
## 40 61 216 187 84
## 41 12 108 18 22
## 42 114 203 188 119
## 43 192 222 225 191
## 44 140 166 204 76
## 45 42 121 12 22
## 46 107 198 206 150
## 47 160 171 179 112
## 48 15 89 21 11
## 49 104 164 112 41
## 50 14 104 32 10
## medicalDoctor yogaInstructor pilatesInstructor
## 1 235 27 31
## 2 203 4 NA
## 3 232 152 111
## 4 204 20 9
## 5 233 209 198
## 6 237 155 157
## 7 234 151 159
## 8 223 72 42
## 9 234 173 116
## 10 217 84 74
## 11 205 48 28
## 12 203 110 81
## 13 226 179 168
## 14 218 57 56
## 15 238 21 17
## 16 231 134 102
## 17 223 26 18
## 18 220 42 14
## 19 141 16 13
## 20 234 197 174
## 21 230 132 122
## 22 231 138 82
## 23 228 129 122
## 24 221 36 17
## 25 232 124 72
## 26 219 8 8
## 27 217 120 62
## 28 226 47 16
## 29 225 55 55
## 30 231 196 168
## 31 220 102 72
## 32 230 89 82
## 33 227 118 122
## 34 204 13 4
## 35 236 137 133
## 36 228 131 113
## 37 206 81 63
## 38 228 121 108
## 39 240 140 67
## 40 222 97 67
## 41 192 14 4
## 42 223 71 23
## 43 223 171 189
## 44 222 70 52
## 45 201 30 8
## 46 225 77 112
## 47 234 148 113
## 48 206 12 8
## 49 232 40 42
## 50 187 8 8colnames(avgSalaryAll) <- gsub('_.*$','',colnames(avgSalaryAll))
colnames(avgSalaryAll) <- gsub(' .*$','',colnames(avgSalaryAll))
avgSalaryAll## state LMT cashier server personalTrainer houseCleaner
## 1 Alabama 37500.00 22234.97 35244.18 62813.49 19339.41
## 2 Alaska 80000.00 26479.10 21368.72 NA 24960.00
## 3 Arizona 69846.70 27653.46 33087.06 47196.19 35871.79
## 4 Arkansas 42500.00 21463.68 27535.24 45182.22 28741.82
## 5 California 66450.98 34324.58 43944.64 65728.00 42592.81
## 6 Colorado 43500.00 30101.13 43066.95 57629.80 36946.36
## 7 Connecticut 45000.00 27991.80 41940.28 95236.40 33449.92
## 8 Delaware 41600.00 25323.74 28323.18 97255.07 29112.68
## 9 Florida 59075.88 22366.93 45017.14 64169.98 34630.93
## 10 Georgia 42500.00 21320.00 28979.60 52756.36 35044.65
## 11 Hawaii 97500.00 29869.41 38220.00 NA 29428.15
## 12 Idaho NA 21408.40 35412.99 44720.00 32240.00
## 13 Illinois 57920.35 26514.12 37863.32 89440.00 38960.00
## 14 Indiana 73166.67 22408.06 32583.75 70325.52 31200.00
## 15 Iowa 42068.42 23029.62 28476.44 51365.85 26166.79
## 16 Kansas 47486.36 23102.16 46028.93 28905.35 31407.38
## 17 Kentucky 81500.00 22735.42 32986.67 46800.00 29640.00
## 18 Louisiana 97500.00 21135.20 23562.72 45987.13 28647.27
## 19 Maine NA 28080.00 37177.82 NA 32916.46
## 20 Maryland 54739.08 28761.72 33761.06 84028.82 39436.43
## 21 Massachusetts 54198.10 34065.88 35587.35 72473.43 35624.38
## 22 Michigan 55641.35 26448.28 41582.37 66298.02 31009.89
## 23 Minnesota 66724.96 26936.00 30767.54 78612.20 32344.00
## 24 Mississippi 47727.95 20831.86 21008.79 49235.12 23450.73
## 25 Missouri 45416.15 23143.16 30728.11 31511.29 30996.79
## 26 Montana NA 24141.10 22544.34 NA 27240.00
## 27 Nebraska 48386.79 23098.67 48884.54 59158.90 24328.31
## 28 Nevada 76647.83 29980.00 32031.52 92537.75 38227.88
## 29 New Hampshire 49199.64 24427.49 24960.00 62314.75 33961.55
## 30 New Jersey 68595.34 25517.66 35680.67 114184.98 42640.00
## 31 New Mexico 42542.50 23857.83 26260.00 51755.29 29120.00
## 32 New York 60724.49 31458.76 32838.85 56368.00 37321.14
## 33 North Carolina 71884.24 23075.30 29014.63 59387.80 30968.89
## 34 North Dakota 45569.00 24442.46 28785.53 NA 26834.93
## 35 Ohio 50376.85 24798.27 51355.15 61091.96 31189.95
## 36 Oklahoma 42470.80 22207.20 29645.59 40792.34 27108.42
## 37 Oregon 97500.00 29766.41 30509.89 62907.32 37706.50
## 38 Pennsylvania 41992.19 23677.67 27428.86 76757.91 40258.06
## 39 Rhode Island 44686.03 29640.00 46918.60 46800.00 36400.00
## 40 South Carolina NA 23589.86 32468.35 87269.57 28477.65
## 41 South Dakota NA 22272.22 21384.17 26000.00 26585.00
## 42 Tennessee 55739.94 31225.51 26834.86 42701.58 31194.58
## 43 Texas 55427.35 26865.89 33006.38 82186.13 37338.54
## 44 Utah 42634.82 26143.35 34814.00 70671.85 34795.58
## 45 Vermont NA 25827.59 30768.78 42640.00 27040.00
## 46 Virginia 78988.50 22816.56 29380.00 48507.07 29611.68
## 47 Washington 77003.42 30880.72 37009.22 109567.52 36382.47
## 48 West Virginia NA 20715.28 29107.38 32240.00 27040.00
## 49 Wisconsin 42375.30 22978.19 29460.81 59154.61 30119.48
## 50 Wyoming NA 24730.72 23083.62 NA 35880.00
## warehouse security nanny clerical tutor teacher dataScientist
## 1 28153.61 22975.66 28129.52 28439.10 37794.74 52559.76 131461.6
## 2 45871.07 35064.00 26678.26 42781.07 42588.00 64408.54 126078.1
## 3 40661.79 39687.53 36400.00 40678.63 57608.22 42322.79 103422.7
## 4 32428.38 23030.25 27245.43 30585.14 25628.57 44115.93 124034.1
## 5 47334.89 41414.16 52888.44 51181.31 87743.41 52693.01 120606.1
## 6 35222.80 38807.82 37327.39 45192.67 52005.02 51821.86 125833.5
## 7 35163.61 35719.10 57200.00 45909.53 95218.63 62185.10 116314.8
## 8 33811.91 29007.57 35141.05 59453.33 45120.45 56105.37 123509.3
## 9 35004.26 35653.39 40427.75 37049.43 54956.06 48095.28 125201.1
## 10 34429.66 40980.73 32410.00 38934.95 33273.66 61490.47 120000.0
## 11 38074.45 38973.52 26000.00 40298.19 45635.20 51117.77 115069.3
## 12 31968.98 23744.41 46280.00 31896.43 41600.00 51480.38 135000.0
## 13 38583.14 33804.48 46560.00 38534.74 47413.69 49464.88 114525.7
## 14 34433.82 27888.29 31358.89 32486.88 36853.92 50319.77 122267.6
## 15 37310.99 34269.19 31026.67 34320.00 47821.43 41605.92 135000.0
## 16 36910.85 36594.46 28660.00 36298.79 38427.25 54960.91 112330.0
## 17 36269.35 31124.81 31827.59 31617.05 39741.28 51644.82 101754.6
## 18 32305.00 30719.58 27040.00 31306.52 46477.60 46072.04 135383.5
## 19 37127.27 30703.28 31200.00 41958.01 49285.42 57117.97 135000.0
## 20 35755.37 38652.69 41955.12 57200.00 76162.67 65715.16 117309.5
## 21 43690.27 34637.16 44687.50 46083.86 80820.75 49906.98 129275.7
## 22 40228.13 40583.92 31200.00 37673.00 36570.05 53481.01 135000.0
## 23 35443.69 33533.67 40966.62 43822.83 55640.00 49212.10 135000.0
## 24 30579.92 23935.15 23685.16 32682.70 35343.23 60215.15 135000.0
## 25 33875.34 32248.72 31060.17 39220.67 56335.77 51356.47 108927.1
## 26 36888.80 29744.32 24000.00 39809.05 NA 53775.14 135000.0
## 27 34273.02 39796.40 33344.33 37038.28 52215.17 41000.00 135000.0
## 28 37316.84 33270.80 28261.21 38507.04 41013.52 53799.85 135000.0
## 29 29796.00 42080.00 36080.79 41604.64 48551.58 57289.16 125691.8
## 30 35093.22 38943.19 52000.00 67796.39 122873.21 62929.87 167227.6
## 31 34984.30 33343.90 26637.42 30947.03 41600.00 66487.53 122872.7
## 32 37008.76 34943.39 44293.80 69956.11 89904.77 46233.16 143371.1
## 33 38296.07 36320.76 42057.60 35052.85 41288.00 38264.15 103970.1
## 34 37521.42 31947.18 18720.00 38596.59 44720.00 59007.25 135000.0
## 35 38278.78 27846.53 35955.56 35363.68 41232.37 37946.95 124467.2
## 36 35039.11 38328.64 28112.16 30100.80 60754.53 38251.50 135000.0
## 37 38391.16 38158.15 42405.16 41226.23 36365.64 58435.04 135000.0
## 38 37319.86 36874.78 34475.03 36151.86 52350.94 39561.37 100740.7
## 39 37028.44 36920.00 38480.00 42156.98 47164.24 47774.16 135000.0
## 40 33454.06 29477.01 28296.10 35945.28 41243.70 46776.50 126012.6
## 41 34962.91 36754.01 26000.00 28527.68 NA 59311.67 135000.0
## 42 32322.16 29266.91 31384.07 33240.50 39892.93 49833.55 128660.7
## 43 32973.57 39028.31 34564.34 39497.09 47205.74 48638.03 128494.0
## 44 35431.65 37554.54 31814.17 39068.15 40511.33 41766.20 114840.0
## 45 37105.24 35033.53 32240.00 32193.70 33280.00 55244.64 135000.0
## 46 37185.68 34036.25 34320.00 32138.89 41630.59 28475.95 117500.0
## 47 44994.77 40159.67 50266.67 48450.05 54775.13 62904.69 154694.3
## 48 31166.84 23534.86 22927.27 60161.89 NA 55596.89 135000.0
## 49 59395.56 31338.67 33589.79 43177.48 38320.37 50731.25 131449.9
## 50 33327.69 39360.00 NA 29443.77 21668.90 63735.50 126396.4
## nurses personalAssistant chiropractor physicalTherapist esthetician
## 1 94567.88 51537.01 93340.00 85666.05 55521.82
## 2 98421.42 88788.57 NA NA 46800.00
## 3 93065.11 64438.57 48474.33 76497.09 60413.65
## 4 89259.51 62182.59 27040.00 106080.00 31200.00
## 5 115737.33 99507.74 61537.56 121261.39 133970.05
## 6 82143.21 75734.44 83442.84 125424.00 75623.66
## 7 90638.61 77819.00 110668.24 77437.84 68720.00
## 8 140545.64 83724.20 74635.83 100629.01 72672.00
## 9 80583.89 100297.56 45554.17 126500.98 94673.91
## 10 83417.04 74218.64 62937.53 NA 70749.71
## 11 82937.05 66274.58 67600.00 95680.00 73840.00
## 12 88370.50 61870.50 57200.00 NA 33280.00
## 13 85820.50 62505.59 48287.10 112815.47 65351.76
## 14 89407.29 55191.18 52000.00 81120.00 60332.24
## 15 75335.96 62516.89 38907.40 NA 39520.00
## 16 79476.97 64977.80 35360.00 78145.45 42120.00
## 17 83420.50 66253.62 37568.00 99467.46 49140.00
## 18 105411.54 77304.08 31662.22 93050.88 36336.97
## 19 79549.30 32500.00 70720.00 82160.00 43680.00
## 20 104918.31 69145.70 43878.10 64458.95 65343.20
## 21 76566.18 65796.38 44026.67 134450.45 66607.04
## 22 76339.48 63472.25 112301.91 88400.00 63492.75
## 23 98586.40 59915.82 56647.31 114400.00 160956.73
## 24 103941.82 63156.28 NA NA 36426.67
## 25 87616.28 53045.50 179668.81 83320.86 62452.00
## 26 68534.66 57542.37 41221.82 81120.00 18720.00
## 27 83436.77 54360.67 31200.00 91520.00 37440.00
## 28 100866.98 61182.82 91520.00 121383.57 82131.74
## 29 94381.00 77572.92 29120.00 86240.61 30928.00
## 30 108746.99 82842.92 74880.00 103959.06 165229.47
## 31 70031.68 91370.36 NA 92077.77 31200.00
## 32 80865.08 77129.17 49582.00 106692.60 110003.17
## 33 76354.40 50298.27 51746.67 94120.00 91456.00
## 34 103032.32 55930.92 NA 101920.00 38480.00
## 35 87126.47 51427.47 54114.44 73825.56 45149.45
## 36 73000.87 55123.52 41782.86 93604.50 101920.00
## 37 84483.06 50425.16 67600.00 102960.00 175568.74
## 38 77206.35 53925.44 61544.23 92499.45 59710.94
## 39 58456.72 92715.58 39318.71 63120.79 89440.00
## 40 74507.46 78620.94 83200.00 40145.93 51132.41
## 41 64326.19 65721.59 NA 91520.00 NA
## 42 98727.22 61158.14 33444.75 NA 41843.40
## 43 89414.57 59751.68 68932.50 95239.64 72716.80
## 44 96266.97 62217.39 53225.71 NA 58209.41
## 45 139476.99 83750.00 31200.00 NA NA
## 46 91389.80 83700.97 50960.00 NA 69680.00
## 47 95335.03 89489.96 105846.00 101662.78 85041.79
## 48 52875.47 52607.75 NA NA 33280.00
## 49 74300.82 67664.93 94840.00 93600.00 59669.61
## 50 96064.66 40154.50 NA 74680.00 31200.00
## medicalSpaEsthetician medicalDoctor yogaInstructor pilatesInstructor
## 1 53936.55 230392.43 101513.04 130000.00
## 2 65520.00 250002.48 NA NA
## 3 54918.12 108732.73 119497.37 116480.00
## 4 65520.00 211226.50 114400.00 130000.00
## 5 57070.00 90671.67 119600.00 119368.89
## 6 54580.38 114840.22 119620.13 124231.78
## 7 59847.27 88064.28 119379.60 124427.17
## 8 65520.00 166414.43 121741.18 130000.00
## 9 56109.68 100301.62 125551.45 125454.48
## 10 50330.14 177901.84 99455.20 116480.00
## 11 65520.00 211766.83 156000.00 156000.00
## 12 65520.00 241453.20 121648.80 121648.80
## 13 52065.45 61615.04 70801.34 81250.00
## 14 58480.00 187377.72 110750.88 119600.00
## 15 65520.00 189517.56 130000.00 110619.29
## 16 65520.00 98080.81 127252.54 130000.00
## 17 57408.00 164865.82 107380.00 130000.00
## 18 65520.00 278183.12 120640.00 130000.00
## 19 56134.63 261418.44 130000.00 130000.00
## 20 56683.08 135313.00 117461.93 114400.00
## 21 55429.19 86574.45 124788.18 126338.78
## 22 56884.29 175915.58 130000.00 120183.41
## 23 52923.10 77048.49 119600.00 95304.92
## 24 65520.00 185730.77 53733.33 85800.00
## 25 65520.00 127364.36 123029.42 125233.33
## 26 52596.10 106050.23 130000.00 130000.00
## 27 56368.00 168907.38 107882.67 130000.00
## 28 53238.96 196847.35 130000.00 130000.00
## 29 65520.00 126054.35 130000.00 128298.18
## 30 56160.00 121250.87 146640.00 115440.00
## 31 54917.07 165227.27 130000.00 130000.00
## 32 48234.94 163185.20 137136.00 120625.75
## 33 54452.62 139693.85 120776.84 125928.14
## 34 65520.00 158103.12 119600.00 130000.00
## 35 53386.67 144104.49 115204.67 104625.56
## 36 61822.22 122863.73 126257.94 108574.16
## 37 57049.28 209603.73 130000.00 130000.00
## 38 55002.58 80897.72 89998.68 79714.07
## 39 53040.00 142581.00 124800.00 124800.00
## 40 53485.71 192733.66 107302.27 130000.00
## 41 65520.00 234702.90 130000.00 130000.00
## 42 54918.99 170108.78 78200.00 130000.00
## 43 58686.49 150276.48 113706.67 117921.69
## 44 57268.42 149630.63 123760.00 123760.00
## 45 65520.00 248134.33 130000.00 130000.00
## 46 54385.07 215669.32 130000.00 130000.00
## 47 55895.36 152145.04 114512.43 119213.91
## 48 65520.00 213932.41 130000.00 130000.00
## 49 52012.68 233949.70 118791.11 119381.05
## 50 65520.00 193219.12 130000.00 130000.00Lets take a look at the health and wellness number of jobs available by using the listedJobsAll columns for LMT, personalTrainer, chiropractor, physicalTherapist, esthetician, and medicalSpaEsthetician.
wellnessJobs <- listedJobsAll[,c(1,2,5,16,17,21,22)]
wellnessSalary <- avgSalaryAll[,c(1,2,5,16,17,21,22)]
wellnessJobs## state LMT personalTrainer chiropractor physicalTherapist
## 1 Alabama 88 92 42 215
## 2 Alaska 39 47 36 87
## 3 Arizona 212 168 155 231
## 4 Arkansas 73 72 27 179
## 5 California 223 190 205 215
## 6 Colorado 241 196 197 215
## 7 Connecticut 203 211 126 234
## 8 Delaware 159 157 115 193
## 9 Florida 222 200 192 225
## 10 Georgia 105 103 89 208
## 11 Hawaii 99 38 31 174
## 12 Idaho 203 87 129 217
## 13 Illinois 226 206 214 212
## 14 Indiana 109 96 87 153
## 15 Iowa 152 82 76 125
## 16 Kansas 179 172 51 212
## 17 Kentucky 123 67 77 201
## 18 Louisiana 110 87 32 200
## 19 Maine 73 28 36 75
## 20 Maryland 237 229 210 247
## 21 Massachusetts 229 207 108 222
## 22 Michigan 237 175 207 220
## 23 Minnesota 187 150 175 205
## 24 Mississippi 93 82 19 190
## 25 Missouri 190 147 118 222
## 26 Montana 46 18 25 111
## 27 Nebraska 169 146 39 150
## 28 Nevada 230 187 126 207
## 29 New Hampshire 139 183 60 187
## 30 New Jersey 236 237 226 254
## 31 New Mexico 162 102 31 179
## 32 New York 196 135 44 219
## 33 North Carolina 203 164 156 219
## 34 North Dakota 46 27 18 75
## 35 Ohio 235 194 151 212
## 36 Oklahoma 192 133 91 231
## 37 Oregon 171 123 147 203
## 38 Pennsylvania 227 211 175 197
## 39 Rhode Island 215 192 62 202
## 40 South Carolina 176 184 61 216
## 41 South Dakota 56 33 12 108
## 42 Tennessee 174 152 114 203
## 43 Texas 234 199 192 222
## 44 Utah 224 162 140 166
## 45 Vermont 70 4 42 121
## 46 Virginia 177 198 107 198
## 47 Washington 218 133 160 171
## 48 West Virginia 61 52 15 89
## 49 Wisconsin 166 141 104 164
## 50 Wyoming 35 17 14 104
## yogaInstructor pilatesInstructor
## 1 27 31
## 2 4 NA
## 3 152 111
## 4 20 9
## 5 209 198
## 6 155 157
## 7 151 159
## 8 72 42
## 9 173 116
## 10 84 74
## 11 48 28
## 12 110 81
## 13 179 168
## 14 57 56
## 15 21 17
## 16 134 102
## 17 26 18
## 18 42 14
## 19 16 13
## 20 197 174
## 21 132 122
## 22 138 82
## 23 129 122
## 24 36 17
## 25 124 72
## 26 8 8
## 27 120 62
## 28 47 16
## 29 55 55
## 30 196 168
## 31 102 72
## 32 89 82
## 33 118 122
## 34 13 4
## 35 137 133
## 36 131 113
## 37 81 63
## 38 121 108
## 39 140 67
## 40 97 67
## 41 14 4
## 42 71 23
## 43 171 189
## 44 70 52
## 45 30 8
## 46 77 112
## 47 148 113
## 48 12 8
## 49 40 42
## 50 8 8Now look at the LMT with esthetician and medical spa esthetician jobs available.Nurses are needed in medical spas that are also estheticians, so we will add that class of jobs listed to our spaJobs data table.
spaJobs <- listedJobsAll[,c(1,2,14,18,19)]
spaSalary <- avgSalaryAll[,c(1,2,14,18,19)]
spaJobs## state LMT nurses esthetician medicalSpaEsthetician
## 1 Alabama 88 238 88 29
## 2 Alaska 39 231 28 10
## 3 Arizona 212 248 211 170
## 4 Arkansas 73 260 47 40
## 5 California 223 234 219 72
## 6 Colorado 241 254 186 159
## 7 Connecticut 203 233 195 44
## 8 Delaware 159 241 144 32
## 9 Florida 222 245 184 124
## 10 Georgia 105 235 74 71
## 11 Hawaii 99 234 68 33
## 12 Idaho 203 247 72 36
## 13 Illinois 226 259 221 143
## 14 Indiana 109 249 94 52
## 15 Iowa 152 249 42 22
## 16 Kansas 179 236 164 118
## 17 Kentucky 123 228 62 25
## 18 Louisiana 110 249 118 12
## 19 Maine 73 170 57 41
## 20 Maryland 237 242 200 169
## 21 Massachusetts 229 247 199 74
## 22 Michigan 237 245 147 112
## 23 Minnesota 187 245 171 129
## 24 Mississippi 93 234 195 36
## 25 Missouri 190 244 170 83
## 26 Montana 46 245 8 41
## 27 Nebraska 169 254 57 30
## 28 Nevada 230 242 184 115
## 29 New Hampshire 139 240 153 52
## 30 New Jersey 236 230 247 183
## 31 New Mexico 162 240 52 41
## 32 New York 196 261 202 79
## 33 North Carolina 203 243 195 70
## 34 North Dakota 46 246 22 10
## 35 Ohio 235 234 201 40
## 36 Oklahoma 192 238 117 81
## 37 Oregon 171 244 91 69
## 38 Pennsylvania 227 240 181 62
## 39 Rhode Island 215 239 191 39
## 40 South Carolina 176 244 187 84
## 41 South Dakota 56 230 18 22
## 42 Tennessee 174 253 188 119
## 43 Texas 234 246 225 191
## 44 Utah 224 229 204 76
## 45 Vermont 70 239 12 22
## 46 Virginia 177 268 206 150
## 47 Washington 218 262 179 112
## 48 West Virginia 61 236 21 11
## 49 Wisconsin 166 245 112 41
## 50 Wyoming 35 228 32 10Lets used our list of demanded LMT states where there weren’t as many jobs available for LMT, but the pay was higher for LMT than the national LMT average pay.
demanded## [1] Georgia Indiana Mississippi Wyoming
## 50 Levels: Alabama Alaska Arizona Arkansas California Colorado ... WyomingLets look at Georgia and see how the jobs available in the wellness and the spa industries compare by plotting the wellness jobs available in GA, then the salary of those jobs in GA against the median income for GA and the median two bedroom home value.
GA <- subset(slr2, slr2=='Georgia')
GA_2BR <- GA$Zillow_2BR_3cityAverageHomeValue
GA_medIncome <- GA$median2018IncomeByState
GA_wellness <- subset(wellnessJobs, wellnessJobs$state=='Georgia')
GA_wellnessSalary <- subset(wellnessSalary,wellnessSalary$state=='Georgia')
GA_wellness2 <- gather(GA_wellness, key='jobTitle',value='jobsListed',2:7)
GA_wellnessSalary2 <- gather(GA_wellnessSalary, key='jobTitle', value='annualSalary',2:7)ggplot(data = GA_wellness2, aes(y=GA_wellness2$jobsListed, x=GA_wellness2$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('GA Wellness Jobs Available')+
ylab(NULL)+
xlab(NULL)
ggplot(data = GA_wellnessSalary2, aes(y=GA_wellnessSalary2$annualSalary, x=GA_wellnessSalary2$jobTitle)) +
geom_bar(stat='identity', position=position_dodge(),na.rm=TRUE)+
scale_fill_brewer(palette='Paired') +
theme_classic()+
geom_hline(yintercept=GA_medIncome, linetype="dashed", color = "red")+
geom_hline(yintercept=GA_2BR, linetype='dashed',color='blue')+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('GA Wellness Jobs Annual Salary')+
ylab(NULL)+
xlab(NULL)
The blue line is the May 2020 Zillow average home value for the top 3 populated cities in GA for a two bedroom home, and the red line is the 2018 median income in GA, according to the data.census.gov data. There wasn’t any salary data for physical therapists in GA, so it doesn’t have a bar for salary information. But chiropractors’ annual salary is above the GA 2018 median income and the average 2020 two bedroom home value. Also, personal trainers make more than LMTs and have slightly less personal training jobs available than LMTs, but both have pay below the median income of GA. The available jobs for chiropractors, LMTs, and personal trainers is roughly the same in GA, while the demand for physical therapists is high with more than twice as many jobs available for physical therapists as any one other wellness category jobs available.Pilates and Yoga Instuctors get paid more annually than chiropractors, LMTs, or personal trainers. But their salary was approximated as full time at the average hourly rate. Many yoga and Pilates intructors might only work 20-32 hours a week. But then again, a great yoga or pilates instructor brings in clients who pay an average of $20 per class three times a week, with bargains on Groupon. Many physical therapists prescribe or recommend yoga and pilates to athletes or trauma patients with tendon injuries who need structural re-alignment.
Now, lets look at the chart of the medical spas jobs available and annual salary in GA with the same home value and median income values for GA used in the wellness jobs above.
GA_spa <- subset(spaJobs, spaJobs$state=='Georgia')
GA_spaSalary <- subset(spaSalary,spaSalary$state=='Georgia')
GA_spa2 <- gather(GA_spa, key='jobTitle',value='jobsListed',2:5)
GA_spaSalary2 <- gather(GA_spaSalary, key='jobTitle', value='annualSalary',2:5)ggplot(data = GA_spa2, aes(y=GA_spa2$jobsListed, x=GA_spa2$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('GA Spa and Medical Spa Jobs Available')+
ylab(NULL)+
xlab(NULL)
ggplot(data = GA_spaSalary2, aes(y=GA_spaSalary2$annualSalary,
x=GA_spaSalary2$jobTitle)) +
geom_bar(stat='identity', position=position_dodge())+
scale_fill_brewer(palette='Paired') +
theme_classic()+
geom_hline(yintercept=GA_medIncome, linetype="dashed", color = "red")+
geom_hline(yintercept=GA_2BR, linetype='dashed',color='blue')+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('GA Spa and Medical Spa Jobs Annual Salary')+
ylab(NULL)+
xlab(NULL)
The blue line above is the same two bedroom Zillow average home value for GA and the red line is the 2018 median income for GA from data.census.gov data. The above medical spa information for GA, shows that medical spa estheticians don’t make as much annually as estheticians or nurses. And that estheticians make more than nurses in GA. A medical spa esthetician is supposed to be an esthetician and a nurse to be able to give botox injections. But there could be some differences in state to state requirements and regulations for estheticians. Estheticians call it ‘lancing’ when they can drain a white head with a needle. Logically, on the surface the above doesn’t make sense, but it is possible that estheticians do make 150,000 USD a year. It is not unlikely that nurse make close to 100,000 USD a year either. The demand of nurses in GA is more than double that of the other medical spa jobs available and nearly triple it at around 250 available nursing jobs in GA’s top three populated cities.
There is now a script that did similar to what the indeed function did but for yellowpages.com. This script takes the first five webpages of yellowpages.com for the business searched in the city and state and returns a table of the number of businesses of that type in the same three top populated cities per state used in this program. The script is called ‘yellowPages-number-of-businesses.Rmd’ and it was used to get the number of businesses in those three cities per state for coffee, massage, gym, tanning, hair salon, tanning, yoga, chiropractic, wellness, and health food businesses. Lets add these counts to our table slr2 of all features used so far.
ypChiro <- read.csv('./number of businesses/statesRates- chiropractor .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypWellness <- read.csv('./number of businesses/statesRates- wellness clinic .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypMassage <- read.csv('./number of businesses/statesRates- massage spa .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypYoga <- read.csv('./number of businesses/statesRates- yoga .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypGym <- read.csv('./number of businesses/statesRates- gym .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypCoffee <- read.csv('./number of businesses/statesRates- coffee .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypHealthFood <- read.csv('./number of businesses/statesRates- health food .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypHairSalon <- read.csv('./number of businesses/statesRates- hair salon .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))
ypTanning <- read.csv('./number of businesses/statesRates- tanning .csv',
sep=',', header=TRUE, na.strings=c('',' ','NA'))ypChiro$states <- states
ypWellness$states <- states
ypMassage$states <- states
ypYoga$states <- states
ypGym$states <- states
ypCoffee$states <- states
ypHealthFood$states <- states
ypHairSalon$states <- states
ypTanning$states <- statesypChiro <- ypChiro[,-1]
ypWellness <- ypWellness[,-1]
ypMassage <- ypMassage[,-1]
ypYoga <- ypYoga[,-1]
ypGym <- ypGym[,-1]
ypCoffee <- ypCoffee[,-1]
ypHealthFood <- ypHealthFood[,-1]
ypHairSalon <- ypHairSalon[,-1]
ypTanning <- ypTanning[,-1]slr3 <- merge(slr2,ypChiro, by.x='state', by.y='states')
slr3 <- merge(slr3,ypWellness,by.x='state', by.y='states')
slr3 <- merge(slr3,ypMassage,by.x='state', by.y='states')
slr3 <- merge(slr3,ypYoga,by.x='state', by.y='states')
slr3 <- merge(slr3,ypGym,by.x='state', by.y='states')
slr3 <- merge(slr3,ypCoffee,by.x='state', by.y='states')
slr3 <- merge(slr3,ypHealthFood,by.x='state', by.y='states')
slr3 <- merge(slr3,ypHairSalon,by.x='state', by.y='states')
slr3 <- merge(slr3,ypTanning,by.x='state', by.y='states')colnames(slr3)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_AvgJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyAvgPayRangeAdvertised_Indeed"
## [34] "LMT_AnualAvgPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityAverageHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"
## [45] "cashier_jobsListed"
## [46] "cashier_avgHourly"
## [47] "cashier_avgAnnualSalary"
## [48] "server_jobsListed"
## [49] "server_avgHourly"
## [50] "server_avgAnnualSalary"
## [51] "personalTrainer_jobsListed"
## [52] "personalTrainer_avgHourly"
## [53] "personalTrainer_avgAnnualSalary"
## [54] "houseCleaner_jobsListed"
## [55] "houseCleaner_avgHourly"
## [56] "houseCleaner_avgAnnualSalary"
## [57] "warehouse_jobsListed"
## [58] "warehouse_avgHourly"
## [59] "warehouse_avgAnnualSalary"
## [60] "security_jobsListed"
## [61] "security_avgHourly"
## [62] "security_avgAnnualSalary"
## [63] "nanny_jobsListed"
## [64] "nanny_avgHourly"
## [65] "nanny_avgAnnualSalary"
## [66] "clerical_jobsListed"
## [67] "clerical_avgHourly"
## [68] "clerical_avgAnnualSalary"
## [69] "tutor_jobsListed"
## [70] "tutor_avgHourly"
## [71] "tutor_avgAnnualSalary"
## [72] "teacher_jobsListed"
## [73] "teacher_avgHourly"
## [74] "teacher_avgAnualSalary"
## [75] "dataScientist_jobsListed"
## [76] "dataScientist_avgHourly"
## [77] "dataScientist_avgAnualSalary"
## [78] "nurses jobsListed"
## [79] "nurses avgHourly"
## [80] "nurses avgAnualSalary"
## [81] "personalAssistant jobsListed"
## [82] "personalAssistant avgHourly"
## [83] "personalAssistant avgAnualSalary"
## [84] "chiropractor jobsListed"
## [85] "chiropractor avgHourly"
## [86] "chiropractor avgAnualSalary"
## [87] "physicalTherapist jobsListed"
## [88] "physicalTherapist avgHourly"
## [89] "physicalTherapist avgAnualSalary"
## [90] "esthetician jobsListed"
## [91] "esthetician avgHourly"
## [92] "esthetician avgAnualSalary"
## [93] "medicalSpaEsthetician jobsListed"
## [94] "medicalSpaEsthetician avgHourly"
## [95] "medicalSpaEsthetician avgAnualSalary"
## [96] "medicalDoctor jobsListed"
## [97] "medicalDoctor avgHourly"
## [98] "medicalDoctor avgAnualSalary"
## [99] "yogaInstructor jobsListed"
## [100] "yogaInstructor avgHourly"
## [101] "yogaInstructor avgAnualSalary"
## [102] "pilatesInstructor jobsListed"
## [103] "pilatesInstructor avgHourly"
## [104] "pilatesInstructor avgAnualSalary"
## [105] "chiropractor_businessListings"
## [106] "wellness.clinic_businessListings"
## [107] "massage.spa_businessListings"
## [108] "yoga_businessListings"
## [109] "gym_businessListings"
## [110] "coffee_businessListings"
## [111] "health.food_businessListings"
## [112] "hair.salon_businessListings"
## [113] "tanning_businessListings"write.csv(slr3, 'stateLicensingDemographicsAddedAndUpdated.csv', row.names=FALSE)We currently have the price of 2 bedroom homes using Zillow data, but now we can add in apartments.com data for rental apartments of 2-3 bedroom, 2 bathroom, dog/cat friendly pricing to our data set to compare rental prices in the three top populated cities in each state. It didn’t happen by magic either, there is a script for it very similar to the indeed and yellowpages web scraping scripts. Originally, I was going to use rent.com, but wouldn’t you know they have a web scraping blocker in place that makes sure their terms of use aren’t violated for ‘not downloading their data.’ I did download apartments.com data, but only to share the aggregate results. Each individual file is downloaded for the date pulled but not added to githu. The script is ‘aps-com-2BR-2BA.Rmd’ in github with all these other shared files.I was booted from apartments.com due to so many downloads, but used a VPN that allowed me to take the average of the average, minimum, and maximum prices of up to ten cities that are the most populated cities (rather than the top 3 cities) with prices on aparmtnents.com. It will produce this table in csv format, ‘apts_2BR2BA_prices.csv’ to add to our updated and added state licensing requirements csv table of data to compare state by state. This last file was modified to read in the listings if available of the top 10 most populated cities, as was the Zillow home value listings for 10 most populated cities, instead of for the 3 most populated as the original scripts were done.
apt2and2 <- read.csv('./apartments Dot Com/apts_2BR2BA_prices.csv',sep=',',
header=TRUE, na.strings=c('',' ','NA'))head(apt2and2,10)## state TwoBedroomApartment_Listings Rent2BR2BA_MinPrice Rent2BR2BA_MaxPrice
## 1 AK 66 1389.373 1843.812
## 2 AL 426 1019.231 1412.166
## 3 AR 251 941.657 1347.389
## 4 AZ 783 1307.573 1938.583
## 5 CA 750 2835.572 4201.762
## 6 CO 575 1698.809 2688.151
## 7 CT 171 2085.466 3086.279
## 8 DE 326 1797.702 2964.835
## 9 FL 774 1582.641 2207.818
## 10 GA 600 1439.029 2287.278
## Rent2BR2BA_AvgPrice
## 1 1528.712
## 2 1153.883
## 3 1074.461
## 4 1575.056
## 5 3395.412
## 6 2147.120
## 7 2407.294
## 8 2318.318
## 9 1839.048
## 10 1814.311The above information takes the price of each listing in those 50 states’ three most populated cities, and gets the minimum, maximum, and average prices of a 2 bedroom, 2 bath dog/cat friendly apartment for rent. The average minimum is the average of all minimum price boundaries of all three cities for each state, and the average maximum is the average of all maximum boundaries of the price range in all three cities of each state, and the average is the average price of all prices listed in each state.
Now lets add these new data columns to our table of 50 states.
colnames(apt2and2)[1] <- 'stateAbbreviation'
colnames(apt2and2)[2:5] <- paste(colnames(apt2and2)[2:5],'_10cities')
apt2and2$stateName <- statesslr4 <- merge(slr3,apt2and2, by.x='state',by.y='stateName')colnames(slr4)## [1] "state"
## [2] "massageBoard"
## [3] "licenseByReciprocity"
## [4] "proofOtherOrAllStateLicense"
## [5] "stateResidencyProof"
## [6] "passportSizePhoto"
## [7] "driversLicensePhotoCopy"
## [8] "nameChangeProof"
## [9] "socialSecurityCopy"
## [10] "Hours"
## [11] "MBLEX_or_NCBTMB"
## [12] "goodHealthClearance"
## [13] "BoardBackgroundCheckFee"
## [14] "stateApplyingBackgroundCheck"
## [15] "DOJ_backgroundCheck"
## [16] "applicationCost"
## [17] "licensingCost"
## [18] "licenseRenewalFee"
## [19] "CPR_certification"
## [20] "licensingETA"
## [21] "healthReferences"
## [22] "CEU"
## [23] "timeLicenseValidYears"
## [24] "liabilityInsurance"
## [25] "schoolTranscripts"
## [26] "MBLEX_transcript"
## [27] "notes"
## [28] "notes2"
## [29] "notes3"
## [30] "notes4"
## [31] "citiesGreaterThan300k_orTop3"
## [32] "LMT_AvgJobsListed_IndeedFirst5pages"
## [33] "LMT_HourlyAvgPayRangeAdvertised_Indeed"
## [34] "LMT_AnualAvgPayAdvertised_Indeed"
## [35] "Zillow_2BR_3cityAverageHomeValue"
## [36] "median2018IncomeByState"
## [37] "total_state_population"
## [38] "percent_black"
## [39] "percent_white"
## [40] "percent_two_or_more"
## [41] "percent_Native_American"
## [42] "percent_Asian"
## [43] "percent_Pacific_Islander"
## [44] "percent_Latino"
## [45] "cashier_jobsListed"
## [46] "cashier_avgHourly"
## [47] "cashier_avgAnnualSalary"
## [48] "server_jobsListed"
## [49] "server_avgHourly"
## [50] "server_avgAnnualSalary"
## [51] "personalTrainer_jobsListed"
## [52] "personalTrainer_avgHourly"
## [53] "personalTrainer_avgAnnualSalary"
## [54] "houseCleaner_jobsListed"
## [55] "houseCleaner_avgHourly"
## [56] "houseCleaner_avgAnnualSalary"
## [57] "warehouse_jobsListed"
## [58] "warehouse_avgHourly"
## [59] "warehouse_avgAnnualSalary"
## [60] "security_jobsListed"
## [61] "security_avgHourly"
## [62] "security_avgAnnualSalary"
## [63] "nanny_jobsListed"
## [64] "nanny_avgHourly"
## [65] "nanny_avgAnnualSalary"
## [66] "clerical_jobsListed"
## [67] "clerical_avgHourly"
## [68] "clerical_avgAnnualSalary"
## [69] "tutor_jobsListed"
## [70] "tutor_avgHourly"
## [71] "tutor_avgAnnualSalary"
## [72] "teacher_jobsListed"
## [73] "teacher_avgHourly"
## [74] "teacher_avgAnualSalary"
## [75] "dataScientist_jobsListed"
## [76] "dataScientist_avgHourly"
## [77] "dataScientist_avgAnualSalary"
## [78] "nurses jobsListed"
## [79] "nurses avgHourly"
## [80] "nurses avgAnualSalary"
## [81] "personalAssistant jobsListed"
## [82] "personalAssistant avgHourly"
## [83] "personalAssistant avgAnualSalary"
## [84] "chiropractor jobsListed"
## [85] "chiropractor avgHourly"
## [86] "chiropractor avgAnualSalary"
## [87] "physicalTherapist jobsListed"
## [88] "physicalTherapist avgHourly"
## [89] "physicalTherapist avgAnualSalary"
## [90] "esthetician jobsListed"
## [91] "esthetician avgHourly"
## [92] "esthetician avgAnualSalary"
## [93] "medicalSpaEsthetician jobsListed"
## [94] "medicalSpaEsthetician avgHourly"
## [95] "medicalSpaEsthetician avgAnualSalary"
## [96] "medicalDoctor jobsListed"
## [97] "medicalDoctor avgHourly"
## [98] "medicalDoctor avgAnualSalary"
## [99] "yogaInstructor jobsListed"
## [100] "yogaInstructor avgHourly"
## [101] "yogaInstructor avgAnualSalary"
## [102] "pilatesInstructor jobsListed"
## [103] "pilatesInstructor avgHourly"
## [104] "pilatesInstructor avgAnualSalary"
## [105] "chiropractor_businessListings"
## [106] "wellness.clinic_businessListings"
## [107] "massage.spa_businessListings"
## [108] "yoga_businessListings"
## [109] "gym_businessListings"
## [110] "coffee_businessListings"
## [111] "health.food_businessListings"
## [112] "hair.salon_businessListings"
## [113] "tanning_businessListings"
## [114] "stateAbbreviation"
## [115] "TwoBedroomApartment_Listings _10cities"
## [116] "Rent2BR2BA_MinPrice _10cities"
## [117] "Rent2BR2BA_MaxPrice _10cities"
## [118] "Rent2BR2BA_AvgPrice _10cities"slr5 <- slr4[,c(114,1:113,115:118)]write.csv(slr5,'stateLicensingDemographicsAddedAndUpdated.csv',row.names=FALSE)Remember to update the stateLicensingRequirements.csv file with the actual massage board licensing requirements of each state, then adding the additional updated and added features with this script, after using those helper script files to update information as needed.
Getting back to the motivation for this project, comparing TN to CA, to satisfy some gamey choices in moving from CA to TN as a massage therapist to make my youngest and greatly adored neice’s life easier and happier, Lets compare TN to CA in demographics, number of data science and massage therapist jobs, annual salary on average for those jobs, and for alternate jobs, how many health food stores, salons, spas, chiropractors, and coffee shops in each state’s top three most populated cities, what the average and average minimum price range for a two bedroom, two bathroom and pet friendly apartment is, the median income from 2018 data for each state, and see how compromisable the effects of moving from CA to TN would be.
Lets first get all the fields used for this comparison.
comparing_TN_CA <- slr5[,c(1,11,12,17,18,19,23,33,35,37:45,55,57,58,60,
64,66,67,69,70,72,76,78,82,84,108:118)]
colnames(comparing_TN_CA)## [1] "stateAbbreviation"
## [2] "Hours"
## [3] "MBLEX_or_NCBTMB"
## [4] "applicationCost"
## [5] "licensingCost"
## [6] "licenseRenewalFee"
## [7] "CEU"
## [8] "LMT_AvgJobsListed_IndeedFirst5pages"
## [9] "LMT_AnualAvgPayAdvertised_Indeed"
## [10] "median2018IncomeByState"
## [11] "total_state_population"
## [12] "percent_black"
## [13] "percent_white"
## [14] "percent_two_or_more"
## [15] "percent_Native_American"
## [16] "percent_Asian"
## [17] "percent_Pacific_Islander"
## [18] "percent_Latino"
## [19] "houseCleaner_jobsListed"
## [20] "houseCleaner_avgAnnualSalary"
## [21] "warehouse_jobsListed"
## [22] "warehouse_avgAnnualSalary"
## [23] "nanny_jobsListed"
## [24] "nanny_avgAnnualSalary"
## [25] "clerical_jobsListed"
## [26] "clerical_avgAnnualSalary"
## [27] "tutor_jobsListed"
## [28] "tutor_avgAnnualSalary"
## [29] "dataScientist_jobsListed"
## [30] "dataScientist_avgAnualSalary"
## [31] "personalAssistant jobsListed"
## [32] "personalAssistant avgAnualSalary"
## [33] "massage.spa_businessListings"
## [34] "yoga_businessListings"
## [35] "gym_businessListings"
## [36] "coffee_businessListings"
## [37] "health.food_businessListings"
## [38] "hair.salon_businessListings"
## [39] "tanning_businessListings"
## [40] "TwoBedroomApartment_Listings _10cities"
## [41] "Rent2BR2BA_MinPrice _10cities"
## [42] "Rent2BR2BA_MaxPrice _10cities"
## [43] "Rent2BR2BA_AvgPrice _10cities"Lets compare demographics of each state by the states’ three most populated cities. This is important as it skews necessary information if the cities most populated aren’t near the place planning to live. For CA, the cities were San Diego, Los Angeles, and San Jose. The distance between San Diego and Los Angeles is about 120 miles, and from San Jose to Los Angeles it is more than 400 miles. San Jose is also a popularly expensive high rent city due to silicon valley. For TN, the cities most populated are Nashville, Memphis, and knoxville. The idea is that the pricing will be similar as an average across the state’s three most populated cities.
CA <- subset(comparing_TN_CA, comparing_TN_CA$stateAbbreviation=='CA')
TN <- subset(comparing_TN_CA, comparing_TN_CA$stateAbbreviation=='TN')Lets look at demographics from 2018 state only data using data.census.gov.
CA_demographics <- CA[,c(1,11:18)]
TN_demographics <- TN[,c(1,11:18)]CA_demoTidy <- gather(CA_demographics, key='race', value='TotalPopulationPercent',
3:9)## Warning: attributes are not identical across measure variables;
## they will be droppedCA_demoTidy$TotalPopulationPercent <- as.numeric(CA_demoTidy$TotalPopulationPercent)
CA_pop <- CA_demographics$total_state_population
gg1 <- ggplot(data = CA_demoTidy,
aes(y=CA_demoTidy$TotalPopulationPercent,
x=CA_demoTidy$race)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(0, 100))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA demographics, Percent of: ',CA_pop)+
ylab(NULL)+
xlab(NULL)
gg1
TN_demoTidy <- gather(TN_demographics, key='race', value='TotalPopulationPercent',
3:9)## Warning: attributes are not identical across measure variables;
## they will be droppedTN_demoTidy$TotalPopulationPercent <- as.numeric(TN_demoTidy$TotalPopulationPercent)
TN_pop <- TN_demographics$total_state_population
gg2 <- ggplot(data = TN_demoTidy,
aes(y=TN_demoTidy$TotalPopulationPercent,
x=TN_demoTidy$race)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(0, 100))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('TN demographics, Percent of: ',TN_pop)+
ylab(NULL)+
xlab(NULL)
gg2
CA_TN_demographics <- rbind(CA_demographics,TN_demographics)
CA_TN_demographics## stateAbbreviation total_state_population percent_black percent_white
## 5 CA 39557045 5.8 59.5
## 42 TN 6770010 16.8 77.3
## percent_two_or_more percent_Native_American percent_Asian
## 5 5.1 0.8 14.7
## 42 2.2 0.3 1.8
## percent_Pacific_Islander percent_Latino
## 5 0.4 39.3
## 42 0.1 5.5grid.arrange(gg1, gg2, ncol = 2)
Now lets look at the two bedroom, two bath, pet friendly apartments for rent in CA and TN.
CA_2BR <- CA[,c(1,40:43)]
TN_2BR <- TN[,c(1,40:43)]CA_listings <- CA_2BR$TwoBedroomApartment_Listings
CA_2BR_tidy <- gather(CA_2BR, key='range',value='price',3:5)
CA_2BR_tidy$range <- gsub('Rent2BR2BA_','',CA_2BR_tidy$range)
gg3 <- ggplot(data = CA_2BR_tidy,
aes(y=CA_2BR_tidy$price,
x=CA_2BR_tidy$range)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(500, 5000))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA 2 Bedroom/2 Bath: ',CA_listings)+
ylab(NULL)+
xlab('San Diego, San Jose, and Los Angeles Prices Averaged')
TN_listings <- TN_2BR$TwoBedroomApartment_Listings
TN_2BR_tidy <- gather(TN_2BR, key='range', value='price', 3:5)
TN_2BR_tidy$range <- gsub('Rent2BR2BA_','',TN_2BR_tidy$range)
gg4 <- ggplot(data = TN_2BR_tidy,
aes(y=TN_2BR_tidy$price,
x=TN_2BR_tidy$range)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(500, 5000))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('TN 2 Bedroom/2 Bath: ',TN_listings)+
ylab(NULL)+
xlab('Nashville,Memphis, and Knoxville Prices Averaged')CA_TN_2BR <- rbind(CA_2BR,TN_2BR)
CA_TN_2BR## stateAbbreviation TwoBedroomApartment_Listings _10cities
## 5 CA 750
## 42 TN 451
## Rent2BR2BA_MinPrice _10cities Rent2BR2BA_MaxPrice _10cities
## 5 2835.572 4201.762
## 42 1268.962 1910.188
## Rent2BR2BA_AvgPrice _10cities
## 5 3395.412
## 42 1507.706grid.arrange(gg3, gg4, ncol = 2)
Lets now look at the number of massage and alternate jobs in CA and TN to compare their annual salaries against number of jobs available.
CA_jobsAvailable <- CA[,c(1,8,19,21,23,25,27,29,31)]
TN_jobsAvailable <- TN[,c(1,8,19,21,23,25,27,29,31)]
colnames(CA_jobsAvailable) <- gsub('_.*$','',colnames(CA_jobsAvailable))
colnames(TN_jobsAvailable) <- gsub('_.*$','',colnames(TN_jobsAvailable))
colnames(CA_jobsAvailable) <- gsub('jobsListed','',colnames(CA_jobsAvailable))
colnames(TN_jobsAvailable) <- gsub('jobsListed','',colnames(TN_jobsAvailable))
CA_jobsAvailable_tidy <- gather(CA_jobsAvailable, key='job',value='listings', 2:9)
TN_jobsAvailable_tidy <- gather(TN_jobsAvailable, key='job',value='listings', 2:9)gg5 <- ggplot(data = CA_jobsAvailable_tidy,
aes(y=CA_jobsAvailable_tidy$listings,
x=CA_jobsAvailable_tidy$job)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(0,300))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA Number of Advertised Jobs: ')+
ylab(NULL)+
xlab('San Diego, San Jose, and Los Angeles')
gg6 <- ggplot(data = TN_jobsAvailable_tidy,
aes(y=TN_jobsAvailable_tidy$listings,
x=TN_jobsAvailable_tidy$job)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
coord_cartesian(ylim = c(0,300))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('TN Number of Advertised Jobs: ')+
ylab(NULL)+
xlab('Nashville, Memphis, and Knoxville')CA_TN_jobsAvailable <- rbind(CA_jobsAvailable,TN_jobsAvailable)
CA_TN_jobsAvailable## stateAbbreviation LMT houseCleaner warehouse nanny clerical tutor
## 5 CA 223 216 243 199 225 217
## 42 TN 174 192 230 113 238 198
## dataScientist personalAssistant
## 5 231 223
## 42 152 218grid.arrange(gg5,gg6,ncol=2)
Now, lets look at the average annual salary for those jobs available in CA and TN.
CA_salary <- CA[,c(1,9,20,22,24,26,28,30,32)]
TN_salary <- TN[,c(1,9,20,22,24,26,28,30,32)]
colnames(CA_salary) <- gsub('_.*$','',colnames(CA_salary))
colnames(CA_salary) <- gsub(' avgAnualSalary', '',colnames(CA_salary))
colnames(TN_salary) <- gsub('_.*$','',colnames(TN_salary))
colnames(TN_salary) <- gsub(' avgAnualSalary', '',colnames(TN_salary))
CA_salary_tidy <- gather(CA_salary, key='job',value='salary',2:9)
TN_salary_tidy <- gather(TN_salary, key='job', value='salary',2:9)CA_medianIncome <- CA$median2018IncomeByState
TN_medianIncome <- TN$median2018IncomeByState
gg7 <- ggplot(data = CA_salary_tidy,
aes(y=CA_salary_tidy$salary,
x=CA_salary_tidy$job)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
geom_hline(yintercept=CA_medianIncome, linetype="dashed", color = "red")+
coord_cartesian(ylim = c(10000, 120000))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('CA Advertised Salaries: ')+
ylab(NULL)+
xlab('San Diego, San Jose, and Los Angeles')
gg8 <- ggplot(data = TN_salary_tidy,
aes(y=TN_salary_tidy$salary,
x=TN_salary_tidy$job)) +
geom_bar(stat='identity', position=position_dodge())+
theme_classic()+
geom_hline(yintercept=TN_medianIncome, linetype="dashed", color = "red")+
coord_cartesian(ylim = c(10000,120000))+
theme(legend.position="bottom")+
theme(axis.text = element_text(colour = "black", angle=90, size = rel(.75)))+
ggtitle('TN Advertised Salaries: ')+
ylab(NULL)+
xlab('Nashville, Memphis, and Knoxville')CA_TN_salaries <- rbind(CA_salary,TN_salary)
CA_TN_salaries## stateAbbreviation LMT houseCleaner warehouse nanny clerical tutor
## 5 CA 66450.98 42592.81 47334.89 52888.44 51181.31 87743.41
## 42 TN 55739.94 31194.58 32322.16 31384.07 33240.50 39892.93
## dataScientist personalAssistant
## 5 120606.1 99507.74
## 42 128660.7 61158.14The red dashed line across the CA and TN advertised salaries is the state’s respective 2018 median income.
grid.arrange(gg7,gg8, ncol=2)
We notice a large gap in LMT annual pay in the CA advertised pay ranges for jobs selected when compared to the gap of the TN median income and annual salaries of jobs selected.