CUNY Data 607 - Project 2: Marriage Rates
Emilie M Bolduc
10/7/2017
Assignment
Choose any three of the “wide” datasets identified in the Week 6 Discussion items. (You may use your own dataset; please don’t use my Sample Post dataset, since that was used in your Week 6 assignment!)
For each of the three chosen datasets:
Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below.
Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!]
Perform the analysis requested in the discussion item.
Your code should be in an R Markdown file, posted to rpubs.com, and should include narrative descriptions of your data cleanup work, analysis, and conclusions.
Solution
Data
Data Source
Michele Bradley posted the following data set about marriage rates from FiveThirtyEight’s GitHub: https://github.com/fivethirtyeight/data/blob/master/marriage/both_sexes.csv
Note: the values in it represent share of the relevant population that has never been married.
Read CSV from URL
theURL <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/marriage/both_sexes.csv"
marriage <- read.csv(theURL, header = TRUE, sep = ",")
head(marriage)## X year date all_2534 HS_2534 SC_2534 BAp_2534 BAo_2534
## 1 1 1960 1960-01-01 0.1233145 0.1095332 0.1522818 0.2389952 0.2389952
## 2 2 1970 1970-01-01 0.1269715 0.1094000 0.1495096 0.2187031 0.2187031
## 3 3 1980 1980-01-01 0.1991767 0.1617313 0.2236916 0.2881646 0.2881646
## 4 4 1990 1990-01-01 0.2968306 0.2777491 0.2780912 0.3612968 0.3656655
## 5 5 2000 2000-01-01 0.3450087 0.3316545 0.3249205 0.3874906 0.3939579
## 6 6 2001 2001-01-01 0.3527767 0.3446069 0.3341101 0.3835686 0.3925148
## GD_2534 White_2534 Black_2534 Hisp_2534 NE_2534 MA_2534
## 1 NA 0.1164848 0.1621855 0.1393736 0.1504184 0.1628934
## 2 NA 0.1179043 0.1855163 0.1298769 0.1517231 0.1640680
## 3 NA 0.1824126 0.3137500 0.1885440 0.2414327 0.2505925
## 4 0.3474505 0.2639256 0.4838556 0.2962372 0.3500384 0.3623321
## 5 0.3691740 0.3127149 0.5144994 0.3180681 0.4091852 0.4175565
## 6 0.3590304 0.3183506 0.5437985 0.3321214 0.4200581 0.4294281
## Midwest_2534 South_2534 Mountain_2534 Pacific_2534 poor_2534 mid_2534
## 1 0.1121467 0.1090562 0.09152117 0.1198758 0.1371597 0.07514929
## 2 0.1153741 0.1126220 0.10293602 0.1374964 0.1717202 0.08159207
## 3 0.1828339 0.1688435 0.17434230 0.2334279 0.3100591 0.14825303
## 4 0.2755046 0.2639794 0.25264326 0.3319579 0.4199108 0.24320008
## 5 0.3308022 0.3099712 0.30621032 0.3753061 0.5033676 0.30202036
## 6 0.3344332 0.3182688 0.30980779 0.3844799 0.5178771 0.31716118
## rich_2534 all_3544 HS_3544 SC_3544 BAp_3544 BAo_3544 GD_3544
## 1 0.2066776 0.07058157 0.06860309 0.06663695 0.1326265 0.1326265 NA
## 2 0.1724093 0.06732520 0.06511964 0.06271724 0.1116899 0.1116899 NA
## 3 0.1851082 0.06883378 0.06429102 0.06531333 0.1056102 0.1056102 NA
## 4 0.2783226 0.11191800 0.11210043 0.09699372 0.1285172 0.1258567 0.1328018
## 5 0.2717386 0.15605881 0.16993703 0.13800404 0.1541238 0.1536299 0.1550970
## 6 0.2532041 0.15642529 0.16870156 0.13986044 0.1548151 0.1524923 0.1595169
## White_3544 Black_3544 Hisp_3544 NE_3544 MA_3544 Midwest_3544
## 1 0.06825586 0.08836728 0.07307651 0.09194322 0.09347468 0.06863360
## 2 0.06250372 0.10290904 0.07070500 0.08570110 0.09040725 0.06156272
## 3 0.05966739 0.13140081 0.08110790 0.07997323 0.09744428 0.06070641
## 4 0.09611312 0.22010298 0.12194206 0.12785915 0.14354989 0.10157576
## 5 0.13207032 0.30239381 0.15469520 0.17327422 0.18819256 0.14539201
## 6 0.13287455 0.30857796 0.14953050 0.16653497 0.18315109 0.14794407
## South_3544 Mountain_3544 Pacific_3544 poor_3544 mid_3544 rich_3544
## 1 0.06026353 0.04739747 0.05822486 0.1019749 0.04717272 0.08553870
## 2 0.05966057 0.04651163 0.06347796 0.1117548 0.04566838 0.06499159
## 3 0.05914089 0.04880077 0.07552538 0.1291426 0.05050321 0.04445951
## 4 0.09637035 0.09189904 0.13134638 0.2012208 0.09024739 0.06573916
## 5 0.14230600 0.13584194 0.17480047 0.2813137 0.12815751 0.08622046
## 6 0.14312592 0.13943820 0.17694864 0.2919112 0.13267625 0.06803283
## all_4554 HS_4554 SC_4554 BAp_4554 BAo_4554 GD_4554
## 1 0.07254649 0.06840792 0.07903755 0.15360889 0.15360889 NA
## 2 0.05968794 0.05833439 0.05443478 0.10466047 0.10466047 NA
## 3 0.05250871 0.05036563 0.04816180 0.08623774 0.08623774 NA
## 4 0.05947824 0.05988244 0.04654087 0.07301884 0.06416529 0.08394886
## 5 0.08804394 0.09442809 0.07558786 0.09208417 0.09097472 0.09362802
## 6 0.08823342 0.09189007 0.07795481 0.09333365 0.09313480 0.09362876
## White_4554 Black_4554 Hisp_4554 NE_4554 MA_4554 Midwest_4554
## 1 0.07246692 0.06913249 0.06636058 0.10236412 0.09264788 0.07285321
## 2 0.05754799 0.07899168 0.05810740 0.08028082 0.07860635 0.05791163
## 3 0.04765354 0.08624602 0.06522951 0.06930253 0.07508466 0.04807290
## 4 0.05092552 0.11617699 0.07613556 0.07047502 0.08373134 0.05398391
## 5 0.07578174 0.17587334 0.09418009 0.10232170 0.11269659 0.08302437
## 6 0.07516912 0.18154531 0.09409896 0.09868408 0.10953635 0.08207629
## South_4554 Mountain_4554 Pacific_4554 poor_4554 mid_4554 rich_4554
## 1 0.05977295 0.04754183 0.05996993 0.1030055 0.05364421 0.07908591
## 2 0.05174462 0.03970134 0.04826312 0.1016489 0.04221637 0.05142867
## 3 0.04485348 0.03374438 0.04958992 0.1003011 0.03830266 0.03311296
## 4 0.05043636 0.04459411 0.06461875 0.1148335 0.04562332 0.03136386
## 5 0.07631858 0.07637774 0.09896832 0.1718976 0.07055672 0.03897342
## 6 0.07886513 0.07405971 0.10119511 0.1759369 0.07407508 0.02857320
## nokids_all_2534 kids_all_2534 nokids_HS_2534 nokids_SC_2534
## 1 0.4640564 0.002820625 0.4430148 0.5000402
## 2 0.4309043 0.009868596 0.4246779 0.4333479
## 3 0.4464304 0.025285667 0.4319342 0.4505900
## 4 0.5425242 0.060277451 0.5464881 0.5238446
## 5 0.5714531 0.099472713 0.5711395 0.5700042
## 6 0.5852213 0.110178467 0.6045475 0.5810912
## nokids_BAp_2534 nokids_BAo_2534 nokids_GD_2534 kids_HS_2534 kids_SC_2534
## 1 0.5619099 0.5619099 NA 0.003318886 0.001150824
## 2 0.4554766 0.4554766 NA 0.012465915 0.003699982
## 3 0.4719700 0.4719700 NA 0.031930752 0.018135401
## 4 0.5560765 0.5633301 0.5332628 0.078470444 0.052032702
## 5 0.5729677 0.5862213 0.5367160 0.127193577 0.097625310
## 6 0.5698644 0.5864967 0.5258800 0.141395652 0.110030662
## kids_BAp_2534 kids_BAo_2534 kids_GD_2534 nokids_poor_2534
## 1 0.0005751073 0.0005751073 NA 0.4933061
## 2 0.0014683425 0.0014683425 NA 0.5097742
## 3 0.0062544364 0.0062544364 NA 0.5740402
## 4 0.0171241042 0.0181766027 0.01374234 0.6546908
## 5 0.0370024452 0.0401009875 0.02761467 0.7055451
## 6 0.0399801447 0.0445838012 0.02645041 0.7147334
## nokids_mid_2534 nokids_rich_2534 kids_poor_2534 kids_mid_2534
## 1 0.4100080 0.4921184 0.008722711 0.0007532065
## 2 0.3764538 0.4288948 0.029974945 0.0033771145
## 3 0.3998250 0.3848089 0.077926214 0.0102368871
## 4 0.5186604 0.4750156 0.170763774 0.0274655254
## 5 0.5690228 0.4458023 0.256281918 0.0597845173
## 6 0.5864741 0.4461111 0.280146488 0.0677954572
## kids_rich_2534
## 1 0.0008027331
## 2 0.0030435661
## 3 0.0068317224
## 4 0.0182329127
## 5 0.0295644698
## 6 0.0336540502Write CSV file
write.csv(marriage, file = "marriage.csv", row.names = FALSE)Tidy Data
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)1. Subset demographic data to include marriage rate data for ages 25-34:
marriage2534 <- marriage[c(2,4:21)]
head(marriage2534)## year all_2534 HS_2534 SC_2534 BAp_2534 BAo_2534 GD_2534
## 1 1960 0.1233145 0.1095332 0.1522818 0.2389952 0.2389952 NA
## 2 1970 0.1269715 0.1094000 0.1495096 0.2187031 0.2187031 NA
## 3 1980 0.1991767 0.1617313 0.2236916 0.2881646 0.2881646 NA
## 4 1990 0.2968306 0.2777491 0.2780912 0.3612968 0.3656655 0.3474505
## 5 2000 0.3450087 0.3316545 0.3249205 0.3874906 0.3939579 0.3691740
## 6 2001 0.3527767 0.3446069 0.3341101 0.3835686 0.3925148 0.3590304
## White_2534 Black_2534 Hisp_2534 NE_2534 MA_2534 Midwest_2534
## 1 0.1164848 0.1621855 0.1393736 0.1504184 0.1628934 0.1121467
## 2 0.1179043 0.1855163 0.1298769 0.1517231 0.1640680 0.1153741
## 3 0.1824126 0.3137500 0.1885440 0.2414327 0.2505925 0.1828339
## 4 0.2639256 0.4838556 0.2962372 0.3500384 0.3623321 0.2755046
## 5 0.3127149 0.5144994 0.3180681 0.4091852 0.4175565 0.3308022
## 6 0.3183506 0.5437985 0.3321214 0.4200581 0.4294281 0.3344332
## South_2534 Mountain_2534 Pacific_2534 poor_2534 mid_2534 rich_2534
## 1 0.1090562 0.09152117 0.1198758 0.1371597 0.07514929 0.2066776
## 2 0.1126220 0.10293602 0.1374964 0.1717202 0.08159207 0.1724093
## 3 0.1688435 0.17434230 0.2334279 0.3100591 0.14825303 0.1851082
## 4 0.2639794 0.25264326 0.3319579 0.4199108 0.24320008 0.2783226
## 5 0.3099712 0.30621032 0.3753061 0.5033676 0.30202036 0.2717386
## 6 0.3182688 0.30980779 0.3844799 0.5178771 0.31716118 0.2532041colnames(marriage2534)## [1] "year" "all_2534" "HS_2534" "SC_2534"
## [5] "BAp_2534" "BAo_2534" "GD_2534" "White_2534"
## [9] "Black_2534" "Hisp_2534" "NE_2534" "MA_2534"
## [13] "Midwest_2534" "South_2534" "Mountain_2534" "Pacific_2534"
## [17] "poor_2534" "mid_2534" "rich_2534"2. Change column names to be more readable:
colnames(marriage2534)<- c("Year", "All", "High School Graduate", "Some College", "Bachelor's Degree", "Bachelor's and Some Graduate", "Graduate Degree", "White", "Black", "Hispanic", "New England", "Mid-Atlantic", "Midwest", "South", "Mountain West", "Pacific", "Low-Income", "Middle-Income", "Upper-Income")
colnames(marriage2534)## [1] "Year" "All"
## [3] "High School Graduate" "Some College"
## [5] "Bachelor's Degree" "Bachelor's and Some Graduate"
## [7] "Graduate Degree" "White"
## [9] "Black" "Hispanic"
## [11] "New England" "Mid-Atlantic"
## [13] "Midwest" "South"
## [15] "Mountain West" "Pacific"
## [17] "Low-Income" "Middle-Income"
## [19] "Upper-Income"head(marriage2534)## Year All High School Graduate Some College Bachelor's Degree
## 1 1960 0.1233145 0.1095332 0.1522818 0.2389952
## 2 1970 0.1269715 0.1094000 0.1495096 0.2187031
## 3 1980 0.1991767 0.1617313 0.2236916 0.2881646
## 4 1990 0.2968306 0.2777491 0.2780912 0.3612968
## 5 2000 0.3450087 0.3316545 0.3249205 0.3874906
## 6 2001 0.3527767 0.3446069 0.3341101 0.3835686
## Bachelor's and Some Graduate Graduate Degree White Black
## 1 0.2389952 NA 0.1164848 0.1621855
## 2 0.2187031 NA 0.1179043 0.1855163
## 3 0.2881646 NA 0.1824126 0.3137500
## 4 0.3656655 0.3474505 0.2639256 0.4838556
## 5 0.3939579 0.3691740 0.3127149 0.5144994
## 6 0.3925148 0.3590304 0.3183506 0.5437985
## Hispanic New England Mid-Atlantic Midwest South Mountain West
## 1 0.1393736 0.1504184 0.1628934 0.1121467 0.1090562 0.09152117
## 2 0.1298769 0.1517231 0.1640680 0.1153741 0.1126220 0.10293602
## 3 0.1885440 0.2414327 0.2505925 0.1828339 0.1688435 0.17434230
## 4 0.2962372 0.3500384 0.3623321 0.2755046 0.2639794 0.25264326
## 5 0.3180681 0.4091852 0.4175565 0.3308022 0.3099712 0.30621032
## 6 0.3321214 0.4200581 0.4294281 0.3344332 0.3182688 0.30980779
## Pacific Low-Income Middle-Income Upper-Income
## 1 0.1198758 0.1371597 0.07514929 0.2066776
## 2 0.1374964 0.1717202 0.08159207 0.1724093
## 3 0.2334279 0.3100591 0.14825303 0.1851082
## 4 0.3319579 0.4199108 0.24320008 0.2783226
## 5 0.3753061 0.5033676 0.30202036 0.2717386
## 6 0.3844799 0.5178771 0.31716118 0.25320413. Make observations from variables with tidyr ‘gather’ function:
ncol(marriage2534)## [1] 19marriage2534 <- gather(marriage2534, "Demographics", "n", 2:19)
head(marriage2534, 30)## Year Demographics n
## 1 1960 All 0.1233145
## 2 1970 All 0.1269715
## 3 1980 All 0.1991767
## 4 1990 All 0.2968306
## 5 2000 All 0.3450087
## 6 2001 All 0.3527767
## 7 2002 All 0.3535249
## 8 2003 All 0.3620345
## 9 2004 All 0.3673247
## 10 2005 All 0.3793451
## 11 2006 All 0.4147656
## 12 2007 All 0.4269222
## 13 2008 All 0.4394414
## 14 2009 All 0.4625638
## 15 2010 All 0.4697332
## 16 2011 All 0.4833335
## 17 2012 All 0.4943453
## 18 1960 High School Graduate 0.1095332
## 19 1970 High School Graduate 0.1094000
## 20 1980 High School Graduate 0.1617313
## 21 1990 High School Graduate 0.2777491
## 22 2000 High School Graduate 0.3316545
## 23 2001 High School Graduate 0.3446069
## 24 2002 High School Graduate 0.3490367
## 25 2003 High School Graduate 0.3581877
## 26 2004 High School Graduate 0.3708102
## 27 2005 High School Graduate 0.3870680
## 28 2006 High School Graduate 0.4312162
## 29 2007 High School Graduate 0.4441386
## 30 2008 High School Graduate 0.45991624. Add a column that is the inverse of “n” and name it “Marriage_Rate”:
marriage_tidy <- marriage2534 %>%
mutate(Marriage_Rate = 1 - n)5. Rename “n” to “Single_Rate”:
The figures in “n” represent the share of the relevant population that has never been married, so “Single_Rate” seems a better descriptor.
marriage_tidy <- dplyr::rename(marriage_tidy, Single_Rate = n)
head(marriage_tidy, 20)## Year Demographics Single_Rate Marriage_Rate
## 1 1960 All 0.1233145 0.8766855
## 2 1970 All 0.1269715 0.8730285
## 3 1980 All 0.1991767 0.8008233
## 4 1990 All 0.2968306 0.7031694
## 5 2000 All 0.3450087 0.6549913
## 6 2001 All 0.3527767 0.6472233
## 7 2002 All 0.3535249 0.6464751
## 8 2003 All 0.3620345 0.6379655
## 9 2004 All 0.3673247 0.6326753
## 10 2005 All 0.3793451 0.6206549
## 11 2006 All 0.4147656 0.5852344
## 12 2007 All 0.4269222 0.5730778
## 13 2008 All 0.4394414 0.5605586
## 14 2009 All 0.4625638 0.5374362
## 15 2010 All 0.4697332 0.5302668
## 16 2011 All 0.4833335 0.5166665
## 17 2012 All 0.4943453 0.5056547
## 18 1960 High School Graduate 0.1095332 0.8904668
## 19 1970 High School Graduate 0.1094000 0.8906000
## 20 1980 High School Graduate 0.1617313 0.83826876. Change “Marriage_Rate” and “Single_Rate”" from characters to numeric:
marriage_tidy$Marriage_Rate <- as.numeric(as.character(marriage_tidy$Marriage_Rate))
marriage_tidy$Single_Rate <- as.numeric(as.character(marriage_tidy$Single_Rate))
head(marriage_tidy, 20)## Year Demographics Single_Rate Marriage_Rate
## 1 1960 All 0.1233145 0.8766855
## 2 1970 All 0.1269715 0.8730285
## 3 1980 All 0.1991767 0.8008233
## 4 1990 All 0.2968306 0.7031694
## 5 2000 All 0.3450087 0.6549913
## 6 2001 All 0.3527767 0.6472233
## 7 2002 All 0.3535249 0.6464751
## 8 2003 All 0.3620345 0.6379655
## 9 2004 All 0.3673247 0.6326753
## 10 2005 All 0.3793451 0.6206549
## 11 2006 All 0.4147656 0.5852344
## 12 2007 All 0.4269222 0.5730778
## 13 2008 All 0.4394414 0.5605586
## 14 2009 All 0.4625638 0.5374362
## 15 2010 All 0.4697332 0.5302668
## 16 2011 All 0.4833335 0.5166665
## 17 2012 All 0.4943453 0.5056547
## 18 1960 High School Graduate 0.1095332 0.8904668
## 19 1970 High School Graduate 0.1094000 0.8906000
## 20 1980 High School Graduate 0.1617313 0.83826877. Round “Single_Rate”" and “Marriage_Rate”" to the hundredth decimal place:
marriage_tidy[, "Marriage_Rate" ] = format(round(marriage_tidy[, "Marriage_Rate" ], 2), nsmall = 2)
marriage_tidy[, "Single_Rate" ] = format(round(marriage_tidy[, "Single_Rate" ], 2), nsmall = 2)8. Convert to a local data frame:
marriage_tidy <- tbl_df(marriage_tidy)
marriage_tidy## # A tibble: 306 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 1960 All 0.12 0.88
## 2 1970 All 0.13 0.87
## 3 1980 All 0.20 0.80
## 4 1990 All 0.30 0.70
## 5 2000 All 0.35 0.65
## 6 2001 All 0.35 0.65
## 7 2002 All 0.35 0.65
## 8 2003 All 0.36 0.64
## 9 2004 All 0.37 0.63
## 10 2005 All 0.38 0.62
## # ... with 296 more rowsPretty Data Table
library(DT)
datatable(marriage_tidy)Analysis
Interested in exploring the declining marriage rates for people ages 25-34 from 2000-2012 by Race, Education, Income Level, and Region (US).
library(ggplot2)All People Ages 25-34 from 2000 - 2012
All <- filter(marriage_tidy, Demographics == "All", Year >= 2000)
All## # A tibble: 13 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 2000 All 0.35 0.65
## 2 2001 All 0.35 0.65
## 3 2002 All 0.35 0.65
## 4 2003 All 0.36 0.64
## 5 2004 All 0.37 0.63
## 6 2005 All 0.38 0.62
## 7 2006 All 0.41 0.59
## 8 2007 All 0.43 0.57
## 9 2008 All 0.44 0.56
## 10 2009 All 0.46 0.54
## 11 2010 All 0.47 0.53
## 12 2011 All 0.48 0.52
## 13 2012 All 0.49 0.51All$Marriage_Rate <- as.numeric(as.character(All$Marriage_Rate))
ggplot(All, aes(Year, Marriage_Rate, group=1)) +
geom_line() +
geom_point() +
expand_limits(y=.5) +
scale_x_continuous(limits = c(2000, 2013)) +
theme_linedraw() +
ggtitle("Declining Marriage Rates in All People Ages 25-34") +
ylab("Marriage Rate") +
theme(plot.title = element_text(lineheight = .8, face = "bold"))
People Ages 25-34 by Race from 2000 - 2012
Race <- filter(marriage_tidy, Demographics == "White" | Demographics == "Black" | Demographics == "Hispanic", Year >= 2000)
Race## # A tibble: 39 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 2000 White 0.31 0.69
## 2 2001 White 0.32 0.68
## 3 2002 White 0.32 0.68
## 4 2003 White 0.33 0.67
## 5 2004 White 0.33 0.67
## 6 2005 White 0.34 0.66
## 7 2006 White 0.38 0.62
## 8 2007 White 0.39 0.61
## 9 2008 White 0.40 0.60
## 10 2009 White 0.42 0.58
## # ... with 29 more rowsRace$Marriage_Rate <- as.numeric(as.character(Race$Marriage_Rate))
ggplot(Race, aes(x = Year, y = Marriage_Rate, group = Demographics, colour = Demographics)) +
geom_line() +
geom_point() +
scale_y_continuous() +
scale_x_continuous(limits = c(2000, 2013)) +
theme_linedraw() +
ggtitle("Declining Marriage Rates by Race in People Ages 25-34") +
ylab("Marriage Rate") +
theme(plot.title = element_text(lineheight = .8, face = "bold"))
People Ages 25-34 by Education Level from 2000 - 2012
Education <- filter(marriage_tidy, Demographics == "Graduate Degree" | Demographics == "Bachelor's and Some Graduate" | Demographics == "Bachelor's Degree" | Demographics == "Some College" | Demographics == "High School Graduate", Year >= 2000)
Education## # A tibble: 65 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 2000 High School Graduate 0.33 0.67
## 2 2001 High School Graduate 0.34 0.66
## 3 2002 High School Graduate 0.35 0.65
## 4 2003 High School Graduate 0.36 0.64
## 5 2004 High School Graduate 0.37 0.63
## 6 2005 High School Graduate 0.39 0.61
## 7 2006 High School Graduate 0.43 0.57
## 8 2007 High School Graduate 0.44 0.56
## 9 2008 High School Graduate 0.46 0.54
## 10 2009 High School Graduate 0.48 0.52
## # ... with 55 more rowsEducation$Marriage_Rate <- as.numeric(as.character(Education$Marriage_Rate))
ggplot(Education, aes(x = Year, y = Marriage_Rate, group = Demographics, colour = Demographics)) +
geom_line() +
geom_point() +
scale_y_continuous() +
scale_x_continuous(limits = c(2000, 2013)) +
ggtitle("Declining Marriage Rates by Education Level in People Ages 25-34") +
theme_classic() +
ylab("Marriage Rate") +
theme(plot.title = element_text(lineheight = .8, face = "bold"))
People Ages 25-34 by Income Level from 2000 - 2012
Income <- filter(marriage_tidy, Demographics == "Low-Income" | Demographics == "Middle-Income" | Demographics == "Upper-Income", Year >= 2000)
Income## # A tibble: 39 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 2000 Low-Income 0.50 0.50
## 2 2001 Low-Income 0.52 0.48
## 3 2002 Low-Income 0.52 0.48
## 4 2003 Low-Income 0.53 0.47
## 5 2004 Low-Income 0.54 0.46
## 6 2005 Low-Income 0.55 0.45
## 7 2006 Low-Income 0.57 0.43
## 8 2007 Low-Income 0.59 0.41
## 9 2008 Low-Income 0.61 0.39
## 10 2009 Low-Income 0.62 0.38
## # ... with 29 more rowsIncome$Marriage_Rate <- as.numeric(as.character(Income$Marriage_Rate))
ggplot(Income, aes(x = Year, y = Marriage_Rate, group = Demographics, colour = Demographics)) +
geom_line() +
geom_point() +
scale_y_continuous() +
scale_x_continuous(limits = c(2000, 2013)) +
ggtitle("Declining Marriage Rates by Income Level in People Ages 25-34") +
theme_classic() +
ylab("Marriage Rate") +
theme(plot.title = element_text(lineheight = .8, face = "bold"))
People Ages 25-34 by Region from 2000 - 2012
Region <- filter(marriage_tidy, Demographics == "New England" | Demographics == "Mid-Atlantic" | Demographics == "Midwest" | Demographics == "South" | Demographics == "Mountain West" | Demographics == "Pacific", Year >= 2000)
Region## # A tibble: 78 x 4
## Year Demographics Single_Rate Marriage_Rate
## <int> <chr> <chr> <chr>
## 1 2000 New England 0.41 0.59
## 2 2001 New England 0.42 0.58
## 3 2002 New England 0.41 0.59
## 4 2003 New England 0.43 0.57
## 5 2004 New England 0.45 0.55
## 6 2005 New England 0.45 0.55
## 7 2006 New England 0.48 0.52
## 8 2007 New England 0.49 0.51
## 9 2008 New England 0.51 0.49
## 10 2009 New England 0.53 0.47
## # ... with 68 more rowsRegion$Marriage_Rate <- as.numeric(as.character(Region$Marriage_Rate))
ggplot(Region, aes(x = Year, y = Marriage_Rate, group = Demographics, colour = Demographics)) +
geom_line() +
geom_point() +
scale_y_continuous() +
scale_x_continuous(limits = c(2000, 2013)) +
ggtitle("Declining Marriage Rates by Income Level in People Ages 25-34") +
theme_classic() +
ylab("Marriage Rate") +
theme(plot.title = element_text(lineheight = .8, face = "bold"))
Conclusion
The marriage rate for people ages 25-34 has been steadily declining since 2000 regardless of race, region, income level, and education. The steepest decline looks to have been from 2005-2007 when the economy was booming.