Topics covered in this homework include:

Let’s return to Prof. Rodriguez’s final exam from last semester. Now you will redo some of the same problems using dplyr and ggplot2.

From your exam: “The data fraem Robey in the car package has data on fertility and contraceptive use in 50 developing countries around 1990. There are three variables (1) region, a factor with four levels (Africa, Asia, Latin America, and Near East) (2) tfr, the total fertiility rate, the average number of children that a women would have over her lifetime at current rates (usually abbreviated TFR), and (3) contraceptors, the precentage of contraception among women of childbearing age.”

  1. “Let us say that a country has undergone a contraceptive revolution when at least half of the women in childbearing age use contraception. Calculate and plot the proportion of countries that have achived this milestone by region.” Be sure to use dplyr and ggplot2.
library(car)
library(dplyr)
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)

data.to.plot <- Robey %>% 
                  mutate(revolution = (contraceptors > 50) ) %>%
                  group_by(region) %>%
                  summarise(prop.revolution = mean(revolution))

p <- ggplot(data.to.plot, aes(x=region, y=prop.revolution)) 
p + geom_bar(stat="identity")

# Now let's improve labels and order (alphabetical order is not a good order)
data.to.plot$region <- reorder(data.to.plot$region, data.to.plot$prop.revolution)
# to learn more about reordering factors see: https://kohske.wordpress.com/2010/12/29/faq-how-to-order-the-factor-variables-in-ggplot2/

p <- ggplot(data.to.plot, aes(x=region, y=prop.revolution)) 
p + geom_bar(stat="identity") + 
  scale_x_discrete(name = "Region") + 
  scale_y_continuous(name = "Proportion with > 50% contraceptive use")

  1. “Compute the average TFR in each region and plot your results.” Be sure to use dplyr and ggplot2.
data.to.plot <- Robey %>% 
                  group_by(region) %>%
                  summarise(mean.tfr = mean(tfr))

p <- ggplot(data.to.plot, aes(x=region, y=mean.tfr)) 
p + geom_point()

# Now let's improve labels and order (alphabetical order is not a good order)
data.to.plot$region <- reorder(data.to.plot$region, data.to.plot$mean.tfr)
# to learn more about reordering factors see: https://kohske.wordpress.com/2010/12/29/faq-how-to-order-the-factor-variables-in-ggplot2/

p <- ggplot(data.to.plot, aes(x=mean.tfr, y=region)) 
p + geom_point() + 
  scale_x_continuous(name = "Mean Total Fertility Rate") +
  scale_y_discrete(name = "Region")

  1. “Plot the TFR against the proportion of married women using contraception and superimpose a regression line.” 
p <- ggplot(Robey, aes(x=contraceptors, y=tfr)) 
p + geom_point() + stat_smooth(method = "lm")

  1. Next, create the same plot but with 4 factets, one for each region. 
p <- ggplot(Robey, aes(x=contraceptors, y=tfr)) 
p + geom_point() + stat_smooth(method = "lm") + facet_grid(. ~ region)

  1. In words, what would you conclude from the graph above. 
There is a negative relationship between contraceptive use and TFR in all four regions in this data.

Next you are going to reproduce and extend this graph by Brad Boehmke that shows median income growth rate by state.

You can read about it and see all of his code here: https://rpubs.com/bradleyboehmke/median_income_trends

The start code below gets you to where Brad Boehmke’s code begins.

## Loading required package: rJava
## Loading required package: xlsxjars
##             State X2013 X2012 X2011 X2010 X2009 X2008 X2007 X2006 X2005
## 1   United States 51939 51758 51842 52646 54059 54423 56436 55689 55278
## 2         Alabama 41381 44096 44112 43733 43420 48119 47424 43848 44329
## 3          Alaska 61137 64573 59483 61804 66904 69230 70771 65183 66691
## 4         Arizona 50602 47728 50358 50103 49674 50757 53045 53905 53988
## 5        Arkansas 39919 39585 42778 41226 39681 42828 45832 42814 43742
## 6      California 57528 57849 55274 57996 60963 61684 62616 63913 61756
## 7        Colorado 63371 58087 60724 64353 60742 65935 68691 64350 60197
## 8     Connecticut 67781 65181 67752 70512 70430 69980 72061 72099 67817
## 9        Delaware 52219 49684 56613 58990 56597 54855 61330 60584 61135
## 10           D.C. 60675 66194 57225 60822 57713 60143 57054 56008 53687
## 11        Florida 47886 46740 46716 47080 49557 48531 51449 52772 51297
## 12        Georgia 47439 48820 47615 47134 47069 50013 54647 57010 54800
## 13         Hawaii 61408 57081 61156 63611 60437 66560 71928 69864 71100
## 14          Idaho 51767 48618 49154 50268 50802 51304 55257 53392 52712
## 15       Illinois 57196 52490 52446 54198 57418 57616 58989 56232 57750
## 16        Indiana 50553 46829 46033 49295 48117 50330 53313 52461 50637
## 17           Iowa 54855 54219 52013 52368 55085 54249 54947 55603 55485
## 18         Kansas 51485 50730 47796 49204 48564 51799 54485 52629 50148
## 19       Kentucky 42158 41683 41280 43915 46334 44518 44324 45619 43790
## 20      Louisiana 39622 39653 42111 41988 49342 42804 46414 42157 44431
## 21          Maine 50121 49872 51468 51209 51589 51096 53808 52733 52410
## 22       Maryland 65262 72880 71337 68592 69708 68930 73734 73559 72205
## 23  Massachusetts 62963 64581 65575 65102 64481 65261 65682 63926 66841
## 24       Michigan 48801 50742 50625 49441 49951 53866 55466 56204 54809
## 25      Minnesota 60907 62693 59886 55899 60916 59424 65227 64944 64691
## 26    Mississippi 40850 37173 42558 40770 38096 39431 41882 40129 39228
## 27       Missouri 50311 50487 47409 48951 52965 49809 51686 51504 51292
## 28        Montana 44132 45743 41716 44103 43916 46414 49046 47491 44523
## 29       Nebraska 53774 52954 57603 56095 53862 54883 55246 55624 57183
## 30         Nevada 45369 48021 48724 54702 55859 59228 60733 60404 57525
## 31  New Hampshire 71322 68805 68234 71190 69648 71596 75920 71597 67995
## 32     New Jersey 61782 67661 64565 67275 70350 70655 67980 78632 75613
## 33     New Mexico 42127 44055 43482 48221 47288 45551 49833 46246 46473
## 34       New York 53843 48373 52445 53186 54536 54594 54988 55713 56292
## 35 North Carolina 41208 42157 46821 46828 45511 46446 48886 45980 50183
## 36   North Dakota 52888 56576 58375 54495 54383 53696 53034 47424 50345
## 37           Ohio 46398 45020 46243 49024 49826 50778 55162 53031 52744
## 38       Oklahoma 43777 49110 50186 46051 49825 49888 48552 44872 44919
## 39         Oregon 56307 52527 53367 54063 53322 55964 56439 54407 52692
## 40   Pennsylvania 53952 52658 51693 51618 52316 55612 54418 56008 55247
## 41   Rhode Island 57812 56880 50785 55154 56076 57602 60904 62084 59046
## 42 South Carolina 43749 45046 41516 44550 44637 45608 49672 45772 48004
## 43   South Dakota 54453 50133 48910 48454 49768 55827 52150 52484 51489
## 44      Tennessee 42499 43620 43789 41230 44003 42954 46282 47015 47021
## 45          Texas 53027 52681 50799 50499 51559 50298 51740 50035 49426
## 46           Utah 62967 59189 57475 60579 63523 67659 60139 63115 65405
## 47        Vermont 54842 56390 53715 59753 56819 54859 53242 60056 60502
## 48       Virginia 67620 65571 64853 64496 65706 67062 66466 65993 61946
## 49     Washington 60106 63091 58881 60004 65588 61270 65252 63224 60432
## 50  West Virginia 40241 44186 43315 45703 43973 41106 47288 44388 43487
## 51      Wisconsin 55258 53850 53918 53795 55645 55394 57609 59723 53278
## 52        Wyoming 55700 58348 56456 55771 56984 57706 54763 54349 53359
##    X2004 X2003 X2002 X2001 X2000 X1999 X1998 X1997 X1996 X1995 X1994 X1993
## 1  54674 54865 54913 55562 56800 56895 55497 53551 52471 51719 50148 49594
## 2  45172 47186 48690 46262 47918 50680 51759 46220 44798 39448 42271 39817
## 3  67906 65655 68335 75476 71487 71854 72348 69453 78028 72782 70514 68152
## 4  54073 52140 51450 56189 53815 51720 52935 47379 46772 46842 48639 48434
## 5  43144 40533 41936 43866 40171 41497 39484 37860 40098 39179 39736 36574
## 6  60702 62442 61424 62186 63328 60995 58421 57442 57379 56170 54915 54090
## 7  62755 63252 62534 64995 65255 67353 66506 62563 60540 61781 58804 54749
## 8  67951 69617 69128 70192 67868 70731 66376 63652 62268 61079 63877 62731
## 9  59256 62086 64289 65265 68129 65188 59169 62274 58114 53012 55757 57251
## 10 53585 57051 50590 54169 55761 54062 47716 46105 47258 46668 46809 43344
## 11 49989 49361 49235 47922 52561 50093 49822 46966 45299 45145 45531 45322
## 12 50543 53751 55600 56020 56680 55118 55183 53056 48042 51754 48909 50264
## 13 69360 65651 61250 62419 69727 62218 58268 59237 61755 65037 65677 67725
## 14 54704 53667 48835 50316 50877 50050 52350 48340 51314 49594 49016 49227
## 15 56824 57189 55303 60750 62311 64771 61624 59742 58476 57782 54526 52160
## 16 52202 53734 53150 53129 55278 57093 56704 56277 51961 50670 43300 46791
## 17 53511 52416 53152 53915 55449 57457 52834 48888 49096 53909 51414 45502
## 18 50644 56023 55185 54493 55541 52214 52394 52778 48173 46050 44021 47259
## 19 43916 46782 47601 50574 49056 47167 51739 48409 47919 45244 41336 38696
## 20 44926 42439 44035 43844 41553 45652 45292 48131 44739 42419 39908 41770
## 21 50968 47006 47719 48173 50410 54331 50865 47425 51294 51388 47120 43557
## 22 70422 66259 73039 70433 73770 72985 71383 67559 65039 62290 60925 63402
## 23 64152 64538 64555 68753 63243 61521 60435 60812 58388 58546 62949 58838
## 24 52112 57023 55310 59271 61564 64434 59687 56064 57990 55285 54842 51850
## 25 69190 66904 70727 69316 73386 65761 68400 61595 60601 57573 52293 53469
## 26 42861 41452 39988 39685 46397 45405 41560 41242 39439 40278 39479 35228
## 27 51965 55428 55389 54393 61003 57855 57375 52897 50657 52856 46924 45532
## 28 41876 43200 45106 42270 44338 43392 45067 42273 42406 42128 42947 42020
## 29 53999 55696 55415 57382 56476 54001 51969 50204 50286 49978 49417 49224
## 30 58214 57229 58214 59740 61897 57964 56740 56227 56977 54766 55754 56854
## 31 70066 70379 71633 67540 68888 64387 64164 59329 58259 59452 54781 60267
## 32 68167 70985 70658 68119 68183 69530 71112 69492 70176 66665 65716 64293
## 33 48789 44463 45912 43584 47471 45540 45018 43538 37087 39448 41818 42478
## 34 55063 54194 54340 55412 55115 55906 53369 51804 52350 50128 49580 50318
## 35 49623 47216 47282 50212 51832 52083 51148 51865 52632 48536 46806 45751
## 36 48368 51182 46874 47095 48692 45664 43250 45817 46525 44150 43952 44637
## 37 53097 55121 55269 54979 58115 55207 55554 52290 50369 53032 49512 49664
## 38 48853 45472 47208 46853 43871 45692 48135 45369 40563 39933 41952 41687
## 39 50555 52737 54127 54306 57489 56787 55756 53901 52471 55207 48892 52606
## 40 54393 54378 55029 57235 57052 52787 55682 54292 51594 52399 49840 49204
## 41 59115 56630 54924 60161 57080 59723 58067 50356 54680 53666 49625 53195
## 42 47715 48736 48961 49652 50821 50975 47479 49581 51248 44122 46389 41358
## 43 50695 50057 49040 52198 49340 50089 46792 42971 43651 44892 46214 44032
## 44 46952 47525 47948 47082 46122 51059 48655 44334 45520 44037 44513 39849
## 45 51052 49739 51987 53762 52227 54087 51070 50758 48893 48627 47802 45603
## 46 62736 62410 61973 62291 64321 64380 63224 61901 54757 55367 55513 56809
## 47 58368 54793 55677 53675 53559 58136 56192 50726 47838 51336 55647 49315
## 48 63069 69386 64265 66106 63798 63881 61875 62164 57969 54976 58514 57836
## 49 61566 60172 58505 55907 57524 63573 67679 64487 54222 53983 52120 56601
## 50 41157 41497 38016 39043 39785 40958 38112 39779 37325 37762 36625 35593
## 51 56398 58603 59438 59665 60991 63844 58982 57299 59137 62159 55003 50428
## 52 55985 53899 51487 52261 53606 52074 50309 48367 45761 47853 51509 46738
##    X1992 X1991 X1990 X1989 X1988 X1987 X1986 X1985 X1984
## 1  49836 50249 51735 52432 51514 51121 50488 48761 47866
## 2  41982 40608 40356 38607 37745 38710 38797 37850 36965
## 3  68000 67739 67898 65311 62636 65189 63586 71810 69095
## 4  47757 51268 50493 51790 50019 52470 51710 49296 45752
## 5  38849 39089 39369 38877 38168 36931 37982 36029 33471
## 6  56778 56150 57518 59875 57307 59134 58828 55704 53999
## 7  52842 52539 53100 48623 49601 51935 55142 58184 55097
## 8  66437 70311 67159 76766 68520 64462 66354 64188 63959
## 9  58038 54351 53222 58168 57720 57365 51966 47444 55135
## 10 49204 49847 47327 48525 50598 53855 49322 43513 43580
## 11 44489 45455 46106 47315 48072 48037 46335 44064 42250
## 12 46845 45389 47619 49958 50267 52402 49419 43457 42675
## 13 68506 62125 67247 63550 62486 68699 58814 59792 61666
## 14 45067 43561 43721 44720 44371 40713 42076 42863 45041
## 15 51325 53181 56225 56775 55864 53128 53761 51346 50721
## 16 46410 45184 46526 46976 49750 44173 46089 46814 48624
## 17 46757 47625 47148 47642 45989 43528 45544 43205 42417
## 18 49365 48863 51690 48725 48375 50183 48519 47048 52594
## 19 38204 39638 42814 42233 37667 40552 40302 35843 37755
## 20 41382 42198 38711 41467 38783 41878 42362 43726 40465
## 21 48179 46483 47452 51190 49956 46293 47501 42363 44093
## 22 60519 61635 67136 65329 69162 68597 62061 62218 63440
## 23 59146 59570 62627 65456 62844 63243 61523 58235 57570
## 24 52489 53570 51724 55823 55765 54340 53951 50049 49041
## 25 50398 49170 54365 54752 55037 55085 53623 49252 52182
## 26 33462 32484 34863 36127 34373 36315 33486 33886 32950
## 27 44509 46580 47224 48063 44358 46529 44461 45295 44364
## 28 43149 41411 40387 42975 42064 40161 41222 41779 41718
## 29 48880 49287 47483 47740 47605 45642 44151 45006 45692
## 30 51906 54938 55329 53220 52948 52723 53164 48051 55044
## 31 64151 60100 70502 68079 65516 63434 61947 54511 55338
## 32 63442 66800 66924 70959 68660 67167 64314 63961 59314
## 33 42067 44268 43262 40998 36511 40719 40243 42165 44054
## 34 50511 53031 54582 57130 54711 51754 50747 48804 47038
## 35 45176 44790 45491 47898 46197 44646 44331 44287 43924
## 36 43855 43187 43651 45763 45586 44285 43615 43779 44356
## 37 51086 49689 51856 52641 52488 50556 50930 51974 49378
## 38 41130 42470 42130 42929 44781 42549 42480 43779 45161
## 39 51936 50356 50591 51749 52503 49114 50236 45202 45697
## 40 48610 50651 50114 52041 50600 49871 48277 47231 43448
## 41 49504 51433 55234 54642 56465 55497 53819 50840 46151
## 42 44862 45807 49648 43167 48312 49136 44548 41366 43369
## 43 42716 41097 42453 43729 42184 41489 40350 37456 41447
## 44 39559 40787 39034 41014 39463 41544 37021 36704 35837
## 45 45472 46258 48772 46954 47234 48492 48997 49019 49167
## 46 55717 46730 52079 55717 49788 52039 53294 52106 49237
## 47 53283 48630 53730 56766 54850 49854 49883 53679 48214
## 48 62138 60275 60598 61886 61775 58840 60258 58694 56643
## 49 55146 56661 55482 57974 61167 53588 54511 49550 53423
## 50 32975 38608 38248 39320 36619 33753 33387 32998 35967
## 51 54183 51929 53062 52826 55960 51725 53596 47993 44296
## 52 49142 48454 50900 53548 49989 54120 47774 45588 50858
  1. Write code to reproduce the graph above.

A few notes:

# clean overall U.S. data and turn it into long format
us <- data %>%
        filter(State == "United States") %>%
        gather(Year, Income, X2013:X1984) %>%
        separate(Year, c("left","Year"), sep="X") %>%
        select(-left) %>%
        arrange(Year)

# extract 1984 value as the baseline value and add to us dataframe
us_base <- us[us$Year==1984,3]
us$us_baseline <- us_base

# calculate the percent change in U.S. median income for each year as compared
# to 1984 (aka the baseline)
us <- us %>% mutate(us_change = (Income-us_baseline)/us_baseline)


# create a states dataframe, clean and turn into long format
states <- data %>%
        filter(State != "United States") %>%
        gather(Year, Income, X2013:X1984) %>%
        separate(Year, c("left","Year"), sep="X") %>%
        select(-left) %>%
        arrange(Year) %>%
        filter(Income != "NA")

# create baselines for each state
state_base <- states %>%
        filter(Year == 1984) %>%
        select(State, State_Baseline = Income)

# add baseline to the states and calculate the percent change in median income
# for each state as compared to 1984 (aka the baseline)
states <- states %>%
        left_join(state_base) %>%
        arrange(State) %>%
        mutate(state_change = (Income-State_Baseline)/State_Baseline)
## Joining by: "State"
# change year variables from character to numeric
states$Year <- as.numeric(states$Year)
us$Year <- as.numeric(us$Year)

# get top 5 and bottom 5 states which will allow me to identify them
# graphically
rank <- states %>% 
        filter(Year == 2013) %>% 
        arrange(desc(state_change)) %>%
        mutate(rank = seq(1,length(State), by=1)) %>%
        filter(rank < 6 | rank > 46 )



p <- ggplot(states, aes(Year, state_change, group=State)) +
        theme_bw() +
        theme(plot.background = element_blank(),
              panel.grid.minor = element_blank(),
              panel.grid.major.x = element_blank(),
              panel.grid.major.y = element_line(linetype = 3, colour = "grey50"),
              panel.border = element_blank(),
              panel.background = element_blank(),
              axis.ticks = element_blank(),  
              axis.title = element_blank()) +
        geom_line(colour="grey90", alpha=.9)

print(p)

p <- p +
        geom_line(data=us, aes(Year, us_change, group=1), linetype=5)

print(p)

p <- p +
        geom_line(data=filter(states, State=="Ohio"), 
                  aes(Year, state_change, group=State), colour="dodgerblue", size = 1)

print(p)

### Step 4
p <- p +
        geom_line(data=filter(states, State=="D.C."), 
                  aes(Year, state_change, group=State), colour="grey70") +
        geom_line(data=filter(states, State=="Nevada"), 
                  aes(Year, state_change, group=State), colour="grey70")

print(p)

### Step 5

p <- p +
        geom_point(data=rank, aes(Year, state_change), shape=21, size=1.5, alpha=.6) +
        geom_point(data=filter(us, Year == 2013), aes(Year, us_change), size=2.5, alpha=.6)

print(p)

### Step 6

p <- p +
        scale_y_continuous(limits=c(-.2,.55), breaks=seq(-.2,.4,by=.2), label=percent) +
        scale_x_continuous(limits=c(1983,2013),breaks=seq(1985,2010,by=5), expand=c(0,.25))

print(p)

### Step 7

# Note that you should do as many of these annotations as possible in ggplot2, do not use illustrator
  1. Modify or extend the graph somehow.
# [ your code here ]

The command below is helpful for debugging, please don’t change it

## R version 3.1.2 (2014-10-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] xlsx_0.5.7     xlsxjars_0.6.1 rJava_0.9-6    scales_0.2.4  
## [5] tidyr_0.2.0    ggplot2_1.0.0  dplyr_0.4.1    car_2.0-24    
## 
## loaded via a namespace (and not attached):
##  [1] assertthat_0.1   colorspace_1.2-4 DBI_0.3.1        digest_0.6.8    
##  [5] evaluate_0.5.5   formatR_1.0      gtable_0.1.2     htmltools_0.2.6 
##  [9] knitr_1.9        labeling_0.3     lattice_0.20-29  lazyeval_0.1.10 
## [13] lme4_1.1-7       magrittr_1.5     MASS_7.3-35      Matrix_1.1-4    
## [17] mgcv_1.8-3       minqa_1.2.4      munsell_0.4.2    nlme_3.1-118    
## [21] nloptr_1.0.4     nnet_7.3-8       parallel_3.1.2   pbkrtest_0.4-2  
## [25] plyr_1.8.1       proto_0.3-10     quantreg_5.11    Rcpp_0.11.4     
## [29] reshape2_1.4.1   rmarkdown_0.5.1  SparseM_1.6      splines_3.1.2   
## [33] stringi_0.4-1    stringr_0.6.2    tools_3.1.2      yaml_2.1.13