Answer
counties %>%
ggplot(aes(x = pop)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Harold Nelson
9/27/2021
Make the packages tidyverse and socviz available. This assumes you have already installed them on your computer.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.6
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Do a glimpse() of the file county_data from the socviz package. Also look in the package documentation.
## Rows: 3,195
## Columns: 32
## $ id <chr> "0", "01000", "01001", "01003", "01005", "01007", "01…
## $ name <chr> NA, "1", "Autauga County", "Baldwin County", "Barbour…
## $ state <fct> NA, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, AL, A…
## $ census_region <fct> NA, South, South, South, South, South, South, South, …
## $ pop_dens <fct> "[ 50, 100)", "[ 50, 100)", "[ 50, 100)", "[…
## $ pop_dens4 <fct> "[ 45, 118)", "[ 45, 118)", "[ 45, 118)", "[118,71…
## $ pop_dens6 <fct> "[ 82, 215)", "[ 82, 215)", "[ 82, 215)", "[ 82, …
## $ pct_black <fct> "[10.0,15.0)", "[25.0,50.0)", "[15.0,25.0)", "[ 5.0,1…
## $ pop <int> 318857056, 4849377, 55395, 200111, 26887, 22506, 5771…
## $ female <dbl> 50.8, 51.5, 51.5, 51.2, 46.5, 46.0, 50.6, 45.2, 53.4,…
## $ white <dbl> 77.7, 69.8, 78.1, 87.3, 50.2, 76.3, 96.0, 27.2, 54.3,…
## $ black <dbl> 13.2, 26.6, 18.4, 9.5, 47.6, 22.1, 1.8, 69.9, 43.6, 2…
## $ travel_time <dbl> 25.5, 24.2, 26.2, 25.9, 24.6, 27.6, 33.9, 26.9, 24.0,…
## $ land_area <dbl> 3531905.43, 50645.33, 594.44, 1589.78, 884.88, 622.58…
## $ hh_income <int> 53046, 43253, 53682, 50221, 32911, 36447, 44145, 3203…
## $ su_gun4 <fct> NA, NA, "[11,54]", "[11,54]", "[ 5, 8)", "[11,54]", "…
## $ su_gun6 <fct> NA, NA, "[10,12)", "[10,12)", "[ 7, 8)", "[10,12)", "…
## $ fips <dbl> 0, 1000, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 10…
## $ votes_dem_2016 <int> NA, NA, 5908, 18409, 4848, 1874, 2150, 3530, 3716, 13…
## $ votes_gop_2016 <int> NA, NA, 18110, 72780, 5431, 6733, 22808, 1139, 4891, …
## $ total_votes_2016 <int> NA, NA, 24661, 94090, 10390, 8748, 25384, 4701, 8685,…
## $ per_dem_2016 <dbl> NA, NA, 0.23956855, 0.19565310, 0.46660250, 0.2142203…
## $ per_gop_2016 <dbl> NA, NA, 0.7343579, 0.7735147, 0.5227141, 0.7696616, 0…
## $ diff_2016 <int> NA, NA, 12202, 54371, 583, 4859, 20658, 2391, 1175, 1…
## $ per_dem_2012 <dbl> NA, NA, 0.2657577, 0.2156657, 0.5125229, 0.2621857, 0…
## $ per_gop_2012 <dbl> NA, NA, 0.7263374, 0.7738975, 0.4833755, 0.7306638, 0…
## $ diff_2012 <int> NA, NA, 11012, 47443, 334, 3931, 17780, 2808, 714, 14…
## $ winner <chr> NA, NA, "Trump", "Trump", "Trump", "Trump", "Trump", …
## $ partywinner16 <chr> NA, NA, "Republican", "Republican", "Republican", "Re…
## $ winner12 <chr> NA, NA, "Romney", "Romney", "Obama", "Romney", "Romne…
## $ partywinner12 <chr> NA, NA, "Republican", "Republican", "Democrat", "Repu…
## $ flipped <chr> NA, NA, "No", "No", "Yes", "No", "No", "No", "No", "N…Read about the FIPS codes at https://www.census.gov/library/reference/code-lists/ansi/ansi-codes-for-states.html
Using the FIPS codes, filter the dataframe to counties, which contains only the counties of the 50 states.
counties = county_data %>%
mutate(numeric_fips = as.numeric(id)) %>%
filter(numeric_fips %% 1000 != 0 &
numeric_fips %/% 1000 != 11) %>%
mutate(state = factor(state))
table(counties$state)##
## AK AL AR AZ CA CO CT DE FL GA HI IA ID IL IN KS KY LA MA MD
## 29 67 75 15 58 64 8 3 67 159 5 99 44 102 92 105 120 64 14 24
## ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC
## 16 83 87 115 82 56 100 53 93 10 21 33 17 62 88 77 36 67 5 46
## SD TN TX UT VA VT WA WI WV WY
## 66 95 254 29 134 14 39 72 55 23Examine the variable pop in counties. Use summary().
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 11025 25702 101272 67690 10116705Counties with zero population are strange. What are they.
## state name pop
## 1 VA Bedford city 0Restrict counties to have non-zero populations.
## id name state census_region
## Length:3141 Length:3141 TX : 254 Midwest :1055
## Class :character Class :character GA : 159 Northeast: 217
## Mode :character Mode :character VA : 133 South :1421
## KY : 120 West : 448
## MO : 115
## KS : 105
## (Other):2255
## pop_dens pop_dens4 pop_dens6 pct_black
## [ 0, 10): 575 [ 0, 17):793 [ 0, 9):529 [ 0.0, 2.0):1470
## [ 10, 50):1117 [ 17, 45):791 [ 9, 25):527 [ 2.0, 5.0): 512
## [ 50, 100): 568 [ 45, 118):782 [ 25, 45):528 [ 5.0,10.0): 349
## [ 100, 500): 624 [118,71672]:775 [ 45, 82):523 [10.0,15.0): 181
## [ 500, 1000): 109 [ 82, 215):517 [15.0,25.0): 221
## [ 1000, 5000): 130 [215,71672]:517 [25.0,50.0): 312
## [ 5000,71672]: 18 [50.0,85.3]: 96
## pop female white black
## Min. : 86 Min. :29.90 Min. : 4.90 Min. : 0.000
## 1st Qu.: 11027 1st Qu.:49.50 1st Qu.:80.70 1st Qu.: 0.700
## Median : 25713 Median :50.40 Median :92.20 Median : 2.300
## Mean : 101305 Mean :49.93 Mean :85.28 Mean : 9.164
## 3rd Qu.: 67694 3rd Qu.:51.00 3rd Qu.:96.10 3rd Qu.:10.700
## Max. :10116705 Max. :57.00 Max. :99.10 Max. :85.300
##
## travel_time land_area hh_income su_gun4
## Min. : 4.40 Min. : 2.0 Min. : 19986 [ 0, 5):784
## 1st Qu.:19.20 1st Qu.: 431.1 1st Qu.: 38187 [ 5, 8):787
## Median :22.80 Median : 616.4 Median : 44157 [ 8,11):786
## Mean :22.97 Mean : 1124.4 Mean : 45934 [11,54]:784
## 3rd Qu.:26.40 3rd Qu.: 924.0 3rd Qu.: 51164
## Max. :44.20 Max. :145504.8 Max. :122238
##
## su_gun6 fips votes_dem_2016 votes_gop_2016
## [ 0, 4):522 Min. : 1001 Min. : 4 Min. : 57
## [ 4, 7):524 1st Qu.:18179 1st Qu.: 1175 1st Qu.: 3238
## [ 7, 8):525 Median :29177 Median : 3194 Median : 7262
## [ 8,10):524 Mean :30390 Mean : 20616 Mean : 20626
## [10,12):523 3rd Qu.:45081 3rd Qu.: 10031 3rd Qu.: 18134
## [12,54]:523 Max. :56045 Max. :1893770 Max. :620285
## NA's :1 NA's :1
## total_votes_2016 per_dem_2016 per_gop_2016 diff_2016
## Min. : 64 Min. :0.03145 Min. :0.08322 Min. : 2
## 1st Qu.: 4868 1st Qu.:0.20537 1st Qu.:0.54615 1st Qu.: 1608
## Median : 11140 Median :0.28630 Median :0.66542 Median : 3797
## Mean : 43493 Mean :0.31743 Mean :0.63535 Mean : 11784
## 3rd Qu.: 29773 3rd Qu.:0.39794 3rd Qu.:0.75028 3rd Qu.: 8810
## Max. :2652072 Max. :0.89329 Max. :0.95273 Max. :1273485
## NA's :1 NA's :1 NA's :1 NA's :1
## per_dem_2012 per_gop_2012 diff_2012 winner
## Min. :0.03448 Min. :0.06006 Min. : 1 Length:3141
## 1st Qu.:0.27797 1st Qu.:0.50762 1st Qu.: 1048 Class :character
## Median :0.37135 Median :0.60942 Median : 2565 Mode :character
## Mean :0.38477 Mean :0.59800 Mean : 9503
## 3rd Qu.:0.47518 3rd Qu.:0.70327 3rd Qu.: 6468
## Max. :0.93355 Max. :0.95862 Max. :972564
## NA's :30 NA's :30 NA's :30
## partywinner16 winner12 partywinner12 flipped
## Length:3141 Length:3141 Length:3141 Length:3141
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## numeric_fips
## Min. : 1001
## 1st Qu.:18179
## Median :29177
## Mean :30390
## 3rd Qu.:45081
## Max. :56045
## Do a histogram of the pop variable.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
This is very difficult use logs instead.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Facet the histogram by census region
counties %>%
ggplot(aes(x = pop)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~census_region,ncol = 1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Free the scale on the y-axis.
counties %>%
ggplot(aes(x = pop)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~census_region,
ncol = 1,
scales = "free_y")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Exercise
Use geom_density() instead of geom_histogram().
counties %>%
ggplot(aes(x = pop)) +
geom_density() +
scale_x_log10() +
facet_wrap(~census_region,
ncol = 1,
scales = "free_y")
Create a new dataframe PNW from counties. Take the counties from Washington, Oregon and Idaho.
## Rows: 119
## Columns: 33
## $ id <chr> "16001", "16003", "16005", "16007", "16009", "16011",…
## $ name <chr> "Ada County", "Adams County", "Bannock County", "Bear…
## $ state <fct> ID, ID, ID, ID, ID, ID, ID, ID, ID, ID, ID, ID, ID, I…
## $ census_region <fct> West, West, West, West, West, West, West, West, West,…
## $ pop_dens <fct> "[ 100, 500)", "[ 0, 10)", "[ 50, 100)", "[…
## $ pop_dens4 <fct> "[118,71672]", "[ 0, 17)", "[ 45, 118)", "[ 0, …
## $ pop_dens6 <fct> "[215,71672]", "[ 0, 9)", "[ 45, 82)", "[ 0, …
## $ pct_black <fct> "[ 0.0, 2.0)", "[ 0.0, 2.0)", "[ 0.0, 2.0)", "[ 0.0, …
## $ pop <int> 426236, 3861, 83347, 5957, 9118, 45269, 21482, 6824, …
## $ female <dbl> 50.0, 48.9, 50.2, 50.3, 49.1, 49.7, 49.1, 48.7, 50.0,…
## $ white <dbl> 92.5, 96.6, 91.9, 97.3, 86.7, 89.5, 95.7, 95.4, 95.9,…
## $ black <dbl> 1.3, 0.2, 0.8, 0.3, 0.3, 0.5, 0.3, 0.4, 0.3, 0.7, 0.4…
## $ travel_time <dbl> 19.6, 19.3, 18.8, 20.0, 21.7, 21.1, 16.9, 31.5, 23.3,…
## $ land_area <dbl> 1052.58, 1363.06, 1111.99, 974.79, 776.62, 2093.98, 2…
## $ hh_income <int> 55210, 35434, 43534, 44964, 39049, 47941, 64042, 4105…
## $ su_gun4 <fct> "[ 5, 8)", "[11,54]", "[11,54]", "[11,54]", "[11,54]"…
## $ su_gun6 <fct> "[ 7, 8)", "[12,54]", "[10,12)", "[12,54]", "[12,54]"…
## $ fips <dbl> 16001, 16003, 16005, 16007, 16009, 16011, 16013, 1601…
## $ votes_dem_2016 <int> 75676, 415, 10342, 255, 780, 2924, 6416, 777, 5140, 8…
## $ votes_gop_2016 <int> 93748, 1556, 17180, 2203, 3101, 10907, 3340, 2673, 10…
## $ total_votes_2016 <int> 195587, 2183, 33405, 2928, 4192, 16625, 10723, 3812, …
## $ per_dem_2016 <dbl> 0.38691733, 0.19010536, 0.30959437, 0.08709016, 0.186…
## $ per_gop_2016 <dbl> 0.4793161, 0.7127806, 0.5142943, 0.7523907, 0.7397424…
## $ diff_2016 <int> 18072, 1141, 6838, 1948, 2321, 7983, 3076, 1896, 5080…
## $ per_dem_2012 <dbl> 0.42715997, 0.28009709, 0.37475893, 0.10701630, 0.299…
## $ per_gop_2012 <dbl> 0.5404217, 0.6859223, 0.5958593, 0.8819986, 0.6683831…
## $ diff_2012 <int> 20428, 836, 7796, 2187, 1432, 9618, 2053, 1331, 4867,…
## $ winner <chr> "Trump", "Trump", "Trump", "Trump", "Trump", "Trump",…
## $ partywinner16 <chr> "Republican", "Republican", "Republican", "Republican…
## $ winner12 <chr> "Romney", "Romney", "Romney", "Romney", "Romney", "Ro…
## $ partywinner12 <chr> "Republican", "Republican", "Republican", "Republican…
## $ flipped <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No",…
## $ numeric_fips <dbl> 16001, 16003, 16005, 16007, 16009, 16011, 16013, 1601…Run the table command on PNW to see how the states compare on the number of counties.
##
## 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 MT
## 0 0 0 0 0 0 0 0 0 0 0 0 44 0 0 0 0 0 0 0 0 0 0 0 0 0
## NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
## 0 0 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 0 0 0 39 0 0 0Fix PNW to get rid of the states that don’t exist. Then rerun the table.
##
## ID OR WA
## 44 36 39Make a density plot of the populations of the counties in these state.
We have another right skew problem. Fix it.
