Harold Nelson
2023-02-01
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()## Rows: 50 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): State
## dbl (2): Latitude, Longitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 3,135
## Columns: 14
## $ name <fct> Autauga County, Baldwin County, Barbour County, Bibb…
## $ state <fct> Alabama, Alabama, Alabama, Alabama, Alabama, Alabama…
## $ pop2000 <dbl> 43671, 140415, 29038, 20826, 51024, 11714, 21399, 11…
## $ pop2010 <dbl> 54571, 182265, 27457, 22915, 57322, 10914, 20947, 11…
## $ pop2017 <int> 55504, 212628, 25270, 22668, 58013, 10309, 19825, 11…
## $ pop_change <dbl> 1.48, 9.19, -6.22, 0.73, 0.68, -2.28, -2.69, -1.51, …
## $ poverty <dbl> 13.7, 11.8, 27.2, 15.2, 15.6, 28.5, 24.4, 18.6, 18.8…
## $ homeownership <dbl> 77.5, 76.7, 68.0, 82.9, 82.0, 76.9, 69.0, 70.7, 71.4…
## $ multi_unit <dbl> 7.2, 22.6, 11.1, 6.6, 3.7, 9.9, 13.7, 14.3, 8.7, 4.3…
## $ unemployment_rate <dbl> 3.86, 3.99, 5.90, 4.39, 4.02, 4.93, 5.49, 4.93, 4.08…
## $ metro <fct> yes, yes, no, yes, yes, no, no, yes, no, no, yes, no…
## $ median_edu <fct> some_college, some_college, hs_diploma, hs_diploma, …
## $ per_capita_income <dbl> 27841.70, 27779.85, 17891.73, 20572.05, 21367.39, 15…
## $ median_hh_income <int> 55317, 52562, 33368, 43404, 47412, 29655, 36326, 436…## name state pop2000 pop2010
## Washington County: 30 Texas : 254 Min. : 67 Min. : 82
## Jefferson County : 25 Georgia : 159 1st Qu.: 11260 1st Qu.: 11154
## Franklin County : 24 Virginia: 133 Median : 24663 Median : 25910
## Jackson County : 23 Kentucky: 120 Mean : 89757 Mean : 98455
## Lincoln County : 23 Missouri: 115 3rd Qu.: 61896 3rd Qu.: 67016
## Madison County : 19 Kansas : 105 Max. :9519338 Max. :9818605
## (Other) :2991 (Other) :2249
## pop2017 pop_change poverty homeownership
## Min. : 134 Min. :-33.6300 Min. : 2.40 Min. :20.7
## 1st Qu.: 11004 1st Qu.: -1.9750 1st Qu.:11.35 1st Qu.:69.5
## Median : 25862 Median : -0.0700 Median :15.20 Median :74.6
## Mean : 103867 Mean : 0.5292 Mean :15.98 Mean :73.3
## 3rd Qu.: 67756 3rd Qu.: 2.3700 3rd Qu.:19.40 3rd Qu.:78.4
## Max. :10163507 Max. : 37.1900 Max. :52.00 Max. :91.3
##
## multi_unit unemployment_rate metro median_edu
## Min. : 0.00 Min. : 1.620 no :1971 below_hs : 2
## 1st Qu.: 6.10 1st Qu.: 3.520 yes:1164 hs_diploma :1397
## Median : 9.70 Median : 4.360 some_college:1691
## Mean :12.32 Mean : 4.607 bachelors : 45
## 3rd Qu.:15.90 3rd Qu.: 5.350
## Max. :98.50 Max. :19.070
##
## per_capita_income median_hh_income
## Min. :10467 Min. : 19264
## 1st Qu.:21765 1st Qu.: 41124
## Median :25442 Median : 48038
## Mean :26074 Mean : 49739
## 3rd Qu.:29250 3rd Qu.: 55751
## Max. :69533 Max. :129588
## Create county_small using the variables at the beginning of the dataframe ending with homeownership.
## Rows: 3,135
## Columns: 8
## $ name <fct> Autauga County, Baldwin County, Barbour County, Bibb Cou…
## $ state <fct> Alabama, Alabama, Alabama, Alabama, Alabama, Alabama, Al…
## $ pop2000 <dbl> 43671, 140415, 29038, 20826, 51024, 11714, 21399, 112249…
## $ pop2010 <dbl> 54571, 182265, 27457, 22915, 57322, 10914, 20947, 118572…
## $ pop2017 <int> 55504, 212628, 25270, 22668, 58013, 10309, 19825, 114728…
## $ pop_change <dbl> 1.48, 9.19, -6.22, 0.73, 0.68, -2.28, -2.69, -1.51, -1.2…
## $ poverty <dbl> 13.7, 11.8, 27.2, 15.2, 15.6, 28.5, 24.4, 18.6, 18.8, 16…
## $ homeownership <dbl> 77.5, 76.7, 68.0, 82.9, 82.0, 76.9, 69.0, 70.7, 71.4, 77…Alaska, Hawaii, and District of Columbia.
Create a new version of county_small with these entities eliminated.
county_small = county_small %>%
filter(!state %in% c("Alaska",
"Hawaii",
"District of Columbia"))
str(county_small)## tibble [3,105 × 8] (S3: tbl_df/tbl/data.frame)
## $ name : Factor w/ 1877 levels "Abbeville County",..: 83 90 101 150 165 226 236 249 297 319 ...
## $ state : Factor w/ 51 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ pop2000 : num [1:3105] 43671 140415 29038 20826 51024 ...
## $ pop2010 : num [1:3105] 54571 182265 27457 22915 57322 ...
## $ pop2017 : int [1:3105] 55504 212628 25270 22668 58013 10309 19825 114728 33713 25857 ...
## $ pop_change : num [1:3105] 1.48 9.19 -6.22 0.73 0.68 -2.28 -2.69 -1.51 -1.2 -0.6 ...
## $ poverty : num [1:3105] 13.7 11.8 27.2 15.2 15.6 28.5 24.4 18.6 18.8 16.1 ...
## $ homeownership: num [1:3105] 77.5 76.7 68 82.9 82 76.9 69 70.7 71.4 77.5 ...Create a file states with one record per state. In addition to the name of the state the file should contain (based on pop2017) the total state population, and the mean and median values of the populations of the counties. Arrange the file in descending order based on the mean of the county populations.
states = county_small %>%
group_by(state) %>%
summarize(total_pop = sum(pop2017),
mean_pop = mean(pop2017),
median_pop = median(pop2017)) %>%
ungroup() %>%
arrange(desc(mean_pop))
head(states)## # A tibble: 6 × 4
## state total_pop mean_pop median_pop
## <fct> <int> <dbl> <dbl>
## 1 California 39536653 681666. 185908.
## 2 Massachusetts 6859819 489987. 492480
## 3 Arizona 7016270 467751. 124756
## 4 Connecticut 3588184 448523 225605
## 5 New Jersey 9005644 428840. 448596
## 6 Delaware 961939 320646. 225322## tibble [48 × 4] (S3: tbl_df/tbl/data.frame)
## $ state : Factor w/ 51 levels "Alabama","Alaska",..: 5 22 3 7 31 8 33 10 21 40 ...
## $ total_pop : int [1:48] 39536653 6859819 7016270 3588184 9005644 961939 19849399 20984400 6052177 1059639 ...
## $ mean_pop : num [1:48] 681666 489987 467751 448523 428840 ...
## $ median_pop: num [1:48] 185908 492480 124756 225605 448596 ...Convert the data to a long format by collapsing the values in the population into a single column named ‘value’ with a description in an adjoining column named ‘measure’. Name the new dataframe ‘states_long’.
states_long = states %>%
pivot_longer(cols = ends_with("_pop"),
values_to = 'value',
names_to = 'measure')
head(states_long,12)## # A tibble: 12 × 3
## state measure value
## <fct> <chr> <dbl>
## 1 California total_pop 39536653
## 2 California mean_pop 681666.
## 3 California median_pop 185908.
## 4 Massachusetts total_pop 6859819
## 5 Massachusetts mean_pop 489987.
## 6 Massachusetts median_pop 492480
## 7 Arizona total_pop 7016270
## 8 Arizona mean_pop 467751.
## 9 Arizona median_pop 124756
## 10 Connecticut total_pop 3588184
## 11 Connecticut mean_pop 448523
## 12 Connecticut median_pop 225605The dataframe State_Centroids contains the coordinates of the centroids of each state. Left join this file to states. Note that the variable ‘state’ in states is spelled ‘State’ in the centroids file. Rename the version in states to simplify the join. Call the joined dataframe ‘with_centroids’.
## Joining, by = "State"## tibble [48 × 6] (S3: tbl_df/tbl/data.frame)
## $ State : chr [1:48] "California" "Massachusetts" "Arizona" "Connecticut" ...
## $ total_pop : int [1:48] 39536653 6859819 7016270 3588184 9005644 961939 19849399 20984400 6052177 1059639 ...
## $ mean_pop : num [1:48] 681666 489987 467751 448523 428840 ...
## $ median_pop: num [1:48] 185908 492480 124756 225605 448596 ...
## $ Latitude : num [1:48] 36.1 42.2 33.7 41.6 40.3 ...
## $ Longitude : num [1:48] -119.7 -71.5 -111.4 -72.8 -74.5 ...