The dataset contains 3,142 rows and 35 columns.
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsacs <- read_csv("acs_2015_county_data_revised.csv")## Rows: 3142 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): state, county
## dbl (33): census_id, total_pop, men, women, hispanic, white, black, native, ...
##
## 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.dim(acs)## [1] 3142 35Most variables had appropriate numeric types. I converted
state to a factor and census_id to a
character.
glimpse(acs)## Rows: 3,142
## Columns: 35
## $ census_id <dbl> 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1…
## $ state <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", …
## $ county <chr> "Autauga", "Baldwin", "Barbour", "Bibb", "Blount", "Bul…
## $ total_pop <dbl> 55221, 195121, 26932, 22604, 57710, 10678, 20354, 11664…
## $ men <dbl> 26745, 95314, 14497, 12073, 28512, 5660, 9502, 56274, 1…
## $ women <dbl> 28476, 99807, 12435, 10531, 29198, 5018, 10852, 60374, …
## $ hispanic <dbl> 2.6, 4.5, 4.6, 2.2, 8.6, 4.4, 1.2, 3.5, 0.4, 1.5, 7.6, …
## $ white <dbl> 75.8, 83.1, 46.2, 74.5, 87.9, 22.2, 53.3, 73.0, 57.3, 9…
## $ black <dbl> 18.5, 9.5, 46.7, 21.4, 1.5, 70.7, 43.8, 20.3, 40.3, 4.8…
## $ native <dbl> 0.4, 0.6, 0.2, 0.4, 0.3, 1.2, 0.1, 0.2, 0.2, 0.6, 0.4, …
## $ asian <dbl> 1.0, 0.7, 0.4, 0.1, 0.1, 0.2, 0.4, 0.9, 0.8, 0.3, 0.3, …
## $ pacific <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, …
## $ citizen <dbl> 40725, 147695, 20714, 17495, 42345, 8057, 15581, 88612,…
## $ income <dbl> 51281, 50254, 32964, 38678, 45813, 31938, 32229, 41703,…
## $ income_per_cap <dbl> 24974, 27317, 16824, 18431, 20532, 17580, 18390, 21374,…
## $ poverty <dbl> 12.9, 13.4, 26.7, 16.8, 16.7, 24.6, 25.4, 20.5, 21.6, 1…
## $ child_poverty <dbl> 18.6, 19.2, 45.3, 27.9, 27.2, 38.4, 39.2, 31.6, 37.2, 3…
## $ professional <dbl> 33.2, 33.1, 26.8, 21.5, 28.5, 18.8, 27.5, 27.3, 23.3, 2…
## $ service <dbl> 17.0, 17.7, 16.1, 17.9, 14.1, 15.0, 16.6, 17.7, 14.5, 1…
## $ office <dbl> 24.2, 27.1, 23.1, 17.8, 23.9, 19.7, 21.9, 24.2, 26.3, 1…
## $ construction <dbl> 8.6, 10.8, 10.8, 19.0, 13.5, 20.1, 10.3, 10.5, 11.5, 13…
## $ production <dbl> 17.1, 11.2, 23.1, 23.7, 19.9, 26.4, 23.7, 20.4, 24.4, 2…
## $ drive <dbl> 87.5, 84.7, 83.8, 83.2, 84.9, 74.9, 84.5, 85.3, 85.1, 8…
## $ carpool <dbl> 8.8, 8.8, 10.9, 13.5, 11.2, 14.9, 12.4, 9.4, 11.9, 12.1…
## $ transit <dbl> 0.1, 0.1, 0.4, 0.5, 0.4, 0.7, 0.0, 0.2, 0.2, 0.2, 0.2, …
## $ walk <dbl> 0.5, 1.0, 1.8, 0.6, 0.9, 5.0, 0.8, 1.2, 0.3, 0.6, 1.1, …
## $ other_transp <dbl> 1.3, 1.4, 1.5, 1.5, 0.4, 1.7, 0.6, 1.2, 0.4, 0.7, 1.4, …
## $ work_at_home <dbl> 1.8, 3.9, 1.6, 0.7, 2.3, 2.8, 1.7, 2.7, 2.1, 2.5, 1.9, …
## $ mean_commute <dbl> 26.5, 26.4, 24.1, 28.8, 34.9, 27.5, 24.6, 24.1, 25.1, 2…
## $ employed <dbl> 23986, 85953, 8597, 8294, 22189, 3865, 7813, 47401, 136…
## $ private_work <dbl> 73.6, 81.5, 71.8, 76.8, 82.0, 79.5, 77.4, 74.1, 85.1, 7…
## $ public_work <dbl> 20.9, 12.3, 20.8, 16.1, 13.5, 15.1, 16.2, 20.8, 12.1, 1…
## $ self_employed <dbl> 5.5, 5.8, 7.3, 6.7, 4.2, 5.4, 6.2, 5.0, 2.8, 7.9, 4.1, …
## $ family_work <dbl> 0.0, 0.4, 0.1, 0.4, 0.4, 0.0, 0.2, 0.1, 0.0, 0.5, 0.5, …
## $ unemployment <dbl> 7.6, 7.5, 17.6, 8.3, 7.7, 18.0, 10.9, 12.3, 8.9, 7.9, 9…acs <- acs %>%
mutate(
state = as_factor(state),
census_id = as.character(census_id)
)
glimpse(acs)## Rows: 3,142
## Columns: 35
## $ census_id <chr> "1001", "1003", "1005", "1007", "1009", "1011", "1013",…
## $ state <fct> Alabama, Alabama, Alabama, Alabama, Alabama, Alabama, A…
## $ county <chr> "Autauga", "Baldwin", "Barbour", "Bibb", "Blount", "Bul…
## $ total_pop <dbl> 55221, 195121, 26932, 22604, 57710, 10678, 20354, 11664…
## $ men <dbl> 26745, 95314, 14497, 12073, 28512, 5660, 9502, 56274, 1…
## $ women <dbl> 28476, 99807, 12435, 10531, 29198, 5018, 10852, 60374, …
## $ hispanic <dbl> 2.6, 4.5, 4.6, 2.2, 8.6, 4.4, 1.2, 3.5, 0.4, 1.5, 7.6, …
## $ white <dbl> 75.8, 83.1, 46.2, 74.5, 87.9, 22.2, 53.3, 73.0, 57.3, 9…
## $ black <dbl> 18.5, 9.5, 46.7, 21.4, 1.5, 70.7, 43.8, 20.3, 40.3, 4.8…
## $ native <dbl> 0.4, 0.6, 0.2, 0.4, 0.3, 1.2, 0.1, 0.2, 0.2, 0.6, 0.4, …
## $ asian <dbl> 1.0, 0.7, 0.4, 0.1, 0.1, 0.2, 0.4, 0.9, 0.8, 0.3, 0.3, …
## $ pacific <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, …
## $ citizen <dbl> 40725, 147695, 20714, 17495, 42345, 8057, 15581, 88612,…
## $ income <dbl> 51281, 50254, 32964, 38678, 45813, 31938, 32229, 41703,…
## $ income_per_cap <dbl> 24974, 27317, 16824, 18431, 20532, 17580, 18390, 21374,…
## $ poverty <dbl> 12.9, 13.4, 26.7, 16.8, 16.7, 24.6, 25.4, 20.5, 21.6, 1…
## $ child_poverty <dbl> 18.6, 19.2, 45.3, 27.9, 27.2, 38.4, 39.2, 31.6, 37.2, 3…
## $ professional <dbl> 33.2, 33.1, 26.8, 21.5, 28.5, 18.8, 27.5, 27.3, 23.3, 2…
## $ service <dbl> 17.0, 17.7, 16.1, 17.9, 14.1, 15.0, 16.6, 17.7, 14.5, 1…
## $ office <dbl> 24.2, 27.1, 23.1, 17.8, 23.9, 19.7, 21.9, 24.2, 26.3, 1…
## $ construction <dbl> 8.6, 10.8, 10.8, 19.0, 13.5, 20.1, 10.3, 10.5, 11.5, 13…
## $ production <dbl> 17.1, 11.2, 23.1, 23.7, 19.9, 26.4, 23.7, 20.4, 24.4, 2…
## $ drive <dbl> 87.5, 84.7, 83.8, 83.2, 84.9, 74.9, 84.5, 85.3, 85.1, 8…
## $ carpool <dbl> 8.8, 8.8, 10.9, 13.5, 11.2, 14.9, 12.4, 9.4, 11.9, 12.1…
## $ transit <dbl> 0.1, 0.1, 0.4, 0.5, 0.4, 0.7, 0.0, 0.2, 0.2, 0.2, 0.2, …
## $ walk <dbl> 0.5, 1.0, 1.8, 0.6, 0.9, 5.0, 0.8, 1.2, 0.3, 0.6, 1.1, …
## $ other_transp <dbl> 1.3, 1.4, 1.5, 1.5, 0.4, 1.7, 0.6, 1.2, 0.4, 0.7, 1.4, …
## $ work_at_home <dbl> 1.8, 3.9, 1.6, 0.7, 2.3, 2.8, 1.7, 2.7, 2.1, 2.5, 1.9, …
## $ mean_commute <dbl> 26.5, 26.4, 24.1, 28.8, 34.9, 27.5, 24.6, 24.1, 25.1, 2…
## $ employed <dbl> 23986, 85953, 8597, 8294, 22189, 3865, 7813, 47401, 136…
## $ private_work <dbl> 73.6, 81.5, 71.8, 76.8, 82.0, 79.5, 77.4, 74.1, 85.1, 7…
## $ public_work <dbl> 20.9, 12.3, 20.8, 16.1, 13.5, 15.1, 16.2, 20.8, 12.1, 1…
## $ self_employed <dbl> 5.5, 5.8, 7.3, 6.7, 4.2, 5.4, 6.2, 5.0, 2.8, 7.9, 4.1, …
## $ family_work <dbl> 0.0, 0.4, 0.1, 0.4, 0.4, 0.0, 0.2, 0.1, 0.0, 0.5, 0.5, …
## $ unemployment <dbl> 7.6, 7.5, 17.6, 8.3, 7.7, 18.0, 10.9, 12.3, 8.9, 7.9, 9…I found that only two variables had missing values:
`income: 1 missing value
child_poverty: 1 missing value
colSums(is.na(acs))## census_id state county total_pop men
## 0 0 0 0 0
## women hispanic white black native
## 0 0 0 0 0
## asian pacific citizen income income_per_cap
## 0 0 0 1 0
## poverty child_poverty professional service office
## 0 1 0 0 0
## construction production drive carpool transit
## 0 0 0 0 0
## walk other_transp work_at_home mean_commute employed
## 0 0 0 0 0
## private_work public_work self_employed family_work unemployment
## 0 0 0 0 0I removed these two observations. Since they account for less than 0.1% of the dataset (2 out of 3,142 rows), the data loss is negligible.
acs_clean <- acs %>% drop_na()
colSums(is.na(acs_clean))## census_id state county total_pop men
## 0 0 0 0 0
## women hispanic white black native
## 0 0 0 0 0
## asian pacific citizen income income_per_cap
## 0 0 0 0 0
## poverty child_poverty professional service office
## 0 0 0 0 0
## construction production drive carpool transit
## 0 0 0 0 0
## walk other_transp work_at_home mean_commute employed
## 0 0 0 0 0
## private_work public_work self_employed family_work unemployment
## 0 0 0 0 0I used the summary() function to examine unusual
values.
summary(acs)## census_id state county total_pop
## Length:3142 Texas : 254 Length:3142 Min. : 85
## Class :character Georgia : 159 Class :character 1st Qu.: 11028
## Mode :character Virginia: 133 Mode :character Median : 25768
## Kentucky: 120 Mean : 100737
## Missouri: 115 3rd Qu.: 67552
## Kansas : 105 Max. :10038388
## (Other) :2256
## men women hispanic white
## Min. : 42 Min. : 43 Min. : 0.000 Min. : 0.90
## 1st Qu.: 5546 1st Qu.: 5466 1st Qu.: 1.900 1st Qu.:65.60
## Median : 12826 Median : 12907 Median : 3.700 Median :84.60
## Mean : 49565 Mean : 51172 Mean : 8.826 Mean :77.28
## 3rd Qu.: 33319 3rd Qu.: 34122 3rd Qu.: 9.000 3rd Qu.:93.30
## Max. :4945351 Max. :5093037 Max. :98.700 Max. :99.80
##
## black native asian pacific
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00000
## 1st Qu.: 0.600 1st Qu.: 0.100 1st Qu.: 0.200 1st Qu.: 0.00000
## Median : 2.100 Median : 0.300 Median : 0.500 Median : 0.00000
## Mean : 8.879 Mean : 1.766 Mean : 1.258 Mean : 0.08475
## 3rd Qu.:10.175 3rd Qu.: 0.600 3rd Qu.: 1.200 3rd Qu.: 0.00000
## Max. :85.900 Max. :92.100 Max. :41.600 Max. :35.30000
##
## citizen income income_per_cap poverty
## Min. : 80 Min. : 19328 Min. : 8292 Min. : 1.4
## 1st Qu.: 8254 1st Qu.: 38826 1st Qu.:20471 1st Qu.:12.0
## Median : 19434 Median : 45111 Median :23577 Median :16.0
## Mean : 70804 Mean : 46830 Mean :24338 Mean :16.7
## 3rd Qu.: 50728 3rd Qu.: 52250 3rd Qu.:27138 3rd Qu.:20.3
## Max. :6046749 Max. :123453 Max. :65600 Max. :53.3
## NA's :1
## child_poverty professional service office
## Min. : 0.00 Min. :13.50 Min. : 5.00 Min. : 4.10
## 1st Qu.:16.10 1st Qu.:26.70 1st Qu.:15.90 1st Qu.:20.20
## Median :22.50 Median :30.00 Median :18.00 Median :22.40
## Mean :23.29 Mean :31.04 Mean :18.26 Mean :22.13
## 3rd Qu.:29.50 3rd Qu.:34.40 3rd Qu.:20.20 3rd Qu.:24.30
## Max. :72.30 Max. :74.00 Max. :36.60 Max. :35.40
## NA's :1
## construction production drive carpool
## Min. : 1.70 Min. : 0.00 Min. : 5.20 Min. : 0.00
## 1st Qu.: 9.80 1st Qu.:11.53 1st Qu.:76.60 1st Qu.: 8.50
## Median :12.20 Median :15.40 Median :80.60 Median : 9.90
## Mean :12.74 Mean :15.82 Mean :79.08 Mean :10.33
## 3rd Qu.:15.00 3rd Qu.:19.40 3rd Qu.:83.60 3rd Qu.:11.88
## Max. :40.30 Max. :55.60 Max. :94.60 Max. :29.90
##
## transit walk other_transp work_at_home
## Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.1000 1st Qu.: 1.400 1st Qu.: 0.900 1st Qu.: 2.800
## Median : 0.4000 Median : 2.400 Median : 1.300 Median : 4.000
## Mean : 0.9675 Mean : 3.307 Mean : 1.614 Mean : 4.697
## 3rd Qu.: 0.8000 3rd Qu.: 4.000 3rd Qu.: 1.900 3rd Qu.: 5.700
## Max. :61.7000 Max. :71.200 Max. :39.100 Max. :37.200
##
## mean_commute employed private_work public_work
## Min. : 4.90 Min. : 62 Min. :25.00 Min. : 5.80
## 1st Qu.:19.30 1st Qu.: 4524 1st Qu.:70.90 1st Qu.:13.10
## Median :22.90 Median : 10644 Median :75.80 Median :16.10
## Mean :23.15 Mean : 46387 Mean :74.44 Mean :17.35
## 3rd Qu.:26.60 3rd Qu.: 29254 3rd Qu.:79.80 3rd Qu.:20.10
## Max. :44.00 Max. :4635465 Max. :88.30 Max. :66.20
##
## self_employed family_work unemployment
## Min. : 0.000 Min. :0.0000 Min. : 0.000
## 1st Qu.: 5.400 1st Qu.:0.1000 1st Qu.: 5.500
## Median : 6.900 Median :0.2000 Median : 7.500
## Mean : 7.921 Mean :0.2915 Mean : 7.815
## 3rd Qu.: 9.400 3rd Qu.:0.3000 3rd Qu.: 9.700
## Max. :36.600 Max. :9.8000 Max. :29.400
## I did not find any invalid values (all variables that cannot be negative had positive minimum values), so no corrections were necessary. I chose to keep these observations to preserve data integrity.
##Data Manipulation and Insights ### Question 05 There are 1985 counties with more women than men.
more_women <- acs %>%
filter(women > men) %>%
nrow()
more_women## [1] 1985There are 2,420 counties with an unemployment rate lower than 10%.
low_unemp <- acs %>%
filter(unemployment < 10) %>%
nrow()
low_unemp## [1] 2420top10 <- acs %>%
select(census_id, county, state, mean_commute) %>%
arrange(desc(mean_commute)) %>%
top_n(10, wt = mean_commute)
top10## # A tibble: 10 × 4
## census_id county state mean_commute
## <chr> <chr> <fct> <dbl>
## 1 42103 Pike Pennsylvania 44
## 2 36005 Bronx New York 43
## 3 24017 Charles Maryland 42.8
## 4 51187 Warren Virginia 42.7
## 5 36081 Queens New York 42.6
## 6 36085 Richmond New York 42.6
## 7 51193 Westmoreland Virginia 42.5
## 8 8093 Park Colorado 42.4
## 9 36047 Kings New York 41.7
## 10 54015 Clay West Virginia 41.4Create a new variable that calculates the percentage of women for each county. \[ percent\_women = \frac{women}{total\_pop} \times 100 \]
acs <- acs %>%
mutate(percent_women = (women/total_pop) * 100 )
names(acs)## [1] "census_id" "state" "county" "total_pop"
## [5] "men" "women" "hispanic" "white"
## [9] "black" "native" "asian" "pacific"
## [13] "citizen" "income" "income_per_cap" "poverty"
## [17] "child_poverty" "professional" "service" "office"
## [21] "construction" "production" "drive" "carpool"
## [25] "transit" "walk" "other_transp" "work_at_home"
## [29] "mean_commute" "employed" "private_work" "public_work"
## [33] "self_employed" "family_work" "unemployment" "percent_women"lowest10 <- acs %>%
select(census_id, county, state, percent_women) %>%
top_n(-10, wt = percent_women) %>%
arrange(percent_women)
lowest10## # A tibble: 10 × 4
## census_id county state percent_women
## <chr> <chr> <fct> <dbl>
## 1 42053 Forest Pennsylvania 26.8
## 2 8011 Bent Colorado 31.4
## 3 51183 Sussex Virginia 31.5
## 4 13309 Wheeler Georgia 32.1
## 5 6035 Lassen California 33.2
## 6 48095 Concho Texas 33.3
## 7 13053 Chattahoochee Georgia 33.4
## 8 2013 Aleutians East Borough Alaska 33.5
## 9 22125 West Feliciana Louisiana 33.6
## 10 32027 Pershing Nevada 33.7Create a new variable that calculates the sum of all race percentage variables
acs <- acs %>%
mutate(sum_race = hispanic + white + black + native + asian + pacific)
summary(acs$sum_race)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 76.4 97.7 98.4 98.1 99.0 100.1lowest10_race <- acs %>%
select(census_id, county, state, sum_race) %>%
top_n(-10, wt = sum_race) %>%
arrange(sum_race)
lowest10_race## # A tibble: 10 × 4
## census_id county state sum_race
## <chr> <chr> <fct> <dbl>
## 1 15001 Hawaii Hawaii 76.4
## 2 15009 Maui Hawaii 79.2
## 3 40097 Mayes Oklahoma 79.7
## 4 15003 Honolulu Hawaii 81.5
## 5 40123 Pontotoc Oklahoma 82.8
## 6 47061 Grundy Tennessee 83
## 7 2282 Yakutat City and Borough Alaska 83.4
## 8 40069 Johnston Oklahoma 84
## 9 15007 Kauai Hawaii 84.1
## 10 40003 Alfalfa Oklahoma 85.1Create a new variable representing the sum of these race percentage at the state level.
sum_race_state <- acs %>%
group_by(state) %>%
summarise(avg_sum_race = mean(sum_race, na.rm = TRUE)) %>%
arrange(avg_sum_race)
sum_race_state## # A tibble: 51 × 2
## state avg_sum_race
## <fct> <dbl>
## 1 Hawaii 84
## 2 Alaska 92.7
## 3 Oklahoma 92.8
## 4 Washington 96.7
## 5 California 96.9
## 6 Oregon 97.1
## 7 Delaware 97.3
## 8 Massachusetts 97.5
## 9 Maryland 97.6
## 10 District of Columbia 97.6
## # ℹ 41 more rowsFind the loweset state
lowest_state <- sum_race_state %>%
top_n(-1, wt = avg_sum_race)
lowest_state## # A tibble: 1 × 2
## state avg_sum_race
## <fct> <dbl>
## 1 Hawaii 84There are 11 counties where the sum greater than 100%.
over100 <- acs %>%
filter(sum_race > 100) %>%
select(county, sum_race)
nrow(over100)## [1] 11over100## # A tibble: 11 × 2
## county sum_race
## <chr> <dbl>
## 1 Claiborne 100
## 2 Gosper 100.
## 3 Hooker 100.
## 4 Nance 100.
## 5 Bailey 100.
## 6 Duval 100
## 7 Edwards 100.
## 8 Kenedy 100
## 9 Kent 100
## 10 Presidio 100
## 11 Beaver 100There are no states with a sum of exactly 100%.
equal100 <- sum_race_state %>%
filter(avg_sum_race == 100)
nrow(equal100)## [1] 0Create a new variable Carpool_rank
acs <- acs %>%
mutate(carpool_rank = min_rank(desc(carpool)))
names(acs)## [1] "census_id" "state" "county" "total_pop"
## [5] "men" "women" "hispanic" "white"
## [9] "black" "native" "asian" "pacific"
## [13] "citizen" "income" "income_per_cap" "poverty"
## [17] "child_poverty" "professional" "service" "office"
## [21] "construction" "production" "drive" "carpool"
## [25] "transit" "walk" "other_transp" "work_at_home"
## [29] "mean_commute" "employed" "private_work" "public_work"
## [33] "self_employed" "family_work" "unemployment" "percent_women"
## [37] "sum_race" "carpool_rank"Top 10
acs %>%
select(census_id, county, state, carpool, carpool_rank) %>%
arrange(carpool_rank) %>%
head(10)## # A tibble: 10 × 5
## census_id county state carpool carpool_rank
## <chr> <chr> <fct> <dbl> <int>
## 1 13061 Clay Georgia 29.9 1
## 2 18087 LaGrange Indiana 27 2
## 3 13165 Jenkins Georgia 25.3 3
## 4 5133 Sevier Arkansas 24.4 4
## 5 20175 Seward Kansas 23.4 5
## 6 48079 Cochran Texas 22.8 6
## 7 48247 Jim Hogg Texas 22.6 7
## 8 48393 Roberts Texas 22.4 8
## 9 39075 Holmes Ohio 21.8 9
## 10 21197 Powell Kentucky 21.6 10Lowest 10
lowest10_carpool <- acs %>%
select(census_id, county, state, carpool, carpool_rank) %>%
arrange(desc(carpool_rank)) %>%
head(10)
lowest10_carpool## # A tibble: 10 × 5
## census_id county state carpool carpool_rank
## <chr> <chr> <fct> <dbl> <int>
## 1 48261 Kenedy Texas 0 3141
## 2 48269 King Texas 0 3141
## 3 48235 Irion Texas 0.9 3140
## 4 31183 Wheeler Nebraska 1.3 3139
## 5 36061 New York New York 1.9 3138
## 6 13309 Wheeler Georgia 2.3 3136
## 7 38029 Emmons North Dakota 2.3 3136
## 8 30019 Daniels Montana 2.6 3134
## 9 31057 Dundy Nebraska 2.6 3134
## 10 46069 Hyde South Dakota 2.8 3132Arizona is the best ranked state for carpooling on average.
carpool_rank_state <- acs %>%
group_by(state) %>%
summarise(avg_carpool_rank = mean(carpool_rank, na.rm = TRUE)) %>%
arrange(avg_carpool_rank)
head(carpool_rank_state, 3)## # A tibble: 3 × 2
## state avg_carpool_rank
## <fct> <dbl>
## 1 Arizona 971.
## 2 Utah 1019.
## 3 Arkansas 1055.carpool_rank_state <- acs %>%
group_by(state) %>%
summarise(avg_carpool_rank = mean(carpool_rank, na.rm = TRUE)) %>%
arrange(avg_carpool_rank)
head(carpool_rank_state, 5)## # A tibble: 5 × 2
## state avg_carpool_rank
## <fct> <dbl>
## 1 Arizona 971.
## 2 Utah 1019.
## 3 Arkansas 1055.
## 4 Hawaii 1072.
## 5 Alaska 1087.