Harold Nelson
2/1/2022
Create a subset of cdc, cdc2 which contains only gender, height and weight. Use head() to look at it.
## gender height weight
## 1 m 70 175
## 2 f 64 125
## 3 f 60 105
## 4 f 66 132
## 5 f 61 150
## 6 f 64 114Read the documentation on select in the dplyr package. Then create a dataframe cdc2 starting with cdc and eliminating the variable exerany.
## Rows: 20,000
## Columns: 8
## $ genhlth <fct> good, good, good, good, very good, very good, very good, very…
## $ hlthplan <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ smoke100 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0…
## $ height <dbl> 70, 64, 60, 66, 61, 64, 71, 67, 65, 70, 69, 69, 66, 70, 69, 7…
## $ weight <int> 175, 125, 105, 132, 150, 114, 194, 170, 150, 180, 186, 168, 1…
## $ wtdesire <int> 175, 115, 105, 124, 130, 114, 185, 160, 130, 170, 175, 148, 2…
## $ age <int> 77, 33, 49, 42, 55, 55, 31, 45, 27, 44, 46, 62, 21, 69, 23, 7…
## $ gender <fct> m, f, f, f, f, f, m, m, f, m, m, m, m, m, m, m, m, m, m, f, f…## Rows: 20,000
## Columns: 8
## $ genhlth <fct> good, good, good, good, very good, very good, very good, very…
## $ hlthplan <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ smoke100 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0…
## $ height <dbl> 70, 64, 60, 66, 61, 64, 71, 67, 65, 70, 69, 69, 66, 70, 69, 7…
## $ weight <int> 175, 125, 105, 132, 150, 114, 194, 170, 150, 180, 186, 168, 1…
## $ wtdesire <int> 175, 115, 105, 124, 130, 114, 185, 160, 130, 170, 175, 148, 2…
## $ age <int> 77, 33, 49, 42, 55, 55, 31, 45, 27, 44, 46, 62, 21, 69, 23, 7…
## $ gender <fct> m, f, f, f, f, f, m, m, f, m, m, m, m, m, m, m, m, m, m, f, f…Create cdc2 by removing the variables exerany and genhlth from cdc.
## Rows: 20,000
## Columns: 7
## $ hlthplan <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ smoke100 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0…
## $ height <dbl> 70, 64, 60, 66, 61, 64, 71, 67, 65, 70, 69, 69, 66, 70, 69, 7…
## $ weight <int> 175, 125, 105, 132, 150, 114, 194, 170, 150, 180, 186, 168, 1…
## $ wtdesire <int> 175, 115, 105, 124, 130, 114, 185, 160, 130, 170, 175, 148, 2…
## $ age <int> 77, 33, 49, 42, 55, 55, 31, 45, 27, 44, 46, 62, 21, 69, 23, 7…
## $ gender <fct> m, f, f, f, f, f, m, m, f, m, m, m, m, m, m, m, m, m, m, f, f…## Rows: 20,000
## Columns: 7
## $ hlthplan <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ smoke100 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0…
## $ height <dbl> 70, 64, 60, 66, 61, 64, 71, 67, 65, 70, 69, 69, 66, 70, 69, 7…
## $ weight <int> 175, 125, 105, 132, 150, 114, 194, 170, 150, 180, 186, 168, 1…
## $ wtdesire <int> 175, 115, 105, 124, 130, 114, 185, 160, 130, 170, 175, 148, 2…
## $ age <int> 77, 33, 49, 42, 55, 55, 31, 45, 27, 44, 46, 62, 21, 69, 23, 7…
## $ gender <fct> m, f, f, f, f, f, m, m, f, m, m, m, m, m, m, m, m, m, m, f, f…Create a dataframe strong_couch which contains people who don’t exercise and do smoke but have excellent general health. Start with cdc. Use glimpse() to examine the results. Get a table of gender.
## Rows: 304
## Columns: 9
## $ genhlth <fct> excellent, excellent, excellent, excellent, excellent, excell…
## $ exerany <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ hlthplan <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1…
## $ smoke100 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ height <dbl> 70, 66, 66, 69, 70, 68, 72, 69, 67, 60, 67, 63, 73, 64, 64, 7…
## $ weight <int> 235, 134, 138, 210, 180, 180, 248, 150, 199, 113, 135, 200, 2…
## $ wtdesire <int> 175, 134, 138, 185, 175, 180, 225, 165, 145, 113, 135, 150, 2…
## $ age <int> 56, 60, 21, 45, 25, 56, 71, 35, 49, 68, 50, 69, 42, 31, 22, 3…
## $ gender <fct> m, f, f, m, m, m, m, m, f, f, m, f, m, m, m, m, f, f, m, f, m…##
## m f
## 153 151Create cdc2 from cdc by adding a factor version of smoke100 with labels “Non-smoker” and “Smoker”.
## Rows: 20,000
## Columns: 10
## $ genhlth <fct> good, good, good, good, very good, very good, very good, very…
## $ exerany <dbl> 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1…
## $ hlthplan <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ smoke100 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0…
## $ height <dbl> 70, 64, 60, 66, 61, 64, 71, 67, 65, 70, 69, 69, 66, 70, 69, 7…
## $ weight <int> 175, 125, 105, 132, 150, 114, 194, 170, 150, 180, 186, 168, 1…
## $ wtdesire <int> 175, 115, 105, 124, 130, 114, 185, 160, 130, 170, 175, 148, 2…
## $ age <int> 77, 33, 49, 42, 55, 55, 31, 45, 27, 44, 46, 62, 21, 69, 23, 7…
## $ gender <fct> m, f, f, f, f, f, m, m, f, m, m, m, m, m, m, m, m, m, m, f, f…
## $ smokef <fct> Non-smoker, Smoker, Smoker, Non-smoker, Non-smoker, Non-smoke…Use mutate to create a variable odd. This variable is true if a man weighs less than 150 or a woman weighs more than 200. Use mean() and sum() in summarize() to get the fraction of odd people and the count of odd people in cdc.
cdc %>%
mutate(odd = (gender == "m" & weight < 150) |
(gender == "f" & weight > 200)) %>%
summarize(mean(odd) , sum(odd))## mean(odd) sum(odd)
## 1 0.07895 1579Create a dataframe health_gender from cdc with one row for every combination of genhlth and gender. Use summarize to get the count of cases and the mean of weight. Don’t forget to ungroup(). Use glimpse to see the result.
health_gender = cdc %>%
group_by(genhlth,gender) %>%
summarize(count = n(),
mean_weight = mean(weight)) %>%
ungroup() %>%
glimpse()## `summarise()` has grouped output by 'genhlth'. You can override using the
## `.groups` argument.## Rows: 10
## Columns: 4
## $ genhlth <fct> excellent, excellent, very good, very good, good, good, fa…
## $ gender <fct> m, f, m, f, m, f, m, f, m, f
## $ count <int> 2298, 2359, 3382, 3590, 2722, 2953, 884, 1135, 283, 394
## $ mean_weight <dbl> 182.8742, 142.1174, 189.1470, 150.4309, 193.1227, 154.8629…EXtend your code from the previous exercise to sort your dataframe in ascending order by count. Use head() to see the result.
health_gender = cdc %>%
group_by(genhlth,gender) %>%
summarize(count = n(),
mean_weight = mean(weight)) %>%
ungroup() %>%
arrange(count) ## `summarise()` has grouped output by 'genhlth'. You can override using the
## `.groups` argument.## # A tibble: 6 × 4
## genhlth gender count mean_weight
## <fct> <fct> <int> <dbl>
## 1 poor m 283 193.
## 2 poor f 394 165.
## 3 fair m 884 194.
## 4 fair f 1135 162.
## 5 excellent m 2298 183.
## 6 excellent f 2359 142.Repeat the previous exercise, but sort in descending order by mean_weight.
health_gender = cdc %>%
group_by(genhlth,gender) %>%
summarize(count = n(),
mean_weight = mean(weight)) %>%
ungroup() %>%
arrange(desc(mean_weight)) ## `summarise()` has grouped output by 'genhlth'. You can override using the
## `.groups` argument.## # A tibble: 6 × 4
## genhlth gender count mean_weight
## <fct> <fct> <int> <dbl>
## 1 fair m 884 194.
## 2 poor m 283 193.
## 3 good m 2722 193.
## 4 very good m 3382 189.
## 5 excellent m 2298 183.
## 6 poor f 394 165.Use the dataframe county from the file “county.rda”. Do a summary of the dataframe to look for anomalies.
## name state pop2000 pop2010
## Washington County: 30 Texas : 254 Min. : 67 Min. : 82
## Jefferson County : 25 Georgia : 159 1st Qu.: 11224 1st Qu.: 11114
## Franklin County : 24 Virginia: 133 Median : 24621 Median : 25872
## Jackson County : 23 Kentucky: 120 Mean : 89650 Mean : 98262
## Lincoln County : 23 Missouri: 115 3rd Qu.: 61775 3rd Qu.: 66780
## Madison County : 19 Kansas : 105 Max. :9519338 Max. :9818605
## (Other) :2998 (Other) :2256 NA's :3
## pop2017 pop_change poverty homeownership
## Min. : 88 Min. :-33.6300 Min. : 2.40 Min. : 0.00
## 1st Qu.: 10976 1st Qu.: -1.9700 1st Qu.:11.30 1st Qu.:69.50
## Median : 25857 Median : -0.0600 Median :15.20 Median :74.60
## Mean : 103763 Mean : 0.5339 Mean :15.97 Mean :73.27
## 3rd Qu.: 67756 3rd Qu.: 2.3750 3rd Qu.:19.40 3rd Qu.:78.40
## Max. :10163507 Max. : 37.1900 Max. :52.00 Max. :91.30
## NA's :3 NA's :3 NA's :2
## multi_unit unemployment_rate metro median_edu
## Min. : 0.00 Min. : 1.620 no :1974 below_hs : 2
## 1st Qu.: 6.10 1st Qu.: 3.520 yes :1165 hs_diploma :1397
## Median : 9.70 Median : 4.360 NA's: 3 some_college:1695
## Mean :12.32 Mean : 4.611 bachelors : 46
## 3rd Qu.:15.90 3rd Qu.: 5.355 NA's : 2
## Max. :98.50 Max. :19.070
## NA's :3
## per_capita_income median_hh_income smoking_ban
## Min. :10467 Min. : 19264 none :1927
## 1st Qu.:21772 1st Qu.: 41126 partial : 635
## Median :25445 Median : 48072 complete: 0
## Mean :26093 Mean : 49765 NA's : 580
## 3rd Qu.:29276 3rd Qu.: 55771
## Max. :69533 Max. :129588
## NA's :2 NA's :2Where are the NA values coming from?
county %>%
filter(is.na(pop2017) |
is.na(pop2000) |
is.na(pop_change) |
is.na(median_edu) |
is.na(median_hh_income) |
is.na(unemployment_rate) |
is.na(metro) |
is.na(per_capita_income))## # A tibble: 7 × 15
## name state pop2000 pop2010 pop2017 pop_c…¹ poverty homeo…² multi…³ unemp…⁴
## <fct> <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Hoonah–… Alas… 3436 2150 NA NA 11.1 64 8.6 12.6
## 2 Skagway Alas… NA 968 NA NA 5.6 59.1 27.2 10.6
## 3 Kusilva… Alas… 7028 7459 8202 2.91 NA 64.8 4.1 NA
## 4 Wrangell Alas… NA 2369 NA NA 11.7 78.7 11.9 7.71
## 5 Broomfi… Colo… NA 55889 68341 13.8 5.2 74.4 21.7 2.63
## 6 Kalawao… Hawa… 147 90 88 -1.12 12.7 0 22.3 NA
## 7 Oglala … Sout… 12466 13586 14354 1.59 NA 51.3 3.8 NA
## # … with 5 more variables: metro <fct>, median_edu <fct>,
## # per_capita_income <dbl>, median_hh_income <int>, smoking_ban <fct>, and
## # abbreviated variable names ¹pop_change, ²homeownership, ³multi_unit,
## # ⁴unemployment_rateReplace county with county_clean, from which all the bad data has been removed. Research: Remove cases with missing values using dplyr.
Repeat the summary and verify.
Research. Remove cases with missing values using dplyr.
## 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
## ## 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…Calculate the total population in each state for 2017. Sort the results in ascending order. Use head() and tail() to examine the results.
state_pop = county_clean %>%
select(state,pop2017) %>%
group_by(state) %>%
summarize(pop = sum(pop2017)) %>%
arrange(pop)
head(state_pop)## # A tibble: 6 × 2
## state pop
## <fct> <int>
## 1 Wyoming 579315
## 2 Vermont 623657
## 3 District of Columbia 693972
## 4 Alaska 725770
## 5 North Dakota 755393
## 6 South Dakota 855312## # A tibble: 6 × 2
## state pop
## <fct> <int>
## 1 Illinois 12802023
## 2 Pennsylvania 12805537
## 3 New York 19849399
## 4 Florida 20984400
## 5 Texas 28304596
## 6 California 39536653What happens if we do this with county instead of county_clean?
state_pop = county %>%
select(state,pop2017) %>%
group_by(state) %>%
summarize(pop = sum(pop2017)) %>%
arrange(pop)
head(state_pop)## # A tibble: 6 × 2
## state pop
## <fct> <int>
## 1 Wyoming 579315
## 2 Vermont 623657
## 3 District of Columbia 693972
## 4 North Dakota 755393
## 5 South Dakota 869666
## 6 Delaware 961939## # A tibble: 6 × 2
## state pop
## <fct> <int>
## 1 Pennsylvania 12805537
## 2 New York 19849399
## 3 Florida 20984400
## 4 Texas 28304596
## 5 California 39536653
## 6 Alaska NACallculate State per capita income for 2017 and arrange in ascending order. Examine the head and the tail.
Hint: Begin by calculating total income for each county.
state_per_cap_income = county_clean %>%
mutate(inc_17 = pop2017 * per_capita_income) %>%
group_by(state) %>%
summarize(total_pop = sum(pop2017),
total_inc = sum(inc_17)) %>%
ungroup() %>%
mutate(state_per_cap = total_inc/total_pop) %>%
select(state,state_per_cap) %>%
arrange(state_per_cap)
head(state_per_cap_income)## # A tibble: 6 × 2
## state state_per_cap
## <fct> <dbl>
## 1 Mississippi 22305.
## 2 Arkansas 24335.
## 3 New Mexico 24375.
## 4 West Virginia 24623.
## 5 Idaho 25245.
## 6 Alabama 25344.## # A tibble: 6 × 2
## state state_per_cap
## <fct> <dbl>
## 1 New Hampshire 37343.
## 2 New Jersey 38598.
## 3 Maryland 38662.
## 4 Massachusetts 40252.
## 5 Connecticut 41442.
## 6 District of Columbia 50917.