In this lab you can use the interactive console to explore but please
record your commands here. Remember anything you type here can be “sent”
to the console with Cmd-Enter (OS-X) or Ctrl-Enter (Windows/Linux) (But
only inside the code chunks designated with the {r}
areas).
First let’s get the OCSdata package if we haven’t already. We added an argument here so you don’t have to select a mirror.
install.packages("OCSdata", repos='http://cran.us.r-project.org')## Installing package into '/usr/local/lib/R/site-library'
## (as 'lib' is unspecified)# don't forget to load the packages that you will need!
library(dplyr)
library(tidyverse)
library(OCSdata)Now let’s load the opioid related datasets by running this chunk.
OCSdata::load_imported_data("ocs-bp-opioid-rural-urban")Check that it worked by seeing if you have the
county_pop data.
county_pop## # A tibble: 28,265 × 10
## BUYER_COUNTY BUYER_STATE countyfips STATE COUNTY county_name NAME variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 AUTAUGA AL 01001 1 1 Autauga Autaug… B01003_…
## 2 BALDWIN AL 01003 1 3 Baldwin Baldwi… B01003_…
## 3 BARBOUR AL 01005 1 5 Barbour Barbou… B01003_…
## 4 BIBB AL 01007 1 7 Bibb Bibb C… B01003_…
## 5 BLOUNT AL 01009 1 9 Blount Blount… B01003_…
## 6 BULLOCK AL 01011 1 11 Bullock Bulloc… B01003_…
## 7 BUTLER AL 01013 1 13 Butler Butler… B01003_…
## 8 CALHOUN AL 01015 1 15 Calhoun Calhou… B01003_…
## 9 CHAMBERS AL 01017 1 17 Chambers Chambe… B01003_…
## 10 CHEROKEE AL 01019 1 19 Cherokee Cherok… B01003_…
## # ℹ 28,255 more rows
## # ℹ 2 more variables: year <int>, population <int>What class is county_pop?
class(county_pop)## [1] "tbl_df" "tbl" "data.frame"How many observations (rows) and variables (columns) are in the
dataset - try the dim() function?
dim(county_pop)## [1] 28265 10nrow(county_pop)## [1] 28265ncol(county_pop)## [1] 10Next, rename the column BUYER_STATE to be
State_name (hint - use rename() and watch out
for the order of the new and old names!).
county_pop <- rename(county_pop, State_name = BUYER_STATE)
head(county_pop)## # A tibble: 6 × 10
## BUYER_COUNTY State_name countyfips STATE COUNTY county_name NAME variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 AUTAUGA AL 01001 1 1 Autauga Autauga … B01003_…
## 2 BALDWIN AL 01003 1 3 Baldwin Baldwin … B01003_…
## 3 BARBOUR AL 01005 1 5 Barbour Barbour … B01003_…
## 4 BIBB AL 01007 1 7 Bibb Bibb Cou… B01003_…
## 5 BLOUNT AL 01009 1 9 Blount Blount C… B01003_…
## 6 BULLOCK AL 01011 1 11 Bullock Bullock … B01003_…
## # ℹ 2 more variables: year <int>, population <int>Convert the column names of county_pop to be all lower
case. Use rename_with(), and the tolower
command.
county_pop <- rename_with(county_pop, tolower)
head(county_pop)## # A tibble: 6 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 AUTAUGA AL 01001 1 1 Autauga Autauga … B01003_…
## 2 BALDWIN AL 01003 1 3 Baldwin Baldwin … B01003_…
## 3 BARBOUR AL 01005 1 5 Barbour Barbour … B01003_…
## 4 BIBB AL 01007 1 7 Bibb Bibb Cou… B01003_…
## 5 BLOUNT AL 01009 1 9 Blount Blount C… B01003_…
## 6 BULLOCK AL 01011 1 11 Bullock Bullock … B01003_…
## # ℹ 2 more variables: year <int>, population <int>dim(county_pop)## [1] 28265 10How can you print the first 3 rows and the last 3 rows of
county_pop (in two lines of code)?
head(county_pop, 3)## # A tibble: 3 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 AUTAUGA AL 01001 1 1 Autauga Autauga … B01003_…
## 2 BALDWIN AL 01003 1 3 Baldwin Baldwin … B01003_…
## 3 BARBOUR AL 01005 1 5 Barbour Barbour … B01003_…
## # ℹ 2 more variables: year <int>, population <int>tail(county_pop, 3)## # A tibble: 3 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 SKAGWAY AK 02230 2 230 Skagway Skagw… B01003_…
## 2 HOONAH ANGOON AK 02105 2 105 Hoonah Angoon Hoona… B01003_…
## 3 PETERSBURG AK 02195 2 195 Petersburg Peter… B01003_…
## # ℹ 2 more variables: year <int>, population <int>Create a subset of the county_pop that only contains the
columns: county_name, year, and
population and assign this object to pop_sub -
what are the dimensions of this dataset?
pop_sub <- select(county_pop, county_name, year, population)
dim(pop_sub)## [1] 28265 3Start with county_pop again instead of the dataset you
just made. Subset the data to only include the population
column and the columns that end with “_name”. Hint: use
select() and ends_with(). Assign this subset
of the data to be pop2. Again take a look at the data and
check the dimensions.
pop2 <- select(county_pop, population, ends_with("_name"))
pop2## # A tibble: 28,265 × 3
## population state_name county_name
## <int> <chr> <chr>
## 1 51328 AL Autauga
## 2 168121 AL Baldwin
## 3 27861 AL Barbour
## 4 22099 AL Bibb
## 5 55485 AL Blount
## 6 10776 AL Bullock
## 7 20815 AL Butler
## 8 115388 AL Calhoun
## 9 34945 AL Chambers
## 10 25466 AL Cherokee
## # ℹ 28,255 more rowsdim(pop2)## [1] 28265 3Pull the variable county_name from pop2.
How does this differ form selecting it? Use head() to take a look at
both options.
head(pull(pop2, county_name))## [1] "Autauga" "Baldwin" "Barbour" "Bibb" "Blount" "Bullock"head(select(pop2, county_name))## # A tibble: 6 × 1
## county_name
## <chr>
## 1 Autauga
## 2 Baldwin
## 3 Barbour
## 4 Bibb
## 5 Blount
## 6 BullockSubset the rows of county_pop that have
more than 100000 for population - how many rows are
there? Use filter().
county_pop_sub <- filter(county_pop, population > 100000)
nrow(county_pop_sub)## [1] 5159Subset the rows of county_pop that have a year value
less than 2010 and more than 100000
population - how many are there?
filter(county_pop, year < 2010 & population > 100000) # all of these options work## # A tibble: 2,260 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 BALDWIN AL 01003 1 3 Baldwin Baldwin… B01003_…
## 2 CALHOUN AL 01015 1 15 Calhoun Calhoun… B01003_…
## 3 ETOWAH AL 01055 1 55 Etowah Etowah … B01003_…
## 4 JEFFERSON AL 01073 1 73 Jefferson Jeffers… B01003_…
## 5 LEE AL 01081 1 81 Lee Lee Cou… B01003_…
## 6 MADISON AL 01089 1 89 Madison Madison… B01003_…
## 7 MOBILE AL 01097 1 97 Mobile Mobile … B01003_…
## 8 MONTGOMERY AL 01101 1 101 Montgomery Montgom… B01003_…
## 9 MORGAN AL 01103 1 103 Morgan Morgan … B01003_…
## 10 SHELBY AL 01117 1 117 Shelby Shelby … B01003_…
## # ℹ 2,250 more rows
## # ℹ 2 more variables: year <int>, population <int>nrow(filter(county_pop, year < 2010 & population > 100000))## [1] 2260nrow(filter(county_pop, year < 2010, population > 100000))## [1] 2260Subset the rows of county_pop that have a year value of
less than or equal to 2010 and more
than 100000 population - how many are there?
filter(county_pop, year <= 2010 & population > 100000) # all of these options work## # A tibble: 2,834 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 BALDWIN AL 01003 1 3 Baldwin Baldwin… B01003_…
## 2 CALHOUN AL 01015 1 15 Calhoun Calhoun… B01003_…
## 3 ETOWAH AL 01055 1 55 Etowah Etowah … B01003_…
## 4 JEFFERSON AL 01073 1 73 Jefferson Jeffers… B01003_…
## 5 LEE AL 01081 1 81 Lee Lee Cou… B01003_…
## 6 MADISON AL 01089 1 89 Madison Madison… B01003_…
## 7 MOBILE AL 01097 1 97 Mobile Mobile … B01003_…
## 8 MONTGOMERY AL 01101 1 101 Montgomery Montgom… B01003_…
## 9 MORGAN AL 01103 1 103 Morgan Morgan … B01003_…
## 10 SHELBY AL 01117 1 117 Shelby Shelby … B01003_…
## # ℹ 2,824 more rows
## # ℹ 2 more variables: year <int>, population <int>nrow(filter(county_pop, year <= 2010 & population > 100000))## [1] 2834nrow(filter(county_pop, year <= 2010, population > 100000))## [1] 2834Why do the answers for 2.5 and 2.6 differ?
# They differ because there are rows with values that are exactly 2010.Subset the rows of county_pop for rows that have
state_name of NM, or
less than 10000 population. How many rows
have both?
filter(county_pop, state_name == "NM" | population < 10000)## # A tibble: 6,526 × 10
## buyer_county state_name countyfips state county county_name name variable
## <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 GREENE AL 01063 1 63 Greene Gree… B01003_…
## 2 ALEUTIANS EAST AK 02013 2 13 Aleutians … Aleu… B01003_…
## 3 ALEUTIANS WEST AK 02016 2 16 Aleutians … Aleu… B01003_…
## 4 BRISTOL BAY AK 02060 2 60 Bristol Bay Bris… B01003_…
## 5 DENALI AK 02068 2 68 Denali Dena… B01003_…
## 6 DILLINGHAM AK 02070 2 70 Dillingham Dill… B01003_…
## 7 HAINES AK 02100 2 100 Haines Hain… B01003_…
## 8 LAKE AND PENIN… AK 02164 2 164 Lake and P… Lake… B01003_…
## 9 NOME AK 02180 2 180 Nome Nome… B01003_…
## 10 NORTH SLOPE AK 02185 2 185 North Slope Nort… B01003_…
## # ℹ 6,516 more rows
## # ℹ 2 more variables: year <int>, population <int>nrow(filter(county_pop, state_name == "NM" & population < 10000))## [1] 72Select the variables that contain the letter “a” from
cars_mt_copy.
select(county_pop, contains("a"))## # A tibble: 28,265 × 7
## state_name state county_name name variable year population
## <chr> <int> <chr> <chr> <chr> <int> <int>
## 1 AL 1 Autauga Autauga County, Alaba… B01003_… 2006 51328
## 2 AL 1 Baldwin Baldwin County, Alaba… B01003_… 2006 168121
## 3 AL 1 Barbour Barbour County, Alaba… B01003_… 2006 27861
## 4 AL 1 Bibb Bibb County, Alabama B01003_… 2006 22099
## 5 AL 1 Blount Blount County, Alabama B01003_… 2006 55485
## 6 AL 1 Bullock Bullock County, Alaba… B01003_… 2006 10776
## 7 AL 1 Butler Butler County, Alabama B01003_… 2006 20815
## 8 AL 1 Calhoun Calhoun County, Alaba… B01003_… 2006 115388
## 9 AL 1 Chambers Chambers County, Alab… B01003_… 2006 34945
## 10 AL 1 Cherokee Cherokee County, Alab… B01003_… 2006 25466
## # ℹ 28,255 more rowsCreate a subset called county_2012 from
county_pop that only contains the rows for the year 2012
and only the columns: county_name, state_name,
population. year should not be included in
county_sub. What are the dimensions of this dataset? Don’t
use pipes (%>%) and instead do this in two steps
creating the county_2012 object with filter
and updating it with select.
county_2012 <- filter(county_pop, year == 2012)
county_2012 <- select(county_2012, county_name, state_name, population)
county_2012 # can get dimensions just from viewing the tibble## # A tibble: 3,140 × 3
## county_name state_name population
## <chr> <chr> <int>
## 1 Autauga AL 54590
## 2 Baldwin AL 183226
## 3 Barbour AL 27469
## 4 Bibb AL 22769
## 5 Blount AL 57466
## 6 Bullock AL 10779
## 7 Butler AL 20730
## 8 Calhoun AL 117834
## 9 Chambers AL 34228
## 10 Cherokee AL 25917
## # ℹ 3,130 more rowsdim(county_2012) # alternatively can use dim() function## [1] 3140 3Try the same thing again with pipes %>%.
county_2012 <- county_pop %>%
filter(year == 2012) %>%
select(county_name, state_name, population)
county_2012## # A tibble: 3,140 × 3
## county_name state_name population
## <chr> <chr> <int>
## 1 Autauga AL 54590
## 2 Baldwin AL 183226
## 3 Barbour AL 27469
## 4 Bibb AL 22769
## 5 Blount AL 57466
## 6 Bullock AL 10779
## 7 Butler AL 20730
## 8 Calhoun AL 117834
## 9 Chambers AL 34228
## 10 Cherokee AL 25917
## # ℹ 3,130 more rowsdim(county_2012)## [1] 3140 3What happens if you do the steps in a different order? Why does this not work?
#county_2012 <- county_pop %>%
# select(county_name, state_name, population) %>%
# filter(year == 2012)
# you get an error because there is now year variable to filter fromRe-order the rows of county_2012 by population in
increasing order. Use arrange(). What is county with the
smallest population?
county_2012 <- arrange(county_2012, population)
county_2012## # A tibble: 3,140 × 3
## county_name state_name population
## <chr> <chr> <int>
## 1 Loving TX 85
## 2 King TX 227
## 3 McPherson NE 348
## 4 Kenedy TX 451
## 5 Arthur NE 490
## 6 Loup NE 534
## 7 Petroleum MT 566
## 8 Blaine NE 584
## 9 Grant NE 608
## 10 Harding NM 612
## # ℹ 3,130 more rowsarrange(county_2012, population) %>%
head(n = 1) %>%
pull(county_name)## [1] "Loving"Create a new variable in county_2012 called
pop1000, which is equal to population divided
by 1000, using mutate()(don’t forget to reassign
county_2012). Use pipes %>%.
# General format
NEWDATA <- OLD_DATA %>% mutate(NEW_COLUMN = OLD_COLUMN)county_2012 <- county_2012 %>% mutate(pop1000 = population/1000)Move the state_name column to be before
county_name in the county_2012 dataset. Use
relocate().
county_2012 <- county_2012 %>% relocate(state_name, .before = county_name)
county_2012## # A tibble: 3,140 × 4
## state_name county_name population pop1000
## <chr> <chr> <int> <dbl>
## 1 TX Loving 85 0.085
## 2 TX King 227 0.227
## 3 NE McPherson 348 0.348
## 4 TX Kenedy 451 0.451
## 5 NE Arthur 490 0.49
## 6 NE Loup 534 0.534
## 7 MT Petroleum 566 0.566
## 8 NE Blaine 584 0.584
## 9 NE Grant 608 0.608
## 10 NM Harding 612 0.612
## # ℹ 3,130 more rowsHow can you find the value of population for Chambers AL
without just looking at the data manually and instead use functions we
learned today?
county_2012 %>%
filter(county_name == "Chambers", state_name == "AL") %>%
select(population)## # A tibble: 1 × 1
## population
## <int>
## 1 34228