Refresh the website and get the latest version of the labs and slides! We are constantly making improvements.

• Use <- to save (assign) values to objects
  
• Use c() to combine vectors
  
• length(), class(), and str() tell you information about an object
  
• The sequence seq() function helps you create numeric vectors (from,to, by, and length.out arguments)
  
• The repeat rep() function helps you create vectors with the each and times arguments
  
• Reproducible science makes everyone’s life easier!
  
• readrhas helpful functions like read_csv() that can help you import data into R

📃Cheatsheet

In this module, we will show you how to:

1. Look at your data in different ways
2. Create a data frame and a tibble
3. Create new variables/make rownames a column
4. Rename columns of a data frame
5. Subset rows of a data frame
6. Subset columns of a data frame
7. Add/remove new columns to a data frame
8. Order the columns of a data frame
9. Order the rows of a data frame

We will largely focus on the dplyr package which is part of the tidyverse.

Some resources on how to use dplyr:

• https://dplyr.tidyverse.org/
• https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
• https://www.opencasestudies.org/

The dplyr package is one of the most helpful packages for altering your data to get it into a form that is useful for creating visualizations, summarizing, or more deeply analyzing.

So you can imagine using pliers on your data.

See this website for a list of the packages included in the tidyverse: https://www.tidyverse.org/packages/

library(tidyverse) # loads dplyr and other packages!

We will use a dataset from a project we worked on called Open Case Studies.

See https://www.opencasestudies.org/.

We have added the specific data to our website.

We will load data from one of the case studies about opioid shipments.

See https://www.opencasestudies.org/ocs-bp-opioid-rural-urban/ about this data.

We will work with data called annualDosage (number of shipments (count) of either oxycodone or hydrocodone pills (DOSAGE_UNIT)).

annualDosage <- 
  read_csv("https://jhudatascience.org/intro_to_r/data/annualDosage.csv")

Rows: 27758 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): BUYER_COUNTY, BUYER_STATE, countyfips
dbl (3): year, count, DOSAGE_UNIT

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.

The dim(), nrow(), and ncol() functions are good options to check the dimensions of your data before moving forward.

dim(annualDosage) # rows, columns

[1] 27758     6

nrow(annualDosage) # number of rows

[1] 27758

ncol(annualDosage) # number of columns

[1] 6

In addition to head() and tail(), the glimpse()function of the dplyr package is another great function to look at your data.

glimpse(annualDosage)

Rows: 27,758
Columns: 6
$ BUYER_COUNTY <chr> "ABBEVILLE", "ABBEVILLE", "ABBEVILLE", "ABBEVILLE", "ABBE…
$ BUYER_STATE  <chr> "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "LA…
$ year         <dbl> 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 200…
$ count        <dbl> 877, 908, 871, 930, 1197, 1327, 1509, 1572, 1558, 5802, 5…
$ DOSAGE_UNIT  <dbl> 363620, 402940, 424590, 467230, 539280, 566560, 589010, 5…
$ countyfips   <chr> "45001", "45001", "45001", "45001", "45001", "45001", "45…

What if you want to see the middle of your data? You can use the slice_sample() function of the dplyr package to see a random set of rows. You can specify the number of rows with the n argument.

slice_sample(annualDosage, n = 2)

# A tibble: 2 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 NICOLLET     MN           2014  1218      327780 27103     
2 WASHINGTON   ID           2009   762      400020 16087     

slice_sample(annualDosage, n = 2)

# A tibble: 2 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 MONO         CA           2008   523      227475 06051     
2 HENRY        KY           2014  1039      567530 21103     

An older version of data in tables is called a data frame. The mtcars dataset is an example of this.

class(mtcars)

[1] "data.frame"

head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Tibbles are a fancier version of data frames:

• We don’t have to use head to see a preview of it
• We see the dimensions
• We see the data types for each column

annualDosage

# A tibble: 27,758 × 6
   BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
   <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
 1 ABBEVILLE    SC           2006   877      363620 45001     
 2 ABBEVILLE    SC           2007   908      402940 45001     
 3 ABBEVILLE    SC           2008   871      424590 45001     
 4 ABBEVILLE    SC           2009   930      467230 45001     
 5 ABBEVILLE    SC           2010  1197      539280 45001     
 6 ABBEVILLE    SC           2011  1327      566560 45001     
 7 ABBEVILLE    SC           2012  1509      589010 45001     
 8 ABBEVILLE    SC           2013  1572      596420 45001     
 9 ABBEVILLE    SC           2014  1558      641350 45001     
10 ACADIA       LA           2006  5802     1969720 22001     
# ℹ 27,748 more rows

If we wanted to create a tibble (“fancy” data frame), we can using the tibble() function on a data frame.

tbl_mtcars <- tibble(mtcars) 
tbl_mtcars

# A tibble: 32 × 11
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# ℹ 22 more rows

Note don’t necessarily need to use head() with tibbles, as they conveniently print a portion of the data.

We generally recommend using tibbles, but you are likely to run into lots of data frames with your work.

Most functions work for both so you don’t need to worry about it much!

It can be helpful to convert data frames to tibbles though just to see more about the data more easily. The tibble() function helps us do that.

Note that this conversion can remove row names - which some data frames have. For example, mtcars (part of R) has row names. In this case we would want to make the rownames a new column first before making into a tibble.

head(mtcars, n = 2)

              mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4

head(tibble(mtcars), n = 2)

# A tibble: 2 × 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1    21     6   160   110   3.9  2.62  16.5     0     1     4     4
2    21     6   160   110   3.9  2.88  17.0     0     1     4     4

Let’s stick with the tibble annualDosage data for our next lesson

head(annualDosage)

# A tibble: 6 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE    SC           2006   877      363620 45001     
2 ABBEVILLE    SC           2007   908      402940 45001     
3 ABBEVILLE    SC           2008   871      424590 45001     
4 ABBEVILLE    SC           2009   930      467230 45001     
5 ABBEVILLE    SC           2010  1197      539280 45001     
6 ABBEVILLE    SC           2011  1327      566560 45001     

“Artwork by @allison_horst”. https://allisonhorst.com/

To rename columns in dplyr, you can use the rename function.

For example, let’s rename BUYER_COUNTY to County. Notice the new name is listed first, similar to how a new object is assigned on the left!

# general format! not code!
{data you are creating or changing} <- rename({data you are using}, 
                                          {New Name} = {Old name})

renamed_annualDosage<- rename(annualDosage, County = BUYER_COUNTY)
head(renamed_annualDosage)

# A tibble: 6 × 6
  County    BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>     <chr>       <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE SC           2006   877      363620 45001     
2 ABBEVILLE SC           2007   908      402940 45001     
3 ABBEVILLE SC           2008   871      424590 45001     
4 ABBEVILLE SC           2009   930      467230 45001     
5 ABBEVILLE SC           2010  1197      539280 45001     
6 ABBEVILLE SC           2011  1327      566560 45001     

When you can, avoid spaces, special punctuation, or numbers in column names, as these require special treatment to refer to them.

See https://jhudatascience.org/intro_to_r/resources/quotes_vs_backticks.html for more guidance.

 # this will cause an error
renamed_annualDosage <- rename(annualDosage, County! = BUYER_COUNTY)

# this will work
renamed_annualDosage <- rename(annualDosage, `County!` = BUYER_COUNTY) 
head(renamed_annualDosage, 2)

# A tibble: 2 × 6
  `County!` BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>     <chr>       <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE SC           2006   877      363620 45001     
2 ABBEVILLE SC           2007   908      402940 45001     

It’s best to avoid unusual column names where possible, as things get tricky later.

We just showed the use of ` backticks` . You may see people use quotes as well.

Other atypical column names are those with:

• spaces
• number without characters
• number starting the name
• other punctuation marks (besides “_” or “.” and not at the beginning)

Rename tricky column names so that you don’t have to deal with them later!

Curly quotes will not work!

# this will cause an error!
renamed_annualDosage <- rename(annualDosage, ‘County!’ = BUYER_COUNTY) 

# this will work!
renamed_annualDosage <- rename(annualDosage, 'County!' = BUYER_COUNTY) 

Also true for double quotes

 # this will cause an error!
renamed_annualDosage <- rename(annualDosage, “County!” = BUYER_COUNTY)

# this will work!
renamed_annualDosage <- rename(annualDosage, "County!" = BUYER_COUNTY) 

A comma can separate different column names to change.

renamed_annualDosage <- rename(annualDosage, 
                               County = BUYER_COUNTY, 
                               State = BUYER_STATE)
head(renamed_annualDosage, 3)

# A tibble: 3 × 6
  County    State  year count DOSAGE_UNIT countyfips
  <chr>     <chr> <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE SC     2006   877      363620 45001     
2 ABBEVILLE SC     2007   908      402940 45001     
3 ABBEVILLE SC     2008   871      424590 45001     

If you need to do lots of naming fixes - look into the janitor package!

#install.packages("janitor")
library(janitor)

The clean_names function can intuit what fixes you might need. Here it makes everything consistent.

head(annualDosage, 2)

# A tibble: 2 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE    SC           2006   877      363620 45001     
2 ABBEVILLE    SC           2007   908      402940 45001     

clean_AD <- clean_names(annualDosage)
head(clean_AD, 2)

# A tibble: 2 × 6
  buyer_county buyer_state  year count dosage_unit countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 ABBEVILLE    SC           2006   877      363620 45001     
2 ABBEVILLE    SC           2007   908      402940 45001     

clean_names can also get rid of spaces and replace them with _.

test <- tibble(`col 1` = c(1,2,3), `col 2` = c(2,3,4))
test

# A tibble: 3 × 2
  `col 1` `col 2`
    <dbl>   <dbl>
1       1       2
2       2       3
3       3       4

clean_names(test)

# A tibble: 3 × 2
  col_1 col_2
  <dbl> <dbl>
1     1     2
2     2     3
3     3     4

A. Renaming it using rename function to something simpler like counties_over_10thous.

B. Keeping it as is and use backticks around the column name when you use it.

C. Keeping it as is and use quotes around the column name when you use it.

• data frames are simpler version of a data table
• tibbles are fancier tidyverse version
• tibbles are made with tibble()
• if your original data has rownames, you need to use rownames_to_column before converting to tibble
• the rename() function of dplyr can help you rename columns
• avoid using punctuation (except underscores), spaces, and numbers (to start or alone) in column names
• if you must do a nonstandard column name - typically use backticks around it. See https://jhudatascience.org/intro_to_r/resources/quotes_vs_backticks.html.
• avoid copy and pasting code from other sources - quotation marks will change!
• check out janitor if you need to make lots of column name changes

🏠 Class Website

💻 Lab

📃 Day 3 Cheatsheet

📃 Posit’s dplyr Cheatsheet # Subsetting Columns

This time lets also make it a smaller subset so it is easier for us to see the full dataset as we work through examples.

#read_csv("https://jhudatascience.org/intro_to_r/data/annualDosage.csv")
set.seed(1234)
AD <-slice_sample(annualDosage, n = 30)

To grab a vector version (or “pull” out) the year column the tidyverse way we can use the pull function:

pull(AD, year)

 [1] 2006 2014 2013 2012 2009 2010 2010 2010 2009 2012 2010 2006 2007 2010 2011
[16] 2008 2009 2010 2007 2008 2006 2007 2013 2012 2011 2006 2013 2008 2013 2010

The select command from dplyr allows you to subset (still a tibble!)

select(AD, year)

# A tibble: 30 × 1
    year
   <dbl>
 1  2006
 2  2014
 3  2013
 4  2012
 5  2009
 6  2010
 7  2010
 8  2010
 9  2009
10  2012
# ℹ 20 more rows

A. pull()

B. select()

We can use select to select for multiple columns.

select(AD, year, BUYER_COUNTY)

# A tibble: 30 × 2
    year BUYER_COUNTY     
   <dbl> <chr>            
 1  2006 EASTLAND         
 2  2014 FALLS CHURCH CITY
 3  2013 DOUGLAS          
 4  2012 FAYETTE          
 5  2009 STAFFORD         
 6  2010 GILES            
 7  2010 ANDREW           
 8  2010 LUNENBURG        
 9  2009 HAWKINS          
10  2012 ATCHISON         
# ℹ 20 more rows

Note that if you want the values (not a tibble), use pull - as it pulls out the data:

pull(AD, year)

 [1] 2006 2014 2013 2012 2009 2010 2010 2010 2009 2012 2010 2006 2007 2010 2011
[16] 2008 2009 2010 2007 2008 2006 2007 2013 2012 2011 2006 2013 2008 2013 2010

# pull with select works too!

pull(select(AD, year))

 [1] 2006 2014 2013 2012 2009 2010 2010 2010 2009 2012 2010 2006 2007 2010 2011
[16] 2008 2009 2010 2007 2008 2006 2007 2013 2012 2011 2006 2013 2008 2013 2010

The select command from dplyr allows you to subset columns matching patterns:

head(AD, 2)

# A tibble: 2 × 6
  BUYER_COUNTY      BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>             <chr>       <dbl> <dbl>       <dbl> <chr>     
1 EASTLAND          TX           2006  1980      723690 48133     
2 FALLS CHURCH CITY VA           2014  1340      531270 51610     

select(AD, starts_with("B"))

# A tibble: 30 × 2
   BUYER_COUNTY      BUYER_STATE
   <chr>             <chr>      
 1 EASTLAND          TX         
 2 FALLS CHURCH CITY VA         
 3 DOUGLAS           CO         
 4 FAYETTE           GA         
 5 STAFFORD          VA         
 6 GILES             TN         
 7 ANDREW            MO         
 8 LUNENBURG         VA         
 9 HAWKINS           TN         
10 ATCHISON          MO         
# ℹ 20 more rows

Here are a few:

last_col()
starts_with()
ends_with()
contains() # like searching

Type tidyselect:: in the console and see what RStudio suggests:

head(AD, 2)

# A tibble: 2 × 6
  BUYER_COUNTY      BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>             <chr>       <dbl> <dbl>       <dbl> <chr>     
1 EASTLAND          TX           2006  1980      723690 48133     
2 FALLS CHURCH CITY VA           2014  1340      531270 51610     

select(AD, starts_with("B"), year)

# A tibble: 30 × 3
   BUYER_COUNTY      BUYER_STATE  year
   <chr>             <chr>       <dbl>
 1 EASTLAND          TX           2006
 2 FALLS CHURCH CITY VA           2014
 3 DOUGLAS           CO           2013
 4 FAYETTE           GA           2012
 5 STAFFORD          VA           2009
 6 GILES             TN           2010
 7 ANDREW            MO           2010
 8 LUNENBURG         VA           2010
 9 HAWKINS           TN           2009
10 ATCHISON          MO           2012
# ℹ 20 more rows

Follows OR logic.

select(AD, starts_with("B"), ends_with("r"))

# A tibble: 30 × 3
   BUYER_COUNTY      BUYER_STATE  year
   <chr>             <chr>       <dbl>
 1 EASTLAND          TX           2006
 2 FALLS CHURCH CITY VA           2014
 3 DOUGLAS           CO           2013
 4 FAYETTE           GA           2012
 5 STAFFORD          VA           2009
 6 GILES             TN           2010
 7 ANDREW            MO           2010
 8 LUNENBURG         VA           2010
 9 HAWKINS           TN           2009
10 ATCHISON          MO           2012
# ℹ 20 more rows

The command in dplyr for subsetting rows is filter.

filter(AD, count > 10000)

# A tibble: 6 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 DOUGLAS      CO           2013 20961     6277640 08035     
2 FAYETTE      GA           2012 12978     3701320 13113     
3 BUTTE        CA           2006 20443    13802710 06007     
4 PUEBLO       CO           2008 23932     9386790 08101     
5 ROGERS       OK           2008 11150     4499860 40131     
6 CABELL       WV           2013 18481     7806600 54011     

You can have multiple logical conditions using the following:

• == : equals to
• !=: not equal to (! : not/negation)
• > / <: greater than / less than
• >= or <=: greater than or equal to / less than or equal to
• & : AND
• | : OR

If you try to filter for a column that does not exist it will not work:

• misspelled column name
• column that was already removed

Remember that filter works by keeping rows with values that match the conditions specified for particular columns.

“Artwork by @allison_horst”. https://allisonhorst.com/

If you want OR statements (meaning the data can meet either condition does not need to meet both), you need to use | between conditions:

filter(AD, count > 10000 | year == 2012)

# A tibble: 8 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 DOUGLAS      CO           2013 20961     6277640 08035     
2 FAYETTE      GA           2012 12978     3701320 13113     
3 ATCHISON     MO           2012   601      204700 29005     
4 BUTTE        CA           2006 20443    13802710 06007     
5 PUEBLO       CO           2008 23932     9386790 08101     
6 LEE          KY           2012  1654     1196860 21129     
7 ROGERS       OK           2008 11150     4499860 40131     
8 CABELL       WV           2013 18481     7806600 54011     

This will not work the way you might expect! Best to stick with nothing but the column name if it is a typical name.

filter(AD, "year" > 2014)

# A tibble: 30 × 6
   BUYER_COUNTY      BUYER_STATE  year count DOSAGE_UNIT countyfips
   <chr>             <chr>       <dbl> <dbl>       <dbl> <chr>     
 1 EASTLAND          TX           2006  1980      723690 48133     
 2 FALLS CHURCH CITY VA           2014  1340      531270 51610     
 3 DOUGLAS           CO           2013 20961     6277640 08035     
 4 FAYETTE           GA           2012 12978     3701320 13113     
 5 STAFFORD          VA           2009  7921     2904600 51179     
 6 GILES             TN           2010  4210     2074530 47055     
 7 ANDREW            MO           2010  1167      315180 29003     
 8 LUNENBURG         VA           2010   763      246130 51111     
 9 HAWKINS           TN           2009  7148     3420480 47073     
10 ATCHISON          MO           2012   601      204700 29005     
# ℹ 20 more rows

Atypical names are those with punctuation, spaces, start with a number, or are just a number.

AD_rename <- rename(AD, `year!` = year)

filter(AD_rename, "year!" > 2013) # will not work correctly

# A tibble: 30 × 6
   BUYER_COUNTY      BUYER_STATE `year!` count DOSAGE_UNIT countyfips
   <chr>             <chr>         <dbl> <dbl>       <dbl> <chr>     
 1 EASTLAND          TX             2006  1980      723690 48133     
 2 FALLS CHURCH CITY VA             2014  1340      531270 51610     
 3 DOUGLAS           CO             2013 20961     6277640 08035     
 4 FAYETTE           GA             2012 12978     3701320 13113     
 5 STAFFORD          VA             2009  7921     2904600 51179     
 6 GILES             TN             2010  4210     2074530 47055     
 7 ANDREW            MO             2010  1167      315180 29003     
 8 LUNENBURG         VA             2010   763      246130 51111     
 9 HAWKINS           TN             2009  7148     3420480 47073     
10 ATCHISON          MO             2012   601      204700 29005     
# ℹ 20 more rows

Using backticks works!

filter(AD_rename, `year!` > 2013) 

# A tibble: 1 × 6
  BUYER_COUNTY      BUYER_STATE `year!` count DOSAGE_UNIT countyfips
  <chr>             <chr>         <dbl> <dbl>       <dbl> <chr>     
1 FALLS CHURCH CITY VA             2014  1340      531270 51610     

filter(AD, "BUYER_STATE" == "CO") # this will not work

# A tibble: 0 × 6
# ℹ 6 variables: BUYER_COUNTY <chr>, BUYER_STATE <chr>, year <dbl>,
#   count <dbl>, DOSAGE_UNIT <dbl>, countyfips <chr>

filter(AD, BUYER_STATE == "CO")# this works!

# A tibble: 2 × 6
  BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
  <chr>        <chr>       <dbl> <dbl>       <dbl> <chr>     
1 DOUGLAS      CO           2013 20961     6277640 08035     
2 PUEBLO       CO           2008 23932     9386790 08101     

Try not use anything special for the column names in filter(). This is why it is good to not use atypical column names. Then you can just use the column name!

Did the filter work the way you expected? Did the dimensions change?

https://media.giphy.com/media/5b5OU7aUekfdSAER5I/giphy.gif

A. filter() with |

B. filter() with &

• pull() to get values out of a data frame/tibble
• select() is the tidyverse way to get a tibble with only certain columns
• you can select() based on patterns in the column names
• you can also select() based on column class with the where() function
• you can combine multiple tidyselect functions together like select(starts_with("C"), ends_with("state"))
• you can combine multiple patterns with the c() function like select(starts_with(c("A", "C"))) (see extra slides at the end for more info!)
• filter() can be used to filter out rows based on logical conditions
• avoid using quotes when referring to column names with filter()

• == is the same as equivalent to
• & means both conditions must be met to remain after filter()
• | means either conditions needs to be met to remain after filter()

🏠 Class Website

💻 Lab

📃 Day 3 Cheatsheet

📃 Posit’s dplyr Cheatsheet

#read_csv("https://jhudatascience.org/intro_to_r/data/annualDosage.csv")
set.seed(1234)
AD <-slice_sample(annualDosage, n = 30)

You can combine filter and select to subset the rows and columns, respectively, of a data frame:

select(filter(AD, year > 2012), BUYER_STATE)

# A tibble: 5 × 1
  BUYER_STATE
  <chr>      
1 VA         
2 CO         
3 TX         
4 IA         
5 WV         

In R, the common way to perform multiple operations is to wrap functions around each other in a “nested” form.

head(select(AD, year, BUYER_STATE), 2)

# A tibble: 2 × 2
   year BUYER_STATE
  <dbl> <chr>      
1  2006 TX         
2  2014 VA         

select(filter(AD, year > 2000 & BUYER_STATE == "CO"), year, count)

# A tibble: 2 × 2
   year count
  <dbl> <dbl>
1  2013 20961
2  2008 23932

One can also create temporary objects and reassign them:

AD_CO <- filter(AD, year > 2000 & BUYER_STATE == "CO")
AD_CO <- select(AD_CO, year, count)

head(AD_CO)

# A tibble: 2 × 2
   year count
  <dbl> <dbl>
1  2013 20961
2  2008 23932

The pipe %>% makes this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut. Pipe AD into filter, then pipe that into select:

AD %>% filter(year > 2000 & BUYER_STATE == "CO") %>% select(year, count)

# A tibble: 2 × 2
   year count
  <dbl> <dbl>
1  2013 20961
2  2008 23932

There are multiple ways to write a pipe and you might see these (they work the same!):

     |>

     %>%
     

Note that %>% is not the same as %in% (which helps you check for a value in a vector).