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

📃 Day 2 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 work with data called er about heat-related ER visits between 2011 and 2022, as reported by the state of Colorado, specifically made available by the Colorado Environmental Public Health Tracking program website. Full dataset available at https://coepht.colorado.gov/heat-related-illness.

er <- 
  read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")

Rows: 768 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): county
dbl (5): rate, lower95cl, upper95cl, visits, year

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(er) # rows, columns

[1] 768   6

nrow(er) # number of rows

[1] 768

ncol(er) # 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(er)

Rows: 768
Columns: 6
$ county    <chr> "Adams", "Adams", "Adams", "Adams", "Adams", "Adams", "Adams…
$ rate      <dbl> 6.729918, 4.843983, 6.836648, 3.080950, 3.356538, 8.848504, …
$ lower95cl <dbl> NA, 2.848937, 4.359735, 1.711087, 1.892912, 6.124961, 4.2920…
$ upper95cl <dbl> 9.236776, NA, 9.313561, 4.846996, 5.232461, 11.572046, 8.977…
$ visits    <dbl> 29, 23, 31, 15, 16, 42, 32, 37, 36, 24, 35, 45, 0, 0, NA, 0,…
$ year      <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, …

This dataset has information about both the rate of ER visits for heat-related illness and the number of visits, broken out by both year and CO county.

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(er, n = 2)

# A tibble: 2 × 6
  county      rate lower95cl upper95cl visits  year
  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Teller        NA        NA        NA     NA  2021
2 Broomfield    NA        NA        NA     NA  2021

slice_sample(er, n = 2)

# A tibble: 2 × 6
  county    rate lower95cl upper95cl visits  year
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Mesa      8.88      4.53      14.7     12  2012
2 Huerfano  0         0          0        0  2020

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

er

# A tibble: 768 × 6
   county  rate lower95cl upper95cl visits  year
   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
 1 Adams   6.73     NA         9.24     29  2011
 2 Adams   4.84      2.85     NA        23  2012
 3 Adams   6.84      4.36      9.31     31  2013
 4 Adams   3.08      1.71      4.85     15  2014
 5 Adams   3.36      1.89      5.23     16  2015
 6 Adams   8.85      6.12     11.6      42  2016
 7 Adams   6.63      4.29      8.98     32  2017
 8 Adams   7.11      4.77      9.44     37  2018
 9 Adams   6.76      4.53      8.99     36  2019
10 Adams   4.76      2.82      6.70     24  2020
# ℹ 758 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 ER data for our next lesson

head(er)

# A tibble: 6 × 6
  county  rate lower95cl upper95cl visits  year
  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Adams   6.73     NA         9.24     29  2011
2 Adams   4.84      2.85     NA        23  2012
3 Adams   6.84      4.36      9.31     31  2013
4 Adams   3.08      1.71      4.85     15  2014
5 Adams   3.36      1.89      5.23     16  2015
6 Adams   8.85      6.12     11.6      42  2016

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

colnames(er)

[1] "county"    "rate"      "lower95cl" "upper95cl" "visits"    "year"     

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

Let’s rename lower95CI to lower_95_CI_limit. Notice the new name is listed first, similar to how a new object is assigned on the left!

The lower95CI is the lower bound of the estimated rate of ER visits for a particular county and year.

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

renamed_er<- rename(er, lower_95_CI_limit = lower95cl)
head(renamed_er)

# A tibble: 6 × 6
  county  rate lower_95_CI_limit upper95cl visits  year
  <chr>  <dbl>             <dbl>     <dbl>  <dbl> <dbl>
1 Adams   6.73             NA         9.24     29  2011
2 Adams   4.84              2.85     NA        23  2012
3 Adams   6.84              4.36      9.31     31  2013
4 Adams   3.08              1.71      4.85     15  2014
5 Adams   3.36              1.89      5.23     16  2015
6 Adams   8.85              6.12     11.6      42  2016

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

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

 # this will cause an error
renamed_er <- rename(er, lower_95%_CI_limit = lower95cl)

# this will work
renamed_er <- rename(er, `lower_95%_CI_limit` = lower95cl) 
head(renamed_er, 2)

# A tibble: 2 × 6
  county  rate `lower_95%_CI_limit` upper95cl visits  year
  <chr>  <dbl>                <dbl>     <dbl>  <dbl> <dbl>
1 Adams   6.73                NA         9.24     29  2011
2 Adams   4.84                 2.85     NA        23  2012

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 like % (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_er <- rename(er, ‘lower_95%_CI_limit’ = lower95cl)

# this will work!
renamed_er <- rename(er, 'lower_95%_CI_limit' = lower95cl)

Also true for double quotes

 # this will cause an error!
renamed_er <- rename(er, “lower_95%_CI_limit” = lower95cl)

# this will work!
renamed_er <- rename(er, "lower_95%_CI_limit" = lower95cl)

A comma can separate different column names to change.

renamed_er <- rename(er, 
                        lower_95perc_CI_limit = lower95cl, 
                        upper_95perc_CI_limit = upper95cl)
head(renamed_er, 3)

# A tibble: 3 × 6
  county  rate lower_95perc_CI_limit upper_95perc_CI_limit visits  year
  <chr>  <dbl>                 <dbl>                 <dbl>  <dbl> <dbl>
1 Adams   6.73                 NA                     9.24     29  2011
2 Adams   4.84                  2.85                 NA        23  2012
3 Adams   6.84                  4.36                  9.31     31  2013

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.

The yearly_co2_emissions dataset contains estimated CO2 emissions for 265 countries between the years 1751 and 2014.

yearly_co2 <- 
  read_csv("https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv")

Rows: 192 Columns: 265
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr   (1): country
dbl (264): 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761,...

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.

head(yearly_co2, n = 2)

# A tibble: 2 × 265
  country  `1751` `1752` `1753` `1754` `1755` `1756` `1757` `1758` `1759` `1760`
  <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Afghani…     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
2 Albania      NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
# ℹ 254 more variables: `1761` <dbl>, `1762` <dbl>, `1763` <dbl>, `1764` <dbl>,
#   `1765` <dbl>, `1766` <dbl>, `1767` <dbl>, `1768` <dbl>, `1769` <dbl>,
#   `1770` <dbl>, `1771` <dbl>, `1772` <dbl>, `1773` <dbl>, `1774` <dbl>,
#   `1775` <dbl>, `1776` <dbl>, `1777` <dbl>, `1778` <dbl>, `1779` <dbl>,
#   `1780` <dbl>, `1781` <dbl>, `1782` <dbl>, `1783` <dbl>, `1784` <dbl>,
#   `1785` <dbl>, `1786` <dbl>, `1787` <dbl>, `1788` <dbl>, `1789` <dbl>,
#   `1790` <dbl>, `1791` <dbl>, `1792` <dbl>, `1793` <dbl>, `1794` <dbl>, …

The clean_names function can intuit what fixes you might need. Here it make sure year names aren’t just a number, so that the colnames don’t need ticks or quotes to be used.

clean_names(yearly_co2)

# A tibble: 192 × 265
   country     x1751 x1752 x1753 x1754 x1755 x1756 x1757 x1758 x1759 x1760 x1761
   <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 Afghanistan    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 2 Albania        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 3 Algeria        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 4 Andorra        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 5 Angola         NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 6 Antigua an…    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 7 Argentina      NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 8 Armenia        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 9 Australia      NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
10 Austria        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# ℹ 182 more rows
# ℹ 253 more variables: x1762 <dbl>, x1763 <dbl>, x1764 <dbl>, x1765 <dbl>,
#   x1766 <dbl>, x1767 <dbl>, x1768 <dbl>, x1769 <dbl>, x1770 <dbl>,
#   x1771 <dbl>, x1772 <dbl>, x1773 <dbl>, x1774 <dbl>, x1775 <dbl>,
#   x1776 <dbl>, x1777 <dbl>, x1778 <dbl>, x1779 <dbl>, x1780 <dbl>,
#   x1781 <dbl>, x1782 <dbl>, x1783 <dbl>, x1784 <dbl>, x1785 <dbl>,
#   x1786 <dbl>, x1787 <dbl>, x1788 <dbl>, x1789 <dbl>, x1790 <dbl>, …

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 seattle_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://daseh.org/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

We’ll work with the CO heat-related ER visits dataset again.

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.

# er<-read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")
set.seed(1234)
er_30 <-slice_sample(er, n = 30)

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

pull(er_30, year)

 [1] 2018 2015 2021 2019 2014 2012 2017 2016 2012 2012 2017 2016 2020 2014 2020
[16] 2014 2011 2022 2018 2013 2017 2021 2015 2020 2012 2016 2013 2014 2017 2022

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

select(er_30, year)

# A tibble: 30 × 1
    year
   <dbl>
 1  2018
 2  2015
 3  2021
 4  2019
 5  2014
 6  2012
 7  2017
 8  2016
 9  2012
10  2012
# ℹ 20 more rows

A. pull()

B. select()

We can use select to select for multiple columns.

select(er_30, year, rate, county)

# A tibble: 30 × 3
    year  rate county    
   <dbl> <dbl> <chr>     
 1  2018  NA   Garfield  
 2  2015  NA   Chaffee   
 3  2021  15.9 Pueblo    
 4  2019   0   Rio Grande
 5  2014   0   Lake      
 6  2012  NA   Chaffee   
 7  2017  NA   Chaffee   
 8  2016   0   Teller    
 9  2012  NA   Prowers   
10  2012   0   Hinsdale  
# ℹ 20 more rows

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

head(er_30, 2)

# A tibble: 2 × 6
  county    rate lower95cl upper95cl visits  year
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Garfield    NA        NA        NA     NA  2018
2 Chaffee     NA        NA        NA     NA  2015

select(er_30, ends_with("cl"))

# A tibble: 30 × 2
   lower95cl upper95cl
       <dbl>     <dbl>
 1     NA         NA  
 2     NA         NA  
 3      9.54      22.2
 4      0          0  
 5      0          0  
 6     NA         NA  
 7     NA         NA  
 8      0          0  
 9     NA         NA  
10      0          0  
# ℹ 20 more rows

Here are a few:

last_col()
starts_with()
ends_with()
contains()

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

head(er_30, 2)

# A tibble: 2 × 6
  county    rate lower95cl upper95cl visits  year
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Garfield    NA        NA        NA     NA  2018
2 Chaffee     NA        NA        NA     NA  2015

select(er_30, ends_with("cl"), year)

# A tibble: 30 × 3
   lower95cl upper95cl  year
       <dbl>     <dbl> <dbl>
 1     NA         NA    2018
 2     NA         NA    2015
 3      9.54      22.2  2021
 4      0          0    2019
 5      0          0    2014
 6     NA         NA    2012
 7     NA         NA    2017
 8      0          0    2016
 9     NA         NA    2012
10      0          0    2012
# ℹ 20 more rows

Follows OR logic.

select(er_30, ends_with("cl"), starts_with("r"))

# A tibble: 30 × 3
   lower95cl upper95cl  rate
       <dbl>     <dbl> <dbl>
 1     NA         NA    NA  
 2     NA         NA    NA  
 3      9.54      22.2  15.9
 4      0          0     0  
 5      0          0     0  
 6     NA         NA    NA  
 7     NA         NA    NA  
 8      0          0     0  
 9     NA         NA    NA  
10      0          0     0  
# ℹ 20 more rows

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

The command in dplyr for subsetting rows is filter.

filter(er_30, year > 2020)

# A tibble: 4 × 6
  county   rate lower95cl upper95cl visits  year
  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Pueblo   15.9      9.54      22.2     26  2021
2 Otero    NA       NA         NA       NA  2022
3 Mesa     12.0      7.12      18.2     19  2021
4 Chaffee   0        0          0        0  2022

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

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(er_30, rate > 9 | year == 2012)

# A tibble: 6 × 6
  county    rate lower95cl upper95cl visits  year
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Pueblo    15.9      9.54      22.2     26  2021
2 Chaffee   NA       NA         NA       NA  2012
3 Prowers   NA       NA         NA       NA  2012
4 Hinsdale   0        0          0        0  2012
5 Mesa      12.0      7.12      18.2     19  2021
6 Phillips   0        0          0        0  2012

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(er_30, "year" > 2014)

# A tibble: 30 × 6
   county      rate lower95cl upper95cl visits  year
   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>
 1 Garfield    NA       NA         NA       NA  2018
 2 Chaffee     NA       NA         NA       NA  2015
 3 Pueblo      15.9      9.54      22.2     26  2021
 4 Rio Grande   0        0          0        0  2019
 5 Lake         0        0          0        0  2014
 6 Chaffee     NA       NA         NA       NA  2012
 7 Chaffee     NA       NA         NA       NA  2017
 8 Teller       0        0          0        0  2016
 9 Prowers     NA       NA         NA       NA  2012
10 Hinsdale     0        0          0        0  2012
# ℹ 20 more rows

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

er_30_rename <- rename(er_30, `year!` = year)

filter(er_30_rename, "year!" > 2013) # will not work correctly

# A tibble: 30 × 6
   county      rate lower95cl upper95cl visits `year!`
   <chr>      <dbl>     <dbl>     <dbl>  <dbl>   <dbl>
 1 Garfield    NA       NA         NA       NA    2018
 2 Chaffee     NA       NA         NA       NA    2015
 3 Pueblo      15.9      9.54      22.2     26    2021
 4 Rio Grande   0        0          0        0    2019
 5 Lake         0        0          0        0    2014
 6 Chaffee     NA       NA         NA       NA    2012
 7 Chaffee     NA       NA         NA       NA    2017
 8 Teller       0        0          0        0    2016
 9 Prowers     NA       NA         NA       NA    2012
10 Hinsdale     0        0          0        0    2012
# ℹ 20 more rows

Using backticks works!

filter(er_30_rename, `year!` > 2013) 

# A tibble: 23 × 6
   county      rate lower95cl upper95cl visits `year!`
   <chr>      <dbl>     <dbl>     <dbl>  <dbl>   <dbl>
 1 Garfield   NA        NA        NA        NA    2018
 2 Chaffee    NA        NA        NA        NA    2015
 3 Pueblo     15.9       9.54     22.2      26    2021
 4 Rio Grande  0         0         0         0    2019
 5 Lake        0         0         0         0    2014
 6 Chaffee    NA        NA        NA        NA    2017
 7 Teller      0         0         0         0    2016
 8 Boulder     3.51      1.77      5.83     12    2017
 9 Fremont    NA        NA        NA        NA    2016
10 Kiowa      NA        NA        NA        NA    2020
# ℹ 13 more rows

filter(er_30, "county" == "Denver") # this will not work

# A tibble: 0 × 6
# ℹ 6 variables: county <chr>, rate <dbl>, lower95cl <dbl>, upper95cl <dbl>,
#   visits <dbl>, year <dbl>

filter(er_30, county == "Denver")# this works!

# A tibble: 1 × 6
  county  rate lower95cl upper95cl visits  year
  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1 Denver  2.95      1.75      4.46     19  2013

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()

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#er <- read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")
set.seed(1234)
er_30 <-slice_sample(er, n = 30)

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

select(filter(er_30, year > 2012), county)

# A tibble: 25 × 1
   county    
   <chr>     
 1 Garfield  
 2 Chaffee   
 3 Pueblo    
 4 Rio Grande
 5 Lake      
 6 Chaffee   
 7 Teller    
 8 Boulder   
 9 Fremont   
10 Kiowa     
# ℹ 15 more rows

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

head(select(er_30, year, county), 2)

# A tibble: 2 × 2
   year county  
  <dbl> <chr>   
1  2018 Garfield
2  2015 Chaffee 

select(filter(er_30, year > 2000 & county == "Denver"), year, rate)

# A tibble: 1 × 2
   year  rate
  <dbl> <dbl>
1  2013  2.95

One can also create temporary objects and reassign them:

er_30_Den <- filter(er_30, year > 2000 & county == "Denver")
er_30_Den <- select(er_30_Den, year, rate)

head(er_30_Den)

# A tibble: 1 × 2
   year  rate
  <dbl> <dbl>
1  2013  2.95

The pipe %>% makes this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut. Pipe er_30 into filter, then pipe that into select:

er_30 %>% filter(year > 2000 & county == "Denver") %>% select(year, rate)

# A tibble: 1 × 2
   year  rate
  <dbl> <dbl>
1  2013  2.95

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

     |>

     %>%

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

The select function can reorder columns.

head(er_30, 2)

# A tibble: 2 × 7
  county    rate lower95cl upper95cl visits  year newcol
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Garfield    NA        NA        NA     NA  2018     NA
2 Chaffee     NA        NA        NA     NA  2015     NA

er_30 %>% select(year, rate, county) %>%
head(2)

# A tibble: 2 × 3
   year  rate county  
  <dbl> <dbl> <chr>   
1  2018    NA Garfield
2  2015    NA Chaffee 

The arrange function can reorder rows By default, arrange orders in increasing order:

arrange(er_30, year)

# A tibble: 30 × 7
   county      rate lower95cl upper95cl visits  year newcol
   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
 1 Saguache    0         0         0         0  2011   0   
 2 Chaffee    NA        NA        NA        NA  2012  NA   
 3 Prowers    NA        NA        NA        NA  2012  NA   
 4 Hinsdale    0         0         0         0  2012   0   
 5 Phillips    0         0         0         0  2012   0   
 6 Denver      2.95      1.75      4.46     19  2013   1.47
 7 San Miguel NA        NA        NA        NA  2013  NA   
 8 Lake        0         0         0         0  2014   0   
 9 Delta       0         0         0         0  2014   0   
10 Adams       3.08      1.71      4.85     15  2014   1.54
# ℹ 20 more rows

Use the desc to arrange the rows in descending order:

arrange(er_30, desc(year))

# A tibble: 30 × 7
   county      rate lower95cl upper95cl visits  year newcol
   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
 1 Otero       NA       NA         NA       NA  2022  NA   
 2 Chaffee      0        0          0        0  2022   0   
 3 Pueblo      15.9      9.54      22.2     26  2021   7.95
 4 Mesa        12.0      7.12      18.2     19  2021   6.01
 5 Kiowa       NA       NA         NA       NA  2020  NA   
 6 Park        NA       NA         NA       NA  2020  NA   
 7 Rio Blanco  NA       NA         NA       NA  2020  NA   
 8 Rio Grande   0        0          0        0  2019   0   
 9 Garfield    NA       NA         NA       NA  2018  NA   
10 Dolores      0        0          0        0  2018   0   
# ℹ 20 more rows

You can combine increasing and decreasing orderings:

arrange(er_30, rate, desc(year)) %>% head(n = 2)

# A tibble: 2 × 7
  county      rate lower95cl upper95cl visits  year newcol
  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Chaffee        0         0         0      0  2022      0
2 Rio Grande     0         0         0      0  2019      0

arrange(er_30, desc(year), rate) %>% head(n = 2)

# A tibble: 2 × 7
  county   rate lower95cl upper95cl visits  year newcol
  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Chaffee     0         0         0      0  2022      0
2 Otero      NA        NA        NA     NA  2022     NA

A. filter()

B. select()

C. mutate()

A. Get the er_30 data, then filter it for rows with year values over 2020, then select only the year and rate columns.

B. Get the er_30 data, then filter it rows with year values over 2020, then select for rows that have values for year and rate.

• select() and filter() can be combined together
• you can do sequential steps in a few ways:
  1. nesting them inside one another using parentheses ()
  2. creating intermediate data objects in between
  3. using pipes %>% (like “then” statements)
• select() and relocate() can be used to reorder columns
• arrange() can be used to reorder rows
• can remove rows with filter()
• can remove a column in a few ways:
  1. using select() with exclamation mark in front of column name(s)
     
  2. not selecting it (without exclamation mark)

• mutate() can be used to create new variables or modify them

# General format - Not the code!
{data object to update} <- mutate({data to use}, 
                                {new variable name} = {new variable source}) 

er_30 <- mutate(ER_30, newcol = count/2.2)

The $ operator is similar to pull(). This is the base R way to do this:

er_30$year

 [1] 2018 2015 2021 2019 2014 2012 2017 2016 2012 2012 2017 2016 2020 2014 2020
[16] 2014 2011 2022 2018 2013 2017 2021 2015 2020 2012 2016 2013 2014 2017 2022

Although it is easier (for this one task), mixing and matching the $ operator with tidyverse functions usually doesn’t work. Therefore, we want to let you know about it in case you see it, but we suggest that you try working with the tidyverse way.

You can add a new column (or modify an existing one) using the $ operator instead of mutate.

Just want you to be aware of this as it is very common.

er_30$newcol <- er_30$rate/2.2
head(er_30,3)

# A tibble: 3 × 7
  county    rate lower95cl upper95cl visits  year newcol
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Garfield  NA       NA         NA       NA  2018  NA   
2 Chaffee   NA       NA         NA       NA  2015  NA   
3 Pueblo    15.9      9.54      22.2     26  2021   7.22

Even though $ is easier for creating new columns, mutate is really powerful, so it’s worth getting used to.

🏠 Class Website

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Image by Gerd Altmann from Pixabay

Need to combine the patterns with the c() function.

select(er_30, ends_with("cl"), starts_with("r"))

# A tibble: 30 × 3
   lower95cl upper95cl  rate
       <dbl>     <dbl> <dbl>
 1     NA         NA    NA  
 2     NA         NA    NA  
 3      9.54      22.2  15.9
 4      0          0     0  
 5      0          0     0  
 6     NA         NA    NA  
 7     NA         NA    NA  
 8      0          0     0  
 9     NA         NA    NA  
10      0          0     0  
# ℹ 20 more rows

select(er_30, starts_with(c("r", "l"))) # here we combine two patterns

# A tibble: 30 × 2
    rate lower95cl
   <dbl>     <dbl>
 1  NA       NA   
 2  NA       NA   
 3  15.9      9.54
 4   0        0   
 5   0        0   
 6  NA       NA   
 7  NA       NA   
 8   0        0   
 9  NA       NA   
10   0        0   
# ℹ 20 more rows

test <- tibble(A = c(1,2,3,4), B = c(1,2,3,4))
test

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

# These are technically the same but >= is easier to read
# Separating can cause issues
filter(test,  B > 2 | B==2)

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

filter(test, B >= 2)

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

Order can matter. Think of individual statements separately first.

filter(test,  A>3 | B==2 & B>2) # A is greater than 3 or B is equal to 2 AND (think but also) B must be greater than 2 , thus 2 is dropped.

# A tibble: 1 × 2
      A     B
  <dbl> <dbl>
1     4     4

filter(test,  A>3 & B>2 | B==2) # A is greater than 3 AND B is greater than 2 leaving only 4s OR B is equal to 2, (since this comes later, 2 is preserved)

# A tibble: 2 × 2
      A     B
  <dbl> <dbl>
1     2     2
2     4     4

Instead of removing rows like filter, which() simply shows where the values occur if they pass a specific condition. We will see that this can be helpful later when we want to select and filter in more complicated ways.

which(select(er_30, year) == 2012)

[1]  6  9 10 25

select(er_30, year) == 2012 %>% head(10)

       year
 [1,] FALSE
 [2,] FALSE
 [3,] FALSE
 [4,] FALSE
 [5,] FALSE
 [6,]  TRUE
 [7,] FALSE
 [8,] FALSE
 [9,]  TRUE
[10,]  TRUE
[11,] FALSE
[12,] FALSE
[13,] FALSE
[14,] FALSE
[15,] FALSE
[16,] FALSE
[17,] FALSE
[18,] FALSE
[19,] FALSE
[20,] FALSE
[21,] FALSE
[22,] FALSE
[23,] FALSE
[24,] FALSE
[25,]  TRUE
[26,] FALSE
[27,] FALSE
[28,] FALSE
[29,] FALSE
[30,] FALSE

er_30$year <- NULL

We can use the colnames function to extract and/or directly reassign column names of df:

colnames(er_30) # just prints

[1] "county"    "rate"      "lower95cl" "upper95cl" "visits"    "year"     
[7] "newcol"   

colnames(er_30)[1:2] <- c("County", "Rate") # reassigns
head(er_30)

# A tibble: 6 × 7
  County      Rate lower95cl upper95cl visits  year newcol
  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Garfield    NA       NA         NA       NA  2018  NA   
2 Chaffee     NA       NA         NA       NA  2015  NA   
3 Pueblo      15.9      9.54      22.2     26  2021   7.22
4 Rio Grande   0        0          0        0  2019   0   
5 Lake         0        0          0        0  2014   0   
6 Chaffee     NA       NA         NA       NA  2012  NA   

Let’s select rows 1 and 3 from df using brackets:

er_30[ c(1, 3), ]

# A tibble: 2 × 7
  County    Rate lower95cl upper95cl visits  year newcol
  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
1 Garfield  NA       NA         NA       NA  2018  NA   
2 Pueblo    15.9      9.54      22.2     26  2021   7.22

We can also subset a data frame using the bracket [, ] subsetting.

For data frames and matrices (2-dimensional objects), the brackets are [rows, columns] subsetting. We can grab the x column using the index of the column or the column name (“year”)

er_30[, 6]

# A tibble: 30 × 1
    year
   <dbl>
 1  2018
 2  2015
 3  2021
 4  2019
 5  2014
 6  2012
 7  2017
 8  2016
 9  2012
10  2012
# ℹ 20 more rows

er_30[, "year"]

# A tibble: 30 × 1
    year
   <dbl>
 1  2018
 2  2015
 3  2021
 4  2019
 5  2014
 6  2012
 7  2017
 8  2016
 9  2012
10  2012
# ℹ 20 more rows

We can select multiple columns using multiple column names:

er_30[, c("County", "Rate")]

# A tibble: 30 × 2
   County      Rate
   <chr>      <dbl>
 1 Garfield    NA  
 2 Chaffee     NA  
 3 Pueblo      15.9
 4 Rio Grande   0  
 5 Lake         0  
 6 Chaffee     NA  
 7 Chaffee     NA  
 8 Teller       0  
 9 Prowers     NA  
10 Hinsdale     0  
# ℹ 20 more rows