Data Summarization

Recap

select(): subset and/or reorder columns
filter(): remove rows
arrange(): reorder rows
mutate(): create new columns or modify them
select() and filter() can be combined together
remove a column: select() with ! mark (!col_name)
you can do sequential steps: especially using pipes %>%

The Data

We can use the CO heat-related ER visits dataset to explore different ways of summarizing data.

Reminder: This dataset contains information about the number and rate of visits for heat-related illness to ERs in Colorado from 2011-2022, adjusted for age.

The Data

We can use the CO heat-related ER visits dataset to explore different ways of summarizing data.

The head function displays the first rows of an object:

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

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

Behavior of `pull()` function

pull() converts a single data column into a vector.

er %>% pull(visits)

Data Summarization

Now that we have a vector of numbers.. what can we do with it?

Basic statistical summarization
- mean(x): takes the mean of x
- sd(x): takes the standard deviation of x
- median(x): takes the median of x
- quantile(x): displays sample quantiles of x. Default is min, IQR, max
- range(x): displays the range. Same as c(min(x), max(x))
- sum(x): sum of x
- max(x): maximum value in x
- min(x): minimum value in x

Pipe (`%>%`) vectors into summarizing functions

A vector can be summarized:

er %>% pull(visits) %>% mean()

[1] NA

Add the na.rm = argument for missing data

er %>% pull(visits) %>% mean(na.rm=T)

[1] 7.189247

GUT CHECK!

What kind of object do we need to run summary operators like mean() ?

A. A vector of numbers

B. A vector of characters

C. A dataset

Summarization on tibbles (data frames)

Summarize the data: `dplyr` `summarize()` function

summarize works on datasets without pull().

Multiple summary statistics can be calculated at once!

# General format - Not the code!
{data to use} %>% 
   summarize({summary column name} = {function(source column)},
             {summary column name} = {function(source column)})

Summarize the data: `dplyr` `summarize()` function

er %>% 
  summarize(mean_visits = mean(visits))

# A tibble: 1 × 1
  mean_visits
        <dbl>
1          NA

er %>% 
  summarize(mean_visits = mean(visits, na.rm = TRUE))

# A tibble: 1 × 1
  mean_visits
        <dbl>
1        7.19

Summarize the data: `dplyr` `summarize()` function

summarize() can do multiple operations at once. Just separate by a comma.

er %>% 
  summarize(mean_visits = mean(visits, na.rm = TRUE),
            median_visits = median(visits, na.rm = TRUE),
            mean_rate = mean(rate, na.rm = TRUE))

# A tibble: 1 × 3
  mean_visits median_visits mean_rate
        <dbl>         <dbl>     <dbl>
1        7.19             0      2.43

Summarize the data: `dplyr` `summarize()` function

Note that summarize() creates a separate tibble from the original data.

If you want to save a summary statistic in the original data, use mutate() instead to create a new column for the summary statistic.

`summary()` Function

Using summary() can give you rough snapshots of each numeric column (character columns are skipped):

summary(er)

    county               rate          lower95cl        upper95cl      
 Length:768         Min.   : 0.000   Min.   : 0.000   Min.   :  0.000  
 Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:  0.000  
 Mode  :character   Median : 0.000   Median : 0.000   Median :  0.000  
                    Mean   : 2.431   Mean   : 1.449   Mean   :  3.526  
                    3rd Qu.: 3.509   3rd Qu.: 1.895   3rd Qu.:  5.173  
                    Max.   :89.275   Max.   :43.398   Max.   :151.420  
                    NA's   :303      NA's   :304      NA's   :304      
     visits            year     
 Min.   : 0.000   Min.   :2011  
 1st Qu.: 0.000   1st Qu.:2014  
 Median : 0.000   Median :2016  
 Mean   : 7.189   Mean   :2016  
 3rd Qu.:13.000   3rd Qu.:2019  
 Max.   :48.000   Max.   :2022  
 NA's   :303

Summary & Lab Part 1

pull() creates a vector
don’t forget the na.rm = TRUE argument!
summary(x): quantile information
summarize: creates a summary table of columns of interest
summary stats (mean()) work with vectors or with summarize()

🏠 Class Website

💻 Lab

`distinct()` values

distinct(x) will return the unique elements of column x.

er %>%
  distinct(county)

# A tibble: 64 × 1
   county    
   <chr>     
 1 Adams     
 2 Alamosa   
 3 Arapahoe  
 4 Archuleta 
 5 Baca      
 6 Bent      
 7 Boulder   
 8 Broomfield
 9 Chaffee   
10 Cheyenne  
# ℹ 54 more rows

How many `distinct()` values?

n_distinct() tells you the number of unique elements.

It needs a vector so you must pull the column first!

er %>%
  pull(county) %>% 
  n_distinct()

[1] 64

Use `count()` to return row count per category.

er %>% count(county)

# A tibble: 64 × 2
   county         n
   <chr>      <int>
 1 Adams         12
 2 Alamosa       12
 3 Arapahoe      12
 4 Archuleta     12
 5 Baca          12
 6 Bent          12
 7 Boulder       12
 8 Broomfield    12
 9 Chaffee       12
10 Cheyenne      12
# ℹ 54 more rows

Looks like 12 rows/observations per county!

Multiple columns listed further subdivides the `count()`

er %>% count(county, year)

# A tibble: 768 × 3
   county  year     n
   <chr>  <dbl> <int>
 1 Adams   2011     1
 2 Adams   2012     1
 3 Adams   2013     1
 4 Adams   2014     1
 5 Adams   2015     1
 6 Adams   2016     1
 7 Adams   2017     1
 8 Adams   2018     1
 9 Adams   2019     1
10 Adams   2020     1
# ℹ 758 more rows

Looks like 1 row/observation per county and year!

GUT CHECK!

The count() function can help us tally:

A. Sample size

B. Rows per each category

C. How many categories

Grouping

Goal

We want to find the mean number of ER visits per year in the dataset.

How do we do this?

Perform Operations By Groups: dplyr

First, let’s group the data.

group_by allows you group the data set by variables/columns you specify:

er_grouped <- er %>% group_by(year)
er_grouped

# A tibble: 768 × 6
# Groups:   year [12]
   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

Summarize the grouped data

It’s grouped! Grouping doesn’t change the data in any way, but how functions operate on it. Now we can summarize visits by group:

er_grouped %>% 
  summarize(avg_visits = mean(visits, na.rm = TRUE))

# A tibble: 12 × 2
    year avg_visits
   <dbl>      <dbl>
 1  2011       5.20
 2  2012       5.89
 3  2013       5.63
 4  2014       4.12
 5  2015       6.4 
 6  2016      10.1 
 7  2017       7.24
 8  2018      11.7 
 9  2019       9.12
10  2020       6.26
11  2021       8.06
12  2022       9.29

Do it in one step: use `%>%` to string these together!

Pipe er into group_by, then pipe that into summarize:

er %>%
  group_by(year) %>%
  summarize(avg_visits = mean(visits, na.rm = TRUE))

# A tibble: 12 × 2
    year avg_visits
   <dbl>      <dbl>
 1  2011       5.20
 2  2012       5.89
 3  2013       5.63
 4  2014       4.12
 5  2015       6.4 
 6  2016      10.1 
 7  2017       7.24
 8  2018      11.7 
 9  2019       9.12
10  2020       6.26
11  2021       8.06
12  2022       9.29

Group by as many variables as you want

group_by county and year:

er %>%
  group_by(year, county) %>%
  summarize(avg_visits = mean(visits, na.rm = TRUE))

# A tibble: 768 × 3
# Groups:   year [12]
    year county     avg_visits
   <dbl> <chr>           <dbl>
 1  2011 Adams              29
 2  2011 Alamosa             0
 3  2011 Arapahoe           33
 4  2011 Archuleta           0
 5  2011 Baca                0
 6  2011 Bent                0
 7  2011 Boulder            12
 8  2011 Broomfield        NaN
 9  2011 Chaffee             0
10  2011 Cheyenne            0
# ℹ 758 more rows

Counting rows/observations

There are other summarizing functions, such as n() count the number of rows/observations (NAs included).

er %>%
  group_by(year) %>%
  summarize(n = n(),
            mean = mean(visits, na.rm = TRUE))

# A tibble: 12 × 3
    year     n  mean
   <dbl> <int> <dbl>
 1  2011    64  5.20
 2  2012    64  5.89
 3  2013    64  5.63
 4  2014    64  4.12
 5  2015    64  6.4 
 6  2016    64 10.1 
 7  2017    64  7.24
 8  2018    64 11.7 
 9  2019    64  9.12
10  2020    64  6.26
11  2021    64  8.06
12  2022    64  9.29

Counting: `count()` and `n()`

count() and n() can give very similar information.

# Here we use count()
er %>% count(year)

# A tibble: 12 × 2
    year     n
   <dbl> <int>
 1  2011    64
 2  2012    64
 3  2013    64
 4  2014    64
 5  2015    64
 6  2016    64
 7  2017    64
 8  2018    64
 9  2019    64
10  2020    64
11  2021    64
12  2022    64

Counting: `count()` and `n()`

count() and n() can give very similar information.

# n() with summarize
er %>% group_by(year) %>% summarize(n())

# A tibble: 12 × 2
    year `n()`
   <dbl> <int>
 1  2011    64
 2  2012    64
 3  2013    64
 4  2014    64
 5  2015    64
 6  2016    64
 7  2017    64
 8  2018    64
 9  2019    64
10  2020    64
11  2021    64
12  2022    64

A few miscellaneous topics ..

Base R functions you might see: `length` and `unique`

These functions require a column as a vector using pull().

er_year <- er %>% pull(year) # pull() to make a vector

er_year %>% unique() # similar to distinct()

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

Base R functions you might see: `length` and `unique`

These functions require a column as a vector using pull().

er_year %>% unique() %>% length() # similar to n_distinct()

[1] 12

`summary()` vs. `summarize()`

summary() (base R) gives statistics table on a dataset.
summarize() (dplyr) creates a more customized summary tibble/dataframe.

Summary

count(x): what unique values do you have?
- distinct(): what are the distinct values?
- n_distinct() with pull(): how many distinct values?
group_by(): changes all subsequent functions
- combine with summarize() to get statistics per group
summarize() with n() gives the count (NAs included)

Lab Part 2

🏠 Class Website

💻 Lab

📃 Day 4 Cheatsheet

📃 Posit’s data transformation Cheatsheet

For more advanced learning:

https://www.danieldsjoberg.com/gtsummary/ for tables
extra slides in this file.

The End

Image by Gerd Altmann from Pixabay

Extra Slides: More advanced summarization

Data Summarization on data frames

Statistical summarization across the data frame
- rowMeans(x): takes the means of each row of x
- colMeans(x): takes the means of each column of x
- rowSums(x): takes the sum of each row of x
- colSums(x): takes the sum of each column of x

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

`rowMeans()` example

Get means for each row.

Let’s see what the mean CO2 emissions is across the years 2010-2014 for each row (country):

yearly_co2 %>%
  select(starts_with("201")) %>%
  rowMeans(na.rm = TRUE) %>%
  head(n = 5)

[1]  10254   5106 129800    487  32040

yearly_co2 %>%
  group_by(country) %>%
  summarize(mean = rowMeans(across(starts_with("201")), na.rm = TRUE)) %>%
  head(n = 5)

# A tibble: 5 × 2
  country       mean
  <chr>        <dbl>
1 Afghanistan  10254
2 Albania       5106
3 Algeria     129800
4 Andorra        487
5 Angola       32040

`colMeans()` example

Get means for each column.

Let’s see what the mean is across each column (year):

yearly_co2 %>%
  select(starts_with("201")) %>%
  colMeans(na.rm = TRUE) %>%
  head(n = 5)

    2010     2011     2012     2013     2014 
165334.1 171764.9 174033.4 174856.2 175992.5

yearly_co2 %>%
  summarize(across(starts_with("201"), ~mean(.x, na.rm = TRUE)))

# A tibble: 1 × 5
   `2010`  `2011`  `2012`  `2013`  `2014`
    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 165334. 171765. 174033. 174856. 175993.

* New! * Many dplyr functions now have a `.by=` argument

Pipe er into group_by, then pipe that into summarize:

er %>%
  group_by(year) %>%
  summarize(avg_visits = mean(visits, na.rm = TRUE),
            max_visits = max(visits, na.rm = TRUE))

is the same as..

er %>%
  summarize(avg_visits = mean(visits, na.rm = TRUE),
            max_visits = max(visits, na.rm = TRUE),
            .by = year)

Recap

The Data

The Data

Behavior of pull() function

Data Summarization

Pipe (%>%) vectors into summarizing functions

GUT CHECK!

Summarization on tibbles (data frames)

Summarize the data: dplyr summarize() function

Summarize the data: dplyr summarize() function

Summarize the data: dplyr summarize() function

Summarize the data: dplyr summarize() function

summary() Function

Summary & Lab Part 1

distinct() values

How many distinct() values?

Use count() to return row count per category.

Multiple columns listed further subdivides the count()

GUT CHECK!

Grouping

Goal

Perform Operations By Groups: dplyr

Summarize the grouped data

Do it in one step: use %>% to string these together!

Group by as many variables as you want

Counting rows/observations

Counting: count() and n()

Counting: count() and n()

A few miscellaneous topics ..

Base R functions you might see: length and unique

Base R functions you might see: length and unique

summary() vs. summarize()

Summary

Lab Part 2

Extra Slides: More advanced summarization

Data Summarization on data frames

rowMeans() example

colMeans() example

* New! * Many dplyr functions now have a .by= argument

Behavior of `pull()` function

Pipe (`%>%`) vectors into summarizing functions

Summarize the data: `dplyr` `summarize()` function

Summarize the data: `dplyr` `summarize()` function

Summarize the data: `dplyr` `summarize()` function

Summarize the data: `dplyr` `summarize()` function

`summary()` Function

`distinct()` values

How many `distinct()` values?

Use `count()` to return row count per category.

Multiple columns listed further subdivides the `count()`

Do it in one step: use `%>%` to string these together!

Counting: `count()` and `n()`

Counting: `count()` and `n()`

Base R functions you might see: `length` and `unique`

Base R functions you might see: `length` and `unique`

`summary()` vs. `summarize()`

`rowMeans()` example

`colMeans()` example

* New! * Many dplyr functions now have a `.by=` argument