Week 1 - Getting Situated in R and tidyverse

Emorie D Beck

Course Goals & Learning Outcomes

Understand the cognitive and psychological underpinnings of perceiving data visualization.
Identify good data visualizations and describe what makes them good.
Produce data visualizations according to types of questions, data, and more, with a particular emphasis on building a toolbox that you can carry into your own research.

Course Expectations

~50% of the course will be in R
You will get the most from this course if you:
- have your own data you can apply course content to
- know how to clean clean, transform, and manage that data
- today’s workshop is a good litmus test for this

Course Materials

All materials (required and optional) are free and online
- Wickham & Grolemond: R for Data Science https://r4ds.had.co.nz
- Wickham: Advanced R http://adv-r.had.co.nz
- Wilke: Fundamentals of Data Visualization https://clauswilke.com/dataviz/
- Healy: Data Visualization: A Practical Introduction https://socviz.co
- Data Camp: All paid content unlocked

Assignments

Assignment Weights	Percent
Class Participation	20%
Response Papers + Visualizations	20%
Final Project Proposal	10%
Class Presentation	20%
Final Project	30%
Total	100%

Assignments

Class Participation

There are lots of ways to participate, both in and outside class meetings
Classes will be technologically hybrid
The goal of this is for accessibility and to create recordings
If you need to miss 2 classes (i.e. 20% of total class time), maybe consider taking the course in a different year
If you must miss class, please let me know ahead of time if possible

Assignments

Response Papers / Visualizations

The main homework in the course is your weekly visualization assignment
The goal is to demonstrate how the principles and skills you learn in the class function “in the wild.”
These should be fun and not taken too seriously! No one is judging you for a pulling a graphic from Instagram instead of Nature.
Due 12:01 AM each Monday (last class is “free points”)

Assigments

Final Projects

I don’t want you to walk out of this course and not know how to apply what you learned
Final project replaces final exam (there are no exams)
Create a visualization for an ongoing project!
- Stage 1: Proposal (due 10/31/22)
- Stage 2: 1-on-1 meetings + feedback
- Stage 3: In-class presentations (11/28)
- Stage 4: Final visualization + brief write-up (??)

Assignments

Extra Credit

Lots of job searches this fall
1 pt extra credit for each one you:
- go to,
- take a snap of a data viz,
- and critique it according to what you’ve learned in class
max 5 pts

Class Time

~5-10 min: welcome and review (if needed)
~10-20 min: data viz of the week
~30-45 min: discussion of readings
~5-10 min: break
~60-90 min: workshop
~20-30 min: open lab

Course Topics

Intro and Overview
Cognitive Perspectives
Proportions and Probability
Differences and Associations
Change and Time Series
Uncertainty
Piecing Visualizations Together

Polishing Visualizations
Interactive Visualizations Additional Topics:

Spatial Information
Automated Reports
Diagrams
More?

Questions on the Syllabus and Course?

Data Viz of the Week and Discussion

Good or Bad?

Data Visualization

Why Should I Care About Data Visualization

Summarizing huge amounts of information
Seeing the forest and the trees
Errors in probabilistic reasoning
It’s fun!

Why Visualize Data in R

Open source and freeeee
Flexible
Reproducible
Flexible formatting / output
Lots of model- and package-specific support
Did I mention free?

Why Use RStudio (Pivot)

Also free
Basically a GUI for R
Organize files, import data, etc. with ease
RMarkdown, Quarto, and more are powerful tools (they were used to create these slides!)
Lots of new features and support

Why Use the `tidyverse`

Maintained by RStudio (Pivot)
No one should have to use a for loop to change data from long to wide
Tons of integrated tools for data cleaning, manipulation, transformation, and visualization
Even increasing support for modeling (e.g., tidymodels)

Why Use the `tidyverse`

Goals for Today

Review core principles of:
- dyplr (data manipulation)
- tidyr (data transformation and reshaping)

Data Manipulation in dplyr

Core Functions

%>%: The pipe. Read as “and then.”
filter(): Pick observations (rows) by their values.
select(): Pick variables (columns) by their names.
arrange(): Reorder the rows.
group_by(): Implicitly split the data set by grouping by names (columns).
mutate(): Create new variables with functions of existing variables.
summarize() / summarise(): Collapse many values down to a single summary.

Core Functions

%>%
filter()
select()
arrange()
group_by()
mutate()
summarize()

Although each of these functions are powerful alone, they are incredibly powerful in conjunction with one another. So below, I’ll briefly introduce each function, then link them all together using an example of basic data cleaning and summary.

1. `%>%`

The pipe %>% is wonderful. It makes coding intuitive. Often in coding, you need to use so-called nested functions. For example, you might want to round a number after taking the square of 43.

sqrt(43)

[1] 6.557439

round(sqrt(43), 2)

[1] 6.56

1. `%>%`

The issue with this comes whenever we need to do a series of operations on a data set or other type of object. In such cases, if we run it in a single call, then we have to start in the middle and read our way out.

round(sqrt(43/2), 2)

[1] 4.64

1. `%>%`

The pipe solves this by allowing you to read from left to right (or top to bottom). The easiest way to think of it is that each call of %>% reads and operates as “and then.” So with the rounded square root of 43, for example:

sqrt(43) %>%
  round(2)

[1] 6.56

2. `filter()`

Often times, when conducting research (experiments or otherwise), there are observations (people, specific trials, etc.) that you don’t want to include.

data(bfi) # grab the bfi data from the psych package
bfi <- bfi %>% as_tibble()
head(bfi)

# A tibble: 6 × 28
     A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2    E3
  <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1     2     4     3     4     4     2     3     3     4     4     3     3     3
2     2     4     5     2     5     5     4     4     3     4     1     1     6
3     5     4     5     4     4     4     5     4     2     5     2     4     4
4     4     4     6     5     5     4     4     3     5     5     5     3     4
5     2     3     3     4     5     4     4     5     3     2     2     2     5
6     6     6     5     6     5     6     6     6     1     3     2     1     6
# … with 15 more variables: E4 <int>, E5 <int>, N1 <int>, N2 <int>, N3 <int>,
#   N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>, O4 <int>, O5 <int>,
#   gender <int>, education <int>, age <int>

2. `filter()`

Often times, when conducting research (experiments or otherwise), there are observations (people, specific trials, etc.) that you don’t want to include.

summary(bfi$age) # get age descriptives

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00   20.00   26.00   28.78   35.00   86.00

2. `filter()`

Often times, when conducting research (experiments or otherwise), there are observations (people, specific trials, etc.) that you don’t want to include.

bfi2 <- bfi %>% # see a pipe!
  filter(age <= 18) # filter to age up to 18

summary(bfi2$age) # summary of the new data

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    3.0    16.0    17.0    16.3    18.0    18.0

But this isn’t quite right. We still have folks below 12. But, the beauty of filter() is that you can do sequence of OR and AND statements when there is more than one condition, such as up to 18 AND at least 12.

2. `filter()`

Often times, when conducting research (experiments or otherwise), there are observations (people, specific trials, etc.) that you don’t want to include.

bfi2 <- bfi %>%
  filter(age <= 18 & age >= 12) # filter to age up to 18 and at least 12

summary(bfi2$age) # summary of the new data

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   12.0    16.0    17.0    16.4    18.0    18.0

Got it!

2. `filter()`

But filter works for more use cases than just conditional
- <, >, <=, and >=
It can also be used for cases where we want a single values to match cases with text.
To do that, let’s convert one of the variables in the bfi data frame to a string.
So let’s change gender (1 = male, 2 = female) to text (we’ll get into factors later).

bfi$education <- plyr::mapvalues(bfi$education, 1:5, c("Below HS", "HS", "Some College", "College", "Higher Degree"))

2. `filter()`

Now let’s try a few things:

1. Create a data set with only individuals with some college (==).

bfi2 <- bfi %>% 
  filter(education == "Some College")
unique(bfi2$education)

[1] "Some College"

2. `filter()`

Now let’s try a few things:

2. Create a data set with only people age 18 (==).

bfi2 <- bfi %>%
  filter(age == 18)
summary(bfi2$age)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     18      18      18      18      18      18

2. `filter()`

Now let’s try a few things:

3. Create a data set with individuals with some college or above (%in%).

bfi2 <- bfi %>%
  filter(education %in% c("Some College", "College", "Higher Degree"))
unique(bfi2$education)

[1] "Some College"  "Higher Degree" "College"

%in% is great. It compares a column to a vector rather than just a single value, you can compare it to several

bfi2 <- bfi %>%
  filter(age %in% 12:18)
summary(bfi2$age)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   12.0    16.0    17.0    16.4    18.0    18.0

3. `select()`

If filter() is for pulling certain observations (rows), then select() is for pulling certain variables (columns).
it’s good practice to remove these columns to stop your environment from becoming cluttered and eating up your RAM.

3. `select()`

In our bfi data, most of these have been pre-removed, so instead, we’ll imagine we don’t want to use any indicators of Agreeableness (A1-A5) and that we aren’t interested in gender.
With select(), there are few ways choose variables. We can bare quote name the ones we want to keep, bare quote names we want to remove, or use any of a number of select() helper functions.

3. `select()`:

A. Bare quote columns we want to keep:

bfi %>%
  select(C1, C2, C3, C4, C5) %>%
  print(n = 6)

# A tibble: 2,800 × 5
     C1    C2    C3    C4    C5
  <int> <int> <int> <int> <int>
1     2     3     3     4     4
2     5     4     4     3     4
3     4     5     4     2     5
4     4     4     3     5     5
5     4     4     5     3     2
6     6     6     6     1     3
# … with 2,794 more rows

bfi %>%
  select(C1:C5) %>%
  print(n = 6)

# A tibble: 2,800 × 5
     C1    C2    C3    C4    C5
  <int> <int> <int> <int> <int>
1     2     3     3     4     4
2     5     4     4     3     4
3     4     5     4     2     5
4     4     4     3     5     5
5     4     4     5     3     2
6     6     6     6     1     3
# … with 2,794 more rows

3. `select()`:

B. Bare quote columns we don’t want to keep:

bfi %>% 
  select(-(A1:A5), -gender) %>% # Note the `()` around the columns
  print(n = 6)

# A tibble: 2,800 × 22
     C1    C2    C3    C4    C5    E1    E2    E3    E4    E5    N1    N2    N3
  <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1     2     3     3     4     4     3     3     3     4     4     3     4     2
2     5     4     4     3     4     1     1     6     4     3     3     3     3
3     4     5     4     2     5     2     4     4     4     5     4     5     4
4     4     4     3     5     5     5     3     4     4     4     2     5     2
5     4     4     5     3     2     2     2     5     4     5     2     3     4
6     6     6     6     1     3     2     1     6     5     6     3     5     2
# … with 2,794 more rows, and 9 more variables: N4 <int>, N5 <int>, O1 <int>,
#   O2 <int>, O3 <int>, O4 <int>, O5 <int>, education <chr>, age <int>

3. `select()`:

C. Add or remove using `select()` helper functions.

starts_with()
ends_with()
contains()
matches()
num_range()
one_of()
all_of()

bfi %>%
  select(starts_with("C"))

# A tibble: 2,800 × 5
      C1    C2    C3    C4    C5
   <int> <int> <int> <int> <int>
 1     2     3     3     4     4
 2     5     4     4     3     4
 3     4     5     4     2     5
 4     4     4     3     5     5
 5     4     4     5     3     2
 6     6     6     6     1     3
 7     5     4     4     2     3
 8     3     2     4     2     4
 9     6     6     3     4     5
10     6     5     6     2     1
# … with 2,790 more rows

4. `arrange()`

Sometimes, either in order to get a better sense of our data or in order to well, order our data, we want to sort it
Although there is a base R sort() function, the arrange() function is tidyverse version that plays nicely with other tidyverse functions.

4. `arrange()`

So in our previous examples, we could also arrange() our data by age or education, rather than simply filtering. (Or as we’ll see later, we can do both!)

# sort by age
bfi %>% 
  select(gender:age) %>%
  arrange(age) %>% 
  print(n = 6)

# A tibble: 2,800 × 3
  gender education       age
   <int> <chr>         <int>
1      1 Higher Degree     3
2      2 <NA>              9
3      2 Some College     11
4      2 <NA>             11
5      2 <NA>             11
6      2 <NA>             12
# … with 2,794 more rows

# sort by education
bfi %>%
  select(gender:age) %>%
  arrange(education) %>%
  print(n = 6)

# A tibble: 2,800 × 3
  gender education   age
   <int> <chr>     <int>
1      1 Below HS     19
2      1 Below HS     21
3      1 Below HS     17
4      1 Below HS     18
5      1 Below HS     18
6      2 Below HS     18
# … with 2,794 more rows

4. `arrange()`

We can also arrange by multiple columns, like if we wanted to sort by gender then education:

bfi %>%
  select(gender:age) %>%
  arrange(gender, education) %>% 
  print(n = 6)

# A tibble: 2,800 × 3
  gender education   age
   <int> <chr>     <int>
1      1 Below HS     19
2      1 Below HS     21
3      1 Below HS     17
4      1 Below HS     18
5      1 Below HS     18
6      1 Below HS     32
# … with 2,794 more rows

Bringing it all together: Split-Apply-Combine

Much of the power of dplyr functions lay in the split-apply-combine method
A given set of of data are:
- split into smaller chunks
- then a function or series of functions are applied to each chunk
- and then the chunks are combined back together

5. `group_by()`

The group_by() function is the “split” of the method
It basically implicitly breaks the data set into chunks by whatever bare quoted column(s)/variable(s) are supplied as arguments.

5. `group_by()`

So imagine that we wanted to group_by() education levels to get average ages at each level

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  group_by(education) %>%
  print(n = 6)

# A tibble: 2,800 × 8
# Groups:   education [6]
     C1    C2    C3    C4    C5   age gender education   
  <int> <int> <int> <int> <int> <int>  <int> <chr>       
1     2     3     3     4     4    16      1 <NA>        
2     5     4     4     3     4    18      2 <NA>        
3     4     5     4     2     5    17      2 <NA>        
4     4     4     3     5     5    17      2 <NA>        
5     4     4     5     3     2    17      1 <NA>        
6     6     6     6     1     3    21      2 Some College
# … with 2,794 more rows

5. `group_by()`

Hadley’s first law of data cleaning: “What is split, must be combined”
This is super easy with the ungroup() function:

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  group_by(education) %>%
  ungroup() %>%
  print(n = 6)

# A tibble: 2,800 × 8
     C1    C2    C3    C4    C5   age gender education   
  <int> <int> <int> <int> <int> <int>  <int> <chr>       
1     2     3     3     4     4    16      1 <NA>        
2     5     4     4     3     4    18      2 <NA>        
3     4     5     4     2     5    17      2 <NA>        
4     4     4     3     5     5    17      2 <NA>        
5     4     4     5     3     2    17      1 <NA>        
6     6     6     6     1     3    21      2 Some College
# … with 2,794 more rows

5. `group_by()`

Multiple group_by() calls overwrites previous calls:

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  group_by(education) %>%
  group_by(gender, age) %>%
  print(n = 6)

# A tibble: 2,800 × 8
# Groups:   gender, age [115]
     C1    C2    C3    C4    C5   age gender education   
  <int> <int> <int> <int> <int> <int>  <int> <chr>       
1     2     3     3     4     4    16      1 <NA>        
2     5     4     4     3     4    18      2 <NA>        
3     4     5     4     2     5    17      2 <NA>        
4     4     4     3     5     5    17      2 <NA>        
5     4     4     5     3     2    17      1 <NA>        
6     6     6     6     1     3    21      2 Some College
# … with 2,794 more rows

6. `mutate()`

mutate() is one of your “apply” functions
When you use mutate(), the resulting data frame will have the same number of rows you started with
You are directly mutating the existing data frame, either modifying existing columns or creating new ones

6. `mutate()`

To demonstrate, let’s add a column that indicated average age levels within each age group

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  arrange(education) %>%
  group_by(education) %>% 
  mutate(age_by_edu = mean(age, na.rm = T)) %>%
  print(n = 6)

# A tibble: 2,800 × 9
# Groups:   education [6]
     C1    C2    C3    C4    C5   age gender education age_by_edu
  <int> <int> <int> <int> <int> <int>  <int> <chr>          <dbl>
1     6     6     3     4     5    19      1 Below HS        25.1
2     4     3     5     3     2    21      1 Below HS        25.1
3     5     5     5     2     2    17      1 Below HS        25.1
4     5     5     4     1     1    18      1 Below HS        25.1
5     4     5     4     3     3    18      1 Below HS        25.1
6     3     2     3     4     6    18      2 Below HS        25.1
# … with 2,794 more rows

6. `mutate()`

mutate() is also super useful even when you aren’t grouping

We can create a new category

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  mutate(gender_cat = plyr::mapvalues(gender, c(1,2), c("Male", "Female")))

# A tibble: 2,800 × 9
      C1    C2    C3    C4    C5   age gender education    gender_cat
   <int> <int> <int> <int> <int> <int>  <int> <chr>        <chr>     
 1     2     3     3     4     4    16      1 <NA>         Male      
 2     5     4     4     3     4    18      2 <NA>         Female    
 3     4     5     4     2     5    17      2 <NA>         Female    
 4     4     4     3     5     5    17      2 <NA>         Female    
 5     4     4     5     3     2    17      1 <NA>         Male      
 6     6     6     6     1     3    21      2 Some College Female    
 7     5     4     4     2     3    18      1 <NA>         Male      
 8     3     2     4     2     4    19      1 HS           Male      
 9     6     6     3     4     5    19      1 Below HS     Male      
10     6     5     6     2     1    17      2 <NA>         Female    
# … with 2,790 more rows

6. `mutate()`

mutate() is also super useful even when you aren’t grouping

We could also just overwrite it:

bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  mutate(gender = plyr::mapvalues(gender, c(1,2), c("Male", "Female")))

# A tibble: 2,800 × 8
      C1    C2    C3    C4    C5   age gender education   
   <int> <int> <int> <int> <int> <int> <chr>  <chr>       
 1     2     3     3     4     4    16 Male   <NA>        
 2     5     4     4     3     4    18 Female <NA>        
 3     4     5     4     2     5    17 Female <NA>        
 4     4     4     3     5     5    17 Female <NA>        
 5     4     4     5     3     2    17 Male   <NA>        
 6     6     6     6     1     3    21 Female Some College
 7     5     4     4     2     3    18 Male   <NA>        
 8     3     2     4     2     4    19 Male   HS          
 9     6     6     3     4     5    19 Male   Below HS    
10     6     5     6     2     1    17 Female <NA>        
# … with 2,790 more rows

7. `summarize()` / `summarise()`

summarize() is one of your “apply” functions
The resulting data frame will have the same number of rows as your grouping variable
You number of groups is 1 for ungrouped data frames

# group_by() education
bfi %>%
  select(starts_with("C"), age, gender, education) %>%
  arrange(education) %>%
  group_by(education) %>% 
  summarize(age_by_edu = mean(age, na.rm = T))

# A tibble: 6 × 2
  education     age_by_edu
  <chr>              <dbl>
1 Below HS            25.1
2 College             33.0
3 Higher Degree       35.3
4 HS                  31.5
5 Some College        27.2
6 <NA>                18.0

7. `summarize()` / `summarise()`

summarize() is one of your “apply” functions
The resulting data frame will have the same number of rows as your grouping variable
You number of groups is 1 for ungrouped data frames

# no groups  
bfi %>% 
  select(starts_with("C"), age, gender, education) %>%
  arrange(education) %>%
  summarize(age_by_edu = mean(age, na.rm = T))

# A tibble: 1 × 1
  age_by_edu
       <dbl>
1       28.8

Data Wrangling in tidyr

`tidyr`

Now, let’s build off what we learned from dplyr and focus on reshaping and merging our data.
First, the reshapers:

pivot_longer(), which takes a “wide” format data frame and makes it long.
pivot_wider(), which takes a “long” format data frame and makes it wide.

`tidyr`

Next, the mergers:

full_join(), which merges all rows in either data frame
inner_join(), which merges rows whose keys are present in both data frames
left_join(), which “prioritizes” the first data set
right_join(), which “prioritizes” the second data set

(See also:anti_join() and semi_join())

Key `tidyr` Functions

1. `pivot_longer()`

(Formerly gather()) Makes wide data long, based on a key
Core arguments:
- data: the data, blank if piped
- cols: columns to be made long, selected via select() calls
- names_to: name(s) of key column(s) in new long data frame (string or string vector)
- values_to: name of values in new long data frame (string)
- names_sep: separator in column headers, if multiple keys
- values_drop_na: drop missing cells (similar to na.rm = T)

1. `pivot_longer()`: Basic Application

Let’s start with an easy one – one key, one value:

bfi %>%
  rownames_to_column("SID") %>%
  pivot_longer(
    cols = A1:O5
    , names_to = "item"
    , values_to = "values"
    , values_drop_na = T
  ) %>%
  print(n = 8)

# A tibble: 69,492 × 6
  SID   gender education   age item  values
  <chr>  <int> <chr>     <int> <chr>  <int>
1 1          1 <NA>         16 A1         2
2 1          1 <NA>         16 A2         4
3 1          1 <NA>         16 A3         3
4 1          1 <NA>         16 A4         4
5 1          1 <NA>         16 A5         4
6 1          1 <NA>         16 C1         2
7 1          1 <NA>         16 C2         3
8 1          1 <NA>         16 C3         3
# … with 69,484 more rows

1. `pivot_longer()`: More Advanced Application

Now a harder one – two keys, one value:

bfi %>%
  rownames_to_column("SID") %>%
  pivot_longer(
    cols = A1:O5
    , names_to = c("trait", "item_num")
    , names_sep = -1
    , values_to = "values"
    , values_drop_na = T
  ) %>%
  print(n = 8)

# A tibble: 69,492 × 7
  SID   gender education   age trait item_num values
  <chr>  <int> <chr>     <int> <chr> <chr>     <int>
1 1          1 <NA>         16 A     1             2
2 1          1 <NA>         16 A     2             4
3 1          1 <NA>         16 A     3             3
4 1          1 <NA>         16 A     4             4
5 1          1 <NA>         16 A     5             4
6 1          1 <NA>         16 C     1             2
7 1          1 <NA>         16 C     2             3
8 1          1 <NA>         16 C     3             3
# … with 69,484 more rows

2. `pivot_wider()`

(Formerly spread()) Makes wide data long, based on a key
Core arguments:
- data: the data, blank if piped
- names_from: name(s) of key column(s) in new long data frame (string or string vector)
- names_sep: separator in column headers, if multiple keys
- names_glue: specify multiple or custom separators of multiple keys
- values_from: name of values in new long data frame (string)
- values_fn: function applied to data with duplicate labels

2. `pivot_wider()`: Basic Application

bfi_long %>%
  pivot_wider(
    names_from = "item"
    , values_from = "values"
  )

# A tibble: 2,800 × 29
   SID   gender education    age    A1    A2    A3    A4    A5    C1    C2    C3
   <chr>  <int> <chr>      <int> <int> <int> <int> <int> <int> <int> <int> <int>
 1 1          1 <NA>          16     2     4     3     4     4     2     3     3
 2 2          2 <NA>          18     2     4     5     2     5     5     4     4
 3 3          2 <NA>          17     5     4     5     4     4     4     5     4
 4 4          2 <NA>          17     4     4     6     5     5     4     4     3
 5 5          1 <NA>          17     2     3     3     4     5     4     4     5
 6 6          2 Some Coll…    21     6     6     5     6     5     6     6     6
 7 7          1 <NA>          18     2     5     5     3     5     5     4     4
 8 8          1 HS            19     4     3     1     5     1     3     2     4
 9 9          1 Below HS      19     4     3     6     3     3     6     6     3
10 10         2 <NA>          17     2     5     6     6     5     6     5     6
# … with 2,790 more rows, and 17 more variables: C4 <int>, C5 <int>, E1 <int>,
#   E2 <int>, E3 <int>, E4 <int>, E5 <int>, N1 <int>, N2 <int>, N3 <int>,
#   N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>, O4 <int>, O5 <int>

2. `pivot_wider()`: More Advanced

bfi_long %>%
  pivot_wider(
    names_from = c("trait", "item_num")
    , values_from = "values"
    , names_sep = "_"
  )

# A tibble: 2,800 × 29
   SID   gender education    age   A_1   A_2   A_3   A_4   A_5   C_1   C_2   C_3
   <chr>  <int> <chr>      <int> <int> <int> <int> <int> <int> <int> <int> <int>
 1 1          1 <NA>          16     2     4     3     4     4     2     3     3
 2 2          2 <NA>          18     2     4     5     2     5     5     4     4
 3 3          2 <NA>          17     5     4     5     4     4     4     5     4
 4 4          2 <NA>          17     4     4     6     5     5     4     4     3
 5 5          1 <NA>          17     2     3     3     4     5     4     4     5
 6 6          2 Some Coll…    21     6     6     5     6     5     6     6     6
 7 7          1 <NA>          18     2     5     5     3     5     5     4     4
 8 8          1 HS            19     4     3     1     5     1     3     2     4
 9 9          1 Below HS      19     4     3     6     3     3     6     6     3
10 10         2 <NA>          17     2     5     6     6     5     6     5     6
# … with 2,790 more rows, and 17 more variables: C_4 <int>, C_5 <int>,
#   E_1 <int>, E_2 <int>, E_3 <int>, E_4 <int>, E_5 <int>, N_1 <int>,
#   N_2 <int>, N_3 <int>, N_4 <int>, N_5 <int>, O_1 <int>, O_2 <int>,
#   O_3 <int>, O_4 <int>, O_5 <int>

2. `pivot_wider()`: A Little More Advanced

bfi_long %>%
  select(-item_num) %>%
  pivot_wider(
    names_from = "trait"
    , values_from = "values"
    , names_sep = "_"
    , values_fn = mean
  )

# A tibble: 2,800 × 9
   SID   gender education      age     A     C     E     N     O
   <chr>  <int> <chr>        <int> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 1          1 <NA>            16   3.4   3.2  3.4    2.8   3.8
 2 2          2 <NA>            18   3.6   4    3      3.8   3.2
 3 3          2 <NA>            17   4.4   4    3.8    3.6   3.6
 4 4          2 <NA>            17   4.8   4.2  4      2.8   3.6
 5 5          1 <NA>            17   3.4   3.6  3.6    3.2   3.2
 6 6          2 Some College    21   5.6   4.4  4      3     3.8
 7 7          1 <NA>            18   4     3.6  4.2    1.4   3.8
 8 8          1 HS              19   2.8   3    3.2    4.2   3.4
 9 9          1 Below HS        19   3.8   4.8  3.75   3.6   5  
10 10         2 <NA>            17   4.8   4    3.6    4.2   3.6
# … with 2,790 more rows

More `dplyr` Functions

The `_join()` Functions

Often we may need to pull different data from different sources
There are lots of reasons to need to do this
We don’t have time to get into all the use cases here, so we’ll talk about them in high level terms

We’ll focus on:
- full_join()
- inner_join()
- left_join()
- right_join()

The `_join()` Functions

Let’s separate demographic and BFI data

bfi_only <- bfi %>% 
  rownames_to_column("SID") %>%
  select(SID, matches("[0-9]"))
bfi_only %>% print(n = 6)

# A tibble: 2,800 × 26
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 13 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>

bfi_dem <- bfi %>%
  rownames_to_column("SID") %>%
  select(SID, education, gender, age)
bfi_dem %>% print(n = 6)

# A tibble: 2,800 × 4
  SID   education    gender   age
  <chr> <chr>         <int> <int>
1 1     <NA>              1    16
2 2     <NA>              2    18
3 3     <NA>              2    17
4 4     <NA>              2    17
5 5     <NA>              1    17
6 6     Some College      2    21
# … with 2,794 more rows

3. `full_join()`

Most simply, we can put those back together keeping all observations.

bfi_only %>%
  full_join(bfi_dem) %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 16 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>, education <chr>, gender <int>, age <int>

bfi %>%
  rownames_to_column("SID") %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 16 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>, gender <int>, education <chr>, age <int>

4. `inner_join()`

We can also keep all rows present in both data frames

bfi_dem %>%
  filter(row_number() %in% 1:1700) %>%
  inner_join(
    bfi_only %>%
      filter(row_number() %in% 1200:2800)
  ) %>%
  print(n = 6)

# A tibble: 501 × 29
  SID   education   gender   age    A1    A2    A3    A4    A5    C1    C2    C3
  <chr> <chr>        <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1200  Some Colle…      2    18     1     5     6     5     5     5     6     5
2 1201  College          2    29     1     5     6     5     5     2     1     4
3 1202  Higher Deg…      1    46     2     5     6     5     6     6     6     6
4 1203  Higher Deg…      1    58     5     4     4     4     5     4     4     5
5 1204  Higher Deg…      2    38     1     4     6     6     6     4     4     5
6 1205  Higher Deg…      2    27     2     3     1     1     1     4     2     2
# … with 495 more rows, and 17 more variables: C4 <int>, C5 <int>, E1 <int>,
#   E2 <int>, E3 <int>, E4 <int>, E5 <int>, N1 <int>, N2 <int>, N3 <int>,
#   N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>, O4 <int>, O5 <int>

bfi %>%
  rownames_to_column("SID") %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 16 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>, gender <int>, education <chr>, age <int>

5. `left_join()`

Or all rows present in the left (first) data frame, perhaps if it’s a subset of people with complete data

bfi_dem %>%
  drop_na() %>%
  left_join(bfi_only) %>%
  print(n = 6)

# A tibble: 2,577 × 29
  SID   education   gender   age    A1    A2    A3    A4    A5    C1    C2    C3
  <chr> <chr>        <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 6     Some Colle…      2    21     6     6     5     6     5     6     6     6
2 8     HS               1    19     4     3     1     5     1     3     2     4
3 9     Below HS         1    19     4     3     6     3     3     6     6     3
4 11    Below HS         1    21     4     4     5     6     5     4     3     5
5 15    Below HS         1    17     4     5     2     2     1     5     5     5
6 23    Higher Deg…      1    68     1     5     6     5     6     4     3     2
# … with 2,571 more rows, and 17 more variables: C4 <int>, C5 <int>, E1 <int>,
#   E2 <int>, E3 <int>, E4 <int>, E5 <int>, N1 <int>, N2 <int>, N3 <int>,
#   N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>, O4 <int>, O5 <int>

bfi %>%
  rownames_to_column("SID") %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 16 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>, gender <int>, education <chr>, age <int>

6. `right_join()`

Or all rows present in the right (second) data frame, such as I do when I join a codebook with raw data

bfi_dem %>%
  drop_na() %>%
  right_join(bfi_only) %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID   education   gender   age    A1    A2    A3    A4    A5    C1    C2    C3
  <chr> <chr>        <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 6     Some Colle…      2    21     6     6     5     6     5     6     6     6
2 8     HS               1    19     4     3     1     5     1     3     2     4
3 9     Below HS         1    19     4     3     6     3     3     6     6     3
4 11    Below HS         1    21     4     4     5     6     5     4     3     5
5 15    Below HS         1    17     4     5     2     2     1     5     5     5
6 23    Higher Deg…      1    68     1     5     6     5     6     4     3     2
# … with 2,794 more rows, and 17 more variables: C4 <int>, C5 <int>, E1 <int>,
#   E2 <int>, E3 <int>, E4 <int>, E5 <int>, N1 <int>, N2 <int>, N3 <int>,
#   N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>, O4 <int>, O5 <int>

bfi %>%
  rownames_to_column("SID") %>%
  print(n = 6)

# A tibble: 2,800 × 29
  SID      A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
  <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 1         2     4     3     4     4     2     3     3     4     4     3     3
2 2         2     4     5     2     5     5     4     4     3     4     1     1
3 3         5     4     5     4     4     4     5     4     2     5     2     4
4 4         4     4     6     5     5     4     4     3     5     5     5     3
5 5         2     3     3     4     5     4     4     5     3     2     2     2
6 6         6     6     5     6     5     6     6     6     1     3     2     1
# … with 2,794 more rows, and 16 more variables: E3 <int>, E4 <int>, E5 <int>,
#   N1 <int>, N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>,
#   O3 <int>, O4 <int>, O5 <int>, gender <int>, education <chr>, age <int>

Week 1 - Getting Situated in R and tidyverse

Course Goals & Learning Outcomes

Course Expectations

Course Materials

Assignments

Assignments

Class Participation

Assignments

Response Papers / Visualizations

Assigments

Final Projects

Assignments

Extra Credit

Class Time

Course Topics

Questions on the Syllabus and Course?

Data Viz of the Week and Discussion

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Good or Bad?

Data Visualization

Why Should I Care About Data Visualization

Why Visualize Data in R

Why Use RStudio (Pivot)

Why Use the tidyverse

Why Use the tidyverse

Goals for Today

Core Functions

Core Functions

1. %>%

1. %>%

1. %>%

2. filter()

2. filter()

2. filter()

2. filter()

2. filter()

2. filter()

2. filter()

2. filter()

3. select()

3. select()

3. select():

A. Bare quote columns we want to keep:

3. select():

B. Bare quote columns we don’t want to keep:

3. select():

C. Add or remove using select() helper functions.

4. arrange()

4. arrange()

4. arrange()

Bringing it all together: Split-Apply-Combine

Bringing it all together: Split-Apply-Combine

5. group_by()

5. group_by()

5. group_by()

5. group_by()

6. mutate()

6. mutate()

6. mutate()

6. mutate()

7. summarize() / summarise()

7. summarize() / summarise()

tidyr

tidyr

Key tidyr Functions

1. pivot_longer()

1. pivot_longer(): Basic Application

1. pivot_longer(): More Advanced Application

2. pivot_wider()

2. pivot_wider(): Basic Application

2. pivot_wider(): More Advanced

2. pivot_wider(): A Little More Advanced

Why Use the `tidyverse`

Why Use the `tidyverse`

1. `%>%`

1. `%>%`

1. `%>%`

2. `filter()`

2. `filter()`

2. `filter()`

2. `filter()`

2. `filter()`

2. `filter()`

2. `filter()`

2. `filter()`

3. `select()`

3. `select()`

3. `select()`:

3. `select()`:

3. `select()`:

C. Add or remove using `select()` helper functions.

4. `arrange()`

4. `arrange()`

4. `arrange()`

5. `group_by()`

5. `group_by()`

5. `group_by()`

5. `group_by()`

6. `mutate()`

6. `mutate()`

6. `mutate()`

6. `mutate()`

7. `summarize()` / `summarise()`

7. `summarize()` / `summarise()`

`tidyr`

`tidyr`

Key `tidyr` Functions

1. `pivot_longer()`

1. `pivot_longer()`: Basic Application

1. `pivot_longer()`: More Advanced Application

2. `pivot_wider()`

2. `pivot_wider()`: Basic Application

2. `pivot_wider()`: More Advanced

2. `pivot_wider()`: A Little More Advanced

More `dplyr` Functions

The `_join()` Functions

The `_join()` Functions

3. `full_join()`

4. `inner_join()`

5. `left_join()`

6. `right_join()`