How the class will now be delivered: Lectorials

• Lectorials are now recorded using Echo360
• Do not come into class, listen to the lectorials online and complete the exercises on rstudio cloud or locally.

How the class will now be delivered: Lab/quizzes

These will still be posted weekly, but we will give you an extra day or two to complete them

• Reading quizzes we expect you to complete before the lecture starts

  • So, Reading quiz 2A should be completed prior to lecture 2A
    • These will be closed shortly after lecture 2a starts (With some leeway as we transition into online classes to give you all a chance to get used to things)

• Lab quizzes require knowledge from the lecture - these need to be completed after the lecture

  • So, lab quiz 2A should be completed after Lecture 2a
  • Again with the same leeway as for reading quiz 2a above

How the class will now be delivered

Assessignments

• Assignment 1 will be posted today at the end of class
• Assignments will be submitted online
  • Please get in touch with us (if you haven’t already) if you are a group of 1, or cannot get in touch with your group members.

Other assessments

• We will update you on this in more detail, but in short, these will be delivered and submitted online

Consult times

These will now be delivered online via a link to a zoom meeting, or other online video meeting service

There is a lot of change

• There is a lot of change in the air, and things might seem uncertain.
• I am committed to helping you all learn how to do data analysis.
• Thank you all for your patience as we have changed this course. We are dealing with daily updates, and need to change on the fly.
• Perhaps now more than ever it is becoming so very relevant to our daily lives that we understand data, and that we can communicate it to others.
• Remember to get your information from reliable sources, like the WHO, the Australian Government, and see the latest data from Johns Hopkins.

Practice the most effective strategies we know

1. Wash your hands often, practice good cough & sneeze etiquette.
2. Try to touch your face as little as possible (mouth, nose, and eyes).
3. Practice social distancing (no hugs, kisses, handshakes, high fives)
4. Do not attend concerts, stage plays, sporting events, or any other mass entertainment events.
5. Refrain from visiting museums, exhibitions, movie theaters, night clubs, and other entertainment venues.
6. Stay away from social gatherings and events, (club meetings, religious services, parties)
7. Reduce travel to a minimum. Don't travel long distances if not absolutely necessary.
8. Do not use public transportation if not absolutely necessary.

Social distancing is hard

• How do we know it works?
• We have data from the last pandemic, the spanish flu.
• Places that practice social distancing vs those who did not had drastically different numbers:

(from (Hatchett et al, 2007))

There is a lot of change

To brighten things up, here are two youtubers I’ve been watching lately to destress and have “COVID19 free time”

• Lofty Pursuits
• SteveMRE1989

The "pipe" operator - %>%

The symbol, %>% is referred to as the "pipe operator"

What you need to know:

• Read it as "then"
• It passes the output along to the next function

data %>%  
  select(age, height, hair_colour) %>% 
  filter(nationality == "australian")

" Use the data, THEN select the variables (columns), age, height, and hair_colour THEN filter so nationality is equal to "australian" "

That is all you need to know for the moment, but you can read more here

Problem solving (demo)

Some common questions you can ask yourself when something isn't working:

• Have I got my data?
• Does the thing exist? (Check environment)
• Have I run the code from the top down to where I am now?
• Did none of that work? (Now Restart R)
• Is the column I want there?
• Try using quotes "", or no quotes, or (last resort) backticks

Style guide

"Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread." -- Hadley Wickham

• Style guide for this course is based on the Tidyverse style guide: http://style.tidyverse.org/
• There's more to it than what we'll cover today, we'll mention more as we introduce more functionality, and do a recap later in the semester

File names and code chunk labels

• Do not use spaces in file names, use - or _ to separate words
• Use all lowercase letters

# Good
ucb-admit.csv
# Bad
UCB Admit.csv

Object names

• Use _ to separate words in object names
• Use informative but short object names
• Do not reuse object names within an analysis

# Good
acs_employed
# Bad
acs.employed
acs2
acs_subset
acs_subsetted_for_males

Spacing

• Put a space before and after all infix operators (=, +, -, <-, etc.), and when naming arguments in function calls.
• Always put a space after a comma, and never before (just like in regular English).

# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)
# Bad
average<-mean(feet/12+inches,na.rm=TRUE)

ggplot

• Always end a line with +
• Always indent the next line

# Good
ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram()
# Bad
ggplot(diamonds,mapping=aes(x=price))+geom_histogram()

Long lines

• Limit your code to 80 characters per line. This fits comfortably on a printed page with a reasonably sized font.
• Take advantage of RStudio editor's auto formatting for indentation at line breaks.

Assignment

• Use <- not =

# Good
x <- 2
# Bad
x = 2

Quotes

Use ", not ', for quoting text. The only exception is when the text already contains double quotes and no single quotes.

ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram() +
  # Good
  labs(title = "`Shine bright like a diamond`",
  # Good
       x = "Diamond prices",
  # Bad
       y = 'Frequency')

Overview

Name clashes

library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0          ✓ purrr   0.3.3.9000
## ✓ tibble  2.1.3          ✓ dplyr   0.8.5     
## ✓ tidyr   1.0.2          ✓ stringr 1.4.0     
## ✓ readr   1.3.1          ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter()     masks stats::filter()
## x dplyr::group_rows() masks kableExtra::group_rows()
## x purrr::is_null()    masks testthat::is_null()
## x dplyr::lag()        masks stats::lag()
## x dplyr::matches()    masks tidyr::matches(), testthat::matches()

Many R packages

• A blessing & a curse!
• So many packages available, it can make it hard to choose!
• Many of the packages are designed to solve a specific problem
• The tidyverse is designed to work with many other packages following a consistent philosophy
• What this means is that you shouldn't notice it!

Example: french fries

• Experiment in Food Sciences at Iowa State University.
• Aim: find if cheaper oil could be used to make hot chips
• Question: Can people distinguish between chips fried in the new oils relative to those current market leader oil.
• 12 tasters recruited
• Each sampled two chips from each batch
• Over a period of ten weeks.

Same oil kept for a period of 10 weeks! May be a bit gross!

Example: french-fries - pivoting into long form

french_fries <- read_csv("data/french_fries.csv")
french_fries

## # A tibble: 6 x 9
##    time treatment subject   rep potato buttery grassy rancid painty
##   <dbl>     <dbl>   <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
## 1     1         1       3     1    2.9     0      0      0      5.5
## 2     1         1       3     2   14       0      0      1.1    0  
## 3     1         1      10     1   11       6.4    0      0      0  
## 4     1         1      10     2    9.9     5.9    2.9    2.2    0  
## 5     1         1      15     1    1.2     0.1    0      1.1    5.1
## 6     1         1      15     2    8.8     3      3.6    1.5    2.3

This data set was brought to R by Hadley Wickham, and was one of the problems that inspired the thinking about tidy data and the tidyverse set of tools

Example: french-fries - pivoting into long form

fries_long <- french_fries %>% 
  pivot_longer(cols = potato:painty,
               names_to = "type", 
               values_to = "rating")
fries_long

## # A tibble: 3,480 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 3,470 more rows

Example: french-fries - pivoting back

fries_long
## # A tibble: 3,480 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 3,470 more rows

fries_long %>% 
  pivot_wider(names_from = type,
              values_from = rating)
## # A tibble: 696 x 9
##     time treatment subject   rep potato buttery grassy rancid painty
##    <dbl>     <dbl>   <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
##  1     1         1       3     1    2.9     0      0      0      5.5
##  2     1         1       3     2   14       0      0      1.1    0  
##  3     1         1      10     1   11       6.4    0      0      0  
##  4     1         1      10     2    9.9     5.9    2.9    2.2    0  
##  5     1         1      15     1    1.2     0.1    0      1.1    5.1
##  6     1         1      15     2    8.8     3      3.6    1.5    2.3
##  7     1         1      16     1    9       2.6    0.4    0.1    0.2
##  8     1         1      16     2    8.2     4.4    0.3    1.4    4  
##  9     1         1      19     1    7       3.2    0      4.9    3.2
## 10     1         1      19     2   13       0      3.1    4.3   10.3
## # … with 686 more rows

filter(): example

fries_long %>%
  filter(subject == 10)
## # A tibble: 300 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1      10     1 potato    11  
##  2     1         1      10     1 buttery    6.4
##  3     1         1      10     1 grassy     0  
##  4     1         1      10     1 rancid     0  
##  5     1         1      10     1 painty     0  
##  6     1         1      10     2 potato     9.9
##  7     1         1      10     2 buttery    5.9
##  8     1         1      10     2 grassy     2.9
##  9     1         1      10     2 rancid     2.2
## 10     1         1      10     2 painty     0  
## # … with 290 more rows

filter(): details

Filtering requires comparison to find the subset of observations of interest. What do you think the following mean?

• subject != 10
• x > 10
• x >= 10
• class %in% c("A", "B")
• !is.na(y)

filter(): details

subject != 10

Find rows corresponding to all subjects except subject 10

x > 10

find all rows where variable x has values bigger than 10

x >= 10

finds all rows variable x is greater than or equal to 10.

class %in% c("A", "B")

finds all rows where variable class is either A or B

!is.na(y)

finds all rows that DO NOT have a missing value for variable y

Your turn: open french-fries.Rmd

Filter the french fries data to have:

• only week 1
• oil type 1 (oil type is called treatment)
• oil types 1 and 3 but not 2
• weeks 1-4 only

French Fries Filter: only week 1

fries_long %>% filter(time == 1)
## # A tibble: 360 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 350 more rows

French Fries Filter: oil type 1

fries_long %>% filter(treatment == 1)
## # A tibble: 1,160 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 1,150 more rows

French Fries Filter: oil types 1 and 3 but not 2

fries_long %>% filter(treatment != 2)
## # A tibble: 2,320 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 2,310 more rows

French Fries Filter: weeks 1-4 only

fries_long %>% filter(time %in% c("1", "2", "3", "4"))
## # A tibble: 1,440 x 6
##     time treatment subject   rep type    rating
##    <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
##  1     1         1       3     1 potato     2.9
##  2     1         1       3     1 buttery    0  
##  3     1         1       3     1 grassy     0  
##  4     1         1       3     1 rancid     0  
##  5     1         1       3     1 painty     5.5
##  6     1         1       3     2 potato    14  
##  7     1         1       3     2 buttery    0  
##  8     1         1       3     2 grassy     0  
##  9     1         1       3     2 rancid     1.1
## 10     1         1       3     2 painty     0  
## # … with 1,430 more rows

select()

• Chooses which variables to keep in the data set.
• Useful when there are many variables but you only need some of them for an analysis.

select(): a comma separated list of variables, by name.

french_fries %>% 
  select(time, 
         treatment, 
         subject)
## # A tibble: 696 x 3
##     time treatment subject
##    <dbl>     <dbl>   <dbl>
##  1     1         1       3
##  2     1         1       3
##  3     1         1      10
##  4     1         1      10
##  5     1         1      15
##  6     1         1      15
##  7     1         1      16
##  8     1         1      16
##  9     1         1      19
## 10     1         1      19
## # … with 686 more rows

select(): drop selected variables by prefixing with -

french_fries %>% 
  select(-time, 
         -treatment, 
         -subject)
## # A tibble: 696 x 6
##      rep potato buttery grassy rancid painty
##    <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
##  1     1    2.9     0      0      0      5.5
##  2     2   14       0      0      1.1    0  
##  3     1   11       6.4    0      0      0  
##  4     2    9.9     5.9    2.9    2.2    0  
##  5     1    1.2     0.1    0      1.1    5.1
##  6     2    8.8     3      3.6    1.5    2.3
##  7     1    9       2.6    0.4    0.1    0.2
##  8     2    8.2     4.4    0.3    1.4    4  
##  9     1    7       3.2    0      4.9    3.2
## 10     2   13       0      3.1    4.3   10.3
## # … with 686 more rows

select()

french_fries %>% 
  select(contains("e"))
## # A tibble: 696 x 5
##     time treatment subject   rep buttery
##    <dbl>     <dbl>   <dbl> <dbl>   <dbl>
##  1     1         1       3     1     0  
##  2     1         1       3     2     0  
##  3     1         1      10     1     6.4
##  4     1         1      10     2     5.9
##  5     1         1      15     1     0.1
##  6     1         1      15     2     3  
##  7     1         1      16     1     2.6
##  8     1         1      16     2     4.4
##  9     1         1      19     1     3.2
## 10     1         1      19     2     0  
## # … with 686 more rows

select(): Using it

french_fries %>% 
  select(time:subject)
## # A tibble: 696 x 3
##     time treatment subject
##    <dbl>     <dbl>   <dbl>
##  1     1         1       3
##  2     1         1       3
##  3     1         1      10
##  4     1         1      10
##  5     1         1      15
##  6     1         1      15
##  7     1         1      16
##  8     1         1      16
##  9     1         1      19
## 10     1         1      19
## # … with 686 more rows

mutate(): create a new variable; keep existing ones

french_fries 
## # A tibble: 696 x 9
##     time treatment subject   rep potato buttery grassy rancid painty
##    <dbl>     <dbl>   <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
##  1     1         1       3     1    2.9     0      0      0      5.5
##  2     1         1       3     2   14       0      0      1.1    0  
##  3     1         1      10     1   11       6.4    0      0      0  
##  4     1         1      10     2    9.9     5.9    2.9    2.2    0  
##  5     1         1      15     1    1.2     0.1    0      1.1    5.1
##  6     1         1      15     2    8.8     3      3.6    1.5    2.3
##  7     1         1      16     1    9       2.6    0.4    0.1    0.2
##  8     1         1      16     2    8.2     4.4    0.3    1.4    4  
##  9     1         1      19     1    7       3.2    0      4.9    3.2
## 10     1         1      19     2   13       0      3.1    4.3   10.3
## # … with 686 more rows

mutate(): create a new variable; keep existing ones

french_fries %>% 
  mutate(rainty = rancid + painty)
## # A tibble: 696 x 10
##     time treatment subject   rep potato buttery grassy rancid painty rainty
##    <dbl>     <dbl>   <dbl> <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
##  1     1         1       3     1    2.9     0      0      0      5.5   5.5 
##  2     1         1       3     2   14       0      0      1.1    0     1.1 
##  3     1         1      10     1   11       6.4    0      0      0     0   
##  4     1         1      10     2    9.9     5.9    2.9    2.2    0     2.2 
##  5     1         1      15     1    1.2     0.1    0      1.1    5.1   6.20
##  6     1         1      15     2    8.8     3      3.6    1.5    2.3   3.8 
##  7     1         1      16     1    9       2.6    0.4    0.1    0.2   0.3 
##  8     1         1      16     2    8.2     4.4    0.3    1.4    4     5.4 
##  9     1         1      19     1    7       3.2    0      4.9    3.2   8.1 
## 10     1         1      19     2   13       0      3.1    4.3   10.3  14.6 
## # … with 686 more rows

summarise(): boil data down to one row observation

fries_long

## # A tibble: 6 x 6
##    time treatment subject   rep type    rating
##   <dbl>     <dbl>   <dbl> <dbl> <chr>    <dbl>
## 1     1         1       3     1 potato     2.9
## 2     1         1       3     1 buttery    0  
## 3     1         1       3     1 grassy     0  
## 4     1         1       3     1 rancid     0  
## 5     1         1       3     1 painty     5.5
## 6     1         1       3     2 potato    14

fries_long %>% 
  summarise(rating = mean(rating, na.rm = TRUE))
## # A tibble: 1 x 1
##   rating
##    <dbl>
## 1   3.16

Using summarise() + group_by()

Produce summaries for every group:

fries_long %>% 
  group_by(type) %>%
  summarise(rating = mean(rating, na.rm=TRUE))
## # A tibble: 5 x 2
##   type    rating
##   <chr>    <dbl>
## 1 buttery  1.82 
## 2 grassy   0.664
## 3 painty   2.52 
## 4 potato   6.95 
## 5 rancid   3.85

french fries answers

fries_long %>% 
  group_by(subject) %>%
  summarise(rating = mean(rating, na.rm=TRUE))
## # A tibble: 12 x 2
##    subject rating
##      <dbl>  <dbl>
##  1       3   2.46
##  2      10   4.24
##  3      15   2.16
##  4      16   3.00
##  5      19   4.54
##  6      31   4.00
##  7      51   4.39
##  8      52   2.72
##  9      63   3.48
## 10      78   1.94
## 11      79   1.94
## 12      86   2.94

french fries answers

fries_long %>% 
  filter(type == "rancid") %>%
  group_by(time) %>%
  summarise(rating = mean(rating, na.rm=TRUE))
## # A tibble: 10 x 2
##     time rating
##    <dbl>  <dbl>
##  1     1   2.36
##  2     2   2.85
##  3     3   3.72
##  4     4   3.60
##  5     5   3.53
##  6     6   4.08
##  7     7   3.89
##  8     8   4.27
##  9     9   4.67
## 10    10   6.07

arrange(): orders data by a given variable.

Useful for display of results (but there are other uses!)

fries_long %>% 
  group_by(type) %>%
  summarise(rating = mean(rating, na.rm=TRUE)) 
## # A tibble: 5 x 2
##   type    rating
##   <chr>    <dbl>
## 1 buttery  1.82 
## 2 grassy   0.664
## 3 painty   2.52 
## 4 potato   6.95 
## 5 rancid   3.85

arrange()

fries_long %>% 
  group_by(type) %>%
  summarise(rating = mean(rating, na.rm=TRUE)) %>%
  arrange(rating)
## # A tibble: 5 x 2
##   type    rating
##   <chr>    <dbl>
## 1 grassy   0.664
## 2 buttery  1.82 
## 3 painty   2.52 
## 4 rancid   3.85 
## 5 potato   6.95

arrange() answers

fries_long %>% 
  group_by(type) %>%
  summarise(rating = mean(rating, na.rm=TRUE)) %>%
  arrange(desc(rating))
## # A tibble: 5 x 2
##   type    rating
##   <chr>    <dbl>
## 1 potato   6.95 
## 2 rancid   3.85 
## 3 painty   2.52 
## 4 buttery  1.82 
## 5 grassy   0.664

arrange() answers

fries_long %>% 
  group_by(subject) %>%
  summarise(rating = mean(rating, na.rm=TRUE)) %>%
  arrange(rating)
## # A tibble: 12 x 2
##    subject rating
##      <dbl>  <dbl>
##  1      78   1.94
##  2      79   1.94
##  3      15   2.16
##  4       3   2.46
##  5      52   2.72
##  6      86   2.94
##  7      16   3.00
##  8      63   3.48
##  9      31   4.00
## 10      10   4.24
## 11      51   4.39
## 12      19   4.54

count() the number of things in a given column

fries_long %>% 
  count(type, sort = TRUE)
## # A tibble: 5 x 2
##   type        n
##   <chr>   <int>
## 1 buttery   696
## 2 grassy    696
## 3 painty    696
## 4 potato    696
## 5 rancid    696

French Fries: Are ratings similar?

fries_long %>% 
  group_by(type) %>%
  summarise(
    m = mean(rating, 
             na.rm = TRUE), 
    sd = sd(rating, 
            na.rm = TRUE)) %>%
  arrange(-m)
## # A tibble: 5 x 3
##   type        m    sd
##   <chr>   <dbl> <dbl>
## 1 potato  6.95   3.58
## 2 rancid  3.85   3.78
## 3 painty  2.52   3.39
## 4 buttery 1.82   2.41
## 5 grassy  0.664  1.32

French Fries: Are ratings similar?

ggplot(fries_long,
       aes(x = type, 
           y = rating)) +
  geom_boxplot()

French Fries: Are reps like each other?

fries_spread <- fries_long %>% 
  pivot_wider(names_from = rep, 
              values_from = rating)
fries_spread
## # A tibble: 1,740 x 6
##     time treatment subject type      `1`   `2`
##    <dbl>     <dbl>   <dbl> <chr>   <dbl> <dbl>
##  1     1         1       3 potato    2.9  14  
##  2     1         1       3 buttery   0     0  
##  3     1         1       3 grassy    0     0  
##  4     1         1       3 rancid    0     1.1
##  5     1         1       3 painty    5.5   0  
##  6     1         1      10 potato   11     9.9
##  7     1         1      10 buttery   6.4   5.9
##  8     1         1      10 grassy    0     2.9
##  9     1         1      10 rancid    0     2.2
## 10     1         1      10 painty    0     0  
## # … with 1,730 more rows

French Fries: Are reps like each other?

summarise(fries_spread,
          r = cor(`1`, `2`, use = "complete.obs"))
## # A tibble: 1 x 1
##       r
##   <dbl>
## 1 0.668

French Fries:

  ggplot(fries_spread,
         aes(x = `1`, 
             y = `2`)) + 
  geom_point() + 
  labs(title = "Data is poor quality: the replicates do not look like each other!")

French Fries: Replicates by rating type

fries_spread %>%
  group_by(type) %>%
  summarise(r = cor(x = `1`, 
                    y = `2`, 
                    use = "complete.obs"))
## # A tibble: 5 x 2
##   type        r
##   <chr>   <dbl>
## 1 buttery 0.650
## 2 grassy  0.239
## 3 painty  0.479
## 4 potato  0.616
## 5 rancid  0.391

French Fries: Replicates by rating type

ggplot(fries_spread, aes(x=`1`, y=`2`)) + 
  geom_point() + facet_wrap(~type, ncol = 5)

Potato'y and buttery have better replication than the other scales, but there is still a lot of variation from rep 1 to 2.

When to use quotes? " ', nothing, or backtick?

• Use no quotes (bare variable names) when the variable exists
• Otherwise use strings

Example:

fries_long %>% 
  pivot_wider(names_from = type,
              values_from = rating)

vs

french_fries %>% 
  pivot_longer(cols = potato:painty,
               names_to = "type", 
               values_to = "rating")

When to use quotes? " ', nothing, or backtick?

Variables with unusual names (starting with numbers, spaces, or containing special characters like !@#$%^&*()- need to be referenced with backticks:

data %>% select(`name with spaces`)

Lab exercise: Exploring data PISA data

Open pisa.Rmd on rstudio cloud.

Assignment 1

It will be launched later today

• Instructions to appear on ED and the course website

When is the assignment due?

• 1st April, 2020 5pm

How do I complete the assignment?

• You should complete as much of the assignment as you can by yourself
• Then once you have done as much as you can, work with your group to

I don't have a group / I can't get in contact with my group

• If you don't have a group, make sure you have filled in this form here (it has also been posted on ED)
• I will assign everyone into a group who has filled in the form

Assignment 1

How do I stay in touch with my group?

• Get in touch with your group and decide how you will work together
  • you can use zoom through Monash to create video/audio group calls
  • you could create a Slack team
  • You can communicate via email, WhatsApp, Messenger, whatever you all agree on

How do I submit the assignment?

• You submit the assignment via ED - instructions to follow