Data Wrangling with R: Day 2

<div class="bg-black white"  style="width:45%;right:0;bottom:0;padding-left:5px;border: solid 4px white;margin: auto;">
<i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. See here for <a href=day2-session4.pdf>PDF <i class="fas fa-file-pdf"></i></a>. 
</div>

<br>

---

count: false
background-image: url(images/bg1.jpg)
background-size: cover
class: hide-slide-number title-slide

.item.center[

# <span style="text-shadow: 2px 2px 30px white;">Data Wrangling with R: Day 2</span>

]

.center.shade_black.animated.bounceInUp.slower[
<br><br>
## <span style="color: #ccf2ff; text-shadow: 10px 10px 100px white;">Advanced data wrangling starring `purrr`</span>

<br>

Presented by Emi Tanaka

Department of Econometrics and Business Statistics

<img src="images/monash-one-line-reversed.png" style="width:500px"><br>
<span><i class="fas  fa-envelope faa-float animated "></i></span>  emi.tanaka@monash.edu
<span><i class="fab  fa-twitter faa-float animated faa-fast "></i></span>  @statsgen

.bottom_abs.width100.bg-black[
2nd December 2020 @ Statistical Society of Australia | Zoom
]

]

</div>

---

# Repetitive tasks

```r
df <- forcats::gss_cat
```
* 🎯  Let's calculate the number of distinct values for each column and store it in `n`

```r
n <- vector("integer", length = ncol(df))
n[1] <- n_distinct(df[[1]])
n[2] <- n_distinct(df[[2]])
n[3] <- n_distinct(df[[3]])
n[4] <- n_distinct(df[[4]])
n[5] <- n_distinct(df[[5]])
n[6] <- n_distinct(df[[6]])
n[7] <- n_distinct(df[[6]])
n[8] <- n_distinct(df[[8]])
n[9] <- n_distinct(df[[9]])
```
--

* Anything you notice from above?

---

# Using for loops

```r
n <- vector("integer", length = ncol(df))
for(i in 1:ncol(df)) {
  n[i] <- n_distinct(df[[i]])
}
```
--

* But R is notoriously known for being slow using for loops
--

* The new `across` function in `dplyr` can do this without for loops

```r
df %>% 
  summarise(across(everything(), n_distinct))
```

```
## # A tibble: 1 x 9
##    year marital   age  race rincome partyid relig denom tvhours
##   <int>   <int> <int> <int>   <int>   <int> <int> <int>   <int>
## 1     8       6    73     3      16      10    15    30      25
```
--

* But the result is a data.frame

---

# Functional programming .font_small[`purrr` .font_small[Part] 1]

* `purrr` is part of the core `tidyverse` packages
* It contains a series of `map` and `walk` functions

```r
map_int(df, n_distinct)
```

```
##    year marital     age    race rincome partyid   relig   denom tvhours 
##       8       6      73       3      16      10      15      30      25
```

```r
map_chr(df, n_distinct)
```

```
##    year marital     age    race rincome partyid   relig   denom tvhours 
##     "8"     "6"    "73"     "3"    "16"    "10"    "15"    "30"    "25"
```

```r
map_df(df, n_distinct)
```

```
## # A tibble: 1 x 9
##    year marital   age  race rincome partyid relig denom tvhours
##   <int>   <int> <int> <int>   <int>   <int> <int> <int>   <int>
## 1     8       6    73     3      16      10    15    30      25
```

<div class="info-box" style="position:absolute;right:10px;bottom:10px;width:800px;margin-right:10px;">
<ul>
<li>The related functions in <code>purrr</code> have been design so that that input are consistent</li>
<li>The user is required to think of the expected output before seeing the output, e.g. </li>
<ul>
<li><code>map_int</code> returns a vector of integer</li>
<li><code>map_df</code> returns a data frame </li>
</ul>
</ul>
</div>

---

#  `map` functions in `purrr`

* .monash-blue[`map`]`(.x, .f, ...)` returns list
* .monash-blue[`map_chr`]`(.x, .f, ...)` returns a vector of character
* .monash-blue[`map_dbl`]`(.x, .f, ...)` returns a vector of numeric
* .monash-blue[`map_int`]`(.x, .f, ...)` returns a vector of integer
* .monash-blue[`map_lgl`]`(.x, .f, ...)` returns a vector of logical
* .monash-blue[`map_raw`]`(.x, .f, ...)` returns a vector of raw
* .monash-blue[`map_df`]`(.x, .f, ...)`, .monash-blue[`map_dfr`]`(.x, .f, ...)` returns a data frame, combining data.frame by row
* .monash-blue[`map_dfc`]`(.x, .f, ...)` returns a data frame, combining data.frame by column

---

# Conditional maps in `purrr`

* .monash-blue[`map_if`]`(.x, .p, .f, ...)` uses `.p` to determine if `.f` will be applied to `.x` 
* .monash-blue[`map_at`]`(.x, .at, .f, ...)` applies `.f` to `.x` at `.at` (name or position)
* .monash-blue[`map_depth`]`(.x, .depth, .f, ...)` apples `.f` to `.x` at a specific depth level of a nested vector
* **The return object is always a list**

```r
map_if(df, is.factor, as.character) %>% as_tibble()
```

```
## # A tibble: 21,483 x 9
##     year marital     age race  rincome    partyid     relig     denom    tvhours
##    <int> <chr>     <int> <chr> <chr>      <chr>       <chr>     <chr>      <int>
##  1  2000 Never ma…    26 White $8000 to … Ind,near r… Protesta… Souther…      12
##  2  2000 Divorced     48 White $8000 to … Not str re… Protesta… Baptist…      NA
##  3  2000 Widowed      67 White Not appli… Independent Protesta… No deno…       2
##  4  2000 Never ma…    39 White Not appli… Ind,near r… Orthodox… Not app…       4
##  5  2000 Divorced     25 White Not appli… Not str de… None      Not app…       1
##  6  2000 Married      25 White $20000 - … Strong dem… Protesta… Souther…      NA
##  7  2000 Never ma…    36 White $25000 or… Not str re… Christian Not app…       3
##  8  2000 Divorced     44 White $7000 to … Ind,near d… Protesta… Luthera…      NA
##  9  2000 Married      44 White $25000 or… Not str de… Protesta… Other          0
## 10  2000 Married      47 White $25000 or… Strong rep… Protesta… Souther…       3
## # … with 21,473 more rows
```

---

# Functional programming .font_small[Base R]

* `lapply`, `Map`, `mapply`, `sapply`, `tapply`, `apply`, and `vapply` are variants of functional programming in Base R
--

* Some function outputs in Base R are more predictable than others:
  * `purrr::map` is a variant of `lapply` (which always returns list)
  * `purrr::pmap` is a variant of `Map` (which takes more than one input)
--

* <img src="images/no-toilet-roll.png" height = "40px"> `sapply` doesn't require users to specify the output type, instead it'll try to figure out what looks best for the user... great for interactive use but require great caution for programming
--

* So setting return type expectation is the reason to use `purrr`?

---

# Anonymous functions .font_small[.font_small[Part] 1]

* Also called **lambda expression** in computer programming
--

* These are functions without names
--

```r
map_int(df, function(x) length(unique(x)))
```

```
##    year marital     age    race rincome partyid   relig   denom tvhours 
##       8       6      73       3      16      10      15      30      25
```
--
* Tidyverse often employs a special shorthand using a formula and `.x` as a special placeholder for input

```r
map_int(df, ~length(unique(.x)))
```

```
##    year marital     age    race rincome partyid   relig   denom tvhours 
##       8       6      73       3      16      10      15      30      25
```

---

# Anonymous functions .font_small[.font_small[Part] 2]

* And yes, it's not just for `purrr` functions:

```r
df %>% 
  summarise(across(everything(), ~length(unique(.x))))
```

* This formula anonymous function is expanded to an actual anonymous function under the hood using `rlang::as_function()`
--

* Most `tidyverse` functions would support this formula approach to anonymous function, but likely not outside of that ecosystem unless developers adopt the same system

---

# Functions with two inputs

* For functions with two inputs, you can use the `map2` variants in `purrr`

```r
x <- c(1, 2, 3)
y <- c(0.1, 0.2, 0.3)
map2_dbl(x, y, function(.x, .y)  .x + .y)
```

```
## [1] 1.1 2.2 3.3
```
--

* For anonymous functions with two inputs, the first input is `.x` (as before) and the second is `.y`

```r
map2_dbl(x, y, ~.x + .y)
```

```
## [1] 1.1 2.2 3.3
```
---

# Functions with more than two inputs

* What about if there are more than two input?
--

* You can use `pmap` variants in `purrr`
--

* But no formula anonymous function supported:

```r
x <- c(1, 2, 3)
y <- c(0.1, 0.2, 0.3)
z <- c(10, 20, 30)
pmap_dbl(list(x, y, z), function(.x, .y, .z)  .x + .y + .z)
```

```
## [1] 11.1 22.2 33.3
```

---

# Other functions in `purrr`

**Using names of input**
* The `imap(x)` variants are shorthand for `map2(x, names(x))`

```r
imap_chr(list(x = 1L, y = 4L), ~paste0(.y, ":", .x))
```

```
##     x     y 
## "x:1" "y:4"
```

--
**Expecting no return object**

* If you are looking to get a side effect rather than return, you can use the `walk` variants

```r
iwalk(df, ~write.csv(.x, file = paste0(.y, ".csv")))
```

---

# A simulated study .font_small[.font_small[Part] 1]

* Let's simulate response of size 400 from a data generating process where it is a simple linear model with slope as -2 and intercept as 1 with error from `$N(0, 1)$`

```r
set.seed(1)
dat <- tibble(id = 1:400) %>% # 400 observations
  mutate(x = runif(n(), 0, 10), # independent covariate
         y = 1 - 2 * x + rnorm(n())) # dependent response 
```
--

* Let's then fit a linear model and a robust linear model to compare their estimates

```r
fit_lm <- lm(y ~ x, data = dat)
fit_rlm <- MASS::rlm(y ~ x, data = dat)
```
--
.grid[
.item[

```r
coef(fit_lm)
```

```
## (Intercept)           x 
##   0.9965522  -1.9994216
```
]
.item[

```r
coef(fit_rlm)
```

```
## (Intercept)           x 
##    1.012063   -1.999890
```

]

---

# A simulated study .font_small[.font_small[Part] 2]

* Let's make it interesting but adding a few outliers

```r
set.seed(1)
dat$y[1:3] <- 100
```
--

* Let's refit the model

```r
fit_lm <- lm(y ~ x, data = dat)
fit_rlm <- MASS::rlm(y ~ x, data = dat)
```
--
.grid[
.item[

```r
coef(fit_lm)
```

```
## (Intercept)           x 
##    2.233095   -2.089164
```
]
.item[

```r
coef(fit_rlm)
```

```
## (Intercept)           x 
##    1.019283   -2.000755
```

]

---

# A simulated study .font_small[.font_small[Part] 3]

* For a proper simulation, you need to run it a multiple times
* Let's do the same simulation 200 times

```r
set.seed(1)
map_dfr(1:200, ~{
  dat <- tibble(id = 1:400) %>% # 400 observations
    mutate(x = runif(n(), 0, 10), # independent covariate
           y = 1 - 2 * x + rnorm(n())) # dependent response 
  dat$y[1:3] <- 100
  fit_lm <- lm(y ~ x, data = dat)
  fit_rlm <- MASS::rlm(y ~ x, data = dat)
  
  tibble(intercept_lm = coef(fit_lm)[1], intercept_rlm = coef(fit_rlm)[1],
         slope_lm = coef(fit_lm)[2], slope_rlm = coef(fit_rlm)[2], sim = .x)})
```

```
## # A tibble: 200 x 5
##    intercept_lm intercept_rlm slope_lm slope_rlm   sim
##           <dbl>         <dbl>    <dbl>     <dbl> <int>
##  1        2.23          1.02     -2.09     -2.00     1
##  2        0.982         1.04     -1.83     -2.01     2
##  3        2.22          1.00     -2.09     -2.00     3
##  4        0.972         1.06     -1.82     -2.01     4
##  5        1.75          1.04     -1.97     -1.99     5
##  6        0.977         1.06     -1.82     -2.00     6
##  7        1.54          0.849    -1.95     -1.97     7
##  8        1.99          0.955    -2.04     -1.99     8
##  9        1.38          1.04     -1.91     -2.01     9
## 10        0.550         0.989    -1.73     -1.99    10
## # … with 190 more rows
```

---

# A simulated study .font_small[.font_small[Part] 4]

* You can speed up your simulations by using parallel processing with `furrr` package

```r
set.seed(1)
library(furrr)
*plan(multisession, workers = 2)
*future_map_dfr(1:200, ~{
  dat <- tibble(id = 1:400) %>% # 400 observations
    mutate(x = runif(n(), 0, 10), # independent covariate
           y = 1 - 2 * x + rnorm(n())) # dependent response 
  dat$y[1:3] <- 100
  fit_lm <- lm(y ~ x, data = dat)
  fit_rlm <- MASS::rlm(y ~ x, data = dat)
  
  tibble(intercept_lm = coef(fit_lm)[1], intercept_rlm = coef(fit_rlm)[1],
         slope_lm = coef(fit_lm)[2], slope_rlm = coef(fit_rlm)[2], sim = .x)})
```

```
## # A tibble: 200 x 5
##    intercept_lm intercept_rlm slope_lm slope_rlm   sim
##           <dbl>         <dbl>    <dbl>     <dbl> <int>
##  1        2.17          1.12     -2.07     -2.01     1
##  2        1.51          0.969    -1.94     -2.00     2
##  3        1.93          0.881    -2.04     -2.00     3
##  4        1.47          0.901    -1.94     -1.98     4
##  5        2.47          0.968    -2.15     -2.00     5
##  6        1.27          0.948    -1.90     -2.00     6
##  7        0.942         0.970    -1.81     -1.98     7
##  8        2.42          0.835    -2.13     -1.96     8
##  9        1.44          1.04     -1.93     -2.01     9
## 10        1.81          1.10     -2.00     -2.02    10
## # … with 190 more rows
```

---

.info-box[
* There are many functions in `tidyverse`, the key is _not_ to remember them all but remember key points to have a mental trigger where to look
{{content}}
]

* There are multiple of ways to data wrangle to get to the same result
{{content}}
--

* What's more important that code is readable and done in a way to satisfy your objective to you _and_ others that you share your work with, e.g. in production, backward compatibility in Base R may be more important

---

# Top downloaded CRAN packages

<div class="grid" style="grid: 1fr / 1.5fr 1fr">
.item[
<br>
.scroll-vert[

| rank|package    |   count|from       |to         |
|----:|:----------|-------:|:----------|:----------|
|    1|jsonlite   | 2322852|2020-11-01 |2020-11-30 |
|    2|rlang      | 1813779|2020-11-01 |2020-11-30 |
|    3|ggplot2    | 1801691|2020-11-01 |2020-11-30 |
|    4|vctrs      | 1651019|2020-11-01 |2020-11-30 |
|    5|fs         | 1618762|2020-11-01 |2020-11-30 |
|    6|devtools   | 1590942|2020-11-01 |2020-11-30 |
|    7|usethis    | 1520368|2020-11-01 |2020-11-30 |
|    8|dplyr      | 1394366|2020-11-01 |2020-11-30 |
|    9|tibble     | 1250293|2020-11-01 |2020-11-30 |
|   10|glue       | 1190160|2020-11-01 |2020-11-30 |
|   11|tidyselect | 1131151|2020-11-01 |2020-11-30 |
|   12|lifecycle  | 1125890|2020-11-01 |2020-11-30 |
|   13|xfun       | 1057799|2020-11-01 |2020-11-30 |
|   14|pillar     | 1001912|2020-11-01 |2020-11-30 |
|   15|ellipsis   |  958145|2020-11-01 |2020-11-30 |
|   16|magrittr   |  930052|2020-11-01 |2020-11-30 |
|   17|cli        |  908144|2020-11-01 |2020-11-30 |
|   18|Hmisc      |  905340|2020-11-01 |2020-11-30 |
|   19|Rcpp       |  897014|2020-11-01 |2020-11-30 |
|   20|digest     |  894615|2020-11-01 |2020-11-30 |
|   21|testthat   |  868069|2020-11-01 |2020-11-30 |
|   22|R6         |  860787|2020-11-01 |2020-11-30 |
|   23|knitr      |  858255|2020-11-01 |2020-11-30 |
|   24|waldo      |  848136|2020-11-01 |2020-11-30 |
|   25|cpp11      |  843535|2020-11-01 |2020-11-30 |
|   26|callr      |  840748|2020-11-01 |2020-11-30 |
|   27|processx   |  822638|2020-11-01 |2020-11-30 |
|   28|rmarkdown  |  822220|2020-11-01 |2020-11-30 |
|   29|diffobj    |  817737|2020-11-01 |2020-11-30 |
|   30|tinytex    |  814966|2020-11-01 |2020-11-30 |
|   31|brio       |  798659|2020-11-01 |2020-11-30 |
|   32|ps         |  790416|2020-11-01 |2020-11-30 |
|   33|sf         |  771887|2020-11-01 |2020-11-30 |
|   34|stringr    |  770111|2020-11-01 |2020-11-30 |
|   35|tidyverse  |  757729|2020-11-01 |2020-11-30 |
|   36|purrr      |  755636|2020-11-01 |2020-11-30 |
|   37|stringi    |  753381|2020-11-01 |2020-11-30 |
|   38|rstudioapi |  745300|2020-11-01 |2020-11-30 |
|   39|broom      |  744498|2020-11-01 |2020-11-30 |
|   40|pkgbuild   |  743352|2020-11-01 |2020-11-30 |

]
.center[
scroll for more
]]
.item[

* How many of the top downloaded package in the past month is `tidyverse` related?

]

</div>

* It's hard to ignore what is widely used 
* Also a duty to be aware with the latest if you work with data?

---

# Software language is an evolving language

Tidyverse evolution has at least now slowed down compared to before

<br>

## Enjoy your evolving data wrangling journey!

---

# <i class="fas fa-code"></i> If you installed the `dwexercise` package, <br> run below in your R console

```r
learnr::run_tutorial("day2-exercise-04", package = "dwexercise")
```

<br>

# <i class="fas fa-link"></i> If the above doesn't work for you, go [here](https://ebsmonash.shinyapps.io/dw-day2-exercise-04).

# <i class="fas fa-question"></i> Questions or issues, let us know!
<center>
<div class="countdown clock" id="timer_5fc6f45b" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">15</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
</center>

---

# Session Information

```r
devtools::session_info()
```

```
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.1 (2020-06-06)
##  os       macOS Catalina 10.15.7      
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_AU.UTF-8                 
##  ctype    en_AU.UTF-8                 
##  tz       Australia/Melbourne         
##  date     2020-12-02                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version    date       lib source                              
##  anicon        0.1.0      2020-06-21 [1] Github (emitanaka/anicon@0b756df)   
##  assertthat    0.2.1      2019-03-21 [2] CRAN (R 4.0.0)                      
##  backports     1.2.0      2020-11-02 [1] CRAN (R 4.0.2)                      
##  broom         0.7.2      2020-10-20 [1] CRAN (R 4.0.2)                      
##  callr         3.5.1      2020-10-13 [1] CRAN (R 4.0.2)                      
##  cellranger    1.1.0      2016-07-27 [2] CRAN (R 4.0.0)                      
##  cli           2.2.0      2020-11-20 [1] CRAN (R 4.0.1)                      
##  codetools     0.2-18     2020-11-04 [2] CRAN (R 4.0.1)                      
##  colorspace    2.0-0      2020-11-11 [1] CRAN (R 4.0.2)                      
##  countdown     0.3.5      2020-07-20 [1] Github (gadenbuie/countdown@a544fa4)
##  cranlogs      2.1.1      2019-04-29 [1] CRAN (R 4.0.0)                      
##  crayon        1.3.4      2017-09-16 [2] CRAN (R 4.0.0)                      
##  curl          4.3        2019-12-02 [2] CRAN (R 4.0.0)                      
##  DBI           1.1.0      2019-12-15 [1] CRAN (R 4.0.2)                      
##  dbplyr        2.0.0      2020-11-03 [1] CRAN (R 4.0.2)                      
##  desc          1.2.0      2018-05-01 [2] CRAN (R 4.0.0)                      
##  devtools      2.3.2      2020-09-18 [1] CRAN (R 4.0.2)                      
##  digest        0.6.27     2020-10-24 [1] CRAN (R 4.0.2)                      
##  dplyr       * 1.0.2      2020-08-18 [1] CRAN (R 4.0.2)                      
##  ellipsis      0.3.1      2020-05-15 [2] CRAN (R 4.0.0)                      
##  evaluate      0.14       2019-05-28 [2] CRAN (R 4.0.0)                      
##  fansi         0.4.1      2020-01-08 [2] CRAN (R 4.0.0)                      
##  forcats     * 0.5.0      2020-03-01 [2] CRAN (R 4.0.0)                      
##  fs            1.5.0      2020-07-31 [1] CRAN (R 4.0.2)                      
##  furrr       * 0.2.1      2020-10-21 [1] CRAN (R 4.0.2)                      
##  future      * 1.20.1     2020-11-03 [1] CRAN (R 4.0.2)                      
##  generics      0.1.0      2020-10-31 [2] CRAN (R 4.0.2)                      
##  ggplot2     * 3.3.2      2020-06-19 [1] CRAN (R 4.0.2)                      
##  globals       0.13.1     2020-10-11 [1] CRAN (R 4.0.2)                      
##  glue          1.4.2      2020-08-27 [1] CRAN (R 4.0.2)                      
##  gtable        0.3.0      2019-03-25 [2] CRAN (R 4.0.0)                      
##  haven         2.3.1      2020-06-01 [2] CRAN (R 4.0.0)                      
##  highr         0.8        2019-03-20 [2] CRAN (R 4.0.0)                      
##  hms           0.5.3      2020-01-08 [2] CRAN (R 4.0.0)                      
##  htmltools     0.5.0      2020-06-16 [1] CRAN (R 4.0.2)                      
##  httr          1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                      
##  icon          0.1.0      2020-06-21 [1] Github (emitanaka/icon@8458546)     
##  jsonlite      1.7.1      2020-09-07 [1] CRAN (R 4.0.2)                      
##  knitr         1.30       2020-09-22 [1] CRAN (R 4.0.2)                      
##  lifecycle     0.2.0      2020-03-06 [1] CRAN (R 4.0.0)                      
##  listenv       0.8.0      2019-12-05 [1] CRAN (R 4.0.2)                      
##  lubridate     1.7.9      2020-06-08 [2] CRAN (R 4.0.1)                      
##  magrittr      2.0.1      2020-11-17 [1] CRAN (R 4.0.2)                      
##  MASS          7.3-53     2020-09-09 [1] CRAN (R 4.0.2)                      
##  memoise       1.1.0      2017-04-21 [2] CRAN (R 4.0.0)                      
##  modelr        0.1.8      2020-05-19 [2] CRAN (R 4.0.0)                      
##  munsell       0.5.0      2018-06-12 [2] CRAN (R 4.0.0)                      
##  parallelly    1.21.0     2020-10-27 [1] CRAN (R 4.0.2)                      
##  pillar        1.4.7      2020-11-20 [1] CRAN (R 4.0.1)                      
##  pkgbuild      1.1.0      2020-07-13 [2] CRAN (R 4.0.1)                      
##  pkgconfig     2.0.3      2019-09-22 [2] CRAN (R 4.0.0)                      
##  pkgload       1.1.0      2020-05-29 [2] CRAN (R 4.0.0)                      
##  prettyunits   1.1.1      2020-01-24 [2] CRAN (R 4.0.0)                      
##  processx      3.4.4      2020-09-03 [1] CRAN (R 4.0.2)                      
##  ps            1.4.0      2020-10-07 [1] CRAN (R 4.0.2)                      
##  purrr       * 0.3.4      2020-04-17 [2] CRAN (R 4.0.0)                      
##  R6            2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                      
##  Rcpp          1.0.5      2020-07-06 [1] CRAN (R 4.0.0)                      
##  readr       * 1.4.0      2020-10-05 [2] CRAN (R 4.0.2)                      
##  readxl        1.3.1      2019-03-13 [2] CRAN (R 4.0.0)                      
##  remotes       2.2.0      2020-07-21 [1] CRAN (R 4.0.2)                      
##  reprex        0.3.0.9001 2020-08-08 [1] Github (tidyverse/reprex@9594ee9)   
##  rlang         0.4.8      2020-10-08 [1] CRAN (R 4.0.2)                      
##  rmarkdown     2.5        2020-10-21 [1] CRAN (R 4.0.1)                      
##  rprojroot     2.0.2      2020-11-15 [1] CRAN (R 4.0.2)                      
##  rstudioapi    0.13       2020-11-12 [1] CRAN (R 4.0.1)                      
##  rvest         0.3.6      2020-07-25 [1] CRAN (R 4.0.2)                      
##  scales        1.1.1      2020-05-11 [2] CRAN (R 4.0.0)                      
##  sessioninfo   1.1.1      2018-11-05 [2] CRAN (R 4.0.0)                      
##  stringi       1.5.3      2020-09-09 [2] CRAN (R 4.0.2)                      
##  stringr     * 1.4.0      2019-02-10 [2] CRAN (R 4.0.0)                      
##  testthat      3.0.0      2020-10-31 [1] CRAN (R 4.0.2)                      
##  tibble      * 3.0.4.9000 2020-11-26 [1] Github (tidyverse/tibble@9eeef4d)   
##  tidyr       * 1.1.2      2020-08-27 [1] CRAN (R 4.0.2)                      
##  tidyselect    1.1.0      2020-05-11 [2] CRAN (R 4.0.0)                      
##  tidyverse   * 1.3.0      2019-11-21 [1] CRAN (R 4.0.2)                      
##  usethis       1.6.3      2020-09-17 [1] CRAN (R 4.0.2)                      
##  utf8          1.1.4      2018-05-24 [2] CRAN (R 4.0.0)                      
##  vctrs         0.3.5.9000 2020-11-26 [1] Github (r-lib/vctrs@957baf7)        
##  whisker       0.4        2019-08-28 [2] CRAN (R 4.0.0)                      
##  withr         2.3.0      2020-09-22 [1] CRAN (R 4.0.2)                      
##  xaringan      0.18       2020-10-21 [1] CRAN (R 4.0.2)                      
##  xfun          0.19       2020-10-30 [1] CRAN (R 4.0.2)                      
##  xml2          1.3.2      2020-04-23 [2] CRAN (R 4.0.0)                      
##  yaml          2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                      
## 
## [1] /Users/etan0038/Library/R/4.0/library
## [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
```
]

These slides are licensed under <br><center><a href="https://creativecommons.org/licenses/by-sa/3.0/au/"><img src="images/cc.svg" style="height:2em;"/><img src="images/by.svg" style="height:2em;"/><img src="images/sa.svg" style="height:2em;"/></a></center>