Data Wrangling with R: Day 1

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---

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.item.center[

# Data Wrangling with R: Day 1

]

.center.shade_black.animated.bounceInUp.slower[
 
## Base R and `tidyverse`

Presented by Emi Tanaka

Department of Econometrics and Business Statistics

<img src="images/monash-one-line-reversed.png" style="width:500px"> 
 emi.tanaka@monash.edu
 @statsgen

.bottom_abs.width100.bg-black[
1st December 2020 @ Statistical Society of Australia | Zoom
]

]

</div>

---

# Base R

.margin-lr[
* **Base R** refers to the state of R when just launched in a clean interactive development environment (IDE, e.g. RStudio).

]

* In this state, R attached the following packages: 
 - `base`,
 - `methods`, for complex reasons, you may need to explicitly call this when using Rscript and package development!
 - `stats`, 
 - `graphics`, 
 - `grDevices`, 
 - `utils`, and
 - `datasets`.

---

# Tidyverse

* What packages does `library(tidyverse)` load?
--

* **Tidyverse** refers to a collection of R-packages: `ggplot2`, `dplyr`, `tidyr`, `readr`, `purrr`, `tibble`, `stringr`, `forcats`, `DBI`, `haven`, `httr`, `readxl`, `rvest`, `jsonlite`, `xml2`, `lubridate`, `hms`, `blob`, `magrittr`, `glue` and more recently, `tidymodels`.

---

# Tidyverse

* What packages does `library(tidyverse)` load?
* **Tidyverse** refers to a collection of R-packages: .monash-blue[`ggplot2`], .monash-blue[`dplyr`], .monash-blue[`tidyr`], .monash-blue[`readr`], .monash-blue[`purrr`], .monash-blue[`tibble`], .monash-blue[`stringr`], .monash-blue[`forcats`], `DBI`, `haven`, `httr`, `readxl`, `rvest`, `jsonlite`, `xml2`, `lubridate`, `hms`, `blob`, `magrittr`, `glue` and more recently, `tidymodels`. 
* Eight of these packages form the **core tidyverse**.

<center>
<img height="130px" src="images/tidyverse.png"> <img height="100px" src="images/ggplot2.png"><img height="100px" src="images/dplyr.png"><img height="100px" src="images/tidyr.png"><img height="100px" src="images/readr.png"><img height="100px" src="images/purrr.png"><img height="100px" src="images/tibble.png"><img height="100px" src="images/stringr.png"><img height="100px" src="images/forcats.png">
</center>
* .monash-blue[`library(tidyverse)`] is a short hand for `library(ggplot2)`, `library(dplyr)`, ..., `library(forcats)`

.footnote[
Wickham, H. et al. 2019. “Welcome to the Tidyverse.” Journal of Open. https://joss.theoj.org/papers/10.21105/joss.01686.
]

---

# Base R and Tidyverse

--
* Knowing Base R is essential to use Tidyverse effectively 📗
{{content}} 
--
* Data wrangling in Tidyverse just gives you a different flavour of how you can do things in Base R 🍭
{{content}}
--
* All data wrangling can be achieved using Base R, so why load extra package(s) to deal with the data wrangling? 🤔
{{content}}
--
* Tidyverse packages share a common design philosophy, grammar and data structure
{{content}}
--
* This trains your mental model to do data science tasks in a certain manner which may make it easier, faster, and/or fun for you to do these tasks
{{content}}
--
* It's an opinionated approach so you should make a decision for yourself of what works for you _and_ others that you share your work with

---

# `data.frame` .font_small[Base R]

* In R, `data.frame` is a special class of `list` 
* Every column in the `data.frame` is a vector of the same length
* Each entry in a vector have the same type, e.g. logical, integer, double (or numeric), character or factor.
* It has an attribute `row.names` which could be a sequence of integers indexing the row number or some unique identifier

```r
cars
```

```
##    speed dist
## 1      4    2
## 2      4   10
## 3      7    4
## 4      7   22
## 5      8   16
## 6      9   10
## 7     10   18
## 8     10   26
## 9     10   34
## 10    11   17
## 11    11   28
## 12    12   14
## 13    12   20
## 14    12   24
## 15    12   28
## 16    13   26
## 17    13   34
## 18    13   34
## 19    13   46
## 20    14   26
## 21    14   36
## 22    14   60
## 23    14   80
## 24    15   20
## 25    15   26
## 26    15   54
## 27    16   32
## 28    16   40
## 29    17   32
## 30    17   40
## 31    17   50
## 32    18   42
## 33    18   56
## 34    18   76
## 35    18   84
## 36    19   36
## 37    19   46
## 38    19   68
## 39    20   32
## 40    20   48
## 41    20   52
## 42    20   56
## 43    20   64
## 44    22   66
## 45    23   54
## 46    24   70
## 47    24   92
## 48    24   93
## 49    24  120
## 50    25   85
```
]
.item[

```r
mtcars
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
```

]

---

# Tidy data

.info-box[
**Definition of a tidy data**
* Each variable must have its own column
* Each observation must have its own row
* Each value must have its own cell
]

.footnote[
Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software, Articles 59 (10): 1–23.
]

---

# Data wrangling with Base R

---

# Subsetting by column .font_small[Base R .font_small[Part ]1]

```r
mtcars[, c("mpg", "cyl")] 
```

```
##                      mpg cyl
## Mazda RX4           21.0   6
## Mazda RX4 Wag       21.0   6
## Datsun 710          22.8   4
## Hornet 4 Drive      21.4   6
## Hornet Sportabout   18.7   8
## Valiant             18.1   6
## Duster 360          14.3   8
## Merc 240D           24.4   4
## Merc 230            22.8   4
## Merc 280            19.2   6
## Merc 280C           17.8   6
## Merc 450SE          16.4   8
## Merc 450SL          17.3   8
## Merc 450SLC         15.2   8
## Cadillac Fleetwood  10.4   8
## Lincoln Continental 10.4   8
## Chrysler Imperial   14.7   8
## Fiat 128            32.4   4
## Honda Civic         30.4   4
## Toyota Corolla      33.9   4
## Toyota Corona       21.5   4
## Dodge Challenger    15.5   8
## AMC Javelin         15.2   8
## Camaro Z28          13.3   8
## Pontiac Firebird    19.2   8
## Fiat X1-9           27.3   4
## Porsche 914-2       26.0   4
## Lotus Europa        30.4   4
## Ford Pantera L      15.8   8
## Ferrari Dino        19.7   6
## Maserati Bora       15.0   8
## Volvo 142E          21.4   4
```

]
.item[
By index

```r
mtcars[, 1:2] 
```

]

---

# Subsetting by column .font_small[Base R .font_small[Part ]2]

```r
mtcars[c("mpg", "cyl")] 
```

]
.item[
By index

```r
mtcars[1:2] 
```

]

---

# Lists .font_small[Base R]

* Remember, `data.frame` is just a special type of `list` and inherit methods applied for `list`.

```r
x <- list( int = 1:3, 
 shop = c("apple", "pear"), 
 c(2020, 12, 1, 1.3))
x
```

```
## $int
## [1] 1 2 3
## 
## $shop
## [1] "apple" "pear" 
## 
## [[3]]
## [1] 2020.0   12.0    1.0    1.3
```

]
.item[
{{content}}
]
]

```r
x[c("int", "shop")]
```

```
## $int
## [1] 1 2 3
## 
## $shop
## [1] "apple" "pear"
```
{{content}}
--

Double square bracket to access inside the list

```r
x[["int"]] # or x$int
```

```
## [1] 1 2 3
```

---

# `list` and `data.frame` .font_small[Base R]

```r
x[, "int"] # this is a special operation for data frames
```

```
## Error in x[, "int"]: incorrect number of dimensions
```
--

```r
mtcars[["cyl"]]
```

```
##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
```

```r
mtcars$cyl
```

```
##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
```
--

```r
mtcars["cyl"]
```

```
##                     cyl
## Mazda RX4             6
## Mazda RX4 Wag         6
## Datsun 710            4
## Hornet 4 Drive        6
## Hornet Sportabout     8
## Valiant               6
## Duster 360            8
## Merc 240D             4
## Merc 230              4
## Merc 280              6
## Merc 280C             6
## Merc 450SE            8
## Merc 450SL            8
## Merc 450SLC           8
## Cadillac Fleetwood    8
## Lincoln Continental   8
## Chrysler Imperial     8
## Fiat 128              4
## Honda Civic           4
## Toyota Corolla        4
## Toyota Corona         4
## Dodge Challenger      8
## AMC Javelin           8
## Camaro Z28            8
## Pontiac Firebird      8
## Fiat X1-9             4
## Porsche 914-2         4
## Lotus Europa          4
## Ford Pantera L        8
## Ferrari Dino          6
## Maserati Bora         8
## Volvo 142E            4
```

<div class="info-box pad20" style="position:absolute!important;bottom:10px;width:500px;margin-left:40%;">
<code>data.frame</code> inherits methods for <code>list</code>, but <code>list</code> does not inherit methods for <code>data.frame</code>
</div>

---

# Subsetting by column .font_small[Base R]

* If you subset _a single column_ using `[, ]`, then by default the output is a vector and not a `data.frame`.

```r
mtcars[, "mpg"]
```

```
##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
```
--
* If you want to preserve the output as a `data.frame` then:

```r
mtcars[, "mpg", drop = FALSE]
```

```
##                      mpg
## Mazda RX4           21.0
## Mazda RX4 Wag       21.0
## Datsun 710          22.8
## Hornet 4 Drive      21.4
## Hornet Sportabout   18.7
## Valiant             18.1
## Duster 360          14.3
## Merc 240D           24.4
## Merc 230            22.8
## Merc 280            19.2
## Merc 280C           17.8
## Merc 450SE          16.4
## Merc 450SL          17.3
## Merc 450SLC         15.2
## Cadillac Fleetwood  10.4
## Lincoln Continental 10.4
## Chrysler Imperial   14.7
## Fiat 128            32.4
## Honda Civic         30.4
## Toyota Corolla      33.9
## Toyota Corona       21.5
## Dodge Challenger    15.5
## AMC Javelin         15.2
## Camaro Z28          13.3
## Pontiac Firebird    19.2
## Fiat X1-9           27.3
## Porsche 914-2       26.0
## Lotus Europa        30.4
## Ford Pantera L      15.8
## Ferrari Dino        19.7
## Maserati Bora       15.0
## Volvo 142E          21.4
```

---

# Subsetting by row .font_small[Base R .font_small[Part ]1]

By index:

```r
mtcars[3:1, ]
```

```
##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
```

If it has row names:

```r
mtcars[c("Datsun 710", "Mazda RX4"), ]
```

```
##             mpg cyl disp  hp drat   wt  qsec vs am gear carb
## Datsun 710 22.8   4  108  93 3.85 2.32 18.61  1  1    4    1
## Mazda RX4  21.0   6  160 110 3.90 2.62 16.46  0  1    4    4
```

---

# Subsetting by row .font_small[Base R .font_small[Part ]2]

Using a logical vector,

```r
mtcars[mtcars$mpg > 31, ]
```

```
##                 mpg cyl disp hp drat    wt  qsec vs am gear carb
## Fiat 128       32.4   4 78.7 66 4.08 2.200 19.47  1  1    4    1
## Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.90  1  1    4    1
```
--

Or using **non-standard evaluation**:

```r
subset(mtcars, mpg > 31)
```

```
##                 mpg cyl disp hp drat    wt  qsec vs am gear carb
## Fiat 128       32.4   4 78.7 66 4.08 2.200 19.47  1  1    4    1
## Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.90  1  1    4    1
```

Non-standard evaluation? We'll come back to this later.

---

# Adding or modifying a column .font_small[Base R]

Append the new column:

```r
df1 <- cbind(mtcars, gpm = 1 / mtcars$mpg)
```
--

Create a new column as you would a create a new object in a list:

```r
df1$gpm <- 1 / df1$mpg
df1[["gpm"]] <- 1 / df1$mpg
```

Overwrite column if modifying an existing column:

```r
df1$wt <- df1$gpm^2
df1[["wt"]] <- df1$gpm^2
```
--
Modify only a part of it:

```r
df1$wt[df1$cyl==6] <- 10
```

---

# Adding a row .font_small[Base R]

What do you notice with the order of the new entry?

```r
df2 <- rbind(cars, data.frame(dist = 10, speed = 3))
tail(df2, 3)
```

```
##    speed dist
## 49    24  120
## 50    25   85
## 51     3   10
```

```r
df2 <- rbind(cars, c(10, 3))
tail(df2, 3)
```

```
##    speed dist
## 49    24  120
## 50    25   85
## 51    10    3
```

---

# Sorting columns .font_small[Base R]

```r
mtcars[, sort(names(mtcars))]
```

```
##                     am carb cyl  disp drat gear  hp  mpg  qsec vs    wt
## Mazda RX4            1    4   6 160.0 3.90    4 110 21.0 16.46  0 2.620
## Mazda RX4 Wag        1    4   6 160.0 3.90    4 110 21.0 17.02  0 2.875
## Datsun 710           1    1   4 108.0 3.85    4  93 22.8 18.61  1 2.320
## Hornet 4 Drive       0    1   6 258.0 3.08    3 110 21.4 19.44  1 3.215
## Hornet Sportabout    0    2   8 360.0 3.15    3 175 18.7 17.02  0 3.440
## Valiant              0    1   6 225.0 2.76    3 105 18.1 20.22  1 3.460
## Duster 360           0    4   8 360.0 3.21    3 245 14.3 15.84  0 3.570
## Merc 240D            0    2   4 146.7 3.69    4  62 24.4 20.00  1 3.190
## Merc 230             0    2   4 140.8 3.92    4  95 22.8 22.90  1 3.150
## Merc 280             0    4   6 167.6 3.92    4 123 19.2 18.30  1 3.440
## Merc 280C            0    4   6 167.6 3.92    4 123 17.8 18.90  1 3.440
## Merc 450SE           0    3   8 275.8 3.07    3 180 16.4 17.40  0 4.070
## Merc 450SL           0    3   8 275.8 3.07    3 180 17.3 17.60  0 3.730
## Merc 450SLC          0    3   8 275.8 3.07    3 180 15.2 18.00  0 3.780
## Cadillac Fleetwood   0    4   8 472.0 2.93    3 205 10.4 17.98  0 5.250
## Lincoln Continental  0    4   8 460.0 3.00    3 215 10.4 17.82  0 5.424
## Chrysler Imperial    0    4   8 440.0 3.23    3 230 14.7 17.42  0 5.345
## Fiat 128             1    1   4  78.7 4.08    4  66 32.4 19.47  1 2.200
## Honda Civic          1    2   4  75.7 4.93    4  52 30.4 18.52  1 1.615
## Toyota Corolla       1    1   4  71.1 4.22    4  65 33.9 19.90  1 1.835
## Toyota Corona        0    1   4 120.1 3.70    3  97 21.5 20.01  1 2.465
## Dodge Challenger     0    2   8 318.0 2.76    3 150 15.5 16.87  0 3.520
## AMC Javelin          0    2   8 304.0 3.15    3 150 15.2 17.30  0 3.435
## Camaro Z28           0    4   8 350.0 3.73    3 245 13.3 15.41  0 3.840
## Pontiac Firebird     0    2   8 400.0 3.08    3 175 19.2 17.05  0 3.845
## Fiat X1-9            1    1   4  79.0 4.08    4  66 27.3 18.90  1 1.935
## Porsche 914-2        1    2   4 120.3 4.43    5  91 26.0 16.70  0 2.140
## Lotus Europa         1    2   4  95.1 3.77    5 113 30.4 16.90  1 1.513
## Ford Pantera L       1    4   8 351.0 4.22    5 264 15.8 14.50  0 3.170
## Ferrari Dino         1    6   6 145.0 3.62    5 175 19.7 15.50  0 2.770
## Maserati Bora        1    8   8 301.0 3.54    5 335 15.0 14.60  0 3.570
## Volvo 142E           1    2   4 121.0 4.11    4 109 21.4 18.60  1 2.780
```

---

# Sorting rows .font_small[Base R]

```r
order(mtcars$mpg)
```

```
##  [1] 15 16 24  7 17 31 14 23 22 29 12 13 11  6  5 10 25 30  1  2  4 32 21  3  9
## [26]  8 27 26 19 28 18 20
```

```r
mtcars[order(mtcars$mpg),]
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
```

---

# Calculating statistical summaries by group .font_small[Base R]

🎯 Calculate the _average_ weight (`wt`) of a car for each gear type in (`gear`)  `mtcars`

```r
tapply(mtcars$wt, mtcars$gear, mean)
```

```
##        3        4        5 
## 3.892600 2.616667 2.632600
```
--

🎯 Calculate the _median_ weight (`wt`) of a car for each gear (`gear`) and engine (`vs`) type in  `mtcars`
--

```r
tapply(mtcars$wt, list(mtcars$gear, mtcars$vs), median)
```

```
##        0     1
## 3 3.8100 3.215
## 4 2.7475 2.550
## 5 2.9700 1.513
```

---

# If you installed the `dwexercise` package, run below in your R console

```r
learnr::run_tutorial("day1-exercise-01", package = "dwexercise")
```

# If the above doesn't work for you, go [here](https://ebsmonash.shinyapps.io/dw-day1-exercise-01).

# Questions or issues, let us know!
<center>
<div class="countdown clock" id="timer_5fc4c936" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time">15:00</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     2020-11-30                  
## 
## ─ 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)                      
##  callr         3.5.1   2020-10-13 [1] CRAN (R 4.0.2)                      
##  cli           2.2.0   2020-11-20 [1] CRAN (R 4.0.1)                      
##  countdown     0.3.5   2020-07-20 [1] Github (gadenbuie/countdown@a544fa4)
##  crayon        1.3.4   2017-09-16 [2] CRAN (R 4.0.0)                      
##  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)                      
##  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)                      
##  fs            1.5.0   2020-07-31 [1] CRAN (R 4.0.2)                      
##  glue          1.4.2   2020-08-27 [1] CRAN (R 4.0.2)                      
##  htmltools     0.5.0   2020-06-16 [1] CRAN (R 4.0.2)                      
##  icon          0.1.0   2020-06-21 [1] Github (emitanaka/icon@8458546)     
##  knitr         1.30    2020-09-22 [1] CRAN (R 4.0.2)                      
##  magrittr      2.0.1   2020-11-17 [1] CRAN (R 4.0.2)                      
##  memoise       1.1.0   2017-04-21 [2] CRAN (R 4.0.0)                      
##  pkgbuild      1.1.0   2020-07-13 [2] CRAN (R 4.0.1)                      
##  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)                      
##  R6            2.5.0   2020-10-28 [1] CRAN (R 4.0.2)                      
##  remotes       2.2.0   2020-07-21 [1] CRAN (R 4.0.2)                      
##  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)                      
##  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)                      
##  usethis       1.6.3   2020-09-17 [1] CRAN (R 4.0.2)                      
##  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)                      
##  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 <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>