# Tidy data and statistical visualisation to support exploration of temporal data with R
### Earo Wang
### May 29, 2017 Slides on <a href="http://bit.ly/tsvis2017" class="uri">http://bit.ly/tsvis2017</a>

---

## Agenda

* Why `tidyverse`? Tidy data

* What does `gg` in `ggplot2` stand for? Grammar of graphics

* Interactive data visualisation on the web
    + plotly: `ggplotly()`, `plot_ly`, animation
    + shiny app: `wanderer4melb`

???

Lemme introduce myself a little bit. I'm currently doing my PhD at Monash, working
with Di and Rob. My research is about visualising temporal data.

This is what I'm going to cover for the next one hour and half. First, I'll talk
about why `tidyverse` is great. `tidyverse` is a collection of R packages that
center around the "tidy data" concept. We'll learn some verbs/functions that do 
the data wrangling to get the tidy temporal data. As data analysts ... Second, we'll learn about the 
`ggplot2` that handles with plotting. You may wonder why it's called `ggplot2` 
and what `gg` means. It's grammar of graphics for plots. I'll talk about how the
`ggplot2` functions powered by the grammar help us to visualise time series data.
In the final bit, I'm going to show you some demos about interactive graphics using
`plotly` and `shiny`.

---

background-image: url(img/stickers.001.png)
background-size: 80%
background-position: 60% 90%

## R packages

* `tidyverse`: a collection of R packages surrounding "tidy data"
* `stringr`: handle string manipulation
* `forcats`: handle categorical variables
* `lubridate`: lubricate date-times process
* `plotly`: create web-based visualisation
* `shiny`: build interactive web applications
* `knitr`: provide tools for dynamic report generation
* `devtools`: help with R packages development

???

Here's a list of R packages that we're going to use for this part. As I said
before, `tidyverse` is a set of packages including `ggplot2`, `dplyr`, `readr`
etc. `stringr` for strings and `forcats` for categorical variables. `lubridate`
is for making dealing with dates easier. `plotly` and `shiny` for interactive
graphics on the web.

---

# Tidy data

???

Okay. Now, let's start with tidy data.

---

## What is tidy data?

* Each variable forms a column.
* Each observation forms a row.
* Each type of observational unit forms a table.

???

What is tidy data? What makes a dataset tidy?

1.2.3

The data structure is a rectangular cases-by-variables data layout that underlines
the `tidyverse`.

I'll use three datasets to explain what tidy data actually mean. How important
is the tidy data for further data analysis and vis.

---

```r
ped_loc <- read_csv("data/sensor_locations.csv")
ped_loc %>% 
 select(
 `Sensor ID`, `Sensor Description`, 
 Longitude, Latitude
 )
```

```
#> # A tibble: 43 x 4
#> `Sensor ID` `Sensor Description` Longitude
#> <int> <chr> <dbl>
#> 1 22 Flinders St-Elizabeth St (East) 144.9651
#> 2 34 Flinders St-Spark La 144.9742
#> 3 11 Waterfront City 144.9396
#> 4 8 Webb Bridge 144.9472
#> 5 7 Birrarung Marr 144.9714
#> 6 13 Flagstaff Station 144.9566
#> 7 15 State Library 144.9645
#> 8 27 QV Market-Peel St 144.9566
#> 9 12 New Quay 144.9429
#> 10 24 Spencer St-Collins St (North) 144.9545
#> # ... with 33 more rows, and 1 more variables:
#> # Latitude <dbl>
```

???

The first dataset we're going to look at is ...

The dataset is sourced from Melbourne Open Data Portal. You can actually click
here to check out the web page that hosts the dataset. Since 2009, the city of
Melb started to install some sensors that capture the foot traffic every hour.
This data can be used for urban planning or business management. For example,
if you're a cafe owner, by looking at the hourly traffic to decide the trading
hours. Until today, there are 43 sensors have been installed across the city.

Here, I read the sensor locations data into R using `read_csv` from `readr` pkg.
Rob has talked about the pipe operator. I pass the data to the `select` function
and select four colnames, that is ...

---

<img src="figure/ped-map-1.png" style="display: block; margin: auto;" />
]

???

As I know sensor's long and lat, I could plot these sensors on the map of Melb.
It clearly shows the locations of these sensors.

---

.left-column[
## Melbourne pedestrian data 🚶‍♀️
### - sensor data
### - sensor map
### - hourly counts
]
.right-column[

```r
ped_2017 <- read_csv("data/pedestrian_03_2017.csv")
ped_2017
```

```
#> # A tibble: 744 x 45
#> Date Hour `State Library` `Collins Place (South)`
#> <chr> <int> <int> <int>
#> 1 01/03/2017 0 140 36
#> 2 01/03/2017 1 64 17
#> 3 01/03/2017 2 29 11
#> 4 01/03/2017 3 13 9
#> 5 01/03/2017 4 13 10
#> 6 01/03/2017 5 31 84
#> 7 01/03/2017 6 92 252
#> 8 01/03/2017 7 327 767
#> 9 01/03/2017 8 908 1997
#> 10 01/03/2017 9 775 1319
#> # ... with 734 more rows, and 41 more variables: `Collins
#> # Place (North)` <int>, `Flagstaff Station` <int>,
#> # `Melbourne Central` <int>, `Town Hall (West)` <int>,
#> # `Bourke Street Mall (North)` <int>, `Bourke Street Mall
#> # (South)` <int>, `Australia on Collins` <int>, `Southern
#> # Cross Station` <int>, `Victoria Point` <int>, `New
#> # Quay` <int>, `Waterfront City` <int>, `Webb
#> # Bridge` <int>, `Princes Bridge` <int>, `Flinders St
#> # Station Underpass` <int>, `Sandridge Bridge` <int>,
#> # `Birrarung Marr` <int>, `QV Market-Elizabeth
#> # (West)` <int>, `Flinders St-Elizabeth St (East)` <int>,
#> # `Spencer St-Collins St (North)` <int>, `Spencer
#> # St-Collins St (South)` <int>, `Bourke St-Russell St
#> # (West)` <int>, `Convention/Exhibition Centre` <int>,
#> # `Chinatown-Swanston St (North)` <int>, `Chinatown-Lt
#> # Bourke St (South)` <int>, `QV Market-Peel St` <int>,
#> # `Vic Arts Centre` <int>, `Lonsdale St (South)` <int>,
#> # `Lygon St (West)` <int>, `Flinders St-Spring St
#> # (West)` <int>, `Flinders St-Spark Lane` <int>, `Alfred
#> # Place` <int>, `Queen Street (West)` <int>, `Lygon
#> # Street (East)` <int>, `Flinders St-Swanston St
#> # (West)` <int>, `Spring St-Lonsdale St (South)` <int>,
#> # `City Square` <int>, `St. Kilda-Alexandra
#> # Gardens` <int>, `Grattan St-Swanston St (West)` <int>,
#> # `Monash Rd-Swanston St (West)` <int>, `Tin
#> # Alley-Swanston St (West)` <int>, Southbank <int>
```
]

???

Besides the locations, we're more interested in learning about the hourly pedestrian
counts at every sensor. I read the second csv file that contains the pedestrian
counts in March.

For this data, there are 744 obs and 45 columns. ...

Date is read into as character ...

Lemme me refer to this kind of format as wide format by contrast to long form.
I'll explain what I mean by wide and long later.

---

.left-column[
## Melbourne pedestrian data 🚶‍♀️
### - sensor data
### - sensor map
### - hourly counts
### - tidy data
]
.right-column[
]

???

The remedy is converting the wide format to the long data.

The top data table is what we have, wide

gather the headers to one key variable, and the counts to the value variable.

By having more than 40 columns, we have four vars to work with. Looking at this
long form, what are the variables become clearer compared to the wide format.
Each variable forms a column.

from tidyr

organise the same data in two different ways. To be consistent, tidy long data used.

---

.left-column[
## Melbourne pedestrian data 🚶‍♀️
### - sensor data
### - sensor map
### - hourly counts
### - tidy data
]
.right-column[

```r
ped_long <- ped_2017 %>% 
 gather(
 key = Sensor_Name, value = Counts, 
 `State Library`:Southbank
 ) %>% 
 mutate(
 Date_Time = dmy_hms(paste(Date, Hour, "00:00")),
 Date = dmy(Date)
 )
ped_long
```

```
#> # A tibble: 31,992 x 5
#> Date Hour Sensor_Name Counts
#> <date> <int> <chr> <int>
#> 1 2017-03-01 0 State Library 140
#> 2 2017-03-01 1 State Library 64
#> 3 2017-03-01 2 State Library 29
#> 4 2017-03-01 3 State Library 13
#> 5 2017-03-01 4 State Library 13
#> 6 2017-03-01 5 State Library 31
#> 7 2017-03-01 6 State Library 92
#> 8 2017-03-01 7 State Library 327
#> 9 2017-03-01 8 State Library 908
#> 10 2017-03-01 9 State Library 775
#> # ... with 31,982 more rows, and 1 more variables:
#> # Date_Time <dttm>
```

]

???

Any questions so far?

---

```r
otway_weather <- read_csv("data/weather_2016.csv")
head(otway_weather)
```

```
#> # A tibble: 6 x 35
#> ID YEAR MONTH ELEMENT VALUE1 VALUE2 VALUE3
#> <chr> <int> <chr> <chr> <int> <int> <int>
#> 1 ASN00090015 2016 01 TMAX 209 195 193
#> 2 ASN00090015 2016 01 TMIN 175 145 162
#> 3 ASN00090015 2016 01 PRCP 0 0 0
#> 4 ASN00090015 2016 01 TAVG 166 174 175
#> 5 ASN00090015 2016 02 TMAX 217 239 185
#> 6 ASN00090015 2016 02 TMIN 120 146 149
#> # ... with 28 more variables: VALUE4 <int>, VALUE5 <int>,
#> # VALUE6 <int>, VALUE7 <int>, VALUE8 <int>, VALUE9 <int>,
#> # VALUE10 <int>, VALUE11 <int>, VALUE12 <int>,
#> # VALUE13 <int>, VALUE14 <int>, VALUE15 <int>,
#> # VALUE16 <int>, VALUE17 <int>, VALUE18 <int>,
#> # VALUE19 <int>, VALUE20 <int>, VALUE21 <int>,
#> # VALUE22 <int>, VALUE23 <int>, VALUE24 <int>,
#> # VALUE25 <int>, VALUE26 <int>, VALUE27 <int>,
#> # VALUE28 <int>, VALUE29 <int>, VALUE30 <int>,
#> # VALUE31 <int>
```
.footnote[.red[*] source: [global historical climatology network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/gsn/)]
]

---

```r
otway_weather %>% 
  gather(DAY, VALUE, VALUE1:VALUE31)
```

```
#> # A tibble: 1,488 x 6
#> ID YEAR MONTH ELEMENT DAY VALUE
#> <chr> <int> <chr> <chr> <chr> <int>
#> 1 ASN00090015 2016 01 TMAX VALUE1 209
#> 2 ASN00090015 2016 01 TMIN VALUE1 175
#> 3 ASN00090015 2016 01 PRCP VALUE1 0
#> 4 ASN00090015 2016 01 TAVG VALUE1 166
#> 5 ASN00090015 2016 02 TMAX VALUE1 217
#> 6 ASN00090015 2016 02 TMIN VALUE1 120
#> 7 ASN00090015 2016 02 PRCP VALUE1 2
#> 8 ASN00090015 2016 02 TAVG VALUE1 187
#> 9 ASN00090015 2016 03 TMAX VALUE1 243
#> 10 ASN00090015 2016 03 TMIN VALUE1 172
#> # ... with 1,478 more rows
```
]

---

]
.right-column[

```
#> # A tibble: 1,464 x 4
#> ID DATE ELEMENT VALUE
#> <chr> <date> <chr> <int>
#> 1 ASN00090015 2016-01-01 TMAX 209
#> 2 ASN00090015 2016-01-01 TMIN 175
#> 3 ASN00090015 2016-01-01 PRCP 0
#> 4 ASN00090015 2016-01-01 TAVG 166
#> 5 ASN00090015 2016-01-02 TMAX 195
#> 6 ASN00090015 2016-01-02 TMIN 145
#> 7 ASN00090015 2016-01-02 PRCP 0
#> 8 ASN00090015 2016-01-02 TAVG 174
#> 9 ASN00090015 2016-01-03 TMAX 193
#> 10 ASN00090015 2016-01-03 TMIN 162
#> # ... with 1,454 more rows
```
]

---

]
.right-column[

```r
otway_weather %>% 
 gather(DAY, VALUE, VALUE1:VALUE31) %>% 
 mutate(
 DAY = str_sub(DAY, start = 6),
 DATE = ymd(paste(YEAR, MONTH, DAY, sep = "-"))
 ) %>% 
 arrange(DATE) %>% 
 select(ID, DATE, ELEMENT, VALUE) %>% 
 filter(!(is.na(DATE))) %>% 
 mutate(
 VALUE = if_else(VALUE < -999, NA_integer_, VALUE),
 VALUE = VALUE / 10
 ) %>% 
 spread(ELEMENT, VALUE)
```

```
#> # A tibble: 366 x 6
#> ID DATE PRCP TAVG TMAX TMIN
#> * <chr> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00090015 2016-01-01 0 16.6 20.9 17.5
#> 2 ASN00090015 2016-01-02 0 17.4 19.5 14.5
#> 3 ASN00090015 2016-01-03 0 17.5 19.3 16.2
#> 4 ASN00090015 2016-01-04 0 17.7 20.2 16.7
#> 5 ASN00090015 2016-01-05 0 17.8 20.6 16.1
#> 6 ASN00090015 2016-01-06 0 17.1 20.3 16.5
#> 7 ASN00090015 2016-01-07 0 15.8 19.7 14.8
#> 8 ASN00090015 2016-01-08 0 15.6 18.8 14.2
#> 9 ASN00090015 2016-01-09 0 15.7 19.0 11.3
#> 10 ASN00090015 2016-01-10 0 18.6 25.4 11.9
#> # ... with 356 more rows
```
]

---

]
.right-column[

```r
otway_tidy <- otway_weather %>% 
 gather(DAY, VALUE, VALUE1:VALUE31) %>% 
 mutate(
 DAY = str_sub(DAY, start = 6),
 DATE = ymd(paste(YEAR, MONTH, DAY, sep = "-"))
 ) %>% 
 arrange(DATE) %>% 
 select(ID, DATE, ELEMENT, VALUE) %>% 
 filter(!(is.na(DATE))) %>% 
 mutate(
 VALUE = if_else(VALUE < -999, NA_integer_, VALUE),
 VALUE = VALUE / 10
 ) %>% 
 spread(ELEMENT, VALUE) %>% 
 mutate(NAVG = (TMAX + TMIN) / 2)
head(otway_tidy)
```

```
#> # A tibble: 6 x 7
#> ID DATE PRCP TAVG TMAX TMIN NAVG
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00090015 2016-01-01 0 16.6 20.9 17.5 19.20
#> 2 ASN00090015 2016-01-02 0 17.4 19.5 14.5 17.00
#> 3 ASN00090015 2016-01-03 0 17.5 19.3 16.2 17.75
#> 4 ASN00090015 2016-01-04 0 17.7 20.2 16.7 18.45
#> 5 ASN00090015 2016-01-05 0 17.8 20.6 16.1 18.35
#> 6 ASN00090015 2016-01-06 0 17.1 20.3 16.5 18.40
```
]

???

TAVG: UTC time zone rather than local time

---

`billboard.csv` records the date a song first entered the Billboard Top 100 in 
2000 and its rank over 76 weeks.

```
#> # A tibble: 6 x 81
#> year artist track
#> <int> <chr> <chr>
#> 1 2000 Backstreet Boys, The Shape Of My Heart
#> 2 2000 Backstreet Boys, The Show Me The Meaning ...
#> 3 2000 Backstreet Boys, The The One
#> 4 2000 N'Sync Bye Bye Bye
#> 5 2000 N'Sync It's Gonna Be Me
#> 6 2000 N'Sync This I Promise You
#> # ... with 78 more variables: time <time>,
#> # date.entered <date>, `1` <int>, `2` <int>, `3` <int>,
#> # `4` <int>, `5` <int>, `6` <int>, `7` <int>, `8` <int>,
#> # `9` <int>, `10` <int>, `11` <int>, `12` <int>,
#> # `13` <int>, `14` <int>, `15` <int>, `16` <int>,
#> # `17` <int>, `18` <int>, `19` <int>, `20` <int>,
#> # `21` <int>, `22` <int>, `23` <int>, `24` <int>,
#> # `25` <int>, `26` <int>, `27` <chr>, `28` <chr>,
#> # `29` <chr>, `30` <chr>, `31` <chr>, `32` <chr>,
#> # `33` <chr>, `34` <chr>, `35` <chr>, `36` <chr>,
#> # `37` <chr>, `38` <chr>, `39` <chr>, `40` <chr>,
#> # `41` <chr>, `42` <chr>, `43` <chr>, `44` <chr>,
#> # `45` <chr>, `46` <chr>, `47` <chr>, `48` <chr>,
#> # `49` <chr>, `50` <chr>, `51` <chr>, `52` <chr>,
#> # `53` <chr>, `54` <chr>, `55` <chr>, `56` <chr>,
#> # `57` <chr>, `58` <chr>, `59` <chr>, `60` <chr>,
#> # `61` <chr>, `62` <chr>, `63` <chr>, `64` <chr>,
#> # `65` <chr>, `66` <chr>, `67` <chr>, `68` <chr>,
#> # `69` <chr>, `70` <chr>, `71` <chr>, `72` <chr>,
#> # `73` <chr>, `74` <chr>, `75` <chr>, `76` <chr>
```

]

???

* Old dataset
* A song first time entered to the billboard Top 100, when it's entered
* The position and if NA, dropped from the Top 100

---

```r
song
```

```
#> # A tibble: 6 x 4
#> id artist track
#> <int> <chr> <chr>
#> 1 1 Backstreet Boys, The Shape Of My Heart
#> 2 2 Backstreet Boys, The Show Me The Meaning ...
#> 3 3 Backstreet Boys, The The One
#> 4 4 N'Sync Bye Bye Bye
#> 5 5 N'Sync It's Gonna Be Me
#> 6 6 N'Sync This I Promise You
#> # ... with 1 more variables: time <time>
```

```r
rank
```

```
#> # A tibble: 456 x 4
#> id date.entered week rank
#> <int> <date> <chr> <chr>
#> 1 1 2000-10-14 1 39
#> 2 2 2000-01-01 1 74
#> 3 3 2000-05-27 1 58
#> 4 4 2000-01-29 1 42
#> 5 5 2000-05-06 1 82
#> 6 6 2000-09-30 1 68
#> 7 1 2000-10-14 2 25
#> 8 2 2000-01-01 2 62
#> 9 3 2000-05-27 2 50
#> 10 4 2000-01-29 2 20
#> # ... with 446 more rows
```
]

???

* convert wide to long
* spit to two data tables
* Follows the third point of the tidy data principles: Each type of observational unit forms a table.
* 40 mins get done

---

# Grammar of graphics

???

* basic/good understanding of tidy data structure
* bc it builds a solid foundation to statistical vis.

---

## What is grammar of graphics?

* In 1999, "The grammar of graphics" by Leland Wilkinson
* In 2008, `ggplot2` by Hadley Wickham
* A conceptual framework for mapping variables to graphical elements
* The analogue of doing a functional mapping of random variables. For example,
  `$n$` independent and identically distributed RVs `$X_1, \ldots, X_n$`, the mean and
  the standard deviation are defined as 
  `$$\bar{X} = \frac{1}{n} \sum_{i = 1}^{n} X_{i} \qquad S_{n-1} = \frac{1}{n-1} \sum_{i = 1}^{n} \left(X_i - \bar{X} \right)^2.$$`

???

we're going to use some short but comprehensive vocabulary to describe different
sorts of graphs.

---

```r
ped_long
```

---

```
data: ped_long
layer:
    mapping: x = Date_Time, y = Counts
    geom: line, point
facet: Sensor_Name
```

```r
ggplot(ped_long, aes(x = Date_Time, y = Counts)) +
  geom_line() +
  geom_point() +
  facet_grid(Sensor_Name ~ ., scale = "free_y")
```
]

???

* `autoplot`: against time index
* This is how we use graphic gramma to describe the plot
* Start with a tidy dataset with clearly defined variables.
* geom and other aes make the graph perceivable.
* facet: trellis plots or lattice plots.
* scale: local for comparing shapes instead of magnitudes.

---

```
data: ped_long
layer:
    mapping: x = Date_Time, y = Counts, colour = Sensor_Name
    geom: line, point
facet: Sensor_Name
```

```r
ggplot(ped_long, aes(x = Date_Time, y = Counts)) +
  geom_line(aes(colour = Sensor_Name)) +
  geom_point(aes(colour = Sensor_Name)) +
  facet_grid(Sensor_Name ~ ., scale = "free_y")
```
]

???

* colour is also the part of aes mapping.
* not adding additional info in this case.
* pedestrian data interesting and rich
* southern x as an example to explore
* seems that sx has distinctive patterns b/t weekdays and weekend

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```r
wday <- c("Monday", "Tuesday", "Wednesday", "Thursday", 
 "Friday")
sx <- ped_long %>% 
 filter(Sensor_Name == "Southern Cross Station") %>% 
 mutate(
 Wday = wday(Date, label = TRUE, abbr = FALSE),
 Wday = if_else(Wday %in% wday, "Weekday", "Weekend"),
 Wday = ordered(Wday)
 )
sx
```

```
#> # A tibble: 744 x 6
#> Date Hour Sensor_Name Counts
#> <date> <int> <chr> <int>
#> 1 2017-03-01 0 Southern Cross Station 16
#> 2 2017-03-01 1 Southern Cross Station 8
#> 3 2017-03-01 2 Southern Cross Station 3
#> 4 2017-03-01 3 Southern Cross Station 4
#> 5 2017-03-01 4 Southern Cross Station 1
#> 6 2017-03-01 5 Southern Cross Station 96
#> 7 2017-03-01 6 Southern Cross Station 581
#> 8 2017-03-01 7 Southern Cross Station 1847
#> 9 2017-03-01 8 Southern Cross Station 3863
#> 10 2017-03-01 9 Southern Cross Station 2063
#> # ... with 734 more rows, and 2 more variables:
#> # Date_Time <dttm>, Wday <ord>
```

]

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```
data: southern-cross
layer:
    mapping: x = Hour, y = Counts, colour = Wday
    geom: line
```

```r
ggplot(sx, aes(Hour, Counts, group = Date)) +
  geom_line(aes(colour = Wday))
```

]

???

* explain activities at some time points.
* more spread out in afternoon than in morning.
* All the daily series are overlaid each other and use color to differentiate
  weekday and weekends.

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```
data: southern-cross
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday
```

```r
ggplot(sx, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2)
```
]

???

* weekday and weekends are not yet sufficient.
* ?what day would make this behave like a weekend. ---> labour day

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

Expertise and Google in action

![](img/adele.png)

]

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```r
labour <- "Labour Day" # 2013-03-13
adele <- "Adele Day" # 2017-03-18 to 19
# Justin Bieber's gig 2017-03-10
sx_more <- sx %>% 
 mutate(
 Wday = fct_expand(Wday, labour, adele),
 Wday = if_else(
 Date == ymd("2017-03-13"), 
 ordered(labour, levels(Wday)), Wday
 ),
 Wday = if_else(
 Date %in% ymd(c("2017-03-18", "2017-03-19")), 
 ordered(adele, levels(Wday)), Wday
 )
 ) 
head(sx_more)
```

```
#> # A tibble: 6 x 6
#> Date Hour Sensor_Name Counts
#> <date> <int> <chr> <int>
#> 1 2017-03-01 0 Southern Cross Station 16
#> 2 2017-03-01 1 Southern Cross Station 8
#> 3 2017-03-01 2 Southern Cross Station 3
#> 4 2017-03-01 3 Southern Cross Station 4
#> 5 2017-03-01 4 Southern Cross Station 1
#> 6 2017-03-01 5 Southern Cross Station 96
#> # ... with 2 more variables: Date_Time <dttm>, Wday <ord>
```

]

???

transform the data

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```
data: southern-cross-more
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday
```

```r
ggplot(sx_more, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2)
```
]

???

grammar remains the same but using transformed data

I've just showed you how to use the graphics to explore the data. Notice something
unexpected and use your expertise or other resources to explain and produce another
graph.

Has anyone noticed there's another weired day that I haven't explained/exploited?

---

.left-column[
## More on pedestrian data
### - revisit
### - `ggplot2`
### - southern x 🚉
]
.right-column[

```
data: southern-cross-more
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday
coord: polar
```

```r
ggplot(sx_more, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2) +
  coord_polar()
```
]

???

known as rose plot

This slide also shows the advantage of using the grammar. Instead of referring
to these as line and rose, the grammar informs you of the difference between
the two plots is simply one linear and one polar coordinates.

polar: periodic behaviour

When you need to decide which plot you should pick up for you presentation, it
should be conducted under the statistical hypothesis testing framework, known
as visual inference.

Be aware of that it provides scientific tools to help with which display is more
powerful.

---

```r
otway_more <- otway_tidy %>% 
 mutate(
 MONTH = month(DATE, label = TRUE),
 DAY = mday(DATE)
 ) 
otway_more
```

```
#> # A tibble: 366 x 9
#> ID DATE PRCP TAVG TMAX TMIN NAVG
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00090015 2016-01-01 0 16.6 20.9 17.5 19.20
#> 2 ASN00090015 2016-01-02 0 17.4 19.5 14.5 17.00
#> 3 ASN00090015 2016-01-03 0 17.5 19.3 16.2 17.75
#> 4 ASN00090015 2016-01-04 0 17.7 20.2 16.7 18.45
#> 5 ASN00090015 2016-01-05 0 17.8 20.6 16.1 18.35
#> 6 ASN00090015 2016-01-06 0 17.1 20.3 16.5 18.40
#> 7 ASN00090015 2016-01-07 0 15.8 19.7 14.8 17.25
#> 8 ASN00090015 2016-01-08 0 15.6 18.8 14.2 16.50
#> 9 ASN00090015 2016-01-09 0 15.7 19.0 11.3 15.15
#> 10 ASN00090015 2016-01-10 0 18.6 25.4 11.9 18.65
#> # ... with 356 more rows, and 2 more variables:
#> # MONTH <ord>, DAY <int>
```
]

---

.left-column[
## Your turn
### - weather data
### - grammar here?
]
.right-column[
<img src="figure/otway-month-a-1.svg" style="display: block; margin: auto;" />
]

???

Discuss with your neighbour what's the graph about and what's the grammar used?

---

```
data: otway_more
layer:
    1. yintercept: year_average
       geom: hline
    2. mapping: xmin = DAY-, xmax = DAY+, ymin = TMIN, ymax = TMAX
       geom: rect
    3. mapping: x = DAY, y = NAVG
       geom: line
facet: MONTH
```

<img src="figure/otway-month-b-1.svg" style="display: block; margin: auto;" />
]

???

Let them do the lab exercise first without explaining the grammar.

70 mins get done

---

background-image: url(img/data-science.png)
background-size: 70%
background-position: center middle

## Workflow

???

* This is the workflow that we followed in the first two parts
* The first two sections: transform and visualise
* missing component is "model", leave it to Rob
* report the findings: communicate using interactive tools

---

# Interactive data visualisation on the web

???

* bringing interactivity to the graphs provides a different dim to look at the data
* serves as an exploratory and communication tool

---

```r
p <- sx %>% 
 ggplot(aes(Hour, Counts, group = Date)) +
 geom_line() +
 facet_wrap(~ Wday, ncol = 2)
ggplotly(p)
```

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]

???

* how do I know when these "weired" behaviour happened? If by looking at static plot.
* `plotly`
* instead of having two different copies of code, `ggplotly` only needs one.
* hover info is quite comprehensive using `ggplotly`.

---

```r
p1 <- sx %>% filter(Wday == "Weekday") %>% 
 group_by(Date) %>% 
 plot_ly(x = ~ Hour, y = ~ Counts) %>% 
 add_lines()
p2 <- sx %>% filter(Wday == "Weekend") %>% 
 group_by(Date) %>% 
 plot_ly(x = ~ Hour, y = ~ Counts) %>% 
 add_lines()
layout(subplot(p1, p2, shareY = TRUE), showlegend = FALSE)
```

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]

???

* `ggplotly` shortcut
* authentic `plot_ly` replicate the plot.
* hover info needs to be customised.

---

```r
a10_df <- broom::tidy(zoo::as.zoo(fpp2::a10)) %>% 
 mutate(
 year = year(index),
 month = month(index)
 )
p3 <- a10_df %>% 
 ggplot(aes(month, value)) +
 geom_line(aes(group = year), alpha = 0.2) +
 geom_line(aes(frame = year, colour = as.factor(year))) +
animation_opts(
 ggplotly(p3), frame = 1000, easing = "elastic"
)
```

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16.503966"],"frame":"2003","type":"scatter","mode":"lines","line":{"width":3.77952755905512,"color":"rgba(97,156,255,1)","dash":"solid"},"hoveron":"points","name":"2003","legendgroup":"2003","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":false},{"x":[1,2,3,4,5,6,7,8,9,10,11,12],"y":[18.00376795,11.93802987,12.9979001,12.88264507,13.94344681,13.9894722,15.33909742,15.37076394,16.142005,16.685754,17.636728,18.869325],"text":["year: 2004 as.factor(year): 2004 month: 1 value: 18.003768","year: 2004 as.factor(year): 2004 month: 2 value: 11.938030","year: 2004 as.factor(year): 2004 month: 3 value: 12.997900","year: 2004 as.factor(year): 2004 month: 4 value: 12.882645","year: 2004 as.factor(year): 2004 month: 5 value: 13.943447","year: 2004 as.factor(year): 2004 month: 6 value: 13.989472","year: 2004 as.factor(year): 2004 month: 7 value: 15.339097","year: 2004 as.factor(year): 2004 month: 8 value: 15.370764","year: 2004 as.factor(year): 2004 month: 9 value: 16.142005","year: 2004 as.factor(year): 2004 month: 10 value: 16.685754","year: 2004 as.factor(year): 2004 month: 11 value: 17.636728","year: 2004 as.factor(year): 2004 month: 12 value: 18.869325"],"frame":"2004","type":"scatter","mode":"lines","line":{"width":3.77952755905512,"color":"rgba(174,135,255,1)","dash":"solid"},"hoveron":"points","name":"2004","legendgroup":"2004","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":false},{"x":[1,2,3,4,5,6,7,8,9,10,11,12],"y":[20.778723,12.154552,13.402392,14.459239,14.795102,15.705248,15.82955,17.554701,18.100864,17.496668,19.347265,20.031291],"text":["year: 2005 as.factor(year): 2005 month: 1 value: 20.778723","year: 2005 as.factor(year): 2005 month: 2 value: 12.154552","year: 2005 as.factor(year): 2005 month: 3 value: 13.402392","year: 2005 as.factor(year): 2005 month: 4 value: 14.459239","year: 2005 as.factor(year): 2005 month: 5 value: 14.795102","year: 2005 as.factor(year): 2005 month: 6 value: 15.705248","year: 2005 as.factor(year): 2005 month: 7 value: 15.829550","year: 2005 as.factor(year): 2005 month: 8 value: 17.554701","year: 2005 as.factor(year): 2005 month: 9 value: 18.100864","year: 2005 as.factor(year): 2005 month: 10 value: 17.496668","year: 2005 as.factor(year): 2005 month: 11 value: 19.347265","year: 2005 as.factor(year): 2005 month: 12 value: 20.031291"],"frame":"2005","type":"scatter","mode":"lines","line":{"width":3.77952755905512,"color":"rgba(219,114,251,1)","dash":"solid"},"hoveron":"points","name":"2005","legendgroup":"2005","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":false},{"x":[1,2,3,4,5,6,7,8,9,10,11,12],"y":[23.486694,12.536987,15.467018,14.233539,17.783058,16.291602,16.980282,18.612189,16.623343,21.430241,23.575517,23.334206],"text":["year: 2006 as.factor(year): 2006 month: 1 value: 23.486694","year: 2006 as.factor(year): 2006 month: 2 value: 12.536987","year: 2006 as.factor(year): 2006 month: 3 value: 15.467018","year: 2006 as.factor(year): 2006 month: 4 value: 14.233539","year: 2006 as.factor(year): 2006 month: 5 value: 17.783058","year: 2006 as.factor(year): 2006 month: 6 value: 16.291602","year: 2006 as.factor(year): 2006 month: 7 value: 16.980282","year: 2006 as.factor(year): 2006 month: 8 value: 18.612189","year: 2006 as.factor(year): 2006 month: 9 value: 16.623343","year: 2006 as.factor(year): 2006 month: 10 value: 21.430241","year: 2006 as.factor(year): 2006 month: 11 value: 23.575517","year: 2006 as.factor(year): 2006 month: 12 value: 23.334206"],"frame":"2006","type":"scatter","mode":"lines","line":{"width":3.77952755905512,"color":"rgba(245,100,227,1)","dash":"solid"},"hoveron":"points","name":"2006","legendgroup":"2006","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":false},{"x":[1,2,3,4,5,6,7,8,9,10,11,12],"y":[28.038383,16.763869,19.792754,16.427305,21.000742,20.681002,21.83489,23.93020353,22.93035694,23.26333992,25.25003022,25.80609],"text":["year: 2007 as.factor(year): 2007 month: 1 value: 28.038383","year: 2007 as.factor(year): 2007 month: 2 value: 16.763869","year: 2007 as.factor(year): 2007 month: 3 value: 19.792754","year: 2007 as.factor(year): 2007 month: 4 value: 16.427305","year: 2007 as.factor(year): 2007 month: 5 value: 21.000742","year: 2007 as.factor(year): 2007 month: 6 value: 20.681002","year: 2007 as.factor(year): 2007 month: 7 value: 21.834890","year: 2007 as.factor(year): 2007 month: 8 value: 23.930204","year: 2007 as.factor(year): 2007 month: 9 value: 22.930357","year: 2007 as.factor(year): 2007 month: 10 value: 23.263340","year: 2007 as.factor(year): 2007 month: 11 value: 25.250030","year: 2007 as.factor(year): 2007 month: 12 value: 25.806090"],"frame":"2007","type":"scatter","mode":"lines","line":{"width":3.77952755905512,"color":"rgba(255,97,195,1)","dash":"solid"},"hoveron":"points","name":"2007","legendgroup":"2007","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","visible":false}],"traces":[1]}],"base_url":"https://plot.ly"},"evals":["config.modeBarButtonsToAdd.0.click"],"jsHooks":{"render":[{"code":"function(el, x) { var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set({\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.2,\"selected\":{\"opacity\":1}}); }","data":null}]}}</script>
]

???

* new release of plotly 4.6 comes with a new feature, animation.
* explain the code.

---

`wanderer4melb`([click me](https://github.com/earowang/wanderer4melb)) is a shiny app for 
visualising Melbourne pedestrian and weather data in 2016.

![demo](img/wanderer4melb.png)

```r
# install.packages("devtools")
devtools::install_github("earowang/wanderer4melb")
library(wanderer4melb)
launch_app()
```

]

???

* let's bind what you've learned before together into a shiny app.
* tidy data, grammar of graphics, and plotly
* I'm showing a demo
* inspiration: we know foot traffic is largely determined by temporal components,
  for example ... and special events. Any other factors affect the number of ped
  too? like temperature and rainfall? If it's extremely cold and heavy rain/storm,
  would you hand out in the city?
* explain the layout and the graphs.

---

`sugrrants` 🐜 is an R package (under development) that supports 
graphics for analysing time series data.

```r
devtools::install_github("earowang/sugrrants")
library(sugrrants)
```

* It fits into the tidyverse and grammar of graphics suite of methods for exploring
  temporal data.
* Calendar-based graphics are useful for visualising people's daily schedules, and
  `frame_calendar` 🗓 is made available for this.

If you find a bug or wanna suggest a new feature, please report/propose it on 
the [Github page](https://github.com/earowang/sugrrants/issues). Thanks.

]

???

---

.left-column[
## bind all together
### - shiny app
### - sugrrants
### - calendar plot
]
.right-column[

`frame_calendar` rearranges the data into a calendar format using linear algebra tools.

```r
sx_cal <- sx %>% 
 frame_calendar(
 x = Hour, y = Counts, date = Date, nrow = 1, ncol = 1
 )
sx_cal
```

```
#> # A tibble: 744 x 9
#> Date Hour Sensor_Name Counts
#> <date> <int> <chr> <int>
#> 1 2017-03-01 0 Southern Cross Station 16
#> 2 2017-03-01 1 Southern Cross Station 8
#> 3 2017-03-01 2 Southern Cross Station 3
#> 4 2017-03-01 3 Southern Cross Station 4
#> 5 2017-03-01 4 Southern Cross Station 1
#> 6 2017-03-01 5 Southern Cross Station 96
#> 7 2017-03-01 6 Southern Cross Station 581
#> 8 2017-03-01 7 Southern Cross Station 1847
#> 9 2017-03-01 8 Southern Cross Station 3863
#> 10 2017-03-01 9 Southern Cross Station 2063
#> # ... with 734 more rows, and 5 more variables:
#> # Date_Time <dttm>, Wday <ord>, .group_id <dbl>,
#> # .x <dbl>, .y <dbl>
```

]

---

.left-column[
## bind all together
### - shiny app
### - sugrrants
### - calendar plot
]
.right-column[

`ggplot2` takes care of plotting a `data.frame` or `tibble` as usual.

```r
p_sx <- sx_cal %>% 
 ggplot(aes(.x, .y, group = .group_id, colour = Wday)) +
 geom_line()
p_sx
```

]

---

.left-column[
## bind all together
### - shiny app
### - sugrrants
### - calendar plot
]
.right-column[

`prettify` takes a `ggplot` object and then makes the calendar plot more readable.

```r
prettify(p_sx)
```

]

---

# Done! 🎉 🎊 hmm... not yet?

---

## Acknowledgements

* Slides powered by the R package [xaringan](https://github.com/yihui/xaringan)
  for [remark.js](https://remarkjs.com/) and [R Markdown](https://rmarkdown.rstudio.com)
    + The source files to reproduce the slides are available [here](https://github.com/earowang/medascin17-tsvis).
    + Yihui is giving a tutorial on [R Markdown Ecosystem](http://www.datasciencemelbourne.com/medascin2017/session/r-markdown-ecosystem-reports-papers-dashboards-books-websites-and-presentations/) (check it out).
* The R packages used for the slides to make analysis easier and more fun: `ggmap`,
  `plotly`, `stringr`, `forcats`, `forecast`, `tidyverse`, `lubridate`, `broom`,
  `zoo`, `shiny`, `emo`

---

# This work is licensed under [ Creative Commons](https://creativecommons.org/licenses/by-nc-nd/3.0/us/)

Notes for current slide

Notes for next slide

Lemme introduce myself a little bit. I'm currently doing my PhD at Monash, working with Di and Rob. My research is about visualising temporal data.

This is what I'm going to cover for the next one hour and half. First, I'll talk about why tidyverse is great. tidyverse is a collection of R packages that center around the "tidy data" concept. We'll learn some verbs/functions that do the data wrangling to get the tidy temporal data. As data analysts ... Second, we'll learn about the ggplot2 that handles with plotting. You may wonder why it's called ggplot2 and what gg means. It's grammar of graphics for plots. I'll talk about how the ggplot2 functions powered by the grammar help us to visualise time series data. In the final bit, I'm going to show you some demos about interactive graphics using plotly and shiny.

Tidy data and statistical visualisation
to support exploration of temporal data with R

Earo Wang

May 29, 2017

Slides on http://bit.ly/tsvis2017

1 / 45

AgendaWhy tidyverse? Tidy data
What does gg in ggplot2 stand for? Grammar of graphics
Interactive data visualisation on the webplotly: ggplotly(), plot_ly, animation
shiny app: wanderer4melb

2 / 45

Lemme introduce myself a little bit. I'm currently doing my PhD at Monash, working with Di and Rob. My research is about visualising temporal data.

R packagestidyverse: a collection of R packages surrounding "tidy data"
stringr: handle string manipulation
forcats: handle categorical variables
lubridate: lubricate date-times process
plotly: create web-based visualisation
shiny: build interactive web applications
knitr: provide tools for dynamic report generation
devtools: help with R packages development
3 / 45

Here's a list of R packages that we're going to use for this part. As I said before, tidyverse is a set of packages including ggplot2, dplyr, readr etc. stringr for strings and forcats for categorical variables. lubridate is for making dealing with dates easier. plotly and shiny for interactive graphics on the web.

Tidy data4 / 45

Okay. Now, let's start with tidy data.

What is tidy data?Each variable forms a column.
Each observation forms a row.
Each type of observational unit forms a table.
5 / 45

What is tidy data? What makes a dataset tidy?

1.2.3

The data structure is a rectangular cases-by-variables data layout that underlines the tidyverse.

I'll use three datasets to explain what tidy data actually mean. How important is the tidy data for further data analysis and vis.

Melbourne pedestrian data 🚶‍♀️

- sensor data

ped_loc <- read_csv("data/sensor_locations.csv")
ped_loc %>% 
  select(
    `Sensor ID`, `Sensor Description`, 
    Longitude, Latitude
  )

#> # A tibble: 43 x 4
#>    `Sensor ID`            `Sensor Description` Longitude
#>          <int>                           <chr>     <dbl>
#>  1          22 Flinders St-Elizabeth St (East)  144.9651
#>  2          34            Flinders St-Spark La  144.9742
#>  3          11                 Waterfront City  144.9396
#>  4           8                     Webb Bridge  144.9472
#>  5           7                  Birrarung Marr  144.9714
#>  6          13               Flagstaff Station  144.9566
#>  7          15                   State Library  144.9645
#>  8          27               QV Market-Peel St  144.9566
#>  9          12                        New Quay  144.9429
#> 10          24   Spencer St-Collins St (North)  144.9545
#> # ... with 33 more rows, and 1 more variables:
#> #   Latitude <dbl>

* source: the city of Melbourne

6 / 45

The first dataset we're going to look at is ...

The dataset is sourced from Melbourne Open Data Portal. You can actually click here to check out the web page that hosts the dataset. Since 2009, the city of Melb started to install some sensors that capture the foot traffic every hour. This data can be used for urban planning or business management. For example, if you're a cafe owner, by looking at the hourly traffic to decide the trading hours. Until today, there are 43 sensors have been installed across the city.

Here, I read the sensor locations data into R using read_csv from readr pkg. Rob has talked about the pipe operator. I pass the data to the select function and select four colnames, that is ...

Melbourne pedestrian data 🚶‍♀️

- sensor data

- sensor map

7 / 45

As I know sensor's long and lat, I could plot these sensors on the map of Melb. It clearly shows the locations of these sensors.

Melbourne pedestrian data 🚶‍♀️
- sensor data
- sensor map
- hourly counts
ped_2017 <- read_csv("data/pedestrian_03_2017.csv")
ped_2017

#> # A tibble: 744 x 45
#>          Date  Hour `State Library` `Collins Place (South)`
#>         <chr> <int>           <int>                   <int>
#>  1 01/03/2017     0             140                      36
#>  2 01/03/2017     1              64                      17
#>  3 01/03/2017     2              29                      11
#>  4 01/03/2017     3              13                       9
#>  5 01/03/2017     4              13                      10
#>  6 01/03/2017     5              31                      84
#>  7 01/03/2017     6              92                     252
#>  8 01/03/2017     7             327                     767
#>  9 01/03/2017     8             908                    1997
#> 10 01/03/2017     9             775                    1319
#> # ... with 734 more rows, and 41 more variables: `Collins
#> #   Place (North)` <int>, `Flagstaff Station` <int>,
#> #   `Melbourne Central` <int>, `Town Hall (West)` <int>,
#> #   `Bourke Street Mall (North)` <int>, `Bourke Street Mall
#> #   (South)` <int>, `Australia on Collins` <int>, `Southern
#> #   Cross Station` <int>, `Victoria Point` <int>, `New
#> #   Quay` <int>, `Waterfront City` <int>, `Webb
#> #   Bridge` <int>, `Princes Bridge` <int>, `Flinders St
#> #   Station Underpass` <int>, `Sandridge Bridge` <int>,
#> #   `Birrarung Marr` <int>, `QV Market-Elizabeth
#> #   (West)` <int>, `Flinders St-Elizabeth St (East)` <int>,
#> #   `Spencer St-Collins St (North)` <int>, `Spencer
#> #   St-Collins St (South)` <int>, `Bourke St-Russell St
#> #   (West)` <int>, `Convention/Exhibition Centre` <int>,
#> #   `Chinatown-Swanston St (North)` <int>, `Chinatown-Lt
#> #   Bourke St (South)` <int>, `QV Market-Peel St` <int>,
#> #   `Vic Arts Centre` <int>, `Lonsdale St (South)` <int>,
#> #   `Lygon St (West)` <int>, `Flinders St-Spring St
#> #   (West)` <int>, `Flinders St-Spark Lane` <int>, `Alfred
#> #   Place` <int>, `Queen Street (West)` <int>, `Lygon
#> #   Street (East)` <int>, `Flinders St-Swanston St
#> #   (West)` <int>, `Spring St-Lonsdale St (South)` <int>,
#> #   `City Square` <int>, `St. Kilda-Alexandra
#> #   Gardens` <int>, `Grattan St-Swanston St (West)` <int>,
#> #   `Monash Rd-Swanston St (West)` <int>, `Tin
#> #   Alley-Swanston St (West)` <int>, Southbank <int>
8 / 45

Besides the locations, we're more interested in learning about the hourly pedestrian counts at every sensor. I read the second csv file that contains the pedestrian counts in March.

For this data, there are 744 obs and 45 columns. ...

Date is read into as character ...

Lemme me refer to this kind of format as wide format by contrast to long form. I'll explain what I mean by wide and long later.

Melbourne pedestrian data 🚶‍♀️
- sensor data
- sensor map
- hourly counts
- tidy data
9 / 45

The remedy is converting the wide format to the long data.

The top data table is what we have, wide

gather the headers to one key variable, and the counts to the value variable.

By having more than 40 columns, we have four vars to work with. Looking at this long form, what are the variables become clearer compared to the wide format. Each variable forms a column.

from tidyr

organise the same data in two different ways. To be consistent, tidy long data used.

Melbourne pedestrian data 🚶‍♀️
- sensor data
- sensor map
- hourly counts
- tidy data
ped_long <- ped_2017 %>% 
  gather(
    key = Sensor_Name, value = Counts, 
    `State Library`:Southbank
  ) %>% 
  mutate(
    Date_Time = dmy_hms(paste(Date, Hour, "00:00")),
    Date = dmy(Date)
  )
ped_long

#> # A tibble: 31,992 x 5
#>          Date  Hour   Sensor_Name Counts
#>        <date> <int>         <chr>  <int>
#>  1 2017-03-01     0 State Library    140
#>  2 2017-03-01     1 State Library     64
#>  3 2017-03-01     2 State Library     29
#>  4 2017-03-01     3 State Library     13
#>  5 2017-03-01     4 State Library     13
#>  6 2017-03-01     5 State Library     31
#>  7 2017-03-01     6 State Library     92
#>  8 2017-03-01     7 State Library    327
#>  9 2017-03-01     8 State Library    908
#> 10 2017-03-01     9 State Library    775
#> # ... with 31,982 more rows, and 1 more variables:
#> #   Date_Time <dttm>
10 / 45

Any questions so far?

Otway weather data ☀️

- what variables?

otway_weather <- read_csv("data/weather_2016.csv")
head(otway_weather)

#> # A tibble: 6 x 35
#>            ID  YEAR MONTH ELEMENT VALUE1 VALUE2 VALUE3
#>         <chr> <int> <chr>   <chr>  <int>  <int>  <int>
#> 1 ASN00090015  2016    01    TMAX    209    195    193
#> 2 ASN00090015  2016    01    TMIN    175    145    162
#> 3 ASN00090015  2016    01    PRCP      0      0      0
#> 4 ASN00090015  2016    01    TAVG    166    174    175
#> 5 ASN00090015  2016    02    TMAX    217    239    185
#> 6 ASN00090015  2016    02    TMIN    120    146    149
#> # ... with 28 more variables: VALUE4 <int>, VALUE5 <int>,
#> #   VALUE6 <int>, VALUE7 <int>, VALUE8 <int>, VALUE9 <int>,
#> #   VALUE10 <int>, VALUE11 <int>, VALUE12 <int>,
#> #   VALUE13 <int>, VALUE14 <int>, VALUE15 <int>,
#> #   VALUE16 <int>, VALUE17 <int>, VALUE18 <int>,
#> #   VALUE19 <int>, VALUE20 <int>, VALUE21 <int>,
#> #   VALUE22 <int>, VALUE23 <int>, VALUE24 <int>,
#> #   VALUE25 <int>, VALUE26 <int>, VALUE27 <int>,
#> #   VALUE28 <int>, VALUE29 <int>, VALUE30 <int>,
#> #   VALUE31 <int>

* source: global historical climatology network

11 / 45

Otway weather data ☀️
- what variables?
- tidying 🛠
otway_weather %>% 
  gather(DAY, VALUE, VALUE1:VALUE31)

#> # A tibble: 1,488 x 6
#>             ID  YEAR MONTH ELEMENT    DAY VALUE
#>          <chr> <int> <chr>   <chr>  <chr> <int>
#>  1 ASN00090015  2016    01    TMAX VALUE1   209
#>  2 ASN00090015  2016    01    TMIN VALUE1   175
#>  3 ASN00090015  2016    01    PRCP VALUE1     0
#>  4 ASN00090015  2016    01    TAVG VALUE1   166
#>  5 ASN00090015  2016    02    TMAX VALUE1   217
#>  6 ASN00090015  2016    02    TMIN VALUE1   120
#>  7 ASN00090015  2016    02    PRCP VALUE1     2
#>  8 ASN00090015  2016    02    TAVG VALUE1   187
#>  9 ASN00090015  2016    03    TMAX VALUE1   243
#> 10 ASN00090015  2016    03    TMIN VALUE1   172
#> # ... with 1,478 more rows
12 / 45

Otway weather data ☀️
- what variables?
- tidying 🛠
otway_weather %>% 
  gather(DAY, VALUE, VALUE1:VALUE31) %>% 
  mutate(
    DAY = str_sub(DAY, start = 6),
    DATE = ymd(paste(YEAR, MONTH, DAY, sep = "-"))
  ) %>% 
  arrange(DATE) %>% 
  select(ID, DATE, ELEMENT, VALUE) %>% 
  filter(!(is.na(DATE)))

#> # A tibble: 1,464 x 4
#>             ID       DATE ELEMENT VALUE
#>          <chr>     <date>   <chr> <int>
#>  1 ASN00090015 2016-01-01    TMAX   209
#>  2 ASN00090015 2016-01-01    TMIN   175
#>  3 ASN00090015 2016-01-01    PRCP     0
#>  4 ASN00090015 2016-01-01    TAVG   166
#>  5 ASN00090015 2016-01-02    TMAX   195
#>  6 ASN00090015 2016-01-02    TMIN   145
#>  7 ASN00090015 2016-01-02    PRCP     0
#>  8 ASN00090015 2016-01-02    TAVG   174
#>  9 ASN00090015 2016-01-03    TMAX   193
#> 10 ASN00090015 2016-01-03    TMIN   162
#> # ... with 1,454 more rows
13 / 45

Otway weather data ☀️
- what variables?
- tidying 🛠
otway_weather %>% 
  gather(DAY, VALUE, VALUE1:VALUE31) %>% 
  mutate(
    DAY = str_sub(DAY, start = 6),
    DATE = ymd(paste(YEAR, MONTH, DAY, sep = "-"))
  ) %>% 
  arrange(DATE) %>% 
  select(ID, DATE, ELEMENT, VALUE) %>% 
  filter(!(is.na(DATE))) %>% 
  mutate(
    VALUE = if_else(VALUE < -999, NA_integer_, VALUE),
    VALUE = VALUE / 10
  ) %>% 
  spread(ELEMENT, VALUE)

#> # A tibble: 366 x 6
#>             ID       DATE  PRCP  TAVG  TMAX  TMIN
#>  *       <chr>     <date> <dbl> <dbl> <dbl> <dbl>
#>  1 ASN00090015 2016-01-01     0  16.6  20.9  17.5
#>  2 ASN00090015 2016-01-02     0  17.4  19.5  14.5
#>  3 ASN00090015 2016-01-03     0  17.5  19.3  16.2
#>  4 ASN00090015 2016-01-04     0  17.7  20.2  16.7
#>  5 ASN00090015 2016-01-05     0  17.8  20.6  16.1
#>  6 ASN00090015 2016-01-06     0  17.1  20.3  16.5
#>  7 ASN00090015 2016-01-07     0  15.8  19.7  14.8
#>  8 ASN00090015 2016-01-08     0  15.6  18.8  14.2
#>  9 ASN00090015 2016-01-09     0  15.7  19.0  11.3
#> 10 ASN00090015 2016-01-10     0  18.6  25.4  11.9
#> # ... with 356 more rows
14 / 45

Otway weather data ☀️
- what variables?
- tidying 🛠
otway_tidy <- otway_weather %>% 
  gather(DAY, VALUE, VALUE1:VALUE31) %>% 
  mutate(
    DAY = str_sub(DAY, start = 6),
    DATE = ymd(paste(YEAR, MONTH, DAY, sep = "-"))
  ) %>% 
  arrange(DATE) %>% 
  select(ID, DATE, ELEMENT, VALUE) %>% 
  filter(!(is.na(DATE))) %>% 
  mutate(
    VALUE = if_else(VALUE < -999, NA_integer_, VALUE),
    VALUE = VALUE / 10
  ) %>% 
  spread(ELEMENT, VALUE) %>% 
  mutate(NAVG = (TMAX + TMIN) / 2)
head(otway_tidy)

#> # A tibble: 6 x 7
#>            ID       DATE  PRCP  TAVG  TMAX  TMIN  NAVG
#>         <chr>     <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00090015 2016-01-01     0  16.6  20.9  17.5 19.20
#> 2 ASN00090015 2016-01-02     0  17.4  19.5  14.5 17.00
#> 3 ASN00090015 2016-01-03     0  17.5  19.3  16.2 17.75
#> 4 ASN00090015 2016-01-04     0  17.7  20.2  16.7 18.45
#> 5 ASN00090015 2016-01-05     0  17.8  20.6  16.1 18.35
#> 6 ASN00090015 2016-01-06     0  17.1  20.3  16.5 18.40
15 / 45

TAVG: UTC time zone rather than local time

Your turn

- billboard data

billboard.csv records the date a song first entered the Billboard Top 100 in 2000 and its rank over 76 weeks.

#> # A tibble: 6 x 81
#>    year               artist                   track
#>   <int>                <chr>                   <chr>
#> 1  2000 Backstreet Boys, The       Shape Of My Heart
#> 2  2000 Backstreet Boys, The Show Me The Meaning ...
#> 3  2000 Backstreet Boys, The                 The One
#> 4  2000               N'Sync             Bye Bye Bye
#> 5  2000               N'Sync        It's Gonna Be Me
#> 6  2000               N'Sync      This I Promise You
#> # ... with 78 more variables: time <time>,
#> #   date.entered <date>, `1` <int>, `2` <int>, `3` <int>,
#> #   `4` <int>, `5` <int>, `6` <int>, `7` <int>, `8` <int>,
#> #   `9` <int>, `10` <int>, `11` <int>, `12` <int>,
#> #   `13` <int>, `14` <int>, `15` <int>, `16` <int>,
#> #   `17` <int>, `18` <int>, `19` <int>, `20` <int>,
#> #   `21` <int>, `22` <int>, `23` <int>, `24` <int>,
#> #   `25` <int>, `26` <int>, `27` <chr>, `28` <chr>,
#> #   `29` <chr>, `30` <chr>, `31` <chr>, `32` <chr>,
#> #   `33` <chr>, `34` <chr>, `35` <chr>, `36` <chr>,
#> #   `37` <chr>, `38` <chr>, `39` <chr>, `40` <chr>,
#> #   `41` <chr>, `42` <chr>, `43` <chr>, `44` <chr>,
#> #   `45` <chr>, `46` <chr>, `47` <chr>, `48` <chr>,
#> #   `49` <chr>, `50` <chr>, `51` <chr>, `52` <chr>,
#> #   `53` <chr>, `54` <chr>, `55` <chr>, `56` <chr>,
#> #   `57` <chr>, `58` <chr>, `59` <chr>, `60` <chr>,
#> #   `61` <chr>, `62` <chr>, `63` <chr>, `64` <chr>,
#> #   `65` <chr>, `66` <chr>, `67` <chr>, `68` <chr>,
#> #   `69` <chr>, `70` <chr>, `71` <chr>, `72` <chr>,
#> #   `73` <chr>, `74` <chr>, `75` <chr>, `76` <chr>

16 / 45

Old dataset
A song first time entered to the billboard Top 100, when it's entered
The position and if NA, dropped from the Top 100

Your turn
- billboard data
- end products
song

#> # A tibble: 6 x 4
#>      id               artist                   track
#>   <int>                <chr>                   <chr>
#> 1     1 Backstreet Boys, The       Shape Of My Heart
#> 2     2 Backstreet Boys, The Show Me The Meaning ...
#> 3     3 Backstreet Boys, The                 The One
#> 4     4               N'Sync             Bye Bye Bye
#> 5     5               N'Sync        It's Gonna Be Me
#> 6     6               N'Sync      This I Promise You
#> # ... with 1 more variables: time <time>
rank

#> # A tibble: 456 x 4
#>       id date.entered  week  rank
#>    <int>       <date> <chr> <chr>
#>  1     1   2000-10-14     1    39
#>  2     2   2000-01-01     1    74
#>  3     3   2000-05-27     1    58
#>  4     4   2000-01-29     1    42
#>  5     5   2000-05-06     1    82
#>  6     6   2000-09-30     1    68
#>  7     1   2000-10-14     2    25
#>  8     2   2000-01-01     2    62
#>  9     3   2000-05-27     2    50
#> 10     4   2000-01-29     2    20
#> # ... with 446 more rows
17 / 45

convert wide to long
spit to two data tables
Follows the third point of the tidy data principles: Each type of observational unit forms a table.
40 mins get done

Grammar of graphics18 / 45

basic/good understanding of tidy data structure
bc it builds a solid foundation to statistical vis.

What is grammar of graphics?In 1999, "The grammar of graphics" by Leland Wilkinson
In 2008, ggplot2 by Hadley Wickham
A conceptual framework for mapping variables to graphical elements
The analogue of doing a functional mapping of random variables. For example,
n independent and identically distributed RVs X1,…,Xn, the mean and
the standard deviation are defined as 
ˉX=1nn∑i=1XiSn−1=1n−1n∑i=1(Xi−ˉX)2.
19 / 45

we're going to use some short but comprehensive vocabulary to describe different sorts of graphs.

More on pedestrian data
- revisit
ped_long

#> # A tibble: 31,992 x 5
#>          Date  Hour   Sensor_Name Counts
#>        <date> <int>         <chr>  <int>
#>  1 2017-03-01     0 State Library    140
#>  2 2017-03-01     1 State Library     64
#>  3 2017-03-01     2 State Library     29
#>  4 2017-03-01     3 State Library     13
#>  5 2017-03-01     4 State Library     13
#>  6 2017-03-01     5 State Library     31
#>  7 2017-03-01     6 State Library     92
#>  8 2017-03-01     7 State Library    327
#>  9 2017-03-01     8 State Library    908
#> 10 2017-03-01     9 State Library    775
#> # ... with 31,982 more rows, and 1 more variables:
#> #   Date_Time <dttm>
20 / 45

More on pedestrian data

- revisit

- `ggplot2`

data: ped_long
layer:
    mapping: x = Date_Time, y = Counts
    geom: line, point
facet: Sensor_Name

ggplot(ped_long, aes(x = Date_Time, y = Counts)) +
  geom_line() +
  geom_point() +
  facet_grid(Sensor_Name ~ ., scale = "free_y")

21 / 45

autoplot: against time index
This is how we use graphic gramma to describe the plot
Start with a tidy dataset with clearly defined variables.
geom and other aes make the graph perceivable.
facet: trellis plots or lattice plots.
scale: local for comparing shapes instead of magnitudes.

More on pedestrian data

- revisit

- `ggplot2`

data: ped_long
layer:
    mapping: x = Date_Time, y = Counts, colour = Sensor_Name
    geom: line, point
facet: Sensor_Name

ggplot(ped_long, aes(x = Date_Time, y = Counts)) +
  geom_line(aes(colour = Sensor_Name)) +
  geom_point(aes(colour = Sensor_Name)) +
  facet_grid(Sensor_Name ~ ., scale = "free_y")

22 / 45

colour is also the part of aes mapping.
not adding additional info in this case.
pedestrian data interesting and rich
southern x as an example to explore
seems that sx has distinctive patterns b/t weekdays and weekend

More on pedestrian data
- revisit
- ggplot2
- southern x 🚉
wday <- c("Monday", "Tuesday", "Wednesday", "Thursday", 
  "Friday")
sx <- ped_long %>% 
  filter(Sensor_Name == "Southern Cross Station") %>% 
  mutate(
    Wday = wday(Date, label = TRUE, abbr = FALSE),
    Wday = if_else(Wday %in% wday, "Weekday", "Weekend"),
    Wday = ordered(Wday)
  )
sx

#> # A tibble: 744 x 6
#>          Date  Hour            Sensor_Name Counts
#>        <date> <int>                  <chr>  <int>
#>  1 2017-03-01     0 Southern Cross Station     16
#>  2 2017-03-01     1 Southern Cross Station      8
#>  3 2017-03-01     2 Southern Cross Station      3
#>  4 2017-03-01     3 Southern Cross Station      4
#>  5 2017-03-01     4 Southern Cross Station      1
#>  6 2017-03-01     5 Southern Cross Station     96
#>  7 2017-03-01     6 Southern Cross Station    581
#>  8 2017-03-01     7 Southern Cross Station   1847
#>  9 2017-03-01     8 Southern Cross Station   3863
#> 10 2017-03-01     9 Southern Cross Station   2063
#> # ... with 734 more rows, and 2 more variables:
#> #   Date_Time <dttm>, Wday <ord>
23 / 45

More on pedestrian data

- revisit

- `ggplot2`

- southern x 🚉

data: southern-cross
layer:
    mapping: x = Hour, y = Counts, colour = Wday
    geom: line

ggplot(sx, aes(Hour, Counts, group = Date)) +
  geom_line(aes(colour = Wday))

24 / 45

explain activities at some time points.
more spread out in afternoon than in morning.
All the daily series are overlaid each other and use color to differentiate weekday and weekends.

More on pedestrian data

- revisit

- `ggplot2`

- southern x 🚉

data: southern-cross
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday

ggplot(sx, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2)

25 / 45

weekday and weekends are not yet sufficient.
?what day would make this behave like a weekend. ---> labour day

More on pedestrian data

- revisit

- `ggplot2`

- southern x 🚉

Expertise and Google in action

26 / 45

More on pedestrian data
- revisit
- ggplot2
- southern x 🚉
labour <- "Labour Day" # 2013-03-13
adele <- "Adele Day" # 2017-03-18 to 19
# Justin Bieber's gig 2017-03-10
sx_more <- sx %>% 
  mutate(
    Wday = fct_expand(Wday, labour, adele),
    Wday = if_else(
      Date == ymd("2017-03-13"), 
      ordered(labour, levels(Wday)), Wday
    ),
    Wday = if_else(
      Date %in% ymd(c("2017-03-18", "2017-03-19")), 
      ordered(adele, levels(Wday)), Wday
    )
  ) 
head(sx_more)

#> # A tibble: 6 x 6
#>         Date  Hour            Sensor_Name Counts
#>       <date> <int>                  <chr>  <int>
#> 1 2017-03-01     0 Southern Cross Station     16
#> 2 2017-03-01     1 Southern Cross Station      8
#> 3 2017-03-01     2 Southern Cross Station      3
#> 4 2017-03-01     3 Southern Cross Station      4
#> 5 2017-03-01     4 Southern Cross Station      1
#> 6 2017-03-01     5 Southern Cross Station     96
#> # ... with 2 more variables: Date_Time <dttm>, Wday <ord>
27 / 45

transform the data

More on pedestrian data

- revisit

- `ggplot2`

- southern x 🚉

data: southern-cross-more
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday

ggplot(sx_more, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2)

28 / 45

grammar remains the same but using transformed data

I've just showed you how to use the graphics to explore the data. Notice something unexpected and use your expertise or other resources to explain and produce another graph.

Has anyone noticed there's another weired day that I haven't explained/exploited?

More on pedestrian data

- revisit

- `ggplot2`

- southern x 🚉

data: southern-cross-more
layer:
    mapping: x = Hour, y = Counts
    geom: line
facet: Wday
coord: polar

ggplot(sx_more, aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2) +
  coord_polar()

29 / 45

known as rose plot

This slide also shows the advantage of using the grammar. Instead of referring to these as line and rose, the grammar informs you of the difference between the two plots is simply one linear and one polar coordinates.

polar: periodic behaviour

When you need to decide which plot you should pick up for you presentation, it should be conducted under the statistical hypothesis testing framework, known as visual inference.

Be aware of that it provides scientific tools to help with which display is more powerful.

Your turn
- weather data
otway_more <- otway_tidy %>% 
  mutate(
    MONTH = month(DATE, label = TRUE),
    DAY = mday(DATE)
  ) 
otway_more

#> # A tibble: 366 x 9
#>             ID       DATE  PRCP  TAVG  TMAX  TMIN  NAVG
#>          <chr>     <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 ASN00090015 2016-01-01     0  16.6  20.9  17.5 19.20
#>  2 ASN00090015 2016-01-02     0  17.4  19.5  14.5 17.00
#>  3 ASN00090015 2016-01-03     0  17.5  19.3  16.2 17.75
#>  4 ASN00090015 2016-01-04     0  17.7  20.2  16.7 18.45
#>  5 ASN00090015 2016-01-05     0  17.8  20.6  16.1 18.35
#>  6 ASN00090015 2016-01-06     0  17.1  20.3  16.5 18.40
#>  7 ASN00090015 2016-01-07     0  15.8  19.7  14.8 17.25
#>  8 ASN00090015 2016-01-08     0  15.6  18.8  14.2 16.50
#>  9 ASN00090015 2016-01-09     0  15.7  19.0  11.3 15.15
#> 10 ASN00090015 2016-01-10     0  18.6  25.4  11.9 18.65
#> # ... with 356 more rows, and 2 more variables:
#> #   MONTH <ord>, DAY <int>
30 / 45

Your turn

- weather data

- grammar here?

31 / 45

Discuss with your neighbour what's the graph about and what's the grammar used?

Your turn

- weather data

- grammar here?

- it's ...

data: otway_more
layer:
    1. yintercept: year_average
       geom: hline
    2. mapping: xmin = DAY-, xmax = DAY+, ymin = TMIN, ymax = TMAX
       geom: rect
    3. mapping: x = DAY, y = NAVG
       geom: line
facet: MONTH

32 / 45

Let them do the lab exercise first without explaining the grammar.

70 mins get done

Workflow

* source: R for Data Science

33 / 45

This is the workflow that we followed in the first two parts
The first two sections: transform and visualise
missing component is "model", leave it to Rob
report the findings: communicate using interactive tools

Interactive data visualisation 
 on the web34 / 45

bringing interactivity to the graphs provides a different dim to look at the data
serves as an exploratory and communication tool

plotly

- `ggplotly`

p <- sx %>% 
  ggplot(aes(Hour, Counts, group = Date)) +
  geom_line() +
  facet_wrap(~ Wday, ncol = 2)
ggplotly(p)

35 / 45

how do I know when these "weired" behaviour happened? If by looking at static plot.
plotly
instead of having two different copies of code, ggplotly only needs one.
hover info is quite comprehensive using ggplotly.

plotly
- ggplotly
- plot_ly
p1 <- sx %>% filter(Wday == "Weekday") %>% 
  group_by(Date) %>% 
  plot_ly(x = ~ Hour, y = ~ Counts) %>% 
  add_lines()
p2 <- sx %>% filter(Wday == "Weekend") %>% 
  group_by(Date) %>% 
  plot_ly(x = ~ Hour, y = ~ Counts) %>% 
  add_lines()
layout(subplot(p1, p2, shareY = TRUE), showlegend = FALSE)
051015200100020003000400005101520
Counts
36 / 45

ggplotly shortcut
authentic plot_ly replicate the plot.
hover info needs to be customised.

plotly
- ggplotly
- plot_ly
- animation
a10_df <- broom::tidy(zoo::as.zoo(fpp2::a10)) %>% 
  mutate(
    year = year(index),
    month = month(index)
  )
p3 <- a10_df %>% 
  ggplot(aes(month, value)) +
  geom_line(aes(group = year), alpha = 0.2) +
  geom_line(aes(frame = year, colour = as.factor(year))) +
animation_opts(
  ggplotly(p3), frame = 1000, easing = "elastic"
)
JFMAMJJASOND102030
monthvalue1991year: 1991199119931995199719992001200320052007Playas.factor(year)
37 / 45

new release of plotly 4.6 comes with a new feature, animation.
explain the code.

bind all together

- shiny app

wanderer4melb(click me) is a shiny app for visualising Melbourne pedestrian and weather data in 2016.

demo

# install.packages("devtools")
devtools::install_github("earowang/wanderer4melb")
library(wanderer4melb)
launch_app()

38 / 45

let's bind what you've learned before together into a shiny app.
tidy data, grammar of graphics, and plotly
I'm showing a demo
inspiration: we know foot traffic is largely determined by temporal components, for example ... and special events. Any other factors affect the number of ped too? like temperature and rainfall? If it's extremely cold and heavy rain/storm, would you hand out in the city?
explain the layout and the graphs.

bind all together

- shiny app

- sugrrants

sugrrants 🐜 is an R package (under development) that supports graphics for analysing time series data.

devtools::install_github("earowang/sugrrants")
library(sugrrants)

It fits into the tidyverse and grammar of graphics suite of methods for exploring temporal data.
Calendar-based graphics are useful for visualising people's daily schedules, and frame_calendar 🗓 is made available for this.

If you find a bug or wanna suggest a new feature, please report/propose it on the Github page. Thanks.

39 / 45

bind all together

- shiny app

- sugrrants

- calendar plot

frame_calendar rearranges the data into a calendar format using linear algebra tools.

sx_cal <- sx %>% 
  frame_calendar(
    x = Hour, y = Counts, date = Date, nrow = 1, ncol = 1
  )
sx_cal

#> # A tibble: 744 x 9
#>          Date  Hour            Sensor_Name Counts
#>        <date> <int>                  <chr>  <int>
#>  1 2017-03-01     0 Southern Cross Station     16
#>  2 2017-03-01     1 Southern Cross Station      8
#>  3 2017-03-01     2 Southern Cross Station      3
#>  4 2017-03-01     3 Southern Cross Station      4
#>  5 2017-03-01     4 Southern Cross Station      1
#>  6 2017-03-01     5 Southern Cross Station     96
#>  7 2017-03-01     6 Southern Cross Station    581
#>  8 2017-03-01     7 Southern Cross Station   1847
#>  9 2017-03-01     8 Southern Cross Station   3863
#> 10 2017-03-01     9 Southern Cross Station   2063
#> # ... with 734 more rows, and 5 more variables:
#> #   Date_Time <dttm>, Wday <ord>, .group_id <dbl>,
#> #   .x <dbl>, .y <dbl>

40 / 45

bind all together

- shiny app

- sugrrants

- calendar plot

ggplot2 takes care of plotting a data.frame or tibble as usual.

p_sx <- sx_cal %>% 
  ggplot(aes(.x, .y, group = .group_id, colour = Wday)) +
  geom_line()
p_sx

41 / 45

bind all together

- shiny app

- sugrrants

- calendar plot

prettify takes a ggplot object and then makes the calendar plot more readable.

prettify(p_sx)

42 / 45

Done! 🎉 🎊 hmm... not yet?43 / 45

Acknowledgements

Slides powered by the R package xaringan for remark.js and R Markdown
- The source files to reproduce the slides are available here.
- Yihui is giving a tutorial on R Markdown Ecosystem (check it out).
The R packages used for the slides to make analysis easier and more fun: ggmap, plotly, stringr, forcats, forecast, tidyverse, lubridate, broom, zoo, shiny, emo

44 / 45

This work is licensed under
Creative Commons

45 / 45

AgendaWhy tidyverse? Tidy data
What does gg in ggplot2 stand for? Grammar of graphics
Interactive data visualisation on the webplotly: ggplotly(), plot_ly, animation
shiny app: wanderer4melb

2 / 45

Lemme introduce myself a little bit. I'm currently doing my PhD at Monash, working with Di and Rob. My research is about visualising temporal data.

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Tidy data and statistical visualisation to support exploration of temporal data with R

Earo Wang

May 29, 2017 Slides on http://bit.ly/tsvis2017

Agenda

R packages

Tidy data

What is tidy data?

Melbourne pedestrian data 🚶‍♀️

- sensor data

Melbourne pedestrian data 🚶‍♀️

- sensor data

- sensor map

Melbourne pedestrian data 🚶‍♀️

- sensor data

- sensor map

- hourly counts

Melbourne pedestrian data 🚶‍♀️

- sensor data

- sensor map

- hourly counts

- tidy data

Melbourne pedestrian data 🚶‍♀️

- sensor data

- sensor map

- hourly counts

- tidy data

Otway weather data ☀️

- what variables?

Otway weather data ☀️

- what variables?

- tidying 🛠

Otway weather data ☀️

- what variables?

- tidying 🛠

Otway weather data ☀️

- what variables?

- tidying 🛠

Otway weather data ☀️

- what variables?

- tidying 🛠

Your turn

- billboard data

Your turn

- billboard data

- end products

Grammar of graphics

What is grammar of graphics?

More on pedestrian data

- revisit

More on pedestrian data

- revisit

- ggplot2

More on pedestrian data

- revisit

- ggplot2

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

- revisit

- ggplot2

- southern x 🚉

More on pedestrian data

Tidy data and statistical visualisation
to support exploration of temporal data with R

May 29, 2017

Slides on http://bit.ly/tsvis2017

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

- `ggplot2`

Interactive data visualisation
on the web

- `ggplotly`

- `ggplotly`

- `plot_ly`

- `ggplotly`

- `plot_ly`

This work is licensed under
Creative Commons