class: center, middle, inverse, title-slide # ETC1010: Data Modelling and Computing ## Lecture 9B: More on networks ### Dr. Nicholas Tierney & Professor Di Cook ### EBS, Monash U. ### 2019-10-09 --- class: bg-main1 # Announcements .vlarge[ - Assignment 3 is due this Friday at 5pm - Project deadlines: - **Deadline 3 (11th October) **: Electronic copy of your data, and a page of data description, and cleaning done, or needing to be done. - **Deadline 4 (18th October) **: Final version of story board uploaded. - Guest Lecture: **16th October**: Dr. James McKeone - Practical exam: **18th October in class at 8am** - Final Exam: I will provide a review of exam content ] --- class: bg-main1 # recap: on network data .huge[ - To make a network analysis, you need: - an association matrix, that describes how nodes (vertices) are connected to each other - a layout algorithm to place the nodes optimally so that the fewest edges cross, or that the nodes that are most closely associated are near to each other. ] --- class: bg-main1 # Quantitative association matrices .huge[ Previous association matrices were black and white: ] <img src="images/network_data.png" width="80%" style="display: block; margin: auto;" /> --- class: bg-main1 ## Quantitative association matrices .huge[ - You could have the association between nodes described as real numbers. ] --- class: bg-main1 ## Quantitative association matrices .huge[ - E.g., these are the number of times that these people called each other in the last week: ] <table> <thead> <tr> <th style="text-align:left;"> </th> <th style="text-align:right;"> Meg </th> <th style="text-align:right;"> Tay </th> <th style="text-align:right;"> Yat </th> <th style="text-align:right;"> Zili </th> <th style="text-align:right;"> Jess </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Meg </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 5 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> Tay </td> <td style="text-align:right;"> 5 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> Yat </td> <td style="text-align:right;"> 4 </td> <td style="text-align:right;"> 4 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 0 </td> </tr> <tr> <td style="text-align:left;"> Zili </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> Jess </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 6 </td> <td style="text-align:right;"> 0 </td> </tr> </tbody> </table> --- class: bg-main1 ## Quantitative association matrices .huge[ We would need to turn this into an edge data set: ] ``` ## # A tibble: 25 x 3 ## from to count ## <chr> <chr> <dbl> ## 1 Meg Meg 0 ## 2 Tay Meg 5 ## 3 Yat Meg 4 ## 4 Zili Meg 1 ## 5 Jess Meg 1 ## 6 Meg Tay 5 ## 7 Tay Tay 0 ## 8 Yat Tay 4 ## 9 Zili Tay 2 ## 10 Jess Tay 1 ## # … with 15 more rows ``` --- class: bg-main1 ## Quantitative association matrices .huge[ - We need to decide what corresponds to a "connection". - Let's say they need to have called each other at least 4 times, to be considered connected. ] ```r d_edges_filter <- d_edges %>% filter(count > 3) ``` --- class: bg-main1 ## Quantitative association matrices ```r d_edges_filter ``` ``` ## # A tibble: 8 x 3 ## from to count ## <chr> <chr> <dbl> ## 1 Tay Meg 5 ## 2 Yat Meg 4 ## 3 Meg Tay 5 ## 4 Yat Tay 4 ## 5 Meg Yat 4 ## 6 Tay Yat 4 ## 7 Jess Zili 6 ## 8 Zili Jess 6 ``` --- class: bg-main1 ## Quantitative association matrices: Make the network diagram. .left-code[ ```r library(geomnet) set.seed(2019-10-09) ggplot(data = d_edges_filter, aes( from_id = from, to_id = to)) + geom_net( layout.alg = "kamadakawai", size = 2, labelon = TRUE, vjust = -0.6, ecolour = "grey60", directed =FALSE, fontsize = 3, ealpha = 0.5 ) + theme_net() ``` ] .right-plot[ <img src="lecture-9b-slides_files/figure-html/geom-net-cals-out-1.png" width="100%" style="display: block; margin: auto;" /> ] --- class: bg-main1 ## Data: Last 4 months of currency USD cross-rates .huge[ 😨 SO let's try this with cross-currency rates across the globe! ] -- .huge[ - Data extracted from http://openexchangerates.org/api/historical - R packages `jsonlite`, processed with `tidyverse`, `lubridate` ] --- class: bg-main1 ## Data: Last 4 months of currency USD cross-rates ``` ## # A tibble: 6 x 171 ## date AED AFN ALL AMD ANG AOA ARS AUD AWG AZN BAM BBD BDT ## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2018-05-14 3.67 71.2 106. 485. 1.79 230. 25.0 1.33 1.79 1.70 1.63 2 84.7 ## 2 2018-05-15 3.67 71.2 107. 485. 1.80 230. 24.1 1.34 1.79 1.70 1.64 2 84.8 ## 3 2018-05-16 3.67 71.0 108. 484. 1.80 232. 24.3 1.33 1.79 1.70 1.66 2 84.8 ## 4 2018-05-17 3.67 71.0 108. 483. 1.80 233. 24.3 1.33 1.79 1.70 1.66 2 84.8 ## 5 2018-05-18 3.67 71.0 108. 483. 1.80 233. 24.4 1.33 1.79 1.70 1.66 2 84.8 ## 6 2018-05-19 3.67 70.9 108. 482. 1.79 233. 24.4 1.33 1.79 1.70 1.66 2 84.8 ## # … with 157 more variables: BGN <dbl>, BHD <dbl>, BIF <dbl>, BMD <dbl>, BND <dbl>, ## # BOB <dbl>, BRL <dbl>, BSD <dbl>, BTC <dbl>, BTN <dbl>, BWP <dbl>, BYN <dbl>, ## # BZD <dbl>, CAD <dbl>, CDF <dbl>, CHF <dbl>, CLF <dbl>, CLP <dbl>, CNH <dbl>, ## # CNY <dbl>, COP <dbl>, CRC <dbl>, CUC <dbl>, CUP <dbl>, CVE <dbl>, CZK <dbl>, ## # DJF <dbl>, DKK <dbl>, DOP <dbl>, DZD <dbl>, EGP <dbl>, ERN <dbl>, ETB <dbl>, ## # EUR <dbl>, FJD <dbl>, FKP <dbl>, GBP <dbl>, GEL <dbl>, GGP <dbl>, GHS <dbl>, ## # GIP <dbl>, GMD <dbl>, GNF <dbl>, GTQ <dbl>, GYD <dbl>, HKD <dbl>, HNL <dbl>, ## # HRK <dbl>, HTG <dbl>, HUF <dbl>, IDR <dbl>, ILS <dbl>, IMP <dbl>, INR <dbl>, ## # IQD <dbl>, IRR <dbl>, ISK <dbl>, JEP <dbl>, JMD <dbl>, JOD <dbl>, JPY <dbl>, ## # KES <dbl>, KGS <dbl>, KHR <dbl>, KMF <dbl>, KPW <dbl>, KRW <dbl>, KWD <dbl>, ## # KYD <dbl>, KZT <dbl>, LAK <dbl>, LBP <dbl>, LKR <dbl>, LRD <dbl>, LSL <dbl>, ## # LYD <dbl>, MAD <dbl>, MDL <dbl>, MGA <dbl>, MKD <dbl>, MMK <dbl>, MNT <dbl>, ## # MOP <dbl>, MRO <dbl>, MRU <dbl>, MUR <dbl>, MVR <dbl>, MWK <dbl>, MXN <dbl>, ## # MYR <dbl>, MZN <dbl>, NAD <dbl>, NGN <dbl>, NIO <dbl>, NOK <dbl>, NPR <dbl>, ## # NZD <dbl>, OMR <dbl>, PAB <dbl>, PEN <dbl>, … ``` --- class: bg-main1 ## Data: Last 4 months of currency USD cross-rates <img src="lecture-9b-slides_files/figure-html/plot-rates-1.png" width="100%" style="display: block; margin: auto;" /> --- class: bg-main1 # Your turn: rstudio cloud .huge[ Make some plots (or google) to answer these questions - Is the NZD more similar to AUD, EUR, or JPY? (What currency is NZD?) - Is SGD more similar to AUD, EUR, or JPY? (What currency is SGD?) - How many currencies are there in the British Isles? ] <div class="countdown" id="timer_5d9d5ad6" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> --- class: bg-main1 <img src="lecture-9b-slides_files/figure-html/show-many-currencies-1.png" width="100%" style="display: block; margin: auto;" /> --- class: bg-main1 ## Pre-processing data: Keep only currencies that change over the period .huge[ - Some currencies don't change very much. - These should be filtered from the analysis, because in a study of currency movement, if it doesn't move then there is nothing more to be said. ] --- class: bg-main1 ## Pre-processing data: Keep only currencies that change over the period .huge[ - To filter out these currencies we use a statistic called [coefficient of variation](https://en.wikipedia.org/wiki/Coefficient_of_variation): $$ Coef Variation = \frac{\sigma}{\mu} $$ ] -- .vlarge[ - Measures standard deviation of currency relative to the mean. - For high means, we expect a currency to change more. - That is, relatively the standard deviation would be larger to consider it to be changing. ] --- class: bg-main1 ```r cv <- function(x){ sd(x)/mean(x) } rates %>% select(-date) %>% summarise_all(funs(cv)) ``` ``` ## # A tibble: 1 x 170 ## AED AFN ALL AMD ANG AOA ARS AUD AWG AZN BAM BBD ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2.22e-5 0.0111 0.00823 0.00276 0.0120 0.0535 0.116 0.0151 0.00136 3.28e-4 0.0106 0 ## # … with 158 more variables: BDT <dbl>, BGN <dbl>, BHD <dbl>, BIF <dbl>, BMD <dbl>, ## # BND <dbl>, BOB <dbl>, BRL <dbl>, BSD <dbl>, BTC <dbl>, BTN <dbl>, BWP <dbl>, ## # BYN <dbl>, BZD <dbl>, CAD <dbl>, CDF <dbl>, CHF <dbl>, CLF <dbl>, CLP <dbl>, ## # CNH <dbl>, CNY <dbl>, COP <dbl>, CRC <dbl>, CUC <dbl>, CUP <dbl>, CVE <dbl>, ## # CZK <dbl>, DJF <dbl>, DKK <dbl>, DOP <dbl>, DZD <dbl>, EGP <dbl>, ERN <dbl>, ## # ETB <dbl>, EUR <dbl>, FJD <dbl>, FKP <dbl>, GBP <dbl>, GEL <dbl>, GGP <dbl>, ## # GHS <dbl>, GIP <dbl>, GMD <dbl>, GNF <dbl>, GTQ <dbl>, GYD <dbl>, HKD <dbl>, ## # HNL <dbl>, HRK <dbl>, HTG <dbl>, HUF <dbl>, IDR <dbl>, ILS <dbl>, IMP <dbl>, ## # INR <dbl>, IQD <dbl>, IRR <dbl>, ISK <dbl>, JEP <dbl>, JMD <dbl>, JOD <dbl>, ## # JPY <dbl>, KES <dbl>, KGS <dbl>, KHR <dbl>, KMF <dbl>, KPW <dbl>, KRW <dbl>, ## # KWD <dbl>, KYD <dbl>, KZT <dbl>, LAK <dbl>, LBP <dbl>, LKR <dbl>, LRD <dbl>, ## # LSL <dbl>, LYD <dbl>, MAD <dbl>, MDL <dbl>, MGA <dbl>, MKD <dbl>, MMK <dbl>, ## # MNT <dbl>, MOP <dbl>, MRO <dbl>, MRU <dbl>, MUR <dbl>, MVR <dbl>, MWK <dbl>, ## # MXN <dbl>, MYR <dbl>, MZN <dbl>, NAD <dbl>, NGN <dbl>, NIO <dbl>, NOK <dbl>, ## # NPR <dbl>, NZD <dbl>, OMR <dbl>, PAB <dbl>, … ``` --- class: bg-main1 ```r cv <- function(x){ sd(x)/mean(x) } rates %>% select(-date) %>% summarise_all(funs(cv)) %>% gather(key = curr, value = cv) ``` ``` ## # A tibble: 170 x 2 ## curr cv ## <chr> <dbl> ## 1 AED 0.0000222 ## 2 AFN 0.0111 ## 3 ALL 0.00823 ## 4 AMD 0.00276 ## 5 ANG 0.0120 ## 6 AOA 0.0535 ## 7 ARS 0.116 ## 8 AUD 0.0151 ## 9 AWG 0.00136 ## 10 AZN 0.000328 ## # … with 160 more rows ``` --- class: bg-main1 ```r cv <- function(x){ sd(x)/mean(x) } rates %>% select(-date) %>% summarise_all(funs(cv)) %>% gather(key = curr, value = cv) %>% filter(cv > 0.0027) ``` ``` ## # A tibble: 130 x 2 ## curr cv ## <chr> <dbl> ## 1 AFN 0.0111 ## 2 ALL 0.00823 ## 3 AMD 0.00276 ## 4 ANG 0.0120 ## 5 AOA 0.0535 ## 6 ARS 0.116 ## 7 AUD 0.0151 ## 8 BAM 0.0106 ## 9 BDT 0.00404 ## 10 BGN 0.00932 ## # … with 120 more rows ``` --- class: bg-main1 ```r # Compute coefficient of variation. We will only analyse # currencies that have changes substantially over this time. # Dates dropped cv <- function(x){ sd(x)/mean(x) } rates_sum <- rates %>% select(-date) %>% summarise_all(funs(cv)) %>% gather(key = curr, value = cv) %>% filter(cv > 0.0027) rates_sub <- select(rates, rates_sum$curr) head(rates_sub) ``` ``` ## # A tibble: 6 x 130 ## AFN ALL AMD ANG AOA ARS AUD BAM BDT BGN BIF BND BRL BTC ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 71.2 106. 485. 1.79 230. 25.0 1.33 1.63 84.7 1.64 1767. 1.33 3.62 0.000115 ## 2 71.2 107. 485. 1.80 230. 24.1 1.34 1.64 84.8 1.65 1773. 1.33 3.65 0.000118 ## 3 71.0 108. 484. 1.80 232. 24.3 1.33 1.66 84.8 1.66 1759. 1.33 3.68 0.000120 ## 4 71.0 108. 483. 1.80 233. 24.3 1.33 1.66 84.8 1.66 1759. 1.33 3.70 0.000124 ## 5 71.0 108. 483. 1.80 233. 24.4 1.33 1.66 84.8 1.66 1762. 1.33 3.74 0.000121 ## 6 70.9 108. 482. 1.79 233. 24.4 1.33 1.66 84.8 1.66 1761. 1.33 3.74 0.000121 ## # … with 116 more variables: BTN <dbl>, BWP <dbl>, BYN <dbl>, CAD <dbl>, CDF <dbl>, ## # CHF <dbl>, CLF <dbl>, CLP <dbl>, CNH <dbl>, CNY <dbl>, COP <dbl>, CRC <dbl>, ## # CVE <dbl>, CZK <dbl>, DKK <dbl>, DOP <dbl>, DZD <dbl>, ERN <dbl>, ETB <dbl>, ## # EUR <dbl>, FJD <dbl>, FKP <dbl>, GBP <dbl>, GEL <dbl>, GGP <dbl>, GHS <dbl>, ## # GIP <dbl>, GMD <dbl>, GNF <dbl>, GTQ <dbl>, GYD <dbl>, HNL <dbl>, HRK <dbl>, ## # HTG <dbl>, HUF <dbl>, IDR <dbl>, ILS <dbl>, IMP <dbl>, INR <dbl>, IRR <dbl>, ## # ISK <dbl>, JEP <dbl>, JMD <dbl>, JPY <dbl>, KES <dbl>, KMF <dbl>, KRW <dbl>, ## # KZT <dbl>, LAK <dbl>, LKR <dbl>, LRD <dbl>, LSL <dbl>, LYD <dbl>, MAD <dbl>, ## # MDL <dbl>, MGA <dbl>, MKD <dbl>, MMK <dbl>, MNT <dbl>, MRU <dbl>, MUR <dbl>, ## # MVR <dbl>, MWK <dbl>, MXN <dbl>, MYR <dbl>, MZN <dbl>, NAD <dbl>, NIO <dbl>, ## # NOK <dbl>, NPR <dbl>, NZD <dbl>, PEN <dbl>, PGK <dbl>, PHP <dbl>, PKR <dbl>, ## # PLN <dbl>, PYG <dbl>, RON <dbl>, RSD <dbl>, RUB <dbl>, RWF <dbl>, SBD <dbl>, ## # SCR <dbl>, SEK <dbl>, SGD <dbl>, SHP <dbl>, SLL <dbl>, SOS <dbl>, STD <dbl>, ## # STN <dbl>, SZL <dbl>, THB <dbl>, TJS <dbl>, TMT <dbl>, TND <dbl>, TOP <dbl>, ## # TRY <dbl>, TWD <dbl>, UAH <dbl>, UGX <dbl>, … ``` --- class: bg-main1 ## Remove currencies that are not currencies .huge[ Some of the currencies ... aren't really currencies. Google these ones: XAG, XDR, XPT - what are they? ] <div class="countdown" id="timer_5d9d5be8" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">02</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ```r # Remove non-currencies rates_dropped <- rates_sub %>% select(-ALL, -XAG, -XDR, -XPT) ``` ??? XAG is Gold XPT is Platinum XDR is special drawing rights --- class: bg-main1 # Standardize the currencies .huge[ To examine overall trend regardless of actual USD cross rate, standardise the values to have mean 0 and standard deviation 1. ] ```r scale01 <- function(x) (x-mean(x))/sd(x) rates_scaled <- rates_dropped %>% mutate_all(funs(scale01)) ``` --- class: bg-main1 # What is `mutate_all()`? .huge[ Instead of: ] ```r rates_dropped %>% mutate(AFN = scale01(AFN), AMD = scale01(AMD), ANG = scale01(ANG), ... ...) ``` --- class: bg-main1 # What is `mutate_all()`? .huge[ We can write: ] ```r rates_scaled <- rates_dropped %>% mutate_all(funs(scale01)) ``` --- class: bg-main1 # These are called `scoped variants` - and there are more: .huge[ - `*_if` = Do this thing **if** some condition is met - `*_at` = Do this thing **at** these select variables - `*_all` = Do this thing **for all** variables ] --- class: bg-main1 # Example: `mutate_if()` ```r iris %>% mutate_if(is.numeric, scale01) ``` ``` ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 -0.89767388 1.01560199 -1.33575163 -1.3110521482 setosa ## 2 -1.13920048 -0.13153881 -1.33575163 -1.3110521482 setosa ## 3 -1.38072709 0.32731751 -1.39239929 -1.3110521482 setosa ## 4 -1.50149039 0.09788935 -1.27910398 -1.3110521482 setosa ## 5 -1.01843718 1.24503015 -1.33575163 -1.3110521482 setosa ## 6 -0.53538397 1.93331463 -1.16580868 -1.0486667950 setosa ## 7 -1.50149039 0.78617383 -1.33575163 -1.1798594716 setosa ## 8 -1.01843718 0.78617383 -1.27910398 -1.3110521482 setosa ## 9 -1.74301699 -0.36096697 -1.33575163 -1.3110521482 setosa ## 10 -1.13920048 0.09788935 -1.27910398 -1.4422448248 setosa ## 11 -0.53538397 1.47445831 -1.27910398 -1.3110521482 setosa ## 12 -1.25996379 0.78617383 -1.22245633 -1.3110521482 setosa ## 13 -1.25996379 -0.13153881 -1.33575163 -1.4422448248 setosa ## 14 -1.86378030 -0.13153881 -1.50569459 -1.4422448248 setosa ## 15 -0.05233076 2.16274279 -1.44904694 -1.3110521482 setosa ## 16 -0.17309407 3.08045544 -1.27910398 -1.0486667950 setosa ## 17 -0.53538397 1.93331463 -1.39239929 -1.0486667950 setosa ## 18 -0.89767388 1.01560199 -1.33575163 -1.1798594716 setosa ## 19 -0.17309407 1.70388647 -1.16580868 -1.1798594716 setosa ## 20 -0.89767388 1.70388647 -1.27910398 -1.1798594716 setosa ## 21 -0.53538397 0.78617383 -1.16580868 -1.3110521482 setosa ## 22 -0.89767388 1.47445831 -1.27910398 -1.0486667950 setosa ## 23 -1.50149039 1.24503015 -1.56234224 -1.3110521482 setosa ## 24 -0.89767388 0.55674567 -1.16580868 -0.9174741184 setosa ## 25 -1.25996379 0.78617383 -1.05251337 -1.3110521482 setosa ## 26 -1.01843718 -0.13153881 -1.22245633 -1.3110521482 setosa ## 27 -1.01843718 0.78617383 -1.22245633 -1.0486667950 setosa ## 28 -0.77691058 1.01560199 -1.27910398 -1.3110521482 setosa ## 29 -0.77691058 0.78617383 -1.33575163 -1.3110521482 setosa ## 30 -1.38072709 0.32731751 -1.22245633 -1.3110521482 setosa ## 31 -1.25996379 0.09788935 -1.22245633 -1.3110521482 setosa ## 32 -0.53538397 0.78617383 -1.27910398 -1.0486667950 setosa ## 33 -0.77691058 2.39217095 -1.27910398 -1.4422448248 setosa ## 34 -0.41462067 2.62159911 -1.33575163 -1.3110521482 setosa ## 35 -1.13920048 0.09788935 -1.27910398 -1.3110521482 setosa ## 36 -1.01843718 0.32731751 -1.44904694 -1.3110521482 setosa ## 37 -0.41462067 1.01560199 -1.39239929 -1.3110521482 setosa ## 38 -1.13920048 1.24503015 -1.33575163 -1.4422448248 setosa ## 39 -1.74301699 -0.13153881 -1.39239929 -1.3110521482 setosa ## 40 -0.89767388 0.78617383 -1.27910398 -1.3110521482 setosa ## 41 -1.01843718 1.01560199 -1.39239929 -1.1798594716 setosa ## 42 -1.62225369 -1.73753594 -1.39239929 -1.1798594716 setosa ## 43 -1.74301699 0.32731751 -1.39239929 -1.3110521482 setosa ## 44 -1.01843718 1.01560199 -1.22245633 -0.7862814418 setosa ## 45 -0.89767388 1.70388647 -1.05251337 -1.0486667950 setosa ## 46 -1.25996379 -0.13153881 -1.33575163 -1.1798594716 setosa ## 47 -0.89767388 1.70388647 -1.22245633 -1.3110521482 setosa ## 48 -1.50149039 0.32731751 -1.33575163 -1.3110521482 setosa ## 49 -0.65614727 1.47445831 -1.27910398 -1.3110521482 setosa ## 50 -1.01843718 0.55674567 -1.33575163 -1.3110521482 setosa ## 51 1.39682886 0.32731751 0.53362088 0.2632599711 versicolor ## 52 0.67224905 0.32731751 0.42032558 0.3944526477 versicolor ## 53 1.27606556 0.09788935 0.64691619 0.3944526477 versicolor ## 54 -0.41462067 -1.73753594 0.13708732 0.1320672944 versicolor ## 55 0.79301235 -0.59039513 0.47697323 0.3944526477 versicolor ## 56 -0.17309407 -0.59039513 0.42032558 0.1320672944 versicolor ## 57 0.55148575 0.55674567 0.53362088 0.5256453243 versicolor ## 58 -1.13920048 -1.50810778 -0.25944625 -0.2615107354 versicolor ## 59 0.91377565 -0.36096697 0.47697323 0.1320672944 versicolor ## 60 -0.77691058 -0.81982329 0.08043967 0.2632599711 versicolor ## 61 -1.01843718 -2.42582042 -0.14615094 -0.2615107354 versicolor ## 62 0.06843254 -0.13153881 0.25038262 0.3944526477 versicolor ## 63 0.18919584 -1.96696410 0.13708732 -0.2615107354 versicolor ## 64 0.30995914 -0.36096697 0.53362088 0.2632599711 versicolor ## 65 -0.29385737 -0.36096697 -0.08950329 0.1320672944 versicolor ## 66 1.03453895 0.09788935 0.36367793 0.2632599711 versicolor ## 67 -0.29385737 -0.13153881 0.42032558 0.3944526477 versicolor ## 68 -0.05233076 -0.81982329 0.19373497 -0.2615107354 versicolor ## 69 0.43072244 -1.96696410 0.42032558 0.3944526477 versicolor ## 70 -0.29385737 -1.27867961 0.08043967 -0.1303180588 versicolor ## 71 0.06843254 0.32731751 0.59026853 0.7880306775 versicolor ## 72 0.30995914 -0.59039513 0.13708732 0.1320672944 versicolor ## 73 0.55148575 -1.27867961 0.64691619 0.3944526477 versicolor ## 74 0.30995914 -0.59039513 0.53362088 0.0008746178 versicolor ## 75 0.67224905 -0.36096697 0.30703027 0.1320672944 versicolor ## 76 0.91377565 -0.13153881 0.36367793 0.2632599711 versicolor ## 77 1.15530226 -0.59039513 0.59026853 0.2632599711 versicolor ## 78 1.03453895 -0.13153881 0.70356384 0.6568380009 versicolor ## 79 0.18919584 -0.36096697 0.42032558 0.3944526477 versicolor ## 80 -0.17309407 -1.04925145 -0.14615094 -0.2615107354 versicolor ## 81 -0.41462067 -1.50810778 0.02379201 -0.1303180588 versicolor ## 82 -0.41462067 -1.50810778 -0.03285564 -0.2615107354 versicolor ## 83 -0.05233076 -0.81982329 0.08043967 0.0008746178 versicolor ## 84 0.18919584 -0.81982329 0.76021149 0.5256453243 versicolor ## 85 -0.53538397 -0.13153881 0.42032558 0.3944526477 versicolor ## 86 0.18919584 0.78617383 0.42032558 0.5256453243 versicolor ## 87 1.03453895 0.09788935 0.53362088 0.3944526477 versicolor ## 88 0.55148575 -1.73753594 0.36367793 0.1320672944 versicolor ## 89 -0.29385737 -0.13153881 0.19373497 0.1320672944 versicolor ## 90 -0.41462067 -1.27867961 0.13708732 0.1320672944 versicolor ## 91 -0.41462067 -1.04925145 0.36367793 0.0008746178 versicolor ## 92 0.30995914 -0.13153881 0.47697323 0.2632599711 versicolor ## 93 -0.05233076 -1.04925145 0.13708732 0.0008746178 versicolor ## 94 -1.01843718 -1.73753594 -0.25944625 -0.2615107354 versicolor ## 95 -0.29385737 -0.81982329 0.25038262 0.1320672944 versicolor ## 96 -0.17309407 -0.13153881 0.25038262 0.0008746178 versicolor ## 97 -0.17309407 -0.36096697 0.25038262 0.1320672944 versicolor ## 98 0.43072244 -0.36096697 0.30703027 0.1320672944 versicolor ## 99 -0.89767388 -1.27867961 -0.42938920 -0.1303180588 versicolor ## 100 -0.17309407 -0.59039513 0.19373497 0.1320672944 versicolor ## 101 0.55148575 0.55674567 1.27004036 1.7063794137 virginica ## 102 -0.05233076 -0.81982329 0.76021149 0.9192233541 virginica ## 103 1.51759216 -0.13153881 1.21339271 1.1816087073 virginica ## 104 0.55148575 -0.36096697 1.04344975 0.7880306775 virginica ## 105 0.79301235 -0.13153881 1.15674505 1.3128013839 virginica ## 106 2.12140867 -0.13153881 1.60992627 1.1816087073 virginica ## 107 -1.13920048 -1.27867961 0.42032558 0.6568380009 virginica ## 108 1.75911877 -0.36096697 1.43998331 0.7880306775 virginica ## 109 1.03453895 -1.27867961 1.15674505 0.7880306775 virginica ## 110 1.63835547 1.24503015 1.32668801 1.7063794137 virginica ## 111 0.79301235 0.32731751 0.76021149 1.0504160307 virginica ## 112 0.67224905 -0.81982329 0.87350679 0.9192233541 virginica ## 113 1.15530226 -0.13153881 0.98680210 1.1816087073 virginica ## 114 -0.17309407 -1.27867961 0.70356384 1.0504160307 virginica ## 115 -0.05233076 -0.59039513 0.76021149 1.5751867371 virginica ## 116 0.67224905 0.32731751 0.87350679 1.4439940605 virginica ## 117 0.79301235 -0.13153881 0.98680210 0.7880306775 virginica ## 118 2.24217198 1.70388647 1.66657392 1.3128013839 virginica ## 119 2.24217198 -1.04925145 1.77986923 1.4439940605 virginica ## 120 0.18919584 -1.96696410 0.70356384 0.3944526477 virginica ## 121 1.27606556 0.32731751 1.10009740 1.4439940605 virginica ## 122 -0.29385737 -0.59039513 0.64691619 1.0504160307 virginica ## 123 2.24217198 -0.59039513 1.66657392 1.0504160307 virginica ## 124 0.55148575 -0.81982329 0.64691619 0.7880306775 virginica ## 125 1.03453895 0.55674567 1.10009740 1.1816087073 virginica ## 126 1.63835547 0.32731751 1.27004036 0.7880306775 virginica ## 127 0.43072244 -0.59039513 0.59026853 0.7880306775 virginica ## 128 0.30995914 -0.13153881 0.64691619 0.7880306775 virginica ## 129 0.67224905 -0.59039513 1.04344975 1.1816087073 virginica ## 130 1.63835547 -0.13153881 1.15674505 0.5256453243 virginica ## 131 1.87988207 -0.59039513 1.32668801 0.9192233541 virginica ## 132 2.48369858 1.70388647 1.49663097 1.0504160307 virginica ## 133 0.67224905 -0.59039513 1.04344975 1.3128013839 virginica ## 134 0.55148575 -0.59039513 0.76021149 0.3944526477 virginica ## 135 0.30995914 -1.04925145 1.04344975 0.2632599711 virginica ## 136 2.24217198 -0.13153881 1.32668801 1.4439940605 virginica ## 137 0.55148575 0.78617383 1.04344975 1.5751867371 virginica ## 138 0.67224905 0.09788935 0.98680210 0.7880306775 virginica ## 139 0.18919584 -0.13153881 0.59026853 0.7880306775 virginica ## 140 1.27606556 0.09788935 0.93015445 1.1816087073 virginica ## 141 1.03453895 0.09788935 1.04344975 1.5751867371 virginica ## 142 1.27606556 0.09788935 0.76021149 1.4439940605 virginica ## 143 -0.05233076 -0.81982329 0.76021149 0.9192233541 virginica ## 144 1.15530226 0.32731751 1.21339271 1.4439940605 virginica ## 145 1.03453895 0.55674567 1.10009740 1.7063794137 virginica ## 146 1.03453895 -0.13153881 0.81685914 1.4439940605 virginica ## 147 0.55148575 -1.27867961 0.70356384 0.9192233541 virginica ## 148 0.79301235 -0.13153881 0.81685914 1.0504160307 virginica ## 149 0.43072244 0.78617383 0.93015445 1.4439940605 virginica ## 150 0.06843254 -0.13153881 0.76021149 0.7880306775 virginica ``` --- class: bg-main1 # Example: `mutate_at()` ```r iris %>% mutate_at(vars(Sepal.Width, Sepal.Length), scale01) ``` ``` ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 -0.89767388 1.01560199 1.4 0.2 setosa ## 2 -1.13920048 -0.13153881 1.4 0.2 setosa ## 3 -1.38072709 0.32731751 1.3 0.2 setosa ## 4 -1.50149039 0.09788935 1.5 0.2 setosa ## 5 -1.01843718 1.24503015 1.4 0.2 setosa ## 6 -0.53538397 1.93331463 1.7 0.4 setosa ## 7 -1.50149039 0.78617383 1.4 0.3 setosa ## 8 -1.01843718 0.78617383 1.5 0.2 setosa ## 9 -1.74301699 -0.36096697 1.4 0.2 setosa ## 10 -1.13920048 0.09788935 1.5 0.1 setosa ## 11 -0.53538397 1.47445831 1.5 0.2 setosa ## 12 -1.25996379 0.78617383 1.6 0.2 setosa ## 13 -1.25996379 -0.13153881 1.4 0.1 setosa ## 14 -1.86378030 -0.13153881 1.1 0.1 setosa ## 15 -0.05233076 2.16274279 1.2 0.2 setosa ## 16 -0.17309407 3.08045544 1.5 0.4 setosa ## 17 -0.53538397 1.93331463 1.3 0.4 setosa ## 18 -0.89767388 1.01560199 1.4 0.3 setosa ## 19 -0.17309407 1.70388647 1.7 0.3 setosa ## 20 -0.89767388 1.70388647 1.5 0.3 setosa ## 21 -0.53538397 0.78617383 1.7 0.2 setosa ## 22 -0.89767388 1.47445831 1.5 0.4 setosa ## 23 -1.50149039 1.24503015 1.0 0.2 setosa ## 24 -0.89767388 0.55674567 1.7 0.5 setosa ## 25 -1.25996379 0.78617383 1.9 0.2 setosa ## 26 -1.01843718 -0.13153881 1.6 0.2 setosa ## 27 -1.01843718 0.78617383 1.6 0.4 setosa ## 28 -0.77691058 1.01560199 1.5 0.2 setosa ## 29 -0.77691058 0.78617383 1.4 0.2 setosa ## 30 -1.38072709 0.32731751 1.6 0.2 setosa ## 31 -1.25996379 0.09788935 1.6 0.2 setosa ## 32 -0.53538397 0.78617383 1.5 0.4 setosa ## 33 -0.77691058 2.39217095 1.5 0.1 setosa ## 34 -0.41462067 2.62159911 1.4 0.2 setosa ## 35 -1.13920048 0.09788935 1.5 0.2 setosa ## 36 -1.01843718 0.32731751 1.2 0.2 setosa ## 37 -0.41462067 1.01560199 1.3 0.2 setosa ## 38 -1.13920048 1.24503015 1.4 0.1 setosa ## 39 -1.74301699 -0.13153881 1.3 0.2 setosa ## 40 -0.89767388 0.78617383 1.5 0.2 setosa ## 41 -1.01843718 1.01560199 1.3 0.3 setosa ## 42 -1.62225369 -1.73753594 1.3 0.3 setosa ## 43 -1.74301699 0.32731751 1.3 0.2 setosa ## 44 -1.01843718 1.01560199 1.6 0.6 setosa ## 45 -0.89767388 1.70388647 1.9 0.4 setosa ## 46 -1.25996379 -0.13153881 1.4 0.3 setosa ## 47 -0.89767388 1.70388647 1.6 0.2 setosa ## 48 -1.50149039 0.32731751 1.4 0.2 setosa ## 49 -0.65614727 1.47445831 1.5 0.2 setosa ## 50 -1.01843718 0.55674567 1.4 0.2 setosa ## 51 1.39682886 0.32731751 4.7 1.4 versicolor ## 52 0.67224905 0.32731751 4.5 1.5 versicolor ## 53 1.27606556 0.09788935 4.9 1.5 versicolor ## 54 -0.41462067 -1.73753594 4.0 1.3 versicolor ## 55 0.79301235 -0.59039513 4.6 1.5 versicolor ## 56 -0.17309407 -0.59039513 4.5 1.3 versicolor ## 57 0.55148575 0.55674567 4.7 1.6 versicolor ## 58 -1.13920048 -1.50810778 3.3 1.0 versicolor ## 59 0.91377565 -0.36096697 4.6 1.3 versicolor ## 60 -0.77691058 -0.81982329 3.9 1.4 versicolor ## 61 -1.01843718 -2.42582042 3.5 1.0 versicolor ## 62 0.06843254 -0.13153881 4.2 1.5 versicolor ## 63 0.18919584 -1.96696410 4.0 1.0 versicolor ## 64 0.30995914 -0.36096697 4.7 1.4 versicolor ## 65 -0.29385737 -0.36096697 3.6 1.3 versicolor ## 66 1.03453895 0.09788935 4.4 1.4 versicolor ## 67 -0.29385737 -0.13153881 4.5 1.5 versicolor ## 68 -0.05233076 -0.81982329 4.1 1.0 versicolor ## 69 0.43072244 -1.96696410 4.5 1.5 versicolor ## 70 -0.29385737 -1.27867961 3.9 1.1 versicolor ## 71 0.06843254 0.32731751 4.8 1.8 versicolor ## 72 0.30995914 -0.59039513 4.0 1.3 versicolor ## 73 0.55148575 -1.27867961 4.9 1.5 versicolor ## 74 0.30995914 -0.59039513 4.7 1.2 versicolor ## 75 0.67224905 -0.36096697 4.3 1.3 versicolor ## 76 0.91377565 -0.13153881 4.4 1.4 versicolor ## 77 1.15530226 -0.59039513 4.8 1.4 versicolor ## 78 1.03453895 -0.13153881 5.0 1.7 versicolor ## 79 0.18919584 -0.36096697 4.5 1.5 versicolor ## 80 -0.17309407 -1.04925145 3.5 1.0 versicolor ## 81 -0.41462067 -1.50810778 3.8 1.1 versicolor ## 82 -0.41462067 -1.50810778 3.7 1.0 versicolor ## 83 -0.05233076 -0.81982329 3.9 1.2 versicolor ## 84 0.18919584 -0.81982329 5.1 1.6 versicolor ## 85 -0.53538397 -0.13153881 4.5 1.5 versicolor ## 86 0.18919584 0.78617383 4.5 1.6 versicolor ## 87 1.03453895 0.09788935 4.7 1.5 versicolor ## 88 0.55148575 -1.73753594 4.4 1.3 versicolor ## 89 -0.29385737 -0.13153881 4.1 1.3 versicolor ## 90 -0.41462067 -1.27867961 4.0 1.3 versicolor ## 91 -0.41462067 -1.04925145 4.4 1.2 versicolor ## 92 0.30995914 -0.13153881 4.6 1.4 versicolor ## 93 -0.05233076 -1.04925145 4.0 1.2 versicolor ## 94 -1.01843718 -1.73753594 3.3 1.0 versicolor ## 95 -0.29385737 -0.81982329 4.2 1.3 versicolor ## 96 -0.17309407 -0.13153881 4.2 1.2 versicolor ## 97 -0.17309407 -0.36096697 4.2 1.3 versicolor ## 98 0.43072244 -0.36096697 4.3 1.3 versicolor ## 99 -0.89767388 -1.27867961 3.0 1.1 versicolor ## 100 -0.17309407 -0.59039513 4.1 1.3 versicolor ## 101 0.55148575 0.55674567 6.0 2.5 virginica ## 102 -0.05233076 -0.81982329 5.1 1.9 virginica ## 103 1.51759216 -0.13153881 5.9 2.1 virginica ## 104 0.55148575 -0.36096697 5.6 1.8 virginica ## 105 0.79301235 -0.13153881 5.8 2.2 virginica ## 106 2.12140867 -0.13153881 6.6 2.1 virginica ## 107 -1.13920048 -1.27867961 4.5 1.7 virginica ## 108 1.75911877 -0.36096697 6.3 1.8 virginica ## 109 1.03453895 -1.27867961 5.8 1.8 virginica ## 110 1.63835547 1.24503015 6.1 2.5 virginica ## 111 0.79301235 0.32731751 5.1 2.0 virginica ## 112 0.67224905 -0.81982329 5.3 1.9 virginica ## 113 1.15530226 -0.13153881 5.5 2.1 virginica ## 114 -0.17309407 -1.27867961 5.0 2.0 virginica ## 115 -0.05233076 -0.59039513 5.1 2.4 virginica ## 116 0.67224905 0.32731751 5.3 2.3 virginica ## 117 0.79301235 -0.13153881 5.5 1.8 virginica ## 118 2.24217198 1.70388647 6.7 2.2 virginica ## 119 2.24217198 -1.04925145 6.9 2.3 virginica ## 120 0.18919584 -1.96696410 5.0 1.5 virginica ## 121 1.27606556 0.32731751 5.7 2.3 virginica ## 122 -0.29385737 -0.59039513 4.9 2.0 virginica ## 123 2.24217198 -0.59039513 6.7 2.0 virginica ## 124 0.55148575 -0.81982329 4.9 1.8 virginica ## 125 1.03453895 0.55674567 5.7 2.1 virginica ## 126 1.63835547 0.32731751 6.0 1.8 virginica ## 127 0.43072244 -0.59039513 4.8 1.8 virginica ## 128 0.30995914 -0.13153881 4.9 1.8 virginica ## 129 0.67224905 -0.59039513 5.6 2.1 virginica ## 130 1.63835547 -0.13153881 5.8 1.6 virginica ## 131 1.87988207 -0.59039513 6.1 1.9 virginica ## 132 2.48369858 1.70388647 6.4 2.0 virginica ## 133 0.67224905 -0.59039513 5.6 2.2 virginica ## 134 0.55148575 -0.59039513 5.1 1.5 virginica ## 135 0.30995914 -1.04925145 5.6 1.4 virginica ## 136 2.24217198 -0.13153881 6.1 2.3 virginica ## 137 0.55148575 0.78617383 5.6 2.4 virginica ## 138 0.67224905 0.09788935 5.5 1.8 virginica ## 139 0.18919584 -0.13153881 4.8 1.8 virginica ## 140 1.27606556 0.09788935 5.4 2.1 virginica ## 141 1.03453895 0.09788935 5.6 2.4 virginica ## 142 1.27606556 0.09788935 5.1 2.3 virginica ## 143 -0.05233076 -0.81982329 5.1 1.9 virginica ## 144 1.15530226 0.32731751 5.9 2.3 virginica ## 145 1.03453895 0.55674567 5.7 2.5 virginica ## 146 1.03453895 -0.13153881 5.2 2.3 virginica ## 147 0.55148575 -1.27867961 5.0 1.9 virginica ## 148 0.79301235 -0.13153881 5.2 2.0 virginica ## 149 0.43072244 0.78617383 5.4 2.3 virginica ## 150 0.06843254 -0.13153881 5.1 1.8 virginica ``` --- class: bg-main1 # Scoped Variants (**if**, **at**, *all**) are available for the five verbs: .huge[ - `mutate()` - `filter()` - `select()` - `summarise()` - `group_by()` ] --- class: bg-main1 # Standardize the currencies <img src="lecture-9b-slides_files/figure-html/currencies-standardized-1.png" width="100%" style="display: block; margin: auto;" /> --- class: bg-main1 # Compute distances between all pairs of currencies .huge[ Euclidean distance is used to compute similarity between all pairs of currencies. `\(d_{ij} = \sqrt{\sum_{i=1}^{t}{(C_{1i}-C_{2i})^2}}\)` ] --- class: bg-main1 # Compute distances between all pairs of currencies ```r # Compute distance between currencies # Need to transpose! Turn matrix around, rows <--> columns rates_dropped_t <- t(rates_dropped) %>% data.frame() dates_dist <- as.matrix(dist(rates_dropped_t, diag = TRUE, upper = TRUE)) colnames(dates_dist) <- as.factor(colnames(rates_dropped)) rownames(dates_dist) <- as.factor(colnames(rates_dropped)) quantile(dates_dist, probs = c(0, 0.25, 0.5, 0.75, 1)) ``` ``` ## 0% 25% 50% 75% 100% ## 0.0000 172.2863 1074.7584 11948.3344 1449147.7492 ``` --- class: bg-main1 # A note on distance matrices: .huge[ - A distance matrix is the inverse of an association matrix. - A distance matrix close to 0 means the pair are most similar. - For an association matrix far from zero means the pair are close. - Either can be used to generate a network. ] --- class: bg-main1 # Make the network: Gather the data into long form, and filter based on similarity .huge[ Here only the pairs of currencies who are closer than "4" to each other are kept. ] ```r d_zero <- d d_zero_tbl <- d_zero %>% as_tibble() %>% mutate(curr1=rownames(d_zero)) %>% gather(curr2, dst, -curr1) %>% filter(dst<3) %>% filter(curr1 != curr2) ``` --- class: bg-main1 # Make the network: Gather the data into long form, and filter based on similarity .huge[ Here only the pairs of currencies who are closer than "4" to each other are kept. ] ```r d_zero_tbl ``` ``` ## # A tibble: 12 x 3 ## curr1 curr2 dst ## <chr> <chr> <dbl> ## 1 Zili Meg 1 ## 2 Jess Meg 1 ## 3 Zili Tay 2 ## 4 Jess Tay 1 ## 5 Zili Yat 0 ## 6 Jess Yat 0 ## 7 Meg Zili 1 ## 8 Tay Zili 2 ## 9 Yat Zili 0 ## 10 Meg Jess 1 ## 11 Tay Jess 1 ## 12 Yat Jess 0 ``` --- class: bg-main1 ### Network laid out .left-code[ ```r # Make network library(geomnet) set.seed(10052016) ggplot(data = d_zero_tbl, aes( from_id = curr1, to_id = curr2 )) + geom_net( layout.alg = "kamadakawai", size = 2, labelon = TRUE, vjust = -0.6, ecolour = "grey60", directed = FALSE, fontsize = 3, ealpha = 0.5 ) + theme_net() + theme( legend.position = "bottom" ) ``` ] .right-plot[ <img src="lecture-9b-slides_files/figure-html/currency-network-out-1.png" width="100%" style="display: block; margin: auto;" /> ] --- class: bg-main1 # Your turn .huge[ - Make a plot of the AUD vs the SGD (using the standardised units). Do they look like they are trending together as suggested by the network? - Finish the rstudio cloud exercise - Reamining time: Ask questions about project / assignment ] --- class: bg-main1 ## Share and share alike <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.