1 Task Data filtering and aggregation is necessary in any project related to artificial intelligence. Before we build an

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1 Task Data Filtering And Aggregation Is Necessary In Any Project Related To Artificial Intelligence Before We Build An 1 (62.89 KiB) Viewed 36 times

1 Task Data Filtering And Aggregation Is Necessary In Any Project Related To Artificial Intelligence Before We Build An 2 (57.82 KiB) Viewed 36 times

1 Task Data filtering and aggregation is necessary in any project related to artificial intelligence. Before we build an algorithm whose purpose is to, for example, predict the future stock market prices, we need to prepare data for training some sophisticated models. This exercise will be a step towards developing such skills. The necessary material is covered in the 3rd Workshop and Appendix D of the LMLCR book (of course, it builds upon what we have learned in Appendix C). Create a single RMarkdown (Rmd) report where you perform what follows. 1. Load the dataset on historical daily EUR to AUD/PLN/CNY/INR ... currency exchange rates: X <- as.natrix(read.csv" - /Data/eurofxref-numeric-20220213.csv")) head(X, 3) a ## Date USD JPY BGN CYP CZK DKK EEK GBP ## [1.] 19034 1.1417 132.24 1.9558 NA 24.405 7.4400 NA 0.83958 ## [2.] 19033 1.1439 132.42 1.9558 NA 24.350 7.4404 NA 8.84248 ## [3.] 19032 1.1435 132.04 1.9558 NA 24.288 7.4437 NA 8.84255 ## HUF LTL LVL MTL PLN ROL RON SEK SIT SKK ## [1.] 353.38 NA NA NA 4.5204 NA 4.9458 10.5530 NA NA ## [2.] 354.02 NA NA NA 4.4921 NA 4.9451 10.5275 NA NA ## [3.] 352.94 NA NA NA 4.5135 NA 4.9449 10.4075 NA NA ## CHF ISK NOK HRK RUB TRL TRY AUD ## [1.] 1.0557 141.8 10.0732 7.5312 85.8550 NA 15.4066 1.5927 ## [2.] 1.0571 141.8 10.6693 7.5275 85.6187 NA 15.4838 1.5894 ## [3.] 1.0555 142.2 10.6585 7.5285 85.5289 NA 15.5510 1.5933 ## BRL CAD CNY HKD IDR ILS INR ## [1.] 5.9263 1.4498 7.2564 8.9054 16339.46 3.6958 85.8535 ## [2.] 5.9668 1.4498 7.2722 8.9142 16390.21 3.6796 85.9373 ## [3.] 6.0198 1.4514 7.2759 8.9106 16390.99 3.6817 85.5765 ## KRW MXN MYR NZD PHP SGD THB ZAR ## [1.] 1363.68 23.3183 4.7832 1.7085 58.482 1.5339 37.282 17.2736

Note that in the Date column, 10595 means 1999-01-04 and 19034 denotes 2022-02-11 - this is the so-called Unix date, the number of days since 1970-01-01. 2. Draw a plot (see ?plot) of the EUR-to-GBP exchange rates between 2021-01-01 and 2021-12-31, i.e., between days 18628 and 18992, respectively. Use the Date column on the x-axis. Make sure that the most recent observations appear on the right while the oldest are given on the lefthand side of the figure. 3. Compute the minimal, average, and maximal exchange rates for all the 41 currencies in the dataset but only for the year 2021. Display them in a nicely formatted table. Why are some exchange rates missing, e.g., the most recent observations for TRL and SIT? 4. Draw a plot of the AUD-to-GBP exchange rates (by appropriately transforming EUR-to-AUD and EUR-to-GBP columns) for the whole dataset (all dates). 5. Compute the average AUD-to-GBP exchange rates for the whole years (separately for each year) of 2019 (between days 17897 and 18261), 2020 (between days 18262 and 18627), and 2021 (between days 18628 and 18992). 2 Artefacts Make sure that the generated PDF document has a readable structure; in particular, that it is divided into sections. Imagine it is a report that you would like to show to your manager or clients—you certainly want to make a good impression Check your spelling and grammar. The report must include both the results and the R code that generates them. Before each code chunk, explain what its purpose is. After each code chunk, summarise and discuss the results. If needed hide unnecessary text output by setting the results="hide' chunk option. All R code should be thoroughly documented (add comments).