Install and load the necessary packages to reproduce the report here:
# This is a chunk where you can load the necessary packages required to reproduce the report
library(readr)
library(knitr)
library(openxlsx)
# Here are some example packages, you may add others if you require
library(readr) # Useful for importing data
library(foreign) # Useful for importing SPSS, SAS, STATA etc. data files
library(rvest) # Useful for scraping HTML data## Loading required package: xml2##
## Attaching package: 'rvest'## The following object is masked from 'package:readr':
##
## guess_encodinglibrary(knitr) # Useful for creating nice tablesLocate an open source of data from the web. This can be a tabular, spreadsheet data (i.e., .txt, .csv, .xls, .xlsx files), data sets from other statistical software (i.e., SPSS, SAS, Stata etc.), or you can scrape HTML table data.
Some sources for open data are provided below, but I encourage you to find others:
As a minimum, your data set should include:
There is no limit on the number of observations and number of variables. But keep in mind that when you have a very large data set, it will increase your reading time. A clear description of data and its source should be provided in this section.
Data: The Daily Adjusted Closing Price data of the top 10 companies on ASX (marketindex) as of 3-9-2021 for the period 1-1-2020 to 3-9-2021.
Source: Yahoo Finance (https://finance.yahoo.com/)
Read/Import the data into R, then save it as a data frame. You can use Base R functions or readr, xlsx, readxl, foreign, rvest packages for this purpose. You must also provide the R codes with outputs (i.e. head() of the data set) that you used to import/read/scrape the data set. You can provide the R codes with outputs using R chunks like this:
# This is an R chunk for importing the data. Provide your R codes here:
DWA1 <- read.xlsx("data/DWA1X.xlsx", sheet = "A1Ret")
head(DWA1)Explain everything that you do in this step using regular text outside the R chunks. You may use bulleted lists like this:
Here is an example of bulleted list:
Provide a clear description of the data and its source (i.e. URL of the web site). Provide variable descriptions.
The Daily Adjusted Closing Price data of the top 10 companies on ASX (marketindex) on 3-9-2021 have been downloaded from Yahoo Finance (https://finance.yahoo.com/quote/.
The prices cover the period from 1-1-2020 to 3-9-2021.
On 4-1-2020, WHO reported on social media that there was a cluster of pneumonia cases in Wuhan, Hubei province (WHO).
The selection of the price data is to evaluate the performance of these top 10 companies since the announcement of the Covid by WHO. Instead of ranking by market capitalization, the evaluation will rank these 10 companies in terms of the return per unit of risk, where risk is measured by the standard deviation.
Systematic risk is dependent on the market factors that affect all businesses, and it cannot be diversified away in theory.
Inspect the data frame and variables using R functions. You should:
Provide your R codes with outputs and explain everything that you do in this step.
# This is a chunk where you inspect the types of variables, data structures, check the attributes in the data.
str(DWA1)## 'data.frame': 425 obs. of 10 variables:
## $ CBA.Ret: num 0.00537 -0.00675 0.01777 -0.0037 0.00727 ...
## $ CSL.Ret: num 0.00818 -0.0018 0.02363 0.00854 0.0182 ...
## $ BHP.Ret: num 0.005122 0.006365 0.0038 0.000506 0.011558 ...
## $ WBC.Ret: num 0.007003 -0.000821 0.011438 -0.003255 -0.002857 ...
## $ NAB.Ret: num 0.006086 -0.006901 0.014156 -0.003218 0.000403 ...
## $ ANZ.Ret: num 0.00731 -0.00405 0.01291 -0.00361 0.00121 ...
## $ WES.Ret: num 0.00961 0.00857 0.02041 -0.00349 0.00858 ...
## $ FMG.Ret: num -0.00279 0.00186 -0.00746 -0.00469 0.01678 ...
## $ MQG.Ret: num 0.00609 -0.00536 0.01357 -0.00727 0.00454 ...
## $ WOW.Ret: num 0.001928 -0.001101 0.017202 0.008357 -0.000806 ...Check if the data conforms the tidy data principles. If your data is untidy, reshape your data into a tidy format. If the data is in a tidy format, you will be expected to explain why the data is originally ‘tidy’.
# This is a chunk where you check if the data conforms the tidy data principles and reshape your data into a tidy format.
# The original price data is not of the required format for the required analysis.
# The original price data were taken from 10 different companies.
# The price data have been used to calculate the Return, the Standard Deviation and the Return per Standard Deviation.
# The sum of the return series is the return for the period from 1-1-2020 to 3-9-2021 for each security due to the property of ln (X/Y) = ln X - ln Y.
# Return per Standard Deviation is a measure of the Return per unit risk, this permits the performance of the security to be measured on a comparable basis, rather than a comparison based on return alone, as high risk security usually requires a high return for compensation.Provide summary statistics (mean, median, minimum, maximum, standard deviation) of numeric variables grouped by one of the qualitative (categorical) variable. For example, if your categorical variable is age groups and quantitative variable is income, provide summary statistics of income grouped by the age groups.
# This is a chunk where you provide summary statistics
Sumstat <- read.xlsx("data/DWA1X.xlsx", sheet = "A1Ret")
#Mean
sapply(Sumstat, mean, na.rm=TRUE)## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret
## 0.0007447446 0.0002823561 0.0005229419 0.0002645284 0.0004939509
## ANZ.Ret WES.Ret FMG.Ret MQG.Ret WOW.Ret
## 0.0004298528 0.0009046196 0.0020127306 0.0006009233 0.0003920370#
# Median
sapply(Sumstat, median, na.rm=TRUE)## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret
## 0.0002277326 0.0006123513 0.0000000000 0.0000000000 0.0008699501
## ANZ.Ret WES.Ret FMG.Ret MQG.Ret WOW.Ret
## 0.0011553420 0.0010602796 0.0016096095 0.0011668180 0.0010860029#
# Minimum
sapply(Sumstat, min, na.rm=TRUE)## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret ANZ.Ret
## -0.1054275 -0.1092873 -0.1556533 -0.1256784 -0.1328331 -0.1335313
## WES.Ret FMG.Ret MQG.Ret WOW.Ret
## -0.1043073 -0.1123291 -0.1664131 -0.1671068#
# Maximum
sapply(Sumstat, max, na.rm=TRUE)## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret ANZ.Ret
## 0.12453260 0.11353842 0.11283246 0.08833145 0.09214871 0.11202514
## WES.Ret FMG.Ret MQG.Ret WOW.Ret
## 0.10694062 0.12464677 0.10383202 0.09275371#
# Standard Deviation
sapply(Sumstat, sd, na.rm=TRUE)## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret ANZ.Ret
## 0.02066647 0.02097091 0.02232177 0.02305426 0.02288352 0.02416212
## WES.Ret FMG.Ret MQG.Ret WOW.Ret
## 0.01767793 0.02775333 0.02382700 0.01772848Create a list that contains a numeric value for each response to the categorical variable. Typically, they are numbered from 1-n.
# This is a chunk where you create a list
company <- c("CBA", "CSL", "BHP", "WBC", "NAB", "ANZ", "WES", "FMG", "MQG", "WOW")
Mean <- c(0.000745, 0.000282, 0.000523, 0.000265, 0.000494, 0.000430, 0.000905, 0.00201, 0.000601, 0.000392)
Median <- c(0.000228, 0.000612, 0, 0, 0.000870, 0.00116, 0.00106, 0.00161, 0.00117, 0.00109)
Min <- c(-0.105, -0.109, -0.156, -0.126, -0.133, -0.134, -0.104, -0.112, -0.166, -0.167)
Max <- c(0.125, 0.114, 0.113, 0.088, 0.092, 0.112, 0.107, 0.125, 0.104, 0.093)
SD <- c(0.021, 0.021, 0.022, 0.023, 0.0230, 0.024, 0.018, 0.028, 0.024, 0.018)
out_list1 <- list(company)
out_list2 <- list(Mean)
out_list3 <- list(Median)
out_list4 <- list(Min)
out_list5 <- list(Max)
out_list6 <- list(SD)
out_list1## [[1]]
## [1] "CBA" "CSL" "BHP" "WBC" "NAB" "ANZ" "WES" "FMG" "MQG" "WOW"out_list2## [[1]]
## [1] 0.000745 0.000282 0.000523 0.000265 0.000494 0.000430 0.000905
## [8] 0.002010 0.000601 0.000392out_list3## [[1]]
## [1] 0.000228 0.000612 0.000000 0.000000 0.000870 0.001160 0.001060
## [8] 0.001610 0.001170 0.001090out_list4## [[1]]
## [1] -0.105 -0.109 -0.156 -0.126 -0.133 -0.134 -0.104 -0.112 -0.166 -0.167out_list5## [[1]]
## [1] 0.125 0.114 0.113 0.088 0.092 0.112 0.107 0.125 0.104 0.093out_list6## [[1]]
## [1] 0.021 0.021 0.022 0.023 0.023 0.024 0.018 0.028 0.024 0.018Join this list on using a join of your choice. Remember that this has to keep the numeric variable, as well as matching to your categorical variable.
# This is a chunk where you join the list
# Original data
my_list <- list(out_list1, out_list2, out_list3, out_list4, out_list5, out_list6)
named_list <- list(Company = out_list1, Mean = out_list2, Median = out_list3, Min = out_list4,
Max = out_list5, SD = out_list6)
c(my_list, named_list)## [[1]]
## [[1]][[1]]
## [1] "CBA" "CSL" "BHP" "WBC" "NAB" "ANZ" "WES" "FMG" "MQG" "WOW"
##
##
## [[2]]
## [[2]][[1]]
## [1] 0.000745 0.000282 0.000523 0.000265 0.000494 0.000430 0.000905
## [8] 0.002010 0.000601 0.000392
##
##
## [[3]]
## [[3]][[1]]
## [1] 0.000228 0.000612 0.000000 0.000000 0.000870 0.001160 0.001060
## [8] 0.001610 0.001170 0.001090
##
##
## [[4]]
## [[4]][[1]]
## [1] -0.105 -0.109 -0.156 -0.126 -0.133 -0.134 -0.104 -0.112 -0.166 -0.167
##
##
## [[5]]
## [[5]][[1]]
## [1] 0.125 0.114 0.113 0.088 0.092 0.112 0.107 0.125 0.104 0.093
##
##
## [[6]]
## [[6]][[1]]
## [1] 0.021 0.021 0.022 0.023 0.023 0.024 0.018 0.028 0.024 0.018
##
##
## $Company
## $Company[[1]]
## [1] "CBA" "CSL" "BHP" "WBC" "NAB" "ANZ" "WES" "FMG" "MQG" "WOW"
##
##
## $Mean
## $Mean[[1]]
## [1] 0.000745 0.000282 0.000523 0.000265 0.000494 0.000430 0.000905
## [8] 0.002010 0.000601 0.000392
##
##
## $Median
## $Median[[1]]
## [1] 0.000228 0.000612 0.000000 0.000000 0.000870 0.001160 0.001060
## [8] 0.001610 0.001170 0.001090
##
##
## $Min
## $Min[[1]]
## [1] -0.105 -0.109 -0.156 -0.126 -0.133 -0.134 -0.104 -0.112 -0.166 -0.167
##
##
## $Max
## $Max[[1]]
## [1] 0.125 0.114 0.113 0.088 0.092 0.112 0.107 0.125 0.104 0.093
##
##
## $SD
## $SD[[1]]
## [1] 0.021 0.021 0.022 0.023 0.023 0.024 0.018 0.028 0.024 0.018Subset the data frame using first 10 observations (include all variables). Then convert it to a matrix. Check the structure of that matrix (i.e. check whether the matrix is character, numeric, integer, factor, or logical) and explain in a few words why you ended up with that structure.
# This is a chunk to subset your data and convert it to a matrix
S1 <- read.xlsx("data/DWA1X.xlsx", sheet = "A1Ret", cols = 1:10, rows= 1: 11)
str(S1)## 'data.frame': 10 obs. of 10 variables:
## $ CBA.Ret: num 0.00537 -0.00675 0.01777 -0.0037 0.00727 ...
## $ CSL.Ret: num 0.00818 -0.0018 0.02363 0.00854 0.0182 ...
## $ BHP.Ret: num 0.005122 0.006365 0.0038 0.000506 0.011558 ...
## $ WBC.Ret: num 0.007003 -0.000821 0.011438 -0.003255 -0.002857 ...
## $ NAB.Ret: num 0.006086 -0.006901 0.014156 -0.003218 0.000403 ...
## $ ANZ.Ret: num 0.00731 -0.00405 0.01291 -0.00361 0.00121 ...
## $ WES.Ret: num 0.00961 0.00857 0.02041 -0.00349 0.00858 ...
## $ FMG.Ret: num -0.00279 0.00186 -0.00746 -0.00469 0.01678 ...
## $ MQG.Ret: num 0.00609 -0.00536 0.01357 -0.00727 0.00454 ...
## $ WOW.Ret: num 0.001928 -0.001101 0.017202 0.008357 -0.000806 ...# The initial matrix shows the name-Ret of the companies in a column, same orientation as str(S1). In order to match the original orientation as per the source data and the Excel Sheet: "A1Ret", as well as the orientation shown above, a double transformation is carried out to bring the matrix to the required orientation, with name-Ret shown horizontally on the first row.
## Change name-Ret of companies from column to row
t(t(S1[1:10,]))## CBA.Ret CSL.Ret BHP.Ret WBC.Ret NAB.Ret
## 1 0.005368665 0.008183389 0.0051215304 0.0070031118 0.0060864241
## 2 -0.006746547 -0.001804790 0.0063654646 -0.0008214224 -0.0069007535
## 3 0.017767699 0.023634762 0.0037997747 0.0114381180 0.0141559833
## 4 -0.003701408 0.008537224 0.0005056133 -0.0032546934 -0.0032180119
## 5 0.007266540 0.018199769 0.0115578676 -0.0028566892 0.0004027757
## 6 0.012318030 0.027779426 -0.0032528395 0.0073290296 0.0040193682
## 7 -0.000242517 -0.016167423 -0.0093165105 -0.0024369477 0.0000000000
## 8 0.008330764 0.006768229 0.0130687872 -0.0008136939 0.0052010328
## 9 0.005874383 0.004173394 0.0032407623 0.0048720927 0.0043798017
## 10 0.009517037 0.010524320 -0.0012452463 0.0084695003 0.0075203767
## ANZ.Ret WES.Ret FMG.Ret MQG.Ret WOW.Ret
## 1 0.007305234 0.009606373 -0.002786896 0.006089294 0.0019275015
## 2 -0.004051945 0.008567390 0.001858835 -0.005362303 -0.0011009330
## 3 0.012908764 0.020406461 -0.007455910 0.013567292 0.0172018171
## 4 -0.003613760 -0.003488836 -0.004688046 -0.007266055 0.0083569304
## 5 0.001205925 0.008583815 0.016775796 0.004538653 -0.0008057017
## 6 0.009198311 0.013992665 -0.011152621 0.008730540 0.0159918594
## 7 -0.004388593 -0.006857156 0.008376167 -0.006074761 -0.0103655325
## 8 0.007568263 -0.003906750 0.018365845 0.011616888 0.0250638949
## 9 0.001982114 0.005510785 -0.012820626 0.002688922 -0.0018255685
## 10 0.006710109 0.007301140 0.013730240 0.013755526 0.0096117718Subset the data frame including only first and the last variable in the data set, save it as an R object file (.RData). Provide the R codes with outputs and explain everything that you do in this step.
# This is a chunk to subset your data and convert it to an R object file Create a data frame with 2 variables. Your data frame has to contain one integer variable and one ordinal variable.
The ordinal variable has to be a factor and ordered properly. Make sure you name your variables.
Show the structure of your variables and the levels of the ordinal variable.
Create another numeric vector and use cbind() to add this vector to your data frame.
After this step you should have 3 variables in the data frame.
Check the attributes and the dimension of your new data frame.
Provide the R codes with outputs and explain everything that you do in this step.
# This is a chunk to create a new data frame with the given specificationsCreate another data frame with a common variable to the dataset created in step 11.
Join the data frame to the dataset above, and ensure that the dataset is joined properly.
Ensuring the new categorical variable is carried to the larger dataset. Eg. A dataset to join could be State, Abbreviation, Municipality, Prevailing Religion.
Provide the R codes with outputs and explain everything that you do in this step.
# This is a chunk to create another data frame with the given specificationsThe report must be uploaded to Assignment 1 section in Canvas as a PDF document with R codes and outputs showing. The easiest way to achieve this is to run all R chunks first, then Preview your notebook in HTML (by clicking Preview), then Open in Browser (Chrome), then Right Click on the report in Chrome , then Click Print and Select the Destination Option to Save as PDF. Upload this PDF report as one single file via the Assignment 1 page in CANVAS.
DELETE the instructional text provided in the template. Failure to do this will INCREASE the SIMILARITY INDEX reported in TURNITIN If you have any questions regarding the assignment instructions and the R template, please post it on Canvas discussion.