# 1. Read the dataset and check the first seven rows
bigmac <- read.csv("big-mac-raw-index_Formatted.csv")
head(bigmac, 7)## date iso_a3 currency_code name local_price dollar_ex dollar_price
## 1 4/1/2000 ARG ARS Argentina 2.50 1.0000000 2.500000
## 2 4/1/2000 AUS AUD Australia 2.59 1.6800000 1.541667
## 3 4/1/2000 BRA BRL Brazil 2.95 1.7900000 1.648045
## 4 4/1/2000 GBR GBP Britain 1.90 0.6329114 3.002000
## 5 4/1/2000 CAN CAD Canada 2.85 1.4700000 1.938776
## 6 4/1/2000 CHL CLP Chile 1260.00 514.0000000 2.451362
## 7 4/1/2000 CHN CNY China 9.90 8.2800000 1.195652
## USD EUR GBP JPY CNY Year_Formatted
## 1 0.11607 0.05007 -0.16722 -0.09864 1.09091 Y2000
## 2 -0.31176 -0.35246 -0.48645 -0.44416 0.28939 Y2000
## 3 -0.26427 -0.30778 -0.45102 -0.40581 0.37836 Y2000
## 4 0.34018 0.26092 0.00000 0.08235 1.51076 Y2000
## 5 -0.13448 -0.18566 -0.35417 -0.30099 0.62152 Y2000
## 6 0.09436 0.02964 -0.18342 -0.11618 1.05023 Y2000
## 7 -0.46623 -0.49779 -0.60171 -0.56891 0.00000 Y2000# 2. Calculate summary statistics for dollar_price by year
summary_stats <- aggregate(dollar_price ~ Year_Formatted, data = bigmac,
FUN = function(x) c(mean = mean(x), min = min(x), max = max(x)))
summary_stats## Year_Formatted dollar_price.mean dollar_price.min dollar_price.max
## 1 Y2000 2.0602601 1.1894737 3.5802469
## 2 Y2001 1.8804949 1.1729622 3.6416185
## 3 Y2002 2.0436859 0.7987220 4.0887850
## 4 Y2003 2.1450423 1.1956522 4.5985401
## 5 Y2004 2.2289945 0.6399659 5.1831628
## 6 Y2005 2.4874191 1.2686675 6.0628673
## 7 Y2006 2.5771867 1.3020672 7.0465234
## 8 Y2007 2.8222464 1.4149184 6.8800633
## 9 Y2008 3.4333239 1.6985794 7.8753335
## 10 Y2009 3.0888727 1.7160183 6.1451012
## 11 Y2010 3.3414435 1.8276762 7.2043226
## 12 Y2011 3.9203287 1.8918919 8.3117075
## 13 Y2012 3.6769645 1.5846168 7.9168268
## 14 Y2013 3.8757716 1.5005002 9.0810660
## 15 Y2014 3.8392883 1.5360983 7.7954348
## 16 Y2015 3.4238243 0.6700508 7.5436662
## 17 Y2016 3.2962137 0.6643227 6.5916236
## 18 Y2017 3.5258859 1.4645711 6.7416896
## 19 Y2018 3.5882339 1.6367752 6.7648436
## 20 Y2019 3.4619033 1.6520957 6.6215046
## 21 Y2020 3.5662031 1.8593492 6.9057105
## 22 Y2021 3.6818914 1.6818182 7.2906735
## 23 Y2022 3.7627851 1.7437918 6.9813651
## 24 Y2023 4.2489444 1.8428548 7.7291342
## 25 Y2024 4.4048045 2.2817158 8.1670213# 3. Create a dataframe bigmac_USA filtering for USA and selecting year and local_price
bigmac_USA <- bigmac[bigmac$iso_a3 == "USA", c("Year_Formatted", "local_price")]
head(bigmac_USA, 5)## Year_Formatted local_price
## 28 Y2000 2.24
## 56 Y2001 2.24
## 88 Y2002 2.35
## 120 Y2003 2.46
## 159 Y2004 2.47# 4. Rename local_price to US_price in bigmac_USA
colnames(bigmac_USA)[2] <- "US_price"# 5. Merge bigmac and bigmac_USA using year
merged_bigmac <- merge(bigmac, bigmac_USA, by = "Year_Formatted")
head(merged_bigmac, 5)## Year_Formatted date iso_a3 currency_code name local_price dollar_ex
## 1 Y2000 4/1/2000 ARG ARS Argentina 2.50 1.0000000
## 2 Y2000 4/1/2000 AUS AUD Australia 2.59 1.6800000
## 3 Y2000 4/1/2000 BRA BRL Brazil 2.95 1.7900000
## 4 Y2000 4/1/2000 GBR GBP Britain 1.90 0.6329114
## 5 Y2000 4/1/2000 CAN CAD Canada 2.85 1.4700000
## dollar_price USD EUR GBP JPY CNY US_price
## 1 2.500000 0.11607 0.05007 -0.16722 -0.09864 1.09091 2.24
## 2 1.541667 -0.31176 -0.35246 -0.48645 -0.44416 0.28939 2.24
## 3 1.648045 -0.26427 -0.30778 -0.45102 -0.40581 0.37836 2.24
## 4 3.002000 0.34018 0.26092 0.00000 0.08235 1.51076 2.24
## 5 1.938776 -0.13448 -0.18566 -0.35417 -0.30099 0.62152 2.24# 6. Sort the merged data by country name
sorted_bigmac <- merged_bigmac[order(merged_bigmac$name), ]# 7. Create a dummy variable undervalued (1 if local_price < US_price)
merged_bigmac$undervalued <- ifelse(merged_bigmac$local_price < merged_bigmac$US_price, 1, 0)# 8. Present a frequency table for undervalued
table(merged_bigmac$undervalued)##
## 0 1
## 3086 268# 9. Custom question: Find the country with the highest local_price in each year
highest_price <- aggregate(local_price ~ Year_Formatted, data = bigmac,
FUN = function(x) bigmac$name[which.max(x)])
highest_price## Year_Formatted local_price
## 1 Y2000 Indonesia
## 2 Y2001 Indonesia
## 3 Y2002 Brazil
## 4 Y2003 Australia
## 5 Y2004 Czech Republic
## 6 Y2005 Malaysia
## 7 Y2006 Israel
## 8 Y2007 Malaysia
## 9 Y2008 Malaysia
## 10 Y2009 Malaysia
## 11 Y2010 Argentina
## 12 Y2011 Mexico
## 13 Y2012 Brazil
## 14 Y2013 Mexico
## 15 Y2014 Turkey
## 16 Y2015 Mexico
## 17 Y2016 Mexico
## 18 Y2017 Mexico
## 19 Y2018 Malaysia
## 20 Y2019 Thailand
## 21 Y2020 Thailand
## 22 Y2021 Poland
## 23 Y2022 South Africa
## 24 Y2023 South Africa
## 25 Y2024 South Africa#I struggled with this assignment more than past assignments and labs. It seemed there was a lot more room for errors to appear.It took me about 3x longer than past assignments. I will admit I had to use chatgpt to help me get past the errors. I tried my best to solve them and I was able to sort out a few, but there were some that I could not figure out for the life of me. I think my coding ability is growing stronger, especially after the challenges I faced with this assignment. I also included some comments in my code blocks to make it more organized.