#Question 1
MacroData <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/USMacroSW.csv")
dfMacroData <- MacroData
summary(dfMacroData)## X unemp cpi ffrate
## Min. : 1 Min. : 3.400 Min. : 27.78 Min. : 0.930
## 1st Qu.: 49 1st Qu.: 5.000 1st Qu.: 35.87 1st Qu.: 3.480
## Median : 97 Median : 5.700 Median : 87.93 Median : 5.400
## Mean : 97 Mean : 5.891 Mean : 91.73 Mean : 5.953
## 3rd Qu.:145 3rd Qu.: 6.833 3rd Qu.:143.07 3rd Qu.: 7.760
## Max. :193 Max. :10.667 Max. :192.17 Max. :19.100
## tbill tbond gbpusd gdpjp
## Min. : 0.830 Min. : 1.01 Min. :112.5 Min. : 10149
## 1st Qu.: 3.500 1st Qu.: 3.91 1st Qu.:159.6 1st Qu.: 57632
## Median : 5.080 Median : 5.62 Median :185.5 Median :254560
## Mean : 5.435 Mean : 6.04 Mean :204.9 Mean :259306
## 3rd Qu.: 6.740 3rd Qu.: 7.55 3rd Qu.:246.9 3rd Qu.:482328
## Max. :15.490 Max. :16.52 Max. :281.5 Max. :523638#Mean unemp~ffrate
MeanRaw.Unemp <- print(mean(dfMacroData$unemp))## [1] 5.891019MeanRaw.ffrate <- print(mean(dfMacroData$ffrate))## [1] 5.953161#Median unemp~ffrate
MedianRaw.Unemp <- print(median(dfMacroData$unemp))## [1] 5.7MedianRaw.ffrate <- print(median(dfMacroData$ffrate))## [1] 5.4#Question 2
dfMacro2 <- subset(dfMacroData, ffrate >= 3.0 & unemp >= 3.0,
select = c(ffrate, unemp))
dfMacro2[1:10,]## ffrate unemp
## 2 3.00 4.100000
## 3 3.47 4.233333
## 10 3.39 5.100000
## 11 3.76 5.266667
## 12 3.99 5.600000
## 13 3.84 5.133333
## 14 3.32 5.233333
## 27 3.48 5.500000
## 28 3.38 5.566667
## 29 3.43 5.466667#Question 3
names(dfMacro2) <- c('Unemployment Rate', 'Federal Funds Rate')
dfMacro2[1:10,]## Unemployment Rate Federal Funds Rate
## 2 3.00 4.100000
## 3 3.47 4.233333
## 10 3.39 5.100000
## 11 3.76 5.266667
## 12 3.99 5.600000
## 13 3.84 5.133333
## 14 3.32 5.233333
## 27 3.48 5.500000
## 28 3.38 5.566667
## 29 3.43 5.466667The mean for both the raw and subset Unemployment have increased, this is because unemployment is generally lower in the US, causing there to be more common in the data set. So when I subset data with conditions, a lot of the lowest unemp rates were removed. The same is also true for Median as well.
#Question 4
summary(dfMacro2)## Unemployment Rate Federal Funds Rate
## Min. : 3.000 Min. : 3.400
## 1st Qu.: 4.700 1st Qu.: 4.808
## Median : 5.870 Median : 5.633
## Mean : 6.816 Mean : 5.877
## 3rd Qu.: 8.450 3rd Qu.: 7.033
## Max. :19.100 Max. :10.667MeanSubset.Unemp <- print(mean(dfMacro2$`Unemployment Rate`))## [1] 6.816329MeanSubset.FederalFunds <- print(mean(dfMacro2$`Federal Funds Rate`))## [1] 5.877004MedianSubset.Unemp <- print(median(dfMacro2$`Unemployment Rate`))## [1] 5.87MedianSubset.FederalFunds <- print(median(dfMacro2$`Federal Funds Rate`))## [1] 5.633333#Mean ~ Raw Data vs. Subset Data
MeanRaw.Unemp <- print(mean(dfMacroData$unemp))## [1] 5.891019MeanSubset.Unemp <- print(mean(dfMacro2$`Unemployment Rate`))## [1] 6.816329MeanRaw.ffrate <- print(mean(dfMacroData$ffrate))## [1] 5.953161MeanSubset.FederalFunds <- print(mean(dfMacro2$`Federal Funds Rate`))## [1] 5.877004#The mean for raw data vs. subset data has increased as the data was subset
#Median ~ Raw Data vs. Subset Data
MedianRaw.Unemp <- print(median(dfMacroData$unemp))## [1] 5.7MedianSubset.Unemp <- print(median(dfMacro2$`Unemployment Rate`))## [1] 5.87MedianRaw.ffrate <- print(median(dfMacroData$ffrate))## [1] 5.4MedianSubset.FederalFunds <- print(median(dfMacro2$`Federal Funds Rate`))## [1] 5.633333#Question 5
head(dfMacro2)## Unemployment Rate Federal Funds Rate
## 2 3.00 4.100000
## 3 3.47 4.233333
## 10 3.39 5.100000
## 11 3.76 5.266667
## 12 3.99 5.600000
## 13 3.84 5.133333dfUnempLevels <- cbind(dfMacro2, UnempLevels = factor(NA,
levels = c("LowUnemp", "MidUnemp", "HighUnemp")))
head(dfUnempLevels)## Unemployment Rate Federal Funds Rate UnempLevels
## 2 3.00 4.100000 <NA>
## 3 3.47 4.233333 <NA>
## 10 3.39 5.100000 <NA>
## 11 3.76 5.266667 <NA>
## 12 3.99 5.600000 <NA>
## 13 3.84 5.133333 <NA>dfUnempLevels[dfUnempLevels$`Unemployment Rate` <= 5.0, "UnempLevels"] <- "LowUnemp"
dfUnempLevels[dfUnempLevels$`Unemployment Rate` > 5.0 &
dfUnempLevels$`Unemployment Rate` <= 6.5, "UnempLevels"] <- "MidUnemp"
dfUnempLevels[dfUnempLevels$`Unemployment Rate` >= 6.5, "UnempLevels"] <- "HighUnemp"
head(dfUnempLevels)## Unemployment Rate Federal Funds Rate UnempLevels
## 2 3.00 4.100000 LowUnemp
## 3 3.47 4.233333 LowUnemp
## 10 3.39 5.100000 LowUnemp
## 11 3.76 5.266667 LowUnemp
## 12 3.99 5.600000 LowUnemp
## 13 3.84 5.133333 LowUnemp#Question 6
dfUnempLevels[1:45, ]## Unemployment Rate Federal Funds Rate UnempLevels
## 2 3.00 4.100000 LowUnemp
## 3 3.47 4.233333 LowUnemp
## 10 3.39 5.100000 LowUnemp
## 11 3.76 5.266667 LowUnemp
## 12 3.99 5.600000 LowUnemp
## 13 3.84 5.133333 LowUnemp
## 14 3.32 5.233333 LowUnemp
## 27 3.48 5.500000 LowUnemp
## 28 3.38 5.566667 LowUnemp
## 29 3.43 5.466667 LowUnemp
## 30 3.50 5.200000 LowUnemp
## 31 3.45 5.000000 LowUnemp
## 32 3.85 4.966667 LowUnemp
## 33 4.04 4.900000 LowUnemp
## 34 4.04 4.666667 LowUnemp
## 35 4.01 4.366667 LowUnemp
## 36 4.32 4.100000 LowUnemp
## 37 4.65 3.866667 LowUnemp
## 38 5.17 3.833333 MidUnemp
## 39 5.40 3.766667 MidUnemp
## 40 5.40 3.700000 MidUnemp
## 41 4.53 3.833333 LowUnemp
## 42 3.98 3.833333 LowUnemp
## 43 3.99 3.800000 LowUnemp
## 44 4.51 3.900000 LowUnemp
## 45 5.05 3.733333 MidUnemp
## 46 6.07 3.566667 MidUnemp
## 47 5.78 3.533333 MidUnemp
## 48 6.02 3.400000 MidUnemp
## 49 6.79 3.400000 HighUnemp
## 50 8.90 3.433333 HighUnemp
## 51 9.15 3.566667 HighUnemp
## 52 8.97 3.566667 HighUnemp
## 53 7.76 4.166667 HighUnemp
## 54 7.60 4.766667 HighUnemp
## 55 6.29 5.166667 MidUnemp
## 56 4.90 5.833333 LowUnemp
## 57 3.71 5.933333 LowUnemp
## 58 4.91 5.900000 LowUnemp
## 59 5.55 6.033333 MidUnemp
## 60 4.14 5.933333 LowUnemp
## 61 3.83 5.766667 LowUnemp
## 62 4.46 5.700000 LowUnemp
## 63 4.87 5.566667 LowUnemp
## 64 5.33 5.366667 MidUnemp#Question 7 ~ Bonus
git.MacroData <- read.csv("https://raw.githubusercontent.com/jonburns2454/USMacroData/master/USMacroSW.csv")
head(git.MacroData)## X unemp cpi ffrate tbill tbond gbpusd gdpjp
## 1 1 3.933333 27.77667 2.96 3.08 3.42 279.3047 10149.0
## 2 2 4.100000 28.01333 3.00 3.29 3.65 279.0237 10904.0
## 3 3 4.233333 28.26333 3.47 3.53 4.07 278.5088 11231.0
## 4 4 4.933333 28.40000 2.98 3.04 3.18 280.5796 11075.0
## 5 5 6.300000 28.73667 1.20 1.30 1.84 281.5398 10973.0
## 6 6 7.366667 28.93000 0.93 0.83 1.23 281.1111 11325.3