Data Science at Paideia - Final Exam
Exercise #1
Using data from the \(\color{red}{\text{nasaweather}}\) package,
* Create a scatterplot between wind and pressure, with color being used to distinguish the type of storm.
#packageDescription("nasaweather")
#glimpse(storms)
g1 <- ggplot(storms, aes(wind,pressure)) + geom_point(aes(color = type))
g1
- Use a geom_path layer to plot the path of each tropical storm. Use color to distinguish the storms from one another, and use faceting to plot each year in its own panel.
g2 <- ggplot(storms, aes(lat,long)) + geom_path(aes(color = name)) + facet_wrap(~year)
g2
Exercise #2
Using data from the \(\color{red}{\text{mlb_teams.csv}}\) file,
* Create an informative data graphic & summary that illustrates an interesting relationship within the MLB data.
glimpse(MLB_Data)## Observations: 210
## Variables: 11
## $ yearID <int> 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008…
## $ teamID <fct> ARI, ATL, BAL, BOS, CHA, CHN, CIN, CLE, COL, DET, FLO, HOU…
## $ lgID <fct> NL, NL, AL, AL, AL, NL, NL, AL, NL, AL, NL, NL, AL, AL, NL…
## $ W <int> 82, 72, 68, 95, 89, 97, 74, 81, 74, 74, 84, 86, 75, 100, 8…
## $ L <int> 80, 90, 93, 67, 74, 64, 88, 81, 88, 88, 77, 75, 87, 62, 78…
## $ WPct <dbl> 0.5061728, 0.4444444, 0.4223602, 0.5864198, 0.5460123, 0.6…
## $ attendance <int> 2509924, 2532834, 1950075, 3048250, 2500648, 3300200, 2058…
## $ normAttend <dbl> 0.5838859, 0.5892155, 0.4536477, 0.7091172, 0.5817280, 0.7…
## $ payroll <int> 66202712, 102365683, 67196246, 133390035, 121189332, 11834…
## $ metroPop <int> 4489109, 5614323, 2785874, 4732161, 9554598, 9554598, 2149…
## $ name <fct> Arizona Diamondbacks, Atlanta Braves, Baltimore Orioles, B…g3 <- ggplot(MLB_Data, aes(WPct,normAttend)) + geom_point(aes(color = lgID)) + geom_smooth(method = lm) + facet_grid(cols = vars(teamID))
g3
# create linear model
lm_fit <- lm(normAttend ~ WPct, data=MLB_Data)
summary(lm_fit)##
## Call:
## lm(formula = normAttend ~ WPct, data = MLB_Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28881 -0.09939 -0.01399 0.10299 0.37451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08979 0.07135 1.258 0.21
## WPct 0.97510 0.14138 6.897 6.27e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.14 on 208 degrees of freedom
## Multiple R-squared: 0.1861, Adjusted R-squared: 0.1822
## F-statistic: 47.57 on 1 and 208 DF, p-value: 6.267e-11Exercise #3
Graph the functions and find the area between \(y = x^2\) and \(y = \sqrt x\): using the integrate function.
fun1 <- function(x) sqrt(x)
fun2 <- function(x) x^2
fun <- function(x) sqrt(x) - x^2
g4 <- ggplot(data.frame(x = c(0, 1)), aes(x = x)) + stat_function(fun = fun1) + stat_function(fun = fun2)
g4
integrate(fun,0,1)## 0.3333334 with absolute error < 7.8e-05Exercise #4
Find the area between \(y = x^2\) and \(y = \sqrt x\): with a simulation - Think Dartboard!
darts <- 100000
x <- runif(darts)
y<- runif(darts)
z<- ifelse(y>x^2 & y<sqrt(x),1,0)
t <- tibble(x,y,z)
g5 <- ggplot(t, aes(x,y)) + geom_point(aes(color = z))
area <- sum(z)/darts
g5
area## [1] 0.33264darts <- 100000
x <- runif(darts)*6-3
y<- runif(darts)*.4
z<- ifelse(y>-0 & y<dnorm(x,0,1),-3,3)
t <- tibble(x,y,z)
g6 <- ggplot(t, aes(x,y)) + geom_point(aes(color = z))
area <- sum(z)/darts
g6
area## [1] 0.513Exercise #5
Compare & Contrast the ages of the Academy Award Winning Actors & Actresses.
#glimpse(Oscars)
g6 <- ggplot(Oscars, aes(Age, fill = Gender)) + geom_histogram(binwidth = 2, color = "Black")
g6
s <- Oscars %>%
group_by(Gender) %>%
summarise(mean = mean(Age),median = median(Age),sd = sd(Age),min = min(Age),max = max(Age))
s## # A tibble: 2 x 6
## Gender mean median sd min max
## <fct> <dbl> <dbl> <dbl> <int> <int>
## 1 F 36.2 33 11.8 21 81
## 2 M 43.7 42 8.86 29 76f <-s[[1,2]]
m <-s[[2,2]]
g7 <- ggplot(Oscars, aes(Year,Age, color = Gender)) + geom_point() + geom_hline(yintercept=f, linetype="dashed", color = "red") + geom_hline(yintercept=m, linetype="dashed", color = "lightblue")
g7