Problem 1a

Convert the cyl column to a factor

mtcars$cyl <- base::as.factor(mtcars$cyl)

Problem 1b

Use ggpairs to create a scatterplot matrix of the dataset. Use only the columns mpg, disp, hp, drat, wt and color by cylinder.

ggpairs(mtcars, aes(col = cyl), columns = c(1, 3, 4, 5, 6))

Problem 3a

Pick three male names in the list and draw a static line chart of number of babies born these names vs. year.

three_male_names <- dplyr::filter(dataset, (name == "John" | name == "Fred" | name == "Aaron") & sex == "M")

ggplot(three_male_names, aes(year, n, color = name)) +
  geom_line(size = 1) +
  ggtitle("Popularity of Three American Baby Names from 1880-2017") +
  ylab("Number of Babies Born")

Problem 3b

# anim <- ggplot(three_male_names, aes(year, n, color = name)) +
#   geom_line(size = 1) +
#   ggtitle("Popularity of Three American Baby Names from 1880-2017") +
#   ylab("Number of Babies Born") +
#   transition_reveal(year)
# 
# gganimate::anim_save("ani1.gif", anim)

Note: Code is incredibly slow so it’s commented out.

Problem 3c

Pick three female names in the list and draw a static line chart of number of babies born with these names vs. year.

three_female_names <- dplyr::filter(dataset, (name == "Linda" | name == "Dina" | name == "Mary") & sex == "F")

ggplot(three_female_names, aes(year, n, color = name)) +
  geom_line(size = 1) +
  ggtitle("Popularity of Three American Baby Names from 1880-2017") +
  ylab("Number of Babies Born")

Problem 3d

# ani2 <- ggplot(three_female_names, aes(year, n, color = name)) +
#   geom_line(size = 1) +
#   ggtitle("Popularity of Three American Baby Names from 1880-2017") +
#   ylab("Number of Babies Born") +
#   transition_reveal(year)
# 
# gganimate::anim_save("ani2.gif", ani2)

Problem 5a

Use ggplot to create a bar graph of the cumulative number of cases by state. To fit all the states on the x labels, use angle = 90. This is a vertical bar graph.

ggplot(USCovid, aes(x=state, y=cases)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_y_continuous(labels = comma) +
  ggtitle("Cumulative Covid19 Cases by State")

Problem 5b

Use ggplot to create a lollipop chart of the cumulative number of cases by state. To fit all the states on the x labels, use angle = 90. This is a vertical lollipop chart.

ggplot(USCovid, aes(x = state, y = cases)) +
  geom_point(size=3) +
  geom_segment(x = USCovid$state, xend = USCovid$state, y = 0, yend = USCovid$cases) +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_y_continuous(labels = comma) +
  ggtitle("Cumulative Covid19 Cases by State")

Problem 5c

Flip both the bar graph and the lollipop chart to horizontal. Adjust fig.asp so that it is readable. Adjust the data so that the state names read alphabetically top down.

# Reorder names in alphabetical order from top-to-bottom
USCovid$state <- forcats::fct_rev(factor(USCovid$state))

ggplot(USCovid, aes(y=state, x=cases)) +
  geom_col() +
  scale_x_continuous(labels = comma) +
  ggtitle("Cumulative Covid19 Cases by State")

ggplot(USCovid, aes(y = state, x = cases)) +
  geom_point(size=3) +
  geom_segment(y = USCovid$state, yend = USCovid$state, x = 0, xend = USCovid$cases) +
  # theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(labels = comma) +
  ggtitle("Cumulative Covid19 Cases by State")

Problem 5d

Create a percentile US map for the cumulative number of cases by state.

# Re-read in the data for ease of manipulation
USCovid <- "USCovidStates.csv" %>%
  readr::read_csv()

## Rows: 51 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (2): cases, deaths
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Cut dataset into percentiles
quant <- stats::quantile(USCovid$cases, c(0, 0.2, 0.4, 0.6, 0.8, 1.0))

USCovid$cases_q <- cut(USCovid$cases, quant, 
                       labels = c("0-20%", "20-40%", "40-60%", "60-80%", "80-100%"),
                       include.lowest = TRUE)
# The above code is so much more elegant than the nonsense solution I came up with for this. That's what I get for only skimming the lab notes! 

# Convert state names to lowercase
USCovid$state <- tolower(USCovid$state)

statesmap <- map_data("state")
head(statesmap)

##        long      lat group order  region subregion
## 1 -87.46201 30.38968     1     1 alabama      <NA>
## 2 -87.48493 30.37249     1     2 alabama      <NA>
## 3 -87.52503 30.37249     1     3 alabama      <NA>
## 4 -87.53076 30.33239     1     4 alabama      <NA>
## 5 -87.57087 30.32665     1     5 alabama      <NA>
## 6 -87.58806 30.32665     1     6 alabama      <NA>

palette <- colorRampPalette(c("bisque", "slateblue4"))(5)

ggplot(USCovid, aes(map_id = state, fill = cases_q)) +
  geom_map(map = statesmap, color = "black") +
  expand_limits(x = statesmap$long, y = statesmap$lat) +
  coord_map("polyconic") + theme_void() +
  scale_fill_manual(values = palette) +
  labs(title = "Cumulative Number of US Covid19 Cases by State", subtitle = "Data up-to-date as of April 16, 2021",
       caption = "Source: New York Times", fill = "Cases \n Percentile")

Problem 6a.

Extract the east Asian countries Japan, China, North Korea and South Korea from the world map.

library(maps)
east_asia <- ggplot2::map_data("world", region = c("Japan", "China", "North Korea", "South Korea"))
east_asia <- dplyr::as_tibble(east_asia)

Problem 6b

Use ggplot to color this map by region

ggplot(east_asia, aes(map_id = region, fill = region)) +
  geom_map(map = east_asia, color = "black") +
  expand_limits(x= east_asia$long, y = east_asia$lat) +
  coord_map("polyconic") + theme_void() +
  labs(title = "Regions of East Asia")

## Warning: Unknown or uninitialised column: `subgroup`.

Problem 7a

Construct a function that will generate 12 colorful frames.

df <- NULL

Hypocycloid <- function(r, R){
  t <- seq(from=0, to=(R + r)*2*pi, len=10000)
  x <<- (R - r)*cos(t) + (r * cos(((R-r)/r)*t))
  y <<- (R - r)*sin(t) - (r * sin(((R-r)/r)*t))} 

r <- 7
R <- 1

for(n in 1:12) {Hypocycloid(r, R + (n-1))
    df <- rbind(df, data.frame(n, x, y))}

Problem 7b

Use a facet wrap to draw a static image of the 12 frames.

ggplot(df, aes(x, y, color = n)) + 
  geom_path() +
  scale_color_gradient(low = "red", high = "blue") +
  facet_wrap(~ n, nrow = 2)

Problem 7c

Animate the frames.

# ani3 <- ggplot(df, aes(x, y, color = n)) + 
#   geom_point() +
#   scale_color_gradient(low = "red", high = "blue") +
#   theme_minimal() +
#   transition_states(n, 3, 1) +
#   ease_aes("cubic-in-out")
# 
# gganimate::anim_save("ani3.gif", ani3)

Problem 8a

Use a facet wrap to draw a static image of the 12 frames.

phyllotaxis <- function() {
a <- pi*(3-sqrt(5))
t <- 1:500
x <- sqrt(t) * cos(a*t)
y <- sqrt(t) * sin(a*t) 

ggplot(data.frame(t, x, y), aes(x, y)) +
  geom_point(size = 4) + theme_void()
  #facet_wrap(seq(from = 0, to = 500, by = 50))
}

phyllotaxis()

#for(n in 1:12) {Hypocycloid( + (n-1))
#    df <- rbind(df, data.frame(n, x, y))}

I’ll be honest here, I had to give up on this one. I couldn’t figure out what I would change with 1:12. For the hypocycloid it’s very intuitive. For this one though I don’t understand what arguments this guy would even take it. I considered changing t so that it was a sequence that would get to 500 over 12 iterations, but that resulted in less dots which ruined the spiral. This is the one I most wanted to solve but I have to throw in the towel.

Problem 9b

Tweak the code to generate two other variations.

x <- 0
y <- 0

df <- data.frame(x = 0, y = 0)
for (i in 2:500) {
  df[i, 1] <- df[i-1, 1] + ((0.50)^i)*cos(i)
  df[i, 2] <- df[i-1, 2] + ((0.50)^i)*sin(i)}

ggplot(df, aes(x, y)) + geom_polygon(fill = "darkgreen") + theme_void()

x <- 0
y <- 0

df <- data.frame(x = 0, y = 0)
for (i in 2:500) {
  df[i, 1] <- df[i-1, 1] + ((0.125)^i)*cos(i)
  df[i, 2] <- df[i-1, 2] + ((0.125)^i)*sin(i)}

ggplot(df, aes(x, y)) + geom_polygon(fill = "orange") + theme_void()