Seminar3.R

library("tidyverse")

urlfile = "https://raw.githubusercontent.com/CWWhitney/teaching_R/master/participants_data.csv"
participants_data <- read_csv(url(urlfile))

# Change the barplot by creating a table of gender 
participants_barplot <- table(participants_data$gender)

barplot(participants_barplot)

# Create a scatterplot of days to email response (y) as a function of the letters in your first name (x) 
ggplot(data = participants_data, 
       aes(x = letters_in_first_name, 
           y = days_to_email_response)) + 
  geom_point()

# Create a scatterplot of days to email response (y) as a function of the letters in your first name (x) 
# with colors representing binary data related to academic parents (color) 
# and working hours per day as bubble sizes (size).
ggplot(data = participants_data, 
       aes(x = letters_in_first_name, 
           y = days_to_email_response, 
           color = academic_parents, 
           size = working_hours_per_day)) + 
  geom_point()

# Create a scatterplot of iris petal length (y) as a function of sepal length (x) 
# with colors representing iris species (color) and petal width as bubble sizes (size).
ggplot(data = iris, 
       aes(x = Sepal.Length, 
           y = Petal.Length, 
           color = Species, 
           size = Petal.Width))+ 
  geom_point()

# Create a plot with the diamonds data of the carat (x) and the price (y)
plot1 <- ggplot(data = diamonds, 
                aes(x = carat, y = price, 
                    alpha = 0.2)) +
  geom_point()
plot1

# Create a plot with the diamonds data of the log of carat (x) and the log of price (y)
ggplot(data = diamonds,
       aes(x = log(carat),
           y = log(price),
           alpha = 0.2)) +
  geom_point()

# Create a smaller diamonds data set (top 100 rows), create a scatterplot with carat on the x-axis 
# and price on the y-axis and with the color of the diamond as the color of the points. 
dsmall <- top_n(diamonds, n = 100)

ggplot(data = dsmall, aes(x = carat, 
                          y = price, 
                          color = color)) + 
  geom_point()

# Create a smaller diamonds data set (top 40 rows), create a scatterplot with carat on the x-axis 
# and price on the y-xis and with the cut of the diamond as the shapes for the points. 
dsmall <- top_n(diamonds, 
                n = 40)

ggplot( data = dsmall, 
        aes(x = carat, 
            y = price, 
            shape = cut)) + 
  geom_point()

# Create a plot of the diamonds data with carat on the x-axis, price on the y-axis. 
# Use the inhibit function to set the alpha to 0.1 and color to blue.
ggplot(data = diamonds, 
       aes(x = carat, 
           y = price, 
           alpha = I(0.1), 
           color = I("blue"))) + 
  geom_point()

# Create a smaller data set of diamonds with 50 rows. Create a scatterplot and smoothed conditional 
# means overlay with carat on the x-axis and price on the y-axis.
dsmall <- top_n(diamonds, 
                n = 50)

ggplot(data = dsmall, 
       aes(x = carat, 
           y = price))+
  geom_point()+
  geom_smooth(method = "loess", formula = y ~ x)

# Create a smaller data set of diamonds with 50 rows. Create a scatterplot and smoothed conditional 
# means overlay with carat on the x-axis and price on the y-axis.
# Use 'glm' as the option for the smoothing
dsmall <- top_n(diamonds, 
                n = 50)

ggplot(data = dsmall, 
       aes(x = carat, 
           y = price))+ 
  geom_point()+ 
  geom_smooth(method = 'glm', formula = y ~ x)

# Change the boxplot so that the x-axis is cut and the y-axis is price divided by carat
ggplot(data = diamonds, 
       aes(x = cut, 
           y = price/carat)) + 
  geom_boxplot()

# Change the jittered boxplot so that the x-axis is cut and the y-axis is price divided by carat
ggplot(data = diamonds, 
       aes(x = cut, 
           y = price/carat)) + 
  geom_boxplot()+ 
  geom_jitter()

# Change the alpha to 0.4 to make the scatter less transparent
ggplot(data = diamonds, 
       aes(x = cut, 
           y = price/carat, 
           alpha = I(0.4))) + 
  geom_boxplot()+ 
  geom_jitter()

# Change the density plot so that the x-axis is carat and the color is the diamond color
ggplot(data = diamonds, 
       aes(x = carat, colour = color)) +
  geom_density()

# Change the density plot so that the x-axis is carat the color is the diamond color
# and the alpha is set to 0.3 using the inhibit function
ggplot(data = diamonds, 
       aes(x = carat, 
           colour = color, 
           alpha = I(0.3))) +
  geom_density()

# Create a plot of the mpg data with manufacturer as the color and a linear model 'lm'
# as the smooth method
ggplot(data = mpg, 
       aes(x = displ, 
           y = hwy,  
           color = manufacturer)) + 
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ x)

# Change the title and labels as you see fit
ggplot(mtcars, 
       aes(mpg, 
           y = hp, 
           col = gear)) +
  geom_point() +
  ggtitle("Horsepower dependent on miles per gallon using different gears") +
  labs(x = "Miles per gallon", 
       y = "Horsepower", 
       col = "Gear")

# Change the title and labels as you see fit  
ggplot(data = mtcars) +
  aes(x = mpg) +
  labs(x = "Miles per gallon") +
  aes(y = hp) +
  labs(y = "Horsepower") +
  geom_point() +
  aes(col = gear) +
  labs(col = "Gear") +
  labs(title = "Horsepower dependent on miles per gallon using different gears")

# subset the data to numeric only with select_if
part_data <- select_if(participants_data, 
                       is.numeric)
# use 'cor' to perform pearson correlation
# use 'round' to reduce correlation 
# results to 1 decimal
cormat <- round(cor(part_data), 
                digits = 1)
# use 'as.data.frame.table' to build a table with correlation values
melted_cormat <- as.data.frame.table(cormat, 
                                     responseName = "value")
# plot the result with 'geom-tile'
ggplot(data = melted_cormat, 
       aes(x = Var1,
           y = Var2,
           fill = value)) + 
  geom_tile()

mpg_wide <- reshape2::melt(select(
  rename(mpg, "City" = cty, "Highway" = hwy), c("year", "City", "Highway")), "year")

png(file = "cars_efficiency.png", width = 7, height = 6, units = "in", res = 300)

ggplot(data = mpg_wide, aes(x = as.factor(year)))+
  geom_col(aes(y = value, fill = variable), position = "dodge")+
  xlab("Year")+
  ylab("Miles per gallon")+
  labs(fill = "Environment")+
  ggtitle("Efficiency of Cars from 1999 and 2008")
  
dev.off()