solved Enter your answers in the empty code chunks. Replace “#

Enter your answers in the empty code chunks. Replace “# your code here” with your code.Make sure you run this chunk before attempting any of the problems:library(tidyverse)2 BasicsCalculate 2+22+2:2+2## [1] 4Calculate 2∗32∗3:# your code hereCalculate (2+2)×(32+5)(6/4)(2+2)×(32+5)(6/4):# your code here3 dplyrLet’s work with the data set diamonds:data(diamonds) # this will load a dataset called “diamonds”Calculate the average price of a diamond. Use the %>% and summarise() syntax (hint: see lectures).# your code hereCalculate the average, median and standard deviation price of a diamond. Use the %>% and summarise() syntax.# your code hereUse group_by() to group diamonds by color, then use summarise() to calculate the average price and the standard deviation in price by color:# your code hereUse filter() to remove observations with a depth greater than 62, then usegroup_by() to group diamonds by clarity, then use summarise() to find the maximum price of a diamond by clarity:# your code hereUse mutate() and log() to create a new variable to the data called “log_price”. Make sure you add the variable to the dataset diamonds.# your code here(Hint: if I wanted to add a variable called “max_price” that calculates the max price, the code would look like this:)diamonds = diamonds %>% _x000D_
mutate(max_price = max(price))4 ggplot2Continue using diamonds.Use geom_histogram() to plot a histogram of prices:# your code hereUse geom_density() to plot the density of log prices (the variable you added to the data frame):# your code hereUse geom_point() to plot carats against log prices (i.e. carats on the x-axis, log prices on the y-axis):# your code hereSame as above, but now add a regression line with geom_smooth():# your code hereUse stat_summary() to make a bar plot of average log price by cut:# your code hereSame as above but change the theme to theme_classic():# your code here5 InferenceUse lm() to estimate the modellog(price)=β0+β1carat+β2table+εlog(price)=β0+β1carat+β2table+εand store the output in an object called “m1”:# your code hereUse summary() to view the output of “m1”:# your code hereUse lm() to estimate the modellog(price)=β0+β1carat+β2table+β3depth+εlog(price)=β0+β1carat+β2table+β3depth+εand store the output in an object called “m2”:# your code hereUse summary() to view the output of “m2”:# your code here