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ckcode ⌲ what-you-learned

require(coursekata) # use the str() function to see what is in the hate_crimes data frame # use the str() function to see what is in the hate_crimes data frame str(hate_crimes) ex() %>% check_function("str", not_called_msg = "Did you use the str() function?") %>% check_arg("object") %>% check_equal(incorrect_msg = "Did you call str on the hate_crimes data set?")
CK Code: ch11-26
require(coursekata) # make a plot to help us explore the variation in avg_hatecrimes_per_100k_fbi # all of these are ways to look at a single continuous variable # some are more helpful than others gf_histogram(~avg_hatecrimes_per_100k_fbi, data = hate_crimes) gf_dhistogram(~avg_hatecrimes_per_100k_fbi, data = hate_crimes) gf_freqpoly(~avg_hatecrimes_per_100k_fbi, data = hate_crimes) gf_density(~avg_hatecrimes_per_100k_fbi, data = hate_crimes) gf_boxplot(avg_hatecrimes_per_100k_fbi ~ 1, data = hate_crimes) gf_violin(avg_hatecrimes_per_100k_fbi ~ 1, data = hate_crimes) ex() %>% check_or( check_function(., "gf_histogram") %>% check_result() %>% check_equal(), check_function(., "gf_dhistogram") %>% check_result() %>% check_equal(), check_function(., "gf_freqpoly") %>% check_result() %>% check_equal(), check_function(., "gf_density") %>% check_result() %>% check_equal(), check_function(., "gf_boxplot") %>% check_result() %>% check_equal(), check_function(., "gf_violin") %>% check_result() %>% check_equal() )
CK Code: ch11-27
require(coursekata) # Can you arrange the data frame to show you the places with the highest hate crime rates? Can you just print the 6 states with the highest crime rates? # Can you arrange the data frame to show you the places with the highest hate crime rates? Can you just print the 6 states with the highest crime rates? # there are multiple ways to approach this task. Here are a few possible solutions: head(arrange(hate_crimes, desc(avg_hatecrimes_per_100k_fbi))) hate_crimes <- arrange(hate_crimes, desc(avg_hatecrimes_per_100k_fbi)) head(select(hate_crimes, state, avg_hatecrimes_per_100k_fbi)) ex() %>% { check_function(., "desc", not_called_msg = "Did you use the desc() function to arrange avg_hatecrimes_per_100k_fbi in descending order?") check_function(., "arrange", not_called_msg = "Did you use the arrange() function to arrange avg_hatecrimes_per_100k_fbi in descending order?") %>% check_result() %>% check_equal() check_function(., "head", not_called_msg = "Did you remember to print the first 6 rows of the data frame?") }
CK Code: ch11-28
require(coursekata) # make a visualization of hate crimes explained by unemployment # make a visualization of hate crimes explained by household income # make a visualization of hate crimes explained by unemployment gf_point(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes, color = "navy", size = 3) %>% gf_lm(color = "orange") # make a visualization of hate crimes explained by household income gf_point(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes, color = "darkgreen", size = 3) %>% gf_lm(color = "orange") ex() %>% check_error()
CK Code: ch11-29
require(coursekata) # find and print the best-fitting estimates for the unemployment model # find and print the best-fitting estimates for the income model # find and print the best-fitting estimates for the unemployment model lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) # find and print the best-fitting estimates for the income model lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) not_called = "Did you use the lm() function to find and print the best-fitting estimates?" ex() %>% { check_function(., "lm", index = 1, not_called_msg = not_called) %>% check_result() %>% check_equal(incorrect_msg = "Did you model avg_hatecrimes_per_100k_fbi as a function of share_unemp_seas in the hate_crimes data frame?") check_function(., "lm", index = 2, not_called_msg = not_called) %>% check_result() %>% check_equal(incorrect_msg = "Did you model avg_hatecrimes_per_100k_fbi as a function of median_house_inc in the hate_crimes data frame?" ) }
CK Code: ch11-30
require(coursekata) # this code fits the models unemp.model <- lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) income.model <- lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # print the supernova table for unemp.model # print the supernova table for income.model # this code fits the models unemp.model <- lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) income.model <- lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # print the supernova table for unemp.model supernova(unemp.model) # print the supernova table for income.model supernova(income.model) msg = "Did you call supernova() on both unemp.model and income.model?" ex() %>% { check_output_expr(., "supernova(unemp.model)") check_output_expr(., "supernova(income.model)") }
CK Code: ch11-31
require(coursekata) hate_crimes <- hate_crimes %>% filter(complete.cases(avg_hatecrimes_per_100k_fbi, median_house_inc)) set.seed(41) # We've saved the sample PRE sample_PRE <- PRE(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # Create a sampling distribution of 1000 randomized PREs SDoPRE <- # Run a tally to calculate p-value tally() # This code will depict the sampling distribution in a histogram gf_histogram(~PRE, data = SDoPRE, fill = ~PRE > sample_PRE) # We've saved the sample PRE sample_PRE <- PRE(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # Create a sampling distribution of 1000 randomized PREs SDoPRE <- do(1000) * PRE(shuffle(avg_hatecrimes_per_100k_fbi) ~ median_house_inc, data = hate_crimes) # NOTE: Best solution is: # Run a tally to calculate p-value tally(~PRE > sample_PRE, data = SDoPRE, format = "proportion") # This code will depict the sampling distribution in a histogram gf_histogram(~PRE, data = SDoPRE, fill = ~PRE > sample_PRE) # This longer solution is there for scoring purposes and can be ignored. # Run a tally to calculate p-value # tally(~PRE > PRE(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes), data = SDoPRE, format = "proportion") # This code will depict the sampling distribution in a histogram # gf_histogram(~PRE, data = SDoPRE, fill = ~PRE > PRE(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes)) eq_fun <- function(x, y) (formula(x) == ~PRE > PRE(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes)) ex() %>% { check_function(., "do") check_function(., "PRE", index = 2) } ex() %>% check_or( check_function(., "gf_histogram") %>% { check_arg(., "data") %>% check_equal() check_arg(., "object") %>% check_equal() check_arg(., "fill") %>% check_equal(eval = FALSE, eq_fun = eq_fun) }, override_solution(., "gf_histogram(~PRE, data = SDoPRE, fill = ~PRE > sample_PRE)") %>% check_function("gf_histogram") %>% check_arg("fill") %>% check_equal(eval = FALSE, eq_fun = function (x,y) (formula(x) == ~PRE > sample_PRE)) ) ex() %>% check_or( check_function(., "tally") %>% { check_arg(., "data") %>% check_equal() check_arg(., "format") %>% check_equal() check_arg(., "x") %>% check_equal(eval = FALSE, eq_fun = eq_fun) }, override_solution(., 'tally(~PRE > sample_PRE, data = SDoPRE, format = "proportion")') %>% check_function("tally") %>% check_arg("x") %>% check_equal(eval = FALSE, eq_fun = function(x,y) (formula(x) == ~PRE > sample_PRE)) )
CK Code: ch11-32
require(coursekata) hate_crimes <- hate_crimes %>% filter(complete.cases(avg_hatecrimes_per_100k_fbi, median_house_inc)) set.seed(41) # We've saved the sample F sample_F <- fVal(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # Create sampling distribution of randomized Fs SDoF <- # Run a tally to calculate p-value tally() # This code will depict the sampling distribution in a histogram gf_histogram(~fVal, data = SDoF, fill = ~fVal > sample_F) # We've saved the sample F sample_F <- fVal(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # Create sampling distribution of randomized Fs SDoF <- do(10) * fVal(shuffle(avg_hatecrimes_per_100k_fbi) ~ median_house_inc, data = hate_crimes) # NOTE: Best solution is: # Run a tally to calculate p-value # tally(~fVal > sample_F, data = SDoF, format = "proportion") # This code will depict the sampling distribution in a histogram # gf_histogram(~fVal, data = SDoF, fill = ~fVal > sample_F) # This longer solution is there for scoring purposes and can be ignored. # Run a tally to calculate p-value tally(~fVal > fVal(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes), data = SDoF, format = "proportion") # This code will depict the sampling distribution in a histogram gf_histogram(~fVal, data = SDoF, fill = ~fVal > fVal(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes)) eq_fun <- function(x, y) (formula(x) == ~fVal > fVal(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes)) ex() %>% { check_function(., "do") check_function(., "fVal", index = 2) } ex() %>% check_or( check_function(., "gf_histogram") %>% { check_arg(., "data") %>% check_equal() check_arg(., "object") %>% check_equal() check_arg(., "fill") %>% check_equal(eval = FALSE, eq_fun = eq_fun) }, override_solution(., "gf_histogram(~fVal, data = SDoF, fill = ~fVal > sample_F)") %>% check_function("gf_histogram") %>% check_arg("fill") %>% check_equal(eval = FALSE, eq_fun = function(x,y) (formula(x) == ~fVal > sample_F)) ) ex() %>% check_or( check_function(., "tally") %>% { check_arg(., "data") %>% check_equal() check_arg(., "format") %>% check_equal() check_arg(., "x") %>% check_equal(eval = FALSE, eq_fun = eq_fun) }, override_solution(., 'tally(~fVal > sample_F, data = SDoF, format = "proportion")') %>% check_function("tally") %>% check_arg("x") %>% check_equal(eval = FALSE, eq_fun = function(x,y) (formula(x) == ~fVal > sample_F)) )
CK Code: ch11-33

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