Course Outline
-
segmentLearnosity
-
segmentCKCode
-
ckcode-chapter-x2-exploring-to-modeling
list Items Test Book
Book
ckcode ⌲ chapter-x2-exploring-to-modeling
require(coursekata)
# Create a histogram of PriceK from the Ames data set
gf_histogram(~ PriceK, data = Ames)
ex() %>%
check_function(., "gf_histogram") %>% {
check_arg(., "object") %>% check_equal()
check_arg(., "data") %>% check_equal()
}
CK Code: X2_Code_Relationships_01
require(coursekata)
# replace the . with Floors
gf_histogram(~ PriceK, data = Ames) %>%
gf_facet_grid(Neighborhood ~ .)
# replace the . with Floors
gf_histogram(~ PriceK, data = Ames) %>%
gf_facet_grid(Neighborhood ~ Floors)
ex() %>%
check_function(., "gf_histogram") %>% {
check_arg(., "object") %>% check_equal()
check_arg(., "data") %>% check_equal()
}
CK Code: X2_Code_Visualizing_01
require(coursekata)
gf_boxplot(PriceK ~ Neighborhood, data = Ames)
gf_boxplot(PriceK ~ Neighborhood, data = Ames) %>%
gf_jitter()
ex() %>%
check_function("gf_jitter") %>%
check_result() %>%
check_equal()
CK Code: X2_Code_TwoVariable_01
require(coursekata)
# make a scatterplot to explore the relationship between PriceK and HomeSizeK
gf_point(PriceK ~ HomeSizeK, data = Ames)
ex() %>% check_function("gf_point") %>%
check_result() %>% check_equal()
CK Code: X2_Code_TwoVariable_02
require(coursekata)
# use R as a calculator to find the mean of these 5 houses
# we have started it out for you by summing the prices together
(50 + 50 + 50 + 100 + 200)
(50 + 50 + 50 + 100 + 200) / 5
ex() %>% check_output_expr("(50 + 50 + 50 + 100 + 200) / 5")
CK Code: X2_Code_Mean_01
require(coursekata)
# here's code to find empty model using favstats()
favstats(~ PriceK, data = Ames)
# write code to find the empty model using lm()
# here's code to find empty model using favstats()
favstats(~ PriceK, data = Ames)
# write code to find the empty model using lm()
lm(PriceK ~ NULL, data = Ames)
ex() %>% {
check_function(., "favstats") %>% check_result() %>% check_equal()
check_function(., "lm") %>% check_result() %>% check_equal()
}
CK Code: X2_Code_Fitting_01
require(coursekata)
# this saves the empty model of PriceK
empty_model <- lm(PriceK ~ NULL, data = Ames)
# write code to print the contents of empty_model
# this saves the empty model of PriceK
empty_model <- lm(PriceK ~ NULL, data = Ames)
# write code to print the contents of empty_model
empty_model
ex() %>% check_output_expr('empty_model')
CK Code: X2_Code_Fitting_02
require(coursekata)
# this saves the empty model
empty_model <- lm(PriceK ~ NULL, data = Ames)
gf_jitter(PriceK ~ Neighborhood, data = Ames, width = .1)
# this saves the empty model
empty_model <- lm(PriceK ~ NULL, data = Ames)
gf_jitter(PriceK ~ Neighborhood, data = Ames, width = .1) %>%
gf_model(empty_model)
ex() %>% check_function("gf_model") %>% {
check_arg(., "model") %>% check_equal()
check_result(.) %>% check_equal()
}
CK Code: X2_Code_Fitting_03
require(coursekata)
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate predictions from this model
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate predictions from this model
predict(empty_model)
ex() %>% check_output_expr("predict(empty_model)")
CK Code: X2_Code_Quantifying_01
require(coursekata)
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate residuals from this model’s predictions
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate residuals from this model’s predictions
resid(empty_model)
ex() %>% check_output_expr("resid(empty_model)")
CK Code: X2_Code_Quantifying_02
require(coursekata)
empty_model <- lm(PriceK ~ NULL, data = Ames)
# saves the predictions and residuals from the empty model
Ames$empty_predict <- predict(empty_model)
Ames$empty_resid <- resid(empty_model)
# this will show us the first 6 rows of PriceK
# modify this code also show prediction and residual variables
head(select(Ames, PriceK))
# saves the predictions and residuals from the empty model
Ames$empty_predict <- predict(empty_model)
Ames$empty_resid <- resid(empty_model)
# this will show us the first 6 rows of PriceK
# modify this code also show prediction and residual variables
head(select(Ames, PriceK, empty_predict, empty_resid))
ex() %>% check_function("head") %>%
check_result() %>% check_equal()
CK Code: X2_Code_Quantifying_03
require(coursekata)
# don't delete this part
empty_model <- lm(PriceK ~ NULL, data = Ames)
Ames$empty_resid <- resid(empty_model)
# this creates the squared residuals
Ames$empty_resid_sqrd <- Ames$empty_resid^2
# write code to sum these squared residuals
# this create the squared residuals
Ames$empty_resid_sqrd <- Ames$empty_resid^2
# write code to sum these squared these residuals
sum(Ames$empty_resid_sqrd)
ex() %>% check_function("sum") %>%
check_result() %>% check_equal()
CK Code: X2_Code_Quantifying_04
require(coursekata)
# we’ve created the empty model
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate the ANOVA table
# we’ve created the empty model
empty_model <- lm(PriceK ~ NULL, data = Ames)
# generate the ANOVA table
supernova(empty_model)
ex() %>% check_function("supernova") %>%
check_result() %>% check_equal()
CK Code: X2_Code_Beauty_01