CourseKata - ckcode-chapter-b1-simple-model

Items Test Book

Book

ckcode ⌲ chapter-b1-simple-model

require(coursekata)

# Modify this line to save the numbers to outcome
outcome <- c()

# This will give you the favstats for outcome
favstats(outcome)

outcome <- c(5, 5, 5, 10, 20)
favstats(outcome)

ex() %>% {
    check_object(., "outcome") %>% check_equal()
    check_function(., "favstats") %>% check_result() %>% check_equal()
}

CK Code: B1_Code_Median_01

require(coursekata)

# modify this code to make a histogram of GradePredict
# the second line adds more tick marks to the x-axis
gf_histogram(~ , data = Fingers, color = "forestgreen") +
 scale_x_continuous(breaks = seq(2.0, 4.0, by = 0.1))

# modify this code to get the favstats for GradePredict
favstats(~ GradePredict, data = )

# modify this code to make a histogram of GradePredict
# the second line adds more tick marks to the x-axis
gf_histogram(~ GradePredict, data = Fingers, color = "forestgreen") +
 scale_x_continuous(breaks = seq(2.0, 4.0, by = 0.1))

# modify this code to get the favstats for GradePredict
favstats(~ GradePredict, data = Fingers)

ex() %>% {
  check_or(.,
    check_function(., "gf_histogram") %>% {
      check_arg(., "object") %>% check_equal()
      check_arg(., "data") %>% check_equal()
    },
    override_solution(., "gf_histogram(Fingers, ~ GradePredict)") %>%
      check_function("gf_histogram") %>% {
        check_arg(., "object") %>% check_equal()
        check_arg(., "gformula") %>% check_equal()
      },
    override_solution(., "gf_histogram(~ Fingers$GradePredict)") %>%
      check_function("gf_histogram") %>%
      check_arg(., "object") %>%
      check_equal()
  )
  check_function(., "favstats") %>%
    check_result() %>%
    check_equal()
}

CK Code: B1_Code_Median_02

require(coursekata)

# modify this code to make a histogram of Thumb
gf_histogram()

# get the favstats for Thumb

# modify this code to make a histogram of Thumb
gf_histogram(~ Thumb, data = Fingers)

# get the favstats for Thumb
favstats(~ Thumb, data = Fingers)

ex() %>% {
  check_or(.,
    check_function(., "gf_histogram") %>% {
      check_arg(., "object") %>% check_equal()
      check_arg(., "data") %>% check_equal()
    },
    override_solution(., "gf_histogram(Fingers, ~ Thumb)") %>%
      check_function("gf_histogram") %>% {
        check_arg(., "object") %>% check_equal()
        check_arg(., "gformula") %>% check_equal()
      },
    override_solution(., "gf_histogram(~ Fingers$Thumb)") %>%
      check_function("gf_histogram") %>%
      check_arg(., "object") %>%
      check_equal()
  )
  check_function(., "favstats") %>%
    check_result() %>%
    check_equal()
}

CK Code: B1_Code_Median_03

require(coursekata)

# make a histogram of Age in the MindsetMatters data frame
# set the fill = "red"

# get the favstats for Age

# make a histogram of Age in the MindsetMatters data frame
# set the fill = "red"
gf_histogram(~ Age, data = MindsetMatters, fill = "red")

# get the favstats for Age
favstats(~ Age, data = MindsetMatters)

ex() %>% {
  check_or(.,
    check_function(., "gf_histogram") %>% {
      check_arg(., "object") %>% check_equal()
      check_arg(., "data") %>% check_equal()
    },
    override_solution(., "gf_histogram(MindsetMatters, ~ Age)") %>%
      check_function("gf_histogram") %>% {
        check_arg(., "object") %>% check_equal()
        check_arg(., "gformula") %>% check_equal()
      },
    override_solution(., "gf_histogram(~ MindsetMatters$Age)") %>%
      check_function("gf_histogram") %>%
      check_arg(., "object") %>%
      check_equal()
  )
  check_function(., "gf_histogram") %>%
    check_arg("fill") %>%
    check_equal()
  check_function(., "favstats") %>%
    check_result() %>%
    check_equal()
}

CK Code: B1_Code_Median_04

require(coursekata)
outcome <- c(5, 5, 5, 10, 20)

# Modify this code to draw a vline representing the median in "purple"
gf_histogram(~outcome) %>%
  gf_vline(xintercept = 9, color = "blue")

# Modify this code to draw a vline representing the median in "purple"
gf_histogram(~outcome) %>%
  gf_vline(xintercept = 5, color = "purple")

ex() %>% {
  check_function(., "gf_histogram") %>%
    check_arg("object") %>%
    check_equal()
  check_function(., "gf_vline") %>% {
    check_arg(., "xintercept") %>% check_equal()
    check_arg(., "color") %>% check_equal()
  }
}

CK Code: B1_Code_Median_05

require(coursekata)

# modify this to fit the empty model of Thumb
empty_model <-

# print out the model estimates


empty_model <- lm(Thumb ~ NULL, data = Fingers)
empty_model

ex() %>% {
    check_object(., "empty_model") %>% check_equal()
    }

CK Code: B1_Code_Empty_01

require(coursekata)
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# saves the best-fitting empty model
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# add gf_model to this scatterplot
gf_point(Thumb ~ Height, data = Fingers)

# add gf_model to this jitter plot
gf_jitter(Thumb ~ Sex, data = Fingers, width = .1)

# saves the best-fitting empty model
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# add gf_model to this scatterplot
gf_point(Thumb ~ Height, data = Fingers) %>%
  gf_model(empty_model)

# add gf_model to this jitter plot
gf_jitter(Thumb ~ Sex, data = Fingers, width = .1) %>%
  gf_model(empty_model)

ex() %>% {
  check_function(., "gf_model", index = 1) %>%
    check_arg("object") %>%
    check_equal()
  check_or(.,
    check_function(., "gf_model", index = 1) %>%
      check_arg("model") %>%
      check_equal(),
    override_solution(., "gf_point(Thumb ~ Height, data = Fingers) %>% gf_model(Thumb ~ NULL)") %>%
      check_function("gf_model", index = 1) %>%
      check_arg("model") %>%
      check_equal()
  )
  check_function(., "gf_model", index = 2) %>%
    check_arg("object") %>%
    check_equal()
  check_or(.,
    check_function(., "gf_model", index = 2) %>%
      check_arg("model") %>%
      check_equal(),
    override_solution(., "gf_model(empty_model); gf_point(Thumb ~ Height, data = Fingers) %>% gf_model(Thumb ~ NULL)") %>%
      check_function("gf_model", index = 2) %>%
      check_arg("model") %>%
      check_equal()
  )
}

CK Code: B1_Code_Empty_02

require(coursekata)

# saves the empty model
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# write code to generate predictions of the empty model

empty_model <- lm(Thumb ~ NULL, data = Fingers)
predict(empty_model)

ex() %>% check_object("empty_model") %>% check_equal()

CK Code: B1_Code_Predictions_01

require(coursekata)

# this saves the empty_model
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# modify this to save the predictions from the empty_model in a new variable
Fingers$Predict <-

# prints out selected variables from Fingers
head(select(Fingers, Thumb, Predict), 10)

# this makes a scatterplot of Thumb by Height and overlays the empty model's predictions as open blue circles
gf_point(Thumb ~ Height, data = Fingers, width = .1) %>%
  gf_point(Predict ~ Height, color = "blue", shape = 1, height = 0)

# this saves the empty_model
empty_model <- lm(Thumb ~ NULL, data = Fingers)

# modify this to save the predictions from the empty_model in a new variable
Fingers$Predict <- predict(empty_model)

# prints out selected variables from Fingers
head(select(Fingers, Thumb, Predict), 10)

# this makes a scatterplot of Thumb by Height and overlays the empty model's predictions as open blue circles
gf_point(Thumb ~ Height, data = Fingers, alpha = .2) %>%
  gf_point(Predict ~ Height, color = "blue", shape = 1)

ex() %>%
  check_object("Fingers") %>%
  check_column("Predict") %>%
  check_equal()

CK Code: B1_Code_Predictions_02

require(coursekata)

Fingers$TinySet <- c(1,1,1,0,0,0,1,0,0,1, rep(0,147))
Fingers$TinySet[142] <- 1
Fingers <- arrange(arrange(Fingers, Height), desc(TinySet))

empty_model <- lm(Thumb ~ NULL, data = Fingers)
Fingers <- Fingers %>% mutate(
    Predict = predict(empty_model),
    Resid = Thumb - Predict
)

# modify this to save the residuals from the empty_model
Fingers$Resid <-

# this prints selected variables from Fingers
select(Fingers, Thumb, Predict, Resid)

Fingers$Resid <- Fingers$Thumb - Fingers$Predict

ex() %>% check_object("Fingers") %>%
    check_column("Resid") %>% check_equal()

CK Code: B1_Code_Thinking_01

require(coursekata)

Fingers$TinySet <- c(1,1,1,0,0,0,1,0,0,1, rep(0,147))
Fingers$TinySet[142] <- 1
Fingers <- arrange(arrange(Fingers, Height), desc(TinySet))

empty_model <- lm(Thumb ~ NULL, data = Fingers)
Fingers <- Fingers %>% mutate(
    Predict = predict(empty_model),
    Resid = Thumb - Predict
)

# calculate the residuals from empty_model the easy way
# and save them in the Fingers data frame
Fingers$EasyResid <-

# this prints select variables from Fingers
head(select(Fingers, Thumb, Predict, Resid, EasyResid))

Fingers$EasyResid <- resid(empty_model)
Fingers

ex() %>% check_object("Fingers") %>%
    check_column("EasyResid") %>% check_equal()

CK Code: Code_Thinking_02

require(coursekata)

empty_model <- lm(Thumb ~ NULL, data = Fingers)
Fingers <- Fingers %>% mutate(
    Predict = predict(empty_model),
    Resid = resid(empty_model)
)

# assume Fingers data frame already has the variable Resid saved in it

sum(Fingers$Resid)

ex() %>% {

    check_output_expr(., "sum(Fingers$Resid)")
}

CK Code: Code_Thinking_03

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_01

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_02

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_03

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_04

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_05

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_06

require(coursekata)

# run your code here

CK Code: B1_Code_Review2_07

ckcode-chapter-a4-explaining-variation ckcode-chapter-b2-quantifying-error

Course Outline

Items Test Book

ckcode ⌲ chapter-b1-simple-model

Responses

list Items Test Book

ckcode ⌲ chapter-b1-simple-model

Items Test Book