CourseKata - ckcode-chapter-a3-examining-distributions

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ckcode ⌲ chapter-a3-examining-distributions

require(coursekata)

# try running this code
gf_histogram(~Thumb, data = Fingers)

# try running this code
gf_histogram(~Thumb, data = Fingers)

ex() %>%
  check_function("gf_histogram") %>% {
    check_arg(., "object", arg_not_specified_msg = "Make sure to keep ~Thumb") %>% check_equal()
    check_arg(., "data", arg_not_specified_msg = "Make sure to specify data") %>% check_equal()
  }

CK Code: ch3-1

require(coursekata)

# Modify this code to play around with labeling the y-axis
gf_histogram(~ Thumb, data = Fingers) %>%
  gf_labs(x = "Thumb length (mm)", y = )

gf_histogram(~ Thumb, data = Fingers) %>%
  gf_labs(x = "Thumb length (mm)", y = "Your Label")

ex() %>% {
    check_function(., "gf_labs") %>% check_arg("x") %>% check_equal(eval = FALSE)
    check_function(., "gf_labs") %>% check_arg("y")
    check_function(., "gf_histogram") %>% check_arg("object") %>% check_equal()
    check_function(., "gf_histogram") %>% check_arg("data") %>% check_equal()
}

CK Code: ch3-2

require(coursekata)

# This sets up our tiny data frame with our outcome variable
outcome <- c(1, 2, 3, 4, 5)
tiny_data <- data.frame(outcome)

# Write code to create a histogram of outcome

outcome <- c(1, 2, 3, 4, 5)
tiny_data <- data.frame(outcome)
gf_histogram(~outcome, data = tiny_data, fill = "aquamarine", color = "gray")

ex() %>% {
    check_object(., "outcome", undefined_msg = "Make sure to not remove `outcome`") %>% check_equal()
    check_object(., "tiny_data") %>% check_column("outcome") %>% check_equal(incorrect_msg = "Make sure to not alter `tiny_data`")
    check_function(., "gf_histogram") %>% {
        check_arg(., "fill", arg_not_specified_msg = "Remember to use `fill =` with your own choice of color")
        check_arg(., "color", arg_not_specified_msg = "Remember to use `color =` with your own choice of color")
    }

    check_or(.,
        check_function(., "gf_histogram") %>% {
            check_arg(., "object") %>% check_equal(eval = FALSE, incorrect_msg = "Make sure you specify `~outcome` as the first argument.")
            check_arg(., "data") %>% check_equal(incorrect_msg = "Did you set `data = tiny_data`?")
        },
        override_solution_code(., '{
            outcome <- c(1, 2, 3, 4, 5)
            tiny_data <- data.frame(outcome)
            gf_histogram(~tiny_data, fill = "aquamarine", color = "gray")
        }') %>%
            check_function("gf_histogram") %>%
                check_arg("object") %>%
                check_equal(eval = FALSE)
    )

}

CK Code: ch3-3

require(coursekata)

# This is the same code as before but we added in another outcome value, 3.2
outcome <- c(1, 2, 3, 4, 5, 3.2)
tiny_data <- data.frame(outcome)

# This makes a histogram with 5 bins
gf_histogram(~ outcome, data = tiny_data, fill = "aquamarine", color = "gray", bins = 5)

outcome <- c(1, 2, 3, 4, 5, 3.2)
tiny_data <- data.frame(outcome)
gf_histogram(~ outcome, data = tiny_data, fill = "aquamarine", color = "gray", bins = 5)

ex() %>% check_object("outcome", undefined_msg = "Make sure not to delete 'outcome'") %>% check_equal(incorrect_msg = "Make sure not to change the content of 'outcome'")
ex() %>% check_object("tiny_data", undefined_msg = "Make sure not to delete 'tiny_data'") %>% check_equal()
ex() %>% check_function("gf_histogram") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "data") %>% check_equal()
}
success_msg("You're doing great!")

CK Code: ch3-4

require(coursekata)

# add 3.7 to the outcome values, then run this code
outcome <- c(1, 2, 3, 4, 5, 3.2)
tiny_data <- data.frame(outcome)

# this makes a histogram with 5 bins
gf_histogram(~ outcome, data = tiny_data, fill = "aquamarine", color = "gray", bins = 5)

# add 3.7 to the outcome values, then run this code
outcome <- c(1, 2, 3, 4, 5, 3.2, 3.7)
tiny_data <- data.frame(outcome)

# this makes a histogram with 5 bins
gf_histogram(~ outcome, data = tiny_data, fill = "aquamarine", color = "gray", bins = 5)

inc_msg = "Don't alter the other code in this exercise -- only the contents of `outcome`."
ex() %>% {
    check_object(., "outcome") %>% check_equal(incorrect_msg = "Did you add 3.7 to the outcome vector?")
    check_object(., "tiny_data") %>% check_equal(incorrect_msg = inc_msg)
    check_function(., "gf_histogram")
}

CK Code: ch3-5

require(coursekata)

# adjust the number of bins to 50
gf_histogram(~ Thumb, data = Fingers, bins = )

# adjust the number of bins to 5
gf_histogram(~ Thumb, data = Fingers)

# adjust the bin width to 3
gf_histogram(~ Thumb, data = Fingers, binwidth = )

# adjust the bin width to 10
gf_histogram(~ Thumb, data = Fingers)

# adjust the number of bins to 50
gf_histogram(~ Thumb, data = Fingers, bins = 50)

# adjust the number of bins to 5
gf_histogram(~ Thumb, data = Fingers, bins = 5)

# adjust the bin width to 3
gf_histogram(~ Thumb, data = Fingers, binwidth = 3)

# adjust the bin width to 10
gf_histogram(~ Thumb, data = Fingers, binwidth = 10)

ex() %>% {
    check_function(., "gf_histogram", index = 1) %>% check_arg("bins") %>% check_equal(incorrect_msg = "Did you set the number of `bins` to 50?")
    check_function(., "gf_histogram", index = 2) %>% check_arg("bins", arg_not_specified_msg = "Did you set the number of `bins` to 5?") %>% check_equal(incorrect_msg = "Did you set the number of `bins` to 5?")
    check_function(., "gf_histogram", index = 3) %>% check_arg("binwidth") %>% check_equal(incorrect_msg = "Did you set the `binwidth` to 3?")
    check_function(., "gf_histogram", index = 4) %>% check_arg("binwidth", arg_not_specified_msg = "Did you set the `binwidth` to 10?") %>% check_equal(incorrect_msg = "Did you set the `binwidth` to 10?")
}

CK Code: ch3-6

require(coursekata)

# This will create a relative frequency histogram of Age
gf_dhistogram(~ Age, data = MindsetMatters, fill = "coral2")

# Add code below to create a frequency histogram of Age

gf_dhistogram(~ Age, data = MindsetMatters, fill = "coral2", bins = 20)
gf_histogram(~ Age, data = MindsetMatters)

ex() %>% check_function(., "gf_histogram") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "data") %>% check_equal()
}

CK Code: ch3-7

require(coursekata)

# make a density histogram of Thumb in the Fingers data frame

# make a density histogram of Thumb in the Fingers data frame
gf_dhistogram(~ Thumb, data = Fingers)

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

CK Code: ch3-8

require(coursekata)

# This creates our model population
model_pop <- 1:6

# Write code to create a relative frequency histogram of our model population.
# Remember to include bins as an argument.

# This creates our model population
model_pop <- 1:6

# Write code to create a relative frequency histogram of our model population.
# Remember to include bins as an argument.
# gf_dhistogram(~ model_pop, color = "black", bins = 6)

ex() %>% {
    check_object(., "model_pop") %>% check_equal()
    override_solution_code(.,
        'gf_dhistogram(~ model_pop, color = "black", bins = 6)'
    ) %>%
        check_function(., "gf_dhistogram") %>% {
            check_arg(., "object") %>% check_equal()
            check_arg(., "bins") %>% check_equal()
        }
}

CK Code: ch3-9

require(coursekata);
allow_solution_error()

model_pop <- 1:6

# This samples the same way 12 times
resample(model_pop, 12)

# Will this accomplish the same thing?
# Do not change the code --- just submit it
sample(model_pop, 12)

model_pop <- 1:6

# This samples the same way 12 times
resample(model_pop, 12)

# Will this accomplish the same thing?
# Do not change the code --- just submit it
sample(model_pop, 12)

success_msg("This code is supposed to throw an error. You can read it in the Console tab.")

CK Code: ch3-10

require(coursekata)
set.seed(4)

model_pop <- 1:6
sample1 <- resample(model_pop, 12)

# Write code to create a relative frequency histogram
# Remember to put in bins as an argument
# Don't use any custom coloring

model_pop <- 1:6
sample1 <- resample(model_pop, 12)

# you have to uncomment this line for it to work
# gf_dhistogram(~ sample1, bins = 6)

ex() %>% {
    override_solution_code(.,
        'model_pop <- 1:6; sample1 <- resample(model_pop, 12); gf_dhistogram(~ sample1, bins = 6)'
    ) %>% {
        check_object(., "sample1") %>% check_equal()
        check_function(., "gf_dhistogram") %>% {
            check_arg(., "bins")
            check_arg(., "object") %>% check_equal(eval = FALSE)
        }
    }
}

CK Code: ch3-11

require(coursekata)
set.seed(5)

model_pop <- 1:6

# Modify this code from 12 dice rolls to 24 dice rolls
sample2 <- resample(model_pop, 12)

# This will create a density histogram
gf_dhistogram(~ sample2, color = "darkgray", fill = "springgreen", bins = 6)

model_pop <- 1:6

# Modify this code from 12 dice rolls to 24 dice rolls
sample2 <- resample(model_pop, 24)

# This will create a density histogram (you have to uncomment the line for it to work)
# gf_dhistogram(~ sample2, color = "darkgray", fill = "springgreen", bins = 6)

ex() %>% check_object("sample2") %>% check_equal()

CK Code: ch3-12

require(coursekata)
set.seed(7)

model_pop <- 1:6

# create samples #3, #4, #5 of 24 dice rolls
sample3 <-
sample4 <-
sample5 <-

# this will create a density histogram of your sample3
# add onto it to include a density plot
gf_dhistogram(~ sample3, color = "darkgray", fill = "springgreen", bins = 6)

# create density histograms of sample4 and sample5 with density plots

model_pop <- 1:6

# create samples
sample3 <- resample(model_pop, 24)
sample4 <- resample(model_pop, 24)
sample5 <- resample(model_pop, 24)

# this will create a density histogram of your sample3
# add onto it to include a density plot
# gf_dhistogram(~ sample3, color = "darkgray", fill = "springgreen", bins = 6) %>%
#   gf_density()

# create density histograms of sample4 and sample5 with density plots
# gf_dhistogram(~ sample4, color = "darkgray", fill = "springgreen", bins = 6) %>%
#   gf_density()
# gf_dhistogram(~ sample5, color = "darkgray", fill = "springgreen", bins = 6) %>%
#   gf_density()

ex() %>% override_solution_code('{
      model_pop <- 1:6
      sample3 <- resample(model_pop, 24)
      sample4 <- resample(model_pop, 24)
      sample5 <- resample(model_pop, 24)
      gf_dhistogram(~ sample3, color = "darkgray", fill = "springgreen", bins = 6) %>%
        gf_density()
      gf_dhistogram(~ sample4, color = "darkgray", fill = "springgreen", bins = 6) %>%
        gf_density()
      gf_dhistogram(~ sample5, color = "darkgray", fill = "springgreen", bins = 6) %>%
        gf_density();
    }') %>%
{
    check_object(., "sample3") %>% check_equal()
    check_object(., "sample4") %>% check_equal()
    check_object(., "sample5") %>% check_equal()

    check_function(., "gf_dhistogram", index = 1) %>%
        check_arg("object") %>% check_equal(eval = FALSE)
    check_function(., "gf_dhistogram", index = 2) %>%
        check_arg("object") %>% check_equal(eval = FALSE)
    check_function(., "gf_dhistogram", index = 3) %>%
        check_arg("object") %>% check_equal(eval = FALSE)

    check_function(., "gf_density", index = 1)
    check_function(., "gf_density", index = 2)
    check_function(., "gf_density", index = 3)
}

CK Code: ch3-13

require(coursekata)
set.seed(7)

model_pop <- 1:6

# create a sample with 1000 rolls of a die
large_sample <-

# this will create a density histogram of your large_sample
gf_dhistogram(~ large_sample, color = "darkgray", fill = "springgreen", bins = 6)

model_pop <- 1:6

# create a sample with 1000 rolls of a die
large_sample <- resample(model_pop, 1000)

# this will create a density histogram of your large_sample
# gf_dhistogram(~ large_sample, color = "darkgray", fill = "springgreen", bins = 6)

ex() %>% override_solution_code('{
    model_pop <- 1:6

    # create a sample with 1000 rolls of a die
    large_sample <- resample(model_pop, 1000)

    # this will create a density histogram of your largesample
    gf_dhistogram(~ large_sample, color="darkgray", fill="springgreen", bins=6)
}') %>% {
    check_object(., "large_sample") %>% check_equal()
    check_function(., "gf_dhistogram") %>%
        check_arg("object") %>%
        check_equal(eval = FALSE)
}

CK Code: ch3-14

require(coursekata)

set.seed(10)

model_pop <- 1:6
w_pop <- c(rep(1,5), 2, rep(3,10), rep(4,10), 5, rep(6,5))

# Create a sample that draws 24 times from w_pop
small_sample <-

# This will create a density histogram of your small_sample
gf_dhistogram(~ small_sample, color = "darkgray", fill = "mistyrose", bins = 6)

# Create a sample that draws 24 times from w_pop
small_sample <- resample(w_pop, 24)

# This will create a density histogram of your small_sample
# gf_dhistogram(~ small_sample, color = "darkgray", fill = "mistyrose", bins = 6)

ex() %>% override_solution_code('{
    # Create a sample that draws 24 times from w_pop
    small_sample <- resample(w_pop, 24)

    # This will create a density histogram of your small_sample
    gf_dhistogram(~ small_sample, color = "darkgray", fill = "mistyrose", bins = 6)
}') %>% {
    check_object(., "small_sample") %>% check_equal()
    check_function(., "gf_dhistogram") %>%
        check_arg("object") %>%
        check_equal()
}

CK Code: ch3-15

require(coursekata)

set.seed(7)
model_pop <- 1:6
w_pop <- c(rep(1,5), 2, rep(3,10), rep(4,10), 5, rep(6,5))

# create a sample that draws 1000 times from w_pop
large_sample <-

# this will create a density histogram of your large_sample
gf_dhistogram(~ large_sample, color = "darkgray", fill = "mistyrose", bins = 6)

# create a sample that draws 1000 times from w_pop
large_sample <- resample(w_pop, 1000)

# this will create a density histogram of your large_sample
# gf_dhistogram(~ large_sample, color = "darkgray", fill = "mistyrose", bins = 6)

ex() %>% override_solution_code('{
    # create a sample that draws 1000 times from w_pop
    large_sample <- resample(w_pop, 1000)

    # this will create a density histogram of your large_sample
    gf_dhistogram(~ large_sample, color = "darkgray", fill = "mistyrose", bins = 6)
}') %>% {
    check_object(., "large_sample") %>% check_equal()
    check_function(., "gf_dhistogram") %>%
        check_arg("object") %>%
        check_equal()
}

CK Code: ch3-16

require(coursekata)

# Write code to sort Wt from lowest to highest

# Solution 1
arrange(MindsetMatters, Wt)

# Solution 2
sort(MindsetMatters$Wt)

3
ex() %>% check_or(
    check_function(., "arrange", not_called_msg = "If you were trying to use `sort`, that's acceptable but your code must have resulted in an error.") %>% check_result() %>% check_equal(),
    check_function(., "sort") %>% check_result() %>% check_equal()
)

CK Code: ch3-17

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)

# Modify the code to get favstats for Population of countries in HappyPlanetIndex
favstats()

favstats(~ Population, data = HappyPlanetIndex)

ex() %>% check_function("favstats") %>% check_result() %>% check_equal()

CK Code: ch3-18

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)

# make a histogram of Population from HappyPlanetIndex using gf_histogram

# make a histogram of Population from HappyPlanetIndex using gf_histogram
gf_histogram(~ Population, data = HappyPlanetIndex)

ex() %>% check_or(
    check_function(., "gf_histogram") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "data") %>% check_equal()
},
    override_solution(., "gf_histogram(HappyPlanetIndex, ~ Population)") %>%
    check_function("gf_histogram") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "gformula") %>% check_equal()
},
    override_solution(., "gf_histogram(~HappyPlanetIndex$Population)") %>%
    check_function("gf_histogram") %>% {
    check_arg(., "object") %>% check_equal()
},
    override_solution(., "gf_histogram(data = HappyPlanetIndex, gformula = ~ Population)") %>%
    check_function("gf_histogram") %>% {
    check_arg(., "data") %>% check_equal()
    check_arg(., "gformula") %>% check_equal()
}
    )

CK Code: ch3-19

require(coursekata)

# Based on the numbers from the favstats results above, use R as a calculator to find the range of Wt in MindsetMatters

# Based on the numbers from the favstats results above, use R as a calculator to find the range of Wt in MindsetMatters
196 - 90

ex() %>% check_output_expr("196 - 90")

CK Code: ch3-20

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)

# Use R as a calculator to find the IQR of Population from the HappyPlanetIndex data set

# Use R as a calculator to find the IQR of Population from the HappyPlanetIndex data set
31.225 - 4.455

ex() %>% check_output_expr("31.225 - 4.455")

CK Code: ch3-21

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)

# Modify this code to create a boxplot of Population from HappyPlanetIndex
gf_boxplot(Wt ~ 1, data = MindsetMatters)

# Modify this code to create a boxplot of Population from HappyPlanetIndex
gf_boxplot(Population ~ 1, data = HappyPlanetIndex)

ex() %>% check_function("gf_boxplot") %>% {
  check_arg(., "object") %>% check_equal()
  check_arg(., "data") %>% check_equal()
}

CK Code: ch3-22

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)

# this calculates the Q3 + 1.5*IQR
upper_boundary <- 31.225 + 1.5*(31.225-4.455)

# modify this code to filter in only countries with population sizes less than the upper_boundary
SmallerCountries <-

# this makes a histogram of the smaller countries' populations
gf_histogram(~ Population, data = SmallerCountries, fill = "slateblue4") %>%
gf_labs(x = "Population (in millions)", title = "Population of Countries (Excludes Outliers)")

upper_boundary <- 31.225 + 1.5*(31.225-4.455)
SmallerCountries <- filter(HappyPlanetIndex, Population < upper_boundary)
gf_histogram(~ Population, data = SmallerCountries, fill = "slateblue4") %>%
gf_labs(x = "Population (in millions)", title = "Population of Countries (Excludes Outliers)")

ex() %>% {
    check_function(., "filter") %>% {
        check_arg(., ".data") %>% check_equal(incorrect_msg="Don't forget to filter in HappyPlanetIndex")
        check_arg(., "...") %>% check_equal(incorrect_msg="Did you use `Population < upper_boundary` as the second argument?")
        check_result(.) %>% check_equal()
    }
    check_object(., "SmallerCountries") %>% check_equal
    check_function(., "gf_histogram") %>% {
        check_arg(., "object") %>% check_equal()
        check_arg(., "data") %>% check_equal()
    }
}

CK Code: ch3-23

require(coursekata)

HappyPlanetIndex$Region <- recode(
    HappyPlanetIndex$Region,
    '1'="Latin America",
    '2'="Western Nations",
    '3'="Middle East and North Africa",
    '4'="Sub-Saharan Africa",
    '5'="South Asia",
    '6'="East Asia",
    '7'="Former Communist Countries"
)
pop_stats <- favstats(~ Population, data = HappyPlanetIndex)
SmallerCountries <- filter(HappyPlanetIndex, Population < (pop_stats$Q3 + 1.5*(pop_stats$Q3 - pop_stats$Q1)))

# Make a boxplot of Population from the SmallerCountries
gf_boxplot(Population ~ 1, data = SmallerCountries)

# Make a boxplot of Population from the SmallerCountries
gf_boxplot(Population ~ 1, data = SmallerCountries)

ex() %>% check_function("gf_boxplot") %>% {
  check_arg(., "object") %>% check_equal()
  check_arg(., "data") %>% check_equal()
}

CK Code: ch3-24

require(coursekata)

# Create a bar graph of RaceEthnic in the Fingers data frame. Use the gf_bar() function

# Create a bar graph of RaceEthnic in the Fingers data frame. Use the gf_bar() function
gf_bar(~ RaceEthnic, data = Fingers)

ex() %>% check_function("gf_bar") %>% {
    check_arg(., "data") %>% check_equal(incorrect_msg="Don't forget to set `data = Fingers`")
    check_arg(., "object") %>% check_equal(incorrect_msg = "Did you use `~ RaceEthnic`?")
}

CK Code: ch3-25

require(coursekata)

# Add arguments color and fill to these bar graphs
# Make sure to add color and fill to both of the graphs
gf_bar(~ Sex, data = Fingers)
gf_bar(~ RaceEthnic, data = Fingers)

# the colors below are just examples. Any color is acceptable as long as you use the color and fill arguments
gf_bar(~ Sex, data = Fingers, color="darkgray", fill="mistyrose" )
gf_bar(~ RaceEthnic, data = Fingers, color="darkgray", fill="mistyrose")

ex() %>% {
    check_function(., "gf_bar", index = 1) %>% {
        check_arg(., "color")
        check_arg(., "fill")
    }
    check_function(., "gf_bar", index = 2) %>% {
        check_arg(., "color")
        check_arg(., "fill")
    }
}

CK Code: ch3-26

require(coursekata)

# This creates a frequency table of Sex
# Do not change this code
tally(~ Sex, data = Fingers)

# Write code to create a frequency table of RaceEthnic

tally(~ Sex, data = Fingers)
tally(~ RaceEthnic, data = Fingers)

ex() %>% check_function(., "tally", index = 2) %>% check_result() %>% check_equal()
success_msg("You're a supe-R coder!")

CK Code: ch3-27

require(coursekata)

# Add margin and format arguments to the tally() function. Set margins to TRUE and format to proportion
tally(~ RaceEthnic, data = Fingers)

tally(~ RaceEthnic, data = Fingers, margins = TRUE, format = "proportion")

ex() %>% check_or(
    check_function(., "tally") %>% {
        check_arg(., "margins") %>% check_equal()
        check_arg(., "format") %>% check_equal()
        check_result(.) %>% check_equal()
    },
    check_output_expr(., 'tally(~ RaceEthnic, data = Fingers, margins = TRUE, format = "proportion")')
)

CK Code: ch3-28

require(coursekata)

# write code to create a frequency table of Thumb

tally(~Thumb, data = Fingers)

ex() %>% check_function("tally") %>% {
    check_arg(., "x") %>% check_equal()
    check_arg(., "data") %>% check_equal()
    check_result(.) %>% check_equal()
}

CK Code: ch3-29

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_01

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_02

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_03

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_04

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_05

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_06

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_07

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_08

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_09

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_10

require(coursekata)

# run your code here

CK Code: A3_Code_Review2_11

ckcode-chapter-a2-understanding-data ckcode-chapter-a4-explaining-variation

Course Outline

Items Test Book

ckcode ⌲ chapter-a3-examining-distributions

Responses

list Items Test Book

ckcode ⌲ chapter-a3-examining-distributions

Items Test Book