These activities are for practice only — there is nothing to turn in. Answer keys are included, so try each question yourself before opening them.
Question 1
The following table summarizes the season information for the eight seasons of AMC’s Breaking Bad show.
Tidy up this data and save it to a tibble. Decide for yourself how to handle season 5 (should it be a single observation or two?). For the first and last aired dates, just store the year as a number.
Save the tibble you created to a CSV file named “breaking_bad.csv”.
Answer key
Part (a)
Version with one observation for the season five parts
library (tidyverse)<- c (1 , 2 , 3 , 4 , 5 )<- c (7 , 13 , 13 , 13 , 16 )<- c (2008 , 2009 , 2010 , 2011 , 2012 )<- c (2008 , 2009 , 2010 , 2011 , 2013 )<- "AMC" <- tibble (season, episodes, first_air, last_air, network)
# A tibble: 5 × 5
season episodes first_air last_air network
<dbl> <dbl> <dbl> <dbl> <chr>
1 1 7 2008 2008 AMC
2 2 13 2009 2009 AMC
3 3 13 2010 2010 AMC
4 4 13 2011 2011 AMC
5 5 16 2012 2013 AMC
Version with two separate observations for the season five parts
library (tidyverse)<- c (1 , 2 , 3 , 4 , 5.1 , 5.2 )<- c (7 , 13 , 13 , 13 , 8 , 8 )<- c (2008 , 2009 , 2010 , 2011 , 2012 , 2013 )<- c (2008 , 2009 , 2010 , 2011 , 2012 , 2013 )<- "AMC" <- tibble (season, episodes, first_air, last_air, network)
# A tibble: 6 × 5
season episodes first_air last_air network
<dbl> <dbl> <dbl> <dbl> <chr>
1 1 7 2008 2008 AMC
2 2 13 2009 2009 AMC
3 3 13 2010 2010 AMC
4 4 13 2011 2011 AMC
5 5.1 8 2012 2012 AMC
6 5.2 8 2013 2013 AMC
Part (b)
write_csv (breaking_bad, "breaking_bad.csv" )
Question 2
Download the cereal.csv
Use {tidyverse} to import this file into R as a tibble named cereal, using an argument to make the missing values (coded as "missing") into NAs.
Run your command again but with the show_col_types = FALSE argument to suppress the column specification message.
Answer key
Part (b)
<- read_csv ("../../data/cereal.csv" , na = "missing" )
Rows: 77 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): name, mfr, type
dbl (5): calories, sodium, carbo, sugars, rating
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 77 × 8
name mfr type calories sodium carbo sugars rating
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 100% Bran Nabisco cold 70 130 5 6 68.4
2 100% Natural Bran Quaker O… cold 120 15 8 8 34.0
3 All-Bran Kelloggs cold 70 260 7 5 59.4
4 All-Bran with Extra Fiber Kelloggs cold 50 140 8 0 93.7
5 Almond Delight Ralston … cold 110 200 14 8 34.4
6 Apple Cinnamon Cheerios General … cold 110 180 10.5 10 29.5
7 Apple Jacks Kelloggs cold 110 125 11 14 33.2
8 Basic 4 General … cold 130 210 18 8 37.0
9 Bran Chex Ralston … cold 90 200 15 6 49.1
10 Bran Flakes Post cold 90 210 13 5 53.3
# ℹ 67 more rows
<- read_csv ("../../data/cereal.csv" , na = "missing" , show_col_types = FALSE )
Question 3
Update the cereal tibble from Question 2 to make the type variable a factor.
Tabulate the type variable using data_tabulate() and plot its discrete distribution using qplot().
Describe the calories variable using describe_distribution() and plot its continuous distribution using qplot() and a “histogram” or “boxplot” geom.
Plot the covariation of the calories and rating variables using qplot() and a “point” or “jitter” geom.
Answer key
Part (a)
$ type <- factor (cereal$ type)
# A tibble: 77 × 8
name mfr type calories sodium carbo sugars rating
<chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 100% Bran Nabisco cold 70 130 5 6 68.4
2 100% Natural Bran Quaker O… cold 120 15 8 8 34.0
3 All-Bran Kelloggs cold 70 260 7 5 59.4
4 All-Bran with Extra Fiber Kelloggs cold 50 140 8 0 93.7
5 Almond Delight Ralston … cold 110 200 14 8 34.4
6 Apple Cinnamon Cheerios General … cold 110 180 10.5 10 29.5
7 Apple Jacks Kelloggs cold 110 125 11 14 33.2
8 Basic 4 General … cold 130 210 18 8 37.0
9 Bran Chex Ralston … cold 90 200 15 6 49.1
10 Bran Flakes Post cold 90 210 13 5 53.3
# ℹ 67 more rows
Part (b)
# Attaching packages: easystats 0.7.6
✔ bayestestR 0.18.1 ✔ correlation 0.8.8
✔ datawizard 1.3.1 ✔ effectsize 1.0.3
✔ insight 1.5.2 ✔ modelbased 0.16.0
✔ performance 0.17.1 ✔ parameters 0.29.2
✔ report 0.6.4 ✔ see 0.14.1
data_tabulate (cereal, type)
type (type) <categorical>
# total N=77 valid N=77
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
cold | 74 | 96.10 | 96.10 | 96.10
hot | 3 | 3.90 | 3.90 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
qplot (x = type, data = cereal, geom = "bar" )
Warning: `qplot()` was deprecated in ggplot2 3.4.0.
Part (c)
describe_distribution (cereal, calories)
Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
----------------------------------------------------------------------------------------
calories | 106.88 | 19.48 | 10 | [50.00, 160.00] | -0.45 | 2.37 | 77 | 0
qplot (x = calories, data = cereal, geom = "histogram" )
`stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Part (d)
qplot (x = calories, y = rating, data = cereal, geom = "point" )
Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
qplot (x = calories, y = rating, data = cereal, geom = "jitter" )
Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Note that there is no need to turn in Activities. These are just for practice!
