AE 07: Data wrangling with rowwise/column-wise operations
Suggested answers
Packages
We will use the following packages in this application exercise.
- tidyverse: For data import, wrangling, and visualization.
- janitor: For cleaning column names.
Powerball
Last class we studied Powerball jackpots over time. Today we will continue this journey and focus on Colorado winners in Match \(N\) Powerball play, prizes available to players who match the red Powerball number and anywhere between 0-4 white ball numbers.
Import and clean the data
The dataset is available for download as a CSV file.
Demo: Import the data file. Store it as powerball_raw
.
powerball_raw <- read_csv(file = "data/POWERBALL-from_0001-01-01_to_2024-09-24.csv")
Rows: 2577 Columns: 61
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (31): Draw date, Last Day To Claim, Winning Numbers, Jackpot, Jackpot Ca...
dbl (30): Powerball, Power Play, Jackpot Winners, Jackpot CO Winners, Match ...
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.
powerball_raw
# A tibble: 2,577 × 61
`Draw date` `Last Day To Claim` `Winning Numbers` Powerball `Power Play`
<chr> <chr> <chr> <dbl> <dbl>
1 Monday, 9/23/24 03/22/2025 15 - 21 - 25 - 3… 19 3
2 Saturday, 9/21/… 03/20/2025 17 - 19 - 21 - 3… 14 2
3 Wednesday, 9/18… 03/17/2025 1 - 11 - 22 - 47… 7 4
4 Monday, 9/16/24 03/15/2025 8 - 9 - 11 - 27 … 17 5
5 Saturday, 9/14/… 03/13/2025 29 - 34 - 38 - 4… 16 2
6 Wednesday, 9/11… 03/10/2025 10 - 12 - 55 - 6… 3 3
7 Monday, 9/9/24 03/08/2025 1 - 16 - 21 - 47… 5 3
8 Saturday, 9/7/24 03/06/2025 14 - 34 - 37 - 5… 20 2
9 Wednesday, 9/4/… 03/03/2025 7 - 10 - 21 - 33… 20 3
10 Monday, 9/2/24 03/01/2025 8 - 42 - 46 - 48… 22 3
# ℹ 2,567 more rows
# ℹ 56 more variables: Jackpot <chr>, `Jackpot Cash Value` <chr>,
# `Jackpot Winners` <dbl>, `Jackpot CO Winners` <dbl>, `Match 5 Prize` <chr>,
# `Match 5 CO Winners` <dbl>, `Match 5 Prize (with Power Play)` <chr>,
# `Match 5 CO Winners (with Power Play)` <dbl>,
# `Match 4 + Powerball Prize` <chr>, `Match 4 + Powerball CO Winners` <dbl>,
# `Match 4 + Powerball Prize (with Power Play)` <chr>, …
Your turn: Clean the raw data to fix the following issues:
- Standardize the column names using
snake_case
format. - Create columns with appropriate data types for any date variables date of the drawing as well as the weekday. Append these columns to the beginning of the data frame.
- Fix all of the currency columns to be formatted as numeric types.
Store the cleaned data frame as powerball
.
powerball <- powerball_raw |>
clean_names() |>
# separate draw_date into two variables, clean both
separate_wider_delim(
cols = draw_date,
delim = ",",
names = c(NA, "draw_date")
) |>
mutate(
draw_date = mdy(draw_date),
last_day_to_claim = mdy(last_day_to_claim),
draw_weekday = wday(x = draw_date, label = TRUE),
.before = last_day_to_claim
) |>
# convert all currency columns to numeric type
mutate(
across(
.cols = c(where(is.character), -contains("winning_numbers")),
.fn = parse_number
)
)
powerball
# A tibble: 2,577 × 62
draw_date draw_weekday last_day_to_claim winning_numbers powerball
<date> <ord> <date> <chr> <dbl>
1 2024-09-23 Mon 2025-03-22 15 - 21 - 25 - 37 - 45 19
2 2024-09-21 Sat 2025-03-20 17 - 19 - 21 - 37 - 45 14
3 2024-09-18 Wed 2025-03-17 1 - 11 - 22 - 47 - 68 7
4 2024-09-16 Mon 2025-03-15 8 - 9 - 11 - 27 - 31 17
5 2024-09-14 Sat 2025-03-13 29 - 34 - 38 - 48 - 56 16
6 2024-09-11 Wed 2025-03-10 10 - 12 - 55 - 65 - 67 3
7 2024-09-09 Mon 2025-03-08 1 - 16 - 21 - 47 - 60 5
8 2024-09-07 Sat 2025-03-06 14 - 34 - 37 - 55 - 63 20
9 2024-09-04 Wed 2025-03-03 7 - 10 - 21 - 33 - 59 20
10 2024-09-02 Mon 2025-03-01 8 - 42 - 46 - 48 - 53 22
# ℹ 2,567 more rows
# ℹ 57 more variables: power_play <dbl>, jackpot <dbl>,
# jackpot_cash_value <dbl>, jackpot_winners <dbl>, jackpot_co_winners <dbl>,
# match_5_prize <dbl>, match_5_co_winners <dbl>,
# match_5_prize_with_power_play <dbl>,
# match_5_co_winners_with_power_play <dbl>, match_4_powerball_prize <dbl>,
# match_4_powerball_co_winners <dbl>, …
Analyze the data
Our goal is to reproduce the following visualization:
In order to accomplish this, we have a few challenges ahead. We will need to:
- Determine the year for every drawing
- Calculate the mean and standard error of the number of winners for each Match \(N\) + Powerball prize for every year
- Structure the data frame so we have one row for each year and prize, and separate columns for the means and standard errors
Generate the year
variable
Your turn: Generate a year
variable from the draw_date
column.
powerball |>
# generate year variable
mutate(
year = year(x = draw_date),
.before = everything()
)
# A tibble: 2,577 × 63
year draw_date draw_weekday last_day_to_claim winning_numbers powerball
<dbl> <date> <ord> <date> <chr> <dbl>
1 2024 2024-09-23 Mon 2025-03-22 15 - 21 - 25 - 37 … 19
2 2024 2024-09-21 Sat 2025-03-20 17 - 19 - 21 - 37 … 14
3 2024 2024-09-18 Wed 2025-03-17 1 - 11 - 22 - 47 -… 7
4 2024 2024-09-16 Mon 2025-03-15 8 - 9 - 11 - 27 - … 17
5 2024 2024-09-14 Sat 2025-03-13 29 - 34 - 38 - 48 … 16
6 2024 2024-09-11 Wed 2025-03-10 10 - 12 - 55 - 65 … 3
7 2024 2024-09-09 Mon 2025-03-08 1 - 16 - 21 - 47 -… 5
8 2024 2024-09-07 Sat 2025-03-06 14 - 34 - 37 - 55 … 20
9 2024 2024-09-04 Wed 2025-03-03 7 - 10 - 21 - 33 -… 20
10 2024 2024-09-02 Mon 2025-03-01 8 - 42 - 46 - 48 -… 22
# ℹ 2,567 more rows
# ℹ 57 more variables: power_play <dbl>, jackpot <dbl>,
# jackpot_cash_value <dbl>, jackpot_winners <dbl>, jackpot_co_winners <dbl>,
# match_5_prize <dbl>, match_5_co_winners <dbl>,
# match_5_prize_with_power_play <dbl>,
# match_5_co_winners_with_power_play <dbl>, match_4_powerball_prize <dbl>,
# match_4_powerball_co_winners <dbl>, …
Calculate means and standard errors
Your turn: Calculate the mean and standard error for each of the number of winners of the Match \(N\) + Powerball prizes for each year.
Recall the formula for the standard error of a sample mean is:
\[ \begin{aligned} \text{s.e.} &= \sqrt{\frac{\text{Variance}(X)}{\text{Sample size}}} \\ &= \frac{\text{Standard deviation}(X)}{\sqrt{\text{Sample size}}} \end{aligned} \]
powerball |>
# generate year variable
mutate(
year = year(x = draw_date),
.before = everything()
) |>
# calculate mean and se for the match N powerball winner columns
summarize(
across(
.cols = starts_with("match") & contains("powerball") & ends_with("winners"),
.fns = list(mean = mean, se = \(x) sd(x) / sqrt(n()))
),
# do this for each year in the dataset
.by = year
)
# A tibble: 24 × 11
year match_4_powerball_co_wi…¹ match_4_powerball_co…² match_3_powerball_co…³
<dbl> <dbl> <dbl> <dbl>
1 2024 0.104 0.0286 7.39
2 2023 0.218 0.0475 11.6
3 2022 0.153 0.0363 10.1
4 2021 0.138 0.0312 9.88
5 2020 0.0762 0.0260 7.49
6 2019 0.125 0.0326 10.8
7 2018 0.221 0.0510 11.3
8 2017 0.192 0.0412 13.4
9 2016 0.295 0.103 19.2
10 2015 0.288 0.0839 12.9
# ℹ 14 more rows
# ℹ abbreviated names: ¹match_4_powerball_co_winners_mean,
# ²match_4_powerball_co_winners_se, ³match_3_powerball_co_winners_mean
# ℹ 7 more variables: match_3_powerball_co_winners_se <dbl>,
# match_2_powerball_co_winners_mean <dbl>,
# match_2_powerball_co_winners_se <dbl>,
# match_1_powerball_co_winners_mean <dbl>, …
Clean up column names
Your turn: Remove "powerball_co_winners_"
from each column name.
rename()
does not allow use of the across()
function. Instead, check out rename_with()
from the dplyr package.
stringr contains many functions for working with character strings. Check out the cheat sheet for examples!
powerball |>
# generate year variable
mutate(
year = year(x = draw_date),
.before = everything()
) |>
# calculate mean and se for the match N powerball winner columns
summarize(
across(
.cols = starts_with("match") & contains("powerball") & ends_with("winners"),
.fns = list(mean = mean, se = \(x) sd(x) / sqrt(n()))
),
# do this for each year in the dataset
.by = year
) |>
# remove extraneous string from columns
rename_with(
.cols = starts_with("match"),
.fn = \(x) str_remove(string = x, pattern = "powerball_co_winners_")
)
# A tibble: 24 × 11
year match_4_mean match_4_se match_3_mean match_3_se match_2_mean match_2_se
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2024 0.104 0.0286 7.39 0.834 153. 16.0
2 2023 0.218 0.0475 11.6 1.15 236. 21.4
3 2022 0.153 0.0363 10.1 1.66 205. 34.4
4 2021 0.138 0.0312 9.88 0.804 198. 15.5
5 2020 0.0762 0.0260 7.49 0.481 156. 7.62
6 2019 0.125 0.0326 10.8 1.18 212. 20.9
7 2018 0.221 0.0510 11.3 1.04 236. 21.4
8 2017 0.192 0.0412 13.4 1.89 270. 34.8
9 2016 0.295 0.103 19.2 5.47 386. 103.
10 2015 0.288 0.0839 12.9 1.55 233. 26.1
# ℹ 14 more rows
# ℹ 4 more variables: match_1_mean <dbl>, match_1_se <dbl>, match_0_mean <dbl>,
# match_0_se <dbl>
Restructure data frame to appropriate form for visualization
Demo: We need the structure to be one row for each year and prize (i.e. 0, 1, 2, 3, 4) and separate columns for the means and standard errors. We can use pivot_longer()
to accomplish this task, but it’s a bit more complicated than past pivoting operations since the column names contain both a variable (e.g. match_4
) and a variable name (i.e. mean
or se
). We can leverage the names_to
argument and its ability to pass in a character vector with multiple values to create multiple columns in the resulting data frame. According to the documentation,
If length > 1, multiple columns will be created. In this case, one of
names_sep
ornames_pattern
must be supplied to specify how the column names should be split. There are also two additional character values you can take advantage of:
NA will discard the corresponding component of the column name.
".value"
indicates that the corresponding component of the column name defines the name of the output column containing the cell values, overridingvalues_to
entirely.
powerball |>
# generate year variable
mutate(
year = year(x = draw_date),
.before = everything()
) |>
# calculate mean and se for the match N powerball winner columns
summarize(
across(
.cols = starts_with("match") & contains("powerball") & ends_with("winners"),
.fns = list(mean = mean, se = \(x) sd(x) / sqrt(n())),
.names = "{.col}_{.fn}"
),
# do this for each year in the dataset
.by = year
) |>
# remove extraneous string from columns
rename_with(
.cols = starts_with("match"),
.fn = \(x) str_remove(string = x, pattern = "powerball_co_winners_")
) |>
# restructure to one row per year per game
# separate columns for mean and se
pivot_longer(
cols = -year,
# columns contain a variable and a variable name
names_to = c("game", ".value"),
# ignore column prefix
names_prefix = "match_",
# separating character
names_sep = "_"
) |>
# reformat game column values for visualization
mutate(game = str_glue("Match {game}"))
# A tibble: 120 × 4
year game mean se
<dbl> <glue> <dbl> <dbl>
1 2024 Match 4 0.104 0.0286
2 2024 Match 3 7.39 0.834
3 2024 Match 2 153. 16.0
4 2024 Match 1 1179. 123.
5 2024 Match 0 2835. 299.
6 2023 Match 4 0.218 0.0475
7 2023 Match 3 11.6 1.15
8 2023 Match 2 236. 21.4
9 2023 Match 1 1797. 160.
10 2023 Match 0 4300. 384.
# ℹ 110 more rows
Plot the data
Demo: Now that we have the appropriate data structure, we can create the visualization.
powerball |>
# generate year variable
mutate(
year = year(x = draw_date),
.before = everything()
) |>
# calculate mean and se for the match N powerball winner columns
summarize(
across(
.cols = starts_with("match") & contains("powerball") & ends_with("winners"),
.fns = list(mean = mean, se = \(x) sd(x) / sqrt(n())),
.names = "{.col}_{.fn}"
),
# do this for each year in the dataset
.by = year
) |>
# remove extraneous string from columns
rename_with(
.cols = starts_with("match"),
.fn = \(x) str_remove(string = x, pattern = "powerball_co_winners_")
) |>
# restructure to one row per year per game
# separate columns for mean and se
pivot_longer(
cols = -year,
# columns contain a variable and a variable name
names_to = c("game", ".value"),
# ignore column prefix
names_prefix = "match_",
# separating character
names_sep = "_"
) |>
# reformat game column values for visualization
mutate(game = str_glue("Match {game}")) |>
ggplot(mapping = aes(x = year, y = mean)) +
geom_point() +
geom_linerange(mapping = aes(
ymin = mean - se,
ymax = mean + se
)) +
facet_wrap(facets = vars(game), scales = "free_y") +
labs(
title = "The number of Match N Powerball Prize winners trends downward",
subtitle = "Average number of prize winners (plus/minus 1 standard error)",
x = "Year",
y = "Number of Colorado winners",
caption = "Source: Colorado Lottery"
) +
theme_minimal()
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os macOS Sonoma 14.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-09-30
pandoc 3.4 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
P bit 4.0.5 2022-11-15 [?] CRAN (R 4.3.0)
P bit64 4.0.5 2020-08-30 [?] CRAN (R 4.3.0)
cli 3.6.3 2024-06-21 [1] RSPM (R 4.4.0)
P colorspace 2.1-0 2023-01-23 [?] CRAN (R 4.3.0)
P crayon 1.5.3 2024-06-20 [?] CRAN (R 4.4.0)
P digest 0.6.35 2024-03-11 [?] CRAN (R 4.3.1)
P dplyr * 1.1.4 2023-11-17 [?] CRAN (R 4.3.1)
P evaluate 0.24.0 2024-06-10 [?] CRAN (R 4.4.0)
P fansi 1.0.6 2023-12-08 [?] CRAN (R 4.3.1)
P farver 2.1.2 2024-05-13 [?] CRAN (R 4.3.3)
P fastmap 1.2.0 2024-05-15 [?] CRAN (R 4.4.0)
P forcats * 1.0.0 2023-01-29 [?] CRAN (R 4.3.0)
P generics 0.1.3 2022-07-05 [?] CRAN (R 4.3.0)
P ggplot2 * 3.5.1 2024-04-23 [?] CRAN (R 4.3.1)
P glue 1.7.0 2024-01-09 [?] CRAN (R 4.3.1)
P gtable 0.3.5 2024-04-22 [?] CRAN (R 4.3.1)
P here 1.0.1 2020-12-13 [?] CRAN (R 4.3.0)
P hms 1.1.3 2023-03-21 [?] CRAN (R 4.3.0)
P htmltools 0.5.8.1 2024-04-04 [?] CRAN (R 4.3.1)
P htmlwidgets 1.6.4 2023-12-06 [?] CRAN (R 4.3.1)
P janitor * 2.2.0 2023-02-02 [?] CRAN (R 4.3.0)
P jsonlite 1.8.8 2023-12-04 [?] CRAN (R 4.3.1)
P knitr 1.47 2024-05-29 [?] CRAN (R 4.4.0)
P labeling 0.4.3 2023-08-29 [?] CRAN (R 4.3.0)
P lifecycle 1.0.4 2023-11-07 [?] CRAN (R 4.3.1)
P lubridate * 1.9.3 2023-09-27 [?] CRAN (R 4.3.1)
P magrittr 2.0.3 2022-03-30 [?] CRAN (R 4.3.0)
P munsell 0.5.1 2024-04-01 [?] CRAN (R 4.3.1)
P pillar 1.9.0 2023-03-22 [?] CRAN (R 4.3.0)
P pkgconfig 2.0.3 2019-09-22 [?] CRAN (R 4.3.0)
P purrr * 1.0.2 2023-08-10 [?] CRAN (R 4.3.0)
P R6 2.5.1 2021-08-19 [?] CRAN (R 4.3.0)
P ragg 1.3.2 2024-05-15 [?] CRAN (R 4.4.0)
P readr * 2.1.5 2024-01-10 [?] CRAN (R 4.3.1)
renv 1.0.7 2024-04-11 [1] CRAN (R 4.4.0)
P rlang 1.1.4 2024-06-04 [?] CRAN (R 4.3.3)
P rmarkdown 2.27 2024-05-17 [?] CRAN (R 4.4.0)
P rprojroot 2.0.4 2023-11-05 [?] CRAN (R 4.3.1)
P rstudioapi 0.16.0 2024-03-24 [?] CRAN (R 4.3.1)
P scales * 1.3.0.9000 2024-05-07 [?] Github (r-lib/scales@c0f79d3)
P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.3.0)
P snakecase 0.11.1 2023-08-27 [?] CRAN (R 4.3.0)
P stringi 1.8.4 2024-05-06 [?] CRAN (R 4.3.1)
P stringr * 1.5.1 2023-11-14 [?] CRAN (R 4.3.1)
P systemfonts 1.1.0 2024-05-15 [?] CRAN (R 4.4.0)
P textshaping 0.4.0 2024-05-24 [?] CRAN (R 4.4.0)
P tibble * 3.2.1 2023-03-20 [?] CRAN (R 4.3.0)
P tidyr * 1.3.1 2024-01-24 [?] CRAN (R 4.3.1)
P tidyselect 1.2.1 2024-03-11 [?] CRAN (R 4.3.1)
P tidyverse * 2.0.0 2023-02-22 [?] CRAN (R 4.3.0)
P timechange 0.3.0 2024-01-18 [?] CRAN (R 4.3.1)
P tzdb 0.4.0 2023-05-12 [?] CRAN (R 4.3.0)
P utf8 1.2.4 2023-10-22 [?] CRAN (R 4.3.1)
P vctrs 0.6.5 2023-12-01 [?] CRAN (R 4.3.1)
P vroom 1.6.5 2023-12-05 [?] CRAN (R 4.3.1)
withr 3.0.1 2024-07-31 [1] RSPM (R 4.4.0)
P xfun 0.45 2024-06-16 [?] CRAN (R 4.4.0)
P yaml 2.3.8 2023-12-11 [?] CRAN (R 4.3.1)
[1] /Users/soltoffbc/Projects/info-5001/course-site/renv/library/macos/R-4.4/aarch64-apple-darwin20
[2] /Users/soltoffbc/Library/Caches/org.R-project.R/R/renv/sandbox/macos/R-4.4/aarch64-apple-darwin20/f7156815
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────