Beer Production
Jim Gruman
March 31, 2020
Last updated: 2021-09-24
Checks: 7 0
Knit directory: myTidyTuesday/
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Import this week’s data
brewing_materials <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewing_materials.csv") %>%
mutate(date = ymd(paste0(year, "/", month, "/1")))
beer_taxed <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_taxed.csv")
brewer_size <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewer_size.csv")
beer_states <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_states.csv")Exploration
skimr::skim(beer_states)
# 19 year/states with missing barrels
skimr::skim(brewing_materials)
table(brewing_materials$type)
# opportunity to examine the proportional trends of Corn vresus Sugar/syrups use
skimr::skim(beer_taxed)
skimr::skim(brewer_size)
table(brewer_size$brewer_size)Total all barrels produced by state per year (regardless of type)
all_barrels <-
beer_states %>%
na.omit() %>%
filter(state != "total") %>%
group_by(state, year) %>%
summarise(
barrels = sum(barrels),
.groups = "drop"
)The top 5 states with largest increase in barrels from 2008 to 2019
increase_states <-
all_barrels %>%
group_by(state) %>%
mutate(total_barrels = cumsum(barrels)) %>%
filter(year %in% c(2008, 2019)) %>%
mutate(pct_chg = (total_barrels - lag(total_barrels)) / lag(total_barrels)) %>%
ungroup() %>%
top_n(5, pct_chg) %>%
pull(state)
increase_states %>%
knitr::kable()| x |
|---|
| AL |
| CT |
| ND |
| RI |
| TN |
# Lets get the state names
states <- tibble(state = state.abb, state_name = state.name)
beer_df <-
beer_states %>%
na.omit() %>%
filter(state != "total") %>%
group_by(state, year) %>%
summarise(
barrels = sum(barrels),
.groups = "drop"
) %>%
mutate(total_barrels = cumsum(barrels)) %>%
left_join(states, by = "state")Lets have a look at the materials trends. What ingredients are used in beer production?
brewing_materials %>%
filter(
!str_detect(material_type, "Total"),
!(month == 12 & year %in% 2014:2015)
) %>% # remove the subtotals) %>%
filter(date == max(date)) %>%
mutate(type = fct_reorder(type, month_current)) %>%
ggplot(aes(type, month_current / 1000000, fill = type)) +
geom_col() +
scale_y_continuous(breaks = scales::pretty_breaks()) +
coord_flip() +
theme(
legend.position = "",
panel.grid.major.y = element_blank()
) +
labs(
x = "",
y = "Million Pounds",
title = "Ingredients in US Beer Production, in the month of December 2019",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
)
brewing_materials %>%
filter(
!str_detect(type, "Total"), year < 2016,
!(month == 12 & year %in% 2014:2015)
) %>% # remove the subtotals
mutate(type = fct_reorder2(type,
date,
month_current,
.fun = last2
)) %>%
ggplot(aes(date,
month_current / 1000000,
color = type
)) +
geom_line() +
labs(
x = "",
y = "Million Pounds",
color = "Material",
title = "Ingredients in US Beer Production, 2008-2016",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
) +
scale_radius(range = c(1, 6)) +
scale_x_date(
date_breaks = "2 years",
date_labels = "%Y"
)
Tidymetrics
cross_by_dimensions <- function(tbl, ..., add = TRUE, max_dimensions = NULL, collect_fun = NULL) {
g_vars <- dplyr::group_vars(tbl)
columns <- ensyms(...)
tbl <- tbl %>%
ungroup() %>%
mutate_at(
vars(!!!columns),
as.character
)
if (!is.null(max_dimensions)) {
tbl <- tbl %>% cross_by_dimensions_limited(
columns,
max_dimensions = max_dimensions,
collect_fun = collect_fun
)
} else {
for (column in columns) {
tbl <- tbl %>%
mutate(`:=`(!!column, "All")) %>%
union_all(tbl)
if (!is.null(collect_fun)) {
tbl <- collect_fun(tbl)
}
}
}
tbl %>%
group_by_at(vars(all_of(g_vars))) %>%
group_by(!!!columns,
.add = add
)
}
brewing_summarized <- brewing_materials %>%
rename(material = type) %>%
filter(
!str_detect(material_type, "Total"),
year < 2016,
!(month == 12 & year %in% 2014:2015)
) %>%
cross_by_dimensions(material_type, material) %>% # duplicates the data to summarize it
cross_by_periods(c("month", "quarter", "year")) %>%
summarize(
total_pounds = sum(month_current),
.groups = "drop"
)
brewing_summarized %>%
filter(
material_type != "All",
material != "All",
period == "month"
) %>%
mutate(material = fct_reorder2(material,
date,
total_pounds,
.fun = last2
)) %>%
ggplot(aes(date,
total_pounds / 1000000,
color = material
)) +
geom_line() +
scale_x_date(
date_breaks = "2 years",
date_labels = "%Y"
) +
labs(
x = "Date",
y = "Million Pounds",
color = "Material",
title = "Ingredients in US Beer Production, 2008-2016: Monthly",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
)
brewing_summarized %>%
filter(
material_type != "All",
material != "All",
period == "quarter"
) %>%
mutate(material = fct_reorder2(material,
date,
total_pounds,
.fun = last2
)) %>%
ggplot(aes(date,
total_pounds / 1000000,
color = material
)) +
geom_line() +
labs(
x = "Date",
y = "Million Pounds",
color = "Material",
title = "Ingredients in US Beer Production, 2008-2016: Quarterly",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
)
brewing_summarized %>%
filter(
material_type != "All",
material != "All",
period == "year"
) %>%
mutate(material = fct_reorder2(material,
date,
total_pounds,
.fun = last2
)) %>%
ggplot(aes(date,
total_pounds / 1000000,
color = material
)) +
geom_line() +
labs(
x = "Date",
y = "Million Pounds",
color = "Material",
title = "Ingredients in US Beer Production, 2008-2016: Annually",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
)
brewing_materials %>%
filter(material_type == "Total Used", year < 2016) %>%
mutate(month = factor(month, labels = month.abb)) %>%
ggplot(aes(
x = month,
y = month_current / 1000000,
group = factor(year),
color = factor(year)
)) +
geom_line() +
expand_limits(y = 0) +
labs(
x = "",
y = "Million Pounds",
color = "Year",
title = "Ingredients in US Beer Production, 2008-2016: Each Month",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
) +
scale_y_continuous(labels = scales::comma_format())
remotes::install_github("ramnathv/shinymetrics")
remotes::install_github("ramnathv/shinybones")
brewing_metrics <- create_metrics(brewing_summarized)
library(shinymetrics)
# interactive plotly visualization of grouped time multi-dimensional BI
preview_metric(brewing_metrics$production_NA_total_pounds)
# change the dropdown from "last year" to "all time" because 2019 is not in this datasetbrewer_size %>%
filter(
brewer_size != "Total",
!is.na(total_barrels)
) %>%
mutate(
brewer_size = fct_lump_n(brewer_size,
n = 5,
w = total_barrels,
other_level = "1 to 100,000 Barrels"
),
barrel_number = coalesce(parse_number(as.character(brewer_size)), 1),
brewer_size = fct_reorder(brewer_size, barrel_number)
) %>%
ggplot(aes(as_factor(year),
total_barrels / 1000000,
fill = brewer_size
)) +
geom_col() +
scale_y_continuous(labels = scales::comma_format()) +
labs(
x = "Year",
y = "Million Barrels",
fill = "Brewer Size",
title = "Total Production, by US Brewer Size in 2019",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
)
Where is Beer produced?
beer_df %>%
ggplot(aes(year, barrels,
group = state
)) +
geom_line(aes(color = state), show.legend = FALSE) +
gghighlight(
state %in% increase_states,
label_key = state_name,
label_params = list(
size = 3,
color = "black",
family = "Arial Narrow",
fill = "grey95",
width = 2,
alpha = 1,
label.r = 0.1,
segment.alpha = 0
),
unhighlighted_params = list(color = "grey88")
) +
labs(
x = "Year",
y = "Total number of barrels produced",
title = "States with the Largest Increase in Beer Production, 2008-2019",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday"
) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_y_log10(labels = scales::label_number()) +
theme(
panel.grid = element_blank(),
panel.grid.major = element_blank()
)
states_percents_2019 <- beer_states %>%
filter(year == max(year), state != "total") %>%
group_by(state) %>%
mutate(percent = barrels / sum(barrels)) %>%
ungroup()
states <- st_as_sf(maps::map("state", plot = FALSE, fill = TRUE))
states_percents_2019 %>%
mutate(ID = if_else(state != "DC",
str_to_lower(state.name[match(state, state.abb)]),
"district of columbia"
)) %>%
inner_join(states, by = "ID") %>%
ggplot() +
geom_sf(aes(geometry = geom, fill = percent)) +
facet_wrap(~type, nrow = 2) +
scale_fill_continuous(labels = scales::percent) +
labs(
title = "In Each State, What is the Highest Proportion of Beer Consumed?",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau",
caption = "@Jim_Gruman | #TidyTuesday",
fill = "% Percent"
) +
theme_jim() +
theme(
legend.position = c(0.7, 0.2),
panel.grid.major = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank()
)
An animated map. Because it’s TidyTuesday
p <- beer_states %>%
filter(state != "total") %>%
group_by(state, year) %>%
mutate(percent = barrels / sum(barrels)) %>%
ungroup() %>%
mutate(ID = if_else(state != "DC",
str_to_lower(state.name[match(state, state.abb)]),
"district of columbia"
)) %>%
inner_join(states, by = "ID") %>%
ggplot(aes(
geometry = geom,
fill = percent
)) +
geom_sf() +
transition_time(year) +
facet_wrap(~type, nrow = 2) +
labs(
title = "In Each State, What is the Highest Proportion of Beer Consumed? {as.integer(frame_time)}",
subtitle = "US Alcohol and Tobacco Tax and Trade Bureau, 2008-2019",
caption = "@Jim_Gruman | #TidyTuesday",
fill = "% Percent"
) +
theme_jim() +
theme(
legend.position = c(0.7, 0.2),
panel.grid.major = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank()
)
animate(p, width = 900, height = 750, end_pause = 50, renderer = gifski_renderer())
What are the relationships between the materials used? (using resampling methods)
For example, how much sugar do beer producers need per pound of malt?
brewing_materials %>%
count(type, wt = month_current, sort = TRUE) %>%
knitr::kable()| type | n |
|---|---|
| Total Used | 53559516695 |
| Total Grain products | 44734903124 |
| Malt and malt products | 32697313882 |
| Total Non-Grain products | 8824613571 |
| Sugar and syrups | 6653104081 |
| Rice and rice products | 5685742541 |
| Corn and corn products | 5207759409 |
| Hops (dry) | 1138840132 |
| Other | 998968470 |
| Barley and barley products | 941444745 |
| Wheat and wheat products | 202642547 |
| Hops (used as extracts) | 33700888 |
brewing_filtered <- brewing_materials %>%
filter(
!str_detect(type, "Total"), year < 2016,
!(month == 12 & year %in% 2014:2015)
)Explore the relationship between ingredients
brewing_df <- brewing_filtered %>%
select(date, type, month_current) %>%
pivot_wider(
names_from = type,
values_from = month_current
) %>%
janitor::clean_names()
# is there a remote correlation between
brewing_df %>%
ggplot(aes(malt_and_malt_products, sugar_and_syrups)) +
geom_smooth(method = "lm") +
geom_point() +
labs(title = "What is the relationship between malt and sugar ingredients?")
A simple linear model
# force the y-intercept to be zero (no beer, no malt, no sugar)
beer_fit <- lm(sugar_and_syrups ~ 0 + malt_and_malt_products, data = brewing_df)
summary(beer_fit)
Call:
lm(formula = sugar_and_syrups ~ 0 + malt_and_malt_products, data = brewing_df)
Residuals:
Min 1Q Median 3Q Max
-29985291 -6468052 174001 7364462 23462837
Coefficients:
Estimate Std. Error t value Pr(>|t|)
malt_and_malt_products 0.205804 0.003446 59.72 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11480000 on 93 degrees of freedom
Multiple R-squared: 0.9746, Adjusted R-squared: 0.9743
F-statistic: 3567 on 1 and 93 DF, p-value: < 2.2e-16broom::tidy(beer_fit) %>%
knitr::kable()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| malt_and_malt_products | 0.205804 | 0.0034461 | 59.72037 | 0 |
Bootstrap Resampling
to arrive at a confidence interval for a linear model
beer_boot <- bootstraps(data = brewing_df, times = 1e3, apparent = FALSE)beer_models <- beer_boot %>%
dplyr::mutate(
model = purrr::map(splits, ~ lm(sugar_and_syrups ~ 0 + malt_and_malt_products,
data = .
)),
coef_info = purrr::map(model, tidy)
)
beer_coefs <- beer_models %>%
unnest(coef_info)Evaluate results
What is the distribution of the estimates for the model?
beer_coefs %>%
ggplot(aes(estimate)) +
geom_histogram(alpha = 0.7) +
labs(title = "Malt Coefficient Estimate for predicting Sugar and Syrups")
Our bootstrap confidence intervals:
int_pctl(beer_models, coef_info)# A tibble: 1 x 6
term .lower .estimate .upper .alpha .method
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 malt_and_malt_products 0.199 0.206 0.213 0.05 percentilebeer_aug <- beer_models %>%
dplyr::mutate(augmented = purrr::map(model, augment)) %>%
unnest(augmented)
beer_aug %>%
ggplot(aes(malt_and_malt_products, sugar_and_syrups)) +
geom_line(aes(y = .fitted, group = id), alpha = 0.1, color = "cyan3") +
geom_point() +
labs(title = "Bootstrap Resampling Model Estimates Confidence Interval")
And finally, my tidytuesday tweet:
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1246645041663221765")h/t to @BrewersStats for the week 14 #TidyTuesday US Beer Production dataset
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) April 5, 2020
code: https://t.co/tAHYewQFrW#r4ds #tidyverse #rstats #dataviz pic.twitter.com/cR4XpzcB5a
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] transformr_0.1.3 gganimate_1.0.7 sf_1.0-2 maps_3.3.0
[5] yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.3 tune_0.1.6
[9] rsample_0.1.0 recipes_0.1.16 parsnip_0.1.7.900 modeldata_0.1.1
[13] infer_1.0.0 dials_0.0.10 scales_1.1.1 broom_0.7.9
[17] tidymodels_0.1.3 gghighlight_0.3.2 GGally_2.1.2 tidymetrics_0.0.1
[21] lubridate_1.7.10 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[25] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3 tibble_3.1.4
[29] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 systemfonts_1.0.2
[4] plyr_1.8.6 splines_4.1.1 listenv_0.8.0
[7] digest_0.6.27 foreach_1.5.1 htmltools_0.5.2
[10] viridis_0.6.1 fansi_0.5.0 magrittr_2.0.1
[13] tzdb_0.1.2 globals_0.14.0 extrafont_0.17
[16] modelr_0.1.8 gower_0.2.2 vroom_1.5.5
[19] R.utils_2.10.1 extrafontdb_1.0 hardhat_0.1.6
[22] lpSolve_5.6.15 prettyunits_1.1.1 colorspace_2.0-2
[25] ggrepel_0.9.1 rvest_1.0.1 textshaping_0.3.5
[28] haven_2.4.3 xfun_0.26 crayon_1.4.1
[31] jsonlite_1.7.2 survival_3.2-11 iterators_1.0.13
[34] glue_1.4.2 gtable_0.3.0 ipred_0.9-12
[37] R.cache_0.15.0 tweetrmd_0.0.9 Rttf2pt1_1.3.9
[40] future.apply_1.8.1 DBI_1.1.1 Rcpp_1.0.7
[43] viridisLite_0.4.0 progress_1.2.2 units_0.7-2
[46] bit_4.0.4 GPfit_1.0-8 proxy_0.4-26
[49] lava_1.6.10 prodlim_2019.11.13 httr_1.4.2
[52] RColorBrewer_1.1-2 ellipsis_0.3.2 R.methodsS3_1.8.1
[55] pkgconfig_2.0.3 reshape_0.8.8 farver_2.1.0
[58] nnet_7.3-16 sass_0.4.0 dbplyr_2.1.1
[61] janitor_2.1.0 utf8_1.2.2 here_1.0.1
[64] labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11
[67] DiceDesign_1.9 later_1.3.0 cachem_1.0.6
[70] munsell_0.5.0 cellranger_1.1.0 tools_4.1.1
[73] cli_3.0.1 generics_0.1.0 gifski_1.4.3-1
[76] evaluate_0.14 fastmap_1.1.0 ragg_1.1.3
[79] yaml_2.2.1 rematch2_2.1.2 bit64_4.0.5
[82] knitr_1.34 fs_1.5.0 nlme_3.1-152
[85] future_1.22.1 whisker_0.4 R.oo_1.24.0
[88] xml2_1.3.2 compiler_4.1.1 rstudioapi_0.13
[91] curl_4.3.2 e1071_1.7-8 reprex_2.0.1
[94] lhs_1.1.3 tweenr_1.0.2 bslib_0.3.0
[97] stringi_1.7.4 highr_0.9 gdtools_0.2.3
[100] hrbrthemes_0.8.0 lattice_0.20-44 Matrix_1.3-4
[103] styler_1.6.1 classInt_0.4-3 conflicted_1.0.4
[106] vctrs_0.3.8 pillar_1.6.2 lifecycle_1.0.1
[109] furrr_0.2.3 jquerylib_0.1.4 httpuv_1.6.3
[112] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[115] gridExtra_2.3 parallelly_1.28.1 codetools_0.2-18
[118] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[121] withr_2.4.2 mgcv_1.8-36 parallel_4.1.1
[124] hms_1.1.0 grid_4.1.1 rpart_4.1-15
[127] timeDate_3043.102 class_7.3-19 snakecase_0.11.0
[130] rmarkdown_2.11 git2r_0.28.0 pROC_1.18.0