Coffee
Jim Gruman
July 7, 2020
Last updated: 2021-09-22
Checks: 7 0
Knit directory: myTidyTuesday/
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coffee <- tidytuesdayR::tt_load("2020-07-07")
Downloading file 1 of 1: `coffee_ratings.csv`coffee_ratings <- coffee$coffee_ratings %>%
mutate(
coffee_id = row_number(),
country_of_origin = case_when(
stringr::str_detect(country_of_origin, "Tanzania") ~ "Tanzania",
stringr::str_detect(country_of_origin, "Hawaii") ~ "US Hawaii", TRUE ~ country_of_origin
)
) %>%
filter(total_cup_points > 0)Let’s build a helper function to add sample counts to categorical charts.
withfreq <- function(x) {
tibble(x) %>%
add_count(x) %>%
mutate(combined = glue::glue("{ str_sub(x, end = 18) } ({ n })")) %>%
pull(combined)
}Cedric Scherer offers great advice on conveying distributions with boxplots and dots here.
coffee_ratings %>%
count(species, sort = TRUE) %>%
knitr::kable()| species | n |
|---|---|
| Arabica | 1310 |
| Robusta | 28 |
coffee_lumped <- coffee_ratings %>%
filter(!is.na(variety)) %>%
mutate(variety = fct_lump(variety, 8), sort = TRUE)
coffee_lumped %>%
mutate(variety = withfreq(fct_reorder(variety, total_cup_points))) %>%
ggplot(aes(total_cup_points, variety)) +
geom_boxplot(
color = "#d95f0e",
width = 0.1,
outlier.shape = NA
) +
ggdist::stat_dots(
## orientation to the left
side = "top",
## move geom up
justification = -0.1,
## adjust grouping (binning) of observations
binwidth = .125,
color = "#d95f0e"
) +
labs(
title = "Coffee Quality Ratings",
subtitle = "(Count of Samples) in study",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "Total Cup Points",
y = "Coffee Variety"
)
coffee_ratings %>%
filter(!is.na(color)) %>%
count(color, sort = TRUE) %>%
knitr::kable(caption = "Count of Coffee Samples by")| color | n |
|---|---|
| Green | 869 |
| Bluish-Green | 114 |
| Blue-Green | 85 |
| None | 52 |
coffee_ratings %>%
filter(!is.na(country_of_origin)) %>%
mutate(
country = withfreq(fct_lump(country_of_origin, 12)),
country = fct_reorder(country, total_cup_points)
) %>%
ggplot(aes(total_cup_points, country)) +
geom_boxplot(
color = "#d95f0e",
width = 0.1,
outlier.shape = NA
) +
ggdist::stat_dots(
## orientation to the left
side = "top",
## move geom up
justification = -0.1,
## adjust grouping (binning) of observations
binwidth = .125,
color = "#d95f0e"
) +
labs(
title = "Coffee Quality Ratings by Country of Origin",
subtitle = "(Count of Samples) in study",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "Total Cup Points",
y = NULL
)
coffee_metrics <- coffee_ratings %>%
dplyr::select(
coffee_id, total_cup_points, variety, company,
country_of_origin,
altitude_mean_meters,
aroma:moisture
) %>%
pivot_longer(aroma:cupper_points, names_to = "metric", values_to = "value")
coffee_metrics %>%
mutate(metric = fct_reorder(metric, value)) %>%
ggplot(aes(value, metric)) +
geom_boxplot(
width = .15,
outlier.shape = NA,
color = "#d95f0e"
) +
geom_point(
## draw horizontal lines instead of points
shape = 124,
size = 4,
alpha = .1,
color = "#d95f0e"
) +
scale_x_continuous(limits = c(5, 10.2)) +
labs(
title = "Coffee Quality Ratings Attribute Ranges",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "Score",
y = ""
)
correlations <- coffee_metrics %>%
pairwise_cor(metric, coffee_id, value, sort = TRUE)
correlations %>%
head(50) %>%
graph_from_data_frame() %>%
ggraph() +
geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE) +
geom_node_point() +
geom_node_text(aes(label = name), repel = TRUE) +
labs(
title = "Coffee Quality",
subtitle = "Network Diagram of Attribute Correlation",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "",
y = ""
)
coffee_metrics %>%
filter(!metric %in% c("sweetness", "clean_cup", "uniformity")) %>%
group_by(metric) %>%
mutate(centered = value - mean(value)) %>%
ungroup() %>%
widely_svd(metric, coffee_id, value) %>%
filter(between(dimension, 1, 6)) %>%
mutate(metric = reorder_within(metric, value, dimension)) %>%
ggplot(aes(value, metric)) +
geom_col(fill = "#d95f0e") +
scale_y_reordered() +
facet_wrap(~dimension, scales = "free_y") +
theme(plot.title.position = "plot") +
labs(
title = "Coffee Quality",
subtitle = "Principal Components of Attributes",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "",
y = ""
)
coffee_ratings %>%
filter(
altitude_mean_meters < 10000,
altitude != 1
) %>%
mutate(altitude_mean_meters = pmin(altitude_mean_meters, 3500)) %>%
ggplot(aes(altitude_mean_meters, total_cup_points)) +
geom_point(color = "#d95f0e", alpha = 0.3) +
geom_smooth(
method = "lm",
formula = "y ~ x"
) +
theme(plot.title.position = "plot") +
labs(
title = "Coffee Quality at Altitude",
caption = "https://github.com/jldbc/coffee-quality-database",
x = "Altitude (meters)",
y = "Total Cup Points"
)
coffee_metrics %>%
filter(altitude_mean_meters < 10000) %>%
mutate(altitude_mean_meters = pmin(altitude_mean_meters, 3000)) %>%
mutate(km = altitude_mean_meters / 1000) %>%
group_by(metric) %>%
summarize(
correlation = cor(altitude_mean_meters, value),
model = list(lm(value ~ km))
) %>%
mutate(tidied = map(model, broom::tidy, conf.int = TRUE)) %>%
unnest(tidied) %>%
filter(term == "km") %>%
ungroup() %>%
mutate(metric = fct_reorder(metric, estimate)) %>%
ggplot(aes(estimate, metric, color = p.value < .05)) +
geom_point() +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = .1) +
theme(
legend.position = "top",
legend.spacing.x = unit(6, "mm"),
panel.grid.major.y = element_blank()
) +
labs(
title = "Coffee Quality",
y = "Evaluation of coffee",
x = "Each kilometer of altitude contributes\n this much to attribute scores (95% confidence interval)",
caption = "https://github.com/jldbc/coffee-quality-database"
)
coffee_lumped %>%
mutate(country_of_origin = fct_lump_lowfreq(country_of_origin, 8)) %>%
group_by(country_of_origin, variety) %>%
summarise(
score = mean(total_cup_points),
.groups = "drop"
) %>%
ungroup() %>%
filter(!variety %in% c("Other", "Yellow Bourbon")) %>%
mutate(country_of_origin = reorder_within(country_of_origin, score, variety)) %>%
ggplot() +
geom_col(aes(
x = score,
y = country_of_origin,
fill = ggplot2::cut_width(score, 2)
), show.legend = FALSE) +
scale_fill_brewer(type = "seq", palette = "YlOrBr") +
scale_y_reordered() +
labs(
title = "Mean Coffee Quality Total Cup Points",
y = "", x = "Variety",
caption = "#TidyTuesday 2020-07-07 @Jim_Gruman\n https://github.com/jldbc/coffee-quality-database"
) +
facet_wrap(~variety, scales = "free_y")
My Tidytuesday tweet:
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1280690395752644608")#TidyTuesday 📊#dataviz on ☕️ Coffee bean ratings by variety and country. Quick take: Ethiopian varieties score very well. #Rstats Code here: https://t.co/R6c8XaMm0I pic.twitter.com/JmsL0FjV15
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) July 8, 2020
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] tidytext_0.3.1 igraph_1.2.6 ggraph_2.0.5 widyr_0.1.4
[5] ggridges_0.5.3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[9] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3 tibble_3.1.4
[13] 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] workflows_0.2.3 selectr_0.4-2 plyr_1.8.6
[7] tidytuesdayR_1.0.1 splines_4.1.1 listenv_0.8.0
[10] SnowballC_0.7.0 usethis_2.0.1 digest_0.6.27
[13] foreach_1.5.1 htmltools_0.5.2 yardstick_0.0.8
[16] viridis_0.6.1 parsnip_0.1.7.900 fansi_0.5.0
[19] magrittr_2.0.1 tune_0.1.6 tzdb_0.1.2
[22] recipes_0.1.16 globals_0.14.0 graphlayouts_0.7.1
[25] modelr_0.1.8 gower_0.2.2 extrafont_0.17
[28] vroom_1.5.5 R.utils_2.10.1 extrafontdb_1.0
[31] hardhat_0.1.6 rsample_0.1.0 dials_0.0.10
[34] colorspace_2.0-2 rvest_1.0.1 ggrepel_0.9.1
[37] ggdist_3.0.0 textshaping_0.3.5 haven_2.4.3
[40] xfun_0.26 crayon_1.4.1 jsonlite_1.7.2
[43] survival_3.2-11 iterators_1.0.13 glue_1.4.2
[46] polyclip_1.10-0 gtable_0.3.0 ipred_0.9-12
[49] distributional_0.2.2 R.cache_0.15.0 tweetrmd_0.0.9
[52] Rttf2pt1_1.3.9 future.apply_1.8.1 scales_1.1.1
[55] infer_1.0.0 DBI_1.1.1 Rcpp_1.0.7
[58] viridisLite_0.4.0 bit_4.0.4 GPfit_1.0-8
[61] lava_1.6.10 prodlim_2019.11.13 httr_1.4.2
[64] RColorBrewer_1.1-2 ellipsis_0.3.2 R.methodsS3_1.8.1
[67] pkgconfig_2.0.3 farver_2.1.0 nnet_7.3-16
[70] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[73] here_1.0.1 reshape2_1.4.4 labeling_0.4.2
[76] tidyselect_1.1.1 rlang_0.4.11 DiceDesign_1.9
[79] later_1.3.0 cachem_1.0.6 munsell_0.5.0
[82] cellranger_1.1.0 tools_4.1.1 cli_3.0.1
[85] generics_0.1.0 broom_0.7.9 evaluate_0.14
[88] fastmap_1.1.0 ragg_1.1.3 yaml_2.2.1
[91] rematch2_2.1.2 bit64_4.0.5 knitr_1.34
[94] fs_1.5.0 tidygraph_1.2.0 nlme_3.1-152
[97] future_1.22.1 whisker_0.4 R.oo_1.24.0
[100] xml2_1.3.2 tokenizers_0.2.1 compiler_4.1.1
[103] rstudioapi_0.13 curl_4.3.2 reprex_2.0.1
[106] lhs_1.1.3 tweenr_1.0.2 bslib_0.3.0
[109] stringi_1.7.4 highr_0.9 gdtools_0.2.3
[112] hrbrthemes_0.8.0 lattice_0.20-44 Matrix_1.3-4
[115] styler_1.6.1 conflicted_1.0.4 vctrs_0.3.8
[118] tidymodels_0.1.3 pillar_1.6.2 lifecycle_1.0.0
[121] furrr_0.2.3 jquerylib_0.1.4 httpuv_1.6.3
[124] R6_2.5.1 promises_1.2.0.1 gridExtra_2.3
[127] janeaustenr_0.1.5 parallelly_1.28.1 codetools_0.2-18
[130] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[133] withr_2.4.2 mgcv_1.8-36 parallel_4.1.1
[136] hms_1.1.0 grid_4.1.1 rpart_4.1-15
[139] timeDate_3043.102 class_7.3-19 rmarkdown_2.11
[142] git2r_0.28.0 ggforce_0.3.3 pROC_1.18.0
[145] lubridate_1.7.10