Apply it to your data 5
Liam Smith
2026-03-06
Import data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorssuppressWarnings(library(correlationfunnel))## ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
## Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
## Try subsetting your data column-wise or row-wise to avoid creating too many columns.
## You can always make a big problem smaller by sampling. :)data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-11-03/ikea.csv')## New names:
## Rows: 3694 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (7): name, category, old_price, link, other_colors, short_description, d... dbl
## (6): ...1, item_id, price, depth, height, width lgl (1): sellable_online
## 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.
## • `` -> `...1`skimr::skim(data)| Name | data |
| Number of rows | 3694 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| logical | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| name | 0 | 1 | 3 | 27 | 0 | 607 | 0 |
| category | 0 | 1 | 4 | 36 | 0 | 17 | 0 |
| old_price | 0 | 1 | 4 | 13 | 0 | 365 | 0 |
| link | 0 | 1 | 52 | 163 | 0 | 2962 | 0 |
| other_colors | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| short_description | 0 | 1 | 3 | 63 | 0 | 1706 | 0 |
| designer | 0 | 1 | 3 | 1261 | 0 | 381 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| sellable_online | 0 | 1 | 0.99 | TRU: 3666, FAL: 28 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1.00 | 1846.50 | 1066.51 | 0 | 923.25 | 1846.5 | 2769.75 | 3693 | ▇▇▇▇▇ |
| item_id | 0 | 1.00 | 48632396.79 | 28887094.10 | 58487 | 20390574.00 | 49288078.0 | 70403572.75 | 99932615 | ▇▇▇▇▇ |
| price | 0 | 1.00 | 1078.21 | 1374.65 | 3 | 180.90 | 544.7 | 1429.50 | 9585 | ▇▁▁▁▁ |
| depth | 1463 | 0.60 | 54.38 | 29.96 | 1 | 38.00 | 47.0 | 60.00 | 257 | ▇▃▁▁▁ |
| height | 988 | 0.73 | 101.68 | 61.10 | 1 | 67.00 | 83.0 | 124.00 | 700 | ▇▂▁▁▁ |
| width | 589 | 0.84 | 104.47 | 71.13 | 1 | 60.00 | 80.0 | 140.00 | 420 | ▇▅▂▁▁ |
data_clean <- data %>%
mutate(across(is.logical, as.factor)) %>%
select(-old_price, -link, -...1) %>%
na.omit() %>%
mutate(price = log(price))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(is.logical, as.factor)`.
## Caused by warning:
## ! Use of bare predicate functions was deprecated in tidyselect 1.1.0.
## ℹ Please use wrap predicates in `where()` instead.
## # Was:
## data %>% select(is.logical)
##
## # Now:
## data %>% select(where(is.logical))Explore data
data_clean %>% count(sellable_online)## # A tibble: 2 × 2
## sellable_online n
## <fct> <int>
## 1 FALSE 13
## 2 TRUE 1886data_clean %>%
ggplot(aes(sellable_online)) +
geom_bar()
sellable_online vs price
data_clean %>%
ggplot(aes(sellable_online, price)) +
geom_boxplot()
Correlation plot
# Step 1: Binarize
data_binarized <- data_clean %>%
select(-item_id) %>%
binarize()
data_binarized %>% glimpse()## Rows: 1,899
## Columns: 88
## $ name__ALGOT <dbl> 0, 0, 0, 0, 0, 0…
## $ name__BEKANT <dbl> 0, 0, 0, 0, 0, 0…
## $ name__BESTÅ <dbl> 0, 0, 0, 0, 0, 0…
## $ `name__BILLY_/_OXBERG` <dbl> 0, 0, 0, 0, 0, 0…
## $ name__BRIMNES <dbl> 0, 0, 0, 0, 0, 0…
## $ name__BROR <dbl> 0, 0, 0, 0, 0, 0…
## $ name__EKET <dbl> 0, 0, 0, 0, 0, 0…
## $ name__GRÖNLID <dbl> 0, 0, 0, 0, 0, 0…
## $ name__HAVSTA <dbl> 0, 0, 0, 0, 0, 0…
## $ name__HAVSTEN <dbl> 0, 0, 0, 0, 0, 0…
## $ name__HEMNES <dbl> 0, 0, 0, 0, 0, 0…
## $ name__IVAR <dbl> 0, 0, 0, 0, 0, 0…
## $ name__JONAXEL <dbl> 0, 0, 0, 0, 0, 0…
## $ name__KALLAX <dbl> 0, 0, 0, 0, 0, 0…
## $ name__LIDHULT <dbl> 0, 0, 0, 0, 0, 0…
## $ name__LIXHULT <dbl> 0, 0, 0, 0, 0, 0…
## $ name__NORDLI <dbl> 0, 0, 0, 0, 0, 0…
## $ name__PAX <dbl> 0, 0, 0, 0, 0, 0…
## $ name__PLATSA <dbl> 0, 0, 0, 0, 0, 0…
## $ `name__STUVA_/_FRITIDS` <dbl> 0, 0, 0, 0, 0, 0…
## $ name__TROFAST <dbl> 0, 0, 0, 0, 0, 0…
## $ name__VALLENTUNA <dbl> 0, 0, 0, 0, 0, 0…
## $ name__VIMLE <dbl> 0, 0, 0, 0, 0, 0…
## $ `name__-OTHER` <dbl> 1, 1, 1, 1, 1, 1…
## $ category__Bar_furniture <dbl> 1, 1, 1, 1, 1, 1…
## $ category__Beds <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Bookcases_&_shelving_units` <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Cabinets_&_cupboards` <dbl> 0, 0, 0, 0, 0, 0…
## $ category__Chairs <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Chests_of_drawers_&_drawer_units` <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Children's_furniture` <dbl> 0, 0, 0, 0, 0, 0…
## $ category__Nursery_furniture <dbl> 0, 0, 0, 0, 0, 0…
## $ category__Outdoor_furniture <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Sideboards,_buffets_&_console_tables` <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Sofas_&_armchairs` <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__Tables_&_desks` <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__TV_&_media_furniture` <dbl> 0, 0, 0, 0, 0, 0…
## $ category__Wardrobes <dbl> 0, 0, 0, 0, 0, 0…
## $ `category__-OTHER` <dbl> 0, 0, 0, 0, 0, 0…
## $ `price__-Inf_5.68697535633982` <dbl> 1, 1, 0, 1, 1, 1…
## $ price__5.68697535633982_6.52209279817015 <dbl> 0, 0, 1, 0, 0, 0…
## $ price__6.52209279817015_7.37085996851068 <dbl> 0, 0, 0, 0, 0, 0…
## $ price__7.37085996851068_Inf <dbl> 0, 0, 0, 0, 0, 0…
## $ sellable_online__TRUE <dbl> 1, 1, 1, 1, 1, 1…
## $ `sellable_online__-OTHER` <dbl> 0, 0, 0, 0, 0, 0…
## $ other_colors__No <dbl> 0, 1, 1, 1, 1, 1…
## $ other_colors__Yes <dbl> 1, 0, 0, 0, 0, 0…
## $ `short_description__3-seat_sofa` <dbl> 0, 0, 0, 0, 0, 0…
## $ `short_description__3-seat_sofa-bed` <dbl> 0, 0, 0, 0, 0, 0…
## $ short_description__Armchair <dbl> 0, 0, 0, 0, 0, 0…
## $ short_description__Chair <dbl> 0, 0, 0, 0, 0, 0…
## $ `short_description__Wardrobe,__________150x66x236_cm` <dbl> 0, 0, 0, 0, 0, 0…
## $ `short_description__-OTHER` <dbl> 1, 1, 1, 1, 1, 1…
## $ designer__Andreas_Fredriksson <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Carina_Bengs <dbl> 0, 0, 1, 0, 0, 0…
## $ designer__Carl_Öjerstam <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Ebba_Strandmark <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Ehlén_Johansson <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__Ehlén_Johansson/IKEA_of_Sweden` <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Eva_Lilja_Löwenhielm <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Francis_Cayouette <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Gillis_Lundgren <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Henrik_Preutz <dbl> 1, 0, 0, 0, 0, 0…
## $ designer__IKEA_of_Sweden <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__IKEA_of_Sweden/Ehlén_Johansson` <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__IKEA_of_Sweden/Jon_Karlsson` <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Johan_Kroon <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Jon_Karlsson <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__Jon_Karlsson/IKEA_of_Sweden` <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__K_Hagberg/M_Hagberg` <dbl> 0, 0, 0, 1, 1, 1…
## $ designer__Mia_Lagerman <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Nike_Karlsson <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Ola_Wihlborg <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Studio_Copenhagen <dbl> 0, 0, 0, 0, 0, 0…
## $ designer__Tord_Björklund <dbl> 0, 0, 0, 0, 0, 0…
## $ `designer__-OTHER` <dbl> 0, 1, 0, 0, 0, 0…
## $ `depth__-Inf_40` <dbl> 0, 0, 0, 0, 0, 0…
## $ depth__40_47 <dbl> 0, 0, 1, 1, 1, 1…
## $ depth__47_60 <dbl> 1, 1, 0, 0, 0, 0…
## $ depth__60_Inf <dbl> 0, 0, 0, 0, 0, 0…
## $ `height__-Inf_71` <dbl> 0, 1, 0, 0, 0, 0…
## $ height__71_92 <dbl> 0, 0, 1, 0, 0, 0…
## $ height__92_171 <dbl> 1, 0, 0, 1, 1, 1…
## $ height__171_Inf <dbl> 0, 0, 0, 0, 0, 0…
## $ `width__-Inf_60` <dbl> 1, 0, 1, 1, 1, 1…
## $ width__60_93 <dbl> 0, 1, 0, 0, 0, 0…
## $ width__93_161.5 <dbl> 0, 0, 0, 0, 0, 0…
## $ width__161.5_Inf <dbl> 0, 0, 0, 0, 0, 0…# Step 2: Correlation
data_correlation <- data_binarized %>%
correlate(sellable_online__TRUE)## Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [52].data_correlation## # A tibble: 88 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 sellable_online TRUE 1
## 2 sellable_online -OTHER -1
## 3 name TROFAST -0.332
## 4 category Children's_furniture -0.144
## 5 price -Inf_5.68697535633982 -0.143
## 6 category Nursery_furniture -0.128
## 7 width -Inf_60 -0.128
## 8 designer Studio_Copenhagen -0.112
## 9 depth -Inf_40 -0.0806
## 10 designer Francis_Cayouette -0.0573
## # ℹ 78 more rows# Step 3: Plot
data_correlation %>%
correlationfunnel::plot_correlation_funnel()## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
## Please report the issue at
## <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
## Please report the issue at
## <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps