Pepper Pirate Paradise Ltd. Analysis
Date : 23/03/2024
- Introduction
- Data Set and Data types view
- Categorical variables EDA
- Numerical variables EDA
- Visualizing The Relation between The numerical variables :
- Visualizing The Relation between The Price variable and other numerical variables :
- The correlation between The numerical variables :
- E. inferential analysis
- Final Conclusions:
Introduction
- On this chapter ,we will use R-Programming Language in order to conduct descriptive and inferential analysis to analyse the Prices of Bell Peppers of different colors (green, red, yellow) to help decision making
Data Set and Data types view
Data Set (1st five rows) :
| week_start_dt | week_end_dt | vietnam_season | p_color | price | total_volume | brazil | india | vietnam | indonesia | china | brazil_season | indonesia_season | india_season | china_season | jordan_max_price | jordan_min_price | demand | supply |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015-12-28 | 2016-01-03 | 2 | green | 6.599075 | 1596040 | 10793.5 | 65658.1 | 1519589 | 0 | 0 | False | False | False | False | 6.625 | 6.325 | 0.50 | 0.1650 |
| 2015-12-28 | 2016-01-03 | 2 | red | 7.175335 | 1596040 | 10793.5 | 65658.1 | 1519589 | 0 | 0 | False | False | False | False | 7.525 | 7.125 | 0.51 | 0.1683 |
| 2015-12-28 | 2016-01-03 | 2 | yellow | 7.300575 | 1596040 | 10793.5 | 65658.1 | 1519589 | 0 | 0 | False | False | False | False | 7.425 | 7.025 | 16.55 | 5.4615 |
| 2016-01-04 | 2016-01-10 | 2 | yellow | 7.379675 | 2295578 | 5677.8 | 15274.4 | 2274626 | 0 | 0 | False | False | False | False | 7.525 | 7.025 | 271.16 | 89.4828 |
| 2016-01-04 | 2016-01-10 | 2 | red | 7.175335 | 2295578 | 5677.8 | 15274.4 | 2274626 | 0 | 0 | False | False | False | False | 7.625 | 7.125 | 42.33 | 13.9689 |
| 2016-01-04 | 2016-01-10 | 2 | green | 6.599075 | 2295578 | 5677.8 | 15274.4 | 2274626 | 0 | 0 | False | False | False | False | 6.625 | 6.325 | 0.58 | 0.1914 |
data set dimensions
## [1] 1215 19Data types and some details using glimps function
## Rows: 1,215
## Columns: 19
## $ week_start_dt <chr> "2015-12-28", "2015-12-28", "2015-12-28", "2016-01-04…
## $ week_end_dt <chr> "2016-01-03", "2016-01-03", "2016-01-03", "2016-01-10…
## $ vietnam_season <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ p_color <chr> "green", "red", "yellow", "yellow", "red", "green", "…
## $ price <dbl> 6.599075, 7.175335, 7.300575, 7.379675, 7.175335, 6.5…
## $ total_volume <dbl> 1596040, 1596040, 1596040, 2295578, 2295578, 2295578,…
## $ brazil <dbl> 10793.5, 10793.5, 10793.5, 5677.8, 5677.8, 5677.8, 26…
## $ india <dbl> 65658.1, 65658.1, 65658.1, 15274.4, 15274.4, 15274.4,…
## $ vietnam <dbl> 1519589, 1519589, 1519589, 2274626, 2274626, 2274626,…
## $ indonesia <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ china <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ brazil_season <chr> "False", "False", "False", "False", "False", "False",…
## $ indonesia_season <chr> "False", "False", "False", "False", "False", "False",…
## $ india_season <chr> "False", "False", "False", "False", "False", "False",…
## $ china_season <chr> "False", "False", "False", "False", "False", "False",…
## $ jordan_max_price <dbl> 6.625, 7.525, 7.425, 7.525, 7.625, 6.625, 7.525, 7.62…
## $ jordan_min_price <dbl> 6.325, 7.125, 7.025, 7.025, 7.125, 6.325, 7.125, 7.32…
## $ demand <dbl> 0.50, 0.51, 16.55, 271.16, 42.33, 0.58, 712.22, 28.52…
## $ supply <dbl> 0.1650, 0.1683, 5.4615, 89.4828, 13.9689, 0.1914, 235…- converting (week_start_dt,week_end_dt) to date factors,converting (vietnam_season,p_color,brazil_season, ,india_season,china_season) to factors
Data types and some details using skim function
| Name | Piped data |
| Number of rows | 1215 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| Date | 2 |
| factor | 6 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| week_start_dt | 0 | 1 | 2015-12-28 | 2023-09-25 | 2019-11-11 | 405 |
| week_end_dt | 0 | 1 | 2016-01-03 | 2023-10-01 | 2019-11-17 | 405 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| vietnam_season | 0 | 1 | FALSE | 3 | 2: 486, 1: 414, 3: 315 |
| p_color | 0 | 1 | FALSE | 3 | gre: 405, red: 405, yel: 405 |
| brazil_season | 0 | 1 | FALSE | 2 | Fal: 690, Tru: 525 |
| indonesia_season | 0 | 1 | FALSE | 2 | Fal: 903, Tru: 312 |
| india_season | 0 | 1 | FALSE | 2 | Fal: 912, Tru: 303 |
| china_season | 0 | 1 | FALSE | 2 | Fal: 801, Tru: 414 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| price | 0 | 1 | 8.01 | 1.37 | 6.30 | 7.00 | 7.60 | 8.60 | 14.08 | ▇▃▂▁▁ |
| total_volume | 0 | 1 | 2244701.29 | 482013.00 | 325471.00 | 1938245.70 | 2254902.30 | 2541122.80 | 3555978.80 | ▁▂▇▇▂ |
| brazil | 0 | 1 | 245303.03 | 238532.96 | 0.00 | 10608.60 | 175493.10 | 443550.60 | 877647.80 | ▇▂▃▂▁ |
| india | 0 | 1 | 28744.10 | 55904.81 | 0.00 | 0.00 | 0.00 | 31022.60 | 348997.40 | ▇▁▁▁▁ |
| vietnam | 0 | 1 | 1806529.53 | 573830.71 | 220121.50 | 1402233.80 | 1785218.90 | 2204094.80 | 3549175.00 | ▁▆▇▅▁ |
| indonesia | 0 | 1 | 154948.08 | 243242.28 | 0.00 | 0.00 | 2150.30 | 278469.20 | 1061050.80 | ▇▂▁▁▁ |
| china | 0 | 1 | 8487.64 | 21366.04 | 0.00 | 0.00 | 0.00 | 7257.40 | 210920.20 | ▇▁▁▁▁ |
| jordan_max_price | 0 | 1 | 8.51 | 1.49 | 6.22 | 7.42 | 8.03 | 9.22 | 14.22 | ▇▇▃▁▁ |
| jordan_min_price | 0 | 1 | 7.93 | 1.37 | 6.03 | 6.92 | 7.53 | 8.53 | 13.72 | ▇▅▂▁▁ |
| demand | 0 | 1 | 229.51 | 225.82 | 0.50 | 75.82 | 117.73 | 391.72 | 1342.04 | ▇▂▁▁▁ |
| supply | 0 | 1 | 94.53 | 113.63 | 0.16 | 18.06 | 50.44 | 118.67 | 616.65 | ▇▁▁▁▁ |
Define categorical variables
## [1] "vietnam_season" "p_color" "brazil_season" "indonesia_season"
## [5] "india_season" "china_season"Define numerical variables
## [1] "price" "total_volume" "brazil" "india"
## [5] "vietnam" "indonesia" "china" "jordan_max_price"
## [9] "jordan_min_price" "demand" "supply"Categorical variables EDA
Categorical variables distribution
| vietnam_season | p_color | brazil_season | indonesia_season | india_season | china_season | |
|---|---|---|---|---|---|---|
| 1:414 | green :405 | False:690 | False:903 | False:912 | False:801 | |
| 2:486 | red :405 | True :525 | True :312 | True :303 | True :414 | |
| 3:315 | yellow:405 | NA | NA | NA | NA |
Categorical variables Visualization
Numerical variables EDA
Numerical variables distribution
| price | total_volume | brazil | india | vietnam | indonesia | china | jordan_max_price | jordan_min_price | demand | supply | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 6.300 | Min. : 325471 | Min. : 0 | Min. : 0 | Min. : 220122 | Min. : 0 | Min. : 0 | Min. : 6.225 | Min. : 6.025 | Min. : 0.50 | Min. : 0.165 | |
| 1st Qu.: 7.000 | 1st Qu.:1938246 | 1st Qu.: 10609 | 1st Qu.: 0 | 1st Qu.:1402234 | 1st Qu.: 0 | 1st Qu.: 0 | 1st Qu.: 7.425 | 1st Qu.: 6.925 | 1st Qu.: 75.82 | 1st Qu.: 18.057 | |
| Median : 7.600 | Median :2254902 | Median :175493 | Median : 0 | Median :1785219 | Median : 2150 | Median : 0 | Median : 8.025 | Median : 7.525 | Median : 117.73 | Median : 50.440 | |
| Mean : 8.009 | Mean :2244701 | Mean :245303 | Mean : 28744 | Mean :1806530 | Mean : 154948 | Mean : 8488 | Mean : 8.509 | Mean : 7.927 | Mean : 229.51 | Mean : 94.526 | |
| 3rd Qu.: 8.601 | 3rd Qu.:2541123 | 3rd Qu.:443551 | 3rd Qu.: 31023 | 3rd Qu.:2204095 | 3rd Qu.: 278469 | 3rd Qu.: 7257 | 3rd Qu.: 9.225 | 3rd Qu.: 8.525 | 3rd Qu.: 391.73 | 3rd Qu.:118.673 | |
| Max. :14.085 | Max. :3555979 | Max. :877648 | Max. :348997 | Max. :3549175 | Max. :1061051 | Max. :210920 | Max. :14.225 | Max. :13.725 | Max. :1342.04 | Max. :616.654 |
Numerical variables Visualization
Visualizing The Relation between The numerical variables :
Visualizing The Relation between The Price variable and other numerical variables :
We can see from the above charts that the relationship between the variables is not linear except when it comes to supply and demand variables)
The correlation between The numerical variables :
- as the data is not normally distributed we will apply spearman methods
| price | total_volume | brazil | india | vietnam | indonesia | china | jordan_max_price | jordan_min_price | demand | supply | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 1.0000000 | -0.1122322 | 0.2782183 | -0.0264911 | -0.3517192 | 0.3648408 | 0.0641676 | 0.9720657 | 0.9714315 | 0.4298274 | 0.3163057 |
| total_volume | -0.1122322 | 1.0000000 | 0.1855243 | -0.3535011 | 0.6964246 | 0.0542286 | 0.3501470 | -0.1220329 | -0.0969187 | 0.1125441 | 0.2829057 |
| brazil | 0.2782183 | 0.1855243 | 1.0000000 | -0.6312192 | -0.3813353 | 0.5656901 | 0.0864702 | 0.2423081 | 0.2533731 | 0.0296134 | 0.0247692 |
| india | -0.0264911 | -0.3535011 | -0.6312192 | 1.0000000 | 0.0638375 | -0.4172503 | -0.2993819 | 0.0003487 | -0.0140451 | -0.0934378 | -0.1787898 |
| vietnam | -0.3517192 | 0.6964246 | -0.3813353 | 0.0638375 | 1.0000000 | -0.5616759 | 0.0860247 | -0.3545445 | -0.3258363 | 0.1054322 | 0.2330524 |
| indonesia | 0.3648408 | 0.0542286 | 0.5656901 | -0.4172503 | -0.5616759 | 1.0000000 | 0.2968556 | 0.3586799 | 0.3509917 | -0.0014067 | 0.0182960 |
| china | 0.0641676 | 0.3501470 | 0.0864702 | -0.2993819 | 0.0860247 | 0.2968556 | 1.0000000 | 0.0652780 | 0.0698367 | 0.0123709 | 0.3439685 |
| jordan_max_price | 0.9720657 | -0.1220329 | 0.2423081 | 0.0003487 | -0.3545445 | 0.3586799 | 0.0652780 | 1.0000000 | 0.9811579 | 0.4037138 | 0.2897715 |
| jordan_min_price | 0.9714315 | -0.0969187 | 0.2533731 | -0.0140451 | -0.3258363 | 0.3509917 | 0.0698367 | 0.9811579 | 1.0000000 | 0.4340305 | 0.3253103 |
| demand | 0.4298274 | 0.1125441 | 0.0296134 | -0.0934378 | 0.1054322 | -0.0014067 | 0.0123709 | 0.4037138 | 0.4340305 | 1.0000000 | 0.7224376 |
| supply | 0.3163057 | 0.2829057 | 0.0247692 | -0.1787898 | 0.2330524 | 0.0182960 | 0.3439685 | 0.2897715 | 0.3253103 | 0.7224376 | 1.0000000 |
- Visualizing The correlation
E. inferential analysis
- as out target is to understand the price variable for each pepper_color we will only focus on these two variables
E.1: Price by pepper_color
- E.1.1 : Price by number of doors-Basic description
| p_color | count | mean | sd | min | Q1 | median | Q3 | max |
|---|---|---|---|---|---|---|---|---|
| green | 405 | 7.215042 | 0.8224482 | 6.299625 | 6.699645 | 6.900000 | 7.539580 | 12.78481 |
| red | 405 | 7.882143 | 1.1570314 | 6.449990 | 7.100440 | 7.529990 | 8.350220 | 13.68538 |
| yellow | 405 | 8.929220 | 1.4515797 | 7.031635 | 7.799655 | 8.600075 | 9.899575 | 14.08459 |
- E.1.2 :Applying kruskal.test (for variables with more than two groups) to find out if there are significant differences in Price Distribution among the p_color groups :
##
## Kruskal-Wallis rank sum test
##
## data: price by p_color
## Kruskal-Wallis chi-squared = 403.28, df = 2, p-value < 2.2e-16As the The p-value is less than 0.05 , we will reject the null hypothesis,indicating that there are significant differences in Price Distribution among the p_color groups.
- E.1.3 :Visualizing The Statistical test
- E.1.4: To find out this difference in details: we will apply pairwise.wilcox.test
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: df$price and df$p_color
##
## green red
## red <2e-16 -
## yellow <2e-16 <2e-16
##
## P value adjustment method: bonferroniAs we can see there is significant difference in Price Distributions when we compare all groups with each other as the p-value adjusted is less than 0.05
Final Conclusions:
- 1. Bell Peppers of different colors (green, red, yellow) do significantly affect the price distribution in the data set.
## * correlation type : generic
## * variable type : numeric
## * correlation method : spearman
##
## * Matrix of Correlation
## price total_volume brazil india vietnam
## price 1.00000000 -0.11773512 0.39102760 -0.02911878 -0.42545151
## total_volume -0.11773512 1.00000000 0.18552426 -0.35350112 0.69642455
## brazil 0.39102760 0.18552426 1.00000000 -0.63121918 -0.38133535
## india -0.02911878 -0.35350112 -0.63121918 1.00000000 0.06383747
## vietnam -0.42545151 0.69642455 -0.38133535 0.06383747 1.00000000
## indonesia 0.44305817 0.05422860 0.56569009 -0.41725026 -0.56167595
## china 0.04146590 0.35014697 0.08647015 -0.29938190 0.08602471
## jordan_max_price 0.95870207 -0.13044284 0.36527989 -0.02090337 -0.43071881
## jordan_min_price 0.95948166 -0.08864000 0.37605574 -0.02631894 -0.38687813
## demand 0.23520333 0.09411858 0.13588593 -0.10557995 0.05014064
## supply -0.05711386 0.31601899 0.04627478 -0.24572925 0.24080334
## indonesia china jordan_max_price jordan_min_price
## price 0.44305817 0.04146590 0.95870207 0.95948166
## total_volume 0.05422860 0.35014697 -0.13044284 -0.08864000
## brazil 0.56569009 0.08647015 0.36527989 0.37605574
## india -0.41725026 -0.29938190 -0.02090337 -0.02631894
## vietnam -0.56167595 0.08602471 -0.43071881 -0.38687813
## indonesia 1.00000000 0.29685559 0.43969053 0.43366220
## china 0.29685559 1.00000000 0.03600303 0.03418858
## jordan_max_price 0.43969053 0.03600303 1.00000000 0.97063336
## jordan_min_price 0.43366220 0.03418858 0.97063336 1.00000000
## demand 0.09461007 -0.02611732 0.25008227 0.26102531
## supply 0.06695820 0.49385444 -0.03733959 -0.02360919
## demand supply
## price 0.23520333 -0.05711386
## total_volume 0.09411858 0.31601899
## brazil 0.13588593 0.04627478
## india -0.10557995 -0.24572925
## vietnam 0.05014064 0.24080334
## indonesia 0.09461007 0.06695820
## china -0.02611732 0.49385444
## jordan_max_price 0.25008227 -0.03733959
## jordan_min_price 0.26102531 -0.02360919
## demand 1.00000000 0.25795877
## supply 0.25795877 1.00000000| price | total_volume | brazil | india | vietnam | indonesia | china | jordan_max_price | jordan_min_price | demand | supply | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 1.0000000 | -0.1177351 | 0.3910276 | -0.0291188 | -0.4254515 | 0.4430582 | 0.0414659 | 0.9587021 | 0.9594817 | 0.2352033 | -0.0571139 |
| total_volume | -0.1177351 | 1.0000000 | 0.1855243 | -0.3535011 | 0.6964246 | 0.0542286 | 0.3501470 | -0.1304428 | -0.0886400 | 0.0941186 | 0.3160190 |
| brazil | 0.3910276 | 0.1855243 | 1.0000000 | -0.6312192 | -0.3813353 | 0.5656901 | 0.0864702 | 0.3652799 | 0.3760557 | 0.1358859 | 0.0462748 |
| india | -0.0291188 | -0.3535011 | -0.6312192 | 1.0000000 | 0.0638375 | -0.4172503 | -0.2993819 | -0.0209034 | -0.0263189 | -0.1055800 | -0.2457293 |
| vietnam | -0.4254515 | 0.6964246 | -0.3813353 | 0.0638375 | 1.0000000 | -0.5616759 | 0.0860247 | -0.4307188 | -0.3868781 | 0.0501406 | 0.2408033 |
| indonesia | 0.4430582 | 0.0542286 | 0.5656901 | -0.4172503 | -0.5616759 | 1.0000000 | 0.2968556 | 0.4396905 | 0.4336622 | 0.0946101 | 0.0669582 |
| china | 0.0414659 | 0.3501470 | 0.0864702 | -0.2993819 | 0.0860247 | 0.2968556 | 1.0000000 | 0.0360030 | 0.0341886 | -0.0261173 | 0.4938544 |
| jordan_max_price | 0.9587021 | -0.1304428 | 0.3652799 | -0.0209034 | -0.4307188 | 0.4396905 | 0.0360030 | 1.0000000 | 0.9706334 | 0.2500823 | -0.0373396 |
| jordan_min_price | 0.9594817 | -0.0886400 | 0.3760557 | -0.0263189 | -0.3868781 | 0.4336622 | 0.0341886 | 0.9706334 | 1.0000000 | 0.2610253 | -0.0236092 |
| demand | 0.2352033 | 0.0941186 | 0.1358859 | -0.1055800 | 0.0501406 | 0.0946101 | -0.0261173 | 0.2500823 | 0.2610253 | 1.0000000 | 0.2579588 |
| supply | -0.0571139 | 0.3160190 | 0.0462748 | -0.2457293 | 0.2408033 | 0.0669582 | 0.4938544 | -0.0373396 | -0.0236092 | 0.2579588 | 1.0000000 |