Deondre Brown 2024-03-03
This is a project documenting my statistical steps and analysis regarding a dataset for Avocado sales.
Step 1.Read in the data
Step 2. Load in the necessary packages we need
## Warning: package 'tidyverse' was built under R version 4.3.3
## Warning: package 'ggplot2' was built under R version 4.3.3
## Warning: package 'tibble' was built under R version 4.3.3
## Warning: package 'tidyr' was built under R version 4.3.3
## Warning: package 'readr' was built under R version 4.3.3
## Warning: package 'purrr' was built under R version 4.3.3
## Warning: package 'dplyr' was built under R version 4.3.3
## Warning: package 'stringr' was built under R version 4.3.3
## Warning: package 'forcats' was built under R version 4.3.3
## Warning: package 'lubridate' was built under R version 4.3.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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 errors## Warning: package 'psych' was built under R version 4.3.3
##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaStep 3. Use these functions to make the sure data was loaded in properly.
## [1] 17490 9## [1] "observationid" "region" "year" "month"
## [5] "quarter" "type" "averageprice" "totalvolume"
## [9] "supplierid"## observationid region year month quarter type averageprice
## 1 17038 Miami/Ft. Lauderdale 2016 10 4 Organic 1.58
## 2 6381 Jacksonville 2018 11 4 Organic 1.65
## 3 6392 Orlando 2018 11 4 Organic 1.39
## 4 16826 Miami/Ft. Lauderdale 2016 10 4 Organic 1.49
## 5 10471 New Orleans/Mobile 2017 6 2 Organic 2.19
## 6 16985 Miami/Ft. Lauderdale 2016 10 4 Organic 1.58
## 7 15109 Boise 2016 2 1 Organic 1.67
## 8 17019 Buffalo/Rochester 2016 10 4 Organic 1.63
## 9 15162 Boise 2016 2 1 Organic 1.84
## 10 10153 New Orleans/Mobile 2017 4 2 Organic 1.95
## totalvolume supplierid
## 1 385.55 A
## 2 404.62 C
## 3 405.29 C
## 4 472.82 A
## 5 515.01 C
## 6 542.85 A
## 7 562.64 A
## 8 563.06 A
## 9 566.57 A
## 10 634.09 C## 'data.frame': 17490 obs. of 9 variables:
## $ observationid: int 17038 6381 6392 16826 10471 16985 15109 17019 15162 10153 ...
## $ region : chr "Miami/Ft. Lauderdale" "Jacksonville" "Orlando" "Miami/Ft. Lauderdale" ...
## $ year : int 2016 2018 2018 2016 2017 2016 2016 2016 2016 2017 ...
## $ month : int 10 11 11 10 6 10 2 10 2 4 ...
## $ quarter : int 4 4 4 4 2 4 1 4 1 2 ...
## $ type : chr "Organic" "Organic" "Organic" "Organic" ...
## $ averageprice : num 1.58 1.65 1.39 1.49 2.19 1.58 1.67 1.63 1.84 1.95 ...
## $ totalvolume : num 386 405 405 473 515 ...
## $ supplierid : chr "A" "C" "C" "A" ...Step 4. Create a new data column to hold dollar sales information. To get the dollar sales information we are going to multiple the average price by the total volume.
Step 5. Look at basic summaries of the data.
## observationid region year month quarter
## Min. : 1 Length:17490 Min. :2016 Min. : 1 Min. :1.000
## 1st Qu.: 4373 Class :character 1st Qu.:2016 1st Qu.: 3 1st Qu.:1.000
## Median : 8746 Mode :character Median :2017 Median : 6 Median :2.000
## Mean : 8746 Mean :2017 Mean : 6 Mean :2.357
## 3rd Qu.:13118 3rd Qu.:2018 3rd Qu.: 9 3rd Qu.:3.000
## Max. :17490 Max. :2019 Max. :12 Max. :4.000
## type averageprice totalvolume supplierid
## Length:17490 Min. :0.440 Min. : 386 Length:17490
## Class :character 1st Qu.:1.120 1st Qu.: 14415 Class :character
## Mode :character Median :1.360 Median : 120285 Mode :character
## Mean :1.393 Mean : 600147
## 3rd Qu.:1.620 3rd Qu.: 449951
## Max. :3.250 Max. :25394903
## totalsales
## Min. : 563
## 1st Qu.: 23080
## Median : 160817
## Mean : 665447
## 3rd Qu.: 546286
## Max. :40361458## vars n mean sd median trimmed mad
## observationid 1 17490 8745.50 5049.07 8745.50 8745.50 6482.67
## region* 2 17490 27.00 15.30 27.00 27.00 19.27
## year 3 17490 2017.13 0.94 2017.00 2017.07 1.48
## month 4 17490 6.00 3.46 6.00 5.93 4.45
## quarter 5 17490 2.36 1.13 2.00 2.32 1.48
## type* 6 17490 1.50 0.50 1.50 1.50 0.74
## averageprice 7 17490 1.39 0.39 1.36 1.37 0.37
## totalvolume 8 17490 600147.13 1385429.67 120285.18 238594.63 167772.12
## supplierid* 9 17490 2.50 0.86 3.00 2.62 0.00
## totalsales 10 17490 665447.32 1519115.25 160817.47 277170.69 220030.78
## min max range skew kurtosis se
## observationid 1.00 17490.00 17489.00 0.00 -1.20 38.18
## region* 1.00 53.00 52.00 0.00 -1.20 0.12
## year 2016.00 2019.00 3.00 0.27 -1.00 0.01
## month 1.00 12.00 11.00 0.12 -1.24 0.03
## quarter 1.00 4.00 3.00 0.15 -1.37 0.01
## type* 1.00 2.00 1.00 0.00 -2.00 0.00
## averageprice 0.44 3.25 2.81 0.66 0.72 0.00
## totalvolume 385.55 25394902.82 25394517.27 4.42 32.59 10475.86
## supplierid* 1.00 3.00 2.00 -1.15 -0.68 0.01
## totalsales 563.35 40361458.14 40360894.79 6.55 110.62 11486.71avocado |>
select(totalvolume, totalsales) |>
describe() # Describes statistics for the total volume and total sales variables## vars n mean sd median trimmed mad min
## totalvolume 1 17490 600147.1 1385430 120285.2 238594.6 167772.1 385.55
## totalsales 2 17490 665447.3 1519115 160817.5 277170.7 220030.8 563.35
## max range skew kurtosis se
## totalvolume 25394903 25394517 4.42 32.59 10475.86
## totalsales 40361458 40360895 6.55 110.62 11486.71Using these statistics we can find out the minimum, maximum, and average sales
Minimum: 563.35 Maximum: 40,361,458 and Average: 665,447.3
Step 6. Look at a data distribution using a histogram. For this histogram we can display the frequency regarding the average price of avocados.
Using this histogram, we can make the observation that the data is positively skewed
Step 7. We can find the correlation value between average price and sales volume to see how they relate to each other.
## [1] -0.3158095We get a value of -0.31 indicating that the average price and sales volume are inversely related
Step 8. We can generate many more insights by gathering summary statistics by grouping.
Get the Summary statistics for price by each supplier
## item group1 vars n mean sd median trimmed
## averageprice1 1 A 1 4335 1.441232 0.3769172 1.410 1.415630
## averageprice2 2 B 1 128 1.166172 0.2572726 1.155 1.161154
## averageprice3 3 C 1 13027 1.379806 0.3941439 1.350 1.355955
## mad min max range skew kurtosis se
## averageprice1 0.340998 0.56 3.25 2.69 0.6910307 0.75940546 0.005724682
## averageprice2 0.266868 0.54 1.88 1.34 0.2340183 0.01419964 0.022739896
## averageprice3 0.385476 0.44 3.17 2.73 0.6643521 0.71053120 0.003453287Average price of avocados by month?
## item group1 vars n mean sd median trimmed
## averageprice1 1 1 1 2014 1.291326 0.3384133 1.27 1.276427
## averageprice2 2 2 1 1697 1.242068 0.3618871 1.23 1.227483
## averageprice3 3 3 1 1697 1.293447 0.3488425 1.26 1.272811
## averageprice4 4 4 1 1484 1.355330 0.3818613 1.33 1.331069
## averageprice5 5 5 1 1378 1.327286 0.3984560 1.31 1.300453
## averageprice6 6 6 1 1270 1.380315 0.3888111 1.32 1.348760
## averageprice7 7 7 1 1590 1.434811 0.3670059 1.39 1.406965
## averageprice8 8 8 1 1272 1.509835 0.3995163 1.46 1.477967
## averageprice9 9 9 1 1378 1.574753 0.4082758 1.53 1.548886
## averageprice10 10 10 1 1484 1.567796 0.4043240 1.53 1.539781
## averageprice11 11 11 1 1272 1.476627 0.3626503 1.45 1.457800
## averageprice12 12 12 1 954 1.360776 0.3539983 1.34 1.344005
## mad min max range skew kurtosis se
## averageprice1 0.355824 0.51 2.70 2.19 0.4685901 0.21133836 0.007540805
## averageprice2 0.385476 0.46 2.59 2.13 0.4075879 -0.06878999 0.008784807
## averageprice3 0.326172 0.44 3.05 2.61 0.7839616 1.55643913 0.008468150
## averageprice4 0.385476 0.50 3.17 2.67 0.6794018 0.67565825 0.009912625
## averageprice5 0.400302 0.51 2.73 2.22 0.6334883 0.30997839 0.010733857
## averageprice6 0.370650 0.57 2.77 2.20 0.7766223 0.54271731 0.010910302
## averageprice7 0.340998 0.64 2.67 2.03 0.6783879 0.28750489 0.009203955
## averageprice8 0.370650 0.58 3.04 2.46 0.7854832 0.69762176 0.011201881
## averageprice9 0.385476 0.64 2.97 2.33 0.6721711 0.58891815 0.010998389
## averageprice10 0.370650 0.67 3.25 2.58 0.7515656 0.88834407 0.010495728
## averageprice11 0.326172 0.60 3.12 2.52 0.6348279 0.97607152 0.010168208
## averageprice12 0.355824 0.56 2.71 2.15 0.5523340 0.51250772 0.011461119