# Load tidyverse
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ 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 errorslibrary(dplyr)
Superstore_data=read.csv("SampleSuperstore_final.csv")
head(Superstore_data)## Ship.Mode Segment Country City State Postal.Code
## 1 Second Class Consumer United States Henderson Kentucky 42420
## 2 Second Class Consumer United States Henderson Kentucky 42420
## 3 Second Class Corporate United States Los Angeles California 90036
## 4 Standard Class Consumer United States Fort Lauderdale Florida 33311
## 5 Standard Class Consumer United States Fort Lauderdale Florida 33311
## 6 Standard Class Consumer United States Los Angeles California 90032
## Region Category Sub.Category Sales Quantity Discount Profit
## 1 South Furniture Bookcases 261.9600 2 0.00 41.9136
## 2 South Furniture Chairs 731.9400 3 0.00 219.5820
## 3 West Office Supplies Labels 14.6200 2 0.00 6.8714
## 4 South Furniture Tables 957.5775 5 0.45 -383.0310
## 5 South Office Supplies Storage 22.3680 2 0.20 2.5164
## 6 West Furniture Furnishings 48.8600 7 0.00 14.1694(The purpose of this week’s data dive is for you to think critically about what might go wrong when it comes time to make conclusions about your data.)
Part 1: A collection of 5-10 random samples of data (with replacement) from at least 6 columns of data
Part 2: Scrutinize these subsamples.
Part 3: Consider how this investigation affects how you might draw conclusions about the data in the future.
1. Part 1 - Collecting 5/6 random samples of data (with replacement) from at least 6 columns of data
Population : -
# Population - count :
# Rows of data in the data set -
nrow(Superstore_data)## [1] 9994Sample size :-
# Sample - should be as long as roughly 50% percent of your data.
# 50 % of 9994
sample_size <- 0.5 * 9994
sample_size## [1] 4997set.seed(10)
df_sample_1 <- Superstore_data |> sample_frac(0.5, replace = TRUE) |> select("Ship.Mode","Segment","Region", "State","Category","Sub.Category","Sales","Profit","Discount")
nrow(df_sample_1)## [1] 4997Random 20 rows from 1st sampledf_sample_1 |> sample_n(20)## Ship.Mode Segment Region State Category
## 1 Standard Class Home Office South Mississippi Furniture
## 2 Standard Class Corporate Central Michigan Furniture
## 3 Standard Class Corporate West Arizona Technology
## 4 Standard Class Consumer West California Technology
## 5 Standard Class Consumer West Washington Office Supplies
## 6 Standard Class Consumer West California Technology
## 7 Same Day Consumer East New York Office Supplies
## 8 Standard Class Consumer Central Michigan Office Supplies
## 9 Second Class Home Office East New York Furniture
## 10 Second Class Consumer Central Texas Furniture
## 11 Standard Class Consumer West Arizona Office Supplies
## 12 Second Class Consumer West California Office Supplies
## 13 Standard Class Corporate Central Michigan Office Supplies
## 14 Standard Class Corporate Central Illinois Office Supplies
## 15 Standard Class Corporate South Virginia Office Supplies
## 16 Standard Class Consumer East Massachusetts Office Supplies
## 17 Standard Class Consumer West California Technology
## 18 Standard Class Corporate East New York Office Supplies
## 19 First Class Consumer Central Illinois Office Supplies
## 20 Standard Class Corporate South Georgia Technology
## Sub.Category Sales Profit Discount
## 1 Furnishings 18.920 7.3788 0.0
## 2 Tables 801.960 200.4900 0.0
## 3 Accessories 62.352 -10.9116 0.2
## 4 Accessories 179.950 37.7895 0.0
## 5 Paper 41.860 18.8370 0.0
## 6 Phones 470.376 52.9173 0.2
## 7 Envelopes 68.460 31.4916 0.0
## 8 Appliances 283.140 72.3580 0.1
## 9 Furnishings 82.640 7.4376 0.0
## 10 Furnishings 30.560 -19.8640 0.6
## 11 Binders 19.194 -12.7960 0.7
## 12 Binders 1016.792 381.2970 0.2
## 13 Appliances 207.144 48.3336 0.1
## 14 Paper 23.520 8.5260 0.2
## 15 Storage 67.900 0.6790 0.0
## 16 Paper 19.440 9.3312 0.0
## 17 Phones 333.576 25.0182 0.2
## 18 Paper 68.520 31.5192 0.0
## 19 Binders 96.784 -145.1760 0.8
## 20 Phones 206.100 55.6470 0.0set.seed(50)
df_sample_2 <- Superstore_data |> sample_frac(0.5, replace = TRUE)|> select("Ship.Mode","Segment","Region", "State","Category","Sub.Category","Sales","Profit","Discount")
nrow(df_sample_2)## [1] 4997
Random 20 rows from 2st sampledf_sample_2 |> sample_n(20)## Ship.Mode Segment Region State Category Sub.Category
## 1 Standard Class Consumer Central Texas Office Supplies Binders
## 2 Standard Class Corporate West California Technology Accessories
## 3 Second Class Consumer Central Illinois Furniture Chairs
## 4 Standard Class Home Office East Pennsylvania Office Supplies Art
## 5 Standard Class Corporate West Washington Furniture Furnishings
## 6 Standard Class Corporate Central Texas Office Supplies Paper
## 7 Standard Class Consumer West California Office Supplies Storage
## 8 Standard Class Corporate South Georgia Furniture Furnishings
## 9 Second Class Home Office South Kentucky Technology Accessories
## 10 Standard Class Consumer West California Office Supplies Appliances
## 11 Second Class Consumer East Ohio Office Supplies Envelopes
## 12 Standard Class Corporate Central Texas Office Supplies Appliances
## 13 Standard Class Corporate South Kentucky Technology Phones
## 14 Standard Class Corporate Central Texas Technology Phones
## 15 First Class Consumer East Pennsylvania Office Supplies Appliances
## 16 Standard Class Consumer West Colorado Office Supplies Binders
## 17 Second Class Home Office East New York Office Supplies Paper
## 18 Standard Class Corporate West California Furniture Bookcases
## 19 First Class Consumer West California Office Supplies Art
## 20 First Class Consumer Central Texas Office Supplies Storage
## Sales Profit Discount
## 1 1.248 -1.9344 0.80
## 2 27.880 3.9032 0.00
## 3 602.651 -163.5767 0.30
## 4 5.248 0.5904 0.20
## 5 137.540 55.0160 0.00
## 6 15.552 5.6376 0.20
## 7 139.040 38.9312 0.00
## 8 39.920 11.1776 0.00
## 9 18.000 3.2400 0.00
## 10 160.960 48.2880 0.00
## 11 46.720 17.5200 0.20
## 12 34.176 -87.1488 0.80
## 13 36.990 9.9873 0.00
## 14 21.072 1.5804 0.20
## 15 434.352 43.4352 0.20
## 16 8.736 -6.1152 0.70
## 17 30.440 14.3068 0.00
## 18 308.499 -18.1470 0.15
## 19 16.020 4.4856 0.00
## 20 18.160 1.8160 0.20set.seed(100)
df_sample_3 <- Superstore_data |> sample_frac(0.5, replace = TRUE)|> select("Ship.Mode","Segment","Region", "State","Category","Sub.Category","Sales","Profit","Discount")
nrow(df_sample_3)## [1] 4997
Random 20 rows from 3rd sampledf_sample_3 |> sample_n(20)## Ship.Mode Segment Region State Category Sub.Category
## 1 Standard Class Home Office Central Michigan Furniture Furnishings
## 2 Second Class Consumer West Utah Technology Phones
## 3 Standard Class Consumer Central Texas Office Supplies Appliances
## 4 Standard Class Consumer Central Texas Furniture Furnishings
## 5 Same Day Home Office Central Illinois Technology Phones
## 6 Standard Class Corporate Central Illinois Technology Phones
## 7 Standard Class Consumer East Pennsylvania Office Supplies Fasteners
## 8 First Class Consumer East Connecticut Office Supplies Paper
## 9 Standard Class Consumer East New York Office Supplies Binders
## 10 Standard Class Home Office West Washington Office Supplies Binders
## 11 Standard Class Corporate East New Jersey Technology Machines
## 12 Standard Class Home Office Central Minnesota Technology Copiers
## 13 Same Day Consumer West Washington Technology Accessories
## 14 Standard Class Consumer West Washington Technology Copiers
## 15 Second Class Consumer West Washington Office Supplies Labels
## 16 First Class Consumer East New York Office Supplies Paper
## 17 Standard Class Consumer West Arizona Office Supplies Labels
## 18 Standard Class Home Office West Colorado Office Supplies Art
## 19 Standard Class Corporate South Alabama Office Supplies Paper
## 20 Standard Class Home Office West California Technology Phones
## Sales Profit Discount
## 1 33.480 8.7048 0.0
## 2 399.960 34.9965 0.2
## 3 58.924 -153.2024 0.8
## 4 66.112 -84.2928 0.6
## 5 34.360 -7.3015 0.2
## 6 239.976 26.9973 0.2
## 7 10.584 -2.3814 0.2
## 8 27.120 12.4752 0.0
## 9 49.536 17.3376 0.2
## 10 6.096 2.1336 0.2
## 11 9099.930 2365.9818 0.0
## 12 549.990 274.9950 0.0
## 13 118.000 20.0600 0.0
## 14 999.980 449.9910 0.0
## 15 87.710 41.2237 0.0
## 16 46.760 22.4448 0.0
## 17 5.040 1.7640 0.2
## 18 14.576 2.3686 0.2
## 19 23.920 11.7208 0.0
## 20 271.960 27.1960 0.2set.seed(120)
df_sample_4 <- Superstore_data |> sample_frac(0.5, replace = TRUE) |> select("Ship.Mode","Segment","Region", "State","Category","Sub.Category","Sales","Profit","Discount")
nrow(df_sample_4)## [1] 4997
Random 20 rows from 4th sampledf_sample_4 |> sample_n(20)## Ship.Mode Segment Region State Category Sub.Category
## 1 Standard Class Corporate West Oregon Furniture Tables
## 2 Standard Class Home Office East New York Office Supplies Paper
## 3 Same Day Consumer Central Texas Furniture Furnishings
## 4 Standard Class Consumer South Mississippi Office Supplies Appliances
## 5 Standard Class Corporate Central Texas Office Supplies Paper
## 6 Standard Class Consumer West California Office Supplies Appliances
## 7 Standard Class Corporate West Nevada Office Supplies Art
## 8 Same Day Consumer West California Office Supplies Storage
## 9 Standard Class Corporate Central Texas Technology Machines
## 10 Standard Class Consumer Central Illinois Office Supplies Paper
## 11 Standard Class Corporate East New York Technology Phones
## 12 Second Class Home Office South Virginia Furniture Furnishings
## 13 First Class Home Office East Pennsylvania Office Supplies Fasteners
## 14 Same Day Corporate West California Office Supplies Fasteners
## 15 Second Class Consumer Central Texas Technology Accessories
## 16 First Class Home Office South Tennessee Office Supplies Art
## 17 Standard Class Consumer East New York Office Supplies Binders
## 18 Standard Class Consumer Central Minnesota Office Supplies Art
## 19 First Class Corporate West Washington Office Supplies Binders
## 20 Standard Class Corporate West California Office Supplies Paper
## Sales Profit Discount
## 1 177.225 -120.5130 0.5
## 2 99.900 47.9520 0.0
## 3 25.160 -11.3220 0.6
## 4 320.640 89.7792 0.0
## 5 98.376 35.6613 0.2
## 6 8.670 2.3409 0.0
## 7 3.640 1.6380 0.0
## 8 31.440 8.4888 0.0
## 9 559.710 -121.2705 0.4
## 10 143.856 48.5514 0.2
## 11 307.980 89.3142 0.0
## 12 47.980 11.0354 0.0
## 13 3.168 -0.7128 0.2
## 14 17.900 8.9500 0.0
## 15 1399.944 52.4979 0.2
## 16 67.920 6.7920 0.2
## 17 106.344 37.2204 0.2
## 18 8.800 2.5520 0.0
## 19 895.920 302.3730 0.2
## 20 38.880 18.6624 0.0set.seed(150)
df_sample_5 <- Superstore_data |> sample_frac(0.5, replace = TRUE)|> select("Ship.Mode","Segment","Region", "State","Category","Sub.Category","Sales","Profit","Discount")
nrow(df_sample_5)## [1] 4997
Random 20 rows from 5th sampledf_sample_5 |> sample_n(20)## Ship.Mode Segment Region State Category
## 1 Standard Class Corporate Central Texas Office Supplies
## 2 Standard Class Consumer West California Office Supplies
## 3 First Class Corporate East New York Office Supplies
## 4 Standard Class Home Office Central Michigan Technology
## 5 Standard Class Consumer Central Oklahoma Office Supplies
## 6 Second Class Corporate East New York Office Supplies
## 7 Standard Class Consumer West California Furniture
## 8 Standard Class Home Office Central Minnesota Office Supplies
## 9 Standard Class Home Office South Florida Furniture
## 10 Standard Class Corporate South Florida Technology
## 11 Standard Class Consumer East New York Furniture
## 12 Same Day Corporate East Connecticut Office Supplies
## 13 Same Day Consumer East Ohio Furniture
## 14 Standard Class Corporate West Washington Technology
## 15 Standard Class Corporate South South Carolina Office Supplies
## 16 Standard Class Corporate Central Indiana Office Supplies
## 17 Second Class Consumer West California Furniture
## 18 Standard Class Corporate Central Texas Office Supplies
## 19 First Class Corporate East Connecticut Office Supplies
## 20 Standard Class Consumer West Arizona Furniture
## Sub.Category Sales Profit Discount
## 1 Storage 32.232 2.4174 0.2
## 2 Storage 777.210 54.4047 0.0
## 3 Storage 83.920 20.1408 0.0
## 4 Accessories 1928.780 829.3754 0.0
## 5 Labels 14.620 6.8714 0.0
## 6 Labels 8.670 4.0749 0.0
## 7 Chairs 194.352 19.4352 0.2
## 8 Art 29.790 12.5118 0.0
## 9 Furnishings 258.072 0.0000 0.2
## 10 Phones 100.792 10.0792 0.2
## 11 Furnishings 28.440 11.3760 0.0
## 12 Binders 23.200 10.4400 0.0
## 13 Furnishings 51.264 7.6896 0.2
## 14 Phones 71.960 25.1860 0.2
## 15 Storage 628.810 12.5762 0.0
## 16 Paper 14.940 7.3206 0.0
## 17 Furnishings 24.140 7.9662 0.0
## 18 Paper 36.288 12.7008 0.2
## 19 Supplies 30.690 7.9794 0.0
## 20 Furnishings 206.112 48.9516 0.2All these sub-samples contain both categorical and continuous (numeric) data.
Check for replacement and if there are common data inbetween the samples
nrow(intersect(df_sample_1, df_sample_2))## [1] 1568- 1568 records are common between sample 1 and 2nrow(intersect(df_sample_2, df_sample_3))## [1] 1572- 1572 records are common between sample 2 and 3 nrow(intersect(df_sample_3, df_sample_4))## [1] 1547- 1547 records are common between sample 3 and 4 nrow(intersect(df_sample_4, df_sample_5))## [1] 1582- 1582 records are common between sample 4 and 5 Part 2:- Scrutinize these sub-samples
Segment :-
count_df_sample_1 <- df_sample_1 |> group_by(Segment) |>
summarise(total_count_segment=n(),
.groups = 'drop')
count_df_sample_1## # A tibble: 3 × 2
## Segment total_count_segment
## <chr> <int>
## 1 Consumer 2596
## 2 Corporate 1535
## 3 Home Office 866 - Sample 2 -count_df_sample_2 <- df_sample_2 |> group_by(Segment) |>
summarise(total_count_segment=n(),
.groups = 'drop')
count_df_sample_2## # A tibble: 3 × 2
## Segment total_count_segment
## <chr> <int>
## 1 Consumer 2574
## 2 Corporate 1515
## 3 Home Office 908 - Sample 3 -count_df_sample_3 <- df_sample_3 |> group_by(Segment) |>
summarise(total_count_segment=n(),
.groups = 'drop')
count_df_sample_3## # A tibble: 3 × 2
## Segment total_count_segment
## <chr> <int>
## 1 Consumer 2560
## 2 Corporate 1511
## 3 Home Office 926 - Sample 4 -count_df_sample_4 <- df_sample_4 |> group_by(Segment) |>
summarise(total_count_segment=n(),
.groups = 'drop')
count_df_sample_4## # A tibble: 3 × 2
## Segment total_count_segment
## <chr> <int>
## 1 Consumer 2592
## 2 Corporate 1487
## 3 Home Office 918 - Sample 5 -count_df_sample_5 <- df_sample_5 |> group_by(Segment) |>
summarise(total_count_segment=n(),
.groups = 'drop')
count_df_sample_5## # A tibble: 3 × 2
## Segment total_count_segment
## <chr> <int>
## 1 Consumer 2563
## 2 Corporate 1576
## 3 Home Office 858From all the above samples, for categorical value - SEGMENT
Sales :-
mean_Sample_1 <- df_sample_1 |> pluck("Sales") |> mean(na.rm=TRUE)
mean_Sample_1## [1] 246.4534- Sample 2 - \
Mean of Sales for sample 2 :-mean_Sample_2 <- df_sample_2 |> pluck("Sales") |> mean(na.rm=TRUE)
mean_Sample_2## [1] 221.7272- Sample 3 -
Mean of Sales for sample 3 :-mean_Sample_3 <- df_sample_3 |> pluck("Sales") |> mean(na.rm=TRUE)
mean_Sample_3## [1] 255.5451- Sample 4 -
Mean of Sales for sample 4 :-mean_Sample_4 <- df_sample_4 |> pluck("Sales") |> mean(na.rm=TRUE)
mean_Sample_4## [1] 217.4075- Sample 5 -
Mean of Sales for sample 5 :-mean_Sample_5 <- df_sample_5 |> pluck("Sales") |> mean(na.rm=TRUE)
mean_Sample_5## [1] 228.1538Mean_of_Sample_average <- mean(mean_Sample_1, mean_Sample_2, mean_Sample_3, mean_Sample_4, mean_Sample_5)
Mean_of_Sample_average ## [1] 246.4534max_Sample_1 <- df_sample_1 |> pluck("Sales") |> max(na.rm=TRUE)
max_Sample_1## [1] 22638.48- Sample 2 - \
Max of Sales for sample 2 :-max_Sample_2 <- df_sample_2 |> pluck("Sales") |> max(na.rm=TRUE)
max_Sample_2## [1] 9099.93- Sample 3 -
Max of Sales for sample 3 :-max_Sample_3 <- df_sample_3 |> pluck("Sales") |> max(na.rm=TRUE)
max_Sample_3## [1] 22638.48- Sample 4 -
Max of Sales for sample 4 :-max_Sample_4 <- df_sample_4 |> pluck("Sales") |> max(na.rm=TRUE)
max_Sample_4## [1] 13999.96- Sample 5 -
Max of Sales for sample 5 :-max_Sample_5 <- df_sample_5 |> pluck("Sales") |> max(na.rm=TRUE)
max_Sample_5## [1] 17499.95min_Sample_1 <- df_sample_1 |> pluck("Sales") |> min(na.rm=TRUE)
min_Sample_1## [1] 0.444- Sample 2 - \
Min of Sales for sample 2 :-min_Sample_2 <- df_sample_2 |> pluck("Sales") |> min(na.rm=TRUE)
min_Sample_2## [1] 0.836- Sample 3 -
Min of Sales for sample 3 :-min_Sample_3 <- df_sample_3 |> pluck("Sales") |> min(na.rm=TRUE)
min_Sample_3## [1] 0.556- Sample 4 -
Min of Sales for sample 4 :-min_Sample_4 <- df_sample_4 |> pluck("Sales") |> min(na.rm=TRUE)
min_Sample_4## [1] 0.852- Sample 5 -
Min of Sales for sample 5 :-min_Sample_5 <- df_sample_5 |> pluck("Sales") |> min(na.rm=TRUE)
min_Sample_5## [1] 0.444State :-
check the various state in each sample -
count_df_sample_1 <- df_sample_1 |> group_by(State) |>
summarise(total_count_state=n(),
.groups = 'drop') |> arrange(desc(total_count_state))
head(count_df_sample_1, 10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 California 1016
## 2 New York 562
## 3 Texas 503
## 4 Pennsylvania 288
## 5 Washington 263
## 6 Illinois 259
## 7 Ohio 258
## 8 Florida 193
## 9 Michigan 137
## 10 North Carolina 13410 states where the purchases were done the least.
tail(count_df_sample_1,10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 Iowa 10
## 2 Idaho 7
## 3 South Dakota 7
## 4 Vermont 7
## 5 District of Columbia 6
## 6 Montana 5
## 7 North Dakota 5
## 8 Maine 4
## 9 West Virginia 1
## 10 Wyoming 1- Sample 2 - \Top 10 states where the purchases were done the most.
count_df_sample_2 <- df_sample_2 |> group_by(State) |>
summarise(total_count_state=n(),
.groups = 'drop') |> arrange(desc(total_count_state))
head(count_df_sample_2, 10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 California 986
## 2 New York 592
## 3 Texas 490
## 4 Pennsylvania 299
## 5 Washington 272
## 6 Illinois 251
## 7 Ohio 231
## 8 Florida 178
## 9 Michigan 127
## 10 North Carolina 12510 states where the purchases were done the least.
tail(count_df_sample_2,10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 New Mexico 12
## 2 Kansas 11
## 3 Nevada 10
## 4 North Dakota 5
## 5 District of Columbia 3
## 6 Vermont 3
## 7 South Dakota 2
## 8 West Virginia 2
## 9 Wyoming 2
## 10 Maine 1- Sample 3 - \Top 10 states where the purchases were done the most.
count_df_sample_3 <- df_sample_3 |> group_by(State) |>
summarise(total_count_state=n(),
.groups = 'drop') |> arrange(desc(total_count_state))
head(count_df_sample_3, 10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 California 994
## 2 New York 541
## 3 Texas 528
## 4 Pennsylvania 289
## 5 Illinois 261
## 6 Ohio 249
## 7 Washington 243
## 8 Florida 197
## 9 Michigan 134
## 10 North Carolina 13010 states where the purchases were done the least.
tail(count_df_sample_3,10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 South Carolina 13
## 2 South Dakota 13
## 3 Iowa 12
## 4 Kansas 11
## 5 Montana 10
## 6 Idaho 8
## 7 Vermont 8
## 8 District of Columbia 5
## 9 Maine 1
## 10 Wyoming 1- Sample 4 - \
Top 10 states where the purchases were done the most.\count_df_sample_4 <- df_sample_4 |> group_by(State) |>
summarise(total_count_state=n(),
.groups = 'drop') |> arrange(desc(total_count_state))
head(count_df_sample_4, 10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 California 983
## 2 New York 570
## 3 Texas 492
## 4 Pennsylvania 298
## 5 Illinois 253
## 6 Washington 236
## 7 Ohio 233
## 8 Florida 209
## 9 North Carolina 131
## 10 Virginia 12510 states where the purchases were done the least.\ tail(count_df_sample_4,10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 Kansas 13
## 2 Vermont 12
## 3 Montana 8
## 4 Idaho 7
## 5 South Dakota 5
## 6 North Dakota 4
## 7 West Virginia 4
## 8 District of Columbia 3
## 9 Maine 3
## 10 Wyoming 1- Sample 5 - \
Top 10 states where the purchases were done the most.\count_df_sample_5 <- df_sample_5 |> group_by(State) |>
summarise(total_count_state=n(),
.groups = 'drop') |> arrange(desc(total_count_state))
head(count_df_sample_5, 10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 California 1027
## 2 New York 546
## 3 Texas 480
## 4 Pennsylvania 270
## 5 Ohio 260
## 6 Washington 256
## 7 Illinois 242
## 8 Florida 187
## 9 North Carolina 135
## 10 Arizona 11910 states where the purchases were done the least.\ tail(count_df_sample_5,10)## # A tibble: 10 × 2
## State total_count_state
## <chr> <int>
## 1 Kansas 11
## 2 Vermont 10
## 3 Idaho 9
## 4 Montana 9
## 5 District of Columbia 7
## 6 South Dakota 6
## 7 North Dakota 5
## 8 West Virginia 3
## 9 Maine 2
## 10 Wyoming 1From all the above samples, for categorical value - STATE
We have calculated the top 10 states which purchase the products.It has been observed that top 5 states are always constant in each of the sample. Even the order is somewhat same.
The remaining states(6 to 10) have certain similarities with the top
5 while occasionally changing their order. The top-performing states
within each sample are, nevertheless, largely stable.
We have calculated the least 10 states which purchase the products.Here it can be observed that there are certain differences in the state with count at the bottom within the samples.
From above samples and their last count on products purchased can see that, West Virginia, Maine and Wyoming is having the least count in all the samples. In the 2nd sample it is seen that Vermont is present in the bottom 5 for one of the samples, a case where the least of all in counts of products are purchased. Also, in 1st and 3rd sample can see Montana and Idaho state present in the sample of least products, which wasnt the case in other samples. But overall, certain states are seen to be similar in case of being the least. No major anomallies detected.
mean_Sample_1 <- df_sample_1 |> pluck("Profit") |> mean(na.rm=TRUE)
mean_Sample_1## [1] 24.96756- Sample 2 - \
Mean of Profit for sample 2 :-mean_Sample_2 <- df_sample_2 |> pluck("Profit") |> mean(na.rm=TRUE)
mean_Sample_2## [1] 29.75024- Sample 3 -
Mean of Profit for sample 3 :-mean_Sample_3 <- df_sample_3 |> pluck("Profit") |> mean(na.rm=TRUE)
mean_Sample_3## [1] 36.66652- Sample 4 -
Mean of Profit for sample 4 :-mean_Sample_4 <- df_sample_4 |> pluck("Profit") |> mean(na.rm=TRUE)
mean_Sample_4## [1] 25.37763- Sample 5 -
Mean of Profit for sample 5 :-mean_Sample_5 <- df_sample_5 |> pluck("Profit") |> mean(na.rm=TRUE)
mean_Sample_5## [1] 30.72369max_Sample_1 <- df_sample_1 |> pluck("Profit") |> max(na.rm=TRUE)
max_Sample_1## [1] 6719.981- Sample 2 - \
Max of Profit for sample 2 :-max_Sample_2 <- df_sample_2 |> pluck("Profit") |> max(na.rm=TRUE)
max_Sample_2## [1] 2591.957- Sample 3 -
Max of Profit for sample 3 :-max_Sample_3 <- df_sample_3 |> pluck("Profit") |> max(na.rm=TRUE)
max_Sample_3## [1] 6719.981- Sample 4 -
Max of Profit for sample 4 :-max_Sample_4 <- df_sample_4 |> pluck("Profit") |> max(na.rm=TRUE)
max_Sample_4## [1] 6719.981- Sample 5 -
Max of Profit for sample 5 :-max_Sample_5 <- df_sample_5 |> pluck("Profit") |> max(na.rm=TRUE)
max_Sample_5## [1] 8399.976min_Sample_1 <- df_sample_1 |> pluck("Profit") |> min(na.rm=TRUE)
min_Sample_1## [1] -6599.978- Sample 2 - \
Min of Profit for sample 2 :-min_Sample_2 <- df_sample_2 |> pluck("Profit") |> min(na.rm=TRUE)
min_Sample_2## [1] -3399.98- Sample 3 -
Min of Profit for sample 3 :-min_Sample_3 <- df_sample_3 |> pluck("Profit") |> min(na.rm=TRUE)
min_Sample_3## [1] -3839.99- Sample 4 -
Min of Profit for sample 4 :-min_Sample_4 <- df_sample_4 |> pluck("Profit") |> min(na.rm=TRUE)
min_Sample_4## [1] -3839.99- Sample 5 -
Min of Profit for sample 5 :-min_Sample_5 <- df_sample_5 |> pluck("Profit") |> min(na.rm=TRUE)
min_Sample_5## [1] -3839.99count_df_sample_1 <- df_sample_1 |> group_by(Region) |>
summarise(total_max_region_sales=max(Sales),
.groups = 'drop') |>
arrange(desc(total_max_region_sales),.by_group= TRUE)
count_df_sample_1## # A tibble: 4 × 2
## Region total_max_region_sales
## <chr> <dbl>
## 1 South 22638.
## 2 West 14000.
## 3 East 11200.
## 4 Central 8160.We can see that when grouping by Region and Segment, products bought in the southern region see the max sale value. Followed by West region.
- Sample 2 -count_df_sample_2 <- df_sample_2 |> group_by(Region) |>
summarise(total_max_region_sales=max(Sales),
.groups = 'drop') |>
arrange(desc(total_max_region_sales),.by_group= TRUE)
count_df_sample_2## # A tibble: 4 × 2
## Region total_max_region_sales
## <chr> <dbl>
## 1 East 9100.
## 2 West 8188.
## 3 Central 5444.
## 4 South 3080We can see that when grouping by Region and Segment, products bought in the Eastern region have products purchased with max sale cost. While Southern is seen to be at the last.
- Sample 3 -
```r
count_df_sample_3 <- df_sample_3 |> group_by(Region) |>
summarise(total_max_region_sales=max(Sales),
.groups = 'drop') |>
arrange(desc(total_max_region_sales),.by_group= TRUE)
count_df_sample_3## # A tibble: 4 × 2
## Region total_max_region_sales
## <chr> <dbl>
## 1 South 22638.
## 2 West 14000.
## 3 East 11200.
## 4 Central 9893.From above grouping can see products from southern region have the highest sale.
- Sample 4 -count_df_sample_4 <- df_sample_4 |> group_by(Region) |>
summarise(total_max_region_sales=max(Sales),
.groups = 'drop') |>
arrange(desc(total_max_region_sales),.by_group= TRUE)
count_df_sample_4## # A tibble: 4 × 2
## Region total_max_region_sales
## <chr> <dbl>
## 1 West 14000.
## 2 East 11200.
## 3 Central 9893.
## 4 South 8000.From above grouping can see products from Western region have the highest sale. But southern is at the last.
- Sample 5 -count_df_sample_5 <- df_sample_5 |> group_by(Region) |>
summarise(total_max_region_sales=max(Sales),
.groups = 'drop') |>
arrange(desc(total_max_region_sales),.by_group= TRUE)
count_df_sample_5## # A tibble: 4 × 2
## Region total_max_region_sales
## <chr> <dbl>
## 1 Central 17500.
## 2 East 11200.
## 3 South 8750.
## 4 West 3611.From above grouping can see products from central region have the highest sale.Followed by Eastern region.For Segment column - we see 3 types of Segment,all the samples saw the segments to be consistent among each other with Home Office being the least and Consumer segment being the most purchased one.This indicates that the data is spread evenly.
For Sales column -
- Mean of Sales :- The sales mean across each sample comes out to be somewhere in the range of 217 to 255. The average of the same comes out to be 246.45. There are no anomalies observed there. All records in each sample somehow depict the population to have a similar average sales.
- Minimum of Sales : - we see the minimum to be varying in each sample. The least of all was 0.444 which is seen in 2 of the samples. If considered to look into separate min values, then we can say that there is an anomaly in minimum of sales, that varies (0.852, 0.556, 0.836) But for the population overall minimum value seems to be 0.444.
- Maximum of Sales : Overall considering the samples the max of sales seems to be 22638.48, which was observed in 2 samples. But anomaly can be observed, where one of the samples had maximum sample to be around 9099.93, which would be an outlier.For State column -
- We can observe the top 5 states to be consistent across each sample,
even the order is somewhat same. Following are the ones:
- For 5 states which purchase the products least, \
- It can be observed that West Virginia, Maine and Wyoming is having the least count in all the samples. So this is consistent across the samples. Considering the same, can conclude the same for the entire population. \
- Few anomalies like Montana and Idaho state are present in the sample of least products, which wasnt the case in other samples. \
- But overall, certain states are seen to be similar in case of being the least. No major anomallies detected. \
Mean of profit :- we see the mean of Profit in each sample to be somewhat similar, within the range of 24 to 36. We can say that the profit for all samples depicts that the populations also has a similar average on profit achieved through each sale.
Minimum of profit : - we see the minimum to be varying in each sample. The least of all was 0.444 which is seen in 2 of the samples. If considered to look into separate min values, then we can say that there is an anomaly in minimum of sales, that varies (0.852, 0.556, 0.836) But for the population overall minimum value seems to be 0.444.
Maximum of profit : we see the maximum profit to be somewhat
8399.976.
3 samples, seems to have the max around 6719.981. This would not
entirely claim to be an anomaly, but if those samples are considered
alone then the assumption would be that products were having that much
of profit. But that is not the case. Sample 2 seems to have max of
2591.957, which would not be considered as a max of Profit, when
compared to rest of the samples. Hence it can be considered as an
outlier , with max profit within that range.
With respect to investigation affecting future, from above data can
incur that if there are similarities in each sample, we can definitely
generalize to attain the whole idea of how the population might
behave.
But if there are certain records that arent observed in each sample then
they would certain outliers that might affect the further
investigation.