20251MBI016_Justine_Mudahogora
Justine Mudahogora
2026-05-24
All Assignment combined
This report demonstrates different data management techniques in R, including importing data, working with database systems, merging datasets, data manipulation using dplyr, graphical visualization, debugging techniques, custom statistical functions, and apply family functions.
Homework 1: Importing Data from Statistical Packages and Database Management Systems
This section demonstrates how to import datasets from statistical packages and external files, as well as how to connect R with a database management system using SQLite.
Importing Data from Statistical Package
data(mtcars)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000The mtcars dataset is a built-in dataset in R containing
information about different car models and their performance
measurements.
Importing E-Commerce Dataset from CSV File
data <- read.csv("C:/Users/justine/Downloads/ecommerce_sales_data.csv")
head(data)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35str(data)## 'data.frame': 3500 obs. of 7 variables:
## $ Order.Date : chr "2024-12-31" "2022-11-27" "2022-05-11" "2024-03-16" ...
## $ Product.Name: chr "Printer" "Mouse" "Tablet" "Mouse" ...
## $ Category : chr "Office" "Accessories" "Electronics" "Accessories" ...
## $ Region : chr "North" "East" "South" "South" ...
## $ Quantity : int 4 7 5 2 1 1 7 7 9 8 ...
## $ Sales : int 3640 1197 5865 786 509 524 6167 3059 5526 672 ...
## $ Profit : num 349 107 503 203 103 ...summary(data)## Order.Date Product.Name Category Region
## Length :3500 Length :3500 Length :3500 Length :3500
## N.unique :1051 N.unique : 10 N.unique : 3 N.unique : 4
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 10 Min.nchar: 5 Min.nchar: 6 Min.nchar: 4
## Max.nchar: 10 Max.nchar: 10 Max.nchar: 11 Max.nchar: 5
##
## Quantity Sales Profit
## Min. :1.000 Min. : 51 Min. : 6.97
## 1st Qu.:3.000 1st Qu.: 1050 1st Qu.: 158.69
## Median :5.000 Median : 2350 Median : 361.07
## Mean :4.932 Mean : 3048 Mean : 527.05
## 3rd Qu.:7.000 3rd Qu.: 4537 3rd Qu.: 729.12
## Max. :9.000 Max. :10782 Max. :2946.93The e-commerce dataset was imported using the read.csv()
function. The dataset contains information related to product sales,
quantity, profit, and product categories.
SQLite Database Management System
# Create database connection
con <- dbConnect(RSQLite::SQLite(), ":memory:")
cat("Database connected!\n")## Database connected!# Write data into database
dbWriteTable(con, "ecommerce_table", data, overwrite = TRUE)
cat("Data written to database!\n")## Data written to database!# Read data from database
data_db <- dbReadTable(con, "ecommerce_table")
head(data_db)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35# Execute SQL query
data_sql <- dbGetQuery(con,
"SELECT * FROM ecommerce_table LIMIT 10")
head(data_sql)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35# Disconnect database
dbDisconnect(con)
cat("Database connection closed!\n")## Database connection closed!SQLite was used as a lightweight database management system. The dataset was written into the database and later retrieved using SQL queries. This demonstrates how R can interact with databases for data storage and analysis.
Homework 2: Merging Datasets
Dataset merging allows information from multiple datasets to be combined into one structured dataset. In this assignment, two datasets were merged using both Base R and dplyr functions.
Creating Sales Dataset
sales_data <- data.frame(
product = c("Laptop", "Phone", "Tablet", "Printer", "Monitor"),
Sales = c(5000, 3000, 2000, 1500, 2500),
Quantity = c(10, 15, 8, 5, 7),
Profit = c(1200, 800, 500, 300, 600)
)
sales_data## product Sales Quantity Profit
## 1 Laptop 5000 10 1200
## 2 Phone 3000 15 800
## 3 Tablet 2000 8 500
## 4 Printer 1500 5 300
## 5 Monitor 2500 7 600Structure of Sales Dataset
head(sales_data)## product Sales Quantity Profit
## 1 Laptop 5000 10 1200
## 2 Phone 3000 15 800
## 3 Tablet 2000 8 500
## 4 Printer 1500 5 300
## 5 Monitor 2500 7 600str(sales_data)## 'data.frame': 5 obs. of 4 variables:
## $ product : chr "Laptop" "Phone" "Tablet" "Printer" ...
## $ Sales : num 5000 3000 2000 1500 2500
## $ Quantity: num 10 15 8 5 7
## $ Profit : num 1200 800 500 300 600Creating Product Dataset
product_info <- data.frame(
product = c("Laptop", "Phone", "Tablet", "Printer", "Monitor"),
price = c(800, 600, 400, 200, 350),
rating = c(4.5, 4.2, 4.0, 3.8, 4.3)
)
product_info## product price rating
## 1 Laptop 800 4.5
## 2 Phone 600 4.2
## 3 Tablet 400 4.0
## 4 Printer 200 3.8
## 5 Monitor 350 4.3Structure of Product Dataset
head(product_info)## product price rating
## 1 Laptop 800 4.5
## 2 Phone 600 4.2
## 3 Tablet 400 4.0
## 4 Printer 200 3.8
## 5 Monitor 350 4.3str(product_info)## 'data.frame': 5 obs. of 3 variables:
## $ product: chr "Laptop" "Phone" "Tablet" "Printer" ...
## $ price : num 800 600 400 200 350
## $ rating : num 4.5 4.2 4 3.8 4.3Selecting Required Columns
product_info <- product_info %>%
select(product, price, rating)
sales_data <- sales_data %>%
select(product, Sales, Quantity, Profit)Merging Using Base R
merged_base <- merge(
sales_data,
product_info,
by = "product",
all.x = TRUE
)
merged_base## product Sales Quantity Profit price rating
## 1 Laptop 5000 10 1200 800 4.5
## 2 Monitor 2500 7 600 350 4.3
## 3 Phone 3000 15 800 600 4.2
## 4 Printer 1500 5 300 200 3.8
## 5 Tablet 2000 8 500 400 4.0Merging Using dplyr
merged_dplyr <- sales_data %>%
left_join(product_info, by = "product")
merged_dplyr## product Sales Quantity Profit price rating
## 1 Laptop 5000 10 1200 800 4.5
## 2 Phone 3000 15 800 600 4.2
## 3 Tablet 2000 8 500 400 4.0
## 4 Printer 1500 5 300 200 3.8
## 5 Monitor 2500 7 600 350 4.3Checking Final Dataset
dim(merged_dplyr)## [1] 5 6names(merged_dplyr)## [1] "product" "Sales" "Quantity" "Profit" "price" "rating"summary(merged_dplyr)## product Sales Quantity Profit price
## Length :5 Min. :1500 Min. : 5 Min. : 300 Min. :200
## N.unique :5 1st Qu.:2000 1st Qu.: 7 1st Qu.: 500 1st Qu.:350
## N.blank :0 Median :2500 Median : 8 Median : 600 Median :400
## Min.nchar:5 Mean :2800 Mean : 9 Mean : 680 Mean :470
## Max.nchar:7 3rd Qu.:3000 3rd Qu.:10 3rd Qu.: 800 3rd Qu.:600
## Max. :5000 Max. :15 Max. :1200 Max. :800
## rating
## Min. :3.80
## 1st Qu.:4.00
## Median :4.20
## Mean :4.16
## 3rd Qu.:4.30
## Max. :4.50The merge operation combined sales information with product details
using the product column as a common key. Both Base R and
dplyr methods successfully produced the merged dataset.
Homework 3: Data Manipulation Using dplyr Functions
This section demonstrates the use of select(),
filter(), arrange(), rename(),
mutate(), group_by(), $, and the
pipe operator %>%.
data$Sales## [1] 3640 1197 5865 786 509 524 6167 3059 5526 672 7074 502
## [13] 7462 4205 570 441 7469 2212 740 662 5712 2739 1400 3477
## [25] 3475 6138 3665 2372 1382 1126 1376 988 10773 3252 2739 2709
## [37] 936 3720 5320 804 1224 8667 3018 4325 1557 3672 333 2384
## [49] 888 3280 1239 5880 1668 4109 595 8155 1872 2124 5210 5761
## [61] 6895 2118 830 851 4730 5460 5795 2008 2842 3404 1065 2149
## [73] 5912 3348 2409 328 6224 2530 4108 1640 111 445 3600 1335
## [85] 3090 3910 219 390 3294 4572 980 2262 7518 873 1512 705
## [97] 3256 556 7101 8802 5592 1852 195 7884 4360 4920 3474 930
## [109] 1421 634 673 3135 882 1192 390 834 612 8073 2640 3234
## [121] 4435 2302 1728 228 3450 7456 4728 945 296 640 797 1456
## [133] 4038 5656 3876 1002 2187 7146 189 940 951 124 7506 8176
## [145] 2125 967 270 10035 6792 714 765 4221 2108 10530 3396 1060
## [157] 480 2529 4792 8056 4228 653 7452 4128 5090 4930 2610 2139
## [169] 226 2244 3150 3388 8736 1102 2640 2547 728 3444 3140 435
## [181] 856 112 2247 1512 4915 2709 1239 572 1708 360 3822 4680
## [193] 2520 2138 2529 236 2730 3021 244 728 453 379 2130 4120
## [205] 3150 2688 1752 7308 2007 7224 6939 5516 2224 3985 2723 746
## [217] 3170 550 480 3284 1748 824 1872 627 5661 2910 9036 4815
## [229] 1187 3339 620 2025 3030 655 2238 3312 9387 5790 2700 2072
## [241] 2991 5568 5509 5922 4380 1760 5571 1082 569 3416 570 3300
## [253] 2584 7650 3600 846 51 7056 384 2928 1904 2314 1028 8307
## [265] 1539 2506 5778 10188 952 3624 7968 1254 705 916 848 2240
## [277] 940 1550 2352 1638 7164 205 766 168 1058 1455 2224 1400
## [289] 7567 3396 704 7767 2050 10458 4700 892 6968 1344 354 3051
## [301] 402 3570 3829 4676 1119 4228 1240 3591 3952 3648 3875 3416
## [313] 908 4532 4168 3543 5124 5751 624 508 4700 3216 2844 2385
## [325] 928 4656 330 1799 1029 5432 5785 1062 163 9162 1440 1465
## [337] 366 3135 630 213 3792 1932 4152 3612 2258 594 8071 5838
## [349] 692 1704 3235 4746 7600 1935 1638 4746 4824 1193 562 6200
## [361] 4128 5405 1371 1405 1644 3414 3272 2655 1673 846 985 7551
## [373] 855 396 2296 1704 1180 411 745 10656 1494 1755 4941 6510
## [385] 5465 3522 1656 1255 1192 1925 1688 260 4880 1134 9621 2836
## [397] 5859 2044 612 1890 2025 1960 4410 8272 5712 1524 5160 2956
## [409] 7944 1815 2706 1356 6384 4248 3954 7110 8113 1830 3732 4062
## [421] 496 1104 2274 4456 4928 1740 178 5296 4156 624 3892 1620
## [433] 2250 1390 5155 1025 1785 2952 773 1640 1017 1964 2994 8046
## [445] 1874 6008 7240 1188 2997 7524 3625 6952 1053 6696 1420 9972
## [457] 3136 225 1662 500 3168 622 1233 573 702 4832 3765 6928
## [469] 1624 68 651 6293 1068 381 2016 1224 2180 2853 272 2640
## [481] 488 9184 280 2889 625 7056 3936 2472 855 3480 846 158
## [493] 3186 6972 1745 4256 1674 6488 3530 632 3675 5592 6432 3069
## [505] 2898 3960 1476 1058 1215 204 4137 250 1185 1504 6888 3588
## [517] 1477 1280 7161 1850 3378 7672 4254 2285 5775 9360 6936 5634
## [529] 2644 247 1869 1988 1920 2577 828 1580 9009 7425 2945 294
## [541] 5875 1308 2360 1008 1344 5635 4088 2192 1952 3955 5256 1163
## [553] 5004 9128 4700 1686 4572 2510 3117 380 2889 3654 5610 668
## [565] 3983 1112 4230 2968 4690 4296 7203 3640 1131 3627 7968 3546
## [577] 2270 1354 6811 1410 1170 4739 7866 3645 5358 1775 4905 4260
## [589] 1632 3324 4590 8496 6444 812 1764 1012 836 723 5628 1660
## [601] 2295 2190 3500 1098 945 5622 2325 1157 531 1760 371 1896
## [613] 514 1876 1182 4255 1341 644 1827 930 528 4236 1664 2727
## [625] 522 8288 1062 1140 3488 5920 3096 3490 4374 3366 822 340
## [637] 763 3408 2282 4151 6279 474 680 8496 7826 4014 687 2943
## [649] 5649 3296 1137 380 4405 120 1176 1770 4302 2988 3033 6363
## [661] 3402 3496 1080 1530 3568 954 1196 2136 8496 7280 4935 9056
## [673] 563 1570 2181 3808 6552 6544 2631 945 760 1392 1242 3183
## [685] 4260 1041 1692 1728 4484 4123 2372 5201 3100 5292 5952 1932
## [697] 3486 1708 4324 2708 7944 684 5904 4970 2288 4662 2532 9328
## [709] 686 1290 1205 7966 1085 6633 5096 4120 1479 1652 1028 4580
## [721] 2152 4566 2244 1344 2622 325 960 2639 1025 1924 1660 985
## [733] 2600 2792 378 968 564 2365 3252 276 3408 1398 904 1178
## [745] 2535 496 3888 4921 4472 603 1899 544 6471 677 1372 7542
## [757] 924 3940 374 1800 3170 5362 5976 8514 810 71 651 1245
## [769] 464 5155 5175 1668 1872 513 2276 3428 1640 7077 9225 3910
## [781] 1995 385 4316 1169 2325 199 375 8010 316 5895 388 3670
## [793] 7731 1596 4192 5264 425 3380 8379 2040 1336 1934 1560 4446
## [805] 1893 4492 6228 4403 4176 486 4880 265 1041 9448 1617 1486
## [817] 7152 1404 122 7533 3888 870 1446 510 6224 686 2840 2562
## [829] 790 4170 1360 5316 3956 726 228 2274 2628 606 7256 1956
## [841] 3954 2262 2804 5850 56 4302 10143 1012 8964 1758 6348 1582
## [853] 1924 120 7752 1114 1765 6834 1520 8141 5176 738 4086 442
## [865] 2478 1860 4968 1446 1722 6573 2481 3828 1197 7623 3384 1296
## [877] 2840 1540 1414 8328 744 1773 6034 756 2256 875 1208 352
## [889] 4266 1841 712 724 5832 4188 392 963 5706 1365 202 2430
## [901] 2670 2758 873 7245 960 1284 512 9729 1580 976 1824 3188
## [913] 5467 5406 2984 919 1686 4165 1281 2829 2211 4740 718 1716
## [925] 1547 739 819 5696 1706 2066 256 1102 2872 2584 1366 1253
## [937] 2618 6822 5286 579 1392 2493 6461 1486 1152 1750 4417 3724
## [949] 2220 9104 3093 3048 161 7238 3561 1850 3522 584 2556 348
## [961] 1602 2853 1024 4485 436 2232 7056 875 315 358 3451 1350
## [973] 3320 8442 166 3720 1090 9783 2432 4060 387 966 3366 7833
## [985] 610 4288 7740 3150 894 7544 6360 2428 574 6744 798 2860
## [997] 5410 4445 7536 4165 3021 310 2170 2832 3945 2104 2060 1060
## [1009] 810 873 595 1562 5337 212 2704 3225 1342 3395 3968 4555
## [1021] 2132 6516 2040 6126 520 4470 5504 3036 2725 4080 3080 1525
## [1033] 1332 4669 5310 9392 1350 3648 300 3408 4302 4575 9304 1928
## [1045] 1042 2214 3024 1104 6056 5295 3668 4293 5789 4440 5515 784
## [1057] 576 705 4458 6780 3970 5131 1750 8337 9810 2658 473 2205
## [1069] 6948 774 2532 6587 7266 6867 4887 864 5190 2128 1718 1270
## [1081] 2569 1518 1210 1128 1077 2244 1608 3685 4080 404 1738 828
## [1093] 4344 1130 1698 844 2286 1026 6984 1672 1092 272 1126 8155
## [1105] 4298 1365 4116 904 70 344 330 518 2763 2298 436 1824
## [1117] 1540 5572 3030 1245 1252 4959 8162 1370 3111 7805 10314 10782
## [1129] 4375 2265 166 7217 1767 2883 1338 9459 168 4536 4404 1952
## [1141] 2004 468 8667 6097 4752 1172 1608 7595 1142 116 3951 1606
## [1153] 2025 2006 9423 2276 4880 1773 2718 3168 1716 214 2092 225
## [1165] 859 2070 3240 1748 2080 2193 2080 5070 469 802 930 495
## [1177] 4938 1300 2100 2248 7096 1992 4056 1986 1635 3132 7064 907
## [1189] 7938 4360 375 826 1011 807 1840 4550 8793 310 1143 2610
## [1201] 1494 1524 9441 3520 390 3219 450 828 1146 1986 7264 7469
## [1213] 1385 4556 456 1984 5178 281 3033 1107 1446 1335 266 648
## [1225] 344 5724 4920 5440 7994 3780 1017 1578 912 2520 2632 6582
## [1237] 3432 1030 1192 7083 3675 4740 1716 3528 777 3368 4240 3285
## [1249] 4872 2466 2465 612 4170 1088 1626 6486 6968 357 454 3582
## [1261] 3762 6642 408 105 2682 497 4823 4048 2758 4096 7264 4302
## [1273] 3297 5960 386 684 1275 5949 5159 1100 290 7144 4200 5022
## [1285] 2354 2032 9528 2372 2703 1264 1624 2976 4998 445 98 4102
## [1297] 1016 1960 2751 4032 5978 8050 4648 1119 2178 918 6903 4855
## [1309] 2158 4550 3592 3488 1214 7200 1808 7083 1060 7182 5532 1800
## [1321] 351 1200 1593 2580 6780 1298 4585 516 1780 365 3896 636
## [1333] 637 1302 719 5106 1215 1791 6255 4165 5990 847 688 1188
## [1345] 3101 8703 9324 749 3224 459 5726 616 1584 3205 958 5139
## [1357] 3795 810 5106 798 3165 5935 3352 8343 2604 526 5220 1737
## [1369] 3186 7014 3411 513 1104 2718 1662 1700 2932 3604 4596 930
## [1381] 2790 4596 962 92 1130 6024 879 2384 4644 607 770 711
## [1393] 1088 4704 660 4680 6797 558 4496 713 1310 2853 2324 330
## [1405] 588 63 847 1037 373 1176 2139 4140 1038 2024 6624 5019
## [1417] 750 6461 2946 618 1774 8586 2000 849 3186 2536 1306 2508
## [1429] 1048 1516 10458 2754 3432 1234 847 7672 7596 7252 732 173
## [1441] 2805 7048 4698 565 672 2604 5124 2455 1796 288 2160 105
## [1453] 7578 365 3752 1384 475 1116 137 738 1821 3654 2592 996
## [1465] 693 513 5736 1458 5496 3183 3990 582 3340 890 4890 2625
## [1477] 1440 505 8192 1920 8768 1140 952 225 1304 1029 3402 8460
## [1489] 104 10665 984 2064 594 6016 2864 724 4098 338 9280 890
## [1501] 1790 8523 7776 3031 4698 4851 4542 978 9378 9464 1720 4752
## [1513] 584 4554 2392 3962 1308 5912 5831 5572 5214 6705 426 2204
## [1525] 2652 3132 1830 2763 9056 5439 828 1910 1874 1032 3264 877
## [1537] 924 417 2670 2716 10134 5500 6228 1092 3255 5645 7578 6282
## [1549] 2364 6880 3288 5292 3522 1518 2457 309 6192 4235 7128 5660
## [1561] 787 375 296 6372 2214 2640 2240 6258 3816 390 630 3868
## [1573] 777 1347 7272 5724 312 1947 1128 4158 2670 85 4136 4332
## [1585] 1600 5075 4152 5635 3108 4270 1350 330 4260 2712 328 7776
## [1597] 2896 1902 3255 633 6209 4698 7424 498 111 7520 182 5890
## [1609] 3244 5229 5202 438 6900 369 4140 1086 2134 5310 2748 3357
## [1621] 10206 2970 1208 945 7040 3684 1262 3504 4464 1324 4140 2384
## [1633] 4624 4812 5640 1840 8071 2400 3570 1758 3072 2185 7664 1710
## [1645] 1878 569 1488 9918 432 9828 3625 2187 5105 1392 744 1724
## [1657] 1940 356 1095 4064 5453 7104 2745 1030 8973 794 2142 414
## [1669] 1416 2103 861 2367 2435 2456 3216 4324 2628 8992 360 2067
## [1681] 3573 1800 3912 5640 552 1428 349 6402 657 5327 4196 2892
## [1693] 2772 4692 2742 8896 2344 4186 5238 2982 2580 2675 2396 1600
## [1705] 304 2070 1230 6234 396 2349 5949 5957 1989 1138 3321 4480
## [1717] 1533 109 2380 2592 5112 9392 2386 465 604 2216 1168 4088
## [1729] 2597 2151 462 5957 6978 282 157 3304 3051 8082 510 3976
## [1741] 828 2457 3924 2018 2043 1035 2724 1120 3438 3170 660 4669
## [1753] 1045 2724 5312 4252 8057 2023 9416 7560 828 527 1160 5430
## [1765] 6874 8952 416 1680 4340 7650 6328 1312 8055 840 9423 6520
## [1777] 1950 5928 4809 356 570 7791 4168 684 3857 3250 1194 3486
## [1789] 1554 2601 3616 3350 2545 545 1470 3126 1368 7083 1994 4716
## [1801] 975 9891 626 5632 2568 5285 516 2139 8136 2130 3445 5376
## [1813] 3006 3941 3350 2019 2416 6160 2796 7700 2095 5990 1206 1330
## [1825] 2706 426 414 5465 726 1001 7362 2284 2940 3068 3477 4014
## [1837] 7200 735 2964 3699 1563 124 416 2624 1636 8200 5615 3085
## [1849] 3198 1848 652 2940 3912 9392 312 7731 1081 1926 936 8550
## [1861] 2468 801 2655 3928 6488 7695 1856 1519 3549 1656 812 3730
## [1873] 1161 2256 359 130 4248 1825 876 9180 1765 1504 1881 7425
## [1885] 720 2985 711 6276 7966 4200 455 5040 856 5230 4635 5508
## [1897] 258 677 4545 4370 372 3177 1659 3360 1132 1815 459 1128
## [1909] 1170 4420 456 1458 2989 3424 1226 6760 111 3275 2508 904
## [1921] 1506 987 2968 4200 1645 3282 4914 3924 420 1544 2046 1548
## [1933] 3184 3385 59 5976 1791 1644 6584 1620 2736 1064 2667 735
## [1945] 58 3136 3936 6102 9408 7875 9900 430 813 4176 3084 8008
## [1957] 4608 4356 566 945 2200 746 3505 4795 4590 2092 3568 10404
## [1969] 7029 664 1923 2471 258 1134 855 3231 3591 254 5930 1344
## [1981] 920 5454 9512 2972 3360 1004 1944 5055 128 633 2523 196
## [1993] 4212 63 2752 1989 8032 3702 456 5355 972 3290 735 3336
## [2005] 2082 1340 1160 1272 2817 1464 307 5706 1112 1147 330 2176
## [2017] 7616 1091 1026 210 180 380 7664 6056 606 750 1218 2162
## [2029] 2223 4452 184 288 834 4025 4732 4025 2416 1897 840 6600
## [2041] 2362 2958 477 942 2084 4584 1118 4005 4610 551 75 6576
## [2053] 6432 7763 2240 5298 116 6728 490 2765 2680 2174 5562 9208
## [2065] 4512 7974 4944 1611 4599 4380 2376 6588 3745 5305 1608 4977
## [2077] 4529 4690 2424 3594 3962 944 6792 6804 816 5916 5730 8288
## [2089] 2046 5031 1764 1880 200 2688 3216 8440 1582 58 608 183
## [2101] 3105 2450 3024 3856 532 3152 8586 1046 3452 2368 1924 4580
## [2113] 3735 2476 162 258 3524 6184 3750 1465 4480 1544 7974 5920
## [2125] 8792 294 1169 9232 1396 10188 222 1328 5085 1245 960 5553
## [2137] 1790 2712 632 4904 361 7290 2052 1842 10593 1218 4240 226
## [2149] 5154 1236 1179 2275 6188 2946 5085 712 648 2735 2910 263
## [2161] 876 913 594 956 7301 5754 1372 920 885 379 1024 4312
## [2173] 5030 1438 419 3519 2912 4005 2075 120 564 486 1488 3096
## [2185] 6488 4284 1864 4435 3296 780 4396 1362 3843 2080 1210 2316
## [2197] 3366 6507 384 3625 400 2880 1608 1574 2478 8872 2320 171
## [2209] 4536 1308 4048 4041 5665 6876 617 1440 472 5640 2640 768
## [2221] 725 1080 393 1264 4797 2080 3812 3695 1159 4149 1446 225
## [2233] 355 3164 498 1315 2805 5180 4045 8793 2592 7819 2919 768
## [2245] 1736 6293 3684 8370 3788 8296 325 2216 7064 2538 1151 717
## [2257] 3048 2070 3630 896 1981 7140 1015 524 1678 3260 720 1792
## [2269] 3096 1400 508 2646 357 373 266 344 1806 7281 3094 252
## [2281] 2691 990 3236 1076 1428 1000 3429 4730 6630 2296 7420 2338
## [2293] 4039 990 733 2421 832 483 2508 4032 3208 3402 541 1806
## [2305] 2433 5880 3375 6960 316 1404 5823 2790 1169 6016 7154 370
## [2317] 430 946 984 1467 2444 8640 3861 868 1746 915 3591 2562
## [2329] 5382 2792 2160 6377 5264 1346 7301 2016 1266 140 6520 582
## [2341] 245 504 6834 5712 4485 2304 6576 10629 706 977 776 3932
## [2353] 1263 7064 4740 4522 2009 2110 95 384 3471 2048 9296 5272
## [2365] 619 448 5004 4716 7506 4095 4459 620 2362 606 1084 2880
## [2377] 4544 556 505 2595 2820 1018 944 610 912 10413 2176 6108
## [2389] 1148 8824 2240 1312 10620 2178 1671 2025 2886 1868 3280 1768
## [2401] 6937 3012 777 4056 3212 952 1674 4315 1722 2895 1280 4295
## [2413] 4434 3816 6335 315 626 1140 590 631 1944 5100 1092 4662
## [2425] 4312 4056 534 3475 4880 8336 9232 2031 2412 1039 696 6160
## [2437] 447 6102 5085 3038 882 2490 635 1083 720 3111 1242 5845
## [2449] 835 2314 6041 369 1635 2067 7128 944 5160 8032 376 4144
## [2461] 2228 1178 2505 1872 340 589 2598 502 3576 2412 1145 2116
## [2473] 707 657 5345 4032 3840 575 9315 3423 3942 574 3024 952
## [2485] 9090 1166 2925 3455 1614 1771 5658 8460 1925 2196 1696 6813
## [2497] 3670 2936 5165 5940 2664 1198 1340 9657 2592 1168 5920 2260
## [2509] 7016 546 839 5176 280 2097 4632 1088 1314 7960 2856 1680
## [2521] 4464 2940 1272 1728 3153 2536 2508 123 2379 6924 9054 9936
## [2533] 393 308 4900 927 266 4750 3272 4350 706 213 1614 6232
## [2545] 3864 2601 4944 4680 2112 7140 750 392 2408 2016 464 832
## [2557] 1296 300 2856 3380 400 722 4596 1176 644 544 586 3835
## [2569] 1415 1125 3399 5370 2331 10728 9056 4581 1074 4572 8141 604
## [2581] 906 1582 3306 847 3222 189 1224 5621 966 1728 6138 964
## [2593] 4697 1232 6384 780 1256 1162 7520 2387 2256 7792 3944 822
## [2605] 1962 1016 1980 3996 663 575 187 6216 189 358 6192 4382
## [2617] 1122 8192 2658 606 448 1674 1944 1716 2982 7497 1026 7392
## [2629] 2604 1746 5682 8664 1887 6013 1578 285 5448 1710 2817 9117
## [2641] 274 6408 5755 2262 1785 5128 5176 1510 947 5427 4560 2100
## [2653] 1054 7026 513 2244 1785 725 2516 6256 452 5285 4872 2495
## [2665] 442 8595 3528 7777 2008 596 4950 630 6144 140 2014 384
## [2677] 3780 5047 2658 528 6507 5887 8224 723 2272 8640 2984 606
## [2689] 170 5283 1350 2500 4648 650 1538 3928 2258 2988 4136 3605
## [2701] 1656 7072 5368 1356 1326 730 4664 3528 1497 2628 4196 1204
## [2713] 7119 1848 2232 5190 1830 1033 2324 1942 5740 6435 1168 7362
## [2725] 862 956 4438 972 1816 596 1233 1055 952 312 294 474
## [2737] 788 2760 908 593 1698 546 4380 2472 1996 2525 4041 858
## [2749] 4221 858 776 3654 384 535 7992 3892 2637 8664 918 5625
## [2761] 6030 432 1884 9225 954 7072 342 2440 510 192 3600 2597
## [2773] 772 646 2947 880 1038 714 1792 5874 2496 795 7238 430
## [2785] 1728 1833 7280 814 4696 1190 591 8100 1500 300 1098 2336
## [2797] 410 594 218 1298 895 2828 813 1800 99 7576 1317 957
## [2809] 1316 5346 464 3752 1962 2310 3028 2994 3393 1209 5784 3078
## [2821] 2464 2445 5312 657 2580 4624 1258 6468 1359 104 1785 7728
## [2833] 4188 3520 2280 2484 1248 850 1412 465 293 9234 1298 9477
## [2845] 6713 5700 4146 6968 1054 1983 875 268 5370 3624 1932 6020
## [2857] 1775 1488 6720 5047 3735 1250 2751 468 632 798 1295 3360
## [2869] 995 550 5384 3534 6813 5508 966 460 7026 910 5796 1050
## [2881] 1043 3330 1896 5170 466 6032 1084 490 558 4024 1560 126
## [2893] 4921 2665 200 186 5456 3321 3766 3150 646 2665 1467 4620
## [2905] 1184 113 3336 4224 1098 4400 5616 4676 2370 4487 6384 2960
## [2917] 897 1178 1026 1148 3185 5180 2025 1359 1836 702 6576 1428
## [2929] 6853 1492 1938 905 2571 1500 8712 1308 2648 9810 3291 758
## [2941] 2436 560 4240 5416 1728 6309 3400 403 2108 1090 1274 1056
## [2953] 5247 6006 5415 632 6160 857 169 1463 7528 1158 200 1514
## [2965] 2632 358 2360 5275 1719 1956 1875 984 3616 4560 7496 5070
## [2977] 4505 1916 8113 1809 1227 2040 6324 2724 1188 8776 5553 970
## [2989] 940 1175 4860 987 2968 2172 3941 2261 8802 3025 4428 7344
## [3001] 2236 536 1980 2100 1497 4200 1242 1570 428 914 465 1062
## [3013] 8720 1308 8514 1212 2576 4800 1440 10251 3038 6993 1632 5706
## [3025] 9657 4405 1720 4557 2380 456 6534 3456 8163 4656 966 2580
## [3037] 8127 2128 6417 3997 879 2415 1064 1156 1184 1950 1088 4380
## [3049] 998 5766 3120 637 3306 7020 5971 750 2379 699 6876 1800
## [3061] 1255 1998 307 235 1506 2464 322 1100 2188 2934 9224 2436
## [3073] 972 5484 3152 6032 6384 3705 5580 1236 7040 909 106 281
## [3085] 6768 4302 7146 582 576 4265 5368 314 2676 3384 3186 1832
## [3097] 4784 381 4527 1612 1918 179 3153 3864 2440 3660 318 483
## [3109] 6774 5310 5154 6741 520 1120 911 1064 1981 4280 5125 1575
## [3121] 9972 4576 6559 619 2488 2536 5416 2334 1080 1324 2304 4736
## [3133] 2925 6345 4221 462 7588 5232 1440 1026 7455 2456 2859 311
## [3145] 1260 466 867 3888 3032 4704 3186 6183 243 897 4563 628
## [3157] 1050 3032 920 668 5990 9324 5768 1123 2336 438 5940 1848
## [3169] 2118 231 978 4960 1377 1958 5607 3504 796 342 6769 10080
## [3181] 3948 1488 1200 4476 1178 2888 1704 4200 4417 764 4000 2331
## [3193] 8172 761 494 194 5820 410 1254 3432 3792 546 1452 9296
## [3205] 1160 4728 1836 6237 5845 6138 1416 1851 7452 1630 4800 61
## [3217] 942 7640 4540 1030 384 8560 1557 707 3405 4722 2150 3619
## [3229] 3892 54 612 3850 3005 5256 3576 3415 1722 4088 5320 3660
## [3241] 5250 328 1596 2443 5432 1254 5495 596 3696 4488 6846 2960
## [3253] 419 6246 1900 3451 1612 4445 720 1428 3532 6528 743 1758
## [3265] 1008 995 4032 480 989 6606 912 2912 845 2804 208 379
## [3277] 1227 5551 423 5982 3732 5904 1810 5604 432 416 1224 5168
## [3289] 6522 1090 1737 7281 5832 7020 2001 1832 3372 3384 351 2092
## [3301] 1309 6696 1020 8240 9810 853 4416 3832 1876 2634 1027 3100
## [3313] 104 1590 1104 1851 1408 4641 2611 3824 2042 6378 4692 6234
## [3325] 1852 3075 370 624 1734 2268 3670 4634 1440 5136 166 1422
## [3337] 1832 668 1272 984 7896 636 2031 3186 134 1765 3628 1000
## [3349] 2134 6706 321 3126 5446 1131 1946 6818 2317 396 558 2034
## [3361] 6080 1378 483 7864 1268 3774 9090 3720 8896 3264 146 1683
## [3373] 6076 2388 882 3561 3395 3366 630 440 6462 5004 1840 4466
## [3385] 3048 4977 2010 5352 4136 6336 5481 6366 623 237 8370 4965
## [3397] 7791 158 4347 3364 1098 1083 1628 1472 3753 6402 2565 2214
## [3409] 975 3312 267 8145 232 300 2248 1614 1476 4375 1064 1068
## [3421] 190 7868 1239 1394 2060 1060 7812 7077 5712 4146 992 1756
## [3433] 3632 3912 3123 700 408 4056 770 531 280 4550 2480 2604
## [3445] 1351 4615 829 177 1086 275 564 684 2656 1190 4854 1060
## [3457] 1527 1168 6328 2193 7600 1764 1270 607 1672 5718 584 7455
## [3469] 7679 847 1278 6592 3544 1959 3384 307 10017 7931 1168 9765
## [3481] 1456 950 2314 915 290 3584 1254 2410 800 990 9702 5888
## [3493] 1768 1022 2883 4064 1117 260 222 10530selected_data <- data %>%
select(Product.Name, Category, Sales, Quantity)
head(selected_data)## Product.Name Category Sales Quantity
## 1 Printer Office 3640 4
## 2 Mouse Accessories 1197 7
## 3 Tablet Electronics 5865 5
## 4 Mouse Accessories 786 2
## 5 Mouse Accessories 509 1
## 6 Camera Electronics 524 1filtered_data <- data %>%
filter(Sales > 500)
head(filtered_data)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35arranged_data <- data %>%
arrange(desc(Sales))
head(arranged_data)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2023-09-29 Keyboard Accessories South 9 10782 2730.14
## 2 2023-04-09 Laptop Electronics North 9 10773 1907.36
## 3 2022-06-27 Laptop Electronics East 9 10728 1967.84
## 4 2023-12-18 Monitor Accessories West 9 10665 1595.08
## 5 2022-05-09 Printer Office West 9 10656 961.30
## 6 2022-11-16 Keyboard Accessories South 9 10629 2843.54renamed_data <- data %>%
rename(
Product_Name = Product.Name,
Total_Sales = Sales
)
head(renamed_data)## Order.Date Product_Name Category Region Quantity Total_Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35mutated_data <- data %>%
mutate(Profit_Per_Unit = Profit / Quantity)
head(mutated_data)## Order.Date Product.Name Category Region Quantity Sales Profit
## 1 2024-12-31 Printer Office North 4 3640 348.93
## 2 2022-11-27 Mouse Accessories East 7 1197 106.53
## 3 2022-05-11 Tablet Electronics South 5 5865 502.73
## 4 2024-03-16 Mouse Accessories South 2 786 202.87
## 5 2022-09-10 Mouse Accessories West 1 509 103.28
## 6 2023-12-01 Camera Electronics West 1 524 106.35
## Profit_Per_Unit
## 1 87.23250
## 2 15.21857
## 3 100.54600
## 4 101.43500
## 5 103.28000
## 6 106.35000grouped_data <- data %>%
group_by(Category) %>%
summarise(
Total_Sales = sum(Sales, na.rm = TRUE),
Avg_Profit = mean(Profit, na.rm = TRUE),
Total_Quantity = sum(Quantity, na.rm = TRUE)
)
grouped_data## # A tibble: 3 × 4
## Category Total_Sales Avg_Profit Total_Quantity
## <chr> <int> <dbl> <int>
## 1 Accessories 4247591 525. 6917
## 2 Electronics 5326074 530. 8610
## 3 Office 1094216 519. 1734final_result <- data %>%
filter(Sales > 500) %>%
select(Product.Name, Category, Sales, Profit) %>%
mutate(Profit_Ratio = Profit / Sales) %>%
arrange(desc(Sales))
head(final_result)## Product.Name Category Sales Profit Profit_Ratio
## 1 Keyboard Accessories 10782 2730.14 0.25321276
## 2 Laptop Electronics 10773 1907.36 0.17705003
## 3 Laptop Electronics 10728 1967.84 0.18343028
## 4 Monitor Accessories 10665 1595.08 0.14956212
## 5 Printer Office 10656 961.30 0.09021209
## 6 Keyboard Accessories 10629 2843.54 0.26752658In this analysis, the tidyverse pipeline operator %>%
was used to streamline data processing and improve readability by
linking multiple transformation steps together. The
group_by() function partitioned the dataset into groups
based on product categories, while the summarise() function
computed aggregated statistics such as total sales and average
profit.
The workflow transformed raw data into structured summaries suitable for analysis and interpretation.
Graph 1: Distribution of Sales
The histogram below shows the distribution of sales values in the dataset.
ggplot(data, aes(x = Sales)) +
geom_histogram(
binwidth = 100,
fill = "steelblue",
color = "white"
) +
labs(
title = "Distribution of Sales",
x = "Sales",
y = "Frequency"
)
The histogram helps visualize the frequency of sales values and identify patterns within the dataset.
Graph 2: Total Sales by Category
The following graph compares total sales across product categories.
category_sales <- data %>%
group_by(Category) %>%
summarise(
Total_Sales = sum(Sales, na.rm = TRUE)
)
ggplot(
category_sales,
aes(
x = reorder(Category, -Total_Sales),
y = Total_Sales
)
) +
geom_bar(
stat = "identity",
fill = "darkgreen"
) +
labs(
title = "Total Sales by Category",
x = "Category",
y = "Total Sales"
) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
This bar chart shows which categories contributed the most to total sales.
Graph 3: Relationship Between Sales and Profit
The scatter plot below examines the relationship between sales and profit.
ggplot(data, aes(x = Sales, y = Profit)) +
geom_point(
color = "red",
size = 2
) +
labs(
title = "Relationship Between Sales and Profit",
x = "Sales",
y = "Profit"
)
The graph indicates that products with higher sales generally generate higher profits.
Graph 4: Quantity Sold by Category
The graph below shows the quantity of products sold in each category.
quantity_data <- data %>%
group_by(Category) %>%
summarise(
Total_Quantity = sum(Quantity, na.rm = TRUE)
)
ggplot(
quantity_data,
aes(
x = Category,
y = Total_Quantity,
fill = Category
)
) +
geom_bar(stat = "identity") +
labs(
title = "Total Quantity Sold by Category",
x = "Category",
y = "Quantity Sold"
) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
This graph helps identify categories with the highest customer demand.
Homework 4: trace() and recover()
Debugging functions in R help identify and monitor program execution.
In this assignment, the trace() function was used to track
function execution.
trace(mean, tracer = quote(cat(">> mean called\n")))## [1] "mean"mean(1:10)## Tracing mean(1:10) on entry
## >> mean called## [1] 5.5mean(c(5, 15, 25))## Tracing mean(c(5, 15, 25)) on entry
## >> mean called## [1] 15untrace(mean)
my_func <- function(x) {
x <- x * 2
x <- x + 10
x
}
trace(
"my_func",
tracer = quote(cat("trace step\n")),
at = 3
)## [1] "my_func"my_func(5)## Tracing my_func(5) step 3
## trace step## [1] 20untrace("my_func")
options(error = NULL)The trace() function inserted debugging messages during
function execution, while untrace() removed the tracing
behavior after testing.
Homework 5: Custom Statistical Functions
Custom statistical functions were created to calculate mean, median, variance, standard deviation, minimum, and maximum values manually.
num_data <- data[, sapply(data, is.numeric)]
# Mean
my_mean <- function(x) {
sum(x, na.rm = TRUE) / length(na.omit(x))
}
# Median
my_median <- function(x) {
x <- sort(na.omit(x))
n <- length(x)
if (n %% 2 == 1) {
x[(n + 1) / 2]
} else {
(x[n / 2] + x[n / 2 + 1]) / 2
}
}
# Variance
my_variance <- function(x) {
x <- na.omit(x)
m <- mean(x)
sum((x - m)^2) / (length(x) - 1)
}
# Standard Deviation
my_sd <- function(x) {
sqrt(my_variance(x))
}
# Minimum
my_min <- function(x) {
min(x, na.rm = TRUE)
}
# Maximum
my_max <- function(x) {
max(x, na.rm = TRUE)
}
# Summary Function
my_summary <- function(x) {
c(
Mean = my_mean(x),
Median = my_median(x),
Variance = my_variance(x),
SD = my_sd(x),
Min = my_min(x),
Max = my_max(x)
)
}
my_summary(num_data$Quantity)## Mean Median Variance SD Min Max
## 4.931714 5.000000 6.635233 2.575895 1.000000 9.000000apply(num_data, 2, my_summary)## Quantity Sales Profit
## Mean 4.931714 3047.966 527.0472
## Median 5.000000 2350.500 361.0700
## Variance 6.635233 5954640.643 254156.8689
## SD 2.575895 2440.213 504.1397
## Min 1.000000 51.000 6.9700
## Max 9.000000 10782.000 2946.9300The custom functions demonstrated how statistical calculations are
performed internally in R. Missing values were handled using
na.omit() and na.rm = TRUE.
Homework 6: Apply Family Functions
Apply family functions simplify repetitive calculations in R.
Functions such as lapply(), sapply(),
vapply(), mapply(), and map()
were used.
numeric_data <- data[, sapply(data, is.numeric)]
# lapply
lapply(numeric_data, mean, na.rm = TRUE)## $Quantity
## [1] 4.931714
##
## $Sales
## [1] 3047.966
##
## $Profit
## [1] 527.0472# sapply
sapply(numeric_data, mean, na.rm = TRUE)## Quantity Sales Profit
## 4.931714 3047.966000 527.047203# vapply
vapply(
numeric_data,
mean,
numeric(1),
na.rm = TRUE
)## Quantity Sales Profit
## 4.931714 3047.966000 527.047203# mapply
sales <- c(100, 200, 300, 400)
quantity <- c(2, 4, 5, 8)
mapply(function(x, y) x / y,
sales,
quantity)## [1] 50 50 60 50# map from purrr
map(numeric_data, mean, na.rm = TRUE)## $Quantity
## [1] 4.931714
##
## $Sales
## [1] 3047.966
##
## $Profit
## [1] 527.0472# Additional examples
sapply(numeric_data, sum, na.rm = TRUE)## Quantity Sales Profit
## 17261 10667881 1844665lapply(numeric_data, max, na.rm = TRUE)## $Quantity
## [1] 9
##
## $Sales
## [1] 10782
##
## $Profit
## [1] 2946.93vapply(
numeric_data,
min,
numeric(1),
na.rm = TRUE
)## Quantity Sales Profit
## 1.00 51.00 6.97The apply family functions reduced code repetition and improved efficiency when performing calculations across multiple variables.
Conclusion
This report demonstrated how R can be used for data importation, database interaction, dataset merging, data manipulation, graphical visualization, debugging, statistical analysis, and functional programming. Packages such as dplyr, ggplot2, DBI, RSQLite, and purrr were used to simplify analysis tasks and improve workflow efficiency.