Basic to Advanced Data Analytics in R-Studio
Tutor: Lumumba Wandera Victor
2023-07-06
- Set up work directory
- MATRIX
- Matrix Creation
- Getting the determinant of a matrix
- Inverse
- Identity Matrix
- Matrix Operation
- View the matrix
- Matrix Addition
- Matrix Subtraction
- Matrix Division
- Matrix Multiplication
- Getting the Identity Matrix
- Data Importation (Comma Seperated Values, csv)
- Manual Data Entry
- Data Framing
- Descriptive Statistics
- Additional Way of Displaying Summary Statistics.
- Display the Summary Statistics
- Correlationa and Covariances
- How many observations do we have in our data set
- Eliminate Missing Observations
- Confirm the Remaining Observations
- Correlation Matrix
- Visualize the Results in Stargazer
- The Covariance Matrix
- Basic R Commands
- Example of how these commands are used
- Data Visualization
- Line Chart
- Dot Plot
- Additional Chart
- Leaf and Stem Plot
- A scatter plot with Regression Equation
- Additional Scatter Plot
- Box Plot
- Bar Graph
- Create Grouped Summaries
- Create the Bar Graph
- Additional Bar Graph
- Pie Chart
- Create pie chart with values and percentages
- Testing the Normality of the Data
- Make a histogram to
Visualize the results
- Parametric Test
- Interpretation!!! (student’s part)
- Two Samples T-test
- Data frame the dataset
- Perform the test
- Student’s part!!!
- Unpaired T-test/ Independent t-test
- One-way ANOVA
- Two-way ANOVA
- Create Factors and Levels
- View the dataset
- Get the grouped summary Statistics for Yields across fertilizer used
- Box plot of Yields for Various Fertilizer Used
- Get the summary statistics for Yields across blocks
- Box plot of Yields for Various Blocks
- Estimate the linear Model and Extract the ANOVA Results
- PARAMETRIC
TESTS
- Wilcoxon Signed-Rank Test:
- Data Frame the data
- Group the Data
- Perform the test
- Mann-Whitney U-Test
- The data
- Perform the Test
- Kruskall Wallis
- The data
- Grouped Summary Statistics
- Use Box Plot to Visualize the Data
- Compute Kruskal-Wallis test
- LINEAR REGRESSION ANALYSIS
- Assumption of Regression Analysis
- Load the data
- To be continued!!!!
Set up work directory
MATRIX
Matrix Creation
[,1] [,2] [,3]
[1,] 5 8 4
[2,] 6 9 5
[3,] 2 2 1Getting the determinant of a matrix
[1] 3Inverse
[,1] [,2] [,3]
[1,] -0.3333333 0 1.3333333
[2,] 1.3333333 -1 -0.3333333
[3,] -2.0000000 2 -1.0000000Identity Matrix
Matrix Operation
View the matrix
[,1] [,2] [,3]
[1,] 3 4 6
[2,] 2 3 4
[3,] 5 2 5 [,1] [,2] [,3]
[1,] 8 5 3
[2,] 2 3 5
[3,] 9 3 3Matrix Addition
[,1] [,2] [,3]
[1,] 11 9 9
[2,] 4 6 9
[3,] 14 5 8Matrix Subtraction
[,1] [,2] [,3]
[1,] -5 -1 3
[2,] 0 0 -1
[3,] -4 -1 2Matrix Division
[,1] [,2] [,3]
[1,] 0.3750000 0.8000000 2.000000
[2,] 1.0000000 1.0000000 0.800000
[3,] 0.5555556 0.6666667 1.666667Matrix Multiplication
[,1] [,2] [,3]
[1,] 86 45 47
[2,] 58 31 33
[3,] 89 46 40Getting the Identity Matrix
Inv
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1Data Importation (Comma Seperated Values, csv)
country year population continent life_exp gdp_cap ln_population
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6.925587
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 6.965716
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 7.011447
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 7.062129
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 7.116590
ln_life_exp ln_gdpPercap
1 1.459408 6.658583
2 1.481901 6.710344
3 1.505109 6.748878
4 1.531734 6.728864
5 1.557363 6.606625Manual Data Entry
Data Framing
age height
1 45 122
2 65 134
3 34 144
4 32 165
5 23 155Descriptive Statistics
========================================================================
Statistic N Mean St. Dev. Min Max
------------------------------------------------------------------------
population 1,704 29,601,212.000 106,157,897.000 60,011 1,318,683,096
life_exp 1,704 59.474 12.917 23.599 82.603
gdp_cap 1,704 7,215.327 9,857.455 241.166 113,523.100
ln_population 1,704 6.847 0.697 4.778 9.120
ln_life_exp 1,704 1.763 0.101 1.373 1.917
ln_gdpPercap 1,704 8.159 1.241 5.485 11.640
------------------------------------------------------------------------Additional Way of Displaying Summary Statistics.
Display the Summary Statistics
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| country* | 1 | 1704 | 71.50 | 41.00 | 71.50 | 71.50 | 52.63 | 1.00 | 1.420000e+02 | 1.410000e+02 | 0.00 | -1.20 | 0.99 |
| year | 2 | 1704 | 1979.50 | 17.27 | 1979.50 | 1979.50 | 22.24 | 1952.00 | 2.007000e+03 | 5.500000e+01 | 0.00 | -1.22 | 0.42 |
| population | 3 | 1704 | 29601212.32 | 106157896.74 | 7023595.50 | 11399459.45 | 7841473.62 | 60011.00 | 1.318683e+09 | 1.318623e+09 | 8.33 | 77.62 | 2571683.45 |
| continent* | 4 | 1704 | 2.33 | 1.21 | 2.00 | 2.27 | 1.48 | 1.00 | 5.000000e+00 | 4.000000e+00 | 0.25 | -1.34 | 0.03 |
| life_exp | 5 | 1704 | 59.47 | 12.92 | 60.71 | 59.92 | 16.10 | 23.60 | 8.260000e+01 | 5.900000e+01 | -0.25 | -1.13 | 0.31 |
| gdp_cap | 6 | 1704 | 7215.33 | 9857.45 | 3531.85 | 5221.44 | 4007.61 | 241.17 | 1.135231e+05 | 1.132820e+05 | 3.84 | 27.40 | 238.80 |
| ln_population | 7 | 1704 | 6.85 | 0.70 | 6.85 | 6.85 | 0.62 | 4.78 | 9.120000e+00 | 4.340000e+00 | 0.08 | 0.47 | 0.02 |
| ln_life_exp | 8 | 1704 | 1.76 | 0.10 | 1.78 | 1.77 | 0.11 | 1.37 | 1.920000e+00 | 5.400000e-01 | -0.57 | -0.66 | 0.00 |
| ln_gdpPercap | 9 | 1704 | 8.16 | 1.24 | 8.17 | 8.14 | 1.51 | 5.49 | 1.164000e+01 | 6.150000e+00 | 0.11 | -0.95 | 0.03 |
Correlationa and Covariances
X Age Sex Job Housing Saving.accounts Checking.account Credit.amount
1 0 67 male 2 own <NA> little 1169
2 1 22 female 2 own little moderate 5951
3 2 49 male 1 own little <NA> 2096
4 3 45 male 2 free little little 7882
5 4 53 male 2 free little little 4870
Duration Purpose
1 6 radio/TV
2 48 radio/TV
3 12 education
4 42 furniture/equipment
5 24 carHow many observations do we have in our data set
'data.frame': 1000 obs. of 10 variables:
$ X : int 0 1 2 3 4 5 6 7 8 9 ...
$ Age : int 67 22 49 45 53 35 53 35 61 28 ...
$ Sex : chr "male" "female" "male" "male" ...
$ Job : int 2 2 1 2 2 1 2 3 1 3 ...
$ Housing : chr "own" "own" "own" "free" ...
$ Saving.accounts : chr NA "little" "little" "little" ...
$ Checking.account: chr "little" "moderate" NA "little" ...
$ Credit.amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
$ Duration : int 6 48 12 42 24 36 24 36 12 30 ...
$ Purpose : chr "radio/TV" "radio/TV" "education" "furniture/equipment" ...Eliminate Missing Observations
Confirm the Remaining Observations
'data.frame': 522 obs. of 10 variables:
$ X : int 1 3 4 7 9 10 11 12 13 14 ...
$ Age : int 22 45 53 35 28 25 24 22 60 28 ...
$ Sex : chr "female" "male" "male" "male" ...
$ Job : int 2 2 2 3 3 2 2 2 1 2 ...
$ Housing : chr "own" "free" "free" "rent" ...
$ Saving.accounts : chr "little" "little" "little" "little" ...
$ Checking.account: chr "moderate" "little" "little" "moderate" ...
$ Credit.amount : int 5951 7882 4870 6948 5234 1295 4308 1567 1199 1403 ...
$ Duration : int 48 42 24 36 30 12 48 12 24 15 ...
$ Purpose : chr "radio/TV" "furniture/equipment" "car" "car" ...
- attr(*, "na.action")= 'omit' Named int [1:478] 1 3 6 7 9 17 18 20 21 25 ...
..- attr(*, "names")= chr [1:478] "1" "3" "6" "7" ...Correlation Matrix
Age Credit.amount Duration
1 67 1169 6
2 22 5951 48
3 49 2096 12
4 45 7882 42
5 53 4870 24 Age Credit.amount Duration
Age 1.00000000 0.03271642 -0.03613637
Credit.amount 0.03271642 1.00000000 0.62498420
Duration -0.03613637 0.62498420 1.00000000Visualize the Results in Stargazer
===========================================
Age Credit.amount Duration
-------------------------------------------
Age 1 0.033 -0.036
Credit.amount 0.033 1 0.625
Duration -0.036 0.625 1
-------------------------------------------The Covariance Matrix
================================================
Age Credit.amount Duration
------------------------------------------------
Age 129.401 1,050.523 -4.957
Credit.amount 1,050.523 7,967,843.000 21,273.750
Duration -4.957 21,273.750 145.415
------------------------------------------------Basic R Commands
- head
- tail
- str
- list
- attach
- Renaming data set
- View
Example of how these commands are used
[[1]]
X Age Sex Job Housing Saving.accounts Checking.account Credit.amount
2 1 22 female 2 own little moderate 5951
4 3 45 male 2 free little little 7882
5 4 53 male 2 free little little 4870
8 7 35 male 3 rent little moderate 6948
10 9 28 male 3 own little moderate 5234
11 10 25 female 2 rent little moderate 1295
12 11 24 female 2 rent little little 4308
13 12 22 female 2 own little moderate 1567
14 13 60 male 1 own little little 1199
15 14 28 female 2 rent little little 1403
16 15 32 female 1 own moderate little 1282
19 18 44 female 3 free little moderate 12579
22 21 44 male 2 rent quite rich little 2647
23 22 48 male 1 rent little little 2241
24 23 44 male 2 own moderate moderate 1804
26 25 36 male 1 own little little 1374
28 27 42 female 2 rent rich rich 409
29 28 34 male 2 own little moderate 2415
30 29 63 male 2 own little little 6836
31 30 36 male 2 own rich moderate 1913
32 31 27 male 2 own little little 4020
33 32 30 male 2 own moderate moderate 5866
35 34 33 female 3 own little rich 1474
36 35 25 male 1 own little moderate 4746
38 37 37 male 2 own little rich 2100
39 38 37 male 2 own little rich 1225
40 39 24 male 2 own little moderate 458
42 41 26 male 2 own quite rich moderate 1158
43 42 44 male 1 own little moderate 6204
44 43 24 male 2 rent moderate little 6187
45 44 58 female 1 free little little 6143
48 47 23 female 0 rent quite rich little 1352
52 51 30 male 3 own little moderate 5965
55 54 57 male 2 free little moderate 2225
59 58 23 female 3 own little rich 1961
60 59 23 female 1 rent little little 6229
61 60 27 male 2 own little moderate 1391
63 62 61 male 3 free little moderate 1953
64 63 25 male 2 own little moderate 14421
68 67 22 male 2 own rich moderate 1007
73 72 51 male 3 free little little 1164
74 73 41 female 1 own little moderate 5954
76 75 66 male 3 free little little 1526
77 76 34 male 2 own little little 3965
78 77 51 male 2 own little moderate 4771
80 79 22 male 2 own little moderate 3832
84 83 58 female 1 own little little 1755
85 84 52 male 1 own little little 2315
87 86 27 female 2 own little moderate 1295
88 87 47 male 2 free moderate moderate 12612
89 88 30 male 3 own moderate little 2249
90 89 28 male 2 own little little 1108
92 91 54 male 2 own little little 1409
95 94 54 male 2 own rich moderate 1318
96 95 58 male 2 rent little moderate 15945
98 97 34 male 2 own moderate moderate 2622
99 98 36 male 2 own little moderate 2337
102 101 24 male 2 rent little moderate 2323
104 103 35 male 2 rent little moderate 1919
106 105 39 male 3 own little moderate 11938
108 107 32 male 2 own little moderate 6078
110 109 35 male 2 own quite rich moderate 1410
111 110 31 male 2 own moderate moderate 1449
112 111 23 female 2 rent little rich 392
113 112 28 male 1 rent little moderate 6260
115 114 35 male 2 own quite rich little 1680
119 118 23 female 2 own quite rich little 4281
120 119 36 male 3 own quite rich moderate 2366
121 120 25 female 2 own little little 1835
124 123 63 male 2 free little rich 781
126 125 30 male 2 own little little 2121
127 126 40 male 1 own little little 701
128 127 30 male 2 own little moderate 639
129 128 34 male 3 own little moderate 1860
130 129 29 female 2 own little little 3499
132 131 29 male 2 own little little 6887
138 137 66 male 1 own quite rich moderate 766
140 139 44 female 1 rent little rich 1881
141 140 27 male 0 own rich rich 709
142 141 30 female 3 own little moderate 4795
143 142 27 male 3 own little little 3416
144 143 22 male 2 own little little 2462
146 145 30 male 2 own moderate moderate 3566
147 146 39 female 2 own little little 860
149 148 28 male 2 own little little 5371
153 152 24 male 2 own little rich 5848
154 153 29 female 2 rent rich moderate 7758
155 154 36 male 3 rent moderate moderate 6967
156 155 20 female 2 rent little little 1282
157 156 48 male 2 own moderate little 1288
158 157 45 male 1 own little little 339
159 158 38 male 2 own moderate moderate 3512
164 163 70 male 3 free little moderate 7308
167 166 33 female 2 own little little 1131
168 167 20 female 2 own rich moderate 1577
170 169 31 male 2 own little moderate 1935
171 170 33 male 2 rent little little 950
173 172 34 female 3 own little moderate 2064
174 173 33 male 2 own little moderate 1414
175 174 26 male 2 own little little 3414
177 176 42 male 2 own little little 2577
178 177 52 male 2 own quite rich little 338
180 179 65 male 2 own little little 571
182 181 30 male 3 own little moderate 4455
185 184 36 male 2 own little moderate 884
187 186 74 female 3 free little moderate 5129
188 187 68 male 0 free little moderate 1175
189 188 20 male 2 own moderate little 674
190 189 33 female 2 own little moderate 3244
192 191 34 male 1 free moderate moderate 3844
193 192 36 male 2 own little moderate 3915
195 194 21 male 2 rent moderate moderate 3031
196 195 34 female 3 own little moderate 1501
198 197 27 female 2 rent moderate moderate 951
200 199 40 male 3 own little moderate 4297
202 201 27 male 1 own little little 1168
204 203 21 male 2 rent little little 902
206 205 38 male 3 free little little 10623
208 207 26 male 2 own little moderate 1424
209 208 21 male 1 own little little 6568
213 212 50 male 2 own little little 5293
214 213 66 male 3 own little rich 1908
217 216 31 male 2 own little little 3104
218 217 23 male 2 own little rich 3913
219 218 24 male 1 rent little little 3021
221 220 26 male 1 own little moderate 625
227 226 27 male 2 own rich moderate 10961
228 227 53 male 3 free little little 7865
230 229 22 male 2 free little little 3149
231 230 26 male 2 own little rich 4210
234 233 25 male 1 own little moderate 866
236 235 30 male 3 own little little 1823
238 237 61 male 1 rent moderate moderate 2767
240 239 39 male 2 own little little 2522
243 242 24 male 2 free little little 4605
249 248 26 male 2 own little rich 1925
251 250 39 female 1 own rich little 666
252 251 46 female 1 own little rich 2251
253 252 24 female 2 own little moderate 2150
258 257 29 male 2 free little little 2149
261 260 27 male 2 own little little 1657
262 261 55 female 2 own little little 1603
263 262 36 male 3 free little little 5302
266 265 37 male 2 own little moderate 802
269 268 45 male 3 own little little 8978
274 273 28 male 2 own little moderate 3060
275 274 34 male 1 own little little 11998
285 284 37 male 2 own moderate moderate 3878
286 285 35 female 1 own little little 10722
287 286 26 male 2 own little little 4788
288 287 31 male 3 free moderate moderate 7582
289 288 49 female 2 own little moderate 1092
290 289 48 male 2 own little little 1024
292 291 28 male 3 rent little moderate 9398
293 292 44 female 3 free little little 6419
294 293 56 male 2 free little rich 4796
296 295 26 female 2 own little moderate 9960
300 299 32 male 2 own rich moderate 2745
302 301 42 female 2 own little moderate 3804
304 303 49 male 2 own little little 1038
308 307 33 male 1 own moderate little 727
309 308 24 female 2 own little moderate 1237
310 309 22 male 1 rent little moderate 276
313 312 26 female 2 own little rich 3749
314 313 25 male 1 own little moderate 685
316 315 31 male 2 own little little 2746
317 316 38 male 1 own little little 708
320 319 27 female 1 own little little 3643
321 320 28 male 3 own little moderate 4249
322 321 32 male 2 own little little 1938
323 322 34 male 3 free little little 2910
324 323 28 male 2 own rich little 2659
326 325 39 male 1 own little little 3398
329 328 31 male 2 own little rich 4473
330 329 28 male 2 own little moderate 1068
331 330 75 male 3 free little little 6615
333 332 24 female 3 own moderate moderate 7408
335 334 23 male 2 rent little little 4110
336 335 44 male 3 rent little little 3384
337 336 23 female 1 own little moderate 2101
339 338 28 male 2 own little little 4169
340 339 31 male 1 own little moderate 1521
341 340 24 female 2 free little moderate 5743
342 341 26 female 1 rent little little 3599
343 342 25 male 2 rent quite rich moderate 3213
344 343 33 male 3 own little moderate 4439
345 344 37 male 1 own little rich 3949
347 346 23 male 2 own little moderate 882
348 347 23 female 0 rent quite rich moderate 3758
350 349 32 male 2 free rich moderate 1136
352 351 29 female 2 own little moderate 959
354 353 28 male 2 rent little little 6199
356 355 23 male 1 own little moderate 1246
360 359 23 female 2 rent little little 2406
363 362 36 female 2 own little rich 2247
365 364 25 male 0 own little little 2473
368 367 22 female 2 rent little little 3650
369 368 42 male 2 own little little 3446
370 369 40 female 2 rent little moderate 3001
375 374 60 female 3 free moderate moderate 14782
376 375 37 female 2 rent little little 7685
379 378 57 male 3 free little moderate 14318
382 381 38 female 3 free little moderate 12976
384 383 26 male 2 own little rich 1330
388 387 40 male 3 own little moderate 7374
389 388 27 male 2 own quite rich moderate 2326
392 391 19 female 1 rent rich moderate 983
393 392 39 male 3 free little little 3249
394 393 31 female 2 own little little 1957
396 395 32 male 2 rent moderate moderate 11760
397 396 55 female 3 free little little 2578
398 397 46 male 2 own little little 2348
399 398 46 male 2 rent little moderate 1223
406 405 22 male 2 own little moderate 2039
408 407 27 male 2 own little little 1053
410 409 28 male 2 own quite rich rich 939
411 410 20 female 2 own little moderate 1967
417 416 33 male 1 own little little 2579
423 422 47 male 1 own little moderate 958
426 425 21 male 2 rent little moderate 2779
430 429 55 female 0 free little little 1190
432 431 29 male 3 own little moderate 11328
433 432 36 male 3 free little little 1872
435 434 25 male 2 own little little 2136
439 438 65 male 0 own little little 3394
440 439 26 female 0 own little rich 609
442 441 30 female 2 own little little 1620
443 442 29 male 2 own little moderate 2629
445 444 30 female 3 own little moderate 5096
447 446 34 female 2 own little little 1842
448 447 35 male 2 own little moderate 2576
450 449 61 male 2 own rich moderate 1512
455 454 31 male 2 own little little 4817
457 456 36 male 2 rent little little 3905
458 457 35 male 2 free little little 3386
459 458 27 female 2 own little little 343
461 460 37 male 2 own little little 3620
462 461 36 male 2 own little little 1721
463 462 34 female 3 rent little moderate 3017
466 465 63 male 2 own little little 2924
467 466 29 female 1 rent little little 1659
471 470 22 male 2 rent moderate moderate 3092
472 471 23 female 2 own little little 448
473 472 28 male 1 own little little 654
475 474 33 male 2 own little moderate 1245
476 475 26 female 2 rent little little 3114
478 477 25 male 2 own little rich 5152
479 478 39 male 1 own moderate moderate 1037
480 479 44 male 2 own little little 1478
481 480 23 female 1 own little moderate 3573
482 481 26 male 2 own little moderate 1201
483 482 57 female 2 rent rich little 3622
486 485 47 male 3 own little moderate 1209
492 491 42 female 3 free little moderate 8318
495 494 39 male 1 rent little little 2122
497 496 29 male 3 rent moderate moderate 9034
499 498 32 male 1 own little moderate 1301
500 499 28 male 2 own moderate rich 1323
501 500 27 female 2 own little little 3123
502 501 42 male 2 free little little 5493
503 502 49 male 2 own moderate rich 1126
504 503 38 male 2 own moderate moderate 1216
505 504 24 female 2 rent little little 1207
507 506 36 male 2 own quite rich rich 2360
508 507 34 male 3 own moderate moderate 6850
511 510 26 male 2 own little little 759
513 512 26 male 2 rent little rich 2687
514 513 20 male 2 rent little moderate 585
516 515 37 female 2 own little little 609
517 516 40 male 1 own little little 1361
519 518 43 male 2 own moderate little 1203
522 521 24 female 2 own little little 3190
523 522 53 male 2 free little little 7119
525 524 26 female 1 own little moderate 1113
526 525 30 male 2 own little moderate 7966
529 528 31 male 2 rent little little 2302
530 529 41 male 1 own little little 662
531 530 32 male 2 own little moderate 2273
532 531 28 female 2 rent moderate moderate 2631
536 535 33 male 2 rent little rich 2319
538 537 37 female 2 own little moderate 3612
539 538 42 male 3 free little little 7763
540 539 45 female 1 own little rich 3049
541 540 23 male 2 rent little moderate 1534
544 543 34 male 1 own little rich 2864
546 545 43 male 2 free little little 1333
549 548 24 female 1 own little little 626
553 552 34 male 2 own little little 6999
554 553 27 male 2 own moderate moderate 1995
555 554 67 female 3 own little moderate 1199
556 555 22 male 2 own little moderate 1331
557 556 28 female 2 own moderate moderate 2278
559 558 27 male 2 own little little 3552
560 559 31 male 1 own little moderate 1928
562 561 24 male 1 rent little little 1546
563 562 29 female 2 own little rich 683
566 565 23 female 2 rent moderate moderate 1553
567 566 36 male 2 own little little 1372
570 569 31 female 2 own little little 6758
571 570 23 female 1 rent little little 3234
574 573 22 female 1 own little little 806
575 574 27 male 1 own little moderate 1082
577 576 27 female 2 own little moderate 2930
579 578 27 male 2 own little moderate 2820
581 580 30 male 2 own little moderate 1056
582 581 49 male 1 own little moderate 3124
584 583 33 male 1 rent little moderate 2384
586 585 20 female 2 rent little little 2039
587 586 36 male 2 rent little little 2799
588 587 21 male 1 own little little 1289
589 588 47 male 1 own little little 1217
590 589 60 male 2 own little little 2246
591 590 58 female 1 own little little 385
594 593 20 female 1 rent little moderate 2718
596 595 32 female 1 own moderate moderate 931
597 596 23 female 2 rent little little 1442
598 597 36 male 1 own little moderate 4241
601 600 45 female 2 own little moderate 2329
602 601 30 female 2 own little moderate 918
603 602 34 female 1 free little moderate 1837
605 604 23 female 2 own little rich 1275
606 605 22 male 2 own quite rich little 2828
608 607 50 female 2 free moderate moderate 2671
611 610 22 female 2 own moderate little 741
612 611 48 female 1 free moderate rich 1240
613 612 29 female 2 own rich little 3357
614 613 22 female 2 rent little little 3632
618 617 37 male 2 rent little little 3676
619 618 21 female 2 rent moderate moderate 3441
621 620 27 male 2 own little moderate 3652
624 623 22 female 2 rent little little 1858
625 624 65 male 2 free little little 2600
627 626 41 male 2 own little rich 2116
628 627 29 male 2 own moderate moderate 1437
631 630 28 female 2 own little little 3660
632 631 44 male 2 own little little 1553
635 634 25 female 1 own little moderate 1355
640 639 26 male 2 own little little 4370
641 640 27 female 0 own little little 750
642 641 38 male 1 own little moderate 1308
645 644 32 male 3 own little little 1880
647 646 32 male 2 own little little 4583
649 648 38 male 2 free little rich 947
650 649 40 male 1 rent little little 684
651 650 50 male 3 free little little 7476
652 651 37 male 1 own little moderate 1922
653 652 45 male 2 own little little 2303
654 653 42 male 3 own moderate moderate 8086
656 655 22 male 2 free little little 3973
657 656 41 male 1 own little moderate 888
659 658 28 female 2 own little moderate 4221
660 659 41 male 2 own little moderate 6361
661 660 23 male 2 rent little rich 1297
664 663 35 male 3 own little moderate 1050
665 664 50 female 1 own little rich 1047
667 666 34 male 2 own rich moderate 3496
669 668 43 male 2 rent little little 4843
670 669 47 male 2 own little rich 3017
678 677 24 male 2 own moderate moderate 5595
679 678 64 male 1 rent little little 2384
685 684 31 male 1 own moderate moderate 9857
688 687 30 male 2 free moderate moderate 2862
690 689 31 male 2 own rich little 3651
691 690 25 male 2 own little little 975
692 691 25 female 1 own moderate moderate 2631
693 692 29 male 2 own moderate moderate 2896
697 696 29 male 2 own little moderate 1103
700 699 40 male 3 rent little rich 1905
702 701 46 male 2 free little little 6331
703 702 47 female 2 free moderate rich 1377
704 703 41 male 2 own moderate moderate 2503
705 704 32 female 2 own little moderate 2528
707 706 24 male 2 own moderate moderate 6560
708 707 25 female 2 rent little moderate 2969
709 708 25 female 2 own little moderate 1206
710 709 37 male 1 own little moderate 2118
712 711 35 female 2 free little little 1198
714 713 25 male 1 own little little 1138
715 714 27 male 3 own little moderate 14027
720 719 31 male 2 own moderate moderate 6148
721 720 34 male 3 own little rich 1337
722 721 24 female 2 rent rich moderate 433
723 722 24 female 1 own little little 1228
724 723 66 female 1 own quite rich moderate 790
728 727 25 female 2 rent little little 1882
729 728 59 female 2 rent little moderate 6416
730 729 36 male 2 own rich rich 1275
731 730 33 male 2 own little moderate 6403
732 731 21 male 1 rent little little 1987
733 732 44 female 1 own little moderate 760
737 736 23 female 3 rent little moderate 11560
738 737 35 male 1 own moderate little 4380
740 739 26 female 1 rent moderate moderate 4280
741 740 32 male 2 own moderate little 2325
742 741 23 male 1 own little moderate 1048
744 743 22 male 2 own quite rich little 2483
746 745 28 male 1 own little little 1797
747 746 23 female 2 rent little little 2511
748 747 37 female 1 own little little 1274
751 750 49 female 2 own little little 428
752 751 23 female 1 own little little 976
753 752 23 female 1 rent moderate moderate 841
757 756 74 male 0 own little rich 1299
760 759 35 male 2 own little little 691
762 761 24 female 2 rent little little 2124
763 762 24 male 1 own little little 2214
766 765 40 male 1 own little moderate 1155
767 766 31 male 1 own little little 3108
769 768 28 male 2 rent little moderate 3617
772 771 25 female 3 own little little 8065
775 774 66 male 0 free quite rich rich 1480
778 777 25 female 2 own little little 3509
780 779 67 female 2 own little moderate 3872
781 780 25 male 2 own little moderate 4933
783 782 31 male 1 own little moderate 1410
784 783 23 female 1 own moderate moderate 836
786 785 35 male 1 own rich moderate 1941
789 788 50 male 2 free little moderate 6224
790 789 27 male 2 own little little 5998
791 790 39 female 2 own little moderate 1188
794 793 51 male 2 free little rich 2892
802 801 48 female 1 rent little moderate 1795
803 802 24 female 2 own little little 4272
806 805 24 male 2 own little little 9271
807 806 26 male 1 own little moderate 590
809 808 55 male 3 free little moderate 9283
810 809 26 female 0 rent little moderate 1778
811 810 26 male 2 own little moderate 907
812 811 28 male 1 own little moderate 484
813 812 24 male 2 own little little 9629
814 813 54 male 2 own little little 3051
815 814 46 male 2 free little little 3931
816 815 54 female 2 rent little moderate 7432
819 818 43 male 3 own little little 15857
820 819 26 male 2 own little little 1345
822 821 24 male 2 own little rich 3016
823 822 41 male 2 own little little 2712
824 823 47 male 1 own little little 731
826 825 30 male 2 own little little 1602
827 826 33 female 2 rent little little 3966
832 831 23 female 2 rent little little 1216
833 832 29 male 2 rent little little 11816
835 834 25 female 1 own little rich 2327
836 835 48 male 2 own little little 1082
839 838 63 male 2 own little little 2957
841 840 29 male 2 own little little 5179
849 848 59 male 2 own little little 1364
850 849 57 male 1 own little little 709
851 850 33 male 2 rent little little 2235
854 853 32 male 1 free little little 1442
859 858 29 female 2 own little little 3959
863 862 35 female 2 own little little 2439
867 866 27 female 2 own little little 2389
870 869 24 female 2 rent little little 652
872 871 46 male 2 own little rich 1343
873 872 26 male 2 own moderate little 1382
875 874 29 male 1 own little little 3590
876 875 40 female 2 own rich moderate 1322
877 876 36 male 3 free little little 1940
879 878 27 male 3 free little little 1422
885 884 43 male 2 own little moderate 4057
886 885 53 female 2 own little little 795
888 887 23 male 2 own little moderate 15672
891 890 43 male 3 own little little 2442
893 892 38 male 1 own little little 2171
894 893 34 male 2 own little moderate 5800
897 896 28 female 3 rent little little 2606
900 899 42 male 2 own little little 4153
901 900 43 male 2 rent little little 2625
906 905 20 male 3 rent little little 1107
912 911 25 female 1 own little moderate 4736
915 914 31 male 2 rent little little 3161
916 915 32 female 3 own little moderate 18424
918 917 68 male 3 own little little 14896
919 918 33 male 2 own moderate little 2359
920 919 39 male 3 rent little little 3345
923 922 22 female 2 rent little little 1366
924 923 30 male 2 rent little moderate 2002
925 924 55 male 2 own little little 6872
926 925 46 male 2 own little little 697
927 926 21 female 2 rent little little 1049
928 927 39 male 2 free little little 10297
930 929 43 male 1 own little little 1344
931 930 24 male 1 own little little 1747
932 931 22 female 2 own little moderate 1670
935 934 23 female 2 own little little 1498
936 935 30 male 3 own moderate moderate 1919
937 936 28 female 1 own little rich 745
938 937 30 male 3 rent little moderate 2063
939 938 42 male 2 free little moderate 6288
945 944 46 female 2 rent little little 1845
946 945 30 female 2 own quite rich moderate 8358
947 946 30 male 2 free quite rich little 3349
951 950 40 male 0 own little moderate 3590
952 951 24 male 2 own little little 2145
953 952 28 female 2 rent quite rich moderate 4113
955 954 29 female 2 own little little 1893
956 955 57 female 3 rent rich little 1231
958 957 37 male 1 own little moderate 1154
959 958 45 male 1 own little little 4006
960 959 30 male 2 free moderate moderate 3069
962 961 47 male 2 own little moderate 2353
965 964 22 male 1 own little moderate 454
967 966 23 male 1 own quite rich moderate 2520
970 969 40 male 1 own little little 3939
971 970 22 male 2 own moderate moderate 1514
973 972 29 female 0 rent little little 1193
974 973 36 male 2 rent little little 7297
976 975 57 female 1 own quite rich rich 1258
977 976 64 female 2 own little moderate 753
980 979 25 male 2 rent moderate moderate 1264
981 980 49 male 2 own little moderate 8386
983 982 28 female 3 own moderate rich 2923
984 983 26 male 2 own little little 8229
986 985 25 female 2 rent little little 1433
987 986 33 male 2 own little rich 6289
989 988 29 male 3 free little little 6579
990 989 48 male 1 own little moderate 1743
994 993 30 male 3 own little little 3959
997 996 40 male 3 own little little 3857
999 998 23 male 2 free little little 1845
1000 999 27 male 2 own moderate moderate 4576
Duration Purpose
2 48 radio/TV
4 42 furniture/equipment
5 24 car
8 36 car
10 30 car
11 12 car
12 48 business
13 12 radio/TV
14 24 car
15 15 car
16 24 radio/TV
19 24 car
22 6 radio/TV
23 10 car
24 12 car
26 6 furniture/equipment
28 12 radio/TV
29 7 radio/TV
30 60 business
31 18 business
32 24 furniture/equipment
33 18 car
35 12 furniture/equipment
36 45 radio/TV
38 18 radio/TV
39 10 domestic appliances
40 9 radio/TV
42 12 radio/TV
43 18 repairs
44 30 car
45 48 car
48 6 car
52 27 car
55 36 car
59 18 car
60 36 furniture/equipment
61 9 business
63 36 business
64 48 business
68 12 car
73 8 vacation/others
74 42 business
76 12 car
77 42 radio/TV
78 11 radio/TV
80 30 furniture/equipment
84 24 vacation/others
85 10 radio/TV
87 18 furniture/equipment
88 36 education
89 18 car
90 12 repairs
92 12 car
95 12 car
96 54 business
98 18 business
99 36 radio/TV
102 36 radio/TV
104 9 furniture/equipment
106 24 vacation/others
108 12 car
110 14 business
111 6 business
112 15 education
113 18 car
115 12 radio/TV
119 33 furniture/equipment
120 12 car
121 21 radio/TV
124 10 car
126 12 car
127 12 radio/TV
128 12 repairs
129 12 car
130 12 car
132 36 education
138 12 radio/TV
140 12 radio/TV
141 6 car
142 36 radio/TV
143 27 radio/TV
144 18 furniture/equipment
146 48 business
147 6 car
149 36 furniture/equipment
153 36 radio/TV
154 24 car
155 24 business
156 12 furniture/equipment
157 9 repairs
158 12 education
159 24 car
164 10 car
167 18 furniture/equipment
168 11 furniture/equipment
170 24 business
171 15 car
173 24 furniture/equipment
174 8 radio/TV
175 21 education
177 12 furniture/equipment
178 6 radio/TV
180 21 car
182 36 business
185 18 car
187 9 car
188 16 car
189 12 radio/TV
190 18 furniture/equipment
192 48 business
193 27 business
195 45 radio/TV
196 9 education
198 12 furniture/equipment
200 18 furniture/equipment
202 12 car
204 12 education
206 30 car
208 12 domestic appliances
209 24 business
213 27 business
214 30 business
217 18 business
218 36 radio/TV
219 24 furniture/equipment
221 12 radio/TV
227 48 radio/TV
228 12 furniture/equipment
230 24 furniture/equipment
231 36 radio/TV
234 18 radio/TV
236 24 radio/TV
238 21 business
240 30 radio/TV
243 48 car
249 24 furniture/equipment
251 6 car
252 12 furniture/equipment
253 30 car
258 12 radio/TV
261 12 furniture/equipment
262 24 radio/TV
263 18 car
266 15 radio/TV
269 14 car
274 48 radio/TV
275 30 repairs
285 24 car
286 47 car
287 48 car
288 48 vacation/others
289 12 radio/TV
290 24 radio/TV
292 36 car
293 24 car
294 42 car
296 48 furniture/equipment
300 21 furniture/equipment
302 36 radio/TV
304 10 car
308 12 radio/TV
309 8 furniture/equipment
310 9 car
313 24 furniture/equipment
314 12 car
316 36 furniture/equipment
317 12 furniture/equipment
320 15 furniture/equipment
321 30 car
322 24 radio/TV
323 24 car
324 18 furniture/equipment
326 8 car
329 36 radio/TV
330 6 radio/TV
331 24 car
333 60 car
335 24 furniture/equipment
336 6 furniture/equipment
337 13 radio/TV
339 24 furniture/equipment
340 10 furniture/equipment
341 24 education
342 21 furniture/equipment
343 18 radio/TV
344 18 business
345 10 car
347 13 radio/TV
348 24 radio/TV
350 9 education
352 9 furniture/equipment
354 12 radio/TV
356 24 car
360 30 furniture/equipment
363 12 car
365 18 furniture/equipment
368 18 furniture/equipment
369 36 furniture/equipment
370 18 furniture/equipment
375 60 vacation/others
376 48 business
379 36 car
382 18 car
384 12 car
388 18 furniture/equipment
389 15 business
392 12 furniture/equipment
393 36 car
394 6 radio/TV
396 39 education
397 12 furniture/equipment
398 36 furniture/equipment
399 12 car
406 24 radio/TV
408 15 radio/TV
410 12 car
411 24 radio/TV
417 12 car
423 12 car
426 18 car
430 18 repairs
432 24 vacation/others
433 6 furniture/equipment
435 9 furniture/equipment
439 42 repairs
440 12 business
442 12 furniture/equipment
443 20 vacation/others
445 48 furniture/equipment
447 36 car
448 7 radio/TV
450 15 repairs
455 24 car
457 11 car
458 12 car
459 6 domestic appliances
461 36 furniture/equipment
462 15 car
463 12 furniture/equipment
466 24 car
467 24 radio/TV
471 24 radio/TV
472 6 education
473 9 car
475 18 radio/TV
476 18 furniture/equipment
478 24 radio/TV
479 12 business
480 15 furniture/equipment
481 12 radio/TV
482 24 car
483 30 furniture/equipment
486 6 car
492 27 business
495 12 car
497 36 furniture/equipment
499 18 radio/TV
500 6 car
501 24 car
502 36 car
503 9 radio/TV
504 24 radio/TV
505 24 car
507 15 car
508 15 car
511 12 car
513 15 business
514 12 radio/TV
516 6 car
517 6 car
519 6 car
522 18 radio/TV
523 48 furniture/equipment
525 18 radio/TV
526 26 car
529 36 radio/TV
530 6 car
531 36 education
532 15 car
536 21 education
538 18 furniture/equipment
539 48 car
540 18 furniture/equipment
541 12 radio/TV
544 18 furniture/equipment
546 24 car
549 12 radio/TV
553 48 radio/TV
554 12 car
555 9 education
556 12 radio/TV
557 18 car
559 24 furniture/equipment
560 18 furniture/equipment
562 24 radio/TV
563 6 radio/TV
566 24 radio/TV
567 12 car
570 48 radio/TV
571 24 furniture/equipment
574 15 business
575 9 radio/TV
577 12 radio/TV
579 36 car
581 18 car
582 12 car
584 36 repairs
586 18 furniture/equipment
587 9 car
588 12 furniture/equipment
589 18 domestic appliances
590 12 furniture/equipment
591 12 radio/TV
594 24 car
596 6 car
597 24 car
598 24 business
601 7 radio/TV
602 9 furniture/equipment
603 24 education
605 10 furniture/equipment
606 24 furniture/equipment
608 36 radio/TV
611 12 domestic appliances
612 10 car
613 21 radio/TV
614 24 car
618 6 car
619 30 furniture/equipment
621 21 business
624 12 furniture/equipment
625 18 radio/TV
627 6 furniture/equipment
628 9 car
631 24 radio/TV
632 18 furniture/equipment
635 24 car
640 42 radio/TV
641 18 education
642 15 repairs
645 18 radio/TV
647 30 furniture/equipment
649 24 car
650 12 education
651 48 education
652 12 furniture/equipment
653 24 car
654 36 car
656 14 car
657 12 car
659 30 business
660 18 furniture/equipment
661 12 radio/TV
664 6 furniture/equipment
665 6 education
667 30 furniture/equipment
669 12 car
670 30 radio/TV
678 72 radio/TV
679 24 radio/TV
685 36 business
688 36 car
690 12 car
691 15 furniture/equipment
692 15 repairs
693 24 radio/TV
697 12 radio/TV
700 15 education
702 48 car
703 24 radio/TV
704 30 business
705 27 business
707 48 car
708 12 furniture/equipment
709 9 radio/TV
710 9 radio/TV
712 6 education
714 9 radio/TV
715 60 car
720 20 car
721 9 radio/TV
722 6 education
723 12 car
724 9 radio/TV
728 18 radio/TV
729 48 business
730 24 business
731 24 radio/TV
732 24 radio/TV
733 8 radio/TV
737 24 car
738 18 car
740 30 business
741 24 car
742 10 radio/TV
744 24 furniture/equipment
746 13 business
747 15 car
748 12 car
751 6 furniture/equipment
752 18 car
753 12 business
757 6 car
760 12 car
762 18 furniture/equipment
763 12 radio/TV
766 12 radio/TV
767 30 furniture/equipment
769 12 furniture/equipment
772 36 education
775 12 car
778 18 radio/TV
780 18 repairs
781 39 radio/TV
783 12 education
784 12 car
786 18 business
789 48 education
790 40 education
791 21 business
794 24 furniture/equipment
802 18 radio/TV
803 20 furniture/equipment
806 36 car
807 6 radio/TV
809 42 car
810 15 car
811 8 business
812 6 radio/TV
813 36 car
814 48 domestic appliances
815 48 car
816 36 car
819 36 vacation/others
820 18 radio/TV
822 12 radio/TV
823 36 furniture/equipment
824 8 car
826 21 car
827 18 car
832 18 car
833 45 business
835 15 radio/TV
836 12 car
839 24 car
841 36 furniture/equipment
849 9 radio/TV
850 12 radio/TV
851 20 car
854 18 car
859 15 car
863 24 radio/TV
867 18 radio/TV
870 12 furniture/equipment
872 6 car
873 24 business
875 12 furniture/equipment
876 11 car
877 18 radio/TV
879 9 car
885 24 furniture/equipment
886 12 education
888 48 business
891 27 business
893 12 car
894 36 car
897 21 radio/TV
900 18 furniture/equipment
901 16 car
906 12 radio/TV
912 24 furniture/equipment
915 24 business
916 48 vacation/others
918 6 car
919 24 furniture/equipment
920 24 furniture/equipment
923 9 radio/TV
924 12 car
925 24 furniture/equipment
926 12 car
927 18 furniture/equipment
928 48 car
930 12 car
931 24 furniture/equipment
932 9 radio/TV
935 12 radio/TV
936 30 radio/TV
937 9 radio/TV
938 6 radio/TV
939 60 education
945 15 furniture/equipment
946 48 car
947 24 furniture/equipment
951 18 business
952 36 business
953 24 car
955 12 car
956 24 radio/TV
958 9 radio/TV
959 28 car
960 24 furniture/equipment
962 21 car
965 6 repairs
967 27 radio/TV
970 11 car
971 15 repairs
973 24 car
974 60 business
976 24 radio/TV
977 6 radio/TV
980 15 car
981 30 furniture/equipment
983 21 car
984 36 car
986 15 furniture/equipment
987 42 business
989 24 car
990 24 radio/TV
994 36 furniture/equipment
997 30 car
999 45 radio/TV
1000 45 carData Visualization
Histogram
Line Chart
year CPI Exch.Rate Lend.Int.Rates
1 1987 7.872727 16.45499 14.0000
2 1988 8.848083 17.74710 15.0000
3 1989 10.035029 20.57247 17.2500
4 1990 11.602322 22.91477 18.7500
5 1991 13.805882 27.50870 18.9975
Dot Plot
Additional Chart
country year population continent life_exp gdp_cap ln_population
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6.925587
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 6.965716
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 7.011447
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 7.062129
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 7.116590
ln_life_exp ln_gdpPercap
1 1.459408 6.658583
2 1.481901 6.710344
3 1.505109 6.748878
4 1.531734 6.728864
5 1.557363 6.606625
Leaf and Stem Plot
The decimal point is 1 digit(s) to the right of the |
2 | 4
2 | 9
3 | 000011222223333344444444444
3 | 55555555566666666666667777777777777777778888888888888888899999999999
4 | 00000000000000000000000000000001111111111111111111111111111112222222+90
4 | 55555555555555555555555555555555555555555555556666666666666666666666+110
5 | 00000000000000000000000000000000000000001111111111111111111111111111+94
5 | 55555555555555555555555555556666666666666666666666666666666666667777+87
6 | 00000000000000000000000000000000000000011111111111111111111111111112+79
6 | 55555555555555555555555555555566666666666666666666666666666666666677+123
7 | 00000000000000000000000000000000000000000000000000000000000000000000+251
7 | 55555555555555555555555555555555555555555555555666666666666666666666+92
8 | 00000000000001111111111122223This kind of a chart is not appropriate for a lage data set. Consider the chart below.
The decimal point is 1 digit(s) to the right of the |
0 |
0 | 5
1 | 134
1 | 789
2 | 012233
2 | 55667888999
3 | 00001112222223333344444
3 | 566666777788889999999
4 | 0011122222222333444444444444
4 | 5555556666677777777888899999999
5 | 000000111222222222333344444
5 | 56666777777899999
6 | 00002333444
6 | 555599
7 | 0112234
7 | 99
8 | 14
8 |
9 |
9 | 5A scatter plot with Regression Equation
Year Income Consumption
1 1950 791.8 733.2
2 1951 819.0 748.7
3 1952 844.3 771.4
4 1953 880.0 802.5
5 1954 894.0 822.7
Additional Scatter Plot
country year population continent life_exp gdp_cap ln_population
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6.925587
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 6.965716
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 7.011447
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 7.062129
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 7.116590
ln_life_exp ln_gdpPercap
1 1.459408 6.658583
2 1.481901 6.710344
3 1.505109 6.748878
4 1.531734 6.728864
5 1.557363 6.606625
Box Plot
Bar Graph
Load the data
Row_ID Order_ID Order_Date Ship_Date Ship_Mode Customer_ID
1 1 CA-2016-152156 08/11/2016 11/11/2016 Second Class CG-12520
2 2 CA-2016-152156 08/11/2016 11/11/2016 Second Class CG-12520
3 3 CA-2016-138688 12/06/2016 16/06/2016 Second Class DV-13045
4 4 US-2015-108966 11/10/2015 18/10/2015 Standard Class SO-20335
5 5 US-2015-108966 11/10/2015 18/10/2015 Standard Class SO-20335
Customer_Name Segment Country City State
1 Claire Gute Consumer United States Henderson Kentucky
2 Claire Gute Consumer United States Henderson Kentucky
3 Darrin Van Huff Corporate United States Los Angeles California
4 Sean O'Donnell Consumer United States Fort Lauderdale Florida
5 Sean O'Donnell Consumer United States Fort Lauderdale Florida
Postal_Code Region Product_ID Category Sub_Category Sales
1 42420 South FUR-BO-10001798 Furniture Bookcases 261.9600
2 42420 South FUR-CH-10000454 Furniture Chairs 731.9400
3 90036 West OFF-LA-10000240 Office Supplies Labels 14.6200
4 33311 South FUR-TA-10000577 Furniture Tables 957.5775
5 33311 South OFF-ST-10000760 Office Supplies Storage 22.3680
Quantity Discount Profit
1 2 0.00 41.9136
2 3 0.00 219.5820
3 2 0.00 6.8714
4 5 0.45 -383.0310
5 2 0.20 2.5164Create Grouped Summaries
# A tibble: 4 × 5
Ship_Mode variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 First Class Sales 1538 228. 630.
2 Same Day Sales 543 236. 555.
3 Second Class Sales 1945 236. 559.
4 Standard Class Sales 5968 228. 647.Create the Bar Graph
Additional Bar Graph
# A tibble: 17 × 5
Sub_Category variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 Accessories Sales 775 216. 335.
2 Appliances Sales 466 231. 389.
3 Art Sales 796 34.1 60.1
4 Binders Sales 1523 134. 563.
5 Bookcases Sales 228 504. 639.
6 Chairs Sales 617 532. 550.
7 Copiers Sales 68 2199. 3176.
8 Envelopes Sales 254 64.9 84.4
9 Fasteners Sales 217 13.9 12.4
10 Furnishings Sales 957 95.8 148.
11 Labels Sales 364 34.3 74.1
12 Machines Sales 115 1646. 2765.
13 Paper Sales 1370 57.3 78.2
14 Phones Sales 889 371. 491.
15 Storage Sales 846 265. 355.
16 Supplies Sales 190 246. 924.
17 Tables Sales 319 649. 616. 
Pie Chart
Create pie chart with values and percentages
Testing the Normality of the Data
Shapiro wilks statistics
country year population continent life_exp gdp_cap ln_population
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6.925587
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 6.965716
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 7.011447
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 7.062129
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 7.116590
ln_life_exp ln_gdpPercap
1 1.459408 6.658583
2 1.481901 6.710344
3 1.505109 6.748878
4 1.531734 6.728864
5 1.557363 6.606625Perform the test
Shapiro-Wilk normality test
data: gdp_cap
W = 0.6522, p-value < 2.2e-16The Shapiro-Wilk test was performed on the variable gdp_cap. The test result shows a test statistic (W) of 0.6522 and an extremely small p-value (p-value < 2.2e-16). Since the p-value is less than 0.05 (assuming a common significance level), we reject the null hypothesis that the data follows a normal distribution. The extremely small p-value indicates strong evidence against the normality assumption for the gdp_cap dataset.
Run the test for normally distributed data
scores
1 27.03187
2 34.64139
3 53.67432
4 17.99033
5 43.50121
Shapiro-Wilk normality test
data: random$scores
W = 0.99238, p-value = 0.3844Make a histogram to Visualize the results
Parametric Test
One sample T-test
One Sample t-test
data: scores
t = 6.1269, df = 199, p-value = 4.715e-09
alternative hypothesis: true mean is not equal to 40
95 percent confidence interval:
43.87582 47.55480
sample estimates:
mean of x
45.71531 Interpretation!!! (student’s part)
Two Samples T-test
Dependent t-test
Data frame the dataset
weight_before weight_after
1 75 73
2 90 85
3 78 70
4 65 59
5 78 72Perform the test
Paired t-test
data: weight_before and weight_after
t = 3.7262, df = 14, p-value = 0.002257
alternative hypothesis: true mean difference is not equal to 0
99 percent confidence interval:
0.7642042 6.8357958
sample estimates:
mean difference
3.8 Student’s part!!!
Unpaired T-test/ Independent t-test
continent life_exp
1 Europe 55.230
2 Europe 59.280
3 Europe 64.820
4 Europe 66.220
5 Europe 67.690
6 Europe 68.930
7 Europe 70.420
8 Europe 72.000
9 Europe 71.581
10 Europe 72.950
11 Europe 75.651
12 Europe 76.423
13 Oceania 69.120
14 Oceania 70.330 continent life_exp
371 Europe 70.845
372 Europe 71.777
373 Europe 69.180
374 Europe 70.420
375 Europe 70.760
376 Europe 71.360
377 Europe 72.010
378 Europe 72.760
379 Europe 74.040
380 Europe 75.007
381 Europe 76.420
382 Europe 77.218
383 Europe 78.471
384 Europe 79.425
Welch Two Sample t-test
data: life_exp by continent
t = -2.9328, df = 29.677, p-value = 0.003208
alternative hypothesis: true difference in means between group Europe and group Oceania is less than 0
99 percent confidence interval:
-Inf -0.391586
sample estimates:
mean in group Europe mean in group Oceania
71.90369 74.32621 One-way ANOVA
Df Sum Sq Mean Sq F value Pr(>F)
continent 4 139343 34836 408.7 <2e-16 ***
Residuals 1699 144805 85
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Two-way ANOVA
S.No Yields Fertilizer.Used Blocks
1 1 54.40207 1 1
2 2 41.00511 1 1
3 3 38.92977 1 1
4 4 54.74481 1 1
5 5 46.72406 1 1Create Factors and Levels
View the dataset
S.No Yields Fertilizer.Used Blocks
1 1 54.40207 DAP Block1
2 2 41.00511 DAP Block1
3 3 38.92977 DAP Block1
4 4 54.74481 DAP Block1
5 5 46.72406 DAP Block1Get the grouped summary Statistics for Yields across fertilizer used
# A tibble: 4 × 5
Fertilizer.Used variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 DAP Yields 100 53.9 11.6
2 NPK Yields 100 54.8 11.2
3 AMONNIA Yields 100 53.8 10.8
4 PHOSPHATE Yields 100 54.0 11.4Box plot of Yields for Various Fertilizer Used
Get the summary statistics for Yields across blocks
# A tibble: 4 × 5
Blocks variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 Block1 Yields 100 50.4 7.27
2 Block2 Yields 100 65.1 4.07
3 Block3 Yields 100 56.1 14.0
4 Block4 Yields 100 44.8 3.8 Box plot of Yields for Various Blocks
Estimate the linear Model and Extract the ANOVA Results
Analysis of Variance Table
Response: Yields
Df Sum Sq Mean Sq F value Pr(>F)
Fertilizer.Used 3 72.6 24.2 0.3436 0.7938
Blocks 3 22433.1 7477.7 106.1659 <2e-16 ***
Residuals 393 27680.6 70.4
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PARAMETRIC TESTS
Wilcoxon Signed-Rank Test:
The Wilcoxon signed-rank test is used to compare paired or related samples. It assesses whether there is a significant difference between the measurements or observations taken from the same subjects or units under two different conditions or time points. The test is appropriate for data that are not normally distributed or when the assumption of normality is violated. #### Create the dataset
Data Frame the data
before after
1 87 80
2 98 90
3 77 68
4 89 77
5 90 81Group the Data
time weight
1 before 87
2 before 98
3 before 77
4 before 89
5 before 90Perform the test
Wilcoxon signed rank test with continuity correction
data: weight by time
V = 0, p-value = 0.0004556
alternative hypothesis: true location shift is not equal to 0Mann-Whitney U-Test
The Mann-Whitney U test (also called the Wilcoxon rank-sum test) compares two independent groups or conditions to determine if there is a significant difference between their distributions or medians. This test is appropriate when comparing two groups without assuming normality or when the data are ordinal or skewed.
The data
continent life_exp
1 Europe 55.23
2 Europe 59.28
3 Europe 64.82
4 Europe 66.22
5 Europe 67.69Perform the Test
Wilcoxon rank sum test with continuity correction
data: life_exp by continent
W = 3271.5, p-value = 0.04653
alternative hypothesis: true location shift is not equal to 0Kruskall Wallis
The Kruskal-Wallis test is a nonparametric test used to compare the medians of three or more independent groups. It is an extension of the Mann-Whitney U test (Wilcoxon rank-sum test) for two groups. The Kruskal-Wallis test does not assume that the data are normally distributed and can handle ordinal or non-normally distributed data.
The data
country life_exp
1 Afghanistan 28.801
2 Afghanistan 30.332
3 Afghanistan 31.997
4 Afghanistan 34.020
5 Afghanistan 36.088Grouped Summary Statistics
# A tibble: 5 × 8
country count mean sd median max min IQR
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 12 37.5 5.10 39.1 43.8 28.8 8.18
2 Canada 12 74.9 3.95 75.0 80.7 68.8 6.19
3 Kenya 12 52.7 5.60 53.8 59.3 42.3 6.83
4 Morocco 12 57.6 9.81 57.7 71.2 42.9 16.2
5 United States 12 73.5 3.34 74.0 78.2 68.4 5.65Use Box Plot to Visualize the Data
Compute Kruskal-Wallis test
We want to see if the median life expectancy in five continents vary significantly. The test can be run using the kruskal.test() function as follows.
Kruskal-Wallis rank sum test
data: life_exp by country
Kruskal-Wallis chi-squared = 49.879, df = 4, p-value = 3.827e-10LINEAR REGRESSION ANALYSIS
Statistical techniques are tools that enable us to answer questions about possible patterns in empirical data. It is not surprising, then, to learn that many important techniques of statistical analysis were developed by scientists who were interested in answering very specific empirical questions. So it was with regression analysis. The history of this particular statistical technique can be traced back to late nineteenth-century England and the pursuits of a gentleman scientist, Francis Galton. Galton was born into a wealthy family that produced more than its share of geniuses; he and Charles Darwin, the famous biologist, were first cousins. During his lifetime, Galton studied everything from fingerprint classification to meteorology, but he gained widespread recognition primarily for his work on inheritance. His most important insight came to him while he was studying the inheritance of one of the most obvious of all human characteristics: height. In order to understand how the characteristic of height was passed from one generation to the next, Galton collected data on the heights of individuals and the heights of their parents. After constructing frequency tables that classified these individuals both by their height and by the average height of their parents, Galton came to the unremarkable conclusion that tall people usually had tall parents and short people usually had short parents.
Assumption of Regression Analysis
1. The error term has a population mean of zero 2. All independent variables are uncorrelated with the error term 3. Observations of the error term are uncorrelated with each other 4. The error term has a constant variance (no heteroscedasticity) 5. No independent variable is a perfect linear function of other explanatory variables 6. The error term is normally distributed (optional)
Load the data
year Unemployment Inflation FedRate
1 1859 5.133333 0.9084719 3.933333
2 1860 5.233333 1.8107772 3.696667
3 1861 5.533333 1.6227203 2.936667
4 1862 6.266667 1.7953352 2.296667
5 1863 6.800000 0.5370330 2.003333