Quiz 1
“Lexi Brenner”
2026-02-11
Overview
This quiz is meant to give you and me a sense of how well you understand the materials up to this point.
It will be worth the same number of points as a regular assignment.
Problem 1
Question 1a
First install a couple of packages. You don’t need to do this in this Rmarkdown document. Just be sure that “tidyverse” and “dslabs” are in your list of packages in the window on the bottom right.
If they aren’t use install.packages(““) directly in the Console in the window on the bottom left.
Question 1b
Load the packages you have just installed.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.2
## ✔ purrr 1.2.0
## ── 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(dslabs)Question 1c
View the different datasets available in dslabs.
data(package ="dslabs")Question 1d
What year are the data in murders from?
?murders## starting httpd help server ... doneThe murders are from 2010.
Question 1e
Load the dataset “murders”.
data("murders")Problem 2
Question 2a
How many columns (variables) are there in “murders” and what type or class is each variable? HINT: There is a single function that you can use to get this information and murders does not need to be in quotes.Put your code in the code chunk. If it runs correctly it will show all of the information I’m asking for and you don’t need to write it out separately.
str(murders)## 'data.frame': 51 obs. of 5 variables:
## $ state : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ abb : chr "AL" "AK" "AZ" "AR" ...
## $ region : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
## $ population: num 4779736 710231 6392017 2915918 37253956 ...
## $ total : num 135 19 232 93 1257 ...Question 2b
List the state abbreviations in order from lowest number of murder to highest.
HINT: In the book, Introduction to Data Science, we were taught to first list the total murders in order and assign that a name. Then use the abb variable with this new name. Feel free to use the book.
ind <- order(murders$total)
murders$abb[ind]## [1] "VT" "ND" "NH" "WY" "HI" "SD" "ME" "ID" "MT" "RI" "AK" "IA" "UT" "WV" "NE"
## [16] "OR" "DE" "MN" "KS" "CO" "NM" "NV" "AR" "WA" "CT" "WI" "DC" "OK" "KY" "MA"
## [31] "MS" "AL" "IN" "SC" "TN" "AZ" "NJ" "VA" "NC" "MD" "OH" "MO" "LA" "IL" "GA"
## [46] "MI" "PA" "NY" "FL" "TX" "CA"Question 2c
Create a new value that provides the crime rate for each state.Finish the r code in the chunk below.
murder_rate <- murders$total / murders$population * 100000
murder_rate## [1] 2.8244238 2.6751860 3.6295273 3.1893901 3.3741383 1.2924531
## [7] 2.7139722 4.2319369 16.4527532 3.3980688 3.7903226 0.5145920
## [13] 0.7655102 2.8369608 2.1900730 0.6893484 2.2081106 2.6732010
## [19] 7.7425810 0.8280881 5.0748655 1.8021791 4.1786225 0.9992600
## [25] 4.0440846 5.3598917 1.2128379 1.7521372 3.1104763 0.3798036
## [31] 2.7980319 3.2537239 2.6679599 2.9993237 0.5947151 2.6871225
## [37] 2.9589340 0.9396843 3.5977513 1.5200933 4.4753235 0.9825837
## [43] 3.4509357 3.2013603 0.7959810 0.3196211 3.1246001 1.3829942
## [49] 1.4571013 1.7056487 0.8871131Question 2d
Now make that value appear as a new variable in murders. HINT: You will be using mutate() check out the example in Basic Statistics using R p 28.
murders_plus <- mutate(murders,
murder_rate = murders$total / murders$population *100000)Question 2e
Show a summary of murders_plus.
summary(murders_plus)## state abb region population
## Length:51 Length:51 Northeast : 9 Min. : 563626
## Class :character Class :character South :17 1st Qu.: 1696962
## Mode :character Mode :character North Central:12 Median : 4339367
## West :13 Mean : 6075769
## 3rd Qu.: 6636084
## Max. :37253956
## total murder_rate
## Min. : 2.0 Min. : 0.3196
## 1st Qu.: 24.5 1st Qu.: 1.2526
## Median : 97.0 Median : 2.6871
## Mean : 184.4 Mean : 2.7791
## 3rd Qu.: 268.0 3rd Qu.: 3.3861
## Max. :1257.0 Max. :16.4528Problem 3
Question 3a
Import the dataset 2018.UCR.PA.xlsx You can copy the code provided from the “import dataset” option in Environment.
library(readxl)
X2018_UCR_PA <- read_excel("C:/Users/Lexib/Downloads/2018.UCR.PA.xlsx")
View(X2018_UCR_PA)Question 3b
Show the names of each variable. Check for strange spellings and correct them. Name 2018.UCR.PA to reflect this change.
HINT: We did this in week 3.
names(X2018_UCR_PA)## [1] "City"
## [2] "Population"
## [3] "Violent\r\ncrime"
## [4] "Murder and\r\nnonnegligent\r\nmanslaughter"
## [5] "Rape"
## [6] "Robbery"
## [7] "Aggravated\r\nassault"
## [8] "Property\r\ncrime"
## [9] "Burglary"
## [10] "Larceny-\r\ntheft"
## [11] "Motor\r\nvehicle\r\ntheft"
## [12] "Arson"X2018_UCR_PA_cleaned <- X2018_UCR_PA %>%
rename(violent.crime = 'Violent\r\ncrime') %>%
rename(murder.manslaughter = 'Murder and\r\nnonnegligent\r\nmanslaughter') %>%
rename(aggravated.assault = 'Aggravated\r\nassault') %>%
rename(property.crime = 'Property\r\ncrime') %>%
rename(larceny.theft = 'Larceny-\r\ntheft') %>%
rename(motor.theft = 'Motor\r\nvehicle\r\ntheft')
summary(X2018_UCR_PA_cleaned)## City Population violent.crime murder.manslaughter
## Length:989 Min. : 132 Min. : 0.00 Min. : 0.0000
## Class :character 1st Qu.: 2066 1st Qu.: 1.00 1st Qu.: 0.0000
## Mode :character Median : 4320 Median : 5.00 Median : 0.0000
## Mean : 10054 Mean : 34.16 Mean : 0.6977
## 3rd Qu.: 9088 3rd Qu.: 15.00 3rd Qu.: 0.0000
## Max. :1586916 Max. :14420.00 Max. :351.0000
## Rape Robbery aggravated.assault property.crime
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 1.00 1st Qu.: 9.0
## Median : 0.000 Median : 0.000 Median : 4.00 Median : 40.0
## Mean : 2.971 Mean : 9.449 Mean : 21.05 Mean : 164.6
## 3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.: 11.00 3rd Qu.: 105.0
## Max. :1095.000 Max. :5262.000 Max. :7712.00 Max. :49145.0
## Burglary larceny.theft motor.theft Arson
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.000
## 1st Qu.: 1.00 1st Qu.: 7.0 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 5.00 Median : 32.0 Median : 1.00 Median : 0.000
## Mean : 21.42 Mean : 131.3 Mean : 11.84 Mean : 1.147
## 3rd Qu.: 12.00 3rd Qu.: 89.0 3rd Qu.: 4.00 3rd Qu.: 0.000
## Max. :6497.00 Max. :36968.0 Max. :5680.00 Max. :430.000Question 3c
Create a new variable called violent_crime_rate as you did in questions 2c and 2d.
violent_crime_rate = X2018_UCR_PA_cleaned$violent.crime / X2018_UCR_PA_cleaned$Population * 100000
X2018_UCR_PA_cleaned <- X2018_UCR_PA_cleaned %>%
mutate(violent_crime_rate = X2018_UCR_PA_cleaned$violent.crime / X2018_UCR_PA_cleaned$Population * 100000)
violent_crime_rate## [1] 79.092592 161.550889 21.269054 0.000000 174.346202 0.000000
## [7] 112.654900 72.167428 0.000000 558.909010 562.908870 86.494501
## [13] 338.417815 376.368613 137.783221 1332.534811 30.590395 704.508857
## [19] 260.416667 201.525838 52.493438 965.488907 149.253731 406.053894
## [25] 0.000000 36.737693 35.910941 250.626566 255.955897 393.959291
## [31] 0.000000 0.000000 0.000000 104.931794 203.665988 86.796692
## [37] 103.519669 0.000000 248.946764 439.814815 417.188152 0.000000
## [43] 184.569952 41.430742 0.000000 15.810277 258.302583 57.636888
## [49] 0.000000 0.000000 115.534429 200.642055 0.000000 202.736949
## [55] 71.174377 0.000000 368.893320 94.161959 33.932813 0.000000
## [61] 65.082981 273.054651 75.053163 0.000000 58.060770 0.000000
## [67] 0.000000 338.669951 66.622252 96.758587 0.000000 247.437257
## [73] 135.317997 164.473684 49.285362 126.262626 710.900474 0.000000
## [79] 1018.922853 232.853514 0.000000 64.655172 224.263135 268.997983
## [85] 326.370757 119.880120 518.134715 180.788237 322.312331 176.961478
## [91] 299.401198 236.259637 105.069609 178.731010 141.944642 4.923441
## [97] 150.808884 31.595577 378.787879 304.878049 307.290466 151.130456
## [103] 81.732734 0.000000 339.065149 267.141585 0.000000 272.422464
## [109] 121.359223 75.528701 25.169897 147.475893 531.632111 166.944908
## [115] 93.095423 0.000000 139.452333 628.140704 383.141762 31.055901
## [121] 25.458248 213.106020 73.592543 151.080224 471.698113 15.831552
## [127] 332.196870 348.211459 150.274251 57.751540 286.232230 398.834177
## [133] 37.797657 186.661689 294.117647 1390.559451 1194.539249 25.015635
## [139] 0.000000 136.612022 349.172613 56.433409 48.309179 612.870276
## [145] 0.000000 551.497988 78.616352 279.069767 551.844322 91.743119
## [151] 116.395078 0.000000 48.053820 727.851700 33.396415 182.062093
## [157] 509.604077 323.624595 87.399854 83.518931 684.116597 0.000000
## [163] 491.065339 315.729047 61.873531 95.693780 0.000000 119.569550
## [169] 91.631032 453.720508 46.232085 665.504674 452.674897 0.000000
## [175] 312.082033 128.555359 25.678886 428.182172 126.903553 39.432177
## [181] 0.000000 192.431046 96.447516 84.486905 584.063413 0.000000
## [187] 75.602117 168.067227 302.964726 1392.263128 204.697264 0.000000
## [193] 0.000000 0.000000 0.000000 103.066220 159.165970 416.594341
## [199] 92.250923 196.335079 203.665988 0.000000 75.159714 113.450145
## [205] 86.058520 186.133085 202.456472 0.000000 0.000000 170.357751
## [211] 177.839635 0.000000 149.812734 1046.176046 185.299568 27.612868
## [217] 117.302053 0.000000 79.863092 75.693065 988.700565 14.753615
## [223] 68.965517 121.967746 260.642919 118.670886 0.000000 0.000000
## [229] 129.586134 52.530205 337.078652 0.000000 379.362671 305.289315
## [235] 254.068783 50.574713 44.189129 54.458816 0.000000 119.379228
## [241] 159.355081 189.155107 0.000000 384.193194 24.824162 64.662140
## [247] 21.727322 995.024876 66.181337 120.992136 67.773636 469.383401
## [253] 0.000000 555.555556 181.175050 68.770536 345.622120 134.318334
## [259] 0.000000 60.922541 162.074554 42.034468 164.803669 95.556617
## [265] 309.018376 149.342891 22.281640 115.141048 131.578947 215.053763
## [271] 92.464170 84.817642 389.863548 0.000000 222.158929 124.062149
## [277] 1032.036121 171.969046 0.000000 40.918623 0.000000 153.400375
## [283] 391.485197 483.749055 0.000000 169.491525 78.976465 51.463493
## [289] 122.189638 60.698027 49.019608 819.672131 148.273671 0.000000
## [295] 248.344371 22.483699 197.335964 20.399837 179.809915 508.228461
## [301] 0.000000 0.000000 268.817204 29.832936 145.306597 0.000000
## [307] 59.066745 0.000000 821.917808 0.000000 0.000000 0.000000
## [313] 392.824407 405.542413 207.612457 206.849000 153.566941 0.000000
## [319] 0.000000 22.261799 0.000000 25.119317 407.962299 120.821587
## [325] 548.245614 52.576236 112.937633 0.000000 293.652586 32.895819
## [331] 76.427558 313.039034 378.124135 164.149705 230.794593 1080.432173
## [337] 0.000000 0.000000 143.781452 185.701021 94.010207 104.522995
## [343] 48.561369 0.000000 474.895482 294.898260 164.068909 138.808560
## [349] 291.095890 133.809099 581.073603 122.335973 83.298626 0.000000
## [355] 675.390460 64.653779 312.337324 0.000000 790.638836 190.114068
## [361] 57.077626 332.778702 173.078436 26.300958 0.000000 145.666424
## [367] 0.000000 0.000000 215.146299 316.729053 0.000000 83.022001
## [373] 897.308076 41.695622 0.000000 92.707046 478.978180 0.000000
## [379] 0.000000 24.491795 21.593608 173.310225 109.565027 176.850296
## [385] 0.000000 0.000000 88.183422 89.385475 225.123818 0.000000
## [391] 487.092060 113.713896 411.879471 436.490615 1149.590458 57.323015
## [397] 278.357690 256.629598 161.498708 24.047132 729.546639 0.000000
## [403] 179.183546 130.472601 22.241993 0.000000 0.000000 553.250346
## [409] 215.285253 157.915515 0.000000 0.000000 701.126153 0.000000
## [415] 208.429829 860.215054 188.442211 0.000000 90.182168 244.131455
## [421] 423.504500 38.270188 303.951368 128.238010 184.501845 408.917029
## [427] 181.818182 163.666121 321.506043 199.800200 84.530854 315.457413
## [433] 170.551450 167.973124 171.772116 345.821326 439.667807 40.371417
## [439] 0.000000 262.295082 125.000000 41.788550 0.000000 100.806452
## [445] 84.674005 0.000000 178.213411 216.966804 643.316655 129.407959
## [451] 71.651569 80.681309 61.425061 25.989777 181.189260 0.000000
## [457] 30.517578 53.997503 30.293850 176.484907 321.569921 121.991793
## [463] 45.336788 92.387288 353.614145 179.694519 79.218379 71.275837
## [469] 0.000000 0.000000 0.000000 101.061142 31.387320 308.861962
## [475] 34.352456 360.317079 0.000000 56.980057 327.198364 123.777695
## [481] 29.682398 98.183603 113.798009 65.919578 1207.327227 342.596117
## [487] 0.000000 52.219321 29.708853 16.756032 61.425061 0.000000
## [493] 0.000000 242.057489 0.000000 92.336103 17.559263 145.208132
## [499] 1530.002391 1686.625063 97.529259 162.576450 88.681964 596.236259
## [505] 159.914712 485.436893 0.000000 611.620795 35.486160 133.351113
## [511] 450.692134 73.033086 280.561122 0.000000 85.421412 129.862464
## [517] 205.549846 603.968939 256.448654 0.000000 236.127509 119.331742
## [523] 0.000000 110.041265 967.883854 216.346154 65.274151 145.666424
## [529] 1029.230136 387.878788 228.029535 65.876153 26.616982 90.497738
## [535] 76.982294 0.000000 77.645780 42.812801 435.161010 116.863387
## [541] 186.150410 207.576544 1067.995728 831.393415 0.000000 146.412884
## [547] 312.049928 71.342163 1089.918256 397.846946 28.546960 84.495142
## [553] 104.384134 108.358371 0.000000 188.146754 20.243939 146.298644
## [559] 39.808917 70.101647 0.000000 54.212295 402.352213 87.145969
## [565] 189.214759 0.000000 116.414435 139.691409 431.474963 26.661927
## [571] 561.387494 0.000000 54.622423 0.000000 15.150367 127.110950
## [577] 318.091451 565.565161 55.699963 68.975031 44.424700 21.853147
## [583] 36.843269 0.000000 182.815356 402.536851 20.234723 30.542900
## [589] 0.000000 268.672757 0.000000 70.497004 139.806812 24.962556
## [595] 97.442144 58.932480 0.000000 59.853359 761.421320 55.418549
## [601] 16.926679 51.565589 40.174088 106.990014 49.319392 240.823783
## [607] 11.732958 51.361068 73.991861 183.635993 109.439124 0.000000
## [613] 288.612188 152.858453 19.971042 0.000000 67.842605 0.000000
## [619] 176.678445 139.189607 149.014214 43.642712 118.273211 184.256321
## [625] 114.140774 40.592653 198.965380 0.000000 101.488498 0.000000
## [631] 0.000000 0.000000 247.875354 245.793156 60.431387 251.455797
## [637] 0.000000 0.000000 180.598555 815.100815 0.000000 23.707918
## [643] 61.462815 30.975096 234.505863 212.765957 320.366133 410.762463
## [649] 45.400890 20.341741 155.464580 133.244504 139.386699 163.532298
## [655] 295.857988 54.210336 908.680737 188.102516 0.000000 0.000000
## [661] 1470.128245 578.758792 208.089478 88.521688 195.372751 0.000000
## [667] 238.322212 77.225498 725.513906 146.429376 194.324780 267.157992
## [673] 247.729149 128.534704 41.339396 0.000000 0.000000 193.798450
## [679] 189.907759 859.950860 325.227290 104.493208 0.000000 307.881773
## [685] 273.556231 86.745316 161.812298 430.643699 107.642626 0.000000
## [691] 97.072178 212.164074 1262.748907 701.964597 34.264177 23.668639
## [697] 0.000000 164.068909 40.160643 61.766523 150.375940 56.195561
## [703] 134.448760 58.626466 53.134963 198.722498 86.566207 0.000000
## [709] 160.170849 0.000000 187.265918 0.000000 121.097955 146.273678
## [715] 263.157895 632.729364 149.981252 118.017309 0.000000 128.700129
## [721] 0.000000 65.485740 72.575524 0.000000 62.800921 0.000000
## [727] 153.374233 0.000000 74.682599 47.709924 57.236889 0.000000
## [733] 0.000000 295.942721 0.000000 0.000000 249.609984 175.918686
## [739] 23.100023 457.721031 108.649725 19.936204 21.039344 1042.054814
## [745] 821.871855 213.219616 136.955033 0.000000 0.000000 71.329220
## [751] 811.965812 628.212450 494.146267 683.610868 538.277512 193.657710
## [757] 962.544465 82.724390 322.580645 75.628663 468.164794 17.908309
## [763] 0.000000 63.451777 0.000000 0.000000 16.594756 72.674419
## [769] 281.101920 46.274873 56.545095 194.678780 0.000000 320.586215
## [775] 34.944671 271.232412 240.690925 101.078167 0.000000 153.080750
## [781] 0.000000 119.303269 1226.993865 0.000000 106.851877 130.264872
## [787] 12.667849 0.000000 49.925112 27.107617 0.000000 30.196276
## [793] 11.866619 22.502250 200.697159 129.293384 295.130349 1515.151515
## [799] 65.267597 130.954330 150.195254 241.109102 62.893082 160.250438
## [805] 118.929633 53.588359 121.512835 388.719512 76.285963 37.950664
## [811] 34.024600 105.820106 0.000000 656.123822 105.871814 218.102508
## [817] 0.000000 436.963910 133.868809 189.182217 60.374321 25.113009
## [823] 520.833333 43.840421 170.415815 0.000000 950.469955 198.150594
## [829] 212.891780 182.561416 63.836578 354.289160 0.000000 241.019167
## [835] 60.618307 0.000000 179.157958 295.387412 0.000000 645.270844
## [841] 185.605279 69.444444 385.405961 197.732665 27.723870 100.755668
## [847] 511.197663 465.116279 132.425274 65.065336 495.049505 0.000000
## [853] 456.769984 541.418517 40.574810 298.507463 80.256822 435.019032
## [859] 0.000000 115.606936 48.804295 38.520801 345.489443 31.625553
## [865] 1394.728746 325.137167 0.000000 1815.384615 34.786066 0.000000
## [871] 252.911422 529.355729 56.398045 78.098851 28.005601 58.969218
## [877] 74.984514 90.976760 86.368216 25.793139 231.440271 38.175224
## [883] 44.998977 11.681561 39.898923 40.489928 0.000000 39.269586
## [889] 84.015963 254.161901 325.401668 424.328147 36.818851 208.159867
## [895] 951.733515 0.000000 100.200401 0.000000 0.000000 77.172403
## [901] 638.204225 73.224310 82.090573 191.152376 81.794015 76.511094
## [907] 126.707025 747.871159 395.604396 59.737157 0.000000 119.047619
## [913] 353.535354 0.000000 1923.864102 0.000000 185.873606 126.702566
## [919] 146.764510 103.626943 451.194013 330.033003 182.274989 93.720712
## [925] 70.671378 98.135427 8.352126 35.281665 124.146493 38.505968
## [931] 146.804836 470.219436 108.446801 59.808612 211.528292 55.928412
## [937] 0.000000 0.000000 73.448402 143.365369 35.306579 177.095632
## [943] 146.664644 402.576490 203.717851 0.000000 23.479690 146.878825
## [949] 0.000000 168.740772 128.700129 475.172250 80.677693 293.772033
## [955] 154.938670 124.019471 60.808756 17.946877 65.242212 112.317484
## [961] 305.810398 80.710250 65.679048 75.869793 182.481752 50.094623
## [967] 91.551138 31.046259 0.000000 515.843773 276.912426 16.170763
## [973] 335.289187 250.608874 27.314941 646.495992 69.492703 0.000000
## [979] 0.000000 0.000000 365.691489 124.354314 281.237445 747.046560
## [985] 1018.791035 204.022677 556.586271 109.920308 55.710306Question 3d
Create a histogram based on violent_crime_rate. HINT: See page 31 in Basic Statistics using R
ggplot(X2018_UCR_PA_cleaned, aes(x=violent_crime_rate)) +
geom_histogram(binwidth = 25)
Problem 3
Knit this Rmarkdown file and submit it via the link in the week 6 folder.