This lab is designed to give you practice acquiring data from an external site and reading it into R. In addition, it should give you more practice using R and Markdown, and provide experience using the R language when producing a
dat<- read.csv("Traffic_Flow_Map_Volumes.csv")Tell me something about the data you downloaded. Why do you think it’s interesting? Since commuting is part of my daily routine. I am interested in where in Seattle has the highest and lowest traffic flow volume in the city.
How large is it, what variables does it contain?
The dataset has 437 rows and 7 columns.
What kind of information is available?
Street Name, Count Location, Year, SEGKEY, Annual Average Weekly Daily Traffic.
What kind of questions might it let you answer?
The locations with the most traffic flow and the locations with least traffic flow.
Use and display a few of the commands we’ve learned (table, head, dim, names) but try to make sure it displays in a readable way. Remember to create separate chunks of code like below and to discuss what you see in the output
table(dat$AAWDT)##
## 468 622 800 860 969 971 1079 1298 1379 1400 1447 1506
## 1 1 1 1 1 1 1 1 1 1 1 2
## 1602 1611 1615 1657 1666 1753 1767 1842 1918 1929 1941 2155
## 1 1 1 1 1 1 1 1 1 1 1 1
## 2212 2214 2228 2279 2308 2366 2372 2383 2396 2398 2454 2470
## 1 1 1 1 1 1 1 1 1 1 1 1
## 2484 2518 2545 2580 2586 2591 2756 2771 2798 2801 2812 2836
## 1 1 1 1 1 1 1 1 1 1 1 1
## 2841 2941 2951 2952 2963 2981 3047 3090 3095 3171 3183 3205
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3210 3458 3467 3505 3510 3541 3542 3578 3607 3659 3683 3692
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3703 3741 3755 3788 3793 3866 3905 3942 3979 4011 4018 4033
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4041 4080 4099 4108 4125 4137 4159 4170 4187 4206 4247 4251
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4283 4297 4306 4337 4340 4343 4347 4360 4396 4408 4435 4488
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4504 4533 4535 4581 4592 4619 4663 4669 4684 4686 4687 4742
## 1 1 1 1 1 1 2 1 1 1 1 1
## 4759 4764 4805 4862 4888 4893 4923 4931 4953 5006 5107 5150
## 1 1 1 1 1 1 1 1 1 1 1 1
## 5153 5288 5375 5377 5440 5447 5532 5537 5547 5559 5566 5576
## 1 1 1 1 1 1 1 1 1 1 1 1
## 5618 5623 5670 5695 5715 5716 5722 5723 5743 5764 5824 5835
## 1 1 1 1 1 1 1 1 1 1 1 1
## 5893 5927 5932 5933 5940 5949 5952 6229 6235 6263 6266 6295
## 1 1 1 1 1 1 1 1 1 2 1 1
## 6297 6346 6453 6494 6498 6530 6545 6548 6589 6593 6648 6665
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6667 6703 6736 6763 6777 6804 6815 6827 6832 6873 6904 6932
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6952 7056 7108 7141 7149 7151 7172 7173 7235 7255 7380 7464
## 1 1 1 1 1 1 1 1 1 1 1 1
## 7477 7494 7543 7592 7626 7690 7692 7726 7728 7817 7840 7880
## 1 1 1 1 1 1 1 2 1 1 1 1
## 7882 7883 7886 7914 7927 7928 7930 7967 7995 8020 8208 8261
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8382 8471 8502 8618 8645 8695 8696 8706 8749 8763 8767 8834
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8988 9111 9173 9221 9260 9313 9327 9334 9341 9342 9392 9399
## 1 1 1 1 1 1 1 1 1 1 1 1
## 9464 9474 9513 9524 9567 9615 9620 9685 9745 9760 9769 9865
## 1 1 1 1 1 1 1 1 1 1 1 1
## 9908 9910 9939 9976 10067 10105 10148 10186 10311 10329 10340 10341
## 1 1 1 2 1 1 1 1 1 1 1 1
## 10467 10657 10661 10663 10677 10835 10840 10910 11076 11149 11211 11258
## 1 1 1 1 1 1 1 1 1 1 1 1
## 11275 11330 11393 11430 11485 11646 11890 11923 11927 11929 11974 12021
## 1 1 1 1 1 1 1 1 1 1 1 1
## 12045 12144 12213 12222 12268 12342 12409 12413 12425 12456 12473 12475
## 1 1 1 1 2 1 1 1 1 1 1 1
## 12557 12636 12679 12706 12765 12808 12869 12883 13054 13133 13381 13396
## 1 1 1 1 1 1 1 1 1 1 1 1
## 13405 13474 13531 13551 13664 13733 13834 13836 13837 13987 14025 14166
## 1 1 1 1 1 1 1 1 1 1 1 1
## 14244 14341 14401 14404 14450 14513 14631 14897 14970 15006 15036 15115
## 1 2 1 1 1 1 1 1 1 1 1 1
## 15203 15261 15287 15469 15473 15793 15903 15978 16056 16105 16118 16123
## 1 1 1 1 1 1 1 2 1 1 1 1
## 16217 16523 16571 17559 17728 17733 17748 17759 17806 17839 17916 17985
## 1 1 1 1 1 1 1 1 1 1 1 1
## 18085 18119 18147 18170 18193 18210 18241 18362 18456 18502 18514 18559
## 1 1 1 1 1 1 1 1 1 1 1 1
## 18764 18786 18809 18883 19841 20383 20404 20452 20772 20898 21010 21028
## 1 1 1 1 1 1 1 1 1 1 1 1
## 21094 21115 21312 21422 21597 21978 22578 23009 23053 23168 23819 24341
## 1 1 1 1 1 1 1 1 1 1 1 1
## 25275 25385 26098 26666 27187 27619 27843 28649 29211 29651 30758 30811
## 1 1 1 1 1 1 1 1 1 1 1 1
## 32238 33004 34537 34754 36598 37707 40714 42194 66361
## 1 1 1 1 1 1 1 1 1head(dat$COUNT_LOCATION)## [1] E BOSTON ST, W/O 11TH AVE E
## [2] E PINE ST, W/O 12TH AVE
## [3] M L KING JR WAY E, N/O E JOHN ST
## [4] EASTLAKE AVE E, N/O THOMAS ST
## [5] SW HENDERSON ST, E/O 11TH AVE SW
## [6] NE RAVENNA EB BV, E/O BROOKLYN AVE NE
## 437 Levels: 10TH AVE E, S/O E MILLER ST ... YALE AVE, NW/O HOWELL STdim(dat)## [1] 437 7names(dat)## [1] "OBJECTID" "STNAME" "COUNT_LOCATION" "YEAR"
## [5] "SEGKEY" "AAWDT" "INPUT_STUDY_ID"summary(dat)## OBJECTID STNAME
## Min. : 352 3RD AVE : 11
## 1st Qu.:12228 1ST AVE : 9
## Median :24033 NE RAVENNA EB BV : 9
## Mean :24570 35TH AVE SW : 7
## 3rd Qu.:35702 AIRPORT WAY S : 7
## Max. :46655 CALIFORNIA AVE SW: 7
## (Other) :387
## COUNT_LOCATION YEAR SEGKEY
## 10TH AVE E, S/O E MILLER ST: 1 Min. :2017 Min. : 1000
## 11TH AVE NE, N/O NE 45TH ST: 1 1st Qu.:2017 1st Qu.: 7911
## 11TH AVE NE, N/O NE 52ND ST: 1 Median :2017 Median : 12359
## 11TH AVE NE, S/O NE 50TH ST: 1 Mean :2017 Mean : 20899
## 11TH AVE W, N/O W DRAVUS ST: 1 3rd Qu.:2018 3rd Qu.: 17899
## 12TH AVE NE, N/O NE 71ST ST: 1 Max. :2018 Max. :652860
## (Other) :431
## AAWDT INPUT_STUDY_ID
## Min. : 468 Min. :323817
## 1st Qu.: 4504 1st Qu.:325393
## Median : 7840 Median :325779
## Mean : 9923 Mean :325675
## 3rd Qu.:13133 3rd Qu.:326138
## Max. :66361 Max. :327691
## If future weeks we’ll learn about modifying data, how to change the orientation of a figure or remove some entries or many other things. Look at your data and think about what would make the raw data more useful. Are dates entered in the wrong form? Do you need locations aggregated to a higher level? Are words entered inconsistently (i.e., Seattle, seattle, SEATTLE)? Start to think forward about what you want to learn to make data as useful as possible for you. If you are experienced with R, go ahead an modify one of the columns.
I would delete the INPUT_STUDY_ID as the dataset already has the objective ID already. I would not include Year as it is either 2017,2018. I would say those are recent and accurate enough. For the first column, I would have OBJECT_ID instead of OBJECTID so it is consistent with others.
Steps(adminstrative), Sleeping Time(adminstrative), Screen Time(adminstrative).
as a second (nested) point on each item, identify whether it is passive, active, or administrative data
answer whether you can find a way to download the data you generate. If yes, post a link to where. If no, can you copy the information each day?
You can export the health data from iphone to computer.
How many do you have, is there any type of data you lack, and is there anything else you’re going to look into acquiring?
I have data for at least 6 months from my phone. I would try to get a heart rate data also and possible dietary application to examine any correlations.
Remember, you’ll want to start data collection next week.