Quiz on Getting Started
Daniel Lee
Suppose that your first job out of college is with the Lakes Region Planning Commission in New Hampshire. The director asks you to analyze recent demographic and economic trends in the region.
The data is posted in Moodle. In your course homepage, scroll all the way down to find “termProject: census.csv”. Click the link to open the data set. The description of the data is posted in Announcement at the top of the course homepage.
Now that you have the data, answer the following questions:
Question 1
How many towns are in the NH Lakes Region Planning Commission region?
Provide your answer here
Question 2
What time period does the data represent?
Provide your answer here
Question 3
What are recent economic and demographic trends? There isn’t right or wrong answer for this question. Explain what approach you would take to identify demographic and economic trends in a sentence or two. In addition, list at least two challenges you might face in your approach.
Provide your answer here
My comments
See below for three simple functions (head(), str(), summary()) to examine some basic feasures of the data you are about to analyze.
From head() you can learn:
- that the row represents towns and the column represents characteristics of towns.
From str() you can learn:
- that there are 64 observations or rows. This is because there are 30 towns in the region plus New Hampshire and the U.S. as benchmarks.
- that makes 32 not 64? Well, there are two years: 2011 and 2016. So each place shows up twice in the data.
From summary() you can learn:
- that each town shows up twice in the data;
- that Belknap County covers the largest area in the Lakes Region representing 22 towns in the region; and
- that the smallest town has the population of 541 people while the largest town the population of more than 300 million people. What? Of coruse, you are right. That is the United States.
In addition, I provided a chart at the bottom just to demonstrate how we can create a beautiful chart to get your points across to your audience. A picture is worth a thousand words!!! Note that you can easily replace medianAge by another variable in the data to perform the same analysis.
Import data
# Import data
data <- read.csv("/resources/rstudio/Bus Statistics/data/census2.csv")
# first six rows of data
# Note that the row represents towns and the column represents characteristics of towns
head(data)
## X Town County popTotal medianAge popNative_bornUSA popNative
## 1 1 Alexandria Grafton 1836 39.2 1783 1790
## 2 2 Alton Belknap 5214 44.6 5023 5040
## 3 3 Andover Merrimack 2422 47.3 2339 2357
## 4 4 Ashland Grafton 1507 39.6 1445 1445
## 5 5 Barnstead Belknap 4564 38.4 4472 4492
## 6 6 Belmont Belknap 7350 41.1 7141 7170
## popNaitve_bornNH popMoved_otherState popMoved_abroad popCommute_car
## 1 1047 27 0 749
## 2 2160 26 34 2312
## 3 1359 63 39 1146
## 4 782 20 29 611
## 5 2247 42 0 2304
## 6 4334 26 0 3359
## popCommute_publicT popCommute_bicycle popCommute_foot popCommute_other
## 1 0 0 10 10
## 2 0 0 80 0
## 3 0 0 86 95
## 4 0 0 22 0
## 5 0 0 24 25
## 6 0 0 88 24
## popCommute_home popBA popPov medianIncome incomeLabor
## 1 31 217 147 56667 30747600
## 2 352 1119 288 60045 126791100
## 3 79 473 180 67900 57250300
## 4 6 218 187 38821 23228600
## 5 89 890 134 65221 100084000
## 6 123 994 502 58561 155553900
## incomeLabor_WageSalary incomeLabor_SelfEmpl incomeInvest incomeTotal
## 1 27640100 3107600 1733400 40645300
## 2 112401800 14389400 5898900 155566200
## 3 50351700 6898600 1834700 71473100
## 4 19907000 3321700 611700 32032600
## 5 90297300 9786700 3353800 119881800
## 6 141222300 14331700 4644100 187212600
## LF LF_Civilian LF_Civilian_Unemployed LF_Not housingTotal
## 1 919 919 85 464 945
## 2 3007 3007 163 1237 4219
## 3 1502 1494 49 531 1124
## 4 691 691 52 544 1261
## 5 2692 2692 108 931 2344
## 6 3782 3782 165 2027 3640
## housingVacant_rent housingVacant_seasonal medianHomeValue
## 1 0 240 206500
## 2 31 2016 263000
## 3 18 122 228500
## 4 65 395 167900
## 5 31 557 205500
## 6 30 317 184900
## medianGrossRent unemplRate LFparticipationRate
## 1 918 9.249184 66.44975
## 2 822 5.420685 70.85297
## 3 937 3.279786 73.88096
## 4 552 7.525326 55.95142
## 5 1133 4.011887 74.30306
## 6 922 4.362771 65.10587
## housingVacant_seasonal_percent housingVacant_rent_percent incomeNonLabor
## 1 25.396825 0.0000000 9897700
## 2 47.783835 0.7347713 28775100
## 3 10.854093 1.6014235 14222800
## 4 31.324346 5.1546392 8804000
## 5 23.762799 1.3225256 19797800
## 6 8.708791 0.8241758 31658700
## incomeTransferPayment incomeNonLabor_percent incomeInvest_percent
## 1 8164300 24.35140 4.264700
## 2 22876200 18.49701 3.791891
## 3 12388100 19.89951 2.566980
## 4 8192300 27.48450 1.909617
## 5 16444000 16.51443 2.797589
## 6 27014600 16.91056 2.480656
## incomeTransferPayment_percent incomeLabor_percent popPov_percent
## 1 20.08670 75.64860 8.006536
## 2 14.70512 81.50299 5.523590
## 3 17.33253 80.10049 7.431874
## 4 25.57488 72.51550 12.408759
## 5 13.71684 83.48557 2.936021
## 6 14.42990 83.08944 6.829932
## popBA_percent popMoved_otherState_percent popMoved_abroad_percent
## 1 11.81917 1.4705882 0.0000000
## 2 21.46145 0.4986575 0.6520905
## 3 19.52931 2.6011561 1.6102395
## 4 14.46583 1.3271400 1.9243530
## 5 19.50044 0.9202454 0.0000000
## 6 13.52381 0.3537415 0.0000000
## popMoved_percent popCommute_car_percent popCommute_publicT_percent
## 1 1.4705882 40.79521 0
## 2 1.1507480 44.34216 0
## 3 4.2113955 47.31627 0
## 4 3.2514930 40.54413 0
## 5 0.9202454 50.48203 0
## 6 0.3537415 45.70068 0
## popCommute_bicycle_percent popCommute_foot_percent
## 1 0 0.5446623
## 2 0 1.5343306
## 3 0 3.5507845
## 4 0 1.4598540
## 5 0 0.5258545
## 6 0 1.1972789
## popCommute_other_percent popCommute_home_percent Year benchM
## 1 0.5446623 1.688453 2011 NA
## 2 0.0000000 6.751055 2011 NA
## 3 3.9223782 3.261767 2011 NA
## 4 0.0000000 0.398142 2011 NA
## 5 0.5477651 1.950044 2011 NA
## 6 0.3265306 1.673469 2011 NA
# structure of data
# Note that there are 64 observations or rows. This is because there are 30 towns in the region plus New Hampshire and the U.S. as benchmarks. That makes 32 not 64? Well, there are two years: 2011 and 2016. So each place show up twice in the data.
str(data)
## 'data.frame': 64 obs. of 56 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Town : Factor w/ 32 levels "Alexandria","Alton",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ County : Factor w/ 6 levels " Belknap"," Carroll",..: 3 1 4 3 1 1 3 3 1 4 ...
## $ popTotal : int 1836 5214 2422 1507 4564 7350 1179 3063 960 1120 ...
## $ medianAge : num 39.2 44.6 47.3 39.6 38.4 41.1 47.7 47.1 49.2 40.5 ...
## $ popNative_bornUSA : int 1783 5023 2339 1445 4472 7141 1143 2961 920 1106 ...
## $ popNative : int 1790 5040 2357 1445 4492 7170 1153 2992 924 1120 ...
## $ popNaitve_bornNH : int 1047 2160 1359 782 2247 4334 545 1538 453 548 ...
## $ popMoved_otherState : int 27 26 63 20 42 26 44 45 5 16 ...
## $ popMoved_abroad : int 0 34 39 29 0 0 0 8 0 0 ...
## $ popCommute_car : int 749 2312 1146 611 2304 3359 575 1337 453 556 ...
## $ popCommute_publicT : int 0 0 0 0 0 0 5 0 0 0 ...
## $ popCommute_bicycle : int 0 0 0 0 0 0 0 10 0 0 ...
## $ popCommute_foot : int 10 80 86 22 24 88 10 58 29 7 ...
## $ popCommute_other : int 10 0 95 0 25 24 21 0 0 5 ...
## $ popCommute_home : int 31 352 79 6 89 123 52 47 6 22 ...
## $ popBA : int 217 1119 473 218 890 994 272 518 319 123 ...
## $ popPov : int 147 288 180 187 134 502 79 437 95 208 ...
## $ medianIncome : int 56667 60045 67900 38821 65221 58561 55208 43242 58571 46845 ...
## $ incomeLabor : num 3.07e+07 1.27e+08 5.73e+07 2.32e+07 1.00e+08 ...
## $ incomeLabor_WageSalary : num 2.76e+07 1.12e+08 5.04e+07 1.99e+07 9.03e+07 ...
## $ incomeLabor_SelfEmpl : num 3107600 14389400 6898600 3321700 9786700 ...
## $ incomeInvest : num 1733400 5898900 1834700 611700 3353800 ...
## $ incomeTotal : num 4.06e+07 1.56e+08 7.15e+07 3.20e+07 1.20e+08 ...
## $ LF : int 919 3007 1502 691 2692 3782 723 1512 501 663 ...
## $ LF_Civilian : int 919 3007 1494 691 2692 3782 723 1512 501 663 ...
## $ LF_Civilian_Unemployed : int 85 163 49 52 108 165 36 36 13 40 ...
## $ LF_Not : int 464 1237 531 544 931 2027 324 997 318 210 ...
## $ housingTotal : int 945 4219 1124 1261 2344 3640 968 2481 730 658 ...
## $ housingVacant_rent : int 0 31 18 65 31 30 12 87 0 0 ...
## $ housingVacant_seasonal : int 240 2016 122 395 557 317 427 1039 306 150 ...
## $ medianHomeValue : int 206500 263000 228500 167900 205500 184900 257400 186700 329200 203500 ...
## $ medianGrossRent : int 918 822 937 552 1133 922 760 704 786 945 ...
## $ unemplRate : num 9.25 5.42 3.28 7.53 4.01 ...
## $ LFparticipationRate : num 66.4 70.9 73.9 56 74.3 ...
## $ housingVacant_seasonal_percent: num 25.4 47.8 10.9 31.3 23.8 ...
## $ housingVacant_rent_percent : num 0 0.735 1.601 5.155 1.323 ...
## $ incomeNonLabor : num 9897700 28775100 14222800 8804000 19797800 ...
## $ incomeTransferPayment : num 8164300 22876200 12388100 8192300 16444000 ...
## $ incomeNonLabor_percent : num 24.4 18.5 19.9 27.5 16.5 ...
## $ incomeInvest_percent : num 4.26 3.79 2.57 1.91 2.8 ...
## $ incomeTransferPayment_percent : num 20.1 14.7 17.3 25.6 13.7 ...
## $ incomeLabor_percent : num 75.6 81.5 80.1 72.5 83.5 ...
## $ popPov_percent : num 8.01 5.52 7.43 12.41 2.94 ...
## $ popBA_percent : num 11.8 21.5 19.5 14.5 19.5 ...
## $ popMoved_otherState_percent : num 1.471 0.499 2.601 1.327 0.92 ...
## $ popMoved_abroad_percent : num 0 0.652 1.61 1.924 0 ...
## $ popMoved_percent : num 1.47 1.15 4.21 3.25 0.92 ...
## $ popCommute_car_percent : num 40.8 44.3 47.3 40.5 50.5 ...
## $ popCommute_publicT_percent : num 0 0 0 0 0 ...
## $ popCommute_bicycle_percent : num 0 0 0 0 0 ...
## $ popCommute_foot_percent : num 0.545 1.534 3.551 1.46 0.526 ...
## $ popCommute_other_percent : num 0.545 0 3.922 0 0.548 ...
## $ popCommute_home_percent : num 1.688 6.751 3.262 0.398 1.95 ...
## $ Year : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ benchM : int NA NA NA NA NA NA NA NA NA NA ...
# summary of data
# You can learn a lot about data from the summary. For example, you can see that each town shows up twice in the data; that Belknap County covers the largest area in the Lakes Region representing 22 towns in the region; and that the smallest town has the population of 541 people while the largest town the population of more than 300 million people. What? Of coruse, you are right. That is the United States.
summary(data)
## X Town County popTotal
## Min. : 1.00 Alexandria: 2 Belknap :22 Min. : 541
## 1st Qu.:16.75 Alton : 2 Carroll :16 1st Qu.: 1496
## Median :32.50 Andover : 2 Grafton :12 Median : 3013
## Mean :32.50 Ashland : 2 Merrimack :10 Mean : 9812983
## 3rd Qu.:48.25 Barnstead : 2 New Hampshire: 2 3rd Qu.: 5516
## Max. :64.00 Belmont : 2 United States: 2 Max. :318558162
## (Other) :52
## medianAge popNative_bornUSA popNative
## Min. :37.00 Min. : 481 Min. : 487
## 1st Qu.:42.27 1st Qu.: 1433 1st Qu.: 1433
## Median :46.30 Median : 2942 Median : 2966
## Mean :46.82 Mean : 8398579 Mean : 8537742
## 3rd Qu.:50.25 3rd Qu.: 5246 3rd Qu.: 5272
## Max. :59.50 Max. :271639606 Max. :276363808
##
## popNaitve_bornNH popMoved_otherState popMoved_abroad
## Min. : 145 Min. : 5 Min. : 0.0
## 1st Qu.: 664 1st Qu.: 27 1st Qu.: 0.0
## Median : 1388 Median : 47 Median : 0.0
## Mean : 5746994 Mean : 188730 Mean : 50896.0
## 3rd Qu.: 2608 3rd Qu.: 103 3rd Qu.: 13.8
## Max. :186708691 Max. :6111964 Max. :1695894.0
##
## popCommute_car popCommute_publicT popCommute_bicycle
## Min. : 173 Min. : 0 Min. : 0.0
## 1st Qu.: 675 1st Qu.: 0 1st Qu.: 0.0
## Median : 1418 Median : 0 Median : 0.0
## Mean : 3854261 Mean : 225044 Mean : 25412.4
## 3rd Qu.: 2323 3rd Qu.: 4 3rd Qu.: 0.2
## Max. :125037241 Max. :7476312 Max. :877995.0
##
## popCommute_foot popCommute_other popCommute_home
## Min. : 0 Min. : 0.0 Min. : 6
## 1st Qu.: 10 1st Qu.: 4.8 1st Qu.: 39
## Median : 24 Median : 13.5 Median : 70
## Mean : 125354 Mean : 54174.8 Mean : 197434
## 3rd Qu.: 74 3rd Qu.: 49.0 3rd Qu.: 145
## Max. :4030730 Max. :1777051.0 Max. :6661892
##
## popBA popPov medianIncome incomeLabor
## Min. : 123 Min. : 30 Min. :38821 Min. :9.157e+06
## 1st Qu.: 360 1st Qu.: 141 1st Qu.:51477 1st Qu.:3.024e+07
## Median : 628 Median : 242 Median :58464 Median :7.037e+07
## Mean : 1912784 Mean : 1404789 Mean :57864 Mean :2.198e+11
## 3rd Qu.: 1025 3rd Qu.: 551 3rd Qu.:63981 3rd Qu.:1.311e+08
## Max. :64767787 Max. :46932225 Max. :76676 Max. :7.290e+12
##
## incomeLabor_WageSalary incomeLabor_SelfEmpl incomeInvest
## Min. :6.118e+06 Min. :1.200e+06 Min. :2.723e+05
## 1st Qu.:2.618e+07 1st Qu.:4.139e+06 1st Qu.:2.141e+06
## Median :6.255e+07 Median :6.469e+06 Median :3.740e+06
## Mean :2.056e+11 Mean :1.412e+10 Mean :1.401e+10
## 3rd Qu.:1.138e+08 3rd Qu.:1.071e+07 3rd Qu.:1.041e+07
## Max. :6.845e+12 Max. :4.534e+11 Max. :4.644e+11
##
## incomeTotal LF LF_Civilian
## Min. :1.554e+07 Min. : 288 Min. : 288
## 1st Qu.:4.338e+07 1st Qu.: 778 1st Qu.: 778
## Median :8.854e+07 Median : 1677 Median : 1677
## Mean :2.837e+11 Mean : 4982562 Mean : 4948950
## 3rd Qu.:1.850e+08 3rd Qu.: 2977 3rd Qu.: 2977
## Max. :9.502e+12 Max. :160818740 Max. :159807099
##
## LF_Civilian_Unemployed LF_Not housingTotal
## Min. : 13 Min. : 187 Min. : 498
## 1st Qu.: 39 1st Qu.: 506 1st Qu.: 1097
## Median : 70 Median : 866 Median : 1948
## Mean : 396637 Mean : 2782613 Mean : 4163607
## 3rd Qu.: 153 3rd Qu.: 1987 3rd Qu.: 4143
## Max. :13488016 Max. :92504969 Max. :134054899
##
## housingVacant_rent housingVacant_seasonal medianHomeValue
## Min. : 0 Min. : 27 Min. :155600
## 1st Qu.: 0 1st Qu.: 300 1st Qu.:184900
## Median : 6 Median : 543 Median :218250
## Mean : 96786 Mean : 162998 Mean :231338
## 3rd Qu.: 38 3rd Qu.: 1244 3rd Qu.:268075
## Max. :3321254 Max. :5368085 Max. :360800
##
## medianGrossRent unemplRate LFparticipationRate
## Min. : 552.0 Min. : 1.310 Min. :45.93
## 1st Qu.: 823.5 1st Qu.: 3.895 1st Qu.:59.26
## Median : 922.0 Median : 4.963 Median :64.54
## Mean : 926.8 Mean : 5.360 Mean :63.78
## 3rd Qu.:1000.5 3rd Qu.: 6.797 3rd Qu.:67.65
## Max. :1315.0 Max. :10.935 Max. :75.95
## NA's :1
## housingVacant_seasonal_percent housingVacant_rent_percent
## Min. : 1.477 Min. :0.0000
## 1st Qu.:16.565 1st Qu.:0.0000
## Median :27.925 Median :0.1363
## Mean :29.913 Mean :0.8662
## 3rd Qu.:41.980 3rd Qu.:1.2604
## Max. :68.020 Max. :5.1546
##
## incomeNonLabor incomeTransferPayment incomeNonLabor_percent
## Min. :4.101e+06 Min. :3.828e+06 Min. :13.60
## 1st Qu.:1.312e+07 1st Qu.:1.012e+07 1st Qu.:19.80
## Median :2.047e+07 Median :1.686e+07 Median :25.93
## Mean :6.394e+10 Mean :4.993e+10 Mean :27.09
## 3rd Qu.:4.423e+07 3rd Qu.:3.312e+07 3rd Qu.:31.85
## Max. :2.212e+12 Max. :1.748e+12 Max. :53.67
##
## incomeInvest_percent incomeTransferPayment_percent incomeLabor_percent
## Min. : 1.031 Min. :10.58 Min. :46.33
## 1st Qu.: 2.542 1st Qu.:15.94 1st Qu.:68.15
## Median : 4.928 Median :18.44 Median :74.07
## Mean : 6.657 Mean :20.43 Mean :72.91
## 3rd Qu.: 8.735 3rd Qu.:24.75 3rd Qu.:80.20
## Max. :36.817 Max. :36.41 Max. :86.40
##
## popPov_percent popBA_percent popMoved_otherState_percent
## Min. : 1.157 Min. :10.58 Min. :0.3537
## 1st Qu.: 6.630 1st Qu.:15.79 1st Qu.:1.1786
## Median : 8.495 Median :21.53 Median :1.9088
## Mean : 9.681 Mean :22.60 Mean :2.1631
## 3rd Qu.:12.705 3rd Qu.:27.98 3rd Qu.:2.7998
## Max. :21.167 Max. :43.13 Max. :7.7385
##
## popMoved_abroad_percent popMoved_percent popCommute_car_percent
## Min. :0.0000 Min. : 0.3537 Min. :31.98
## 1st Qu.:0.0000 1st Qu.: 1.2728 1st Qu.:40.47
## Median :0.0000 Median : 2.0825 Median :44.18
## Mean :0.3554 Mean : 2.5185 Mean :43.82
## 3rd Qu.:0.3727 3rd Qu.: 3.2062 3rd Qu.:47.22
## Max. :7.7634 Max. :11.0906 Max. :54.78
##
## popCommute_publicT_percent popCommute_bicycle_percent
## Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000
## Mean :0.1775 Mean :0.08443
## 3rd Qu.:0.1488 3rd Qu.:0.00840
## Max. :2.3469 Max. :1.08887
##
## popCommute_foot_percent popCommute_other_percent popCommute_home_percent
## Min. :0.0000 Min. :0.0000 Min. : 0.3981
## 1st Qu.:0.4395 1st Qu.:0.2175 1st Qu.: 1.6705
## Median :0.8309 Median :0.5455 Median : 2.3043
## Mean :1.2211 Mean :0.7564 Mean : 2.9100
## 3rd Qu.:1.5008 3rd Qu.:0.8935 3rd Qu.: 3.6064
## Max. :6.6234 Max. :3.9224 Max. :15.8965
##
## Year benchM
## Min. :2011 Min. :1.0
## 1st Qu.:2011 1st Qu.:1.0
## Median :2014 Median :1.5
## Mean :2014 Mean :1.5
## 3rd Qu.:2016 3rd Qu.:2.0
## Max. :2016 Max. :2.0
## NA's :60Simple demonstration of what you can do with R
Note that you can easily replace medianAge by another variable in the data to perform the same analysis
# Load
library(ggplot2)
library(dplyr)
data %>%
filter(Year == 2016) %>%
ggplot(aes(reorder(x = Town, medianAge), y = medianAge, fill = benchM)) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(title = "Lakes Region Towns by Median Age",
x = NULL,
y = NULL,
caption = "Data source: American Community Survey, 5-year estimate, 2012-2016")