Quiz on EDA for Numerical Data
Jake Blackburn
# Load packages
library(ggplot2)
library(dplyr)
# Import data
census <- read.csv("/resources/rstudio/BusStatistics/Data/census.csv")
str(census)
## 'data.frame': 30 obs. of 39 variables:
## $ GEOID : num 3.3e+09 3.3e+09 3.3e+09 3.3e+09 3.3e+09 ...
## $ NAME : Factor w/ 30 levels "Alexandria town, Grafton County, New Hampshire",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ popTotal : int 1876 5285 2658 2159 4631 7281 987 3050 1011 1294 ...
## $ medianAge : num 45 45.8 46.5 37.8 41.8 43.8 57.6 41.4 51.5 44.6 ...
## $ popNative_USA : int 1833 4975 2554 2094 4518 7223 966 3003 989 1268 ...
## $ popNative : int 1835 5041 2573 2142 4533 7236 976 3026 989 1279 ...
## $ popNaitve_NH : int 1008 2450 1418 1008 2273 4420 425 1611 384 659 ...
## $ popMoved_otherState : int 10 72 13 41 37 60 31 84 43 6 ...
## $ popMoved_abroad : int 5 0 13 0 0 11 0 9 0 0 ...
## $ popCommute_car : int 755 2298 1178 993 2537 3615 383 1381 524 696 ...
## $ popCommute_publicT : int 0 0 0 0 13 0 0 0 0 11 ...
## $ popCommute_bicycle : int 0 0 19 0 0 0 0 0 0 0 ...
## $ popCommute_foot : int 6 24 36 143 33 0 0 96 4 0 ...
## $ popCommute_other : int 14 12 5 8 0 54 0 6 3 3 ...
## $ popCommute_home : int 29 277 182 24 79 39 44 48 49 43 ...
## $ popBA : int 199 1274 742 531 783 928 271 484 374 209 ...
## $ popPov : int 159 305 176 457 234 620 45 323 79 32 ...
## $ medianIncome : int 56597 76676 69489 44000 70037 60938 53958 54015 70625 61058 ...
## $ earningsAgg : int 31933300 127223500 65678000 36025500 118527900 150122700 21898000 54045100 27997300 31278900 ...
## $ earningsAgg_WageSalary : int 29137500 118023200 57790900 34825100 112078200 142960400 19791000 51913800 23983000 26628900 ...
## $ earningsAgg_SelfEmpl : int 2795800 9200300 7887100 1200400 6449800 7162300 2106900 2131300 4014200 4650000 ...
## $ LF : int 905 2775 1559 1232 2905 3904 459 1618 611 794 ...
## $ LF_Civilian : int 905 2775 1559 1232 2905 3904 459 1610 611 794 ...
## $ LF_Civilian_Unemployed : int 68 120 129 59 180 174 21 40 31 28 ...
## $ LF_Not : int 631 1526 689 590 992 2117 442 875 313 267 ...
## $ housingTotal : int 960 4437 1251 1445 2415 3610 1033 2333 785 735 ...
## $ housingVacant_rent : int 0 38 0 28 0 83 0 0 0 0 ...
## $ housingVacant_seasonal : int 259 2123 151 326 618 585 529 1017 331 115 ...
## $ medianHomeValue : int 178800 279000 208000 155600 177500 186100 253200 205600 334000 165900 ...
## $ medianGrossRent : int 869 798 1125 834 1068 892 1027 824 1083 941 ...
## $ unemplRate : num 7.51 4.32 8.27 4.79 6.2 ...
## $ LFparticipationRate : num 58.9 64.5 69.4 67.6 74.5 ...
## $ housing_percentOfseasonal : num 27 47.8 12.1 22.6 25.6 ...
## $ housing_percentOfrent : num 0 0.856 0 1.938 0 ...
## $ earnings_percenOfcurrentlabor : num 100 100 100 100 100 ...
## $ popPov_percent : num 8.48 5.77 6.62 21.17 5.05 ...
## $ popBA_percent : num 10.6 24.1 27.9 24.6 16.9 ...
## $ housingVacant_seasonal_percent: num 27 47.8 12.1 22.6 25.6 ...
## $ housingVacant_rent_percent : num 0 0.856 0 1.938 0 ...
summary(census)
## GEOID NAME
## Min. :3.300e+09 Alexandria town, Grafton County, New Hampshire: 1
## 1st Qu.:3.300e+09 Alton town, Belknap County, New Hampshire : 1
## Median :3.300e+09 Andover town, Merrimack County, New Hampshire : 1
## Mean :3.301e+09 Ashland town, Grafton County, New Hampshire : 1
## 3rd Qu.:3.301e+09 Barnstead town, Belknap County, New Hampshire : 1
## Max. :3.301e+09 Belmont town, Belknap County, New Hampshire : 1
## (Other) :24
## popTotal medianAge popNative_USA popNative
## Min. : 582 Min. :37.80 Min. : 550 Min. : 557
## 1st Qu.: 1483 1st Qu.:44.77 1st Qu.: 1452 1st Qu.: 1457
## Median : 2934 Median :48.75 Median : 2865 Median : 2889
## Mean : 3774 Mean :48.59 Mean : 3645 Mean : 3669
## 3rd Qu.: 4774 3rd Qu.:53.00 3rd Qu.: 4576 3rd Qu.: 4615
## Max. :16171 Max. :59.10 Max. :15511 Max. :15613
##
## popNaitve_NH popMoved_otherState popMoved_abroad popCommute_car
## Min. : 202.0 Min. : 6.00 Min. : 0.000 Min. : 234.0
## 1st Qu.: 698.5 1st Qu.: 23.25 1st Qu.: 0.000 1st Qu.: 710.8
## Median :1316.0 Median : 42.00 Median : 0.000 Median :1397.5
## Mean :1881.0 Mean : 77.80 Mean : 6.467 Mean :1670.0
## 3rd Qu.:2405.8 3rd Qu.: 87.25 3rd Qu.:10.500 3rd Qu.:2273.0
## Max. :9378.0 Max. :470.00 Max. :49.000 Max. :7069.0
##
## popCommute_publicT popCommute_bicycle popCommute_foot popCommute_other
## Min. : 0.0 Min. : 0.000 Min. : 0.0 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 6.5 1st Qu.: 3.50
## Median : 0.0 Median : 0.000 Median : 15.0 Median : 13.50
## Mean : 3.9 Mean : 2.567 Mean : 34.6 Mean : 35.47
## 3rd Qu.: 0.0 3rd Qu.: 0.000 3rd Qu.: 34.0 3rd Qu.: 51.00
## Max. :72.0 Max. :27.000 Max. :238.0 Max. :225.00
##
## popCommute_home popBA popPov medianIncome
## Min. : 21.00 Min. : 159.0 Min. : 30.0 Min. :44000
## 1st Qu.: 36.00 1st Qu.: 401.5 1st Qu.: 146.2 1st Qu.:52204
## Median : 58.50 Median : 651.0 Median : 215.0 Median :61438
## Mean : 93.07 Mean : 833.0 Mean : 390.8 Mean :59857
## 3rd Qu.:134.00 3rd Qu.: 973.8 3rd Qu.: 440.5 3rd Qu.:68043
## Max. :360.00 Max. :2995.0 Max. :2557.0 Max. :76676
##
## earningsAgg earningsAgg_WageSalary earningsAgg_SelfEmpl
## Min. : 11992400 Min. : 10745100 Min. : 1200400
## 1st Qu.: 31442500 1st Qu.: 27256050 1st Qu.: 3072075
## Median : 67989550 Median : 60202850 Median : 6436850
## Mean : 84895040 Mean : 77478727 Mean : 7416153
## 3rd Qu.:117084150 3rd Qu.:110519975 3rd Qu.: 9046525
## Max. :353736200 Max. :325047100 Max. :28684400
##
## LF LF_Civilian LF_Civilian_Unemployed LF_Not
## Min. : 288.0 Min. : 288.0 Min. : 17.00 Min. : 233.0
## 1st Qu.: 821.8 1st Qu.: 821.8 1st Qu.: 40.75 1st Qu.: 530.8
## Median :1647.5 Median :1643.5 Median : 68.00 Median : 842.5
## Mean :1979.8 Mean :1978.9 Mean :106.07 Mean :1184.5
## 3rd Qu.:2739.5 3rd Qu.:2739.5 3rd Qu.:120.00 3rd Qu.:1513.8
## Max. :8468.0 Max. :8461.0 Max. :635.00 Max. :5018.0
##
## housingTotal housingVacant_rent housingVacant_seasonal medianHomeValue
## Min. : 498 Min. : 0.0 Min. : 27.0 Min. :155600
## 1st Qu.:1054 1st Qu.: 0.0 1st Qu.: 259.0 1st Qu.:178275
## Median :1826 Median : 0.0 Median : 520.5 Median :206800
## Mean :2501 Mean : 19.7 Mean : 798.9 Mean :226277
## 3rd Qu.:3486 3rd Qu.: 13.5 3rd Qu.:1189.8 3rd Qu.:263650
## Max. :9945 Max. :208.0 Max. :2999.0 Max. :334000
##
## medianGrossRent unemplRate LFparticipationRate
## Min. : 787.0 Min. : 1.310 Min. :45.93
## 1st Qu.: 849.0 1st Qu.: 3.835 1st Qu.:57.72
## Median : 941.0 Median : 4.566 Median :63.42
## Mean : 960.7 Mean : 5.241 Mean :62.30
## 3rd Qu.:1027.0 3rd Qu.: 6.912 3rd Qu.:66.53
## Max. :1223.0 Max. :10.272 Max. :74.84
## NA's :1
## housing_percentOfseasonal housing_percentOfrent
## Min. : 1.477 Min. :0.0000
## 1st Qu.:18.398 1st Qu.:0.0000
## Median :29.462 Median :0.0000
## Mean :30.980 Mean :0.5421
## 3rd Qu.:41.164 3rd Qu.:0.6900
## Max. :68.020 Max. :5.0510
##
## earnings_percenOfcurrentlabor popPov_percent popBA_percent
## Min. :100 Min. : 2.473 Min. :10.61
## 1st Qu.:100 1st Qu.: 5.894 1st Qu.:15.94
## Median :100 Median : 8.145 Median :24.35
## Mean :100 Mean : 9.077 Mean :23.81
## 3rd Qu.:100 3rd Qu.:10.869 3rd Qu.:29.02
## Max. :100 Max. :21.167 Max. :43.13
##
## housingVacant_seasonal_percent housingVacant_rent_percent
## Min. : 1.477 Min. :0.0000
## 1st Qu.:18.398 1st Qu.:0.0000
## Median :29.462 Median :0.0000
## Mean :30.980 Mean :0.5421
## 3rd Qu.:41.164 3rd Qu.:0.6900
## Max. :68.020 Max. :5.0510
## Suppose that you are writing a report on the economic importance of snowbirds in the Lakes Region Planning Commission’s Region. The director ask you what the share of seasonal homes in total (housing_percentOfseasonal) for a typical town in the region is.
Q1 Would you choose the mean or the median for the typical value?
i would choose mean ## Q2 Explain your answer. Because there are no extreme values that would affect the data ## Q3 What is the highest percentage of the seasonal homes by any town in the region? the highest percentage is 68.020%
# Create faceted histogram
ggplot(census, aes(x = housing_percentOfseasonal)) +
geom_histogram() 
# Create box plots of city mpg by UR_aboveAve
ggplot(census, aes(x = 1, y = housing_percentOfseasonal)) +
geom_boxplot()
# Create overlaid density plots for same data
ggplot(census, aes(x = housing_percentOfseasonal)) +
geom_density(alpha = .3)
# If data has extreme values
census %>%
summarize(median = median(housing_percentOfseasonal, na.rm = TRUE),
IQR = IQR(housing_percentOfseasonal, na.rm = TRUE))
## median IQR
## 1 29.46153 22.76686
# If data doesn't have extreme values
census %>%
summarize(mean = mean(housing_percentOfseasonal, na.rm = TRUE),
sd = sd(housing_percentOfseasonal, na.rm = TRUE))
## mean sd
## 1 30.97958 16.97828Suppose that director suspect that the share of seaonsal homes (popBA_percent) may be associated with the educational level of residents. Divide the towns into two groups: 1) educated towns (the share of population with Bachelor’s degree or higher than the average) and 2) other towns (the share of population with Bachelor’s degree or lower than the average). ## Q4 What is the share of seasonal homes in total in a typical educated town? around 27 ## Q5 What is the share of seasonal homes in total in a typical less educated educated town? around 31 ## Q6 What possible explanation you may have for the significant difference, if any? i calculated the data using the medium. If i used the mean, the answers would have increased due to the outliers in percent.
# Create a new variable, UR > or < average
UR_ave <- mean(census$popBA_percent)
census$UR_aboveAve <- ifelse(census$popBA_percent >= UR_ave, "equal or above ave", "below ave")
# Create box plots of total population by UR_aboveAve
ggplot(census, aes(x = UR_aboveAve, y = housingTotal)) +
geom_boxplot()
# If data has extreme values
census %>%
group_by(UR_aboveAve) %>%
summarize(median = median(housing_percentOfseasonal, na.rm = TRUE),
IQR = IQR(housing_percentOfseasonal, na.rm = TRUE))
## # A tibble: 2 x 3
## UR_aboveAve median IQR
## <chr> <dbl> <dbl>
## 1 below ave 24.8 14.5
## 2 equal or above ave 35.8 18.5
# If data doesn't have extreme values
census %>%
group_by(UR_aboveAve) %>%
summarize(mean = mean(housing_percentOfseasonal, na.rm = TRUE),
sd = sd(housing_percentOfseasonal, na.rm = TRUE))
## # A tibble: 2 x 3
## UR_aboveAve mean sd
## <chr> <dbl> <dbl>
## 1 below ave 23.7 17.4
## 2 equal or above ave 37.3 14.3