Homework4-JunyuMeng(jm4655)

Use the Aimes housing prices data set in the HW 4 folder (located in Week 5 folder) to answer the following questions:

1 Convert the data to a tibble data-frame. Use one or more functions from the Purrr package that subsets list data to subset columns in the data to three unique datasets. The first should include columns that are factors only (i.e. - categorical data), the second should include columns that are numeric only, and the third should include columns with logical values only.(Hint: Use split(data_with_sales_price,originaldata$categorical_variable) to split data into list of datasets by each category) Create column means for each column of the sales price variable using the colMeans function with one of the map functions on this list.

data1<-read.csv(file="E://Columbia Spring Semester//Housing_prices_data.csv")
                
library(tidyverse)

## -- Attaching packages --------------------------------------------------------------- tidyverse 1.2.1 --

## √ ggplot2 2.2.1     √ purrr   0.2.4
## √ tibble  1.4.2     √ dplyr   0.7.4
## √ tidyr   0.8.0     √ stringr 1.2.0
## √ readr   1.1.1     √ forcats 0.2.0

## -- Conflicts ------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

housing<-as.tibble(data1)
keep(housing, is.factor)

## # A tibble: 1,460 x 43
##    MSZoning Street Alley LotShape LandContour Utilities LotConfig
##    <fct>    <fct>  <fct> <fct>    <fct>       <fct>     <fct>    
##  1 RL       Pave   <NA>  Reg      Lvl         AllPub    Inside   
##  2 RL       Pave   <NA>  Reg      Lvl         AllPub    FR2      
##  3 RL       Pave   <NA>  IR1      Lvl         AllPub    Inside   
##  4 RL       Pave   <NA>  IR1      Lvl         AllPub    Corner   
##  5 RL       Pave   <NA>  IR1      Lvl         AllPub    FR2      
##  6 RL       Pave   <NA>  IR1      Lvl         AllPub    Inside   
##  7 RL       Pave   <NA>  Reg      Lvl         AllPub    Inside   
##  8 RL       Pave   <NA>  IR1      Lvl         AllPub    Corner   
##  9 RM       Pave   <NA>  Reg      Lvl         AllPub    Inside   
## 10 RL       Pave   <NA>  Reg      Lvl         AllPub    Corner   
## # ... with 1,450 more rows, and 36 more variables: LandSlope <fct>,
## #   Neighborhood <fct>, Condition1 <fct>, Condition2 <fct>,
## #   BldgType <fct>, HouseStyle <fct>, RoofStyle <fct>, RoofMatl <fct>,
## #   Exterior1st <fct>, Exterior2nd <fct>, MasVnrType <fct>,
## #   ExterQual <fct>, ExterCond <fct>, Foundation <fct>, BsmtQual <fct>,
## #   BsmtCond <fct>, BsmtExposure <fct>, BsmtFinType1 <fct>,
## #   BsmtFinType2 <fct>, Heating <fct>, HeatingQC <fct>, CentralAir <fct>,
## #   Electrical <fct>, KitchenQual <fct>, Functional <fct>,
## #   FireplaceQu <fct>, GarageType <fct>, GarageFinish <fct>,
## #   GarageQual <fct>, GarageCond <fct>, PavedDrive <fct>, PoolQC <fct>,
## #   Fence <fct>, MiscFeature <fct>, SaleType <fct>, SaleCondition <fct>

keep(housing, is.numeric)

## # A tibble: 1,460 x 38
##       Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt
##    <int>      <int>       <int>   <int>       <int>       <int>     <int>
##  1     1         60          65    8450           7           5      2003
##  2     2         20          80    9600           6           8      1976
##  3     3         60          68   11250           7           5      2001
##  4     4         70          60    9550           7           5      1915
##  5     5         60          84   14260           8           5      2000
##  6     6         50          85   14115           5           5      1993
##  7     7         20          75   10084           8           5      2004
##  8     8         60          NA   10382           7           6      1973
##  9     9         50          51    6120           7           5      1931
## 10    10        190          50    7420           5           6      1939
## # ... with 1,450 more rows, and 31 more variables: YearRemodAdd <int>,
## #   MasVnrArea <int>, BsmtFinSF1 <int>, BsmtFinSF2 <int>, BsmtUnfSF <int>,
## #   TotalBsmtSF <int>, X1stFlrSF <int>, X2ndFlrSF <int>,
## #   LowQualFinSF <int>, GrLivArea <int>, BsmtFullBath <int>,
## #   BsmtHalfBath <int>, FullBath <int>, HalfBath <int>,
## #   BedroomAbvGr <int>, KitchenAbvGr <int>, TotRmsAbvGrd <int>,
## #   Fireplaces <int>, GarageYrBlt <int>, GarageCars <int>,
## #   GarageArea <int>, WoodDeckSF <int>, OpenPorchSF <int>,
## #   EnclosedPorch <int>, X3SsnPorch <int>, ScreenPorch <int>,
## #   PoolArea <int>, MiscVal <int>, MoSold <int>, YrSold <int>,
## #   SalePrice <int>

keep(housing, is.logical)

## # A tibble: 1,460 x 0

2.Using the second dataset from question #1, calculate column means with one of the apply functions.

dataset2<-keep(housing, is.numeric)
apply(dataset2,2,mean,na.rm=TRUE)

##            Id    MSSubClass   LotFrontage       LotArea   OverallQual 
##  7.305000e+02  5.689726e+01  7.004996e+01  1.051683e+04  6.099315e+00 
##   OverallCond     YearBuilt  YearRemodAdd    MasVnrArea    BsmtFinSF1 
##  5.575342e+00  1.971268e+03  1.984866e+03  1.036853e+02  4.436397e+02 
##    BsmtFinSF2     BsmtUnfSF   TotalBsmtSF     X1stFlrSF     X2ndFlrSF 
##  4.654932e+01  5.672404e+02  1.057429e+03  1.162627e+03  3.469925e+02 
##  LowQualFinSF     GrLivArea  BsmtFullBath  BsmtHalfBath      FullBath 
##  5.844521e+00  1.515464e+03  4.253425e-01  5.753425e-02  1.565068e+00 
##      HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd    Fireplaces 
##  3.828767e-01  2.866438e+00  1.046575e+00  6.517808e+00  6.130137e-01 
##   GarageYrBlt    GarageCars    GarageArea    WoodDeckSF   OpenPorchSF 
##  1.978506e+03  1.767123e+00  4.729801e+02  9.424452e+01  4.666027e+01 
## EnclosedPorch    X3SsnPorch   ScreenPorch      PoolArea       MiscVal 
##  2.195411e+01  3.409589e+00  1.506096e+01  2.758904e+00  4.348904e+01 
##        MoSold        YrSold     SalePrice 
##  6.321918e+00  2.007816e+03  1.809212e+05

3.Using the second dataset from question #1, create a new dataset that only includes the variable indicating sales price (variable name is “SalePrice”).Search for a categorical variable in the full dataset that you think may be related to the sales price of houses.Use the split function to subset this data into a list containing a single dataset for each category of your data.Create column means for each column of the sales price variable using the colMeans function with one of the map functions on this list.

I choose Garage quality as the categorical condition which is related to the sales price of housing. Because people will always choose the one can provide convenience for transportation,especially places like New York and some other big cities. After deriving the average price level of different houses with different qualities of Garage, we could figure out that houses with “Ex” Grarage have higher price while these with “Po” have lower average price. However, probably it is because houses with good quality garage always have some other advantages.

data_2<-keep(housing,is.numeric)$SalePrice

mylist<-split(data_2,housing$GarageQual) 

str(mylist)

## List of 5
##  $ Ex: int [1:3] 475000 120500 127500
##  $ Fa: int [1:48] 129900 68500 83000 100000 105000 115000 125000 100000 200100 135000 ...
##  $ Gd: int [1:14] 118000 239000 233230 90350 316600 325000 157500 174900 185000 180000 ...
##  $ Po: int [1:3] 137000 67000 96500
##  $ TA: int [1:1311] 208500 181500 223500 140000 250000 143000 307000 200000 129500 345000 ...

map(mylist,mean,na.rm=TRUE)

## $Ex
## [1] 241000
## 
## $Fa
## [1] 123573.4
## 
## $Gd
## [1] 215860.7
## 
## $Po
## [1] 100166.7
## 
## $TA
## [1] 187489.8

4. Convert the full original housing dataset to a tibble data frame and then convert the tibble data frame to a list using as.list().Subset the list into two unique lists: 1) a new list that includes a single list element that measures the sales price of Aimes houses (variable name is “SalePrice”). 2) a new list that includes a single list element that measures the building type of each home (variable name is “BldgType”).Lastly, create one more list that appends the the list with building type data to the list with sales price data.

library(tidyverse)

housing<-as.tibble(housing)

list_housing<-as.list(housing)

a<-pluck(list_housing,"SalePrice")

b<-pluck(list_housing,"BldgType")
append(a,b)

##    [1] 208500 181500 223500 140000 250000 143000 307000 200000 129900
##   [10] 118000 129500 345000 144000 279500 157000 132000 149000  90000
##   [19] 159000 139000 325300 139400 230000 129900 154000 256300 134800
##   [28] 306000 207500  68500  40000 149350 179900 165500 277500 309000
##   [37] 145000 153000 109000  82000 160000 170000 144000 130250 141000
##   [46] 319900 239686 249700 113000 127000 177000 114500 110000 385000
##   [55] 130000 180500 172500 196500 438780 124900 158000 101000 202500
##   [64] 140000 219500 317000 180000 226000  80000 225000 244000 129500
##   [73] 185000 144900 107400  91000 135750 127000 136500 110000 193500
##   [82] 153500 245000 126500 168500 260000 174000 164500  85000 123600
##   [91] 109900  98600 163500 133900 204750 185000 214000  94750  83000
##  [100] 128950 205000 178000 118964 198900 169500 250000 100000 115000
##  [109] 115000 190000 136900 180000 383970 217000 259500 176000 139000
##  [118] 155000 320000 163990 180000 100000 136000 153900 181000  84500
##  [127] 128000  87000 155000 150000 226000 244000 150750 220000 180000
##  [136] 174000 143000 171000 230000 231500 115000 260000 166000 204000
##  [145] 125000 130000 105000 222500 141000 115000 122000 372402 190000
##  [154] 235000 125000  79000 109500 269500 254900 320000 162500 412500
##  [163] 220000 103200 152000 127500 190000 325624 183500 228000 128500
##  [172] 215000 239000 163000 184000 243000 211000 172500 501837 100000
##  [181] 177000 200100 120000 200000 127000 475000 173000 135000 153337
##  [190] 286000 315000 184000 192000 130000 127000 148500 311872 235000
##  [199] 104000 274900 140000 171500 112000 149000 110000 180500 143900
##  [208] 141000 277000 145000  98000 186000 252678 156000 161750 134450
##  [217] 210000 107000 311500 167240 204900 200000 179900  97000 386250
##  [226] 112000 290000 106000 125000 192500 148000 403000  94500 128200
##  [235] 216500  89500 185500 194500 318000 113000 262500 110500  79000
##  [244] 120000 205000 241500 137000 140000 180000 277000  76500 235000
##  [253] 173000 158000 145000 230000 207500 220000 231500  97000 176000
##  [262] 276000 151000 130000  73000 175500 185000 179500 120500 148000
##  [271] 266000 241500 290000 139000 124500 205000 201000 141000 415298
##  [280] 192000 228500 185000 207500 244600 179200 164700 159000  88000
##  [289] 122000 153575 233230 135900 131000 235000 167000 142500 152000
##  [298] 239000 175000 158500 157000 267000 205000 149900 295000 305900
##  [307] 225000  89500  82500 360000 165600 132000 119900 375000 178000
##  [316] 188500 260000 270000 260000 187500 342643 354000 301000 126175
##  [325] 242000  87000 324000 145250 214500  78000 119000 139000 284000
##  [334] 207000 192000 228950 377426 214000 202500 155000 202900  82000
##  [343]  87500 266000  85000 140200 151500 157500 154000 437154 318061
##  [352] 190000  95000 105900 140000 177500 173000 134000 130000 280000
##  [361] 156000 145000 198500 118000 190000 147000 159000 165000 132000
##  [370] 162000 172400 134432 125000 123000 219500  61000 148000 340000
##  [379] 394432 179000 127000 187750 213500  76000 240000 192000  81000
##  [388] 125000 191000 426000 119000 215000 106500 100000 109000 129000
##  [397] 123000 169500  67000 241000 245500 164990 108000 258000 168000
##  [406] 150000 115000 177000 280000 339750  60000 145000 222000 115000
##  [415] 228000 181134 149500 239000 126000 142000 206300 215000 113000
##  [424] 315000 139000 135000 275000 109008 195400 175000  85400  79900
##  [433] 122500 181000  81000 212000 116000 119000  90350 110000 555000
##  [442] 118000 162900 172500 210000 127500 190000 199900 119500 120000
##  [451] 110000 280000 204000 210000 188000 175500  98000 256000 161000
##  [460] 110000 263435 155000  62383 188700 124000 178740 167000 146500
##  [469] 250000 187000 212000 190000 148000 440000 251000 132500 208900
##  [478] 380000 297000  89471 326000 374000 155000 164000 132500 147000
##  [487] 156000 175000 160000  86000 115000 133000 172785 155000  91300
##  [496]  34900 430000 184000 130000 120000 113000 226700 140000 289000
##  [505] 147000 124500 215000 208300 161000 124500 164900 202665 129900
##  [514] 134000  96500 402861 158000 265000 211000 234000 106250 150000
##  [523] 159000 184750 315750 176000 132000 446261  86000 200624 175000
##  [532] 128000 107500  39300 178000 107500 188000 111250 158000 272000
##  [541] 315000 248000 213250 133000 179665 229000 210000 129500 125000
##  [550] 263000 140000 112500 255500 108000 284000 113000 141000 108000
##  [559] 175000 234000 121500 170000 108000 185000 268000 128000 325000
##  [568] 214000 316600 135960 142600 120000 224500 170000 139000 118500
##  [577] 145000 164500 146000 131500 181900 253293 118500 325000 133000
##  [586] 369900 130000 137000 143000  79500 185900 451950 138000 140000
##  [595] 110000 319000 114504 194201 217500 151000 275000 141000 220000
##  [604] 151000 221000 205000 152000 225000 359100 118500 313000 148000
##  [613] 261500 147000  75500 137500 183200 105500 314813 305000  67000
##  [622] 240000 135000 168500 165150 160000 139900 153000 135000 168500
##  [631] 124000 209500  82500 139400 144000 200000  60000  93000  85000
##  [640] 264561 274000 226000 345000 152000 370878 143250  98300 155000
##  [649] 155000  84500 205950 108000 191000 135000 350000  88000 145500
##  [658] 149000  97500 167000 197900 402000 110000 137500 423000 230500
##  [667] 129000 193500 168000 137500 173500 103600 165000 257500 140000
##  [676] 148500  87000 109500 372500 128500 143000 159434 173000 285000
##  [685] 221000 207500 227875 148800 392000 194700 141000 755000 335000
##  [694] 108480 141500 176000  89000 123500 138500 196000 312500 140000
##  [703] 361919 140000 213000  55000 302000 254000 179540 109900  52000
##  [712] 102776 189000 129000 130500 165000 159500 157000 341000 128500
##  [721] 275000 143000 124500 135000 320000 120500 222000 194500 110000
##  [730] 103000 236500 187500 222500 131400 108000 163000  93500 239900
##  [739] 179000 190000 132000 142000 179000 175000 180000 299800 236000
##  [748] 265979 260400  98000  96500 162000 217000 275500 156000 172500
##  [757] 212000 158900 179400 290000 127500 100000 215200 337000 270000
##  [766] 264132 196500 160000 216837 538000 134900 102000 107000 114500
##  [775] 395000 162000 221500 142500 144000 135000 176000 175900 187100
##  [784] 165500 128000 161500 139000 233000 107900 187500 160200 146800
##  [793] 269790 225000 194500 171000 143500 110000 485000 175000 200000
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##  [811] 181000 144500  55993 157900 116000 224900 137000 271000 155000
##  [820] 224000 183000  93000 225000 139500 232600 385000 109500 189000
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##  [838] 100000 144000 130500 140000 157500 174900 141000 153900 171000
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##  [883] 178000 118500 100000 328900 145000 135500 268000 149500 122900
##  [892] 172500 154500 165000 118858 140000 106500 142953 611657 135000
##  [901] 110000 153000 180000 240000 125500 128000 255000 250000 131000
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##  [919] 238000 176500 201000 145900 169990 193000 207500 175000 285000
##  [928] 176000 236500 222000 201000 117500 320000 190000 242000  79900
##  [937] 184900 253000 239799 244400 150900 214000 150000 143000 137500
##  [946] 124900 143000 270000 192500 197500 129000 119900 133900 172000
##  [955] 127500 145000 124000 132000 185000 155000 116500 272000 155000
##  [964] 239000 214900 178900 160000 135000  37900 140000 135000 173000
##  [973]  99500 182000 167500 165000  85500 199900 110000 139000 178400
##  [982] 336000 159895 255900 126000 125000 117000 395192 195000 197000
##  [991] 348000 168000 187000 173900 337500 121600 136500 185000  91000
## [1000] 206000  82000  86000 232000 136905 181000 149900 163500  88000
## [1009] 240000 102000 135000 100000 165000  85000 119200 227000 203000
## [1018] 187500 160000 213490 176000 194000  87000 191000 287000 112500
## [1027] 167500 293077 105000 118000 160000 197000 310000 230000 119750
## [1036]  84000 315500 287000  97000  80000 155000 173000 196000 262280
## [1045] 278000 139600 556581 145000 115000  84900 176485 200141 165000
## [1054] 144500 255000 180000 185850 248000 335000 220000 213500  81000
## [1063]  90000 110500 154000 328000 178000 167900 151400 135000 135000
## [1072] 154000  91500 159500 194000 219500 170000 138800 155900 126000
## [1081] 145000 133000 192000 160000 187500 147000  83500 252000 137500
## [1090] 197000  92900 160000 136500 146000 129000 176432 127000 170000
## [1099] 128000 157000  60000 119500 135000 159500 106000 325000 179900
## [1108] 274725 181000 280000 188000 205000 129900 134500 117000 318000
## [1117] 184100 130000 140000 133700 118400 212900 112000 118000 163900
## [1126] 115000 174000 259000 215000 140000 135000  93500 117500 239500
## [1135] 169000 102000 119000  94000 196000 144000 139000 197500 424870
## [1144]  80000  80000 149000 180000 174500 116900 143000 124000 149900
## [1153] 230000 120500 201800 218000 179900 230000 235128 185000 146000
## [1162] 224000 129000 108959 194000 233170 245350 173000 235000 625000
## [1171] 171000 163000 171900 200500 239000 285000 119500 115000 154900
## [1180]  93000 250000 392500 745000 120000 186700 104900  95000 262000
## [1189] 195000 189000 168000 174000 125000 165000 158000 176000 219210
## [1198] 144000 178000 148000 116050 197900 117000 213000 153500 271900
## [1207] 107000 200000 140000 290000 189000 164000 113000 145000 134500
## [1216] 125000 112000 229456  80500  91500 115000 134000 143000 137900
## [1225] 184000 145000 214000 147000 367294 127000 190000 132500 101800
## [1234] 142000 130000 138887 175500 195000 142500 265900 224900 248328
## [1243] 170000 465000 230000 178000 186500 169900 129500 119000 244000
## [1252] 171750 130000 294000 165400 127500 301500  99900 190000 151000
## [1261] 181000 128900 161500 180500 181000 183900 122000 378500 381000
## [1270] 144000 260000 185750 137000 177000 139000 137000 162000 197900
## [1279] 237000  68400 227000 180000 150500 139000 169000 132500 143000
## [1288] 190000 278000 281000 180500 119500 107500 162900 115000 138500
## [1297] 155000 140000 160000 154000 225000 177500 290000 232000 130000
## [1306] 325000 202500 138000 147000 179200 335000 203000 302000 333168
## [1315] 119000 206900 295493 208900 275000 111000 156500  72500 190000
## [1324]  82500 147000  55000  79000 130500 256000 176500 227000 132500
## [1333] 100000 125500 125000 167900 135000  52500 200000 128500 123000
## [1342] 155000 228500 177000 155835 108500 262500 283463 215000 122000
## [1351] 200000 171000 134900 410000 235000 170000 110000 149900 177500
## [1360] 315000 189000 260000 104900 156932 144152 216000 193000 127000
## [1369] 144000 232000 105000 165500 274300 466500 250000 239000  91000
## [1378] 117000  83000 167500  58500 237500 157000 112000 105000 125500
## [1387] 250000 136000 377500 131000 235000 124000 123000 163000 246578
## [1396] 281213 160000 137500 138000 137450 120000 193000 193879 282922
## [1405] 105000 275000 133000 112000 125500 215000 230000 140000  90000
## [1414] 257000 207000 175900 122500 340000 124000 223000 179900 127500
## [1423] 136500 274970 144000 142000 271000 140000 119000 182900 192140
## [1432] 143750  64500 186500 160000 174000 120500 394617 149700 197000
## [1441] 191000 149300 310000 121000 179600 129000 157900 240000 112000
## [1450]  92000 136000 287090 145000  84500 185000 175000 210000 266500
## [1459] 142125 147500      1      1      1      1      1      1      1
## [1468]      1      1      2      1      1      1      1      1      1
## [1477]      1      3      1      1      1      1      1      5      1
## [1486]      1      1      1      1      1      1      1      1      1
## [1495]      5      1      1      1      1      3      1      1      1
## [1504]      1      1      5      1      1      2      1      1      1
## [1513]      3      1      1      1      4      1      1      1      1
## [1522]      1      5      1      1      1      1      1      1      1
## [1531]      1      1      1      1      1      4      1      1      3
## [1540]      1      1      5      1      1      1      1      1      5
## [1549]      1      1      1      1      1      2      1      1      1
## [1558]      1      1      1      1      1      3      1      1      1
## [1567]      1      1      1      1      1      1      1      1      1
## [1576]      5      1      1      1      1      1      1      1      5
## [1585]      1      2      5      1      1      1      1      1      1
## [1594]      1      1      1      1      3      1      1      1      1
## [1603]      1      1      3      4      1      1      1      1      1
## [1612]      1      1      1      1      1      1      1      1      1
## [1621]      1      1      1      1      1      2      1      1      1
## [1630]      1      1      1      5      1      1      1      1      1
## [1639]      1      1      4      1      1      1      1      1      1
## [1648]      1      3      5      1      1      1      4      1      4
## [1657]      1      1      1      1      1      1      1      5      1
## [1666]      1      1      1      1      1      1      1      1      1
## [1675]      1      1      1      1      1      5      1      1      1
## [1684]      1      1      4      1      4      1      5      1      1
## [1693]      4      1      1      5      1      1      1      1      1
## [1702]      1      1      5      1      1      2      1      1      1
## [1711]      1      5      1      1      1      1      1      1      1
## [1720]      1      1      1      1      1      1      1      1      1
## [1729]      1      1      1      1      1      1      1      1      1
## [1738]      1      1      1      1      1      4      1      5      5
## [1747]      1      1      1      1      1      2      1      1      1
## [1756]      1      1      1      1      1      2      1      1      1
## [1765]      1      1      1      1      1      1      1      1      2
## [1774]      1      1      1      1      1      1      1      1      1
## [1783]      1      1      1      1      5      1      1      1      3
## [1792]      1      1      5      1      2      1      1      1      1
## [1801]      1      1      3      5      5      1      1      1      4
## [1810]      1      5      1      1      1      1      1      1      5
## [1819]      1      1      1      1      1      4      1      1      1
## [1828]      1      1      1      1      1      5      1      1      1
## [1837]      1      1      1      1      1      1      1      1      1
## [1846]      5      1      1      1      1      1      1      1      1
## [1855]      1      1      1      1      1      1      5      1      1
## [1864]      1      1      1      1      1      1      1      1      2
## [1873]      1      1      1      1      1      1      1      1      3
## [1882]      1      1      1      1      1      1      1      1      1
## [1891]      4      1      5      1      4      1      1      1      1
## [1900]      1      1      3      1      5      1      1      1      1
## [1909]      1      1      1      1      1      1      3      1      1
## [1918]      1      1      1      1      1      1      1      1      5
## [1927]      1      1      1      1      5      1      5      1      5
## [1936]      1      1      1      1      1      1      1      1      4
## [1945]      1      1      1      1      2      4      5      1      1
## [1954]      1      1      1      1      1      1      1      4      1
## [1963]      1      1      5      3      1      1      1      1      1
## [1972]      5      1      1      1      1      1      1      1      1
## [1981]      2      1      1      1      1      1      1      1      1
## [1990]      1      1      1      1      1      1      2      1      1
## [1999]      1      1      1      1      1      5      1      1      1
## [2008]      1      1      1      5      1      1      1      1      1
## [2017]      1      1      1      5      1      1      1      1      1
## [2026]      1      1      1      1      3      3      1      1      1
## [2035]      1      1      1      1      5      1      1      1      3
## [2044]      1      1      1      1      1      1      1      1      1
## [2053]      1      5      1      1      1      5      1      4      1
## [2062]      1      1      5      1      1      1      1      1      1
## [2071]      1      1      1      1      5      1      1      1      1
## [2080]      1      1      1      1      5      1      1      1      1
## [2089]      1      1      1      4      1      1      3      2      1
## [2098]      2      1      5      5      1      1      1      1      1
## [2107]      1      1      1      4      1      1      1      1      1
## [2116]      4      1      1      1      1      1      1      1      1
## [2125]      1      1      1      1      1      1      1      1      1
## [2134]      1      1      4      1      1      1      1      5      1
## [2143]      1      1      1      5      1      5      1      5      5
## [2152]      1      1      1      1      1      1      1      1      5
## [2161]      1      1      1      2      1      2      1      5      1
## [2170]      1      1      1      5      2      1      1      1      1
## [2179]      1      1      1      5      1      1      1      1      1
## [2188]      1      3      1      5      1      1      1      1      1
## [2197]      3      1      3      1      1      1      1      1      5
## [2206]      1      1      1      1      1      1      1      1      1
## [2215]      1      5      1      1      4      1      1      1      1
## [2224]      1      5      1      1      1      1      1      1      1
## [2233]      1      1      1      5      1      1      3      3      1
## [2242]      1      1      1      1      1      1      1      1      1
## [2251]      5      1      1      1      1      1      1      1      1
## [2260]      1      1      1      1      1      1      1      1      1
## [2269]      1      1      1      5      1      1      1      1      1
## [2278]      1      1      5      1      2      1      1      1      1
## [2287]      1      1      1      4      1      5      1      1      1
## [2296]      1      1      4      1      1      1      1      1      3
## [2305]      1      1      1      1      1      1      5      5      1
## [2314]      1      1      1      1      1      1      1      1      2
## [2323]      1      1      1      1      1      1      1      1      1
## [2332]      1      1      1      1      1      1      1      1      1
## [2341]      1      1      1      1      1      5      3      1      1
## [2350]      1      1      1      1      1      3      1      1      3
## [2359]      1      1      1      1      1      1      1      1      1
## [2368]      1      1      1      3      1      1      3      5      4
## [2377]      1      1      1      1      1      3      1      5      1
## [2386]      1      1      1      1      1      1      1      1      1
## [2395]      1      1      1      1      1      1      3      1      3
## [2404]      3      1      1      1      1      1      1      1      1
## [2413]      1      1      3      3      5      1      1      4      1
## [2422]      1      5      1      1      1      1      1      1      2
## [2431]      1      4      5      1      1      4      1      5      1
## [2440]      1      1      1      1      1      3      2      1      1
## [2449]      1      1      1      1      1      1      1      1      1
## [2458]      1      1      1      1      1      1      3      5      1
## [2467]      1      5      1      1      1      3      1      1      1
## [2476]      1      1      5      1      5      1      1      1      5
## [2485]      1      1      1      1      1      4      2      1      1
## [2494]      1      1      1      1      1      4      5      1      1
## [2503]      4      1      1      1      1      1      1      1      1
## [2512]      1      1      1      1      1      5      1      1      1
## [2521]      5      1      2      1      1      1      1      1      5
## [2530]      1      1      1      1      1      1      1      1      1
## [2539]      5      1      1      1      1      1      1      1      5
## [2548]      1      4      5      3      4      1      1      1      1
## [2557]      1      5      1      1      1      1      1      1      5
## [2566]      1      1      1      1      1      1      1      1      1
## [2575]      1      1      1      1      1      1      1      1      1
## [2584]      1      1      1      5      1      1      3      1      1
## [2593]      1      1      1      1      1      1      1      1      1
## [2602]      1      1      1      2      1      1      1      1      1
## [2611]      1      1      1      1      1      1      1      4      1
## [2620]      1      4      1      1      3      1      1      1      1
## [2629]      1      1      1      1      5      1      1      1      1
## [2638]      1      1      1      1      5      1      1      1      1
## [2647]      2      1      1      1      2      4      1      5      1
## [2656]      1      1      1      1      1      1      1      1      1
## [2665]      1      1      1      1      1      1      1      1      1
## [2674]      1      1      1      3      1      1      4      1      1
## [2683]      1      1      1      1      1      1      5      1      3
## [2692]      3      3      1      1      1      4      1      1      1
## [2701]      1      1      1      1      1      1      1      1      1
## [2710]      1      1      5      1      1      1      1      1      1
## [2719]      1      1      1      1      1      1      5      5      2
## [2728]      1      1      1      1      1      1      1      1      3
## [2737]      1      1      1      1      1      1      1      3      1
## [2746]      1      1      1      5      1      1      4      1      1
## [2755]      1      1      1      5      1      1      1      1      1
## [2764]      1      5      1      5      1      1      1      1      1
## [2773]      1      1      1      1      1      5      1      1      1
## [2782]      1      1      1      1      1      1      1      1      1
## [2791]      1      1      1      1      5      1      3      1      1
## [2800]      1      1      1      1      1      1      1      1      1
## [2809]      1      1      3      1      1      1      1      1      1
## [2818]      1      4      1      1      1      1      1      5      1
## [2827]      1      5      5      1      1      1      1      1      1
## [2836]      1      1      1      4      1      1      1      1      1
## [2845]      1      1      1      1      1      1      1      3      1
## [2854]      2      5      1      1      1      1      1      1      1
## [2863]      1      1      1      5      1      1      1      1      1
## [2872]      1      3      1      1      5      2      1      1      1
## [2881]      1      5      5      1      1      1      1      1      1
## [2890]      1      1      5      1      1      1      1      1      1
## [2899]      1      1      1      5      1      1      1      1      1
## [2908]      1      1      4      3      1      5      1      1      1
## [2917]      1      1      1      1

str(append(a,b))

##  int [1:2920] 208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 ...