1. Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes.

Read file and get data of U. S. State Public-School Expenditures

urlfile <- "https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/carData/Anscombe.csv"
dataset<-read.csv(urlfile)
head(dataset, 52)

##     X education income young urban
## 1  ME       189   2824 350.7   508
## 2  NH       169   3259 345.9   564
## 3  VT       230   3072 348.5   322
## 4  MA       168   3835 335.3   846
## 5  RI       180   3549 327.1   871
## 6  CT       193   4256 341.0   774
## 7  NY       261   4151 326.2   856
## 8  NJ       214   3954 333.5   889
## 9  PA       201   3419 326.2   715
## 10 OH       172   3509 354.5   753
## 11 IN       194   3412 359.3   649
## 12 IL       189   3981 348.9   830
## 13 MI       233   3675 369.2   738
## 14 WI       209   3363 360.7   659
## 15 MN       262   3341 365.4   664
## 16 IO       234   3265 343.8   572
## 17 MO       177   3257 336.1   701
## 18 ND       177   2730 369.1   443
## 19 SD       187   2876 368.7   446
## 20 NE       148   3239 349.9   615
## 21 KA       196   3303 339.9   661
## 22 DE       248   3795 375.9   722
## 23 MD       247   3742 364.1   766
## 24 DC       246   4425 352.1  1000
## 25 VA       180   3068 353.0   631
## 26 WV       149   2470 328.8   390
## 27 NC       155   2664 354.1   450
## 28 SC       149   2380 376.7   476
## 29 GA       156   2781 370.6   603
## 30 FL       191   3191 336.0   805
## 31 KY       140   2645 349.3   523
## 32 TN       137   2579 342.8   588
## 33 AL       112   2337 362.2   584
## 34 MS       130   2081 385.2   445
## 35 AR       134   2322 351.9   500
## 36 LA       162   2634 389.6   661
## 37 OK       135   2880 329.8   680
## 38 TX       155   3029 369.4   797
## 39 MT       238   2942 368.9   534
## 40 ID       170   2668 367.7   541
## 41 WY       238   3190 365.6   605
## 42 CO       192   3340 358.1   785
## 43 NM       227   2651 421.5   698
## 44 AZ       207   3027 387.5   796
## 45 UT       201   2790 412.4   804
## 46 NV       225   3957 385.1   809
## 47 WA       215   3688 341.3   726
## 48 OR       233   3317 332.7   671
## 49 CA       273   3968 348.4   909
## 50 AK       372   4146 439.7   484
## 51 HI       212   3513 382.9   831

Use summary function to gain an overview of the dataset

summary(dataset)

##        X        education         income         young      
##  AK     : 1   Min.   :112.0   Min.   :2081   Min.   :326.2  
##  AL     : 1   1st Qu.:165.0   1st Qu.:2786   1st Qu.:342.1  
##  AR     : 1   Median :192.0   Median :3257   Median :354.1  
##  AZ     : 1   Mean   :196.3   Mean   :3225   Mean   :358.9  
##  CA     : 1   3rd Qu.:228.5   3rd Qu.:3612   3rd Qu.:369.1  
##  CO     : 1   Max.   :372.0   Max.   :4425   Max.   :439.7  
##  (Other):45                                                 
##      urban       
##  Min.   : 322.0  
##  1st Qu.: 552.5  
##  Median : 664.0  
##  Mean   : 664.5  
##  3rd Qu.: 790.5  
##  Max.   :1000.0  
##

Mean and Median for education

mean(dataset$education)

## [1] 196.3137

median(dataset$education)

## [1] 192

Mean and Median for income

mean(dataset$income)

## [1] 3225.294

median(dataset$income)

## [1] 3257

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.

newframe<-c(dataset [1:20, c(1,2,3)]) # new subset with columns 1,2 and 3
newdataset<-(data.frame(newframe))
head(newdataset, 20)

##     X education income
## 1  ME       189   2824
## 2  NH       169   3259
## 3  VT       230   3072
## 4  MA       168   3835
## 5  RI       180   3549
## 6  CT       193   4256
## 7  NY       261   4151
## 8  NJ       214   3954
## 9  PA       201   3419
## 10 OH       172   3509
## 11 IN       194   3412
## 12 IL       189   3981
## 13 MI       233   3675
## 14 WI       209   3363
## 15 MN       262   3341
## 16 IO       234   3265
## 17 MO       177   3257
## 18 ND       177   2730
## 19 SD       187   2876
## 20 NE       148   3239

3. Create new column names for the new data frame.

names(newdataset)<-c("States", "Education Expences($)", "Per-Capita Income($)")
head(newdataset, 20)

##    States Education Expences($) Per-Capita Income($)
## 1      ME                   189                 2824
## 2      NH                   169                 3259
## 3      VT                   230                 3072
## 4      MA                   168                 3835
## 5      RI                   180                 3549
## 6      CT                   193                 4256
## 7      NY                   261                 4151
## 8      NJ                   214                 3954
## 9      PA                   201                 3419
## 10     OH                   172                 3509
## 11     IN                   194                 3412
## 12     IL                   189                 3981
## 13     MI                   233                 3675
## 14     WI                   209                 3363
## 15     MN                   262                 3341
## 16     IO                   234                 3265
## 17     MO                   177                 3257
## 18     ND                   177                 2730
## 19     SD                   187                 2876
## 20     NE                   148                 3239

4. Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare.

Use summary function to gain an overview of the new dataset

summary(newdataset)

##      States   Education Expences($) Per-Capita Income($)
##  CT     : 1   Min.   :148.0         Min.   :2730        
##  IL     : 1   1st Qu.:177.0         1st Qu.:3252        
##  IN     : 1   Median :191.0         Median :3388        
##  IO     : 1   Mean   :199.3         Mean   :3448        
##  MA     : 1   3rd Qu.:218.0         3rd Qu.:3715        
##  ME     : 1   Max.   :262.0         Max.   :4256        
##  (Other):14

Mean and Median for Education Expences

mean(newdataset$`Education Expences($)`)

## [1] 199.35

median(newdataset$`Education Expences($)`)

## [1] 191

Mean and Median for Per-Capita Income of the states

mean(newdataset$`Per-Capita Income($)`)

## [1] 3448.35

median(newdataset$`Per-Capita Income($)`)

## [1] 3387.5

Compare Original and New summary of datasets

summary(dataset)

##        X        education         income         young      
##  AK     : 1   Min.   :112.0   Min.   :2081   Min.   :326.2  
##  AL     : 1   1st Qu.:165.0   1st Qu.:2786   1st Qu.:342.1  
##  AR     : 1   Median :192.0   Median :3257   Median :354.1  
##  AZ     : 1   Mean   :196.3   Mean   :3225   Mean   :358.9  
##  CA     : 1   3rd Qu.:228.5   3rd Qu.:3612   3rd Qu.:369.1  
##  CO     : 1   Max.   :372.0   Max.   :4425   Max.   :439.7  
##  (Other):45                                                 
##      urban       
##  Min.   : 322.0  
##  1st Qu.: 552.5  
##  Median : 664.0  
##  Mean   : 664.5  
##  3rd Qu.: 790.5  
##  Max.   :1000.0  
##

summary(newdataset)

##      States   Education Expences($) Per-Capita Income($)
##  CT     : 1   Min.   :148.0         Min.   :2730        
##  IL     : 1   1st Qu.:177.0         1st Qu.:3252        
##  IN     : 1   Median :191.0         Median :3388        
##  IO     : 1   Mean   :199.3         Mean   :3448        
##  MA     : 1   3rd Qu.:218.0         3rd Qu.:3715        
##  ME     : 1   Max.   :262.0         Max.   :4256        
##  (Other):14

According to the summaries, original dataset has a mean of $196.30 in education expences where as my new subset of first 20 states has a mean of $199.30 in education expences. mean of education expences in new data set is higher than the original dataset.Also, original dataset has a median of $192 in education expences where as new dataset has a median of $191 in education expences.

Also the mean of income in original dataset is $3225 and the mean income of newdataset is $3448 making a $223 higher income in new dataset with 20 states. Median income in original dataset is $3257 where as the median income in new dataset is $3388. New dataset has a higher median in income compared to the original data set median income.

5. For at least 3 values in a column please rename so that every value in that column is renamed.For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as “excellent”

Note: For this Question I am using a different dataset as I can not use the previous dataset because all the columns in previous dataset are with numbers except for the state column.

urlfile <- "https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/carData/States.csv"
dataset2<-read.csv(urlfile)
head(dataset2, 51)

##     X region   pop SATV SATM percent dollars pay
## 1  AL    ESC  4041  470  514       8   3.648  27
## 2  AK    PAC   550  438  476      42   7.887  43
## 3  AZ    MTN  3665  445  497      25   4.231  30
## 4  AR    WSC  2351  470  511       6   3.334  23
## 5  CA    PAC 29760  419  484      45   4.826  39
## 6  CO    MTN  3294  456  513      28   4.809  31
## 7  CN     NE  3287  430  471      74   7.914  43
## 8  DE     SA   666  433  470      58   6.016  35
## 9  DC     SA   607  409  441      68   8.210  39
## 10 FL     SA 12938  418  466      44   5.154  30
## 11 GA     SA  6478  401  443      57   4.860  29
## 12 HI    PAC  1108  404  481      52   5.008  32
## 13 ID    MTN  1007  466  502      17   3.200  25
## 14 IL    ENC 11431  466  528      16   5.062  34
## 15 IN    ENC  5544  408  459      54   5.051  32
## 16 IA    WNC  2777  511  577       5   4.839  28
## 17 KS    WNC  2478  492  548      10   5.009  29
## 18 KY    ESC  3685  473  521      10   4.390  29
## 19 LA    WSC  4220  476  517       9   4.012  26
## 20 ME     NE  1228  423  463      60   5.894  28
## 21 MD     SA  4781  430  478      59   6.184  38
## 22 MA     NE  6016  427  473      72   6.351  36
## 23 MI    ENC  9295  454  514      12   5.257  38
## 24 MN    WNC  4375  477  542      14   5.260  33
## 25 MS    ESC  2573  477  519       4   3.322  24
## 26 MO    WNC  5117  473  522      12   4.415  28
## 27 MT    MTN   799  464  523      20   5.184  26
## 28 NE    WNC  1578  484  546      10   4.381  26
## 29 NV    MTN  1202  434  487      24   4.564  32
## 30 NH     NE  1109  442  486      67   5.504  31
## 31 NJ     MA  7730  418  473      69   9.159  38
## 32 NM    MTN  1515  480  527      12   4.446  26
## 33 NY     MA 17990  412  470      70   8.500  42
## 34 NC     SA  6629  401  440      55   4.802  29
## 35 ND    WNC   639  505  564       6   3.685  23
## 36 OH    ENC 10847  450  499      22   5.639  32
## 37 OK    WSC  3146  478  523       9   3.742  24
## 38 OR    PAC  2842  439  484      49   5.291  32
## 39 PA     MA 11882  420  463      64   6.534  36
## 40 RI     NE  1003  422  461      62   6.989  37
## 41 SC     SA  3487  397  437      54   4.327  28
## 42 SD    WNC   696  506  555       5   3.730  22
## 43 TN    ESC  4877  483  525      12   3.707  28
## 44 TX    WSC 16987  413  461      42   4.238  28
## 45 UT    MTN  1723  492  539       5   2.993  25
## 46 VT     NE   563  431  466      62   5.740  31
## 47 VA     SA  6187  425  470      58   5.360  32
## 48 WA    PAC  4867  437  486      44   5.045  33
## 49 WV     SA  1793  443  490      15   5.046  26
## 50 WI    ENC  4892  476  543      11   5.946  33
## 51 WY    MTN   454  458  519      13   5.255  29

# Creating a subset out of the original table
newframe2<-c(dataset2 [1:20, c(1,2,3,7)]) # new subset with columns 1,2,3 and 7
newdataset2<-(data.frame(newframe2))
head(newdataset2, 20)

##     X region   pop dollars
## 1  AL    ESC  4041   3.648
## 2  AK    PAC   550   7.887
## 3  AZ    MTN  3665   4.231
## 4  AR    WSC  2351   3.334
## 5  CA    PAC 29760   4.826
## 6  CO    MTN  3294   4.809
## 7  CN     NE  3287   7.914
## 8  DE     SA   666   6.016
## 9  DC     SA   607   8.210
## 10 FL     SA 12938   5.154
## 11 GA     SA  6478   4.860
## 12 HI    PAC  1108   5.008
## 13 ID    MTN  1007   3.200
## 14 IL    ENC 11431   5.062
## 15 IN    ENC  5544   5.051
## 16 IA    WNC  2777   4.839
## 17 KS    WNC  2478   5.009
## 18 KY    ESC  3685   4.390
## 19 LA    WSC  4220   4.012
## 20 ME     NE  1228   5.894

# rename columns
names(newdataset2)<-c("States", "Census Regions", "Population", "State Spending on Education")
head(newdataset2, 20)

##    States Census Regions Population State Spending on Education
## 1      AL            ESC       4041                       3.648
## 2      AK            PAC        550                       7.887
## 3      AZ            MTN       3665                       4.231
## 4      AR            WSC       2351                       3.334
## 5      CA            PAC      29760                       4.826
## 6      CO            MTN       3294                       4.809
## 7      CN             NE       3287                       7.914
## 8      DE             SA        666                       6.016
## 9      DC             SA        607                       8.210
## 10     FL             SA      12938                       5.154
## 11     GA             SA       6478                       4.860
## 12     HI            PAC       1108                       5.008
## 13     ID            MTN       1007                       3.200
## 14     IL            ENC      11431                       5.062
## 15     IN            ENC       5544                       5.051
## 16     IA            WNC       2777                       4.839
## 17     KS            WNC       2478                       5.009
## 18     KY            ESC       3685                       4.390
## 19     LA            WSC       4220                       4.012
## 20     ME             NE       1228                       5.894

# plyr package required
require(plyr)

## Loading required package: plyr

## Warning: package 'plyr' was built under R version 3.6.1

# rename the values in Census region column.
x<-mapvalues(newdataset2[,2], c("ESC","PAC","MTN","WSC","NE","SA","ENC","WNC"), c("East South Central","Pacific", "Mountain", "West South Central","New England","South Atlantic","East North Central", "West North Central"))
newdataset2[,2]<-x
head(newdataset2,20)

##    States     Census Regions Population State Spending on Education
## 1      AL East South Central       4041                       3.648
## 2      AK            Pacific        550                       7.887
## 3      AZ           Mountain       3665                       4.231
## 4      AR West South Central       2351                       3.334
## 5      CA            Pacific      29760                       4.826
## 6      CO           Mountain       3294                       4.809
## 7      CN        New England       3287                       7.914
## 8      DE     South Atlantic        666                       6.016
## 9      DC     South Atlantic        607                       8.210
## 10     FL     South Atlantic      12938                       5.154
## 11     GA     South Atlantic       6478                       4.860
## 12     HI            Pacific       1108                       5.008
## 13     ID           Mountain       1007                       3.200
## 14     IL East North Central      11431                       5.062
## 15     IN East North Central       5544                       5.051
## 16     IA West North Central       2777                       4.839
## 17     KS West North Central       2478                       5.009
## 18     KY East South Central       3685                       4.390
## 19     LA West South Central       4220                       4.012
## 20     ME        New England       1228                       5.894

Week2_R_HW

gpadmaperuma

7/29/2019

1. Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes.

Read file and get data of U. S. State Public-School Expenditures

Use summary function to gain an overview of the dataset

Mean and Median for education

Mean and Median for income

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.

3. Create new column names for the new data frame.

4. Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare.

Use summary function to gain an overview of the new dataset

Mean and Median for Education Expences

Mean and Median for Per-Capita Income of the states

Compare Original and New summary of datasets

5. For at least 3 values in a column please rename so that every value in that column is renamed.For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as “excellent”

Note: For this Question I am using a different dataset as I can not use the previous dataset because all the columns in previous dataset are with numbers except for the state column.