WEEK 2 - R ASSIGNMENT

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

States <-read.csv(file="https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/carData/States.csv", header = TRUE, sep = ",")
States

##     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

summary(States)

##        X          region        pop             SATV            SATM      
##  AK     : 1   SA     : 9   Min.   :  454   Min.   :397.0   Min.   :437.0  
##  AL     : 1   MTN    : 8   1st Qu.: 1215   1st Qu.:422.5   1st Qu.:470.0  
##  AR     : 1   WNC    : 7   Median : 3294   Median :443.0   Median :490.0  
##  AZ     : 1   NE     : 6   Mean   : 4877   Mean   :448.2   Mean   :497.4  
##  CA     : 1   ENC    : 5   3rd Qu.: 5780   3rd Qu.:474.5   3rd Qu.:522.5  
##  CN     : 1   PAC    : 5   Max.   :29760   Max.   :511.0   Max.   :577.0  
##  (Other):45   (Other):11                                                  
##     percent         dollars           pay       
##  Min.   : 4.00   Min.   :2.993   Min.   :22.00  
##  1st Qu.:11.50   1st Qu.:4.354   1st Qu.:27.50  
##  Median :25.00   Median :5.045   Median :30.00  
##  Mean   :33.75   Mean   :5.175   Mean   :30.94  
##  3rd Qu.:57.50   3rd Qu.:5.689   3rd Qu.:33.50  
##  Max.   :74.00   Max.   :9.159   Max.   :43.00  
##

mean(States$pay)

## [1] 30.94118

median(States$pay)

## [1] 30

mean(States$pop)

## [1] 4876.647

median(States$pop)

## [1] 3294

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

States2 <- subset(States, pop < 1000 & pay < 30, c('X','region', 'pop','pay'))
States2

##     X region pop pay
## 27 MT    MTN 799  26
## 35 ND    WNC 639  23
## 42 SD    WNC 696  22
## 51 WY    MTN 454  29

Create new column names for the new data frame.

require package plyr

library(plyr)
rename(States2, c("X"="State","region"="Reg" ,"pop"="Population","pay"="Amt"))

##    State Reg Population Amt
## 27    MT MTN        799  26
## 35    ND WNC        639  23
## 42    SD WNC        696  22
## 51    WY MTN        454  29

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.

summary(States2)

##        X         region       pop             pay       
##  MT     :1   MTN    :2   Min.   :454.0   Min.   :22.00  
##  ND     :1   WNC    :2   1st Qu.:592.8   1st Qu.:22.75  
##  SD     :1   ENC    :0   Median :667.5   Median :24.50  
##  WY     :1   ESC    :0   Mean   :647.0   Mean   :25.00  
##  AK     :0   MA     :0   3rd Qu.:721.8   3rd Qu.:26.75  
##  AL     :0   NE     :0   Max.   :799.0   Max.   :29.00  
##  (Other):0   (Other):0

mean(States2$pop)

## [1] 647

median(States$pop)

## [1] 3294

mean(States2$pay)

## [1] 25

median(States$pay)

## [1] 30

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”.

States2$X<-revalue(States2$X,c("MT"="AAA"))
States2$X<-revalue(States2$X,c("ND"="AAA"))
States2$X<-revalue(States2$X,c("SD"="AAA"))
States2$X<-revalue(States2$X,c("WY"="AAA"))
States2

##      X region pop pay
## 27 AAA    MTN 799  26
## 35 AAA    WNC 639  23
## 42 AAA    WNC 696  22
## 51 AAA    MTN 454  29

Display enough rows to see examples of all of steps 1-5 above.

head(States)

##    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

head(States2)

##      X region pop pay
## 27 AAA    MTN 799  26
## 35 AAA    WNC 639  23
## 42 AAA    WNC 696  22
## 51 AAA    MTN 454  29

BONUS – place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

Please See Step 1 above