R Bridge Workshop Week 2 Assignment

Data Source

https://raw.githubusercontent.com/amit-kapoor/r/master/data/hvisob.csv

Data Description

The Boston data frame has 506 rows and 14 columns.

crim - per capita crime rate by town.
zn - proportion of residential land zoned for lots over 25,000 sq.ft.
indus - proportion of non-retail business acres per town.
chas - Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
nox - nitrogen oxides concentration (parts per 10 million).
rm - average number of rooms per dwelling.
age - proportion of owner-occupied units built prior to 1940.
dis - weighted mean of distances to five Boston employment centres.
rad - index of accessibility to radial highways.
tax - full-value property-tax rate per $10,000.
ptratio - pupil-teacher ratio by town.
black - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
lstat - lower status of the population (percent).
medv - median value of owner-occupied homes in $1000s.

theLink <- "https://raw.githubusercontent.com/amit-kapoor/r/master/data/hvisob.csv"

# load data into data frame
df_boston <- read.csv(file=theLink, header = TRUE, sep = ",")

# display header rows
head(df_boston)

##   X    crim zn indus chas   nox    rm  age    dis rad tax ptratio  black
## 1 1 0.00632 18  2.31    0 0.538 6.575 65.2 4.0900   1 296    15.3 396.90
## 2 2 0.02731  0  7.07    0 0.469 6.421 78.9 4.9671   2 242    17.8 396.90
## 3 3 0.02729  0  7.07    0 0.469 7.185 61.1 4.9671   2 242    17.8 392.83
## 4 4 0.03237  0  2.18    0 0.458 6.998 45.8 6.0622   3 222    18.7 394.63
## 5 5 0.06905  0  2.18    0 0.458 7.147 54.2 6.0622   3 222    18.7 396.90
## 6 6 0.02985  0  2.18    0 0.458 6.430 58.7 6.0622   3 222    18.7 394.12
##   lstat medv
## 1  4.98 24.0
## 2  9.14 21.6
## 3  4.03 34.7
## 4  2.94 33.4
## 5  5.33 36.2
## 6  5.21 28.7

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

Summary of dataset

# get summary of data set
summary(df_boston)

##        X              crim                zn             indus      
##  Min.   :  1.0   Min.   : 0.00632   Min.   :  0.00   Min.   : 0.46  
##  1st Qu.:127.2   1st Qu.: 0.08204   1st Qu.:  0.00   1st Qu.: 5.19  
##  Median :253.5   Median : 0.25651   Median :  0.00   Median : 9.69  
##  Mean   :253.5   Mean   : 3.61352   Mean   : 11.36   Mean   :11.14  
##  3rd Qu.:379.8   3rd Qu.: 3.67708   3rd Qu.: 12.50   3rd Qu.:18.10  
##  Max.   :506.0   Max.   :88.97620   Max.   :100.00   Max.   :27.74  
##       chas              nox               rm             age        
##  Min.   :0.00000   Min.   :0.3850   Min.   :3.561   Min.   :  2.90  
##  1st Qu.:0.00000   1st Qu.:0.4490   1st Qu.:5.886   1st Qu.: 45.02  
##  Median :0.00000   Median :0.5380   Median :6.208   Median : 77.50  
##  Mean   :0.06917   Mean   :0.5547   Mean   :6.285   Mean   : 68.57  
##  3rd Qu.:0.00000   3rd Qu.:0.6240   3rd Qu.:6.623   3rd Qu.: 94.08  
##  Max.   :1.00000   Max.   :0.8710   Max.   :8.780   Max.   :100.00  
##       dis              rad              tax           ptratio     
##  Min.   : 1.130   Min.   : 1.000   Min.   :187.0   Min.   :12.60  
##  1st Qu.: 2.100   1st Qu.: 4.000   1st Qu.:279.0   1st Qu.:17.40  
##  Median : 3.207   Median : 5.000   Median :330.0   Median :19.05  
##  Mean   : 3.795   Mean   : 9.549   Mean   :408.2   Mean   :18.46  
##  3rd Qu.: 5.188   3rd Qu.:24.000   3rd Qu.:666.0   3rd Qu.:20.20  
##  Max.   :12.127   Max.   :24.000   Max.   :711.0   Max.   :22.00  
##      black            lstat            medv      
##  Min.   :  0.32   Min.   : 1.73   Min.   : 5.00  
##  1st Qu.:375.38   1st Qu.: 6.95   1st Qu.:17.02  
##  Median :391.44   Median :11.36   Median :21.20  
##  Mean   :356.67   Mean   :12.65   Mean   :22.53  
##  3rd Qu.:396.23   3rd Qu.:16.95   3rd Qu.:25.00  
##  Max.   :396.90   Max.   :37.97   Max.   :50.00

Mean and Median of rm attribute

# mean of rm
rm_mean <- mean(df_boston$rm)
rm_mean

## [1] 6.284634

# median of rm
rm_median <- median(df_boston$rm)
rm_median

## [1] 6.2085

Mean and Median of ptratio attribute

# mean of ptratio
ptratio_mean <- mean(df_boston$ptratio)
ptratio_mean

## [1] 18.45553

# median of ptratio
ptratio_median <- median(df_boston$ptratio)
ptratio_median

## [1] 19.05

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

Subset of 50 rows with columns rm, age, dis, tax and ptratio

df_boston_subset <- df_boston[1:50, c(7,8,9,11,12)]
df_boston_subset

##       rm   age    dis tax ptratio
## 1  6.575  65.2 4.0900 296    15.3
## 2  6.421  78.9 4.9671 242    17.8
## 3  7.185  61.1 4.9671 242    17.8
## 4  6.998  45.8 6.0622 222    18.7
## 5  7.147  54.2 6.0622 222    18.7
## 6  6.430  58.7 6.0622 222    18.7
## 7  6.012  66.6 5.5605 311    15.2
## 8  6.172  96.1 5.9505 311    15.2
## 9  5.631 100.0 6.0821 311    15.2
## 10 6.004  85.9 6.5921 311    15.2
## 11 6.377  94.3 6.3467 311    15.2
## 12 6.009  82.9 6.2267 311    15.2
## 13 5.889  39.0 5.4509 311    15.2
## 14 5.949  61.8 4.7075 307    21.0
## 15 6.096  84.5 4.4619 307    21.0
## 16 5.834  56.5 4.4986 307    21.0
## 17 5.935  29.3 4.4986 307    21.0
## 18 5.990  81.7 4.2579 307    21.0
## 19 5.456  36.6 3.7965 307    21.0
## 20 5.727  69.5 3.7965 307    21.0
## 21 5.570  98.1 3.7979 307    21.0
## 22 5.965  89.2 4.0123 307    21.0
## 23 6.142  91.7 3.9769 307    21.0
## 24 5.813 100.0 4.0952 307    21.0
## 25 5.924  94.1 4.3996 307    21.0
## 26 5.599  85.7 4.4546 307    21.0
## 27 5.813  90.3 4.6820 307    21.0
## 28 6.047  88.8 4.4534 307    21.0
## 29 6.495  94.4 4.4547 307    21.0
## 30 6.674  87.3 4.2390 307    21.0
## 31 5.713  94.1 4.2330 307    21.0
## 32 6.072 100.0 4.1750 307    21.0
## 33 5.950  82.0 3.9900 307    21.0
## 34 5.701  95.0 3.7872 307    21.0
## 35 6.096  96.9 3.7598 307    21.0
## 36 5.933  68.2 3.3603 279    19.2
## 37 5.841  61.4 3.3779 279    19.2
## 38 5.850  41.5 3.9342 279    19.2
## 39 5.966  30.2 3.8473 279    19.2
## 40 6.595  21.8 5.4011 252    18.3
## 41 7.024  15.8 5.4011 252    18.3
## 42 6.770   2.9 5.7209 233    17.9
## 43 6.169   6.6 5.7209 233    17.9
## 44 6.211   6.5 5.7209 233    17.9
## 45 6.069  40.0 5.7209 233    17.9
## 46 5.682  33.8 5.1004 233    17.9
## 47 5.786  33.3 5.1004 233    17.9
## 48 6.030  85.5 5.6894 233    17.9
## 49 5.399  95.3 5.8700 233    17.9
## 50 5.602  62.0 6.0877 233    17.9

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

Rename Columns

colnames(df_boston_subset)[1] <- "rooms_per_dwelling"
colnames(df_boston_subset)[2] <- "age_proportion"
colnames(df_boston_subset)[3] <- "wght_mean_dist"
colnames(df_boston_subset)[4] <- "property_tax"
colnames(df_boston_subset)[5] <- "pupil_teacher_ratio"

df_boston_subset

##    rooms_per_dwelling age_proportion wght_mean_dist property_tax
## 1               6.575           65.2         4.0900          296
## 2               6.421           78.9         4.9671          242
## 3               7.185           61.1         4.9671          242
## 4               6.998           45.8         6.0622          222
## 5               7.147           54.2         6.0622          222
## 6               6.430           58.7         6.0622          222
## 7               6.012           66.6         5.5605          311
## 8               6.172           96.1         5.9505          311
## 9               5.631          100.0         6.0821          311
## 10              6.004           85.9         6.5921          311
## 11              6.377           94.3         6.3467          311
## 12              6.009           82.9         6.2267          311
## 13              5.889           39.0         5.4509          311
## 14              5.949           61.8         4.7075          307
## 15              6.096           84.5         4.4619          307
## 16              5.834           56.5         4.4986          307
## 17              5.935           29.3         4.4986          307
## 18              5.990           81.7         4.2579          307
## 19              5.456           36.6         3.7965          307
## 20              5.727           69.5         3.7965          307
## 21              5.570           98.1         3.7979          307
## 22              5.965           89.2         4.0123          307
## 23              6.142           91.7         3.9769          307
## 24              5.813          100.0         4.0952          307
## 25              5.924           94.1         4.3996          307
## 26              5.599           85.7         4.4546          307
## 27              5.813           90.3         4.6820          307
## 28              6.047           88.8         4.4534          307
## 29              6.495           94.4         4.4547          307
## 30              6.674           87.3         4.2390          307
## 31              5.713           94.1         4.2330          307
## 32              6.072          100.0         4.1750          307
## 33              5.950           82.0         3.9900          307
## 34              5.701           95.0         3.7872          307
## 35              6.096           96.9         3.7598          307
## 36              5.933           68.2         3.3603          279
## 37              5.841           61.4         3.3779          279
## 38              5.850           41.5         3.9342          279
## 39              5.966           30.2         3.8473          279
## 40              6.595           21.8         5.4011          252
## 41              7.024           15.8         5.4011          252
## 42              6.770            2.9         5.7209          233
## 43              6.169            6.6         5.7209          233
## 44              6.211            6.5         5.7209          233
## 45              6.069           40.0         5.7209          233
## 46              5.682           33.8         5.1004          233
## 47              5.786           33.3         5.1004          233
## 48              6.030           85.5         5.6894          233
## 49              5.399           95.3         5.8700          233
## 50              5.602           62.0         6.0877          233
##    pupil_teacher_ratio
## 1                 15.3
## 2                 17.8
## 3                 17.8
## 4                 18.7
## 5                 18.7
## 6                 18.7
## 7                 15.2
## 8                 15.2
## 9                 15.2
## 10                15.2
## 11                15.2
## 12                15.2
## 13                15.2
## 14                21.0
## 15                21.0
## 16                21.0
## 17                21.0
## 18                21.0
## 19                21.0
## 20                21.0
## 21                21.0
## 22                21.0
## 23                21.0
## 24                21.0
## 25                21.0
## 26                21.0
## 27                21.0
## 28                21.0
## 29                21.0
## 30                21.0
## 31                21.0
## 32                21.0
## 33                21.0
## 34                21.0
## 35                21.0
## 36                19.2
## 37                19.2
## 38                19.2
## 39                19.2
## 40                18.3
## 41                18.3
## 42                17.9
## 43                17.9
## 44                17.9
## 45                17.9
## 46                17.9
## 47                17.9
## 48                17.9
## 49                17.9
## 50                17.9

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.

Summary of new dataset

# get summary of new data set
summary(df_boston_subset)

##  rooms_per_dwelling age_proportion   wght_mean_dist   property_tax  
##  Min.   :5.399      Min.   :  2.90   Min.   :3.360   Min.   :222.0  
##  1st Qu.:5.818      1st Qu.: 42.58   1st Qu.:4.091   1st Qu.:242.0  
##  Median :5.997      Median : 74.20   Median :4.590   Median :307.0  
##  Mean   :6.087      Mean   : 66.82   Mean   :4.860   Mean   :281.9  
##  3rd Qu.:6.201      3rd Qu.: 91.35   3rd Qu.:5.721   3rd Qu.:307.0  
##  Max.   :7.185      Max.   :100.00   Max.   :6.592   Max.   :311.0  
##  pupil_teacher_ratio
##  Min.   :15.2       
##  1st Qu.:17.9       
##  Median :19.2       
##  Mean   :19.0       
##  3rd Qu.:21.0       
##  Max.   :21.0

Mean and Median of rooms_per_dwelling attribute

# mean of rm
rooms_per_dwelling_mean <- mean(df_boston_subset$rooms_per_dwelling)
rooms_per_dwelling_mean

## [1] 6.08676

# median of rooms_per_dwelling
rooms_per_dwelling_median <- median(df_boston_subset$rooms_per_dwelling)
rooms_per_dwelling_median

## [1] 5.997

Mean and Median of pupil_teacher_ratio attribute

# mean of pupil_teacher_ratio
pupil_teacher_ratio_mean <- mean(df_boston_subset$pupil_teacher_ratio)
pupil_teacher_ratio_mean

## [1] 18.998

# median of pupil_teacher_ratio
pupil_teacher_ratio_median <- median(df_boston_subset$pupil_teacher_ratio)
pupil_teacher_ratio_median

## [1] 19.2

Compare mean and median of same two attributes in original dataset and its subset

paste("Mean rm of original dataset is ", rm_mean, "while its mean for subset is ", rooms_per_dwelling_mean)

## [1] "Mean rm of original dataset is  6.28463438735178 while its mean for subset is  6.08676"

paste("Median rm of original dataset is ", rm_median, "while its median for subset is ", rooms_per_dwelling_median)

## [1] "Median rm of original dataset is  6.2085 while its median for subset is  5.997"

paste("Mean ptratio of original dataset is ", ptratio_mean, "while its mean for subset is ", pupil_teacher_ratio_mean)

## [1] "Mean ptratio of original dataset is  18.4555335968379 while its mean for subset is  18.998"

paste("Mean ptratio of original dataset is ", ptratio_median, "while its mean for subset is ", pupil_teacher_ratio_median)

## [1] "Mean ptratio of original dataset is  19.05 while its mean for subset is  19.2"

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”.

For column rad in original dataset, lets replace value 4 by 44

df_boston$rad[df_boston$rad == 4] <- 44
subset(df_boston, df_boston$rad == 44)

##       X    crim   zn indus chas    nox    rm   age     dis rad tax ptratio
## 14   14 0.62976  0.0  8.14    0 0.5380 5.949  61.8  4.7075  44 307    21.0
## 15   15 0.63796  0.0  8.14    0 0.5380 6.096  84.5  4.4619  44 307    21.0
## 16   16 0.62739  0.0  8.14    0 0.5380 5.834  56.5  4.4986  44 307    21.0
## 17   17 1.05393  0.0  8.14    0 0.5380 5.935  29.3  4.4986  44 307    21.0
## 18   18 0.78420  0.0  8.14    0 0.5380 5.990  81.7  4.2579  44 307    21.0
## 19   19 0.80271  0.0  8.14    0 0.5380 5.456  36.6  3.7965  44 307    21.0
## 20   20 0.72580  0.0  8.14    0 0.5380 5.727  69.5  3.7965  44 307    21.0
## 21   21 1.25179  0.0  8.14    0 0.5380 5.570  98.1  3.7979  44 307    21.0
## 22   22 0.85204  0.0  8.14    0 0.5380 5.965  89.2  4.0123  44 307    21.0
## 23   23 1.23247  0.0  8.14    0 0.5380 6.142  91.7  3.9769  44 307    21.0
## 24   24 0.98843  0.0  8.14    0 0.5380 5.813 100.0  4.0952  44 307    21.0
## 25   25 0.75026  0.0  8.14    0 0.5380 5.924  94.1  4.3996  44 307    21.0
## 26   26 0.84054  0.0  8.14    0 0.5380 5.599  85.7  4.4546  44 307    21.0
## 27   27 0.67191  0.0  8.14    0 0.5380 5.813  90.3  4.6820  44 307    21.0
## 28   28 0.95577  0.0  8.14    0 0.5380 6.047  88.8  4.4534  44 307    21.0
## 29   29 0.77299  0.0  8.14    0 0.5380 6.495  94.4  4.4547  44 307    21.0
## 30   30 1.00245  0.0  8.14    0 0.5380 6.674  87.3  4.2390  44 307    21.0
## 31   31 1.13081  0.0  8.14    0 0.5380 5.713  94.1  4.2330  44 307    21.0
## 32   32 1.35472  0.0  8.14    0 0.5380 6.072 100.0  4.1750  44 307    21.0
## 33   33 1.38799  0.0  8.14    0 0.5380 5.950  82.0  3.9900  44 307    21.0
## 34   34 1.15172  0.0  8.14    0 0.5380 5.701  95.0  3.7872  44 307    21.0
## 35   35 1.61282  0.0  8.14    0 0.5380 6.096  96.9  3.7598  44 307    21.0
## 51   51 0.08873 21.0  5.64    0 0.4390 5.963  45.7  6.8147  44 243    16.8
## 52   52 0.04337 21.0  5.64    0 0.4390 6.115  63.0  6.8147  44 243    16.8
## 53   53 0.05360 21.0  5.64    0 0.4390 6.511  21.1  6.8147  44 243    16.8
## 54   54 0.04981 21.0  5.64    0 0.4390 5.998  21.4  6.8147  44 243    16.8
## 66   66 0.03584 80.0  3.37    0 0.3980 6.290  17.8  6.6115  44 337    16.1
## 67   67 0.04379 80.0  3.37    0 0.3980 5.787  31.1  6.6115  44 337    16.1
## 68   68 0.05789 12.5  6.07    0 0.4090 5.878  21.4  6.4980  44 345    18.9
## 69   69 0.13554 12.5  6.07    0 0.4090 5.594  36.8  6.4980  44 345    18.9
## 70   70 0.12816 12.5  6.07    0 0.4090 5.885  33.0  6.4980  44 345    18.9
## 71   71 0.08826  0.0 10.81    0 0.4130 6.417   6.6  5.2873  44 305    19.2
## 72   72 0.15876  0.0 10.81    0 0.4130 5.961  17.5  5.2873  44 305    19.2
## 73   73 0.09164  0.0 10.81    0 0.4130 6.065   7.8  5.2873  44 305    19.2
## 74   74 0.19539  0.0 10.81    0 0.4130 6.245   6.2  5.2873  44 305    19.2
## 81   81 0.04113 25.0  4.86    0 0.4260 6.727  33.5  5.4007  44 281    19.0
## 82   82 0.04462 25.0  4.86    0 0.4260 6.619  70.4  5.4007  44 281    19.0
## 83   83 0.03659 25.0  4.86    0 0.4260 6.302  32.2  5.4007  44 281    19.0
## 84   84 0.03551 25.0  4.86    0 0.4260 6.167  46.7  5.4007  44 281    19.0
## 93   93 0.04203 28.0 15.04    0 0.4640 6.442  53.6  3.6659  44 270    18.2
## 94   94 0.02875 28.0 15.04    0 0.4640 6.211  28.9  3.6659  44 270    18.2
## 95   95 0.04294 28.0 15.04    0 0.4640 6.249  77.3  3.6150  44 270    18.2
## 128 128 0.25915  0.0 21.89    0 0.6240 5.693  96.0  1.7883  44 437    21.2
## 129 129 0.32543  0.0 21.89    0 0.6240 6.431  98.8  1.8125  44 437    21.2
## 130 130 0.88125  0.0 21.89    0 0.6240 5.637  94.7  1.9799  44 437    21.2
## 131 131 0.34006  0.0 21.89    0 0.6240 6.458  98.9  2.1185  44 437    21.2
## 132 132 1.19294  0.0 21.89    0 0.6240 6.326  97.7  2.2710  44 437    21.2
## 133 133 0.59005  0.0 21.89    0 0.6240 6.372  97.9  2.3274  44 437    21.2
## 134 134 0.32982  0.0 21.89    0 0.6240 5.822  95.4  2.4699  44 437    21.2
## 135 135 0.97617  0.0 21.89    0 0.6240 5.757  98.4  2.3460  44 437    21.2
## 136 136 0.55778  0.0 21.89    0 0.6240 6.335  98.2  2.1107  44 437    21.2
## 137 137 0.32264  0.0 21.89    0 0.6240 5.942  93.5  1.9669  44 437    21.2
## 138 138 0.35233  0.0 21.89    0 0.6240 6.454  98.4  1.8498  44 437    21.2
## 139 139 0.24980  0.0 21.89    0 0.6240 5.857  98.2  1.6686  44 437    21.2
## 140 140 0.54452  0.0 21.89    0 0.6240 6.151  97.9  1.6687  44 437    21.2
## 141 141 0.29090  0.0 21.89    0 0.6240 6.174  93.6  1.6119  44 437    21.2
## 142 142 1.62864  0.0 21.89    0 0.6240 5.019 100.0  1.4394  44 437    21.2
## 196 196 0.01381 80.0  0.46    0 0.4220 7.875  32.0  5.6484  44 255    14.4
## 204 204 0.03510 95.0  2.68    0 0.4161 7.853  33.2  5.1180  44 224    14.7
## 205 205 0.02009 95.0  2.68    0 0.4161 8.034  31.9  5.1180  44 224    14.7
## 206 206 0.13642  0.0 10.59    0 0.4890 5.891  22.3  3.9454  44 277    18.6
## 207 207 0.22969  0.0 10.59    0 0.4890 6.326  52.5  4.3549  44 277    18.6
## 208 208 0.25199  0.0 10.59    0 0.4890 5.783  72.7  4.3549  44 277    18.6
## 209 209 0.13587  0.0 10.59    1 0.4890 6.064  59.1  4.2392  44 277    18.6
## 210 210 0.43571  0.0 10.59    1 0.4890 5.344 100.0  3.8750  44 277    18.6
## 211 211 0.17446  0.0 10.59    1 0.4890 5.960  92.1  3.8771  44 277    18.6
## 212 212 0.37578  0.0 10.59    1 0.4890 5.404  88.6  3.6650  44 277    18.6
## 213 213 0.21719  0.0 10.59    1 0.4890 5.807  53.8  3.6526  44 277    18.6
## 214 214 0.14052  0.0 10.59    0 0.4890 6.375  32.3  3.9454  44 277    18.6
## 215 215 0.28955  0.0 10.59    0 0.4890 5.412   9.8  3.5875  44 277    18.6
## 216 216 0.19802  0.0 10.59    0 0.4890 6.182  42.4  3.9454  44 277    18.6
## 275 275 0.05644 40.0  6.41    1 0.4470 6.758  32.9  4.0776  44 254    17.6
## 276 276 0.09604 40.0  6.41    0 0.4470 6.854  42.8  4.2673  44 254    17.6
## 277 277 0.10469 40.0  6.41    1 0.4470 7.267  49.0  4.7872  44 254    17.6
## 278 278 0.06127 40.0  6.41    1 0.4470 6.826  27.6  4.8628  44 254    17.6
## 279 279 0.07978 40.0  6.41    0 0.4470 6.482  32.1  4.1403  44 254    17.6
## 291 291 0.03502 80.0  4.95    0 0.4110 6.861  27.9  5.1167  44 245    19.2
## 292 292 0.07886 80.0  4.95    0 0.4110 7.148  27.7  5.1167  44 245    19.2
## 293 293 0.03615 80.0  4.95    0 0.4110 6.630  23.4  5.1167  44 245    19.2
## 294 294 0.08265  0.0 13.92    0 0.4370 6.127  18.4  5.5027  44 289    16.0
## 295 295 0.08199  0.0 13.92    0 0.4370 6.009  42.3  5.5027  44 289    16.0
## 296 296 0.12932  0.0 13.92    0 0.4370 6.678  31.1  5.9604  44 289    16.0
## 297 297 0.05372  0.0 13.92    0 0.4370 6.549  51.0  5.9604  44 289    16.0
## 298 298 0.14103  0.0 13.92    0 0.4370 5.790  58.0  6.3200  44 289    16.0
## 309 309 0.49298  0.0  9.90    0 0.5440 6.635  82.5  3.3175  44 304    18.4
## 310 310 0.34940  0.0  9.90    0 0.5440 5.972  76.7  3.1025  44 304    18.4
## 311 311 2.63548  0.0  9.90    0 0.5440 4.973  37.8  2.5194  44 304    18.4
## 312 312 0.79041  0.0  9.90    0 0.5440 6.122  52.8  2.6403  44 304    18.4
## 313 313 0.26169  0.0  9.90    0 0.5440 6.023  90.4  2.8340  44 304    18.4
## 314 314 0.26938  0.0  9.90    0 0.5440 6.266  82.8  3.2628  44 304    18.4
## 315 315 0.36920  0.0  9.90    0 0.5440 6.567  87.3  3.6023  44 304    18.4
## 316 316 0.25356  0.0  9.90    0 0.5440 5.705  77.7  3.9450  44 304    18.4
## 317 317 0.31827  0.0  9.90    0 0.5440 5.914  83.2  3.9986  44 304    18.4
## 318 318 0.24522  0.0  9.90    0 0.5440 5.782  71.7  4.0317  44 304    18.4
## 319 319 0.40202  0.0  9.90    0 0.5440 6.382  67.2  3.5325  44 304    18.4
## 320 320 0.47547  0.0  9.90    0 0.5440 6.113  58.8  4.0019  44 304    18.4
## 329 329 0.06617  0.0  3.24    0 0.4600 5.868  25.8  5.2146  44 430    16.9
## 330 330 0.06724  0.0  3.24    0 0.4600 6.333  17.2  5.2146  44 430    16.9
## 331 331 0.04544  0.0  3.24    0 0.4600 6.144  32.2  5.8736  44 430    16.9
## 348 348 0.01870 85.0  4.15    0 0.4290 6.516  27.7  8.5353  44 351    17.9
## 349 349 0.01501 80.0  2.01    0 0.4350 6.635  29.7  8.3440  44 280    17.0
## 352 352 0.07950 60.0  1.69    0 0.4110 6.579  35.9 10.7103  44 411    18.3
## 353 353 0.07244 60.0  1.69    0 0.4110 5.884  18.5 10.7103  44 411    18.3
## 355 355 0.04301 80.0  1.91    0 0.4130 5.663  21.9 10.5857  44 334    22.0
## 356 356 0.10659 80.0  1.91    0 0.4130 5.936  19.5 10.5857  44 334    22.0
## 489 489 0.15086  0.0 27.74    0 0.6090 5.454  92.7  1.8209  44 711    20.1
## 490 490 0.18337  0.0 27.74    0 0.6090 5.414  98.3  1.7554  44 711    20.1
## 491 491 0.20746  0.0 27.74    0 0.6090 5.093  98.0  1.8226  44 711    20.1
## 492 492 0.10574  0.0 27.74    0 0.6090 5.983  98.8  1.8681  44 711    20.1
## 493 493 0.11132  0.0 27.74    0 0.6090 5.983  83.5  2.1099  44 711    20.1
##      black lstat medv
## 14  396.90  8.26 20.4
## 15  380.02 10.26 18.2
## 16  395.62  8.47 19.9
## 17  386.85  6.58 23.1
## 18  386.75 14.67 17.5
## 19  288.99 11.69 20.2
## 20  390.95 11.28 18.2
## 21  376.57 21.02 13.6
## 22  392.53 13.83 19.6
## 23  396.90 18.72 15.2
## 24  394.54 19.88 14.5
## 25  394.33 16.30 15.6
## 26  303.42 16.51 13.9
## 27  376.88 14.81 16.6
## 28  306.38 17.28 14.8
## 29  387.94 12.80 18.4
## 30  380.23 11.98 21.0
## 31  360.17 22.60 12.7
## 32  376.73 13.04 14.5
## 33  232.60 27.71 13.2
## 34  358.77 18.35 13.1
## 35  248.31 20.34 13.5
## 51  395.56 13.45 19.7
## 52  393.97  9.43 20.5
## 53  396.90  5.28 25.0
## 54  396.90  8.43 23.4
## 66  396.90  4.67 23.5
## 67  396.90 10.24 19.4
## 68  396.21  8.10 22.0
## 69  396.90 13.09 17.4
## 70  396.90  8.79 20.9
## 71  383.73  6.72 24.2
## 72  376.94  9.88 21.7
## 73  390.91  5.52 22.8
## 74  377.17  7.54 23.4
## 81  396.90  5.29 28.0
## 82  395.63  7.22 23.9
## 83  396.90  6.72 24.8
## 84  390.64  7.51 22.9
## 93  395.01  8.16 22.9
## 94  396.33  6.21 25.0
## 95  396.90 10.59 20.6
## 128 392.11 17.19 16.2
## 129 396.90 15.39 18.0
## 130 396.90 18.34 14.3
## 131 395.04 12.60 19.2
## 132 396.90 12.26 19.6
## 133 385.76 11.12 23.0
## 134 388.69 15.03 18.4
## 135 262.76 17.31 15.6
## 136 394.67 16.96 18.1
## 137 378.25 16.90 17.4
## 138 394.08 14.59 17.1
## 139 392.04 21.32 13.3
## 140 396.90 18.46 17.8
## 141 388.08 24.16 14.0
## 142 396.90 34.41 14.4
## 196 394.23  2.97 50.0
## 204 392.78  3.81 48.5
## 205 390.55  2.88 50.0
## 206 396.90 10.87 22.6
## 207 394.87 10.97 24.4
## 208 389.43 18.06 22.5
## 209 381.32 14.66 24.4
## 210 396.90 23.09 20.0
## 211 393.25 17.27 21.7
## 212 395.24 23.98 19.3
## 213 390.94 16.03 22.4
## 214 385.81  9.38 28.1
## 215 348.93 29.55 23.7
## 216 393.63  9.47 25.0
## 275 396.90  3.53 32.4
## 276 396.90  2.98 32.0
## 277 389.25  6.05 33.2
## 278 393.45  4.16 33.1
## 279 396.90  7.19 29.1
## 291 396.90  3.33 28.5
## 292 396.90  3.56 37.3
## 293 396.90  4.70 27.9
## 294 396.90  8.58 23.9
## 295 396.90 10.40 21.7
## 296 396.90  6.27 28.6
## 297 392.85  7.39 27.1
## 298 396.90 15.84 20.3
## 309 396.90  4.54 22.8
## 310 396.24  9.97 20.3
## 311 350.45 12.64 16.1
## 312 396.90  5.98 22.1
## 313 396.30 11.72 19.4
## 314 393.39  7.90 21.6
## 315 395.69  9.28 23.8
## 316 396.42 11.50 16.2
## 317 390.70 18.33 17.8
## 318 396.90 15.94 19.8
## 319 395.21 10.36 23.1
## 320 396.23 12.73 21.0
## 329 382.44  9.97 19.3
## 330 375.21  7.34 22.6
## 331 368.57  9.09 19.8
## 348 392.43  6.36 23.1
## 349 390.94  5.99 24.5
## 352 370.78  5.49 24.1
## 353 392.33  7.79 18.6
## 355 382.80  8.05 18.2
## 356 376.04  5.57 20.6
## 489 395.09 18.06 15.2
## 490 344.05 23.97  7.0
## 491 318.43 29.68  8.1
## 492 390.11 18.07 13.6
## 493 396.90 13.35 20.1

For column rad in original dataset, lets replace value 5 by 55

df_boston$rad[df_boston$rad == 5] <- 55
subset(df_boston, df_boston$rad == 55)

##       X    crim    zn indus chas    nox    rm   age     dis rad tax
## 7     7 0.08829  12.5  7.87    0 0.5240 6.012  66.6  5.5605  55 311
## 8     8 0.14455  12.5  7.87    0 0.5240 6.172  96.1  5.9505  55 311
## 9     9 0.21124  12.5  7.87    0 0.5240 5.631 100.0  6.0821  55 311
## 10   10 0.17004  12.5  7.87    0 0.5240 6.004  85.9  6.5921  55 311
## 11   11 0.22489  12.5  7.87    0 0.5240 6.377  94.3  6.3467  55 311
## 12   12 0.11747  12.5  7.87    0 0.5240 6.009  82.9  6.2267  55 311
## 13   13 0.09378  12.5  7.87    0 0.5240 5.889  39.0  5.4509  55 311
## 36   36 0.06417   0.0  5.96    0 0.4990 5.933  68.2  3.3603  55 279
## 37   37 0.09744   0.0  5.96    0 0.4990 5.841  61.4  3.3779  55 279
## 38   38 0.08014   0.0  5.96    0 0.4990 5.850  41.5  3.9342  55 279
## 39   39 0.17505   0.0  5.96    0 0.4990 5.966  30.2  3.8473  55 279
## 56   56 0.01311  90.0  1.22    0 0.4030 7.249  21.9  8.6966  55 226
## 58   58 0.01432 100.0  1.32    0 0.4110 6.816  40.5  8.3248  55 256
## 75   75 0.07896   0.0 12.83    0 0.4370 6.273   6.0  4.2515  55 398
## 76   76 0.09512   0.0 12.83    0 0.4370 6.286  45.0  4.5026  55 398
## 77   77 0.10153   0.0 12.83    0 0.4370 6.279  74.5  4.0522  55 398
## 78   78 0.08707   0.0 12.83    0 0.4370 6.140  45.8  4.0905  55 398
## 79   79 0.05646   0.0 12.83    0 0.4370 6.232  53.7  5.0141  55 398
## 80   80 0.08387   0.0 12.83    0 0.4370 5.874  36.6  4.5026  55 398
## 101 101 0.14866   0.0  8.56    0 0.5200 6.727  79.9  2.7778  55 384
## 102 102 0.11432   0.0  8.56    0 0.5200 6.781  71.3  2.8561  55 384
## 103 103 0.22876   0.0  8.56    0 0.5200 6.405  85.4  2.7147  55 384
## 104 104 0.21161   0.0  8.56    0 0.5200 6.137  87.4  2.7147  55 384
## 105 105 0.13960   0.0  8.56    0 0.5200 6.167  90.0  2.4210  55 384
## 106 106 0.13262   0.0  8.56    0 0.5200 5.851  96.7  2.1069  55 384
## 107 107 0.17120   0.0  8.56    0 0.5200 5.836  91.9  2.2110  55 384
## 108 108 0.13117   0.0  8.56    0 0.5200 6.127  85.2  2.1224  55 384
## 109 109 0.12802   0.0  8.56    0 0.5200 6.474  97.1  2.4329  55 384
## 110 110 0.26363   0.0  8.56    0 0.5200 6.229  91.2  2.5451  55 384
## 111 111 0.10793   0.0  8.56    0 0.5200 6.195  54.4  2.7778  55 384
## 143 143 3.32105   0.0 19.58    1 0.8710 5.403 100.0  1.3216  55 403
## 144 144 4.09740   0.0 19.58    0 0.8710 5.468 100.0  1.4118  55 403
## 145 145 2.77974   0.0 19.58    0 0.8710 4.903  97.8  1.3459  55 403
## 146 146 2.37934   0.0 19.58    0 0.8710 6.130 100.0  1.4191  55 403
## 147 147 2.15505   0.0 19.58    0 0.8710 5.628 100.0  1.5166  55 403
## 148 148 2.36862   0.0 19.58    0 0.8710 4.926  95.7  1.4608  55 403
## 149 149 2.33099   0.0 19.58    0 0.8710 5.186  93.8  1.5296  55 403
## 150 150 2.73397   0.0 19.58    0 0.8710 5.597  94.9  1.5257  55 403
## 151 151 1.65660   0.0 19.58    0 0.8710 6.122  97.3  1.6180  55 403
## 152 152 1.49632   0.0 19.58    0 0.8710 5.404 100.0  1.5916  55 403
## 153 153 1.12658   0.0 19.58    1 0.8710 5.012  88.0  1.6102  55 403
## 154 154 2.14918   0.0 19.58    0 0.8710 5.709  98.5  1.6232  55 403
## 155 155 1.41385   0.0 19.58    1 0.8710 6.129  96.0  1.7494  55 403
## 156 156 3.53501   0.0 19.58    1 0.8710 6.152  82.6  1.7455  55 403
## 157 157 2.44668   0.0 19.58    0 0.8710 5.272  94.0  1.7364  55 403
## 158 158 1.22358   0.0 19.58    0 0.6050 6.943  97.4  1.8773  55 403
## 159 159 1.34284   0.0 19.58    0 0.6050 6.066 100.0  1.7573  55 403
## 160 160 1.42502   0.0 19.58    0 0.8710 6.510 100.0  1.7659  55 403
## 161 161 1.27346   0.0 19.58    1 0.6050 6.250  92.6  1.7984  55 403
## 162 162 1.46336   0.0 19.58    0 0.6050 7.489  90.8  1.9709  55 403
## 163 163 1.83377   0.0 19.58    1 0.6050 7.802  98.2  2.0407  55 403
## 164 164 1.51902   0.0 19.58    1 0.6050 8.375  93.9  2.1620  55 403
## 165 165 2.24236   0.0 19.58    0 0.6050 5.854  91.8  2.4220  55 403
## 166 166 2.92400   0.0 19.58    0 0.6050 6.101  93.0  2.2834  55 403
## 167 167 2.01019   0.0 19.58    0 0.6050 7.929  96.2  2.0459  55 403
## 168 168 1.80028   0.0 19.58    0 0.6050 5.877  79.2  2.4259  55 403
## 169 169 2.30040   0.0 19.58    0 0.6050 6.319  96.1  2.1000  55 403
## 170 170 2.44953   0.0 19.58    0 0.6050 6.402  95.2  2.2625  55 403
## 171 171 1.20742   0.0 19.58    0 0.6050 5.875  94.6  2.4259  55 403
## 172 172 2.31390   0.0 19.58    0 0.6050 5.880  97.3  2.3887  55 403
## 173 173 0.13914   0.0  4.05    0 0.5100 5.572  88.5  2.5961  55 296
## 174 174 0.09178   0.0  4.05    0 0.5100 6.416  84.1  2.6463  55 296
## 175 175 0.08447   0.0  4.05    0 0.5100 5.859  68.7  2.7019  55 296
## 176 176 0.06664   0.0  4.05    0 0.5100 6.546  33.1  3.1323  55 296
## 177 177 0.07022   0.0  4.05    0 0.5100 6.020  47.2  3.5549  55 296
## 178 178 0.05425   0.0  4.05    0 0.5100 6.315  73.4  3.3175  55 296
## 179 179 0.06642   0.0  4.05    0 0.5100 6.860  74.4  2.9153  55 296
## 188 188 0.07875  45.0  3.44    0 0.4370 6.782  41.1  3.7886  55 398
## 189 189 0.12579  45.0  3.44    0 0.4370 6.556  29.1  4.5667  55 398
## 190 190 0.08370  45.0  3.44    0 0.4370 7.185  38.9  4.5667  55 398
## 191 191 0.09068  45.0  3.44    0 0.4370 6.951  21.5  6.4798  55 398
## 192 192 0.06911  45.0  3.44    0 0.4370 6.739  30.8  6.4798  55 398
## 193 193 0.08664  45.0  3.44    0 0.4370 7.178  26.3  6.4798  55 398
## 217 217 0.04560   0.0 13.89    1 0.5500 5.888  56.0  3.1121  55 276
## 218 218 0.07013   0.0 13.89    0 0.5500 6.642  85.1  3.4211  55 276
## 219 219 0.11069   0.0 13.89    1 0.5500 5.951  93.8  2.8893  55 276
## 220 220 0.11425   0.0 13.89    1 0.5500 6.373  92.4  3.3633  55 276
## 258 258 0.61154  20.0  3.97    0 0.6470 8.704  86.9  1.8010  55 264
## 259 259 0.66351  20.0  3.97    0 0.6470 7.333 100.0  1.8946  55 264
## 260 260 0.65665  20.0  3.97    0 0.6470 6.842 100.0  2.0107  55 264
## 261 261 0.54011  20.0  3.97    0 0.6470 7.203  81.8  2.1121  55 264
## 262 262 0.53412  20.0  3.97    0 0.6470 7.520  89.4  2.1398  55 264
## 263 263 0.52014  20.0  3.97    0 0.6470 8.398  91.5  2.2885  55 264
## 264 264 0.82526  20.0  3.97    0 0.6470 7.327  94.5  2.0788  55 264
## 265 265 0.55007  20.0  3.97    0 0.6470 7.206  91.6  1.9301  55 264
## 266 266 0.76162  20.0  3.97    0 0.6470 5.560  62.8  1.9865  55 264
## 267 267 0.78570  20.0  3.97    0 0.6470 7.014  84.6  2.1329  55 264
## 268 268 0.57834  20.0  3.97    0 0.5750 8.297  67.0  2.4216  55 264
## 269 269 0.54050  20.0  3.97    0 0.5750 7.470  52.6  2.8720  55 264
## 280 280 0.21038  20.0  3.33    0 0.4429 6.812  32.2  4.1007  55 216
## 281 281 0.03578  20.0  3.33    0 0.4429 7.820  64.5  4.6947  55 216
## 282 282 0.03705  20.0  3.33    0 0.4429 6.968  37.2  5.2447  55 216
## 283 283 0.06129  20.0  3.33    1 0.4429 7.645  49.7  5.2119  55 216
## 299 299 0.06466  70.0  2.24    0 0.4000 6.345  20.1  7.8278  55 358
## 300 300 0.05561  70.0  2.24    0 0.4000 7.041  10.0  7.8278  55 358
## 301 301 0.04417  70.0  2.24    0 0.4000 6.871  47.4  7.8278  55 358
## 321 321 0.16760   0.0  7.38    0 0.4930 6.426  52.3  4.5404  55 287
## 322 322 0.18159   0.0  7.38    0 0.4930 6.376  54.3  4.5404  55 287
## 323 323 0.35114   0.0  7.38    0 0.4930 6.041  49.9  4.7211  55 287
## 324 324 0.28392   0.0  7.38    0 0.4930 5.708  74.3  4.7211  55 287
## 325 325 0.34109   0.0  7.38    0 0.4930 6.415  40.1  4.7211  55 287
## 326 326 0.19186   0.0  7.38    0 0.4930 6.431  14.7  5.4159  55 287
## 327 327 0.30347   0.0  7.38    0 0.4930 6.312  28.9  5.4159  55 287
## 328 328 0.24103   0.0  7.38    0 0.4930 6.083  43.7  5.4159  55 287
## 334 334 0.05083   0.0  5.19    0 0.5150 6.316  38.1  6.4584  55 224
## 335 335 0.03738   0.0  5.19    0 0.5150 6.310  38.5  6.4584  55 224
## 336 336 0.03961   0.0  5.19    0 0.5150 6.037  34.5  5.9853  55 224
## 337 337 0.03427   0.0  5.19    0 0.5150 5.869  46.3  5.2311  55 224
## 338 338 0.03041   0.0  5.19    0 0.5150 5.895  59.6  5.6150  55 224
## 339 339 0.03306   0.0  5.19    0 0.5150 6.059  37.3  4.8122  55 224
## 340 340 0.05497   0.0  5.19    0 0.5150 5.985  45.4  4.8122  55 224
## 341 341 0.06151   0.0  5.19    0 0.5150 5.968  58.5  4.8122  55 224
## 344 344 0.02543  55.0  3.78    0 0.4840 6.696  56.4  5.7321  55 370
## 345 345 0.03049  55.0  3.78    0 0.4840 6.874  28.1  6.4654  55 370
## 354 354 0.01709  90.0  2.02    0 0.4100 6.728  36.1 12.1265  55 187
##     ptratio  black lstat medv
## 7      15.2 395.60 12.43 22.9
## 8      15.2 396.90 19.15 27.1
## 9      15.2 386.63 29.93 16.5
## 10     15.2 386.71 17.10 18.9
## 11     15.2 392.52 20.45 15.0
## 12     15.2 396.90 13.27 18.9
## 13     15.2 390.50 15.71 21.7
## 36     19.2 396.90  9.68 18.9
## 37     19.2 377.56 11.41 20.0
## 38     19.2 396.90  8.77 21.0
## 39     19.2 393.43 10.13 24.7
## 56     17.9 395.93  4.81 35.4
## 58     15.1 392.90  3.95 31.6
## 75     18.7 394.92  6.78 24.1
## 76     18.7 383.23  8.94 21.4
## 77     18.7 373.66 11.97 20.0
## 78     18.7 386.96 10.27 20.8
## 79     18.7 386.40 12.34 21.2
## 80     18.7 396.06  9.10 20.3
## 101    20.9 394.76  9.42 27.5
## 102    20.9 395.58  7.67 26.5
## 103    20.9  70.80 10.63 18.6
## 104    20.9 394.47 13.44 19.3
## 105    20.9 392.69 12.33 20.1
## 106    20.9 394.05 16.47 19.5
## 107    20.9 395.67 18.66 19.5
## 108    20.9 387.69 14.09 20.4
## 109    20.9 395.24 12.27 19.8
## 110    20.9 391.23 15.55 19.4
## 111    20.9 393.49 13.00 21.7
## 143    14.7 396.90 26.82 13.4
## 144    14.7 396.90 26.42 15.6
## 145    14.7 396.90 29.29 11.8
## 146    14.7 172.91 27.80 13.8
## 147    14.7 169.27 16.65 15.6
## 148    14.7 391.71 29.53 14.6
## 149    14.7 356.99 28.32 17.8
## 150    14.7 351.85 21.45 15.4
## 151    14.7 372.80 14.10 21.5
## 152    14.7 341.60 13.28 19.6
## 153    14.7 343.28 12.12 15.3
## 154    14.7 261.95 15.79 19.4
## 155    14.7 321.02 15.12 17.0
## 156    14.7  88.01 15.02 15.6
## 157    14.7  88.63 16.14 13.1
## 158    14.7 363.43  4.59 41.3
## 159    14.7 353.89  6.43 24.3
## 160    14.7 364.31  7.39 23.3
## 161    14.7 338.92  5.50 27.0
## 162    14.7 374.43  1.73 50.0
## 163    14.7 389.61  1.92 50.0
## 164    14.7 388.45  3.32 50.0
## 165    14.7 395.11 11.64 22.7
## 166    14.7 240.16  9.81 25.0
## 167    14.7 369.30  3.70 50.0
## 168    14.7 227.61 12.14 23.8
## 169    14.7 297.09 11.10 23.8
## 170    14.7 330.04 11.32 22.3
## 171    14.7 292.29 14.43 17.4
## 172    14.7 348.13 12.03 19.1
## 173    16.6 396.90 14.69 23.1
## 174    16.6 395.50  9.04 23.6
## 175    16.6 393.23  9.64 22.6
## 176    16.6 390.96  5.33 29.4
## 177    16.6 393.23 10.11 23.2
## 178    16.6 395.60  6.29 24.6
## 179    16.6 391.27  6.92 29.9
## 188    15.2 393.87  6.68 32.0
## 189    15.2 382.84  4.56 29.8
## 190    15.2 396.90  5.39 34.9
## 191    15.2 377.68  5.10 37.0
## 192    15.2 389.71  4.69 30.5
## 193    15.2 390.49  2.87 36.4
## 217    16.4 392.80 13.51 23.3
## 218    16.4 392.78  9.69 28.7
## 219    16.4 396.90 17.92 21.5
## 220    16.4 393.74 10.50 23.0
## 258    13.0 389.70  5.12 50.0
## 259    13.0 383.29  7.79 36.0
## 260    13.0 391.93  6.90 30.1
## 261    13.0 392.80  9.59 33.8
## 262    13.0 388.37  7.26 43.1
## 263    13.0 386.86  5.91 48.8
## 264    13.0 393.42 11.25 31.0
## 265    13.0 387.89  8.10 36.5
## 266    13.0 392.40 10.45 22.8
## 267    13.0 384.07 14.79 30.7
## 268    13.0 384.54  7.44 50.0
## 269    13.0 390.30  3.16 43.5
## 280    14.9 396.90  4.85 35.1
## 281    14.9 387.31  3.76 45.4
## 282    14.9 392.23  4.59 35.4
## 283    14.9 377.07  3.01 46.0
## 299    14.8 368.24  4.97 22.5
## 300    14.8 371.58  4.74 29.0
## 301    14.8 390.86  6.07 24.8
## 321    19.6 396.90  7.20 23.8
## 322    19.6 396.90  6.87 23.1
## 323    19.6 396.90  7.70 20.4
## 324    19.6 391.13 11.74 18.5
## 325    19.6 396.90  6.12 25.0
## 326    19.6 393.68  5.08 24.6
## 327    19.6 396.90  6.15 23.0
## 328    19.6 396.90 12.79 22.2
## 334    20.2 389.71  5.68 22.2
## 335    20.2 389.40  6.75 20.7
## 336    20.2 396.90  8.01 21.1
## 337    20.2 396.90  9.80 19.5
## 338    20.2 394.81 10.56 18.5
## 339    20.2 396.14  8.51 20.6
## 340    20.2 396.90  9.74 19.0
## 341    20.2 396.90  9.29 18.7
## 344    17.6 396.90  7.18 23.9
## 345    17.6 387.97  4.61 31.2
## 354    17.0 384.46  4.50 30.1

For column rad in original dataset, lets replace value 6 by 66

df_boston$rad[df_boston$rad == 6] <- 66
subset(df_boston, df_boston$rad == 66)

##       X    crim   zn indus chas   nox    rm  age    dis rad tax ptratio
## 112 112 0.10084  0.0 10.01    0 0.547 6.715 81.6 2.6775  66 432    17.8
## 113 113 0.12329  0.0 10.01    0 0.547 5.913 92.9 2.3534  66 432    17.8
## 114 114 0.22212  0.0 10.01    0 0.547 6.092 95.4 2.5480  66 432    17.8
## 115 115 0.14231  0.0 10.01    0 0.547 6.254 84.2 2.2565  66 432    17.8
## 116 116 0.17134  0.0 10.01    0 0.547 5.928 88.2 2.4631  66 432    17.8
## 117 117 0.13158  0.0 10.01    0 0.547 6.176 72.5 2.7301  66 432    17.8
## 118 118 0.15098  0.0 10.01    0 0.547 6.021 82.6 2.7474  66 432    17.8
## 119 119 0.13058  0.0 10.01    0 0.547 5.872 73.1 2.4775  66 432    17.8
## 120 120 0.14476  0.0 10.01    0 0.547 5.731 65.2 2.7592  66 432    17.8
## 239 239 0.08244 30.0  4.93    0 0.428 6.481 18.5 6.1899  66 300    16.6
## 240 240 0.09252 30.0  4.93    0 0.428 6.606 42.2 6.1899  66 300    16.6
## 241 241 0.11329 30.0  4.93    0 0.428 6.897 54.3 6.3361  66 300    16.6
## 242 242 0.10612 30.0  4.93    0 0.428 6.095 65.1 6.3361  66 300    16.6
## 243 243 0.10290 30.0  4.93    0 0.428 6.358 52.9 7.0355  66 300    16.6
## 244 244 0.12757 30.0  4.93    0 0.428 6.393  7.8 7.0355  66 300    16.6
## 288 288 0.03871 52.5  5.32    0 0.405 6.209 31.3 7.3172  66 293    16.6
## 289 289 0.04590 52.5  5.32    0 0.405 6.315 45.6 7.3172  66 293    16.6
## 290 290 0.04297 52.5  5.32    0 0.405 6.565 22.9 7.3172  66 293    16.6
## 494 494 0.17331  0.0  9.69    0 0.585 5.707 54.0 2.3817  66 391    19.2
## 495 495 0.27957  0.0  9.69    0 0.585 5.926 42.6 2.3817  66 391    19.2
## 496 496 0.17899  0.0  9.69    0 0.585 5.670 28.8 2.7986  66 391    19.2
## 497 497 0.28960  0.0  9.69    0 0.585 5.390 72.9 2.7986  66 391    19.2
## 498 498 0.26838  0.0  9.69    0 0.585 5.794 70.6 2.8927  66 391    19.2
## 499 499 0.23912  0.0  9.69    0 0.585 6.019 65.3 2.4091  66 391    19.2
## 500 500 0.17783  0.0  9.69    0 0.585 5.569 73.5 2.3999  66 391    19.2
## 501 501 0.22438  0.0  9.69    0 0.585 6.027 79.7 2.4982  66 391    19.2
##      black lstat medv
## 112 395.59 10.16 22.8
## 113 394.95 16.21 18.8
## 114 396.90 17.09 18.7
## 115 388.74 10.45 18.5
## 116 344.91 15.76 18.3
## 117 393.30 12.04 21.2
## 118 394.51 10.30 19.2
## 119 338.63 15.37 20.4
## 120 391.50 13.61 19.3
## 239 379.41  6.36 23.7
## 240 383.78  7.37 23.3
## 241 391.25 11.38 22.0
## 242 394.62 12.40 20.1
## 243 372.75 11.22 22.2
## 244 374.71  5.19 23.7
## 288 396.90  7.14 23.2
## 289 396.90  7.60 22.3
## 290 371.72  9.51 24.8
## 494 396.90 12.01 21.8
## 495 396.90 13.59 24.5
## 496 393.29 17.60 23.1
## 497 396.90 21.14 19.7
## 498 396.90 14.10 18.3
## 499 396.90 12.92 21.2
## 500 395.77 15.10 17.5
## 501 396.90 14.33 16.8

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

df_boston[1:100,]

##       X    crim    zn indus chas    nox    rm   age    dis rad tax ptratio
## 1     1 0.00632  18.0  2.31    0 0.5380 6.575  65.2 4.0900   1 296    15.3
## 2     2 0.02731   0.0  7.07    0 0.4690 6.421  78.9 4.9671   2 242    17.8
## 3     3 0.02729   0.0  7.07    0 0.4690 7.185  61.1 4.9671   2 242    17.8
## 4     4 0.03237   0.0  2.18    0 0.4580 6.998  45.8 6.0622   3 222    18.7
## 5     5 0.06905   0.0  2.18    0 0.4580 7.147  54.2 6.0622   3 222    18.7
## 6     6 0.02985   0.0  2.18    0 0.4580 6.430  58.7 6.0622   3 222    18.7
## 7     7 0.08829  12.5  7.87    0 0.5240 6.012  66.6 5.5605  55 311    15.2
## 8     8 0.14455  12.5  7.87    0 0.5240 6.172  96.1 5.9505  55 311    15.2
## 9     9 0.21124  12.5  7.87    0 0.5240 5.631 100.0 6.0821  55 311    15.2
## 10   10 0.17004  12.5  7.87    0 0.5240 6.004  85.9 6.5921  55 311    15.2
## 11   11 0.22489  12.5  7.87    0 0.5240 6.377  94.3 6.3467  55 311    15.2
## 12   12 0.11747  12.5  7.87    0 0.5240 6.009  82.9 6.2267  55 311    15.2
## 13   13 0.09378  12.5  7.87    0 0.5240 5.889  39.0 5.4509  55 311    15.2
## 14   14 0.62976   0.0  8.14    0 0.5380 5.949  61.8 4.7075  44 307    21.0
## 15   15 0.63796   0.0  8.14    0 0.5380 6.096  84.5 4.4619  44 307    21.0
## 16   16 0.62739   0.0  8.14    0 0.5380 5.834  56.5 4.4986  44 307    21.0
## 17   17 1.05393   0.0  8.14    0 0.5380 5.935  29.3 4.4986  44 307    21.0
## 18   18 0.78420   0.0  8.14    0 0.5380 5.990  81.7 4.2579  44 307    21.0
## 19   19 0.80271   0.0  8.14    0 0.5380 5.456  36.6 3.7965  44 307    21.0
## 20   20 0.72580   0.0  8.14    0 0.5380 5.727  69.5 3.7965  44 307    21.0
## 21   21 1.25179   0.0  8.14    0 0.5380 5.570  98.1 3.7979  44 307    21.0
## 22   22 0.85204   0.0  8.14    0 0.5380 5.965  89.2 4.0123  44 307    21.0
## 23   23 1.23247   0.0  8.14    0 0.5380 6.142  91.7 3.9769  44 307    21.0
## 24   24 0.98843   0.0  8.14    0 0.5380 5.813 100.0 4.0952  44 307    21.0
## 25   25 0.75026   0.0  8.14    0 0.5380 5.924  94.1 4.3996  44 307    21.0
## 26   26 0.84054   0.0  8.14    0 0.5380 5.599  85.7 4.4546  44 307    21.0
## 27   27 0.67191   0.0  8.14    0 0.5380 5.813  90.3 4.6820  44 307    21.0
## 28   28 0.95577   0.0  8.14    0 0.5380 6.047  88.8 4.4534  44 307    21.0
## 29   29 0.77299   0.0  8.14    0 0.5380 6.495  94.4 4.4547  44 307    21.0
## 30   30 1.00245   0.0  8.14    0 0.5380 6.674  87.3 4.2390  44 307    21.0
## 31   31 1.13081   0.0  8.14    0 0.5380 5.713  94.1 4.2330  44 307    21.0
## 32   32 1.35472   0.0  8.14    0 0.5380 6.072 100.0 4.1750  44 307    21.0
## 33   33 1.38799   0.0  8.14    0 0.5380 5.950  82.0 3.9900  44 307    21.0
## 34   34 1.15172   0.0  8.14    0 0.5380 5.701  95.0 3.7872  44 307    21.0
## 35   35 1.61282   0.0  8.14    0 0.5380 6.096  96.9 3.7598  44 307    21.0
## 36   36 0.06417   0.0  5.96    0 0.4990 5.933  68.2 3.3603  55 279    19.2
## 37   37 0.09744   0.0  5.96    0 0.4990 5.841  61.4 3.3779  55 279    19.2
## 38   38 0.08014   0.0  5.96    0 0.4990 5.850  41.5 3.9342  55 279    19.2
## 39   39 0.17505   0.0  5.96    0 0.4990 5.966  30.2 3.8473  55 279    19.2
## 40   40 0.02763  75.0  2.95    0 0.4280 6.595  21.8 5.4011   3 252    18.3
## 41   41 0.03359  75.0  2.95    0 0.4280 7.024  15.8 5.4011   3 252    18.3
## 42   42 0.12744   0.0  6.91    0 0.4480 6.770   2.9 5.7209   3 233    17.9
## 43   43 0.14150   0.0  6.91    0 0.4480 6.169   6.6 5.7209   3 233    17.9
## 44   44 0.15936   0.0  6.91    0 0.4480 6.211   6.5 5.7209   3 233    17.9
## 45   45 0.12269   0.0  6.91    0 0.4480 6.069  40.0 5.7209   3 233    17.9
## 46   46 0.17142   0.0  6.91    0 0.4480 5.682  33.8 5.1004   3 233    17.9
## 47   47 0.18836   0.0  6.91    0 0.4480 5.786  33.3 5.1004   3 233    17.9
## 48   48 0.22927   0.0  6.91    0 0.4480 6.030  85.5 5.6894   3 233    17.9
## 49   49 0.25387   0.0  6.91    0 0.4480 5.399  95.3 5.8700   3 233    17.9
## 50   50 0.21977   0.0  6.91    0 0.4480 5.602  62.0 6.0877   3 233    17.9
## 51   51 0.08873  21.0  5.64    0 0.4390 5.963  45.7 6.8147  44 243    16.8
## 52   52 0.04337  21.0  5.64    0 0.4390 6.115  63.0 6.8147  44 243    16.8
## 53   53 0.05360  21.0  5.64    0 0.4390 6.511  21.1 6.8147  44 243    16.8
## 54   54 0.04981  21.0  5.64    0 0.4390 5.998  21.4 6.8147  44 243    16.8
## 55   55 0.01360  75.0  4.00    0 0.4100 5.888  47.6 7.3197   3 469    21.1
## 56   56 0.01311  90.0  1.22    0 0.4030 7.249  21.9 8.6966  55 226    17.9
## 57   57 0.02055  85.0  0.74    0 0.4100 6.383  35.7 9.1876   2 313    17.3
## 58   58 0.01432 100.0  1.32    0 0.4110 6.816  40.5 8.3248  55 256    15.1
## 59   59 0.15445  25.0  5.13    0 0.4530 6.145  29.2 7.8148   8 284    19.7
## 60   60 0.10328  25.0  5.13    0 0.4530 5.927  47.2 6.9320   8 284    19.7
## 61   61 0.14932  25.0  5.13    0 0.4530 5.741  66.2 7.2254   8 284    19.7
## 62   62 0.17171  25.0  5.13    0 0.4530 5.966  93.4 6.8185   8 284    19.7
## 63   63 0.11027  25.0  5.13    0 0.4530 6.456  67.8 7.2255   8 284    19.7
## 64   64 0.12650  25.0  5.13    0 0.4530 6.762  43.4 7.9809   8 284    19.7
## 65   65 0.01951  17.5  1.38    0 0.4161 7.104  59.5 9.2229   3 216    18.6
## 66   66 0.03584  80.0  3.37    0 0.3980 6.290  17.8 6.6115  44 337    16.1
## 67   67 0.04379  80.0  3.37    0 0.3980 5.787  31.1 6.6115  44 337    16.1
## 68   68 0.05789  12.5  6.07    0 0.4090 5.878  21.4 6.4980  44 345    18.9
## 69   69 0.13554  12.5  6.07    0 0.4090 5.594  36.8 6.4980  44 345    18.9
## 70   70 0.12816  12.5  6.07    0 0.4090 5.885  33.0 6.4980  44 345    18.9
## 71   71 0.08826   0.0 10.81    0 0.4130 6.417   6.6 5.2873  44 305    19.2
## 72   72 0.15876   0.0 10.81    0 0.4130 5.961  17.5 5.2873  44 305    19.2
## 73   73 0.09164   0.0 10.81    0 0.4130 6.065   7.8 5.2873  44 305    19.2
## 74   74 0.19539   0.0 10.81    0 0.4130 6.245   6.2 5.2873  44 305    19.2
## 75   75 0.07896   0.0 12.83    0 0.4370 6.273   6.0 4.2515  55 398    18.7
## 76   76 0.09512   0.0 12.83    0 0.4370 6.286  45.0 4.5026  55 398    18.7
## 77   77 0.10153   0.0 12.83    0 0.4370 6.279  74.5 4.0522  55 398    18.7
## 78   78 0.08707   0.0 12.83    0 0.4370 6.140  45.8 4.0905  55 398    18.7
## 79   79 0.05646   0.0 12.83    0 0.4370 6.232  53.7 5.0141  55 398    18.7
## 80   80 0.08387   0.0 12.83    0 0.4370 5.874  36.6 4.5026  55 398    18.7
## 81   81 0.04113  25.0  4.86    0 0.4260 6.727  33.5 5.4007  44 281    19.0
## 82   82 0.04462  25.0  4.86    0 0.4260 6.619  70.4 5.4007  44 281    19.0
## 83   83 0.03659  25.0  4.86    0 0.4260 6.302  32.2 5.4007  44 281    19.0
## 84   84 0.03551  25.0  4.86    0 0.4260 6.167  46.7 5.4007  44 281    19.0
## 85   85 0.05059   0.0  4.49    0 0.4490 6.389  48.0 4.7794   3 247    18.5
## 86   86 0.05735   0.0  4.49    0 0.4490 6.630  56.1 4.4377   3 247    18.5
## 87   87 0.05188   0.0  4.49    0 0.4490 6.015  45.1 4.4272   3 247    18.5
## 88   88 0.07151   0.0  4.49    0 0.4490 6.121  56.8 3.7476   3 247    18.5
## 89   89 0.05660   0.0  3.41    0 0.4890 7.007  86.3 3.4217   2 270    17.8
## 90   90 0.05302   0.0  3.41    0 0.4890 7.079  63.1 3.4145   2 270    17.8
## 91   91 0.04684   0.0  3.41    0 0.4890 6.417  66.1 3.0923   2 270    17.8
## 92   92 0.03932   0.0  3.41    0 0.4890 6.405  73.9 3.0921   2 270    17.8
## 93   93 0.04203  28.0 15.04    0 0.4640 6.442  53.6 3.6659  44 270    18.2
## 94   94 0.02875  28.0 15.04    0 0.4640 6.211  28.9 3.6659  44 270    18.2
## 95   95 0.04294  28.0 15.04    0 0.4640 6.249  77.3 3.6150  44 270    18.2
## 96   96 0.12204   0.0  2.89    0 0.4450 6.625  57.8 3.4952   2 276    18.0
## 97   97 0.11504   0.0  2.89    0 0.4450 6.163  69.6 3.4952   2 276    18.0
## 98   98 0.12083   0.0  2.89    0 0.4450 8.069  76.0 3.4952   2 276    18.0
## 99   99 0.08187   0.0  2.89    0 0.4450 7.820  36.9 3.4952   2 276    18.0
## 100 100 0.06860   0.0  2.89    0 0.4450 7.416  62.5 3.4952   2 276    18.0
##      black lstat medv
## 1   396.90  4.98 24.0
## 2   396.90  9.14 21.6
## 3   392.83  4.03 34.7
## 4   394.63  2.94 33.4
## 5   396.90  5.33 36.2
## 6   394.12  5.21 28.7
## 7   395.60 12.43 22.9
## 8   396.90 19.15 27.1
## 9   386.63 29.93 16.5
## 10  386.71 17.10 18.9
## 11  392.52 20.45 15.0
## 12  396.90 13.27 18.9
## 13  390.50 15.71 21.7
## 14  396.90  8.26 20.4
## 15  380.02 10.26 18.2
## 16  395.62  8.47 19.9
## 17  386.85  6.58 23.1
## 18  386.75 14.67 17.5
## 19  288.99 11.69 20.2
## 20  390.95 11.28 18.2
## 21  376.57 21.02 13.6
## 22  392.53 13.83 19.6
## 23  396.90 18.72 15.2
## 24  394.54 19.88 14.5
## 25  394.33 16.30 15.6
## 26  303.42 16.51 13.9
## 27  376.88 14.81 16.6
## 28  306.38 17.28 14.8
## 29  387.94 12.80 18.4
## 30  380.23 11.98 21.0
## 31  360.17 22.60 12.7
## 32  376.73 13.04 14.5
## 33  232.60 27.71 13.2
## 34  358.77 18.35 13.1
## 35  248.31 20.34 13.5
## 36  396.90  9.68 18.9
## 37  377.56 11.41 20.0
## 38  396.90  8.77 21.0
## 39  393.43 10.13 24.7
## 40  395.63  4.32 30.8
## 41  395.62  1.98 34.9
## 42  385.41  4.84 26.6
## 43  383.37  5.81 25.3
## 44  394.46  7.44 24.7
## 45  389.39  9.55 21.2
## 46  396.90 10.21 19.3
## 47  396.90 14.15 20.0
## 48  392.74 18.80 16.6
## 49  396.90 30.81 14.4
## 50  396.90 16.20 19.4
## 51  395.56 13.45 19.7
## 52  393.97  9.43 20.5
## 53  396.90  5.28 25.0
## 54  396.90  8.43 23.4
## 55  396.90 14.80 18.9
## 56  395.93  4.81 35.4
## 57  396.90  5.77 24.7
## 58  392.90  3.95 31.6
## 59  390.68  6.86 23.3
## 60  396.90  9.22 19.6
## 61  395.11 13.15 18.7
## 62  378.08 14.44 16.0
## 63  396.90  6.73 22.2
## 64  395.58  9.50 25.0
## 65  393.24  8.05 33.0
## 66  396.90  4.67 23.5
## 67  396.90 10.24 19.4
## 68  396.21  8.10 22.0
## 69  396.90 13.09 17.4
## 70  396.90  8.79 20.9
## 71  383.73  6.72 24.2
## 72  376.94  9.88 21.7
## 73  390.91  5.52 22.8
## 74  377.17  7.54 23.4
## 75  394.92  6.78 24.1
## 76  383.23  8.94 21.4
## 77  373.66 11.97 20.0
## 78  386.96 10.27 20.8
## 79  386.40 12.34 21.2
## 80  396.06  9.10 20.3
## 81  396.90  5.29 28.0
## 82  395.63  7.22 23.9
## 83  396.90  6.72 24.8
## 84  390.64  7.51 22.9
## 85  396.90  9.62 23.9
## 86  392.30  6.53 26.6
## 87  395.99 12.86 22.5
## 88  395.15  8.44 22.2
## 89  396.90  5.50 23.6
## 90  396.06  5.70 28.7
## 91  392.18  8.81 22.6
## 92  393.55  8.20 22.0
## 93  395.01  8.16 22.9
## 94  396.33  6.21 25.0
## 95  396.90 10.59 20.6
## 96  357.98  6.65 28.4
## 97  391.83 11.34 21.4
## 98  396.90  4.21 38.7
## 99  393.53  3.57 43.8
## 100 396.90  6.19 33.2

R Bridge Workshop Week 2 Assignment

Amit Kapoor

28 July 2019

Data Source

Data Description

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

Summary of dataset

Mean and Median of rm attribute

Mean and Median of ptratio attribute

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

Subset of 50 rows with columns rm, age, dis, tax and ptratio

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

Rename Columns

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.

Summary of new dataset

Mean and Median of rooms_per_dwelling attribute

Mean and Median of pupil_teacher_ratio attribute

Compare mean and median of same two attributes in original dataset and its subset

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”.

For column rad in original dataset, lets replace value 4 by 44

For column rad in original dataset, lets replace value 5 by 55

For column rad in original dataset, lets replace value 6 by 66

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