SPS R Bridge Week 2 Assignment

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

theURL <- "https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/datasets/airquality.csv"
air <- read.table(file = theURL, header = TRUE, sep = ",")
summary(air)

##        X           Ozone           Solar.R           Wind       
##  Min.   :  1   Min.   :  1.00   Min.   :  7.0   Min.   : 1.700  
##  1st Qu.: 39   1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400  
##  Median : 77   Median : 31.50   Median :205.0   Median : 9.700  
##  Mean   : 77   Mean   : 42.13   Mean   :185.9   Mean   : 9.958  
##  3rd Qu.:115   3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500  
##  Max.   :153   Max.   :168.00   Max.   :334.0   Max.   :20.700  
##                NA's   :37       NA's   :7                       
##       Temp           Month            Day      
##  Min.   :56.00   Min.   :5.000   Min.   : 1.0  
##  1st Qu.:72.00   1st Qu.:6.000   1st Qu.: 8.0  
##  Median :79.00   Median :7.000   Median :16.0  
##  Mean   :77.88   Mean   :6.993   Mean   :15.8  
##  3rd Qu.:85.00   3rd Qu.:8.000   3rd Qu.:23.0  
##  Max.   :97.00   Max.   :9.000   Max.   :31.0  
##

#print(typeof(air))
mean(air[,"Solar.R"], na.rm = TRUE)

## [1] 185.9315

median(air[,"Solar.R"], na.rm = TRUE)

## [1] 205

mean(air[,"Ozone"], na.rm = TRUE)

## [1] 42.12931

median(air[,"Ozone"], na.rm = TRUE)

## [1] 31.5

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

newAir <- air[1:50, 1:5]
print(newAir)

##     X Ozone Solar.R Wind Temp
## 1   1    41     190  7.4   67
## 2   2    36     118  8.0   72
## 3   3    12     149 12.6   74
## 4   4    18     313 11.5   62
## 5   5    NA      NA 14.3   56
## 6   6    28      NA 14.9   66
## 7   7    23     299  8.6   65
## 8   8    19      99 13.8   59
## 9   9     8      19 20.1   61
## 10 10    NA     194  8.6   69
## 11 11     7      NA  6.9   74
## 12 12    16     256  9.7   69
## 13 13    11     290  9.2   66
## 14 14    14     274 10.9   68
## 15 15    18      65 13.2   58
## 16 16    14     334 11.5   64
## 17 17    34     307 12.0   66
## 18 18     6      78 18.4   57
## 19 19    30     322 11.5   68
## 20 20    11      44  9.7   62
## 21 21     1       8  9.7   59
## 22 22    11     320 16.6   73
## 23 23     4      25  9.7   61
## 24 24    32      92 12.0   61
## 25 25    NA      66 16.6   57
## 26 26    NA     266 14.9   58
## 27 27    NA      NA  8.0   57
## 28 28    23      13 12.0   67
## 29 29    45     252 14.9   81
## 30 30   115     223  5.7   79
## 31 31    37     279  7.4   76
## 32 32    NA     286  8.6   78
## 33 33    NA     287  9.7   74
## 34 34    NA     242 16.1   67
## 35 35    NA     186  9.2   84
## 36 36    NA     220  8.6   85
## 37 37    NA     264 14.3   79
## 38 38    29     127  9.7   82
## 39 39    NA     273  6.9   87
## 40 40    71     291 13.8   90
## 41 41    39     323 11.5   87
## 42 42    NA     259 10.9   93
## 43 43    NA     250  9.2   92
## 44 44    23     148  8.0   82
## 45 45    NA     332 13.8   80
## 46 46    NA     322 11.5   79
## 47 47    21     191 14.9   77
## 48 48    37     284 20.7   72
## 49 49    20      37  9.2   65
## 50 50    12     120 11.5   73

Create new column names for the new data frame.

colnames(newAir) <- c("obs", "newOzone", "solar", "w", "t")
print(newAir)

##    obs newOzone solar    w  t
## 1    1       41   190  7.4 67
## 2    2       36   118  8.0 72
## 3    3       12   149 12.6 74
## 4    4       18   313 11.5 62
## 5    5       NA    NA 14.3 56
## 6    6       28    NA 14.9 66
## 7    7       23   299  8.6 65
## 8    8       19    99 13.8 59
## 9    9        8    19 20.1 61
## 10  10       NA   194  8.6 69
## 11  11        7    NA  6.9 74
## 12  12       16   256  9.7 69
## 13  13       11   290  9.2 66
## 14  14       14   274 10.9 68
## 15  15       18    65 13.2 58
## 16  16       14   334 11.5 64
## 17  17       34   307 12.0 66
## 18  18        6    78 18.4 57
## 19  19       30   322 11.5 68
## 20  20       11    44  9.7 62
## 21  21        1     8  9.7 59
## 22  22       11   320 16.6 73
## 23  23        4    25  9.7 61
## 24  24       32    92 12.0 61
## 25  25       NA    66 16.6 57
## 26  26       NA   266 14.9 58
## 27  27       NA    NA  8.0 57
## 28  28       23    13 12.0 67
## 29  29       45   252 14.9 81
## 30  30      115   223  5.7 79
## 31  31       37   279  7.4 76
## 32  32       NA   286  8.6 78
## 33  33       NA   287  9.7 74
## 34  34       NA   242 16.1 67
## 35  35       NA   186  9.2 84
## 36  36       NA   220  8.6 85
## 37  37       NA   264 14.3 79
## 38  38       29   127  9.7 82
## 39  39       NA   273  6.9 87
## 40  40       71   291 13.8 90
## 41  41       39   323 11.5 87
## 42  42       NA   259 10.9 93
## 43  43       NA   250  9.2 92
## 44  44       23   148  8.0 82
## 45  45       NA   332 13.8 80
## 46  46       NA   322 11.5 79
## 47  47       21   191 14.9 77
## 48  48       37   284 20.7 72
## 49  49       20    37  9.2 65
## 50  50       12   120 11.5 73

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(newAir)

##       obs           newOzone          solar             w        
##  Min.   : 1.00   Min.   :  1.00   Min.   :  8.0   Min.   : 5.70  
##  1st Qu.:13.25   1st Qu.: 12.00   1st Qu.:118.5   1st Qu.: 9.20  
##  Median :25.50   Median : 20.50   Median :246.0   Median :11.50  
##  Mean   :25.50   Mean   : 25.47   Mean   :203.0   Mean   :11.57  
##  3rd Qu.:37.75   3rd Qu.: 33.50   3rd Qu.:286.8   3rd Qu.:13.80  
##  Max.   :50.00   Max.   :115.00   Max.   :334.0   Max.   :20.70  
##                  NA's   :16       NA's   :4                      
##        t        
##  Min.   :56.00  
##  1st Qu.:62.50  
##  Median :69.00  
##  Mean   :71.16  
##  3rd Qu.:79.00  
##  Max.   :93.00  
##

mean(newAir[,"solar"], na.rm = TRUE)

## [1] 202.9783

median(newAir[,"solar"], na.rm = TRUE)

## [1] 246

mean(newAir[,"newOzone"], na.rm = TRUE)

## [1] 25.47059

median(newAir[,"newOzone"], na.rm = TRUE)

## [1] 20.5

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”.

print(newAir)

##    obs newOzone solar    w  t
## 1    1       41   190  7.4 67
## 2    2       36   118  8.0 72
## 3    3       12   149 12.6 74
## 4    4       18   313 11.5 62
## 5    5       NA    NA 14.3 56
## 6    6       28    NA 14.9 66
## 7    7       23   299  8.6 65
## 8    8       19    99 13.8 59
## 9    9        8    19 20.1 61
## 10  10       NA   194  8.6 69
## 11  11        7    NA  6.9 74
## 12  12       16   256  9.7 69
## 13  13       11   290  9.2 66
## 14  14       14   274 10.9 68
## 15  15       18    65 13.2 58
## 16  16       14   334 11.5 64
## 17  17       34   307 12.0 66
## 18  18        6    78 18.4 57
## 19  19       30   322 11.5 68
## 20  20       11    44  9.7 62
## 21  21        1     8  9.7 59
## 22  22       11   320 16.6 73
## 23  23        4    25  9.7 61
## 24  24       32    92 12.0 61
## 25  25       NA    66 16.6 57
## 26  26       NA   266 14.9 58
## 27  27       NA    NA  8.0 57
## 28  28       23    13 12.0 67
## 29  29       45   252 14.9 81
## 30  30      115   223  5.7 79
## 31  31       37   279  7.4 76
## 32  32       NA   286  8.6 78
## 33  33       NA   287  9.7 74
## 34  34       NA   242 16.1 67
## 35  35       NA   186  9.2 84
## 36  36       NA   220  8.6 85
## 37  37       NA   264 14.3 79
## 38  38       29   127  9.7 82
## 39  39       NA   273  6.9 87
## 40  40       71   291 13.8 90
## 41  41       39   323 11.5 87
## 42  42       NA   259 10.9 93
## 43  43       NA   250  9.2 92
## 44  44       23   148  8.0 82
## 45  45       NA   332 13.8 80
## 46  46       NA   322 11.5 79
## 47  47       21   191 14.9 77
## 48  48       37   284 20.7 72
## 49  49       20    37  9.2 65
## 50  50       12   120 11.5 73

newAir$newOzone[is.na(newAir$newOzone)] <- 0
newAir$newOzone[newAir$newOzone==41] <- 0
newAir$newOzone[newAir$newOzone==18] <- 0
print(newAir)

##    obs newOzone solar    w  t
## 1    1        0   190  7.4 67
## 2    2       36   118  8.0 72
## 3    3       12   149 12.6 74
## 4    4        0   313 11.5 62
## 5    5        0    NA 14.3 56
## 6    6       28    NA 14.9 66
## 7    7       23   299  8.6 65
## 8    8       19    99 13.8 59
## 9    9        8    19 20.1 61
## 10  10        0   194  8.6 69
## 11  11        7    NA  6.9 74
## 12  12       16   256  9.7 69
## 13  13       11   290  9.2 66
## 14  14       14   274 10.9 68
## 15  15        0    65 13.2 58
## 16  16       14   334 11.5 64
## 17  17       34   307 12.0 66
## 18  18        6    78 18.4 57
## 19  19       30   322 11.5 68
## 20  20       11    44  9.7 62
## 21  21        1     8  9.7 59
## 22  22       11   320 16.6 73
## 23  23        4    25  9.7 61
## 24  24       32    92 12.0 61
## 25  25        0    66 16.6 57
## 26  26        0   266 14.9 58
## 27  27        0    NA  8.0 57
## 28  28       23    13 12.0 67
## 29  29       45   252 14.9 81
## 30  30      115   223  5.7 79
## 31  31       37   279  7.4 76
## 32  32        0   286  8.6 78
## 33  33        0   287  9.7 74
## 34  34        0   242 16.1 67
## 35  35        0   186  9.2 84
## 36  36        0   220  8.6 85
## 37  37        0   264 14.3 79
## 38  38       29   127  9.7 82
## 39  39        0   273  6.9 87
## 40  40       71   291 13.8 90
## 41  41       39   323 11.5 87
## 42  42        0   259 10.9 93
## 43  43        0   250  9.2 92
## 44  44       23   148  8.0 82
## 45  45        0   332 13.8 80
## 46  46        0   322 11.5 79
## 47  47       21   191 14.9 77
## 48  48       37   284 20.7 72
## 49  49       20    37  9.2 65
## 50  50       12   120 11.5 73

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

head(air)

##   X Ozone Solar.R Wind Temp Month Day
## 1 1    41     190  7.4   67     5   1
## 2 2    36     118  8.0   72     5   2
## 3 3    12     149 12.6   74     5   3
## 4 4    18     313 11.5   62     5   4
## 5 5    NA      NA 14.3   56     5   5
## 6 6    28      NA 14.9   66     5   6

head(newAir)

##   obs newOzone solar    w  t
## 1   1        0   190  7.4 67
## 2   2       36   118  8.0 72
## 3   3       12   149 12.6 74
## 4   4        0   313 11.5 62
## 5   5        0    NA 14.3 56
## 6   6       28    NA 14.9 66

7.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.

Done above from the original source file’s link.

SPS R Bridge Week 2 Assignment

Omar Pineda