# R Bridge Week 2 Assignment
# SPS Cuny - Bridge Program
# Spring 2017
# Duubar E. Villalobos Jimenez
# mydvtech@gmail.com
#One of the challenges in working with data is wrangling. In this assignment we will use R to perform this task.
#Here is a list of data sets:
#http://vincentarelbundock.github.io/Rdatasets/ (click on the csv index for a list)
#Method 1 --- Download File
getwd()
## [1] "C:/Users/mydvtech/Documents/GitHub/CUNY-Bridge/R"
setwd("C:/Users/mydvtech/Documents/GitHub/CUNY-Bridge/R")
airquality <- read.csv("airquality.csv", header = TRUE)
airquality
## 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
## 7 7 23 299 8.6 65 5 7
## 8 8 19 99 13.8 59 5 8
## 9 9 8 19 20.1 61 5 9
## 10 10 NA 194 8.6 69 5 10
## 11 11 7 NA 6.9 74 5 11
## 12 12 16 256 9.7 69 5 12
## 13 13 11 290 9.2 66 5 13
## 14 14 14 274 10.9 68 5 14
## 15 15 18 65 13.2 58 5 15
## 16 16 14 334 11.5 64 5 16
## 17 17 34 307 12.0 66 5 17
## 18 18 6 78 18.4 57 5 18
## 19 19 30 322 11.5 68 5 19
## 20 20 11 44 9.7 62 5 20
## 21 21 1 8 9.7 59 5 21
## 22 22 11 320 16.6 73 5 22
## 23 23 4 25 9.7 61 5 23
## 24 24 32 92 12.0 61 5 24
## 25 25 NA 66 16.6 57 5 25
## 26 26 NA 266 14.9 58 5 26
## 27 27 NA NA 8.0 57 5 27
## 28 28 23 13 12.0 67 5 28
## 29 29 45 252 14.9 81 5 29
## 30 30 115 223 5.7 79 5 30
## 31 31 37 279 7.4 76 5 31
## 32 32 NA 286 8.6 78 6 1
## 33 33 NA 287 9.7 74 6 2
## 34 34 NA 242 16.1 67 6 3
## 35 35 NA 186 9.2 84 6 4
## 36 36 NA 220 8.6 85 6 5
## 37 37 NA 264 14.3 79 6 6
## 38 38 29 127 9.7 82 6 7
## 39 39 NA 273 6.9 87 6 8
## 40 40 71 291 13.8 90 6 9
## 41 41 39 323 11.5 87 6 10
## 42 42 NA 259 10.9 93 6 11
## 43 43 NA 250 9.2 92 6 12
## 44 44 23 148 8.0 82 6 13
## 45 45 NA 332 13.8 80 6 14
## 46 46 NA 322 11.5 79 6 15
## 47 47 21 191 14.9 77 6 16
## 48 48 37 284 20.7 72 6 17
## 49 49 20 37 9.2 65 6 18
## 50 50 12 120 11.5 73 6 19
## 51 51 13 137 10.3 76 6 20
## 52 52 NA 150 6.3 77 6 21
## 53 53 NA 59 1.7 76 6 22
## 54 54 NA 91 4.6 76 6 23
## 55 55 NA 250 6.3 76 6 24
## 56 56 NA 135 8.0 75 6 25
## 57 57 NA 127 8.0 78 6 26
## 58 58 NA 47 10.3 73 6 27
## 59 59 NA 98 11.5 80 6 28
## 60 60 NA 31 14.9 77 6 29
## 61 61 NA 138 8.0 83 6 30
## 62 62 135 269 4.1 84 7 1
## 63 63 49 248 9.2 85 7 2
## 64 64 32 236 9.2 81 7 3
## 65 65 NA 101 10.9 84 7 4
## 66 66 64 175 4.6 83 7 5
## 67 67 40 314 10.9 83 7 6
## 68 68 77 276 5.1 88 7 7
## 69 69 97 267 6.3 92 7 8
## 70 70 97 272 5.7 92 7 9
## 71 71 85 175 7.4 89 7 10
## 72 72 NA 139 8.6 82 7 11
## 73 73 10 264 14.3 73 7 12
## 74 74 27 175 14.9 81 7 13
## 75 75 NA 291 14.9 91 7 14
## 76 76 7 48 14.3 80 7 15
## 77 77 48 260 6.9 81 7 16
## 78 78 35 274 10.3 82 7 17
## 79 79 61 285 6.3 84 7 18
## 80 80 79 187 5.1 87 7 19
## 81 81 63 220 11.5 85 7 20
## 82 82 16 7 6.9 74 7 21
## 83 83 NA 258 9.7 81 7 22
## 84 84 NA 295 11.5 82 7 23
## 85 85 80 294 8.6 86 7 24
## 86 86 108 223 8.0 85 7 25
## 87 87 20 81 8.6 82 7 26
## 88 88 52 82 12.0 86 7 27
## 89 89 82 213 7.4 88 7 28
## 90 90 50 275 7.4 86 7 29
## 91 91 64 253 7.4 83 7 30
## 92 92 59 254 9.2 81 7 31
## 93 93 39 83 6.9 81 8 1
## 94 94 9 24 13.8 81 8 2
## 95 95 16 77 7.4 82 8 3
## 96 96 78 NA 6.9 86 8 4
## 97 97 35 NA 7.4 85 8 5
## 98 98 66 NA 4.6 87 8 6
## 99 99 122 255 4.0 89 8 7
## 100 100 89 229 10.3 90 8 8
## 101 101 110 207 8.0 90 8 9
## 102 102 NA 222 8.6 92 8 10
## 103 103 NA 137 11.5 86 8 11
## 104 104 44 192 11.5 86 8 12
## 105 105 28 273 11.5 82 8 13
## 106 106 65 157 9.7 80 8 14
## 107 107 NA 64 11.5 79 8 15
## 108 108 22 71 10.3 77 8 16
## 109 109 59 51 6.3 79 8 17
## 110 110 23 115 7.4 76 8 18
## 111 111 31 244 10.9 78 8 19
## 112 112 44 190 10.3 78 8 20
## 113 113 21 259 15.5 77 8 21
## 114 114 9 36 14.3 72 8 22
## 115 115 NA 255 12.6 75 8 23
## 116 116 45 212 9.7 79 8 24
## 117 117 168 238 3.4 81 8 25
## 118 118 73 215 8.0 86 8 26
## 119 119 NA 153 5.7 88 8 27
## 120 120 76 203 9.7 97 8 28
## 121 121 118 225 2.3 94 8 29
## 122 122 84 237 6.3 96 8 30
## 123 123 85 188 6.3 94 8 31
## 124 124 96 167 6.9 91 9 1
## 125 125 78 197 5.1 92 9 2
## 126 126 73 183 2.8 93 9 3
## 127 127 91 189 4.6 93 9 4
## 128 128 47 95 7.4 87 9 5
## 129 129 32 92 15.5 84 9 6
## 130 130 20 252 10.9 80 9 7
## 131 131 23 220 10.3 78 9 8
## 132 132 21 230 10.9 75 9 9
## 133 133 24 259 9.7 73 9 10
## 134 134 44 236 14.9 81 9 11
## 135 135 21 259 15.5 76 9 12
## 136 136 28 238 6.3 77 9 13
## 137 137 9 24 10.9 71 9 14
## 138 138 13 112 11.5 71 9 15
## 139 139 46 237 6.9 78 9 16
## 140 140 18 224 13.8 67 9 17
## 141 141 13 27 10.3 76 9 18
## 142 142 24 238 10.3 68 9 19
## 143 143 16 201 8.0 82 9 20
## 144 144 13 238 12.6 64 9 21
## 145 145 23 14 9.2 71 9 22
## 146 146 36 139 10.3 81 9 23
## 147 147 7 49 10.3 69 9 24
## 148 148 14 20 16.6 63 9 25
## 149 149 30 193 6.9 70 9 26
## 150 150 NA 145 13.2 77 9 27
## 151 151 14 191 14.3 75 9 28
## 152 152 18 131 8.0 76 9 29
## 153 153 20 223 11.5 68 9 30
# Method 2 --- Internet File
#Original Location of the file
#airquality <- read.csv(url("http://vincentarelbundock.github.io/Rdatasets/csv/datasets/airquality.csv"), header = TRUE)
#GitHub Raw Location of the file
airquality <- read.csv(url("https://raw.githubusercontent.com/dvillalobos/CUNY-Bridge/master/airquality.csv"), header = TRUE)
airquality
## 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
## 7 7 23 299 8.6 65 5 7
## 8 8 19 99 13.8 59 5 8
## 9 9 8 19 20.1 61 5 9
## 10 10 NA 194 8.6 69 5 10
## 11 11 7 NA 6.9 74 5 11
## 12 12 16 256 9.7 69 5 12
## 13 13 11 290 9.2 66 5 13
## 14 14 14 274 10.9 68 5 14
## 15 15 18 65 13.2 58 5 15
## 16 16 14 334 11.5 64 5 16
## 17 17 34 307 12.0 66 5 17
## 18 18 6 78 18.4 57 5 18
## 19 19 30 322 11.5 68 5 19
## 20 20 11 44 9.7 62 5 20
## 21 21 1 8 9.7 59 5 21
## 22 22 11 320 16.6 73 5 22
## 23 23 4 25 9.7 61 5 23
## 24 24 32 92 12.0 61 5 24
## 25 25 NA 66 16.6 57 5 25
## 26 26 NA 266 14.9 58 5 26
## 27 27 NA NA 8.0 57 5 27
## 28 28 23 13 12.0 67 5 28
## 29 29 45 252 14.9 81 5 29
## 30 30 115 223 5.7 79 5 30
## 31 31 37 279 7.4 76 5 31
## 32 32 NA 286 8.6 78 6 1
## 33 33 NA 287 9.7 74 6 2
## 34 34 NA 242 16.1 67 6 3
## 35 35 NA 186 9.2 84 6 4
## 36 36 NA 220 8.6 85 6 5
## 37 37 NA 264 14.3 79 6 6
## 38 38 29 127 9.7 82 6 7
## 39 39 NA 273 6.9 87 6 8
## 40 40 71 291 13.8 90 6 9
## 41 41 39 323 11.5 87 6 10
## 42 42 NA 259 10.9 93 6 11
## 43 43 NA 250 9.2 92 6 12
## 44 44 23 148 8.0 82 6 13
## 45 45 NA 332 13.8 80 6 14
## 46 46 NA 322 11.5 79 6 15
## 47 47 21 191 14.9 77 6 16
## 48 48 37 284 20.7 72 6 17
## 49 49 20 37 9.2 65 6 18
## 50 50 12 120 11.5 73 6 19
## 51 51 13 137 10.3 76 6 20
## 52 52 NA 150 6.3 77 6 21
## 53 53 NA 59 1.7 76 6 22
## 54 54 NA 91 4.6 76 6 23
## 55 55 NA 250 6.3 76 6 24
## 56 56 NA 135 8.0 75 6 25
## 57 57 NA 127 8.0 78 6 26
## 58 58 NA 47 10.3 73 6 27
## 59 59 NA 98 11.5 80 6 28
## 60 60 NA 31 14.9 77 6 29
## 61 61 NA 138 8.0 83 6 30
## 62 62 135 269 4.1 84 7 1
## 63 63 49 248 9.2 85 7 2
## 64 64 32 236 9.2 81 7 3
## 65 65 NA 101 10.9 84 7 4
## 66 66 64 175 4.6 83 7 5
## 67 67 40 314 10.9 83 7 6
## 68 68 77 276 5.1 88 7 7
## 69 69 97 267 6.3 92 7 8
## 70 70 97 272 5.7 92 7 9
## 71 71 85 175 7.4 89 7 10
## 72 72 NA 139 8.6 82 7 11
## 73 73 10 264 14.3 73 7 12
## 74 74 27 175 14.9 81 7 13
## 75 75 NA 291 14.9 91 7 14
## 76 76 7 48 14.3 80 7 15
## 77 77 48 260 6.9 81 7 16
## 78 78 35 274 10.3 82 7 17
## 79 79 61 285 6.3 84 7 18
## 80 80 79 187 5.1 87 7 19
## 81 81 63 220 11.5 85 7 20
## 82 82 16 7 6.9 74 7 21
## 83 83 NA 258 9.7 81 7 22
## 84 84 NA 295 11.5 82 7 23
## 85 85 80 294 8.6 86 7 24
## 86 86 108 223 8.0 85 7 25
## 87 87 20 81 8.6 82 7 26
## 88 88 52 82 12.0 86 7 27
## 89 89 82 213 7.4 88 7 28
## 90 90 50 275 7.4 86 7 29
## 91 91 64 253 7.4 83 7 30
## 92 92 59 254 9.2 81 7 31
## 93 93 39 83 6.9 81 8 1
## 94 94 9 24 13.8 81 8 2
## 95 95 16 77 7.4 82 8 3
## 96 96 78 NA 6.9 86 8 4
## 97 97 35 NA 7.4 85 8 5
## 98 98 66 NA 4.6 87 8 6
## 99 99 122 255 4.0 89 8 7
## 100 100 89 229 10.3 90 8 8
## 101 101 110 207 8.0 90 8 9
## 102 102 NA 222 8.6 92 8 10
## 103 103 NA 137 11.5 86 8 11
## 104 104 44 192 11.5 86 8 12
## 105 105 28 273 11.5 82 8 13
## 106 106 65 157 9.7 80 8 14
## 107 107 NA 64 11.5 79 8 15
## 108 108 22 71 10.3 77 8 16
## 109 109 59 51 6.3 79 8 17
## 110 110 23 115 7.4 76 8 18
## 111 111 31 244 10.9 78 8 19
## 112 112 44 190 10.3 78 8 20
## 113 113 21 259 15.5 77 8 21
## 114 114 9 36 14.3 72 8 22
## 115 115 NA 255 12.6 75 8 23
## 116 116 45 212 9.7 79 8 24
## 117 117 168 238 3.4 81 8 25
## 118 118 73 215 8.0 86 8 26
## 119 119 NA 153 5.7 88 8 27
## 120 120 76 203 9.7 97 8 28
## 121 121 118 225 2.3 94 8 29
## 122 122 84 237 6.3 96 8 30
## 123 123 85 188 6.3 94 8 31
## 124 124 96 167 6.9 91 9 1
## 125 125 78 197 5.1 92 9 2
## 126 126 73 183 2.8 93 9 3
## 127 127 91 189 4.6 93 9 4
## 128 128 47 95 7.4 87 9 5
## 129 129 32 92 15.5 84 9 6
## 130 130 20 252 10.9 80 9 7
## 131 131 23 220 10.3 78 9 8
## 132 132 21 230 10.9 75 9 9
## 133 133 24 259 9.7 73 9 10
## 134 134 44 236 14.9 81 9 11
## 135 135 21 259 15.5 76 9 12
## 136 136 28 238 6.3 77 9 13
## 137 137 9 24 10.9 71 9 14
## 138 138 13 112 11.5 71 9 15
## 139 139 46 237 6.9 78 9 16
## 140 140 18 224 13.8 67 9 17
## 141 141 13 27 10.3 76 9 18
## 142 142 24 238 10.3 68 9 19
## 143 143 16 201 8.0 82 9 20
## 144 144 13 238 12.6 64 9 21
## 145 145 23 14 9.2 71 9 22
## 146 146 36 139 10.3 81 9 23
## 147 147 7 49 10.3 69 9 24
## 148 148 14 20 16.6 63 9 25
## 149 149 30 193 6.9 70 9 26
## 150 150 NA 145 13.2 77 9 27
## 151 151 14 191 14.3 75 9 28
## 152 152 18 131 8.0 76 9 29
## 153 153 20 223 11.5 68 9 30
airquality <- data.frame(airquality)
#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 Function
summary(airquality)
## 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
##
#Returning Mean and Medican
median(airquality$Wind)
## [1] 9.7
mean(airquality$Wind)
## [1] 9.957516
median(airquality$Temp)
## [1] 79
mean(airquality$Temp)
## [1] 77.88235
#2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.
# Subset filtering process by Temp > 80 and Wind > 10; Reporting Month, Day, Temperature and Wind
airqualitysubsetframe <- data.frame(subset(airquality, Temp > 80 & Wind >10, select = c(Wind, Temp, Month, Day)))
airqualitysubsetframe
## Wind Temp Month Day
## 29 14.9 81 5 29
## 40 13.8 90 6 9
## 41 11.5 87 6 10
## 42 10.9 93 6 11
## 65 10.9 84 7 4
## 67 10.9 83 7 6
## 74 14.9 81 7 13
## 75 14.9 91 7 14
## 78 10.3 82 7 17
## 81 11.5 85 7 20
## 84 11.5 82 7 23
## 88 12.0 86 7 27
## 94 13.8 81 8 2
## 100 10.3 90 8 8
## 103 11.5 86 8 11
## 104 11.5 86 8 12
## 105 11.5 82 8 13
## 129 15.5 84 9 6
## 134 14.9 81 9 11
## 146 10.3 81 9 23
#3. Create new column names for the new data frame.
airqualitysubsetframe <- setNames(airqualitysubsetframe, c("Wind Speed", "Temperature", "Month", "Day"))
airqualitysubsetframe
## Wind Speed Temperature Month Day
## 29 14.9 81 5 29
## 40 13.8 90 6 9
## 41 11.5 87 6 10
## 42 10.9 93 6 11
## 65 10.9 84 7 4
## 67 10.9 83 7 6
## 74 14.9 81 7 13
## 75 14.9 91 7 14
## 78 10.3 82 7 17
## 81 11.5 85 7 20
## 84 11.5 82 7 23
## 88 12.0 86 7 27
## 94 13.8 81 8 2
## 100 10.3 90 8 8
## 103 11.5 86 8 11
## 104 11.5 86 8 12
## 105 11.5 82 8 13
## 129 15.5 84 9 6
## 134 14.9 81 9 11
## 146 10.3 81 9 23
#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 Function
summary(airqualitysubsetframe)
## Wind Speed Temperature Month Day
## Min. :10.30 Min. :81.00 Min. :5.0 Min. : 2.00
## 1st Qu.:10.90 1st Qu.:81.75 1st Qu.:7.0 1st Qu.: 8.75
## Median :11.50 Median :84.00 Median :7.0 Median :11.50
## Mean :12.37 Mean :84.80 Mean :7.3 Mean :13.45
## 3rd Qu.:14.07 3rd Qu.:86.25 3rd Qu.:8.0 3rd Qu.:17.75
## Max. :15.50 Max. :93.00 Max. :9.0 Max. :29.00
#Returning Mean and Medican
median(airqualitysubsetframe$Wind)
## [1] 11.5
mean(airqualitysubsetframe$Wind)
## [1] 12.365
median(airqualitysubsetframe$Temperature)
## [1] 84
mean(airqualitysubsetframe$Temperature)
## [1] 84.8
#By Comparing, we can deduct that the subset frame has a new mean and median for the new data,
#providing new information when the Temperature is more than 80 and Wind > 10.
#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 âeâ in one column. Rename those values so that all 20 would show as âexcellentâ.
airquality$Month[airquality$Month == 5 & airquality$Day < 10] <- 1
airquality$Ozone[is.na(airquality$Ozone)] <- 0
airquality
## X Ozone Solar.R Wind Temp Month Day
## 1 1 41 190 7.4 67 1 1
## 2 2 36 118 8.0 72 1 2
## 3 3 12 149 12.6 74 1 3
## 4 4 18 313 11.5 62 1 4
## 5 5 0 NA 14.3 56 1 5
## 6 6 28 NA 14.9 66 1 6
## 7 7 23 299 8.6 65 1 7
## 8 8 19 99 13.8 59 1 8
## 9 9 8 19 20.1 61 1 9
## 10 10 0 194 8.6 69 5 10
## 11 11 7 NA 6.9 74 5 11
## 12 12 16 256 9.7 69 5 12
## 13 13 11 290 9.2 66 5 13
## 14 14 14 274 10.9 68 5 14
## 15 15 18 65 13.2 58 5 15
## 16 16 14 334 11.5 64 5 16
## 17 17 34 307 12.0 66 5 17
## 18 18 6 78 18.4 57 5 18
## 19 19 30 322 11.5 68 5 19
## 20 20 11 44 9.7 62 5 20
## 21 21 1 8 9.7 59 5 21
## 22 22 11 320 16.6 73 5 22
## 23 23 4 25 9.7 61 5 23
## 24 24 32 92 12.0 61 5 24
## 25 25 0 66 16.6 57 5 25
## 26 26 0 266 14.9 58 5 26
## 27 27 0 NA 8.0 57 5 27
## 28 28 23 13 12.0 67 5 28
## 29 29 45 252 14.9 81 5 29
## 30 30 115 223 5.7 79 5 30
## 31 31 37 279 7.4 76 5 31
## 32 32 0 286 8.6 78 6 1
## 33 33 0 287 9.7 74 6 2
## 34 34 0 242 16.1 67 6 3
## 35 35 0 186 9.2 84 6 4
## 36 36 0 220 8.6 85 6 5
## 37 37 0 264 14.3 79 6 6
## 38 38 29 127 9.7 82 6 7
## 39 39 0 273 6.9 87 6 8
## 40 40 71 291 13.8 90 6 9
## 41 41 39 323 11.5 87 6 10
## 42 42 0 259 10.9 93 6 11
## 43 43 0 250 9.2 92 6 12
## 44 44 23 148 8.0 82 6 13
## 45 45 0 332 13.8 80 6 14
## 46 46 0 322 11.5 79 6 15
## 47 47 21 191 14.9 77 6 16
## 48 48 37 284 20.7 72 6 17
## 49 49 20 37 9.2 65 6 18
## 50 50 12 120 11.5 73 6 19
## 51 51 13 137 10.3 76 6 20
## 52 52 0 150 6.3 77 6 21
## 53 53 0 59 1.7 76 6 22
## 54 54 0 91 4.6 76 6 23
## 55 55 0 250 6.3 76 6 24
## 56 56 0 135 8.0 75 6 25
## 57 57 0 127 8.0 78 6 26
## 58 58 0 47 10.3 73 6 27
## 59 59 0 98 11.5 80 6 28
## 60 60 0 31 14.9 77 6 29
## 61 61 0 138 8.0 83 6 30
## 62 62 135 269 4.1 84 7 1
## 63 63 49 248 9.2 85 7 2
## 64 64 32 236 9.2 81 7 3
## 65 65 0 101 10.9 84 7 4
## 66 66 64 175 4.6 83 7 5
## 67 67 40 314 10.9 83 7 6
## 68 68 77 276 5.1 88 7 7
## 69 69 97 267 6.3 92 7 8
## 70 70 97 272 5.7 92 7 9
## 71 71 85 175 7.4 89 7 10
## 72 72 0 139 8.6 82 7 11
## 73 73 10 264 14.3 73 7 12
## 74 74 27 175 14.9 81 7 13
## 75 75 0 291 14.9 91 7 14
## 76 76 7 48 14.3 80 7 15
## 77 77 48 260 6.9 81 7 16
## 78 78 35 274 10.3 82 7 17
## 79 79 61 285 6.3 84 7 18
## 80 80 79 187 5.1 87 7 19
## 81 81 63 220 11.5 85 7 20
## 82 82 16 7 6.9 74 7 21
## 83 83 0 258 9.7 81 7 22
## 84 84 0 295 11.5 82 7 23
## 85 85 80 294 8.6 86 7 24
## 86 86 108 223 8.0 85 7 25
## 87 87 20 81 8.6 82 7 26
## 88 88 52 82 12.0 86 7 27
## 89 89 82 213 7.4 88 7 28
## 90 90 50 275 7.4 86 7 29
## 91 91 64 253 7.4 83 7 30
## 92 92 59 254 9.2 81 7 31
## 93 93 39 83 6.9 81 8 1
## 94 94 9 24 13.8 81 8 2
## 95 95 16 77 7.4 82 8 3
## 96 96 78 NA 6.9 86 8 4
## 97 97 35 NA 7.4 85 8 5
## 98 98 66 NA 4.6 87 8 6
## 99 99 122 255 4.0 89 8 7
## 100 100 89 229 10.3 90 8 8
## 101 101 110 207 8.0 90 8 9
## 102 102 0 222 8.6 92 8 10
## 103 103 0 137 11.5 86 8 11
## 104 104 44 192 11.5 86 8 12
## 105 105 28 273 11.5 82 8 13
## 106 106 65 157 9.7 80 8 14
## 107 107 0 64 11.5 79 8 15
## 108 108 22 71 10.3 77 8 16
## 109 109 59 51 6.3 79 8 17
## 110 110 23 115 7.4 76 8 18
## 111 111 31 244 10.9 78 8 19
## 112 112 44 190 10.3 78 8 20
## 113 113 21 259 15.5 77 8 21
## 114 114 9 36 14.3 72 8 22
## 115 115 0 255 12.6 75 8 23
## 116 116 45 212 9.7 79 8 24
## 117 117 168 238 3.4 81 8 25
## 118 118 73 215 8.0 86 8 26
## 119 119 0 153 5.7 88 8 27
## 120 120 76 203 9.7 97 8 28
## 121 121 118 225 2.3 94 8 29
## 122 122 84 237 6.3 96 8 30
## 123 123 85 188 6.3 94 8 31
## 124 124 96 167 6.9 91 9 1
## 125 125 78 197 5.1 92 9 2
## 126 126 73 183 2.8 93 9 3
## 127 127 91 189 4.6 93 9 4
## 128 128 47 95 7.4 87 9 5
## 129 129 32 92 15.5 84 9 6
## 130 130 20 252 10.9 80 9 7
## 131 131 23 220 10.3 78 9 8
## 132 132 21 230 10.9 75 9 9
## 133 133 24 259 9.7 73 9 10
## 134 134 44 236 14.9 81 9 11
## 135 135 21 259 15.5 76 9 12
## 136 136 28 238 6.3 77 9 13
## 137 137 9 24 10.9 71 9 14
## 138 138 13 112 11.5 71 9 15
## 139 139 46 237 6.9 78 9 16
## 140 140 18 224 13.8 67 9 17
## 141 141 13 27 10.3 76 9 18
## 142 142 24 238 10.3 68 9 19
## 143 143 16 201 8.0 82 9 20
## 144 144 13 238 12.6 64 9 21
## 145 145 23 14 9.2 71 9 22
## 146 146 36 139 10.3 81 9 23
## 147 147 7 49 10.3 69 9 24
## 148 148 14 20 16.6 63 9 25
## 149 149 30 193 6.9 70 9 26
## 150 150 0 145 13.2 77 9 27
## 151 151 14 191 14.3 75 9 28
## 152 152 18 131 8.0 76 9 29
## 153 153 20 223 11.5 68 9 30
#6. Display enough rows to see examples of all of steps 1-5 above.
#Done
#7. BONUS â 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