Activity (Graded)

Execute the following cell to load the tidyverse library:

library(tidyverse)

Execute the following cell to load the data. Refer to this website http://archive.ics.uci.edu/ml/datasets/Auto+MPG for details on the dataset:

autompg = read.table(
  "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data",
  quote = "\"",
  comment.char = "",
  stringsAsFactors = FALSE)
head(autompg,20)

Task 1: print the structure of the unedited data set. How many samples and features are there?

str(autompg)

'data.frame':   390 obs. of  8 variables:
 $ mpg   : num  18 15 18 16 17 15 14 14 14 15 ...
 $ cyl   : Factor w/ 5 levels "3","4","5","6",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ disp  : num  307 350 318 304 302 429 454 440 455 390 ...
 $ hp    : num  130 165 150 150 140 198 220 215 225 190 ...
 $ wt    : num  3504 3693 3436 3433 3449 ...
 $ acc   : num  12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
 $ year  : int  70 70 70 70 70 70 70 70 70 70 ...
 $ origin: Factor w/ 2 levels "international",..: 2 2 2 2 2 2 2 2 2 2 ...

# There are 398 samples with 8 features.

Execute the following cell to assign names to the columns of the dataframe:

colnames(autompg) = c("mpg", "cyl", "disp", "hp", "wt", "acc", "year", "origin", "name")
colnames(autompg)

[1] "mpg"    "cyl"    "disp"   "hp"     "wt"     "acc"    "year"   "origin" "name"  

Task-2: complete the code segment below to remove samples with missing horsepower (hp) values represented as a “?” in the dataset.

autompg = autompg %>% filter(!hp=="?")
autompg

Task-3: complete the code segment below to remove samples with the name “plymouth reliant”

autompg = autompg %>% filter(!name=="plymouth reliant") 
autompg

Task-4: complete the code segment below to select all features except ‘name’

autompg = autompg %>% select(-name)
autompg

Execute the following cell to change the type of hp values from character to numeric:

autompg$hp = as.numeric(autompg$hp)
autompg$hp

  [1] 130 165 150 150 140 198 220 215 225 190 170 160 150 225  95  95  97  85  88  46  87  90  95 113  90 215 200 210
 [29] 193  88  90  95 100 105 100  88 100 165 175 153 150 180 170 175 110  72 100  88  86  90  70  76  65  69  60  70
 [57]  95  80  54  90  86 165 175 150 153 150 208 155 160 190  97 150 130 140 150 112  76  87  69  86  92  97  80  88
 [85] 175 150 145 137 150 198 150 158 150 215 225 175 105 100 100  88  95  46 150 167 170 180 100  88  72  94  90  85
[113] 107  90 145 230  49  75  91 112 150 110 122 180  95 100 100  67  80  65  75 100 110 105 140 150 150 140 150  83
[141]  67  78  52  61  75  75  75  97  93  67  95 105  72  72 170 145 150 148 110 105 110  95 110 110 129  75  83 100
[169]  78  96  71  97  97  70  90  95  88  98 115  53  86  81  92  79  83 140 150 120 152 100 105  81  90  52  60  70
[197]  53 100  78 110  95  71  70  75  72 102 150  88 108 120 180 145 130 150  68  80  58  96  70 145 110 145 130 110
[225] 105 100  98 180 170 190 149  78  88  75  89  63  83  67  78  97 110 110  48  66  52  70  60 110 140 139 105  95
[253]  85  88 100  90 105  85 110 120 145 165 139 140  68  95  97  75  95 105  85  97 103 125 115 133  71  68 115  85
[281]  88  90 110 130 129 138 135 155 142 125 150  71  65  80  80  77 125  71  90  70  70  65  69  90 115 115  90  76
[309]  60  70  65  90  88  90  90  78  90  75  92  75  65 105  65  48  48  67  67  67  67  62 132 100  88  72  84  92
[337] 110  58  64  60  67  65  62  68  63  65  65  74  75  75 100  74  80  76 116 120 110 105  88  85  88  88  88  85
[365]  84  90  92  74  68  68  63  70  88  75  70  67  67  67 110  85  92 112  96  84  90  86  52  84  79  82

Execute the following code cell to modify ‘origin’ column to reflect local (1) and international models (0)

autompg = autompg %>% mutate(origin = ifelse(!(origin %in% c(2, 3)), 'local', 'international'))
head(autompg, 20)

Task 5: print the structure of the dataframe. What types are the columns ‘cyl’ and ‘origin’?

str(autompg)

'data.frame':   390 obs. of  8 variables:
 $ mpg   : num  18 15 18 16 17 15 14 14 14 15 ...
 $ cyl   : int  8 8 8 8 8 8 8 8 8 8 ...
 $ disp  : num  307 350 318 304 302 429 454 440 455 390 ...
 $ hp    : num  130 165 150 150 140 198 220 215 225 190 ...
 $ wt    : num  3504 3693 3436 3433 3449 ...
 $ acc   : num  12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
 $ year  : int  70 70 70 70 70 70 70 70 70 70 ...
 $ origin: chr  "local" "local" "local" "local" ...

#column "cyl" is integers while "origin" is characters.

Task-6: complete the code segment below to change the types of ‘cyl’ and ‘origin’ columns to factor

catcols = c('cyl', 'origin')
autompg[catcols] = lapply(autompg[catcols],as.factor)
autompg[catcols]

Task-7: complete the code segment below to create a scatter plot of mpg vs. displacement by color coding the points according to the origin (local or international), Comment on what you observe:

p = ggplot(data =autompg , aes(x =mpg , y =disp , color = origin )) +geom_point()+ labs(x = 'mpg ', y = 'Displacement ', title = 'mpg vs.Displacement')
# Below shows the scatter plot of mpg vs displacement.The scatter plots from the same origin (local or international)are colored uniformly or have same color.

  
p