--Basics
--select()
--filter()
--arrange()
--mutate()
--summarize()
--group_by()
library(dplyr)
library(ggplot2)tibble <- tbl_df(diamonds) #Convert diamonds dataset into tbl_df
head(tibble) #View first 6 rows## # A tibble: 6 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
dim(tibble) #Check the dimensions of the dataset## [1] 53940 10
select(tibble, color, cut)## # A tibble: 53,940 × 2
## color cut
## <ord> <ord>
## 1 E Ideal
## 2 E Premium
## 3 E Good
## 4 I Premium
## 5 J Good
## 6 J Very Good
## 7 I Very Good
## 8 H Very Good
## 9 E Fair
## 10 H Very Good
## # ... with 53,930 more rows
select(tibble, clarity:price)## # A tibble: 53,940 × 4
## clarity depth table price
## <ord> <dbl> <dbl> <int>
## 1 SI2 61.5 55 326
## 2 SI1 59.8 61 326
## 3 VS1 56.9 65 327
## 4 VS2 62.4 58 334
## 5 SI2 63.3 58 335
## 6 VVS2 62.8 57 336
## 7 VVS1 62.3 57 336
## 8 SI1 61.9 55 337
## 9 VS2 65.1 61 337
## 10 VS1 59.4 61 338
## # ... with 53,930 more rows
select(tibble, -carat) #Select all columns except the carat column## # A tibble: 53,940 × 9
## cut color clarity depth table price x y z
## <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## # ... with 53,930 more rows
select(tibble, -(carat:clarity)) #Select all columns except the named sequence of columns carat:clarity## # A tibble: 53,940 × 6
## depth table price x y z
## <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 61.5 55 326 3.95 3.98 2.43
## 2 59.8 61 326 3.89 3.84 2.31
## 3 56.9 65 327 4.05 4.07 2.31
## 4 62.4 58 334 4.20 4.23 2.63
## 5 63.3 58 335 4.34 4.35 2.75
## 6 62.8 57 336 3.94 3.96 2.48
## 7 62.3 57 336 3.95 3.98 2.47
## 8 61.9 55 337 4.07 4.11 2.53
## 9 65.1 61 337 3.87 3.78 2.49
## 10 59.4 61 338 4.00 4.05 2.39
## # ... with 53,930 more rows
filter(tibble, cut == "Premium") #Select rows representing only Premium cut diamonds## # A tibble: 13,791 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 2 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 3 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
## 4 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
## 5 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
## 6 0.24 Premium I VS1 62.5 57 355 3.97 3.94 2.47
## 7 0.29 Premium F SI1 62.4 58 403 4.24 4.26 2.65
## 8 0.22 Premium E VS2 61.6 58 404 3.93 3.89 2.41
## 9 0.22 Premium D VS2 59.3 62 404 3.91 3.88 2.31
## 10 0.30 Premium J SI2 59.3 61 405 4.43 4.38 2.61
## # ... with 13,781 more rows
filter(tibble, cut == "Premium", color == "D") #Select rows representing only diamonds of Premium cut AND D color classification ## # A tibble: 1,603 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.22 Premium D VS2 59.3 62 404 3.91 3.88 2.31
## 2 0.30 Premium D SI1 62.6 59 552 4.23 4.27 2.66
## 3 0.71 Premium D SI2 61.7 59 2768 5.71 5.67 3.51
## 4 0.71 Premium D VS2 62.5 60 2770 5.65 5.61 3.52
## 5 0.70 Premium D VS2 58.0 62 2773 5.87 5.78 3.38
## 6 0.72 Premium D SI1 62.7 59 2782 5.73 5.69 3.58
## 7 0.70 Premium D SI1 62.8 60 2782 5.68 5.66 3.56
## 8 0.72 Premium D SI2 62.0 60 2795 5.73 5.69 3.54
## 9 0.71 Premium D SI1 62.7 60 2797 5.67 5.71 3.57
## 10 0.71 Premium D SI1 61.3 58 2797 5.73 5.75 3.52
## # ... with 1,593 more rows
filter(tibble, carat > 1.0000, price < 5000) #Select rows representing only diamonds of carat over 1.0000 AND price less than 5000## # A tibble: 3,756 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 1.17 Very Good J I1 60.2 61 2774 6.83 6.90 4.13
## 2 1.01 Premium F I1 61.8 60 2781 6.39 6.36 3.94
## 3 1.01 Fair E I1 64.5 58 2788 6.29 6.21 4.03
## 4 1.01 Premium H SI2 62.7 59 2788 6.31 6.22 3.93
## 5 1.05 Very Good J SI2 63.2 56 2789 6.49 6.45 4.09
## 6 1.05 Fair J SI2 65.8 59 2789 6.41 6.27 4.18
## 7 1.01 Fair E SI2 67.4 60 2797 6.19 6.05 4.13
## 8 1.04 Premium G I1 62.2 58 2801 6.46 6.41 4.00
## 9 1.20 Fair F I1 64.6 56 2809 6.73 6.66 4.33
## 10 1.02 Premium G I1 60.3 58 2815 6.55 6.50 3.94
## # ... with 3,746 more rows
filter(tibble, carat > 1.0000 | price < 5000) #Select rows representing only diamonds of carat over 1.0000 OR price less than 50000## # A tibble: 52,959 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## # ... with 52,949 more rows
filter(tibble, is.na(cut)) #Select rows representing diamonds which have NA values for cut## # A tibble: 0 × 10
## # ... with 10 variables: carat <dbl>, cut <ord>, color <ord>,
## # clarity <ord>, depth <dbl>, table <dbl>, price <int>, x <dbl>,
## # y <dbl>, z <dbl>
filter(tibble, !is.na(cut)) #Select rows representing diamonds which do NOT have NA values for cut## # A tibble: 53,940 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## # ... with 53,930 more rows
arrange(tibble, color) #Arrange rows based on their color classification, default order is ascending ## # A tibble: 53,940 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Very Good D VS2 60.5 61 357 3.96 3.97 2.40
## 2 0.23 Very Good D VS1 61.9 58 402 3.92 3.96 2.44
## 3 0.26 Very Good D VS2 60.8 59 403 4.13 4.16 2.52
## 4 0.26 Good D VS2 65.2 56 403 3.99 4.02 2.61
## 5 0.26 Good D VS1 58.4 63 403 4.19 4.24 2.46
## 6 0.22 Premium D VS2 59.3 62 404 3.91 3.88 2.31
## 7 0.30 Premium D SI1 62.6 59 552 4.23 4.27 2.66
## 8 0.30 Ideal D SI1 62.5 57 552 4.29 4.32 2.69
## 9 0.30 Ideal D SI1 62.1 56 552 4.30 4.33 2.68
## 10 0.24 Very Good D VVS1 61.5 60 553 3.97 4.00 2.45
## # ... with 53,930 more rows
arrange(tibble, desc(color)) #Arrange rows based on their color classification, specifying in descending order## # A tibble: 53,940 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 2 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 3 0.30 Good J SI1 64.0 55 339 4.25 4.28 2.73
## 4 0.23 Ideal J VS1 62.8 56 340 3.93 3.90 2.46
## 5 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 6 0.30 Good J SI1 63.4 54 351 4.23 4.29 2.70
## 7 0.30 Good J SI1 63.8 56 351 4.23 4.26 2.71
## 8 0.30 Very Good J SI1 62.7 59 351 4.21 4.27 2.66
## 9 0.31 Very Good J SI1 59.4 62 353 4.39 4.43 2.62
## 10 0.31 Very Good J SI1 58.1 62 353 4.44 4.47 2.59
## # ... with 53,930 more rows
arrange(tibble, desc(color), desc(carat)) #Arrange rows based first on their color classification, specifying in descending order, and then within the same color classification, ordered by their carat value, specifying in descending order## # A tibble: 53,940 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 5.01 Fair J I1 65.5 59 18018 10.74 10.54 6.98
## 2 4.50 Fair J I1 65.8 58 18531 10.23 10.16 6.72
## 3 4.01 Premium J I1 62.5 62 15223 10.02 9.94 6.24
## 4 3.51 Premium J VS2 62.5 59 18701 9.66 9.63 6.03
## 5 3.11 Fair J I1 65.9 57 9823 9.15 9.02 5.98
## 6 3.01 Ideal J SI2 61.7 58 16037 9.25 9.20 5.69
## 7 3.01 Ideal J I1 65.4 60 16538 8.99 8.93 5.86
## 8 3.01 Premium J SI2 60.7 59 18710 9.35 9.22 5.64
## 9 3.01 Premium J SI2 59.7 58 18710 9.41 9.32 5.59
## 10 3.00 Good J SI2 59.3 64 14918 9.32 9.19 5.50
## # ... with 53,930 more rows
mutate(tibble, euro_price = price*0.9) #Create new variable euro_price based on existing price variable## # A tibble: 53,940 × 11
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## # ... with 53,930 more rows, and 1 more variables: euro_price <dbl>
summarize(tibble, mean(carat)) #Summarizes mean statistic for carat variable## # A tibble: 1 × 1
## `mean(carat)`
## <dbl>
## 1 0.7979397
grouped <- group_by(tibble, cut) #Group dataset by cut classification and assign to object
summarize(grouped, count = n(), average_price = mean(price)) #Compute a count and average_price value for each cut classification, using the grouped data## # A tibble: 5 × 3
## cut count average_price
## <ord> <int> <dbl>
## 1 Fair 1610 4358.758
## 2 Good 4906 3928.864
## 3 Very Good 12082 3981.760
## 4 Premium 13791 4584.258
## 5 Ideal 21551 3457.542
summarize(grouped, no_unique_prices = n_distinct(price)) #Compute the no. of unique prices for each cut classification, using the grouped data## # A tibble: 5 × 2
## cut no_unique_prices
## <ord> <int>
## 1 Fair 1267
## 2 Good 3086
## 3 Very Good 5840
## 4 Premium 6014
## 5 Ideal 7281