Big Data Day LA 2015 - Using data visualization to find patterns in multidimensional data by Amelia McNamara of UCLA
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The document describes the use of various R libraries to analyze and visualize a sample dataset of diamonds. Key operations include data exploration with the ggplot2, dplyr, and gpairs libraries, along with animations using the tourr library and user interactions with the nullabor library. Additionally, it showcases the structure of the diamonds dataset, which includes variables such as carat, cut, color, clarity, depth, and price.
![library(ggplot2)
data(diamonds)
set.seed(123)
diamonds <- diamonds[sample(500),]
str(diamonds)
## 'data.frame': 500 obs. of 10 variables:
## $ carat : num 0.7 0.32 0.7 0.7 0.86 0.23 0.71 0.9 0.7 0.72 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 3 4 2 5 4 3 4 1 3 5 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 3 6 2 5 3 5 3 7 2 1 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 5 3 4 3 2 5 5 4 4 3 ...
## $ depth : num 60 61 64.1 61.4 59.3 61 61.8 65 62.9 60.8 ...
## $ table : num 57 59 59 56 62 57 59 56 57 57 ...
## $ price : int 2767 554 2777 2815 2818 353 2790 2815 2793 2782 ...
## $ x : num 5.8 4.39 5.64 5.72 6.36 3.94 5.73 6.08 5.66 5.76 ...
## $ y : num 5.87 4.36 5.59 5.77 6.22 3.96 5.69 6.04 5.69 5.75 ...
## $ z : num 3.5 2.67 3.6 3.53 3.73 2.41 3.53 3.94 3.57 3.5 ...](https://image.slidesharecdn.com/usingdatavisualizationtofindpatternsinmultidimensionaldata-150724043312-lva1-app6892/85/Big-Data-Day-LA-2015-Using-data-visualization-to-find-patterns-in-multidimensional-data-by-Amelia-McNamara-of-UCLA-5-320.jpg)
![decrypt("OlCE bQTQ Aw GWPATAWw qd")
## [1] "True data in position 17"](https://image.slidesharecdn.com/usingdatavisualizationtofindpatternsinmultidimensionaldata-150724043312-lva1-app6892/85/Big-Data-Day-LA-2015-Using-data-visualization-to-find-patterns-in-multidimensional-data-by-Amelia-McNamara-of-UCLA-19-320.jpg)
Big Data Day LA 2015 - Using data visualization to find patterns in multidimensional data by Amelia McNamara of UCLA
- 5. library(ggplot2) data(diamonds) set.seed(123) diamonds <- diamonds[sample(500),] str(diamonds) ## 'data.frame': 500 obs. of 10 variables: ## $ carat : num 0.7 0.32 0.7 0.7 0.86 0.23 0.71 0.9 0.7 0.72 ... ## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 3 4 2 5 4 3 4 1 3 5 ... ## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 3 6 2 5 3 5 3 7 2 1 ... ## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 5 3 4 3 2 5 5 4 4 3 ... ## $ depth : num 60 61 64.1 61.4 59.3 61 61.8 65 62.9 60.8 ... ## $ table : num 57 59 59 56 62 57 59 56 57 57 ... ## $ price : int 2767 554 2777 2815 2818 353 2790 2815 2793 2782 ... ## $ x : num 5.8 4.39 5.64 5.72 6.36 3.94 5.73 6.08 5.66 5.76 ... ## $ y : num 5.87 4.36 5.59 5.77 6.22 3.96 5.69 6.04 5.69 5.75 ... ## $ z : num 3.5 2.67 3.6 3.53 3.73 2.41 3.53 3.94 3.57 3.5 ...
- 8. library(dplyr) select(diamonds, carat, depth, table) %>% plot()
- 12. select(diamonds, carat, cut, color, depth) %>% gpairs(outer.rot=c(90, 0))
- 15. library(tourr) select(diamonds, carat, depth, table, x, y, z) %>% animate(grand_tour(), display = display_x
- 18. library(nullabor) library(ggplot2) qplot(carat, depth, data=diamonds) %+% lineup(null_permute('carat'), diamonds) + facet_wrap(~.sample) ## decrypt("OlCE bQTQ Aw GWPATAWw qd")
- 19. decrypt("OlCE bQTQ Aw GWPATAWw qd") ## [1] "True data in position 17"