Computer programming as a form of problem solving
library(tidyverse)
Professor X from X-Men (the Patrick Stewart version, not James Mcavoy)
Computers are not mind-reading machines. They are very efficient at certain tasks, and can perform calculations thousands of times faster than any human. But they are also very dumb: they can only do what you tell them to do. If you are not explicit about what you want the computer to do, or you misspeak and tell the computer to do the wrong thing, it will not correct you.
In order to translate your goal for the program into clear instructions for the computer, you need to break the problem down into a set of smaller, discrete chunks that can be followed by the computer (and also by yourself/other humans).
Decomposing problems using diamonds
library(tidyverse)
glimpse(diamonds)## Observations: 53,940
## Variables: 10
## $ carat <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, ...
## $ cut <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very G...
## $ color <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, ...
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI...
## $ depth <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, ...
## $ table <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54...
## $ price <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339,...
## $ x <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, ...
## $ y <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, ...
## $ z <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, ...The diamonds dataset contains prices and other attributes of almost 54,000 diamonds. Let’s answer the following questions by decomposing the problem into a series of discrete steps we can tell R to follow.
What is the average price of an ideal cut diamond?
Think about what we need to have the computer do to answer this question:
- First we need to identify the input, or the data we’re going to analyze.
- Next we need to select only the observations which are ideal cut diamonds.
- Finally we need to calculate the average value, or mean, of price.
Here’s how we tell the computer to do this:
data("diamonds")
diamonds_ideal <- filter(diamonds, cut == "Ideal")
summarize(diamonds_ideal, avg_price = mean(price))## # A tibble: 1 x 1
## avg_price
## <dbl>
## 1 3457.542The first line of code copies the diamonds data frame from the hard drive into memory so we can actively work with it. The second line creates a new data frame called diamonds_ideal that only contains the observations in diamonds which are ideal cut diamonds. The third line summarizes the new data frame and calculates the mean value for the price variable.
What is the average price of a diamond for each cut?
Exercise: decompose the question into a discrete set of tasks to complete using R.
Click for the solution
- First we need to identify the input, or the data we’re going to analyze.
- Next we need to group the observations together by their value for
cut, so we can make separate calculations for each category. - Finally we need to calculate the average value, or mean, of price for each cut of diamond.
Here’s how we tell the computer to do this:
data("diamonds")
diamonds_cut <- group_by(diamonds, cut)
summarize(diamonds_cut, avg_price = mean(price))## # A tibble: 5 x 2
## cut avg_price
## <ord> <dbl>
## 1 Fair 4358.758
## 2 Good 3928.864
## 3 Very Good 3981.760
## 4 Premium 4584.258
## 5 Ideal 3457.542What is the average carat size and price for each cut of “I” colored diamonds?
Exercise: decompose the question into a discrete set of tasks to complete using R.
Click for the solution
- Use
diamondsas the input - Filter
diamondsto only keep observations where the color is rated as “I” - Group the filtered
diamondsdata frame by cut - Summarize the grouped and filtered
diamondsdata frame by calculating the average carat size and price
data("diamonds")
diamonds_i <- filter(diamonds, color == "I")
diamonds_i_group <- group_by(diamonds_i, cut)
summarize(
diamonds_i_group,
carat = mean(carat),
price = mean(price)
)## # A tibble: 5 x 3
## cut carat price
## <ord> <dbl> <dbl>
## 1 Fair 1.1980571 4685.446
## 2 Good 1.0572222 5078.533
## 3 Very Good 1.0469518 5255.880
## 4 Premium 1.1449370 5946.181
## 5 Ideal 0.9130291 4451.970Session Info
devtools::session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.4.3 (2017-11-30)
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## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2018-04-02## Packages -----------------------------------------------------------------## package * version date source
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## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)This work is licensed under the CC BY-NC 4.0 Creative Commons License.