Working with Colors

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setwd("C:/Users/kk/Downloads/edu/DataScienceJHU/DataScienceWithR/04_Exploratory_Data_Analysis/workspace")
library(ggplot2)
library(swirl)

| Hi! Type swirl() when you are ready to begin.

swirl()

| Welcome to swirl! Please sign in. If you've been here before, use the same name as
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What shall I call you? Krishnakanth Allika

| Please choose a course, or type 0 to exit swirl.

1: Exploratory Data Analysis
2: Take me to the swirl course repository!

Selection: 1

| Please choose a lesson, or type 0 to return to course menu.

 1: Principles of Analytic Graphs   2: Exploratory Graphs             
 3: Graphics Devices in R           4: Plotting Systems               
 5: Base Plotting System            6: Lattice Plotting System        
 7: Working with Colors             8: GGPlot2 Part1                  
 9: GGPlot2 Part2                  10: GGPlot2 Extras                 
11: Hierarchical Clustering        12: K Means Clustering             
13: Dimension Reduction            14: Clustering Example             
15: CaseStudy

Selection: 7

| Attempting to load lesson dependencies...

| Package ‘jpeg’ loaded correctly!

| Package ‘RColorBrewer’ loaded correctly!

| Package ‘datasets’ loaded correctly!

| | 0%

| Working_with_Colors. (Slides for this and other Data Science courses may be found at
| github https://github.com/DataScienceSpecialization/courses/. If you care to use
| them, they must be downloaded as a zip file and viewed locally. This lesson
| corresponds to 04_ExploratoryAnalysis/Colors.)

...

|= | 1%
| This lesson is about using colors in R. It really supplements the lessons on
| plotting with the base and lattice packages which contain functions that are able to
| take the argument col. We'll discuss ways to set this argument more colorfully.

...

|== | 3%
| Of course, color choice is secondary to your data and how you analyze it, but
| effectively using colors can enhance your plots and presentations, emphasizing the
| important points you're trying to convey.

...

|=== | 4%
| The motivation for this lesson is that the default color schemes for most plots in R
| are not optimal. Fortunately there have been recent developments to improve the
| handling and specification of colors in plots and graphs. We'll cover some functions
| in R as well as in external packages that are very handy. If you know how to use
| some of these then you'll have more options when you create your displays.

...

|==== | 6%
| We'll begin with a motivating example - a typical R plot using 3 default colors.

image

...

|====== | 7%
| According to the plot, what is color 2?

1: Blue
2: Empty black circles
3: Red
4: Green

Selection: 3

| Nice work!

|======= | 9%
| So these are the first 3 default values. If you were plotting and just specified
| col=c(1:3) as one of your arguments, these are colors you'd get. Maybe you like
| them, but they might not be the best choice for your application.

...

|======== | 10%
| To show you some options, here's a display of two color palettes that come with the
| grDevices package available to you. The left shows you some colors from the function
| heat.colors. Here low values are represented in red and as the values increase the
| colors move through yellow towards white. This is consistent with the physical
| properties of fire. The right display is from the function topo.colors which uses
| topographical colors ranging from blue (low values) towards brown (higher values).

image

...

|========= | 12%
| So we'll first discuss some functions that the grDevices package offers. The
| function colors() lists the names of 657 predefined colors you can use in any
| plotting function. These names are returned as strings. Run the R command sample
| with colors() as its first argument and 10 as its second to give you an idea of the
| choices you have.

sample(colors(),10)
[1] "gray1" "darkorchid2" "blue3" "darkorchid3" "gray10"
[6] "firebrick1" "magenta3" "gray75" "lemonchiffon4" "rosybrown3"

| Great job!

|========== | 13%
| We see a lot of variety in the colors, some of which are names followed by numbers
| indicating that there are multiple forms of that particular color.

...

|=========== | 14%
| So you're free to use any of these 600+ colors listed by the colors function.
| However, two additional functions from grDevices, colorRamp and colorRampPalette,
| give you more options. Both of these take color names as arguments and use them as
| "palettes", that is, these argument colors are blended in different proportions to
| form new colors.

...

|============ | 16%
| The first, colorRamp, takes a palette of colors (the arguments) and returns a
| function that takes values between 0 and 1 as arguments. The 0 and 1 correspond to
| the extremes of the color palette. Arguments between 0 and 1 return blends of these
| extremes.

...

|============= | 17%
| Let's see what this means. Assign to the variable pal the output of a call to
| colorRamp with the single argument, c("red","blue").

pal<-colorRamp(c("red","blue"))

| You are amazing!

|=============== | 19%
| We don't see any output, but R has created the function pal which we can call with a
| single argument between 0 and 1. Call pal now with the argument 0.

pal(0)
[,1] [,2] [,3]
[1,] 255 0 0

| You are quite good my friend!

|================ | 20%
| We see a 1 by 3 array with 255 as the first entry and 0 in the other 2. This 3 long
| vector corresponds to red, green, blue (RGB) color encoding commonly used in
| televisions and monitors. In R, 24 bits are used to represent colors. Think of these
| 24 bits as 3 sets of 8 bits, each of which represents an intensity for one of the
| colors red, green, and blue.

...

|================= | 22%
| The 255 returned from the pal(0) call corresponds to the largest possible number
| represented with 8 bits, so the vector (255,0,0) contains only red (no green or
| blue), and moreover, it's the highest possible value of red.

...

|================== | 23%
| Given that you created pal with the palette containing "red" and "blue", what color
| do you think will be represented by the vector that pal(1) returns? Recall that pal
| will only take arguments between 0 and 1, so 1 is the largest argument you can pass
| it.

1: blue
2: red
3: green
4: yellow

Selection: 1

| Keep up the great work!

|=================== | 25%
| Check your answer now by calling pal with the argument 1.

pal(1)
[,1] [,2] [,3]
[1,] 0 0 255

| Excellent work!

|==================== | 26%
| You see the vector (0,0,255) which represents the highest intensity of blue. What
| vector do you think the call pal(.5) will return?

1: (0,255,0)
2: (255,255,255)
3: (127.5,0,127.5)
4: (255,0,255)

Selection: 3

| You got it!

|===================== | 28%
| The function pal can take more than one argument. It returns one 3-long (or 4-long,
| but more about this later) vector for each argument. To see this in action, call pal
| with the argument seq(0,1,len=6).

pal(seq(0,1,len=6))

     [,1] [,2] [,3]  
[1,]  255    0    0  
[2,]  204    0   51  
[3,]  153    0  102  
[4,]  102    0  153  
[5,]   51    0  204  
[6,]    0    0  255

| Nice work!

|====================== | 29%
| Six vectors (each of length 3) are returned. The i-th vector is identical to output
| that would be returned by the call pal(i/5) for i=0,...5. We see that the i-th row
| (for i=1,...6) differs from the (i-1)-st row in the following way. Its red entry is
| 51 = 255/5 points lower and its blue entry is 51 points higher.

...

|======================= | 30%
| So pal creates colors using the palette we specified when we called colorRamp. In
| this example none of pal's outputs will ever contain green since it wasn't in our
| initial palette.

...

|========================= | 32%
| We'll turn now to colorRampPalette, a function similar to colorRamp. It also takes a
| palette of colors and returns a function. This function, however, takes integer
| arguments (instead of numbers between 0 and 1) and returns a vector of colors each
| of which is a blend of colors of the original palette.

...

|========================== | 33%
| The argument you pass to the returned function specifies the number of colors you
| want returned. Each element of the returned vector is a 24 bit number, represented
| as 6 hexadecimal characters, which range from 0 to F. This set of 6 hex characters
| represents the intensities of red, green, and blue, 2 characters for each color.

...

|=========================== | 35%
| To see this better, assign to the variable p1 the output of a call to
| colorRampPalette with the single argument, c("red","blue"). We'll compare it to our
| experiments using colorRamp.

p1<-colorRampPalette(c("red","blue"))

| You got it!

|============================ | 36%
| Now call p1 with the argument 2.

p1(2)
[1] "#FF0000" "#0000FF"

| All that hard work is paying off!

|============================= | 38%
| We see a 2-long vector is returned. The first entry FF0000 represents red. The FF is
| hexadecimal for 255, the same value returned by our call pal(0). The second entry
| 0000FF represents blue, also with intensity 255.

...

|============================== | 39%
| Now call p1 with the argument 6. Let's see if we get the same result as we did when
| we called pal with the argument seq(0,1,len=6).

p1(6)
[1] "#FF0000" "#CC0033" "#990066" "#650099" "#3200CC" "#0000FF"

| You are amazing!

|=============================== | 41%
| Now we get the 6-long vector (FF0000, CC0033, 990066, 650099, 3200CC, 0000FF). We
| see the two ends (FF0000 and 0000FF) are consistent with the colors red and blue.
| How about CC0033? Type 0xcc or 0xCC at the command line to see the decimal
| equivalent of this hex number. You must include the 0 before the x to specify that
| you're entering a hexadecimal number.

0xCC
[1] 204

| You are amazing!

|================================ | 42%
| So 0xCC equals 204 and we can easily convert hex 33 to decimal, as in
| 0x33=3*16+3=51. These were exactly the numbers we got in the second row returned
| from our call to pal(seq(0,1,len=6)). We see that 4 of the 6 numbers agree with our
| earlier call to pal. Two of the 6 differ slightly.

...

|================================= | 43%
| We can also form palettes using colors other than red, green and blue. Form a
| palette, p2, by calling colorRampPalette with the colors "red" and "yellow".
| Remember to concatenate them into a single argument.

p2<-colorRampPalette(c("red","yellow"))

| You are really on a roll!

|=================================== | 45%
| Now call p2 with the argument 2. This will show us the two extremes of the blends of
| colors we'll get.

p2(2)
[1] "#FF0000" "#FFFF00"

| Excellent work!

|==================================== | 46%
| Not surprisingly the first color we see is FF0000, which we know represents red. The
| second color returned, FFFF00, must represent yellow, a combination of full
| intensity red and full intensity green. This makes sense, since yellow falls between
| red and green on the color wheel as we see here. (We borrowed this image from
| lucaskrech.com.)

image

...

|===================================== | 48%
| Let's now call p2 with the argument 10. This will show us how the two extremes, red
| and yellow, are blended together.

p2(10)
[1] "#FF0000" "#FF1C00" "#FF3800" "#FF5500" "#FF7100" "#FF8D00" "#FFAA00" "#FFC600"
[9] "#FFE200" "#FFFF00"

| Your dedication is inspiring!

|====================================== | 49%
| So we see the 10-long vector. For each element, the red component is fixed at FF,
| and the green component grows from 00 (at the first element) to FF (at the last).

...

|======================================= | 51%
| This is all fine and dandy but you're probably wondering when you can see how all
| these colors show up in a display. We copied some code from the R documentation
| pages (color.scale if you're interested) and created a function for you, showMe.
| This takes as an argument, a color vector, which as you know, is precisely what
| calls to p1 and p2 return to you. Call showMe now with p1(20).

showMe(p1(20))

image

| That's the answer I was looking for.

|======================================== | 52%
| We see the interpolated palette here. Low values in the lower left corner are red
| and as you move to the upper right, the colors move toward blue. Now call showMe
| with p2(20) as its argument.

showMe(p2(20))

image

| You're the best!

|========================================= | 54%
| Here we see a similar display, the colors moving from red to yellow, the base colors
| of our p2 palette. For fun, see what p2(2) looks like using showMe.

showMe(p2(2))

image

| You are really on a roll!

|========================================== | 55%
| A much more basic pattern, simple but elegant.

...

|============================================ | 57%
| We mentioned before that colorRamp (and colorRampPalette) could return a 3 or 4 long
| vector of colors. We saw 3-long vectors returned indicating red, green, and blue
| intensities. What would the 4th entry be?

...

|============================================= | 58%
| We'll answer this indirectly. First, look at the function p1 that colorRampPalette
| returned to you. Just type p1 at the command prompt.

p1
function (n)
{
x <- ramp(seq.int(0, 1, length.out = n))
if (ncol(x) == 4L)
rgb(x[, 1L], x[, 2L], x[, 3L], x[, 4L], maxColorValue = 255)
else rgb(x[, 1L], x[, 2L], x[, 3L], maxColorValue = 255)
}

<bytecode: 0x00000174e0c71940>

<environment: 0x00000174dbdd5a00>

| Keep up the great work!

|============================================== | 59%
| We see that p1 is a short function with one argument, n. The argument n is used as
| the length in a call to the function seq.int, itself an argument to the function
| ramp. We can infer that ramp is just going to divide the interval from 0 to 1 into n
| pieces.

...

|=============================================== | 61%
| The heart of p1 is really the call to the function rgb with either 4 or 5 arguments.
| Use the ?fun construct to look at the R documentation for rgb now.

?rgb

| You got it!

|================================================ | 62%
| We see that rgb is a color specification function that can be used to produce any
| color with red, green, blue proportions. We see the maxColorValue is 1 by default,
| so if we called rgb with values for red, green and blue, we would specify numbers at
| most 1 (assuming we didn't change the default for maxColorValue). According to the
| documentation, what is the maximum number of arguments rgb can have?

1: 6
2: 4
3: 5
4: 3

Selection: 1

| All that practice is paying off!

|================================================= | 64%
| So the fourth argument is alpha which can be a logical, i.e., either TRUE or FALSE,
| or a numerical value. Create the function p3 now by calling colorRampPalette with
| the colors blue and green (remember to concatenate them into a single argument) and
| setting the alpha argument to .5.

p3<-colorRampPalette(c("blue","green"),alpha=0.5)

| You are really on a roll!

|================================================== | 65%
| Now call p3 with the argument 5.

p3(5)
[1] "#0000FFFF" "#003FBFFF" "#007F7FFF" "#00BF3FFF" "#00FF00FF"

| Perseverance, that's the answer.

|=================================================== | 67%
| We see that in the 5-long vector that the call returned, each element has 32 bits, 4
| groups of 8 bits each. The last 8 bits represent the value of alpha. Since it was
| NOT ZERO in the call to colorRampPalette, it gets the maximum FF value. (The same
| result would happen if alpha had been set to TRUE.) When it was 0 or FALSE (as in
| previous calls to colorRampPalette) it was given the value 00 and wasn't shown. The
| leftmost 24 bits of each element are the same RGB encoding we previously saw.

...

|==================================================== | 68%
| So what is alpha? Alpha represents an opacity level, that is, how transparent should
| the colors be. We can add color transparency with the alpha parameter to calls to
| rgb. We haven't seen any examples of this yet, but we will now.

...

|====================================================== | 70%
| We generated 1000 random normal pairs for you in the variables x and y. We'll plot
| them in a scatterplot by calling plot with 4 arguments. The variables x and y are
| the first 2. The third is the print character argument pch. Set this equal to 19
| (filled circles). The final argument is col which should be set equal to a call to
| rgb. Give rgb 3 arguments, 0, .5, and .5.

plot(x,y,pch=19,col=rgb(0,0.5,0.5))

image

| Your dedication is inspiring!

|======================================================= | 71%
| Well this picture is okay for a scatterplot, a nice mix of blue and green, but it
| really doesn't tell us too much information in the center portion, since the points
| are so thick there. We see there are a lot of points, but is one area more filled
| than another? We can't really discriminate between different point densities. This
| is where the alpha argument can help us. Recall your plot command (use the up arrow)
| and add a 4th argument, .3, to the call to rgb. This will be our value for alpha.

plot(x,y,pch=19,col=rgb(0,0.5,0.5,0.3))

image

| You are amazing!

|======================================================== | 72%
| Clearly this is better. It shows us where, specifically, the densest areas of the
| scatterplot really are.

...

|========================================================= | 74%
| Our last topic for this lesson is the RColorBrewer Package, available on CRAN, that
| contains interesting and useful color palettes, of which there are 3 types,
| sequential, divergent, and qualitative. Which one you would choose to use depends on
| your data.

...

|========================================================== | 75%
| Here's a picture of the palettes available from this package. The top section shows
| the sequential palettes in which the colors are ordered from light to dark. The
| divergent palettes are at the bottom. Here the neutral color (white) is in the
| center, and as you move from the middle to the two ends of each palette, the colors
| increase in intensity. The middle display shows the qualitative palettes which look
| like collections of random colors. These might be used to distinguish factors in
| your data.

image

...

|=========================================================== | 77%
| These colorBrewer palettes can be used in conjunction with the colorRamp() and
| colorRampPalette() functions. You would use colors from a colorBrewer palette as
| your base palette,i.e., as arguments to colorRamp or colorRampPalette which would
| interpolate them to create new colors.

...

|============================================================ | 78%
| As an example of this, create a new object, cols by calling the function brewer.pal
| with 2 arguments, 3 and "BuGn". The string "BuGn" is the second last palette in the
| sequential display. The 3 tells the function how many different colors we want.

cols<-brewer.pal(3,"BuGn")

| That's the answer I was looking for.

|============================================================= | 80%
| Use showMe to look at cols now.

showMe(cols)

image

| Keep up the great work!

|============================================================== | 81%
| We see 3 colors, mixes of blue and green. Now create the variable pal by calling
| colorRampPalette with cols as its argument.

pal<-colorRampPalette(cols)

| All that hard work is paying off!

|================================================================ | 83%
| The call showMe(pal(3)) would be identical to the showMe(cols) call. So use showMe
| to look at pal(20).

showMe(pal(20))

image

| Keep up the great work!

|================================================================= | 84%
| Now we can use the colors in pal(20) to display topographic information on
| Auckland's Maunga Whau Volcano. R provides this information in a matrix called
| volcano which is included in the package datasets. Call the R function image with
| volcano as its first argument and col set equal to pal(20) as its second.

image(volcano,col=pal(20))

image

| Great job!

|================================================================== | 86%
| We see that the colors here of the sequential palette clue us in on the topography.
| The darker colors are more concentrated than the lighter ones. Just for fun, recall
| your last command calling image and instead of pal(20), use p1(20) as the second
| argument.

image(volcano,col=p1(20))

image

| Your dedication is inspiring!

|=================================================================== | 87%
| Not as nice a picture since the palette isn't as well suited to this data, but
| that's okay. It's review time!!!!

...

|==================================================================== | 88%
| True or False? Careful use of colors in plots/maps/etc. can make it easier for the
| reader to understand what points you're trying to convey.

1: False
2: True

Selection: 2

| You got it!

|===================================================================== | 90%
| Which of the following is an R package that provides color palettes for sequential,
| categorical, and diverging data?

1: RColorBluer
2: RColorBrewer
3: RColorStewer
4: RColorVintner

Selection: 2

| Keep working like that and you'll get there!

|====================================================================== | 91%
| True or False? The colorRamp and colorRampPalette functions can be used in
| conjunction with color palettes to connect data to colors.

1: False
2: True

Selection: 2

| You are really on a roll!

|======================================================================= | 93%
| True or False? Transparency can NEVER be used to clarify plots with many points

1: True
2: False

Selection: 2

| Excellent work!

|========================================================================= | 94%
| True or False? The call p7 <- colorRamp("red","blue") would work (i.e., not
| generate an error).

1: True
2: False

Selection: 2

| Excellent job!

|========================================================================== | 96%
| True or False? The function colors returns only 10 colors.

1: False
2: True

Selection: 1

| All that practice is paying off!

|=========================================================================== | 97%
| Transparency is determined by which parameter of the rgb function?

1: beta
2: gamma
3: it's all Greek to me
4: delta
5: alpha

Selection: 5

| You got it right!

|============================================================================ | 99%
| Congratulations! We hope this lesson didn't make you see red. We're green with envy
| that you blue through it.

...

|=============================================================================| 100%
| Would you like to receive credit for completing this course on Coursera.org?

1: Yes
2: No

Selection: 1
What is your email address? [email protected]
What is your assignment token? xXxXxxXXxXxxXXXx
Grade submission succeeded!

| Your dedication is inspiring!

| You've reached the end of this lesson! Returning to the main menu...

| Please choose a course, or type 0 to exit swirl.

1: Exploratory Data Analysis
2: Take me to the swirl course repository!

Selection: 0

| Leaving swirl now. Type swirl() to resume.

rm(list=ls())

Last updated 2020-05-08 19:05:14.923558 IST

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