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2016-r-rtgram

R stuff for instagram vancouver 2016

2017-09-08

See http://rolandtanglao.com/2017/09/08/p1-faceted-r-ggjoy-joyplot-with-colours-and-fill-colour/

2017-09-07

with colours!

colour_named_vector <-
setNames(as.character(average_colour_ig_van_jan2016$colourname), 
         average_colour_ig_van_jan2016$colourname)
ggplot(average_colour_ig_van_jan2016,   
  aes(x=hour, y= colourname , height=..density..))+
  geom_joy(scale=16, aes(colour=colour_named_vector)) +
  scale_colour_manual(values=colour_named_vector)

Output:

average colour instagram vancouver jauary 2016 aes-x-hour-y-colourname, geom_joy-scale16

ggplot(average_colour_ig_van_jan2016, aes(x=hour, y= colourname , height=..density..))+
geom_joy(scale=16)

Output

2017-09-04

Sucess :-)

See http://rolandtanglao.com/2017/09/04/p2-remove-5-from-each-one-hour-period/

2017-09-03

Chromeless i.e. no legend

ggplot(gt5_h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)+
scale_y_continuous(limits = c(0,0.0012))+
theme_void()+theme(legend.position = 'none') +
theme(strip.background = element_blank(),strip.text.x = element_blank())

Output

2017-09-02

III. same as II. below but with 0.0012 instead of 0.002

see http://rolandtanglao.com/2017/09/02/p2-0.0012version-is-better-density-plot-corrupted-for-art-copy/

II. Hex Colours + Greater than 5 occurences of colourname + truncate 0.002: Simple density plot for hour 0 i.e. midnight to 12:59a.m. with continuous colours from plotrix i.e. 600 colours

ggplot(gt5_h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)+
scale_y_continuous(limits = c(0,0.002))

Output

I. Hex Colours + Greater than 5 occurences of colourname + truncate 0.005: Simple density plot for hour 0 i.e. midnight to 12:59a.m. with continuous colours from plotrix i.e. 600 colours

ggplot(gt5_h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)+
scale_y_continuous(limits = c(0,0.035))

Output

2017-09-01

Hex Colours + Greater than 5 occurences of colourname: Simple density plot for hour 0 i.e. midnight to 12:59a.m. with continuous colours from plotrix i.e. 600 colours

# use hex colours
colour_hex_strings_all = sapply(gt5_h00_600colours$sixhundred_colourint, function(x){
  function(x){
    sprintf(“#%6.6X”, x)})
             
colour_named_vector <- setNames(as.character(colour_hex_strings_all), colour_hex_strings_all)
ggplot(gt5_h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)

Output

Greater than 5 occurences of colourname: Simple density plot for hour 0 i.e. midnight to 12:9a.m. with continuous colours from plotrix i.e. 600 colours

# let's remove <= 5
gt5_h00_600colours <- average_colour_ig_van_jan2016 %>%
filter(hour=="00") %>%
add_count(colourname) %>%
filter(n >5) %>%
rowwise() %>%
mutate(sixhundred_colourint = getnumericColour(colourname))

colour_named_vector <- setNames(as.character(gt5_h00_600colours$sixhundred_colourint), gt5_h00_600colours$sixhundred_colourint)

ggplot(gt5_h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)

Output:

Simple density plot for hour 0 i.e. midnight to 12:9a.m. with continuous colours from plotrix i.e. 600 colours

# use 600 values of 24 bit colour
library(tidyverse)
library(plotrix)

getnumericColour <-
  function(colorname) {
    colour_matrix=col2rgb(colorname)
    return(as.numeric(colour_matrix[1,1]) * 65536 +
             as.numeric(colour_matrix[2,1]) * 256 +
             as.numeric(colour_matrix[3,1]))
  }
csv_url = 
"https://raw.githubusercontent.com/rtanglao/2016-r-rtgram/master/JANUARY2016/january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour-colourname.csv"
average_colour_ig_van_jan2016 = read_csv(csv_url)

h00_600colours <- average_colour_ig_van_jan2016 %>%
filter(hour=="00") %>%
rowwise() %>%
mutate(sixhundred_colourint = getnumericColour(colourname))

colour_named_vector <- setNames(as.character(h00_600colours$sixhundred_colourint), h00_600colours$sixhundred_colourint)

ggplot(h00_600colours, aes(x=colour))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)

Output:

August 29, 2017

  • 1. simple plot for hour 0 (i.e. 00:00 to 00:59)
no00 <- singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname %>%
filter(hour=="00") %>%
add_count(colourname) %>%
filter(nn != 1)
colour_named_vector <- setNames(no00$colourname, no00$colourname)
ggplot(no00, aes(x=colourname))+
  geom_density(mapping = aes(colour= colour_named_vector))+
  scale_colour_manual(values=colour_named_vector)

Output:

August 28, 2017

Remove singleton colournames and make a colour vector and plot them, need mapping in geom_density using colour vector and colour vector in scale_colour_manual and try a faceted plot

  • 1. Remove singleton colournames
singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname <-
average_colour_ig_van_jan2016_colourname %>%
add_count(colourname) %>%
filter(n != 1)
nrow(singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname)
[1] 146463
  • 2. naive plot
ggplot(singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname, aes(x=colourname))+
geom_density()

Result:

https://github.com/rtanglao/2016-r-rtgram/blob/master/JANUARY2016/ggplot(singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname%2C%20aes(x%3Dcolourname))%2Bgeom_density().png

  • 3. Make colour vector
# Don't need as.character() since it's already a character
colour_named_vector <- setNames
(singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname$colourname, singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname$colourname)
  • 4. Successful plot with graph "chrome"
ggplot(
singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname, 
aes(x=colourname))+
geom_density(mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)

Output:

  • 5. Successful plot without graph "chrome" i.e. theme_void() + remove legend
ggplot(
singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname,
aes(x=colourname))+
geom_density(
mapping = aes(colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)+
theme_void()+
theme(legend.position = 'none')+
theme(strip.background = element_blank(),strip.text.x = element_blank())

Output:

Zazzle 2100 x 1800 output:

  • 6. Faceted plot by hour
ggplot(
singleton_colours_removed_average_colour_ig_van_jan2016_with_colourname,
aes(x=colourname))+
geom_density(mapping = aes(
colour= colour_named_vector))+
scale_colour_manual(values=colour_named_vector)+
theme_void()+
theme(legend.position = 'none')+
theme(strip.background = element_blank(),strip.text.x = element_blank())+
facet_wrap(~ hour, nrow = 2)

Output:

AES maps but doesn't SET part 8888

  • 1. AES maps but doesn't SET part 8888 :-) the colour instead it maps the variable, in this case colourname, to a set of levels and the levels are mapped to a default colour palette i.e. the plot shows up not in the colours of colourname but in the default colour palette! Code:
ggplot(average_colour_ig_van_jan2016_colourname,
aes(colourname, colour=colourname)) + geom_density()

And here's how it looks ggplot(average_colour_ig_van_jan2016_colourname, aes(colourname, colour=colourname)) + geom_density()-mapping-colourname-instead-of-plotting-colourname<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

August 20, 2017

Blogged here: http://rolandtanglao.com/2017/08/20/p1-naive-density-plot-instagram-vancouver-average-colour-january-2016/

csv_url = 
"https://raw.githubusercontent.com/rtanglao/2016-r-rtgram/master/JANUARY2016/january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour-colourname.csv"
average_colour_ig_van_jan2016_colourname = read_csv(csv_url)
ggplot(average_colour_ig_van_jan2016_colourname,
aes(colourname)) + geom_density()
  • 2. And here is the output (I used R Studio to make the output PNG 9740 x 6020 px) on flickr (on github: pdf, png):

naive-density-plot-instagram-vancouver-jan2016-averagecolour-colourname<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

August 7, 2017

cd JANUARY2016
Rscript ../part2-create-csv-ig-van-average-colour-jan2016.R
  • 4. Create part 3 naive scatterplot, output file is: part3-naive-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour-colourname.png
Rscript ../part3-create-naive-scatterplot-colourname-hour.R
  • 5. Create part 4 with the dots of the scatterplot coloured like colourname i.e. geom_point uses the colour name literally (if you put the colour name in aesit will map the colour name to a level!) output file is: part4-colourname-aesthetic-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour-colourname.png
Rscript ../part4-create-colourname-with-colourname-aesthetic-scatterplot-hour.R

Here's the part 4 output:

July 24, 2017

Rscript ../average-colour-by-hour-ggjoy-from-csv.R 31-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv 
Error in FUN(X[[i]], ...) : need at least 2 data points
Calls: main ... <Anonymous> -> f -> <Anonymous> -> f -> vapply -> FUN
Execution halted
  • 2.But I have two data points! namely for example the first 6 rows I have 3 rows with indianred4
 colour                             id dayofweek.month.dayofmonth daynumber   unixtime  hour colourname
    <chr>                          <chr>                      <chr>     <int>      <int> <int>      <chr>
1 #546363 1174465369140103047_2176611536                   SunJan31        31 1454227203     0     gray37
2 #ACA8A8 1174465451560169285_2137478482                   SunJan31        31 1454227213     0   darkgray
3 #6B3434 1174465462824925338_2250967365                   SunJan31        31 1454227215     0 indianred4
4 #4B3E3E  1174465565628114803_177763144                   SunJan31        31 1454227227     0     gray26
5 #803A3A  1174465617924150379_361059564                   SunJan31        31 1454227233     0 indianred4
6 #704141 1174465676122308807_1537167607                   SunJan31        31 1454227240     0 indianred4

September 11, 2016

24 squares per day

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/24SQUARES-PER-DAY
  2. parallel Rscript ../../twenty-four-square-pie-chart-from-csv.R '{}' ::: ../??-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv
  3. mkdir TRIMMED
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: *.png
  5. cd TRIMMED
  6. ls -1 *.png >31pngs.txt
  7. gm montage -verbose -adjoin -tile 7x6 +frame +shadow +label -adjoin -geometry '1023x684+0+0<' null: null: null: null: null: @31pngs.txt null: null: null: null: null: null: ig-van-2016-one-top-colour-square-per-hour-01-31january2016-square-piechart.png

Here's how the 24 squares per day looks!

ig-van-2016-one-top-colour-square-per-hour-01-31january2016-square-piechart<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

Dataset for January 2016 on octopub

Put the january 1-31, 2016 github dataset up on octopub (which is on github!)

September 8, 2016

24 square plan

  1. for each day.csv
    1. loop over all 24 hours (do i really need a loop? probably not)
    2. get that hour's subset from the CSV file, average the subset, add the average to that hour's dataframe
    3. add colourname to the hour's dataframe and the graph the hour's dataframe

September 6, 2016

  1. What shall I do next besides the pull request for waffle and/or the github issue?:
    1. Maybe average colour over each day and then do a June 1-May 27, 2016 graphic?
    2. Maybe train a neural network with the likes (weight 0.5), comments (weight 1.0) with my instagram photos from 2014-2016?

September 5, 2016

Do 3000 first photos instagram vancouver average colour square pie charts for January 1-31, 2016

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/FIXED-WAFFLE-3000
  2. parallel Rscript ../../file-numphotos-square-piechart.R '{}' 3000 ::: ../*-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv
  3. mkdir TRIMMED
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: 3000*.png
  5. cd TRIMMED
  6. ls -1 3000*.png >31pngs.txt
  7. gm montage -verbose -adjoin -tile 7x6 +frame +shadow +label -adjoin -geometry '1023x308+0+0<' null: null: null: null: null: @31pngs.txt null: null: null: null: null: null: ig-van-2016-top3000-topcolour-sorted-3000-squares-01-31january2016-square-piechart.png

waffle() doesn't work if you have more than 25 levels in your square piechart

The solution which I still haven't tested:

From stack overflow repeating vector of letters:

letters658 = make.unique(rep(letters, length.out = 658), sep='') #use letters658 instead of LETTERS R constant

The above code makes up for the R constant LETTERS only having 26 levels when R has 657 colours (add 1 since waffle() starts at 'B' instead of 'A'). So having 657 letters will allow all R colours to be plotted safely instead of any colour beyond the first 26 being turned into 'not a number' i.e. NA.

September 1, 2016

Write a test case for waffle()

pseudo code: pass in number of squares as a command line argument, do (2500-1000)/2 + 1000 i.e. a binary search for where it breaks startg with 1750

August 31, 2016

Cumulative sum 1000

Algorithm: take 1st 2500 photos, get colournames, sort by descending frequency of colournames and then take the first 1000 photos to form a square pie chart graph of <=1000 squares

  1. /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/CUMULATIVE-SUM-1000
  2. mkdir TRIMMED
  3. ls -1 ../*-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../../cumulativesum-size0.1-first2500-void-square-piechart-from-csv.R
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: cumulative-sum*.png # results in 750x480 and 768x480 png
  5. cd TRIMMED; ls -1 >31pngs.txt
  6. gm montage -verbose -adjoin -tile 7x6 +frame +shadow +label -adjoin -geometry '768x480+0+0<' null: null: null: null: null: @31pngs.txt null: null: null: null: null: null: ig-van-2016-top2500-topcolour-sorted-1000-squares-01-31january2016-square-piechart.png
  7. gm montage -verbose -adjoin -tile 7x6 +frame +shadow +label -adjoin -geometry '750x480+0+0<' null: null: null: null: null: @31pngs.txt null: null: null: null: null: null: 750-ig-van-2016-top2500-topcolour-sorted-1000-squares-01-31january2016-square-piechart.png 768-ig-van-2016-top2500-topcolour-sorted-1000-squares-01-31january2016-square-piechart<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

Let's try 0.1 and divide by 5

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/DIV5-SIZE0.1
  2. mkdir TRIMMED
  3. ls -1 ../*-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../../div5-size0.1-first2500-void-square-piechart-from-csv.R
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: first2500-div5-size0.1-*.png

Let's try 0.1 and divide by 10

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/DIV10-SIZE0.1
  2. ls -1 ../*-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../../div10-size0.1-first2500-void-dquare-piechart-from-csv.R
  3. mkdir TRIMMED
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: first2500-div10-size0.1-square-piechart-colourname-*.png

Tried 0.1 it's better but still missing some datapoints

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016/SIZE0.1
  2. ls -1 ../*-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../../size0.1.-first2500-void-square-piechart-from-csv.R
  3. mkdir TRIMMED
  4. parallel convert -trim '{}' 'TRIMMED/{}' ::: first2500-size0*.png

August 30, 2016

Let's make 1000 square graphs

  1. use cumsum to compute a cumulative sum column ``sept01countcolourname$numphotos <- cumsum(sept01countcolourname$freq)```
  2. then use head with a conditional on the cumulative sum get first 2000 subset(sept01countcolourname, sept01countcolourname$numphotos <2001)

Let's make 2500 photos NOT 2501 31 square pie charts for january 2016

  • fixed bug changed 2501 to 2500!
  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016
  2. ``````ls -1 *-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../first2500-colourname-void-square-piechart-from-csv.R```
  3. parallel convert -trim '{}' 'FIRST2500-TRIMMED/{}' ::: first2500*.png

Creating 6 point square piechart

  1. Rscript first6-ig-van-01january2016-square-piechart.R
    output: 6datapoints-squarepiechart<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

August 28, 2016

1st2500:

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016
  2. ls -1 *-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../first2500-colourname-void-square-piechart-from-csv.R
  3. trim off white space: mkdir FIRST2500-TRIMMED
  4. parallel convert -trim '{}' 'FIRST2500-TRIMMED/{}' ::: first2500*.png citation: O. Tange (2011): GNU Parallel - The Command-Line Power Tool, ;login: The USENIX Magazine, February 2011:42-47. actually the above is wrong because convert -trim truncates white columns, ok then we should use 1026x475px not trim

31 faceted square piecharts just to see how bad the layout will be :-)

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016
  2. ../faceted-by-daynumber-colourname-void-square-piechart-from-csv.R january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv
  3. Use iron() to display all 31? Because it doesn't work, maybe because faceting isn't supported?: Error in layout_base(data, vars, drop = drop) : At least one layer must contain all variables used for facetting

August 14, 2016

Let's make 31 square pie charts for january 2016

  1. cd /Users/rtanglao/Dropbox/GIT/2016-r-rtgram/JANUARY2016
  2. ls -1 *-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../colourname-void-square-piechart-from-csv.R

Simplest possible square pie chart

  1. Rscript first3-ig-van-01january2016-square-piechart.R # theme_void doesn't work, gives us 1 colour only!
  2. open 1st3-ig-van-01january-2016-squarepiechart.png output: 1st3-ig-van-01january-2016-squarepiechart<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

August 6, 2016

  1. mkdir JANUARY 2016; cd !$; cp ../january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv .
  2. ../splitCSVForAMonthInto31CSFfiles.rb january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv
  3. ls -1 *-january2016-ig-van-avgcolour-id-mf-month-day-daynum-unixtime-hour.csv | xargs -n 1 Rscript ../colourname-void-piechart-from-csv.R
  4. ls -1 *.png > 31pngs.txt
  5. gm montage -verbose -adjoin -tile 7x6 +frame +shadow +label -adjoin -geometry '1920x1920+0+0<' null: null: null: null: null: @31pngs.txt null: null: null: null: null: null: 01-31january2016-piechart.png # Week starts on a Sunday and January 1 is a Friday so add 5 nulls at the beginning, January 31 is a Sunday so add 6 nulls at the end
  6. gm convert 01-31january2016-piechart.png 01-31january2016-piechart.jpg# And post jpeg to flickr :-)

Output

31 circles almost all alike :-) 31 jpegs fri jan 1 - sun jan 31 2016 instagram vancouver average colour pie charts 01-31january2016-piechart<script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>

August 1, 2016

How to make a named character vector in R - useful if we ever want have a legend with colours in a pie chart.

July 31, 2016

  1. hourly failed but i got it working!
  2. Rscript scale-color-manual-first3-ig-van01january2016-piechart.R more info: http://rolandtanglao.com/2016/07/31/p3-simplest-ggplot2-pie-chart-with-colors-as-bar-values-and-a-legend/

July 27, 2016

  1. let's try hourly Rscript ig-van-01january2016-piechart.R
  2. output: ig-van-01january-2016-barchart.png

July 25, 2016

today let's try theme_void() - it's great! removed all chrome!

  1. Rscript ig-van-january2016-piechart-as-barchart.R # output ig-van-january-2016-piechart-as-barchart.png
  2. mv ig-van-january-2016-barchart.png theme-void-ig-van-january-2016-barchart.png theme_void() small multiples vancouver 2016 january piechart

July 23, 2016

  1. Rscript ig-van-january2016-piechart-as-barchart.R # output ig-van-january-2016-piechart-as-barchart.png
  2. Rscript ig-van-january2016-piechart.R #output ig-van-january-2016-barchart.png There's a BUG in layout of small multiples hmmm lots of bugs? is there a bug in my script or in my data that causes the large white slices in the following output png:
    small multiples vancouver 2016 january piechart