Collaborative Interactive ggplot2 and R plots

A Plotly workflow

Marianne Corvellec, Plotly [email protected]

Plot.ly is a platform for making and sharing graphs --- A GitHub for Data

plotly_interface

In [1]:
print("Before we dive into plots...")

[1] "Before we dive into plots..."

This is the IPython Notebook for R (a native R kernel lets you run R code): https://github.com/takluyver/IRkernel

Histogram of Batting Averages

Downloaded data from http://www.seanlahman.com/baseball-archive/statistics/ (http://creativecommons.org/licenses/by-sa/3.0/)

In [2]:
batting_table <- read.csv("/home/marianne/Downloads/Batting.csv")
In [3]:
head(batting_table)
Out[3]:
   playerID yearID stint teamID lgID  G G_batting AB R H X2B X3B HR RBI SB CS
1 aardsda01   2004     1    SFN   NL 11        11  0 0 0   0   0  0   0  0  0
2 aardsda01   2006     1    CHN   NL 45        43  2 0 0   0   0  0   0  0  0
3 aardsda01   2007     1    CHA   AL 25         2  0 0 0   0   0  0   0  0  0
4 aardsda01   2008     1    BOS   AL 47         5  1 0 0   0   0  0   0  0  0
5 aardsda01   2009     1    SEA   AL 73         3  0 0 0   0   0  0   0  0  0
6 aardsda01   2010     1    SEA   AL 53         4  0 0 0   0   0  0   0  0  0
  BB SO IBB HBP SH SF GIDP G_old
1  0  0   0   0  0  0    0    11
2  0  0   0   0  1  0    0    45
3  0  0   0   0  0  0    0     2
4  0  1   0   0  0  0    0     5
5  0  0   0   0  0  0    0    NA
6  0  0   0   0  0  0    0    NA
In [4]:
summary(batting_table)
Out[4]:
      playerID         yearID         stint           teamID        lgID      
 mcguide01:   31   Min.   :1871   Min.   :1.000   CHN    : 4720   AA  : 1890  
 henderi01:   29   1st Qu.:1931   1st Qu.:1.000   PHI    : 4621   AL  :44369  
 newsobo01:   29   Median :1970   Median :1.000   PIT    : 4575   FL  :  470  
 johnto01 :   28   Mean   :1962   Mean   :1.077   SLN    : 4535   NL  :49944  
 kaatji01 :   28   3rd Qu.:1995   3rd Qu.:1.000   CIN    : 4393   PL  :  147  
 ansonca01:   27   Max.   :2013   Max.   :5.000   CLE    : 4318   UA  :  332  
 (Other)  :97717                                  (Other):70727   NA's:  737  
       G            G_batting            AB              R         
 Min.   :  1.00   Min.   :  0.00   Min.   :  0.0   Min.   :  0.00  
 1st Qu.: 13.00   1st Qu.:  7.00   1st Qu.:  9.0   1st Qu.:  0.00  
 Median : 35.00   Median : 32.00   Median : 61.0   Median :  5.00  
 Mean   : 51.65   Mean   : 49.13   Mean   :154.1   Mean   : 20.47  
 3rd Qu.: 81.00   3rd Qu.: 81.00   3rd Qu.:260.0   3rd Qu.: 31.00  
 Max.   :165.00   Max.   :165.00   Max.   :716.0   Max.   :192.00  
                  NA's   :1406     NA's   :6413    NA's   :6413    
       H               X2B            X3B               HR        
 Min.   :  0.00   Min.   : 0.0   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:  1.00   1st Qu.: 0.0   1st Qu.: 0.000   1st Qu.: 0.000  
 Median : 12.00   Median : 2.0   Median : 0.000   Median : 0.000  
 Mean   : 40.37   Mean   : 6.8   Mean   : 1.424   Mean   : 3.002  
 3rd Qu.: 66.00   3rd Qu.:10.0   3rd Qu.: 2.000   3rd Qu.: 3.000  
 Max.   :262.00   Max.   :67.0   Max.   :36.000   Max.   :73.000  
 NA's   :6413     NA's   :6413   NA's   :6413     NA's   :6413    
      RBI               SB                CS               BB        
 Min.   :  0.00   Min.   :  0.000   Min.   : 0.000   Min.   :  0.00  
 1st Qu.:  0.00   1st Qu.:  0.000   1st Qu.: 0.000   1st Qu.:  0.00  
 Median :  5.00   Median :  0.000   Median : 0.000   Median :  4.00  
 Mean   : 18.47   Mean   :  3.265   Mean   : 1.385   Mean   : 14.21  
 3rd Qu.: 28.00   3rd Qu.:  2.000   3rd Qu.: 1.000   3rd Qu.: 21.00  
 Max.   :191.00   Max.   :138.000   Max.   :42.000   Max.   :232.00  
 NA's   :6837     NA's   :7713      NA's   :29867    NA's   :6413    
       SO              IBB              HBP               SH        
 Min.   :  0.00   Min.   :  0.00   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:  2.00   1st Qu.:  0.00   1st Qu.: 0.000   1st Qu.: 0.000  
 Median : 11.00   Median :  0.00   Median : 0.000   Median : 1.000  
 Mean   : 21.95   Mean   :  1.28   Mean   : 1.136   Mean   : 2.564  
 3rd Qu.: 31.00   3rd Qu.:  1.00   3rd Qu.: 1.000   3rd Qu.: 3.000  
 Max.   :223.00   Max.   :120.00   Max.   :51.000   Max.   :67.000  
 NA's   :14251    NA's   :42977    NA's   :9233     NA's   :12751   
       SF             GIDP           G_old       
 Min.   : 0.0    Min.   : 0.00   Min.   :  0.00  
 1st Qu.: 0.0    1st Qu.: 0.00   1st Qu.: 11.00  
 Median : 0.0    Median : 1.00   Median : 34.00  
 Mean   : 1.2    Mean   : 3.33   Mean   : 50.99  
 3rd Qu.: 2.0    3rd Qu.: 5.00   3rd Qu.: 82.00  
 Max.   :19.0    Max.   :36.00   Max.   :165.00  
 NA's   :42446   NA's   :32521   NA's   :5189
In [5]:
batting_table <- subset(batting_table, yearID >= 2004)
In [6]:
batting_table$Avg <- with(batting_table, H / AB)
In [7]:
library(ggplot2)
ggplot(data=batting_table) + geom_histogram(aes(Avg), binwidth=0.05)
Out[7]:










    




























Let us filter out entries where players were at bat less than 10 times.













In [8]:



    


batting_table <- subset(batting_table, AB >= 10)
hist <- ggplot(data=batting_table) + geom_histogram(aes(Avg), binwidth=0.05)
hist



    















    
Out[8]:


















    




























Let me share this plot, so that others can contribute!
I want to install the latest version of the plotly package and load it (silently).













In [9]:



    


suppressPackageStartupMessages(library(devtools))
suppressMessages(install_github("ropensci/plotly"))
suppressPackageStartupMessages(library(plotly))



    















Open a Plotly connection (using your own credentials if you prefer).













In [10]:



    


py <- plotly("ggplot2examples", "3gazttckd7")  # Instantiate plotly object



    













In [11]:



    


collab_hist <- py$ggplotly(hist)



    















    






packageStartupMessage in packageStartupMessage(gettextf("Loading required package: %s", : Loading required package: IRdisplay











    



























Let me click "data and graph", then "Save & edit", "Copy", fit the distribution with a Gaussian, add two vertical lines, an annotation, give a title, etc., and "Share"!






























https://plot.ly/~mkcor/305















Declarative Plotly syntax in R: https://plot.ly/~mkcor/305.r (Get the JSON https://plot.ly/~mkcor/305.json and more...)















Now retrieve the plot's DNA, edit it in R, send it back online...













In [12]:



    


enhanc_hist <- py$get_figure("mkcor", 305)



    













In [13]:



    


str(enhanc_hist)



    















    






List of 2
 $ layout:List of 25
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In [14]:



    


enhanc_hist$data[[2]]  # Data for second trace



    















    
Out[14]:








$textfont
named list()

$error_x
named list()

$name
[1] "Good"

$uid
[1] "a3a1f2"

$ysrc
[1] "mkcor:304:095ab6"

$xsrc
[1] "mkcor:304:5a03a8"

$marker
$marker$line
named list()


$mode
[1] "lines"

$y
[1] "0"     "25000"

$x
[1] "0.3" "0.3"

$line
             color 
"rgb(44, 160, 44)" 

$type
[1] "scatter"

$error_y
named list()






















Say we have more ambition.













In [15]:



    


enhanc_hist$data[[2]]$name <- "Very Good"



    













In [16]:



    


enhanc_hist$data[[2]]$x[[1]] <- 0.35
enhanc_hist$data[[2]]$x[[2]] <- 0.35



    













In [17]:



    


enhanc_hist$data[[2]]$x



    















    
Out[17]:








[1] "0.35" "0.35"



















In [18]:



    


py$plotly(enhanc_hist$data, kwargs=list(layout=enhanc_hist$layout))



    















    
Out[18]:








$url
[1] "https://plot.ly/~ggplot2examples/210"

$message
[1] ""

$warning
[1] ""

$filename
[1] "from api (58) (1)"

$error
[1] ""






















Visit the above URL.















Faceted Plots














ggplot2 also uses geom_bar to make histograms...













In [19]:



    


bar <- ggplot(data=batting_table) + geom_bar(aes(Avg), binwidth=0.05) + facet_wrap(~yearID)



    













In [20]:



    


bar



    















    
Out[20]:


















    


























In [21]:



    


py$ggplotly(bar)



    















    






simpleWarning in gg2list(gg): You may want to use geom_histogram.










    



























Thank you!














Join the fun! https://github.com/ropensci/plotly













In [22]:



    


# CSS styling within IPython notebook
display_html(getURL("https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css"))