Plotting in Nextjournal

hist(rnorm(200, 1), breaks=100, main = "Random Deviates of a Normal Distribution", xlab = "Events")

file -b 
__out-0
 

png('/results/my-plot.png')

file -b 
my-plot.png

artists <- read.csv(
artist_data.csv
, header=T)
born <- artists$yearOfBirth

birth_distribution = smoothScatter(born, 1:length(born), axes=FALSE,
                                   xlab="Year", ylab="",
									        				 main="Distribution of Artist's Birth Years at the Tate")                            

axis(1, col.ticks="blue")
birth_distribution

artists

library(tidyverse)
library(plotly)

artists <- read_csv(
artist_data.csv
)
born <- artists$yearOfBirth
df <- data.frame(born)

ggplot(df, mapping=aes(x = born, y = as.numeric(row.names(df)))) + 
           geom_point(size=2.2, alpha=0.4, shape=15) + 
           labs(x = "Year", y=element_blank(),
                title = "Distribution of Artist's Birth Years at the Tate", 
                subtitle = "From the Museum's Permanent Collection") + 
           theme_bw() +
           theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 panel.grid.minor=element_blank(),
                 panel.grid.major.y=element_blank())

artists <- read_csv(
artist_data.csv
)
female_artists <- artists[artists$gender == "Female",]
male_artists <- artists[artists$gender == "Male",]

plot_ly(alpha=0.6) %>%
  add_histogram(data=female_artists, x=~yearOfBirth, name="Females") %>%
  add_histogram(data=male_artists, x=~yearOfBirth, name="Males") %>%
  layout(barmode="overlay", xaxis=list(title="Year of Birth"))

artists <- read_csv(
artist_data.csv
)
born <- artists$yearOfBirth
df <- data.frame(born)
id <- as.numeric(row.names(df))

ggplotly(ggplot(df, mapping=aes(x = born, y = id)) + 
           geom_point(size=1.5, alpha=0.4, shape=15) +
           labs(x = "Year", y="",
                title = "Distribution of Artist's Birth Years at the Tate") +
           theme_bw() +
           theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 panel.grid.minor=element_blank(),
                 panel.grid.major.y=element_blank()))

artworks <- read_csv(
artwork_data.csv
)

drop <- c("accession_number", "artistRole", "artistId", "dateText", "creditLine", "units", "inscription", "thumbnailCopyright", "thumbnailUrl", "url")
artworks_rem <- artworks[ , !(names(artworks) %in% drop)]

artworks_size <- artworks_rem[!(is.na(artworks_rem$height & artworks_rem$width & artworks_rem$year)), ]

artworks_size$size <- artworks_size$height * artworks_size$width

metal <- artworks_size[artworks_size$medium == "Steel" | artworks_size$medium=="Bronze",]

plot_ly(data=metal, x=~acquisitionYear, name="Sculptural Acquisitions")

plot_ly(data=metal, x=~year, y=~acquisitionYear, z=~size, color=~medium, 
        colors = c('#BF382A', '#0C4B8E'), text=~artist,
        marker=list(size=4, opacity=0.5)) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Year Created'),
                     yaxis = list(title = 'Year of Acquisition'),
                     zaxis = list(title = 'Size')),
         annotations = list(
           x = 1.13,
           y = 1.05,
           text = 'Material',
           xref = 'paper',
           yref = 'paper',
           showarrow = FALSE
         ))

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

artwork_data = pd.read_csv(
artwork_data.csv
)
artwork_data.drop(columns=["accession_number", "artistRole", "artistId", "dateText", "acquisitionYear", "dimensions", "width", "height", "depth", "creditLine", "units", "inscription", "thumbnailCopyright", "thumbnailUrl", "url"])

# Drop the rows listed as NaN, otherwise indexing oil, acrylic, and watercolour artworks yeild the error "ValueError: cannot index with vector containing NA / NaN values." Replace this line with something more sensible to get a more complete dataset.
artwork_data.dropna(subset=['medium'],inplace=True)
artwork_data["year"] = pd.to_numeric(artwork_data["year"], errors="coerce")

oil=artwork_data[artwork_data["medium"].str.contains("oil", case=False)]
acrylic=artwork_data[artwork_data["medium"].str.contains("acrylic", case=False)]
watercolour=artwork_data[artwork_data["medium"].str.contains("watercolour", case=False)]

fig, ax = plt.subplots()

ax.set(xlabel='year', ylabel='number of works',
       title='Paintings at the Tate, by Medium')

ax.hist([oil["year"], acrylic["year"], watercolour["year"]], stacked=True)
fig

import pandas as pd

# plotly imports
import plotly.plotly as py
import plotly.figure_factory as ff
# plotly.graph_objs contains all the helper classes to make/style plots
import plotly.graph_objs as go

artist_data = pd.read_csv(
artist_data.csv
)

# Display the first 12 rows and 3 columns of the dataframe
ff.create_table(artist_data.iloc[:12,:3], index=False)

import numpy as np

artist_data = pd.read_csv(
artist_data.csv
)

male = artist_data['gender'] == 'Male'
female = artist_data['gender'] == 'Female'

trace1 = go.Histogram(
    x=np.array((artist_data[female]['yearOfBirth'])),
    name='Female')

trace2 = go.Histogram(
    x=np.array((artist_data[male]['yearOfBirth'])),
    name='Male')

trace_data = [trace1, trace2]
layout = go.Layout(
    bargroupgap=0.3)

go.Figure(data=trace_data, layout=layout)

using Plots; plotly()

scatter(rand(10), rand(10), title="Plot.ly Backend")

using Plots; gr()

scatter(rand(10), rand(10), title="GR Backend")