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jupyter
Weird display in jupyter notebook with R kernel
Hello,
I am using jupyter-notebook with an R kernel. I am trying to plot simple things, but the scale is very weird:
Here is the output of conda list
# packages in environment at /home/bastien/anaconda3/envs/R:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
_r-mutex 1.0.0 anacondar_1
argon2-cffi 21.3.0 pyhd3eb1b0_0
argon2-cffi-bindings 21.2.0 pypi_0 pypi
asttokens 2.0.5 pyhd3eb1b0_0
attrs 21.4.0 pyhd3eb1b0_0
backcall 0.2.0 pyhd3eb1b0_0
beautifulsoup4 4.11.1 pypi_0 pypi
binutils_impl_linux-64 2.33.1 he6710b0_7
binutils_linux-64 2.33.1 h9595d00_15
bleach 4.1.0 pyhd3eb1b0_0
bwidget 1.9.11 1
bzip2 1.0.8 h7b6447c_0
ca-certificates 2022.4.26 h06a4308_0
cairo 1.16.0 h19f5f5c_2
certifi 2022.5.18.1 pypi_0 pypi
cffi 1.15.0 pypi_0 pypi
curl 7.69.1 hbc83047_0
dbus 1.13.18 hb2f20db_0
debugpy 1.5.1 pypi_0 pypi
decorator 5.1.1 pyhd3eb1b0_0
defusedxml 0.7.1 pyhd3eb1b0_0
entrypoints 0.4 pypi_0 pypi
executing 0.8.3 pyhd3eb1b0_0
expat 2.4.4 h295c915_0
fontconfig 2.13.1 h6c09931_0
freetype 2.11.0 h70c0345_0
fribidi 1.0.10 h7b6447c_0
gcc_impl_linux-64 7.3.0 habb00fd_1
gcc_linux-64 7.3.0 h553295d_15
gfortran_impl_linux-64 7.3.0 hdf63c60_1
gfortran_linux-64 7.3.0 h553295d_15
glib 2.69.1 h4ff587b_1
graphite2 1.3.14 h295c915_1
gsl 2.4 h14c3975_4
gst-plugins-base 1.14.0 h8213a91_2
gstreamer 1.14.0 h28cd5cc_2
gxx_impl_linux-64 7.3.0 hdf63c60_1
gxx_linux-64 7.3.0 h553295d_15
harfbuzz 2.8.1 h6f93f22_0
icu 58.2 he6710b0_3
ipykernel 6.9.1 pypi_0 pypi
ipython 8.3.0 pypi_0 pypi
ipython_genutils 0.2.0 pyhd3eb1b0_1
ipywidgets 7.6.5 pyhd3eb1b0_1
jedi 0.18.1 pypi_0 pypi
jinja2 3.0.3 pyhd3eb1b0_0
jpeg 9e h7f8727e_0
jsonschema 4.4.0 pypi_0 pypi
jupyter 1.0.0 pypi_0 pypi
jupyter-client 7.2.2 pypi_0 pypi
jupyter-core 4.10.0 pypi_0 pypi
jupyter_client 7.2.2 py310h06a4308_0
jupyter_console 6.4.3 pyhd3eb1b0_0
jupyter_core 4.10.0 py310h06a4308_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
krb5 1.17.1 h173b8e3_0
ld_impl_linux-64 2.33.1 h53a641e_7
libcurl 7.69.1 h20c2e04_0
libedit 3.1.20210910 h7f8727e_0
libffi 3.3 he6710b0_2
libgcc-ng 11.2.0 h1234567_1
libgfortran-ng 7.5.0 ha8ba4b0_17
libgfortran4 7.5.0 ha8ba4b0_17
libgomp 11.2.0 h1234567_1
libpng 1.6.37 hbc83047_0
libsodium 1.0.18 h7b6447c_0
libssh2 1.10.0 h8f2d780_0
libstdcxx-ng 11.2.0 h1234567_1
libtiff 4.2.0 h2818925_1
libuuid 1.0.3 h7f8727e_2
libwebp-base 1.2.2 h7f8727e_0
libxcb 1.15 h7f8727e_0
libxml2 2.9.14 h74e7548_0
lz4-c 1.9.3 h295c915_1
make 4.2.1 h1bed415_1
markupsafe 2.0.1 pypi_0 pypi
matplotlib-inline 0.1.2 pyhd3eb1b0_2
mistune 0.8.4 pypi_0 pypi
nbclient 0.5.13 pypi_0 pypi
nbconvert 6.4.4 pypi_0 pypi
nbformat 5.3.0 pypi_0 pypi
ncurses 6.3 h7f8727e_2
nest-asyncio 1.5.5 pypi_0 pypi
notebook 6.4.11 pypi_0 pypi
openssl 1.1.1o h7f8727e_0
packaging 21.3 pyhd3eb1b0_0
pandocfilters 1.5.0 pyhd3eb1b0_0
pango 1.45.3 hd140c19_0
parso 0.8.3 pyhd3eb1b0_0
pcre 8.45 h295c915_0
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pip 21.2.4 pypi_0 pypi
pixman 0.40.0 h7f8727e_1
prometheus_client 0.13.1 pyhd3eb1b0_0
prompt-toolkit 3.0.20 pyhd3eb1b0_0
prompt_toolkit 3.0.20 hd3eb1b0_0
ptyprocess 0.7.0 pyhd3eb1b0_2
pure_eval 0.2.2 pyhd3eb1b0_0
pycparser 2.21 pyhd3eb1b0_0
pygments 2.11.2 pyhd3eb1b0_0
pyparsing 3.0.4 pyhd3eb1b0_0
pyqt 5.9.2 py310h295c915_6
pyrsistent 0.18.0 pypi_0 pypi
python 3.10.4 h12debd9_0
python-dateutil 2.8.2 pyhd3eb1b0_0
python-fastjsonschema 2.15.1 pyhd3eb1b0_0
pyzmq 22.3.0 pypi_0 pypi
qt 5.9.7 h5867ecd_1
qtconsole 5.3.0 pyhd3eb1b0_0
qtpy 2.0.1 pyhd3eb1b0_0
r-base 3.6.1 haffb61f_2
r-base64enc 0.1_3 r36h96ca727_4
r-boot 1.3_20 r36h6115d3f_0
r-class 7.3_15 r36h96ca727_0
r-cluster 2.0.8 r36ha65eedd_0
r-codetools 0.2_16 r36h6115d3f_0
r-crayon 1.3.4 r36h6115d3f_0
r-digest 0.6.18 r36h96ca727_0
r-evaluate 0.13 r36h6115d3f_0
r-foreign 0.8_71 r36h96ca727_0
r-htmltools 0.3.6 r36h29659fb_0
r-irdisplay 0.7.0 r36h6115d3f_0
r-irkernel 0.8.15 r36_0
r-jsonlite 1.6 r36h96ca727_0
r-kernsmooth 2.23_15 r36ha65eedd_4
r-lattice 0.20_38 r36h96ca727_0
r-mass 7.3_51.3 r36h96ca727_0
r-matrix 1.2_17 r36h96ca727_0
r-mgcv 1.8_28 r36h96ca727_0
r-nlme 3.1_139 r36ha65eedd_0
r-nnet 7.3_12 r36h96ca727_0
r-pbdzmq 0.3_3 r36h5455479_1
r-rcpp 1.0.1 r36h29659fb_0
r-recommended 3.6.0 r36_0
r-repr 0.19.2 r36h6115d3f_0
r-rpart 4.1_15 r36h96ca727_0
r-spatial 7.3_11 r36h96ca727_4
r-survival 2.44_1.1 r36h96ca727_0
r-uuid 0.1_2 r36h96ca727_4
readline 8.1.2 h7f8727e_1
send2trash 1.8.0 pyhd3eb1b0_1
setuptools 61.2.0 pypi_0 pypi
sip 4.19.13 pypi_0 pypi
six 1.16.0 pyhd3eb1b0_1
soupsieve 2.3.1 pyhd3eb1b0_0
sqlite 3.38.3 hc218d9a_0
stack_data 0.2.0 pyhd3eb1b0_0
terminado 0.13.1 pypi_0 pypi
testpath 0.5.0 pyhd3eb1b0_0
tk 8.6.12 h1ccaba5_0
tktable 2.10 h14c3975_0
tornado 6.1 pypi_0 pypi
traitlets 5.1.1 pyhd3eb1b0_0
typing-extensions 4.1.1 hd3eb1b0_0
typing_extensions 4.1.1 pyh06a4308_0
tzdata 2022a hda174b7_0
wcwidth 0.2.5 pyhd3eb1b0_0
webencodings 0.5.1 pypi_0 pypi
wheel 0.37.1 pyhd3eb1b0_0
widgetsnbextension 3.5.2 pypi_0 pypi
xz 5.2.5 h7f8727e_1
zeromq 4.3.4 h2531618_0
zlib 1.2.12 h7f8727e_2
zstd 1.5.2 ha4553b6_0
Doing the same command in Rstudio gives a nice plot.
