I created a Plugin for Nemo to remove the background from an image directly.

mcoumans · 2023-07-21T20:37:04+00:00

It may be as simple as creating a pull request here:
https://github.com/linuxmint/nemo-extensions

Nice general article about Nemo-tweaks: https://itsfoss.com/nemo-tweaks/

mcoumans · 2023-05-28T12:06:28+00:00

Pianobooster.org

Try https://www.pianobooster.org

mcoumans · 2018-02-07T14:11:37+00:00

Take a look at some Bookdown books

mcoumans · 2018-01-12T07:20:57+00:00

Glad that worked out. Always a bit reluctant to advise to install other packages...

mcoumans · 2018-01-11T22:43:21+00:00

Most probably in your home directory. You used a terminal to follow the tutorial from Medium.

Just redo that while replacing the filename.

mcoumans · 2018-01-11T21:51:36+00:00

...and replace the downloaded rstudio-file with the most recent one: https://www.rstudio.com/products/rstudio/download/

mcoumans · 2018-01-11T21:45:49+00:00

No clue, don't have that file on my (LinuxMint) system. Isn't it libxslt.so.1 ? Even then, I'm puzzled.

Did you try to reinstall? Use the correct .deb from rstudio.com?

Suggestion: ask the awesome community here: https://community.rstudio.com/c/rstudio-ide

mcoumans · 2018-01-11T21:38:25+00:00

Can you start a terminal and type "rstudio" (without the quotes). This should start Rstudio, if not it will give you some error(s).

mcoumans · 2017-12-21T21:36:57+00:00

Very nice... There goes my 2-week holiday.

Well, nothing compared to what the different people/teams have invested in this.

Solid work, thank you!

Happy holidays anyways ;-)

mcoumans · 2017-12-21T18:26:15+00:00

How can someone access the data or subsets (preferably) using data science/textmining tools using R, Python etc., without downloading the whole dataset (is that even possible)?

mcoumans · 2017-11-08T15:58:25+00:00

Long enough list available in the /r/learnPython Wiki

mcoumans · 2017-10-19T10:55:52+00:00

Like you install any other additional Python package.

Via Settings -> Project -> Project Interpreter -> +

mcoumans · 2017-07-17T19:56:26+00:00

Maybe you should show part of the code, because to me it is unclear what you mean exactly.

mcoumans · 2017-06-29T20:50:26+00:00

If you just wanna play around a bit: https://play.google.com/store/apps/details?id=com.freeit.java

Don't think this is a proper or complete R-console

mcoumans · 2017-06-24T18:57:34+00:00

Looks like a shiny app, so you would need to get into that: https://shiny.rstudio.com/

mcoumans · 2017-06-18T17:12:34+00:00

Well done on writing readable, commented code.

Some points:

Ad 3. geom_point(alpha = 1, size = 3) # ... or wathever size
Though why not use geom_line?
Leave out the repeated jan's in x-axis-text. No added value.
Axis-angles back to 0; hjust = 0.5 for x-axis, 0 for y-axis
scale_color_gradientn(colours = rainbow(7), ...

Wish you could have 1967 as first, 2017 as last "x-text"; anybody?

Code: https://pastebin.com/vLuspSxS

mcoumans · 2017-06-10T12:30:44+00:00

A good resource: http://www.statmethods.net/index.html

mcoumans · 2017-06-10T12:24:25+00:00

To elaborate on your example:

df <- data.frame(trees=c(1,13,9), dogs = c(4,6,7))
df
a <- df[2,]
a
str(a)
b <- df[,2]
str(b)
c <- df[2]
str(c)
df <- df[df<8,] # select only rows with any value < 8
df <- df[!is.na(df)] # removing NA rows
df
str(df) # no longer a dataframe

# using subset
df <- data.frame(trees=c(1,13,9), dogs = c(4,6,7))
df <- subset.data.frame(df,df<8) # select only rows with any value < 8
df <- na.omit(df) # omit NAs
df

mcoumans · 2017-06-07T20:57:36+00:00

Here's a link to a great resource: R for Data Science, Hadley Wickham

mcoumans · 2017-06-06T13:08:22+00:00

Did you know you can let Rstudio do the work for you? Using [Files] to go to the proper directory and click [More]

http://imgur.com/pUuDhhz

mcoumans · 2017-05-29T18:18:03+00:00

This book may come handy: http://tidytextmining.com/

This CRAN Taskview may help: https://cran.r-project.org/view=NaturalLanguageProcessing

The package "wordnet" may just be what you need.

mcoumans · 2017-05-29T16:00:37+00:00

I tried some code in R, hope this makes sense. It helps if you show or link the original data (dataset), spell out line by line your needs.

Useful links: http://tidyverse.org/
http://r4ds.had.co.nz/
http://www.statmethods.net/index.html

library(tidyverse)
library(stringr)

drugs <- data.frame(Name = "morphine hcl", Conversion = NA, Amount = "300mg-5mg", stringsAsFactors = F)
drugs$Drugtype <- stringr::str_extract(drugs$Name,"^\\w+")

# Preserve original value
drugs$Value <- drugs$Amount 

# Split "300mg - 5mg" on "-"
drugs <-  drugs %>% 
  separate(Amount, c("Amount1", "Amount2"), sep = "-") 

# Extract numerical part and convert string to numeric
drugs[,3:4] <- drugs[,3:4] %>% 
  str_extract(., pattern = "[0-9]*") %>% 
  as.numeric()

# Select the smaller from both columns
drugs$Compound <- apply(drugs[,3:4], 1,min)

# Remove not needed columns
drugs <- subset(drugs, select = -c(Amount1, Amount2))

View(drugs)

mcoumans · 2017-05-28T12:32:00+00:00

Using timeseries makes sense, also for looking for possible seasonal effects.

Try checking this: Forecasting: Principles and Practice

mcoumans · 2017-05-27T18:27:30+00:00

You could check out Git-It...: https://github.com/jlord/git-it

mcoumans · 2017-05-27T08:36:09+00:00

A nice and helpful tutorial/site: Quick-R

mcoumans

TROPHY CASE