| ################################################### | |
| ### 0.Preparación de los datos | |
| ################################################### | |
| ################################################### | |
| ### 0.1.Asignación de directorios | |
| ################################################### | |
| setwd("/<...>/PEC1") | |
| workingDir <-getwd() | |
| dataDir <-file.path(workingDir, "data") |
wget -r -np -nH --cut-dirs=3 -R index.html http://hostname/aaa/bbb/ccc/ddd/
Explanation:
It will download all files and subfolders in ddd directory: recursively (-r), not going to upper directories, like ccc/… (-np), not saving files to hostname folder (-nH), but to ddd by omitting first 3 folders aaa, bbb, ccc (--cut-dirs=3) excluding index.html files (-R index.html)
How to untar Open a terminal and type: tar xvjf filename.tar.bz2 Which will untar it to the current directory. Normally (99% of the time) it will create it's own subdirectory so you don't need to worry about that. Just so you know: tar - Tape ARchiver And the options: x - extract v - verbose output (lists all files as they are extracted) j - deal with bzipped file
| docker run --rm -it -v $(pwd)/cv:/root/torch-rnn/cv crisbal/torch-rnn:base bash |
In R, if you ever want to read a csv file where the rows have different lengths, and the csv does
not have a header row with all the column names, neither read.csv from {base} nor read_csv from
{readr v.1.0.0}, will read the columns as (I would have) expected.
For example, given the following csv:
A,B
C,D
E,F
G,H
I hereby claim:
To claim this, I am signing this object:
| ``` | |
| "+y | |
| ``` |
| curve(dnorm(x, 178, 20), from = 100, to = 250) | |
| # is the same as | |
| ggplot(tibble(x = seq(100, 250, 10)), aes(x)) + | |
| stat_function(fun = function(x) dnorm(x, 178, 20)) |