Executive 1: Software Overview

NOTE: All instructions in capital letters indicate code that you should fill in with the appropriate information.

Set up your python

Install the reticulate package (do not include this code).
Load the reticulate library.

library(reticulate)

The first you set this up, you need to configure where your Python is located.
Run py_config() to find the python versions you have installed on your machine.
For example, here’s mine:

python:         /usr/bin/python
libpython:      /System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/config/libpython2.7.dylib
pythonhome:     /System/Library/Frameworks/Python.framework/Versions/2.7:/System/Library/Frameworks/Python.framework/Versions/2.7
version:        2.7.16 (default, Oct 17 2019, 17:14:30)  [GCC 4.2.1 Compatible Apple LLVM 11.0.0 (clang-1100.0.32.4) (-macos10.15-objc-s
numpy:          /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy
numpy_version:  1.8.0

python versions found: 
 /usr/bin/python
 /usr/bin/python3
 /usr/local/bin/python3
 /Users/buchanan/anaconda3/bin/python

Next, we want to configure which one to use.
If the one listed next to python on the top line is the one you want to use, you are all done.
If not, you will copy the path (i.e. /Users/buchanan/...) and tell R to talk to that python instead.
To do that, you would use: use_python("PATH", required = TRUE).
However, you can’t run use_python() after running py_config(). You will get an error message:

ERROR: The requested version of Python
('/Users/buchanan/anaconda3/bin/python') cannot be used, as
another version of Python ('/usr/bin/python') has already
been initialized. Please restart the R session if you need
to attach reticulate to a different version of Python.
Error in use_python("/Users/buchanan/anaconda3/bin/python", required = TRUE) : 
  failed to initialize requested version of Python

Click Session > Restart R.
Then use your use_python() code.
Do not include BOTH py_config() and use_python() in the same markdown. Once you know where your path is, just use use_python().

py_config()

## python:         C:\Users\nanl\AppData\Local\CONTIN~1\ANACON~1\python.exe
## libpython:      C:/Users/nanl/AppData/Local/CONTIN~1/ANACON~1/python27.dll
## pythonhome:     C:\Users\nanl\AppData\Local\CONTIN~1\ANACON~1
## version:        2.7.16 |Anaconda, Inc.| (default, Mar 14 2019, 15:42:17) [MSC v.1500 64 bit (AMD64)]
## Architecture:   64bit
## numpy:          C:\Users\nanl\AppData\Local\CONTIN~1\ANACON~1\lib\site-packages\numpy
## numpy_version:  1.16.5
## 
## python versions found: 
##  C:\Users\nanl\AppData\Local\Continuum\anaconda2\envs\r-reticulate\python.exe
##  C:\PROGRA~1\Python37\python.exe
##  C:\Users\nanl\AppData\Local\CONTIN~1\ANACON~1\python.exe
##  C:\PROGRA~1\Python37\\python.exe
##  C:\Users\nanl\AppData\Local\Continuum\anaconda2\python.exe

Let’s do some R

In this chunk, we will load a dataset - use data(rock) to load it.
Use the head() function to print out the first six rows of the dataset.

data(rock)
head(rock,6)

##   area    peri     shape perm
## 1 4990 2791.90 0.0903296  6.3
## 2 7002 3892.60 0.1486220  6.3
## 3 7558 3930.66 0.1833120  6.3
## 4 7352 3869.32 0.1170630  6.3
## 5 7943 3948.54 0.1224170 17.1
## 6 7979 4010.15 0.1670450 17.1

Let’s do some Python

First, let’s install some packages.
You will need numpy, nltk, spacy, seaborn and pandas for starters.
You can check if you have them first by using py_module_available("PACKAGE").

py_module_available('numpy')

## [1] TRUE

py_module_available('nltk')

## [1] TRUE

py_module_available('spacy')

## [1] FALSE

py_module_available('seaborn')

## [1] TRUE

py_module_available('pandas')

## [1] TRUE

If any of these return FALSE, then install them using py_install("PACKAGE").

#if they are all TRUE, leave this blank

Call a dataset in Python

First, load the seaborn library, it has several sample datasets. You load python packages by using import PACKAGE.
Then call the dots dataset by doing: VARNAME = PACKAGENAME.load_dataset("DATASETNAME").
To print out the first six rows, use the .head() function: VARNAME.head().

import seaborn
var1 = seaborn.load_dataset("dots")
var1.head()

##   align choice  time  coherence  firing_rate
## 0  dots     T1   -80        0.0    33.189967
## 1  dots     T1   -80        3.2    31.691726
## 2  dots     T1   -80        6.4    34.279840
## 3  dots     T1   -80       12.8    32.631874
## 4  dots     T1   -80       25.6    35.060487

QUESTION: Look in your environment window. What do you see?

Print out Python information in R

You can have the two environments interact. To print out information from Python in R: py$VARNAME.
Normally, to print out R dataset columns, you do DATAFRAME$COLUMN. Try to print out the time column from your dots variable (whatever you named it above).

py$var1$time

##   [1]  -80  -80  -80  -80  -80  -80  -60  -60  -60  -60  -60  -60  -40  -40
##  [15]  -40  -40  -40  -40  -20  -20  -20  -20  -20  -20    0    0    0    0
##  [29]    0    0   20   20   20   20   20   20   40   40   40   40   40   40
##  [43]   60   60   60   60   60   60   80   80   80   80   80   80  100  100
##  [57]  100  100  100  100  120  120  120  120  120  120  140  140  140  140
##  [71]  140  140  160  160  160  160  160  160  180  180  180  180  180  180
##  [85]  200  200  200  200  200  200  220  220  220  220  220  220  240  240
##  [99]  240  240  240  240  260  260  260  260  260  260  280  280  280  280
## [113]  280  280  300  300  300  300  300  320  320  320  320  320  340  340
## [127]  340  340  340  360  360  360  360  360  380  380  380  380  380  400
## [141]  400  400  400  400  420  420  420  420  420  440  440  440  440  460
## [155]  460  460  460  480  480  480  480  500  500  500  500  520  520  520
## [169]  520  540  540  540  540  560  560  560  560  580  580  580  600  600
## [183]  600  620  620  620  640  640  660  660  680  680  700  720  -80  -80
## [197]  -80  -80  -80  -80  -60  -60  -60  -60  -60  -60  -40  -40  -40  -40
## [211]  -40  -40  -20  -20  -20  -20  -20  -20    0    0    0    0    0    0
## [225]   20   20   20   20   20   20   40   40   40   40   40   40   60   60
## [239]   60   60   60   60   80   80   80   80   80   80  100  100  100  100
## [253]  100  100  120  120  120  120  120  120  140  140  140  140  140  140
## [267]  160  160  160  160  160  160  180  180  180  180  180  180  200  200
## [281]  200  200  200  200  220  220  220  220  220  220  240  240  240  240
## [295]  240  240  260  260  260  260  260  260  280  280  280  280  280  280
## [309]  300  300  300  300  300  300  320  320  320  320  320  320  340  340
## [323]  340  340  340  360  360  360  360  360  380  380  380  380  380  400
## [337]  400  400  400  400  420  420  420  420  420  440  440  440  440  440
## [351]  460  460  460  460  480  480  480  480  500  500  500  500  520  520
## [365]  520  520  540  540  540  540  560  560  560  560  580  580  580  600
## [379]  600  600  620  620  620  640  640  640  660  660  680  680  700  700
## [393]  720  720 -600 -580 -560 -560 -540 -540 -520 -520 -500 -500 -500 -480
## [407] -480 -480 -460 -460 -460 -440 -440 -440 -440 -420 -420 -420 -420 -400
## [421] -400 -400 -400 -380 -380 -380 -380 -360 -360 -360 -360 -340 -340 -340
## [435] -340 -320 -320 -320 -320 -300 -300 -300 -300 -300 -280 -280 -280 -280
## [449] -280 -260 -260 -260 -260 -260 -240 -240 -240 -240 -240 -220 -220 -220
## [463] -220 -220 -200 -200 -200 -200 -200 -180 -180 -180 -180 -180 -160 -160
## [477] -160 -160 -160 -160 -140 -140 -140 -140 -140 -140 -120 -120 -120 -120
## [491] -120 -120 -100 -100 -100 -100 -100 -100  -80  -80  -80  -80  -80  -80
## [505]  -60  -60  -60  -60  -60  -60  -40  -40  -40  -40  -40  -40  -20  -20
## [519]  -20  -20  -20  -20    0    0    0    0    0    0   20   20   20   20
## [533]   20   20   40   40   40   40   40   40   60   60   60   60   60   60
## [547]   80   80   80   80   80   80  100  100  100  100  100  100  120  120
## [561]  120  120  120  120  140  140  140  140  140  140  160  160  160  160
## [575]  160  160  180  180  180  180  180  180  200  200  200  200  200  200
## [589]  220  220  220  220  220  220  240  240  240  240  240  240  260  260
## [603]  260  260  260  260  280  280  280  280  280  280  300  300  300  300
## [617]  300  300 -600 -600 -580 -580 -560 -560 -540 -540 -520 -520 -520 -500
## [631] -500 -500 -480 -480 -480 -460 -460 -460 -440 -440 -440 -440 -420 -420
## [645] -420 -420 -400 -400 -400 -400 -380 -380 -380 -380 -360 -360 -360 -360
## [659] -340 -340 -340 -340 -320 -320 -320 -320 -320 -300 -300 -300 -300 -300
## [673] -280 -280 -280 -280 -280 -260 -260 -260 -260 -260 -240 -240 -240 -240
## [687] -240 -220 -220 -220 -220 -220 -200 -200 -200 -200 -200 -200 -180 -180
## [701] -180 -180 -180 -180 -160 -160 -160 -160 -160 -160 -140 -140 -140 -140
## [715] -140 -140 -120 -120 -120 -120 -120 -120 -100 -100 -100 -100 -100 -100
## [729]  -80  -80  -80  -80  -80  -80  -60  -60  -60  -60  -60  -60  -40  -40
## [743]  -40  -40  -40  -40  -20  -20  -20  -20  -20  -20    0    0    0    0
## [757]    0    0   20   20   20   20   20   20   40   40   40   40   40   40
## [771]   60   60   60   60   60   60   80   80   80   80   80   80  100  100
## [785]  100  100  100  100  120  120  120  120  120  120  140  140  140  140
## [799]  140  140  160  160  160  160  160  160  180  180  180  180  180  180
## [813]  200  200  200  200  200  200  220  220  220  220  220  220  240  240
## [827]  240  240  240  240  260  260  260  260  260  260  280  280  280  280
## [841]  280  280  300  300  300  300  300  300

Print our R in Python

When using R in Python, instead of $, we use . like this: r.VARNAME.
To print out a single column, you use DATAFRAME["COLUMNNAME"]. Try printing out the shape column in the rock dataset.

r.rock["shape"]

## 0     0.090330
## 1     0.148622
## 2     0.183312
## 3     0.117063
## 4     0.122417
## 5     0.167045
## 6     0.189651
## 7     0.164127
## 8     0.203654
## 9     0.162394
## 10    0.150944
## 11    0.148141
## 12    0.228595
## 13    0.231623
## 14    0.172567
## 15    0.153481
## 16    0.204314
## 17    0.262727
## 18    0.200071
## 19    0.144810
## 20    0.113852
## 21    0.291029
## 22    0.240077
## 23    0.161865
## 24    0.280887
## 25    0.179455
## 26    0.191802
## 27    0.133083
## 28    0.225214
## 29    0.341273
## 30    0.311646
## 31    0.276016
## 32    0.197653
## 33    0.326635
## 34    0.154192
## 35    0.276016
## 36    0.176969
## 37    0.438712
## 38    0.163586
## 39    0.253832
## 40    0.328641
## 41    0.230081
## 42    0.464125
## 43    0.420477
## 44    0.200744
## 45    0.262651
## 46    0.182453
## 47    0.200447
## Name: shape, dtype: float64

Get started with PyCharm!

Great job! Here’s what you learned:
You know how to tell Rmarkdown which Python to use.
You know how to install and load the libraries in both languages.
You know how to load built in datasets in both languages.
You know how to print out data from one language to another.
Turn this document in for credit –> hit KNIT –> turn in the HTML file.
Be sure to fill in your name at the top!

Executive 1: Software Overview

Nan Li

2019-11-13

Set up your python

Let’s do some R

Let’s do some Python

Call a dataset in Python

Print out Python information in R

Print our R in Python

Get started with PyCharm!