IPythonMagicWithR.py

$ ipython

In [1]: import pandas as pd

In [2]: mpg = pd.read_csv('mpg.csv')

# move mpg dataframe to R. The "-i mpg" is an input. Dataframe moves from pandas to R.

In [3]: %load_ext rpy2.ipython

In [4]: %%R -i mpg
   ...:     mpg.lm <- lm(hwy ~ ., data = mpg)
   ...:     summary(mpg.lm)
   ...: 

Call:
lm(formula = hwy ~ ., data = mpg)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.56544 -0.54876  0.00631  0.46380  2.54085 

Coefficients: (20 not defined because of singularities)
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 -147.89919   45.12149  -3.278 0.001259 ** 
manufacturerchevrolet         -0.07523    0.79154  -0.095 0.924389    
manufacturerdodge             -5.13203    1.41378  -3.630 0.000371 ***
manufacturerford              -1.55781    1.38801  -1.122 0.263239    
manufacturerhonda             -0.59813    1.41389  -0.423 0.672778    
manufacturerhyundai           -1.28086    1.39510  -0.918 0.359810    
manufacturerjeep              -5.35642    1.40137  -3.822 0.000183 ***
manufacturerland rover        -3.27903    1.40198  -2.339 0.020459 *  
manufacturerlincoln           -3.17958    1.48043  -2.148 0.033095 *  
manufacturermercury           -4.60493    1.42801  -3.225 0.001502 ** 
manufacturernissan            -4.87560    1.41981  -3.434 0.000741 ***
manufacturerpontiac            1.00415    0.80246   1.251 0.212464    
manufacturersubaru            -2.19715    1.24436  -1.766 0.079174 .  
manufacturertoyota            -5.50331    1.38981  -3.960 0.000109 ***
manufacturervolkswagen         0.91416    0.67962   1.345 0.180313    
modela4                        2.09185    1.18317   1.768 0.078783 .  
modela4 quattro                0.93240    1.11315   0.838 0.403370    
modela6 quattro                     NA         NA      NA       NA    
modelaltima                    4.65544    1.19531   3.895 0.000139 ***
modelc1500 suburban 2wd       -3.83985    1.38658  -2.769 0.006217 ** 
modelcamry                     5.07921    1.30992   3.877 0.000149 ***
modelcamry solara              5.13686    0.99411   5.167 6.36e-07 ***
modelcaravan 2wd               2.99227    0.51484   5.812 2.82e-08 ***
modelcivic                          NA         NA      NA       NA    
modelcorolla                   6.35934    1.11122   5.723 4.40e-08 ***
modelcorvette                  1.29816    1.46072   0.889 0.375365    
modeldakota pickup 4wd         0.26640    0.44834   0.594 0.553146    
modeldurango 4wd               0.13012    0.47705   0.273 0.785350    
modelexpedition 2wd           -1.48593    0.68925  -2.156 0.032444 *  
modelexplorer 4wd             -3.38590    0.53383  -6.343 1.83e-09 ***
modelf150 pickup 4wd          -3.89203    0.50532  -7.702 9.14e-13 ***
modelforester awd             -0.64526    0.63354  -1.019 0.309828    
modelgrand cherokee 4wd             NA         NA      NA       NA    
modelgrand prix                     NA         NA      NA       NA    
modelgti                      -1.43019    1.05924  -1.350 0.178675    
modelimpreza awd                    NA         NA      NA       NA    
modeljetta                    -1.20836    1.02930  -1.174 0.241986    
modelk1500 tahoe 4wd          -5.42887    1.42240  -3.817 0.000187 ***
modelland cruiser wagon 4wd    1.11909    0.82087   1.363 0.174520    
modelmalibu                         NA         NA      NA       NA    
modelmaxima                    3.54620    1.39625   2.540 0.011952 *  
modelmountaineer 4wd                NA         NA      NA       NA    
modelmustang                        NA         NA      NA       NA    
modelnavigator 2wd                  NA         NA      NA       NA    
modelnew beetle               -1.17533    1.31465  -0.894 0.372522    
modelpassat                         NA         NA      NA       NA    
modelpathfinder 4wd                 NA         NA      NA       NA    
modelram 1500 pickup 4wd            NA         NA      NA       NA    
modelrange rover                    NA         NA      NA       NA    
modelsonata                    1.06729    1.30742   0.816 0.415409    
modeltiburon                        NA         NA      NA       NA    
modeltoyota tacoma 4wd         0.10859    0.53127   0.204 0.838273    
displ                          0.27043    0.24309   1.112 0.267452    
year                           0.08069    0.02273   3.550 0.000494 ***
cyl                           -0.34384    0.14603  -2.355 0.019644 *  
transauto(l3)                 -0.62059    0.96716  -0.642 0.521920    
transauto(l4)                  0.92040    0.61091   1.507 0.133697    
transauto(l5)                  1.39939    0.58240   2.403 0.017305 *  
transauto(l6)                  1.29868    0.72348   1.795 0.074351 .  
transauto(s4)                  0.63402    0.91104   0.696 0.487389    
transauto(s5)                  2.14954    0.85193   2.523 0.012511 *  
transauto(s6)                  0.68904    0.62041   1.111 0.268239    
transmanual(m5)                1.11020    0.59822   1.856 0.065140 .  
transmanual(m6)                0.90339    0.56318   1.604 0.110476    
drvf                                NA         NA      NA       NA    
drvr                                NA         NA      NA       NA    
cty                            0.91478    0.05681  16.102  < 2e-16 ***
fld                           -1.07125    1.23220  -0.869 0.385819    
fle                           -4.92816    1.09789  -4.489 1.29e-05 ***
flp                           -4.13173    1.04039  -3.971 0.000104 ***
flr                           -3.32290    1.02569  -3.240 0.001429 ** 
classcompact                  -0.44789    0.78249  -0.572 0.567779    
classmidsize                   0.06069    1.18808   0.051 0.959318    
classminivan                        NA         NA      NA       NA    
classpickup                         NA         NA      NA       NA    
classsubcompact                     NA         NA      NA       NA    
classsuv                            NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9467 on 177 degrees of freedom
Multiple R-squared:  0.9808,	Adjusted R-squared:  0.9747 
F-statistic: 161.5 on 56 and 177 DF,  p-value: < 2.2e-16

# The "-o meanHwyMpg" moves the R dataframe, that's calculated within the R cell, back to Python.

In [6]: %%R -o meanHwyMpg
   ...: library(plyr)
   ...: meanHwyMpg <- ddply(mpg, .(manufacturer), summarize, meanHwyMpg = mean(hwy))
   ...: 

# And now, here we are back in Python, with the result from the previous R cell.

In [7]: meanHwyMpg
Out[7]: 
   manufacturer  meanHwyMpg
0          audi   26.444444
1     chevrolet   21.894737
2         dodge   17.945946
3          ford   19.360000
4         honda   32.555556
5       hyundai   26.857143
6          jeep   17.625000
7    land rover   16.500000
8       lincoln   17.000000
9       mercury   18.000000
10       nissan   24.615385
11      pontiac   26.400000
12       subaru   25.571429
13       toyota   24.911765
14   volkswagen   29.222222