如何使用rpy2通过for循环测试重要性？

Question

I am attempting to run a t-test using r (with the help of the rpy2 package), on some variables from a pandas dataframe. I am using magic functions in jupyter notebook to get python to interact with R. The interaction is successful, except for the loop.

Here is the dataframe:

df.head()
Out[60]: 
              ID Category  Num Vert_Horizon Description  Fem_Valence_Mean  \
0  Animals_001_h  Animals    1            h  Dead Stork              2.40   
1  Animals_002_v  Animals    2            v        Lion              6.31   
2  Animals_003_h  Animals    3            h       Snake              5.14   
3  Animals_004_v  Animals    4            v        Wolf              4.55   
4  Animals_005_h  Animals    5            h         Bat              5.29   

   Fem_Valence_SD  Fem_Av/Ap_Mean  Fem_Av/Ap_SD  Arousal_Mean       ...        \
0            1.30            3.03          1.47          6.72       ...         
1            2.19            5.96          2.24          6.69       ...         
2            1.19            5.14          1.75          5.34       ...         
3            1.87            4.82          2.27          6.84       ...         
4            1.56            4.61          1.81          5.50       ...         

   Luminance  Contrast  JPEG_size80   LABL   LABA   LABB  Entropy  \
0     126.05     68.45       263028  51.75  -0.39  16.93     7.86   
1     123.41     32.34       250208  52.39  10.63  30.30     6.71   
2     135.28     59.92       190887  55.45   0.25   4.41     7.83   
3     122.15     75.10       282350  49.84   3.82   1.36     7.69   
4     131.81     59.77       329325  54.26  -0.34  -0.95     7.82   

   Classification  valence_median_split  temp_selection  
0                           Low_Valence             OUT  
1                          High_Valence             NaN  
2                           Low_Valence             OUT  
3                           Low_Valence             OUT  
4                           Low_Valence             OUT  

[5 rows x 35 columns]

Here is how I attempted to do it:

%Rpush df

Variables = 'All_Valence_Mean', 'Male_Valence_Mean', 'Fem_Valence_Mean'

for var in Variables:
    %R var + '_Sig' <- t.test(var ~ valence_median_split, data = df, var.equal = TRUE)

I am attempting to get the results to be saved to the 'var' variable with a "Sig" string added. This component is not crucial, but what I'm really after is getting this command to recognize "var" as a variable in a list of variables.

Here is the error that I got:

Error in model.frame.default(formula = var ~ valence_median_split, data = df) : 
  invalid type (list) for variable 'var'

Error in model.frame.default(formula = var ~ valence_median_split, data = df) : 
  invalid type (list) for variable 'var'

Error in model.frame.default(formula = var ~ valence_median_split, data = df) : 
  invalid type (list) for variable 'var'
/anaconda3/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Error in model.frame.default(formula = var ~ valence_median_split, data = df) : 
  invalid type (list) for variable 'var'

  warnings.warn(x, RRuntimeWarning)

Answer 1

If you are more comfortable with R, push as much of the logical as you can to R. For example, this will store the results in results that you will be able to access from Python in subsequent notebook cells.

%%R -i df -o results

Variables <- c("All_Valence_Mean", "Male_Valence_Mean",
               "Fem_Valence_Mean")
results <- list()

for (var in Variables) {
    results[[paste0(var, '_Sig')]] <- t.test(
        as.formula(paste(var, '~ valence_median_split')),
        data = df, var.equal = TRUE)
}

If you are more comfortable with Python, keep as much as you can in Python:

Variables = ('All_Valence_Mean', 'Male_Valence_Mean',
             'Fem_Valence_Mean')
results = dict()
from rpy2.robjects.packages import importr
from rpy2.robjects import Formula
stats = importr('stats')

for var in Variables:
    results[('%s_Sig' % var] = stats.t_test(
        Formula('%s ~ valence_median_split' % var),
        data=df, var_equal=True)