various changes
This commit is contained in:
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5d9561f88a
commit
fe49a45447
3 changed files with 199 additions and 95 deletions
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@ -55,6 +55,7 @@ os.getcwd()
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#from combining import combine_dfs_with_checks
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from combining_FIXME import detect_common_cols
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from reference_dict import oneletter_aa_dict # CHECK DIR STRUC THERE!
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from reference_dict import low_3letter_dict # CHECK DIR STRUC THERE!
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#=======================================================================
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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@ -79,6 +80,21 @@ gene = args.gene
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datadir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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gene_match = gene + '_p.'
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print('mut pattern for gene', gene, ':', gene_match)
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nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
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print('nsSNP for gene', gene, ':', nssnp_match)
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wt_regex = gene_match.lower()+'([A-Za-z]{3})'
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print('wt regex:', wt_regex)
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mut_regex = r'[0-9]+(\w{3})$'
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print('mt regex:', mut_regex)
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pos_regex = r'([0-9]+)'
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print('position regex:', pos_regex)
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#%%=======================================================================
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#==============
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# directories
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@ -214,7 +230,9 @@ combined_df[merging_cols_m4].apply(len) == len(combined_df)
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# OR merges: TEDIOUSSSS!!!!
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del(mcsm_df, foldx_df, mcsm_foldx_dfs, dssp_kd_dfs, dssp_kd_rd_dfs,rd_df, kd_df, infile_mcsm, infile_foldx, infile_dssp, infile_kd)
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del(merging_cols_m1, merging_cols_m2, merging_cols_m3, merging_cols_m4)
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del(in_filename_dssp, in_filename_foldx, in_filename_kd, in_filename_mcsm, in_filename_rd)
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#%%
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print('==================================='
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, '\nFifth merge: afor_df + afor_kin_df'
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@ -235,7 +253,7 @@ print('Dim of afor_df:', afor_df.shape
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# finding if ALL afor_kin_df muts are present in afor_df
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# i.e all kinship muts should be PRESENT in mycalcs_present
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if len(afor_kin_df[afor_kin_df['mutation'].isin(afor_df['mutation'])]) == afor_kin_df.shape[0]:
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print('PASS: ALL or_kinship muts are present in my or list')
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print('PASS: ALL', len(afor_kin_df), 'or_kinship muts are present in my or list')
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else:
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nf_muts = len(afor_kin_df[~afor_kin_df['mutation'].isin(afor_df['mutation'])])
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nf_muts_df = afor_kin_df[~afor_kin_df['mutation'].isin(afor_df['mutation'])]
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@ -246,10 +264,10 @@ else:
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# Now checking how many afor_df muts are NOT present in afor_kin_df
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common_muts = len(afor_df[afor_df['mutation'].isin(afor_kin_df['mutation'])])
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extra_muts_myor = afor_kin_df.shape[0] - common_muts
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extra_muts_myor = afor_kin_df.shape[0] - common_muts
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print('=========================================='
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, '\nmy or calcs has', extra_muts_myor, 'extra mutations'
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, '\nmy or calcs has', common_muts, 'present in af_or_kin_df'
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, '\n==========================================')
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print('Expected cals for merging with outer_join...')
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@ -257,10 +275,15 @@ print('Expected cals for merging with outer_join...')
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expected_rows = afor_df.shape[0] + extra_muts_myor
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expected_cols = afor_df.shape[1] + afor_kin_df.shape[1] - len(merging_cols_m5)
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afor_df['mutation']
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afor_kin_df['mutation']
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ors_df = pd.merge(afor_df, afor_kin_df, on = merging_cols_m5, how = o_join)
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if ors_df.shape[0] == expected_rows and ors_df.shape[1] == expected_cols:
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print('PASS: OR dfs successfully combined! PHEWWWW!')
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print('PASS but with duplicate muts: OR dfs successfully combined! PHEWWWW!'
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, '\nDuplicate muts present but with different \'ref\' and \'alt\' alleles')
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else:
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print('FAIL: could not combine OR dfs'
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, '\nCheck expected rows and cols calculation and join type')
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@ -269,12 +292,12 @@ print('Dim of merged ors_df:', ors_df.shape)
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ors_df[merging_cols_m5].apply(len)
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ors_df[merging_cols_m5].apply(len) == len(ors_df)
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#%%============================================================================
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# formatting ors_df
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ors_df.columns
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# Dropping unncessary columns: already removed in ealier preprocessing
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#cols_to_drop = ['reference_allele', 'alternate_allele', 'symbol' ]
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cols_to_drop = ['n_miss']
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print('Dropping', len(cols_to_drop), 'columns:\n'
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, cols_to_drop)
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@ -327,7 +350,6 @@ column_order = ['mutation'
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#, 'wt_3let' # old
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#, 'mt_3let' # old
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#, 'symbol'
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#, 'n_miss'
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]
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if ( (len(column_order) == ors_df.shape[1]) and (DataFrame(column_order).isin(ors_df.columns).all().all())):
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@ -343,6 +365,61 @@ else:
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print('\nResult of Sixth merge:', ors_df_ordered.shape
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, '\n===================================================================')
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#%%
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ors_df_ordered.shape
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check = ors_df_ordered[['mutationinformation','mutation', 'wild_type', 'position', 'mutant_type']]
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# populating 'nan' info
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lookup_dict = dict()
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for k, v in low_3letter_dict.items():
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lookup_dict[k] = v['one_letter_code']
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#print(lookup_dict)
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wt = ors_df_ordered['mutation'].str.extract(wt_regex).squeeze()
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#print(wt)
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ors_df_ordered['wild_type'] = wt.map(lookup_dict)
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ors_df_ordered['position'] = ors_df_ordered['mutation'].str.extract(pos_regex)
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mt = ors_df_ordered['mutation'].str.extract(mut_regex).squeeze()
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ors_df_ordered['mutant_type'] = mt.map(lookup_dict)
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ors_df_ordered['mutationinformation'] = ors_df_ordered['wild_type'] + ors_df_ordered.position.map(str) + ors_df_ordered['mutant_type']
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check = ors_df_ordered[['mutationinformation','mutation', 'wild_type', 'position', 'mutant_type']]
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# populate mut_info_f1
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ors_df_ordered['mut_info_f1'].isna().sum()
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ors_df_ordered['mut_info_f1'] = ors_df_ordered['position'].astype(str) + ors_df_ordered['wild_type'] + '>' + ors_df_ordered['position'].astype(str) + ors_df_ordered['mutant_type']
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ors_df_ordered['mut_info_f1'].isna().sum()
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# populate mut_info_f2
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ors_df_ordered['mut_info_f2'] = ors_df_ordered['mutation'].str.replace(gene_match.lower(), 'p.', regex = True)
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# populate mut_type
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ors_df_ordered['mut_type'].isna().sum()
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#mut_type_word = ors_df_ordered['mut_type'].value_counts()
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mut_type_word = 'missense' # FIXME, should be derived
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ors_df_ordered['mut_type'].fillna(mut_type_word, inplace = True)
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ors_df_ordered['mut_type'].isna().sum()
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# populate gene_id
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ors_df_ordered['gene_id'].isna().sum()
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#gene_id_word = ors_df_ordered['gene_id'].value_counts()
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gene_id_word = 'Rv2043c' # FIXME, should be derived
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ors_df_ordered['gene_id'].fillna(gene_id_word, inplace = True)
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ors_df_ordered['gene_id'].isna().sum()
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# populate gene_name
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ors_df_ordered['gene_name'].isna().sum()
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ors_df_ordered['gene_name'].value_counts()
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ors_df_ordered['gene_name'].fillna(gene, inplace = True)
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ors_df_ordered['gene_name'].isna().sum()
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# check numbers
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ors_df_ordered['or_kin'].isna().sum()
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# should be 0
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ors_df_ordered['or_mychisq'].isna().sum()
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#%%============================================================================
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print('==================================='
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, '\nSixth merge: Fourth + Fifth merge'
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@ -351,78 +428,93 @@ print('==================================='
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#combined_df_all = combine_dfs_with_checks(combined_df, ors_df_ordered, my_join = i_join)
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merging_cols_m6 = detect_common_cols(combined_df, ors_df_ordered)
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# dtype problems
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if len(merging_cols_m6) > 1 and 'position'in merging_cols_m6:
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print('Removing \'position\' from merging_cols_m6 to make dtypes consistent'
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, '\norig length of merging_cols_m6:', len(merging_cols_m6))
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merging_cols_m6.remove('position')
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print('\nlength after removing:', len(merging_cols_m6))
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print('Dim of df1:', combined_df.shape
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, '\nDim of df2:', ors_df_ordered.shape
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, '\nNo. of merging_cols:', len(merging_cols_m6))
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print('Checking mutations in the two dfs:'
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, '\nmuts in df1 but NOT in df2:'
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, '\nmuts in df1 present in df2:'
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, combined_df['mutationinformation'].isin(ors_df_ordered['mutationinformation']).sum()
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, '\nmuts in df2 but NOT in df1:'
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, '\nmuts in df2 present in df1:'
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, ors_df_ordered['mutationinformation'].isin(combined_df['mutationinformation']).sum())
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#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df_ordered['mutationinformation']) )
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combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m6, how = l_join)
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#----------
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# merge 6
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#----------
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combined_df_all = pd.merge(combined_df, ors_df_ordered, on = merging_cols_m6, how = o_join)
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combined_df_all.shape
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# populate mut_info_f1
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combined_df_all['mut_info_f1'].isna().sum()
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combined_df_all['mut_info_f1'] = combined_df_all['position'].astype(str) + combined_df_all['wild_type'] + '>' + combined_df_all['position'].astype(str) + combined_df_all['mutant_type']
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combined_df_all['mut_info_f1'].isna().sum()
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# populate mut_type
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combined_df_all['mut_type'].isna().sum()
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#mut_type_word = combined_df_all['mut_type'].value_counts()
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mut_type_word = 'missense' # FIXME, should be derived
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combined_df_all['mut_type'].fillna(mut_type_word, inplace = True)
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combined_df_all['mut_type'].isna().sum()
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# populate gene_id
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combined_df_all['gene_id'].isna().sum()
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#gene_id_word = combined_df_all['gene_id'].value_counts()
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gene_id_word = 'Rv2043c' # FIXME, should be derived
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combined_df_all['gene_id'].fillna(gene_id_word, inplace = True)
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combined_df_all['gene_id'].isna().sum()
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# populate gene_name
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combined_df_all['gene_name'].isna().sum()
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combined_df_all['gene_name'].value_counts()
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combined_df_all['gene_name'].fillna(gene, inplace = True)
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combined_df_all['gene_name'].isna().sum()
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# FIXME: DIM
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# only with left join!
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outdf_expected_rows = len(combined_df)
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# sanity check for merge 6
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outdf_expected_rows = len(combined_df) + extra_muts_myor
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unique_muts = len(combined_df)
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outdf_expected_cols = len(combined_df.columns) + len(ors_df_ordered.columns) - len(merging_cols_m6)
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#if combined_df_all.shape[1] == outdf_expected_cols and combined_df_all.shape[0] == outdf_expected_rows:
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if combined_df_all.shape[1] == outdf_expected_cols and combined_df_all['mutationinformation'].nunique() == outdf_expected_rows:
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if combined_df_all.shape[0] == outdf_expected_rows and combined_df_all.shape[1] == outdf_expected_cols and combined_df_all['mutationinformation'].nunique() == unique_muts:
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print('PASS: Df dimension match'
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, '\nDim of combined_df_all with join type:', l_join
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, '\ncombined_df_all with join type:', o_join
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, '\n', combined_df_all.shape
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, '\n===============================================================')
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else:
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print('FAIL: Df dimension mismatch'
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, 'Cannot generate expected dim. See details of merge performed'
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, '\ndf1 dim:', combined_df.shape
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, '\ndf2 dim:', ors_df.shape
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, '\ndf2 dim:', ors_df_ordered.shape
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, '\nGot:', combined_df_all.shape
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, '\nmuts in df1 but NOT in df2:'
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, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
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, combined_df['mutationinformation'].isin(ors_df_ordered['mutationinformation']).sum()
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, '\nmuts in df2 but NOT in df1:'
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, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
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sys.exit()
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# drop extra cols
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all_cols = combined_df_all.columns
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#pos_cols_check = combined_df_all[['position_x','position_y']]
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c = combined_df_all[['position_x','position_y']].isna().sum()
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pos_col_to_drop = c.index[c>0].to_list()
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cols_to_drop = pos_col_to_drop + ['wild_type_kd']
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print('Dropping', len(cols_to_drop), 'columns:\n', cols_to_drop)
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combined_df_all.drop(cols_to_drop, axis = 1, inplace = True)
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# rename position_x to position
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pos_col_to_rename = c.index[c==0].to_list()
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combined_df_all.shape
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combined_df_all.rename(columns = { pos_col_to_rename[0]: 'position'}, inplace = True)
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combined_df_all.shape
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all_cols = combined_df_all.columns
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#%% reorder cols to for convenience
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first_cols = ['mutationinformation','mutation', 'wild_type', 'position', 'mutant_type']
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last_cols = [col for col in combined_df_all.columns if col not in first_cols]
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df = combined_df_all[first_cols+last_cols]
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#%% IMPORTANT: check if mutation related info is all populated after this merge
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# FIXME: should get fixed with JP's resolved dataset!?
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check_nan = combined_df_all.isna().sum(axis = 0)
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# select string colnames to ensure no NA exist there
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string_cols = combined_df_all.columns[combined_df_all.applymap(lambda x: isinstance(x, str)).all(0)]
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if (combined_df_all[string_cols].isna().sum(axis = 0)== 0).all():
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print('PASS: All string cols are populated with no NAs')
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else:
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print('FAIL: NAs detected in string cols')
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print(combined_df_all[string_cols].isna().sum(axis = 0))
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sys.exit()
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# relevant mut cols
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check_mut_cols = merging_cols_m5 + merging_cols_m6
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count_na_mut_cols = combined_df_all[check_mut_cols].isna().sum().reset_index().rename(columns = {'index': 'col_name', 0: 'na_count'})
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print(check_mut_cols)
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if (count_na_mut_cols['na_count'].sum() > 0).any():
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# FIXME: static override, generate 'mutation' from variable
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@ -434,31 +526,29 @@ else:
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print('No missing \'mutation\' has been detected!')
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lookup_dict = dict()
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for k, v in oneletter_aa_dict.items():
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lookup_dict[k] = v['three_letter_code_lower']
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print(lookup_dict)
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wt_3let = combined_df_all['wild_type'].map(lookup_dict)
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#print(wt_3let)
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pos = combined_df_all['position'].astype(str)
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#print(pos)
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mt_3let = combined_df_all['mutant_type'].map(lookup_dict)
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#print(mt_3let)
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# override the 'mutation' column
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combined_df_all['mutation'] = 'pnca_p.' + wt_3let + pos + mt_3let
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print(combined_df_all['mutation'])
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# populate mut_info_f2
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combined_df_all['mut_info_f2'] = combined_df_all['mutation'].str.replace(gene_match.lower(), 'p.', regex = True)
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#cols_to_drop = ['reference_allele', 'alternate_allele', 'symbol' ]
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col_to_drop = ['wild_type_kd']
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print('Dropping', len(col_to_drop), 'columns:\n'
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, col_to_drop)
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combined_df_all.drop(col_to_drop, axis = 1, inplace = True)
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#%% check
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#cols_check = check_mut_cols + ['mut_info_f1', 'mut_info_f2']
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#foo = combined_df_all[cols_check]
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foo = combined_df_all.drop_duplicates('mutationinformation')
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foo2 = combined_df_all.drop_duplicates('mutation')
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poo = combined_df_all[combined_df_all['mutation'].isna()]
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#%%============================================================================
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output_cols = combined_df_all.columns
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#print('Output cols:', output_cols)
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#%% drop duplicates
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if combined_df_all.shape[0] != outdf_expected_rows:
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print('INFORMARIONAL ONLY: combined_df_all has duplicate muts present but with unique ref and alt allele')
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else:
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print('combined_df_all has no duplicate muts present')
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#%%============================================================================
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# write csv
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print('Writing file: combined output of all params needed for plotting and ML')
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@ -81,16 +81,16 @@ gene = args.gene
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gene_match = gene + '_p.'
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print('mut pattern for gene', gene, ':', gene_match)
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nssnp_match = gene_match +'[A-Z]{3}[0-9]+[A-Z]{3}'
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nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
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print('nsSNP for gene', gene, ':', nssnp_match)
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wt_regex = gene_match.lower()+'(\w{3})'
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wt_regex = gene_match.lower()+'([A-Za-z]{3})'
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print('wt regex:', wt_regex)
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mut_regex = r'\d+(\w{3})$'
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mut_regex = r'[0-9]+(\w{3})$'
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print('mt regex:', mut_regex)
|
||||
|
||||
pos_regex = r'(\d+)'
|
||||
pos_regex = r'([0-9]+)'
|
||||
print('position regex:', pos_regex)
|
||||
|
||||
# building cols to extract
|
||||
|
|
@ -154,30 +154,29 @@ if in_filename_master == 'original_tanushree_data_v2.csv':
|
|||
else:
|
||||
core_cols = ['id'
|
||||
, 'sample'
|
||||
, 'patient_id'
|
||||
, 'strain'
|
||||
#, 'patient_id'
|
||||
#, 'strain'
|
||||
, 'lineage'
|
||||
, 'sublineage'
|
||||
, 'country'
|
||||
#, 'country'
|
||||
, 'country_code'
|
||||
, 'geographic_source'
|
||||
#, 'region'
|
||||
, 'location'
|
||||
, 'host_body_site'
|
||||
, 'environment_material'
|
||||
, 'host_status'
|
||||
, 'host_sex'
|
||||
, 'submitted_host_sex'
|
||||
, 'hiv_status'
|
||||
, 'HIV_status'
|
||||
, 'tissue_type'
|
||||
, 'isolation_source'
|
||||
#, 'location'
|
||||
#, 'host_body_site'
|
||||
#, 'environment_material'
|
||||
#, 'host_status'
|
||||
#, 'host_sex'
|
||||
#, 'submitted_host_sex'
|
||||
#, 'hiv_status'
|
||||
#, 'HIV_status'
|
||||
#, 'tissue_type'
|
||||
#, 'isolation_source'
|
||||
, resistance_col]
|
||||
|
||||
variable_based_cols = [drug
|
||||
, dr_muts_col
|
||||
, other_muts_col]
|
||||
#, resistance_col]
|
||||
|
||||
cols_to_extract = core_cols + variable_based_cols
|
||||
print('Extracting', len(cols_to_extract), 'columns from master data')
|
||||
|
|
@ -200,7 +199,7 @@ print('No. of NAs/column:' + '\n', meta_data.isna().sum()
|
|||
|
||||
#%% Write check file
|
||||
check_file = outdir + '/' + gene.lower() + '_gwas.csv'
|
||||
meta_data.to_csv(check_file)
|
||||
meta_data.to_csv(check_file, index = False)
|
||||
print('Writing subsetted gwas data'
|
||||
, '\nFile', check_file
|
||||
, '\nDim:', meta_data.shape)
|
||||
|
|
@ -215,9 +214,9 @@ print('Writing subsetted gwas data'
|
|||
# drug counts: complete samples for OR calcs
|
||||
meta_data[drug].value_counts()
|
||||
print('===========================================================\n'
|
||||
, 'RESULT: No. of Sus and Res samples:\n', meta_data[drug].value_counts()
|
||||
, 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts()
|
||||
, '\n===========================================================\n'
|
||||
, 'RESULT: Percentage of Sus and Res samples:\n', meta_data[drug].value_counts(normalize = True)*100
|
||||
, 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100
|
||||
, '\n===========================================================')
|
||||
|
||||
#%%
|
||||
|
|
|
|||
|
|
@ -58,6 +58,20 @@ rm(my_df, upos, dup_muts)
|
|||
#in_file1: output of plotting_data.R
|
||||
# my_df_u
|
||||
|
||||
# quick checks
|
||||
head(my_df_u[, c("mutation", "mutation2")])
|
||||
|
||||
cols_to_extract = c("mutationinformation", "mutation", "or_mychisq", "or_kin", "af", "af_kin")
|
||||
foo = my_df_u[, colnames(my_df_u)%in% cols_to_extract]
|
||||
|
||||
|
||||
which(is.na(my_df_u$af_kin)) == which(is.na(my_df_u$af))
|
||||
|
||||
|
||||
baz = cbind(my_df_u$mutation, my_df_u$or_mychisq, bar$mutation, bar$or_mychisq)
|
||||
colnames(baz) = c("my_df_u_muts", "my_df_u_or", "real_muts", "real_or")
|
||||
|
||||
|
||||
# infile 2: gene associated meta data
|
||||
#in_filename_gene_metadata = paste0(tolower(gene), "_meta_data_with_AFandOR.csv")
|
||||
in_filename_gene_metadata = paste0(tolower(gene), "_metadata.csv")
|
||||
|
|
@ -94,6 +108,7 @@ gene_metadata <- read.csv(infile_gene_metadata
|
|||
, header = T)
|
||||
cat("Dim:", dim(gene_metadata))
|
||||
|
||||
|
||||
# counting NAs in AF, OR cols:
|
||||
if (identical(sum(is.na(my_df_u$or_mychisq))
|
||||
, sum(is.na(my_df_u$pval_fisher))
|
||||
|
|
@ -230,9 +245,9 @@ if (identical(sum(is.na(merged_df3$or_kin))
|
|||
if ( identical( which(is.na(merged_df2$or_mychisq)), which(is.na(merged_df2$or_kin)))
|
||||
&& identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin)))
|
||||
&& identical( which(is.na(merged_df2$pval_fisher)), which(is.na(merged_df2$pwald_kin))) ){
|
||||
cat('PASS: Indices match for mychisq and kin ors missing values')
|
||||
cat("PASS: Indices match for mychisq and kin ors missing values")
|
||||
} else{
|
||||
cat('Index mismatch: mychisq and kin ors missing indices match')
|
||||
cat("Index mismatch: mychisq and kin ors missing indices match")
|
||||
quit()
|
||||
}
|
||||
|
||||
|
|
@ -245,7 +260,7 @@ cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
|
|||
,"\nfilename: merged_df2_comp")
|
||||
|
||||
if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
|
||||
print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
|
||||
print("mychisq and kin ors missing indices match. Procedding with omitting NAs")
|
||||
na_count_df2 = sum(is.na(merged_df2$af))
|
||||
merged_df2_comp = merged_df2[!is.na(merged_df2$af),]
|
||||
# sanity check: no +-1 gymnastics
|
||||
|
|
@ -262,7 +277,7 @@ if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
|
|||
,"\nGot no. of rows: ", nrow(merged_df2_comp))
|
||||
}
|
||||
}else{
|
||||
print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
|
||||
print("Index mismatch for mychisq and kin ors. Aborting NA ommission")
|
||||
}
|
||||
|
||||
#=========================
|
||||
|
|
@ -272,7 +287,7 @@ if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
|
|||
#=========================
|
||||
|
||||
if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
|
||||
print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
|
||||
print("mychisq and kin ors missing indices match. Procedding with omitting NAs")
|
||||
na_count_df3 = sum(is.na(merged_df3$af))
|
||||
#merged_df3_comp = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),] # a way
|
||||
merged_df3_comp = merged_df3[!is.na(merged_df3$af),] # another way
|
||||
|
|
@ -289,7 +304,7 @@ if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
|
|||
,"\nGot no. of rows: ", nrow(merged_df3_comp))
|
||||
}
|
||||
} else{
|
||||
print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
|
||||
print("Index mismatch for mychisq and kin ors. Aborting NA ommission")
|
||||
}
|
||||
|
||||
# alternate way of deriving merged_df3_comp
|
||||
|
|
@ -347,7 +362,7 @@ merged_df3_comp_lig = merged_df3_comp[merged_df3_comp$ligand_distance<10,]
|
|||
if (nrow(merged_df3_lig) == nrow(my_df_u_lig)){
|
||||
print("PASS: verified merged_df3_lig")
|
||||
}else{
|
||||
cat(paste0('FAIL: nrow mismatch for merged_df3_lig'
|
||||
cat(paste0("FAIL: nrow mismatch for merged_df3_lig"
|
||||
, "\nExpected:", nrow(my_df_u_lig)
|
||||
, "\nGot:", nrow(merged_df3_lig)))
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue