# long_to_wide_converter works - spread true Code purrr::walk(list(df1, df2, df3, df4), dplyr::glimpse) Output Rows: 150 Columns: 5 $ .rowid 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17~ $ Petal.Length 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~ $ Petal.Width 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~ $ Sepal.Length 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~ $ Sepal.Width 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~ Rows: 32 Columns: 3 $ .rowid 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ~ $ `0` 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, ~ $ `1` NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~ Rows: 88 Columns: 5 $ .rowid 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,~ $ HDHF 10.0, 10.0, 9.0, 8.5, 3.0, 10.0, 10.0, 10.0, 0.0, 10.0, 8.5, 8.~ $ HDLF 9.0, 10.0, 6.0, 5.5, 7.5, 10.0, 9.0, 6.0, 0.0, 8.5, 6.5, 4.0, 6~ $ LDHF 6.0, 10.0, 9.0, 6.5, 0.5, 10.0, 10.0, 9.5, 2.5, 7.5, 8.5, 8.0, ~ $ LDLF 6.0, 5.0, 6.0, 3.0, 2.0, 10.0, 10.0, 9.5, 0.0, 9.5, 7.0, 3.0, 4~ Rows: 51 Columns: 5 $ .rowid 3, 4, 5, 7, 9, 12, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29,~ $ carni 0.0700, 0.0108, 0.0256, 0.3250, 0.0125, 0.1570, 0.0175, 0.0445~ $ herbi NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.42300, 0.09820, 0.11500,~ $ insecti NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~ $ omni NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~ --- Code purrr::map(list(df1, df2, df3, df4), summary) Output [[1]] .rowid Petal.Length Petal.Width Sepal.Length Min. : 1.00 Min. :1.000 Min. :0.100 Min. :4.300 1st Qu.: 38.25 1st Qu.:1.600 1st Qu.:0.300 1st Qu.:5.100 Median : 75.50 Median :4.350 Median :1.300 Median :5.800 Mean : 75.50 Mean :3.758 Mean :1.199 Mean :5.843 3rd Qu.:112.75 3rd Qu.:5.100 3rd Qu.:1.800 3rd Qu.:6.400 Max. :150.00 Max. :6.900 Max. :2.500 Max. :7.900 Sepal.Width Min. :2.000 1st Qu.:2.800 Median :3.000 Mean :3.057 3rd Qu.:3.300 Max. :4.400 [[2]] .rowid 0 1 Min. : 1.00 Min. :2.465 Min. :1.513 1st Qu.: 8.75 1st Qu.:3.438 1st Qu.:1.935 Median :16.50 Median :3.520 Median :2.320 Mean :16.50 Mean :3.769 Mean :2.411 3rd Qu.:24.25 3rd Qu.:3.842 3rd Qu.:2.780 Max. :32.00 Max. :5.424 Max. :3.570 NA's :13 NA's :19 [[3]] .rowid HDHF HDLF LDHF Min. : 1.00 Min. : 0.000 Min. : 0.000 Min. : 0.500 1st Qu.:24.75 1st Qu.: 6.000 1st Qu.: 4.375 1st Qu.: 6.000 Median :47.50 Median : 8.500 Median : 7.750 Median : 8.000 Mean :47.57 Mean : 7.824 Mean : 6.676 Mean : 7.352 3rd Qu.:70.25 3rd Qu.:10.000 3rd Qu.: 9.500 3rd Qu.: 9.500 Max. :93.00 Max. :10.000 Max. :10.000 Max. :10.000 LDLF Min. : 0.000 1st Qu.: 3.500 Median : 6.000 Mean : 5.659 3rd Qu.: 7.500 Max. :10.000 [[4]] .rowid carni herbi insecti Min. : 3.00 Min. :0.01080 Min. :0.000400 Min. :0.00025 1st Qu.:25.50 1st Qu.:0.01750 1st Qu.:0.005125 1st Qu.:0.00030 Median :46.00 Median :0.04450 Median :0.012285 Median :0.00120 Mean :42.96 Mean :0.07926 Mean :0.621598 Mean :0.02155 3rd Qu.:61.50 3rd Qu.:0.07000 3rd Qu.:0.236000 3rd Qu.:0.02500 Max. :76.00 Max. :0.32500 Max. :5.712000 Max. :0.08100 NA's :42 NA's :31 NA's :46 omni Min. :0.00014 1st Qu.:0.00260 Median :0.00660 Mean :0.14573 3rd Qu.:0.17900 Max. :1.32000 NA's :34 # long_to_wide_converter works - spread false Code purrr::walk(list(df1, df2, df3, df4), dplyr::glimpse) Output Rows: 600 Columns: 3 $ .rowid 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, ~ $ condition Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Petal.~ $ value 1.4, 0.2, 5.1, 3.5, 1.4, 0.2, 4.9, 3.0, 1.3, 0.2, 4.7, 3.2, ~ Rows: 32 Columns: 3 $ .rowid 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, ~ $ am 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ~ $ wt 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, ~ Rows: 352 Columns: 3 $ .rowid 1, 1, 1, 1, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, ~ $ condition HDHF, HDLF, LDHF, LDLF, HDHF, HDLF, LDHF, LDLF, HDHF, HDLF, ~ $ desire 10.0, 9.0, 6.0, 6.0, 10.0, 10.0, 10.0, 5.0, 9.0, 6.0, 9.0, 6~ Rows: 51 Columns: 3 $ .rowid 3, 4, 5, 7, 9, 12, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29,~ $ vore carni, carni, carni, carni, carni, carni, carni, carni, carni,~ $ brainwt 0.07000, 0.01080, 0.02560, 0.32500, 0.01250, 0.15700, 0.01750,~ --- Code purrr::map(list(df1, df2, df3, df4), summary) Output [[1]] .rowid condition value Min. : 1.0 Petal.Length:150 Min. :0.100 1st Qu.: 38.0 Petal.Width :150 1st Qu.:1.700 Median : 75.5 Sepal.Length:150 Median :3.200 Mean : 75.5 Sepal.Width :150 Mean :3.465 3rd Qu.:113.0 3rd Qu.:5.100 Max. :150.0 Max. :7.900 [[2]] .rowid am wt Min. : 1.00 0:19 Min. :1.513 1st Qu.: 8.75 1:13 1st Qu.:2.581 Median :16.50 Median :3.325 Mean :16.50 Mean :3.217 3rd Qu.:24.25 3rd Qu.:3.610 Max. :32.00 Max. :5.424 [[3]] .rowid condition desire Min. : 1.00 HDHF:88 Min. : 0.000 1st Qu.:24.75 HDLF:88 1st Qu.: 5.000 Median :47.50 LDHF:88 Median : 7.500 Mean :47.57 LDLF:88 Mean : 6.878 3rd Qu.:70.25 3rd Qu.: 9.500 Max. :93.00 Max. :10.000 [[4]] .rowid vore brainwt Min. : 3.00 carni : 9 Min. :0.00014 1st Qu.:25.50 herbi :20 1st Qu.:0.00375 Median :46.00 insecti: 5 Median :0.01550 Mean :42.96 omni :17 Mean :0.30844 3rd Qu.:61.50 3rd Qu.:0.16300 Max. :76.00 Max. :5.71200