df <- read.csv("C:/rstudio/dataset_pendidikan.csv")
head(df)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar Rata2_Jam_Tidur
## 1 MHS001 3.45 2 58 238 7.5
## 2 MHS002 3.13 5 54 246 7.1
## 3 MHS003 2.75 4 61 91 7.0
## 4 MHS004 3.52 4 67 162 6.6
## 5 MHS005 3.57 4 56 258 7.4
## 6 MHS006 3.52 7 56 222 7.5str(df)## 'data.frame': 200 obs. of 6 variables:
## $ ID_Mahasiswa : chr "MHS001" "MHS002" "MHS003" "MHS004" ...
## $ IPK : num 3.45 3.13 2.75 3.52 3.57 3.52 2.97 2.86 2.74 3.24 ...
## $ Semester : int 2 5 4 4 4 7 8 2 6 3 ...
## $ Total_Kehadiran : int 58 54 61 67 56 56 43 55 63 57 ...
## $ Total_Jam_Belajar: int 238 246 91 162 258 222 153 114 189 201 ...
## $ Rata2_Jam_Tidur : num 7.5 7.1 7 6.6 7.4 7.5 7.4 6.2 7 6.3 ...structure(df)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar
## 1 MHS001 3.45 2 58 238
## 2 MHS002 3.13 5 54 246
## 3 MHS003 2.75 4 61 91
## 4 MHS004 3.52 4 67 162
## 5 MHS005 3.57 4 56 258
## 6 MHS006 3.52 7 56 222
## 7 MHS007 2.97 8 43 153
## 8 MHS008 2.86 2 55 114
## 9 MHS009 2.74 6 63 189
## 10 MHS010 3.24 3 57 201
## 11 MHS011 3.17 8 63 161
## 12 MHS012 3.29 8 61 139
## 13 MHS013 3.35 5 59 166
## 14 MHS014 3.40 6 61 196
## 15 MHS015 3.41 5 51 221
## 16 MHS016 3.24 7 57 264
## 17 MHS017 3.60 4 64 246
## 18 MHS018 2.97 3 56 185
## 19 MHS019 3.88 3 70 290
## 20 MHS020 3.67 3 63 189
## 21 MHS021 2.93 5 60 158
## 22 MHS022 2.70 4 50 148
## 23 MHS023 2.89 6 58 203
## 24 MHS024 3.39 1 60 306
## 25 MHS025 3.22 4 63 128
## 26 MHS026 2.74 4 56 242
## 27 MHS027 3.25 2 60 175
## 28 MHS028 2.51 5 60 179
## 29 MHS029 2.96 4 58 188
## 30 MHS030 2.51 3 44 140
## 31 MHS031 2.69 2 56 169
## 32 MHS032 2.31 7 52 95
## 33 MHS033 2.95 7 53 218
## 34 MHS034 3.57 5 57 202
## 35 MHS035 3.28 3 56 139
## 36 MHS036 3.67 8 52 226
## 37 MHS037 3.07 5 54 141
## 38 MHS038 2.95 4 54 161
## 39 MHS039 2.92 8 50 113
## 40 MHS040 2.46 3 55 166
## 41 MHS041 3.43 6 61 294
## 42 MHS042 3.75 3 59 236
## 43 MHS043 3.96 3 65 291
## 44 MHS044 3.86 6 70 225
## 45 MHS045 3.66 3 66 271
## 46 MHS046 3.85 5 70 329
## 47 MHS047 2.17 5 48 132
## 48 MHS048 3.51 2 61 225
## 49 MHS049 3.48 6 65 160
## 50 MHS050 2.89 5 63 179
## 51 MHS051 3.33 5 67 215
## 52 MHS052 4.00 4 70 377
## 53 MHS053 3.83 4 64 252
## 54 MHS054 3.36 5 57 247
## 55 MHS055 2.62 4 52 132
## 56 MHS056 2.87 6 49 84
## 57 MHS057 3.50 5 55 228
## 58 MHS058 2.88 4 58 162
## 59 MHS059 2.39 1 52 93
## 60 MHS060 3.21 7 50 187
## 61 MHS061 2.94 5 56 210
## 62 MHS062 3.23 3 63 225
## 63 MHS063 2.87 5 44 156
## 64 MHS064 2.99 1 59 140
## 65 MHS065 3.45 8 58 191
## 66 MHS066 2.93 8 60 244
## 67 MHS067 2.40 1 51 77
## 68 MHS068 3.26 4 56 203
## 69 MHS069 3.97 6 70 282
## 70 MHS070 3.29 3 56 221
## 71 MHS071 2.43 5 53 102
## 72 MHS072 3.65 4 55 268
## 73 MHS073 2.83 6 44 126
## 74 MHS074 2.66 8 49 70
## 75 MHS075 3.07 7 65 148
## 76 MHS076 3.19 5 57 187
## 77 MHS077 3.58 2 54 215
## 78 MHS078 2.43 8 38 103
## 79 MHS079 2.57 1 57 45
## 80 MHS080 2.92 3 60 145
## 81 MHS081 2.61 3 57 196
## 82 MHS082 3.54 6 67 262
## 83 MHS083 3.78 4 59 264
## 84 MHS084 2.70 1 55 180
## 85 MHS085 3.39 1 62 252
## 86 MHS086 3.31 5 56 232
## 87 MHS087 2.31 5 44 142
## 88 MHS088 3.34 5 58 196
## 89 MHS089 2.95 5 62 197
## 90 MHS090 2.97 7 56 103
## 91 MHS091 3.73 8 70 264
## 92 MHS092 3.23 2 65 202
## 93 MHS093 3.33 8 58 198
## 94 MHS094 3.61 7 59 213
## 95 MHS095 3.15 5 51 159
## 96 MHS096 3.53 6 68 253
## 97 MHS097 3.35 5 64 249
## 98 MHS098 3.13 8 62 193
## 99 MHS099 2.88 3 55 249
## 100 MHS100 3.68 5 68 182
## 101 MHS101 2.66 7 56 119
## 102 MHS102 3.21 2 62 222
## 103 MHS103 2.58 5 43 116
## 104 MHS104 2.77 4 60 163
## 105 MHS105 3.24 6 49 194
## 106 MHS106 3.33 1 66 247
## 107 MHS107 2.32 5 61 66
## 108 MHS108 3.01 7 63 167
## 109 MHS109 3.66 5 70 281
## 110 MHS110 3.59 3 62 251
## 111 MHS111 4.00 6 61 258
## 112 MHS112 3.53 3 49 270
## 113 MHS113 2.88 6 50 170
## 114 MHS114 3.06 2 62 197
## 115 MHS115 3.51 3 69 219
## 116 MHS116 3.00 1 57 157
## 117 MHS117 3.59 1 70 284
## 118 MHS118 3.43 5 56 233
## 119 MHS119 2.06 3 53 40
## 120 MHS120 3.72 5 64 184
## 121 MHS121 2.88 6 59 178
## 122 MHS122 3.05 7 60 190
## 123 MHS123 4.00 4 66 341
## 124 MHS124 2.77 3 52 169
## 125 MHS125 3.38 3 55 188
## 126 MHS126 2.78 2 56 222
## 127 MHS127 2.64 6 54 136
## 128 MHS128 2.19 7 59 107
## 129 MHS129 3.53 4 55 210
## 130 MHS130 3.30 5 64 273
## 131 MHS131 2.71 8 48 154
## 132 MHS132 2.98 6 48 191
## 133 MHS133 3.31 6 60 169
## 134 MHS134 3.71 7 55 265
## 135 MHS135 3.15 3 52 174
## 136 MHS136 3.58 6 64 195
## 137 MHS137 3.03 6 56 209
## 138 MHS138 2.66 7 51 90
## 139 MHS139 3.16 3 56 175
## 140 MHS140 3.46 6 56 235
## 141 MHS141 3.65 5 62 263
## 142 MHS142 3.02 4 56 220
## 143 MHS143 3.18 2 66 244
## 144 MHS144 2.55 4 58 138
## 145 MHS145 2.58 8 40 167
## 146 MHS146 2.40 3 49 92
## 147 MHS147 2.78 2 60 180
## 148 MHS148 3.24 4 66 204
## 149 MHS149 3.15 2 58 165
## 150 MHS150 3.31 3 62 158
## 151 MHS151 1.89 8 37 69
## 152 MHS152 3.10 3 62 155
## 153 MHS153 2.64 5 60 153
## 154 MHS154 2.95 3 64 172
## 155 MHS155 3.20 6 46 244
## 156 MHS156 3.16 2 61 241
## 157 MHS157 2.95 7 57 189
## 158 MHS158 3.75 4 70 259
## 159 MHS159 2.74 2 59 221
## 160 MHS160 2.16 6 41 40
## 161 MHS161 2.99 5 59 165
## 162 MHS162 2.96 4 54 140
## 163 MHS163 3.44 3 67 180
## 164 MHS164 3.68 7 62 256
## 165 MHS165 2.90 4 57 168
## 166 MHS166 3.78 1 70 207
## 167 MHS167 3.74 8 65 245
## 168 MHS168 2.53 1 53 160
## 169 MHS169 3.18 2 58 177
## 170 MHS170 3.01 4 65 157
## 171 MHS171 2.13 2 59 148
## 172 MHS172 3.80 3 64 380
## 173 MHS173 2.94 5 54 183
## 174 MHS174 2.53 8 49 83
## 175 MHS175 3.00 8 66 206
## 176 MHS176 3.78 1 66 334
## 177 MHS177 3.18 7 53 169
## 178 MHS178 2.59 2 54 212
## 179 MHS179 3.54 7 62 255
## 180 MHS180 2.33 3 50 171
## 181 MHS181 3.25 6 60 195
## 182 MHS182 2.74 6 49 163
## 183 MHS183 2.57 7 49 163
## 184 MHS184 3.52 8 62 173
## 185 MHS185 3.83 3 66 278
## 186 MHS186 3.96 5 69 247
## 187 MHS187 3.14 6 61 143
## 188 MHS188 2.95 1 52 169
## 189 MHS189 3.28 6 59 228
## 190 MHS190 3.45 3 61 213
## 191 MHS191 3.15 2 62 218
## 192 MHS192 3.56 5 66 283
## 193 MHS193 2.54 4 50 141
## 194 MHS194 1.99 5 46 74
## 195 MHS195 3.04 6 62 142
## 196 MHS196 3.15 5 64 164
## 197 MHS197 3.15 8 54 212
## 198 MHS198 3.06 2 55 168
## 199 MHS199 2.63 3 57 102
## 200 MHS200 3.21 4 55 250
## Rata2_Jam_Tidur
## 1 7.5
## 2 7.1
## 3 7.0
## 4 6.6
## 5 7.4
## 6 7.5
## 7 7.4
## 8 6.2
## 9 7.0
## 10 6.3
## 11 6.3
## 12 7.5
## 13 6.7
## 14 6.5
## 15 7.2
## 16 7.2
## 17 6.1
## 18 5.9
## 19 7.4
## 20 7.2
## 21 6.4
## 22 7.6
## 23 6.6
## 24 5.0
## 25 5.7
## 26 5.3
## 27 7.6
## 28 7.0
## 29 6.6
## 30 4.3
## 31 5.1
## 32 6.2
## 33 4.9
## 34 7.4
## 35 6.4
## 36 6.5
## 37 6.5
## 38 5.6
## 39 5.6
## 40 4.7
## 41 6.6
## 42 6.9
## 43 6.6
## 44 6.6
## 45 6.0
## 46 7.1
## 47 4.2
## 48 5.7
## 49 6.1
## 50 6.2
## 51 7.1
## 52 6.8
## 53 6.9
## 54 7.8
## 55 7.7
## 56 7.0
## 57 7.8
## 58 8.1
## 59 6.7
## 60 5.3
## 61 7.6
## 62 5.8
## 63 8.3
## 64 8.1
## 65 6.1
## 66 5.6
## 67 5.8
## 68 6.7
## 69 8.1
## 70 5.6
## 71 7.2
## 72 4.8
## 73 7.4
## 74 6.3
## 75 6.9
## 76 6.5
## 77 7.3
## 78 6.6
## 79 7.9
## 80 5.0
## 81 5.8
## 82 5.5
## 83 7.5
## 84 5.0
## 85 8.9
## 86 6.7
## 87 7.6
## 88 5.7
## 89 6.9
## 90 7.0
## 91 6.1
## 92 6.6
## 93 8.0
## 94 7.8
## 95 6.9
## 96 8.7
## 97 5.0
## 98 6.9
## 99 6.3
## 100 7.9
## 101 7.5
## 102 7.7
## 103 5.9
## 104 7.2
## 105 7.5
## 106 7.9
## 107 5.7
## 108 6.0
## 109 7.3
## 110 8.0
## 111 7.2
## 112 7.8
## 113 4.7
## 114 6.5
## 115 7.3
## 116 6.9
## 117 5.1
## 118 7.6
## 119 6.6
## 120 6.1
## 121 7.5
## 122 5.0
## 123 6.2
## 124 7.3
## 125 6.9
## 126 6.4
## 127 6.4
## 128 6.3
## 129 7.4
## 130 6.5
## 131 7.1
## 132 4.6
## 133 7.7
## 134 8.8
## 135 7.0
## 136 6.5
## 137 6.1
## 138 7.1
## 139 6.0
## 140 5.8
## 141 4.9
## 142 6.7
## 143 6.4
## 144 6.4
## 145 6.4
## 146 5.8
## 147 6.7
## 148 5.7
## 149 6.4
## 150 6.2
## 151 4.6
## 152 5.4
## 153 6.1
## 154 6.2
## 155 6.9
## 156 6.2
## 157 6.5
## 158 6.4
## 159 4.2
## 160 6.1
## 161 6.0
## 162 6.3
## 163 6.1
## 164 7.4
## 165 6.7
## 166 7.4
## 167 7.6
## 168 5.9
## 169 7.4
## 170 6.8
## 171 5.5
## 172 6.8
## 173 6.6
## 174 7.6
## 175 6.3
## 176 8.2
## 177 7.2
## 178 4.6
## 179 5.7
## 180 6.9
## 181 5.2
## 182 6.9
## 183 7.8
## 184 6.3
## 185 8.1
## 186 7.9
## 187 6.6
## 188 6.5
## 189 6.9
## 190 7.1
## 191 5.3
## 192 6.7
## 193 4.9
## 194 6.0
## 195 6.4
## 196 5.0
## 197 5.5
## 198 6.2
## 199 8.5
## 200 5.4library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf %>%
slice_max(IPK, n = 3)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar Rata2_Jam_Tidur
## 1 MHS052 4 4 70 377 6.8
## 2 MHS111 4 6 61 258 7.2
## 3 MHS123 4 4 66 341 6.2data_baru <- mutate(df, persen_kehadiran = Total_Kehadiran/70*100 )
head(data_baru, 200)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar
## 1 MHS001 3.45 2 58 238
## 2 MHS002 3.13 5 54 246
## 3 MHS003 2.75 4 61 91
## 4 MHS004 3.52 4 67 162
## 5 MHS005 3.57 4 56 258
## 6 MHS006 3.52 7 56 222
## 7 MHS007 2.97 8 43 153
## 8 MHS008 2.86 2 55 114
## 9 MHS009 2.74 6 63 189
## 10 MHS010 3.24 3 57 201
## 11 MHS011 3.17 8 63 161
## 12 MHS012 3.29 8 61 139
## 13 MHS013 3.35 5 59 166
## 14 MHS014 3.40 6 61 196
## 15 MHS015 3.41 5 51 221
## 16 MHS016 3.24 7 57 264
## 17 MHS017 3.60 4 64 246
## 18 MHS018 2.97 3 56 185
## 19 MHS019 3.88 3 70 290
## 20 MHS020 3.67 3 63 189
## 21 MHS021 2.93 5 60 158
## 22 MHS022 2.70 4 50 148
## 23 MHS023 2.89 6 58 203
## 24 MHS024 3.39 1 60 306
## 25 MHS025 3.22 4 63 128
## 26 MHS026 2.74 4 56 242
## 27 MHS027 3.25 2 60 175
## 28 MHS028 2.51 5 60 179
## 29 MHS029 2.96 4 58 188
## 30 MHS030 2.51 3 44 140
## 31 MHS031 2.69 2 56 169
## 32 MHS032 2.31 7 52 95
## 33 MHS033 2.95 7 53 218
## 34 MHS034 3.57 5 57 202
## 35 MHS035 3.28 3 56 139
## 36 MHS036 3.67 8 52 226
## 37 MHS037 3.07 5 54 141
## 38 MHS038 2.95 4 54 161
## 39 MHS039 2.92 8 50 113
## 40 MHS040 2.46 3 55 166
## 41 MHS041 3.43 6 61 294
## 42 MHS042 3.75 3 59 236
## 43 MHS043 3.96 3 65 291
## 44 MHS044 3.86 6 70 225
## 45 MHS045 3.66 3 66 271
## 46 MHS046 3.85 5 70 329
## 47 MHS047 2.17 5 48 132
## 48 MHS048 3.51 2 61 225
## 49 MHS049 3.48 6 65 160
## 50 MHS050 2.89 5 63 179
## 51 MHS051 3.33 5 67 215
## 52 MHS052 4.00 4 70 377
## 53 MHS053 3.83 4 64 252
## 54 MHS054 3.36 5 57 247
## 55 MHS055 2.62 4 52 132
## 56 MHS056 2.87 6 49 84
## 57 MHS057 3.50 5 55 228
## 58 MHS058 2.88 4 58 162
## 59 MHS059 2.39 1 52 93
## 60 MHS060 3.21 7 50 187
## 61 MHS061 2.94 5 56 210
## 62 MHS062 3.23 3 63 225
## 63 MHS063 2.87 5 44 156
## 64 MHS064 2.99 1 59 140
## 65 MHS065 3.45 8 58 191
## 66 MHS066 2.93 8 60 244
## 67 MHS067 2.40 1 51 77
## 68 MHS068 3.26 4 56 203
## 69 MHS069 3.97 6 70 282
## 70 MHS070 3.29 3 56 221
## 71 MHS071 2.43 5 53 102
## 72 MHS072 3.65 4 55 268
## 73 MHS073 2.83 6 44 126
## 74 MHS074 2.66 8 49 70
## 75 MHS075 3.07 7 65 148
## 76 MHS076 3.19 5 57 187
## 77 MHS077 3.58 2 54 215
## 78 MHS078 2.43 8 38 103
## 79 MHS079 2.57 1 57 45
## 80 MHS080 2.92 3 60 145
## 81 MHS081 2.61 3 57 196
## 82 MHS082 3.54 6 67 262
## 83 MHS083 3.78 4 59 264
## 84 MHS084 2.70 1 55 180
## 85 MHS085 3.39 1 62 252
## 86 MHS086 3.31 5 56 232
## 87 MHS087 2.31 5 44 142
## 88 MHS088 3.34 5 58 196
## 89 MHS089 2.95 5 62 197
## 90 MHS090 2.97 7 56 103
## 91 MHS091 3.73 8 70 264
## 92 MHS092 3.23 2 65 202
## 93 MHS093 3.33 8 58 198
## 94 MHS094 3.61 7 59 213
## 95 MHS095 3.15 5 51 159
## 96 MHS096 3.53 6 68 253
## 97 MHS097 3.35 5 64 249
## 98 MHS098 3.13 8 62 193
## 99 MHS099 2.88 3 55 249
## 100 MHS100 3.68 5 68 182
## 101 MHS101 2.66 7 56 119
## 102 MHS102 3.21 2 62 222
## 103 MHS103 2.58 5 43 116
## 104 MHS104 2.77 4 60 163
## 105 MHS105 3.24 6 49 194
## 106 MHS106 3.33 1 66 247
## 107 MHS107 2.32 5 61 66
## 108 MHS108 3.01 7 63 167
## 109 MHS109 3.66 5 70 281
## 110 MHS110 3.59 3 62 251
## 111 MHS111 4.00 6 61 258
## 112 MHS112 3.53 3 49 270
## 113 MHS113 2.88 6 50 170
## 114 MHS114 3.06 2 62 197
## 115 MHS115 3.51 3 69 219
## 116 MHS116 3.00 1 57 157
## 117 MHS117 3.59 1 70 284
## 118 MHS118 3.43 5 56 233
## 119 MHS119 2.06 3 53 40
## 120 MHS120 3.72 5 64 184
## 121 MHS121 2.88 6 59 178
## 122 MHS122 3.05 7 60 190
## 123 MHS123 4.00 4 66 341
## 124 MHS124 2.77 3 52 169
## 125 MHS125 3.38 3 55 188
## 126 MHS126 2.78 2 56 222
## 127 MHS127 2.64 6 54 136
## 128 MHS128 2.19 7 59 107
## 129 MHS129 3.53 4 55 210
## 130 MHS130 3.30 5 64 273
## 131 MHS131 2.71 8 48 154
## 132 MHS132 2.98 6 48 191
## 133 MHS133 3.31 6 60 169
## 134 MHS134 3.71 7 55 265
## 135 MHS135 3.15 3 52 174
## 136 MHS136 3.58 6 64 195
## 137 MHS137 3.03 6 56 209
## 138 MHS138 2.66 7 51 90
## 139 MHS139 3.16 3 56 175
## 140 MHS140 3.46 6 56 235
## 141 MHS141 3.65 5 62 263
## 142 MHS142 3.02 4 56 220
## 143 MHS143 3.18 2 66 244
## 144 MHS144 2.55 4 58 138
## 145 MHS145 2.58 8 40 167
## 146 MHS146 2.40 3 49 92
## 147 MHS147 2.78 2 60 180
## 148 MHS148 3.24 4 66 204
## 149 MHS149 3.15 2 58 165
## 150 MHS150 3.31 3 62 158
## 151 MHS151 1.89 8 37 69
## 152 MHS152 3.10 3 62 155
## 153 MHS153 2.64 5 60 153
## 154 MHS154 2.95 3 64 172
## 155 MHS155 3.20 6 46 244
## 156 MHS156 3.16 2 61 241
## 157 MHS157 2.95 7 57 189
## 158 MHS158 3.75 4 70 259
## 159 MHS159 2.74 2 59 221
## 160 MHS160 2.16 6 41 40
## 161 MHS161 2.99 5 59 165
## 162 MHS162 2.96 4 54 140
## 163 MHS163 3.44 3 67 180
## 164 MHS164 3.68 7 62 256
## 165 MHS165 2.90 4 57 168
## 166 MHS166 3.78 1 70 207
## 167 MHS167 3.74 8 65 245
## 168 MHS168 2.53 1 53 160
## 169 MHS169 3.18 2 58 177
## 170 MHS170 3.01 4 65 157
## 171 MHS171 2.13 2 59 148
## 172 MHS172 3.80 3 64 380
## 173 MHS173 2.94 5 54 183
## 174 MHS174 2.53 8 49 83
## 175 MHS175 3.00 8 66 206
## 176 MHS176 3.78 1 66 334
## 177 MHS177 3.18 7 53 169
## 178 MHS178 2.59 2 54 212
## 179 MHS179 3.54 7 62 255
## 180 MHS180 2.33 3 50 171
## 181 MHS181 3.25 6 60 195
## 182 MHS182 2.74 6 49 163
## 183 MHS183 2.57 7 49 163
## 184 MHS184 3.52 8 62 173
## 185 MHS185 3.83 3 66 278
## 186 MHS186 3.96 5 69 247
## 187 MHS187 3.14 6 61 143
## 188 MHS188 2.95 1 52 169
## 189 MHS189 3.28 6 59 228
## 190 MHS190 3.45 3 61 213
## 191 MHS191 3.15 2 62 218
## 192 MHS192 3.56 5 66 283
## 193 MHS193 2.54 4 50 141
## 194 MHS194 1.99 5 46 74
## 195 MHS195 3.04 6 62 142
## 196 MHS196 3.15 5 64 164
## 197 MHS197 3.15 8 54 212
## 198 MHS198 3.06 2 55 168
## 199 MHS199 2.63 3 57 102
## 200 MHS200 3.21 4 55 250
## Rata2_Jam_Tidur persen_kehadiran
## 1 7.5 82.85714
## 2 7.1 77.14286
## 3 7.0 87.14286
## 4 6.6 95.71429
## 5 7.4 80.00000
## 6 7.5 80.00000
## 7 7.4 61.42857
## 8 6.2 78.57143
## 9 7.0 90.00000
## 10 6.3 81.42857
## 11 6.3 90.00000
## 12 7.5 87.14286
## 13 6.7 84.28571
## 14 6.5 87.14286
## 15 7.2 72.85714
## 16 7.2 81.42857
## 17 6.1 91.42857
## 18 5.9 80.00000
## 19 7.4 100.00000
## 20 7.2 90.00000
## 21 6.4 85.71429
## 22 7.6 71.42857
## 23 6.6 82.85714
## 24 5.0 85.71429
## 25 5.7 90.00000
## 26 5.3 80.00000
## 27 7.6 85.71429
## 28 7.0 85.71429
## 29 6.6 82.85714
## 30 4.3 62.85714
## 31 5.1 80.00000
## 32 6.2 74.28571
## 33 4.9 75.71429
## 34 7.4 81.42857
## 35 6.4 80.00000
## 36 6.5 74.28571
## 37 6.5 77.14286
## 38 5.6 77.14286
## 39 5.6 71.42857
## 40 4.7 78.57143
## 41 6.6 87.14286
## 42 6.9 84.28571
## 43 6.6 92.85714
## 44 6.6 100.00000
## 45 6.0 94.28571
## 46 7.1 100.00000
## 47 4.2 68.57143
## 48 5.7 87.14286
## 49 6.1 92.85714
## 50 6.2 90.00000
## 51 7.1 95.71429
## 52 6.8 100.00000
## 53 6.9 91.42857
## 54 7.8 81.42857
## 55 7.7 74.28571
## 56 7.0 70.00000
## 57 7.8 78.57143
## 58 8.1 82.85714
## 59 6.7 74.28571
## 60 5.3 71.42857
## 61 7.6 80.00000
## 62 5.8 90.00000
## 63 8.3 62.85714
## 64 8.1 84.28571
## 65 6.1 82.85714
## 66 5.6 85.71429
## 67 5.8 72.85714
## 68 6.7 80.00000
## 69 8.1 100.00000
## 70 5.6 80.00000
## 71 7.2 75.71429
## 72 4.8 78.57143
## 73 7.4 62.85714
## 74 6.3 70.00000
## 75 6.9 92.85714
## 76 6.5 81.42857
## 77 7.3 77.14286
## 78 6.6 54.28571
## 79 7.9 81.42857
## 80 5.0 85.71429
## 81 5.8 81.42857
## 82 5.5 95.71429
## 83 7.5 84.28571
## 84 5.0 78.57143
## 85 8.9 88.57143
## 86 6.7 80.00000
## 87 7.6 62.85714
## 88 5.7 82.85714
## 89 6.9 88.57143
## 90 7.0 80.00000
## 91 6.1 100.00000
## 92 6.6 92.85714
## 93 8.0 82.85714
## 94 7.8 84.28571
## 95 6.9 72.85714
## 96 8.7 97.14286
## 97 5.0 91.42857
## 98 6.9 88.57143
## 99 6.3 78.57143
## 100 7.9 97.14286
## 101 7.5 80.00000
## 102 7.7 88.57143
## 103 5.9 61.42857
## 104 7.2 85.71429
## 105 7.5 70.00000
## 106 7.9 94.28571
## 107 5.7 87.14286
## 108 6.0 90.00000
## 109 7.3 100.00000
## 110 8.0 88.57143
## 111 7.2 87.14286
## 112 7.8 70.00000
## 113 4.7 71.42857
## 114 6.5 88.57143
## 115 7.3 98.57143
## 116 6.9 81.42857
## 117 5.1 100.00000
## 118 7.6 80.00000
## 119 6.6 75.71429
## 120 6.1 91.42857
## 121 7.5 84.28571
## 122 5.0 85.71429
## 123 6.2 94.28571
## 124 7.3 74.28571
## 125 6.9 78.57143
## 126 6.4 80.00000
## 127 6.4 77.14286
## 128 6.3 84.28571
## 129 7.4 78.57143
## 130 6.5 91.42857
## 131 7.1 68.57143
## 132 4.6 68.57143
## 133 7.7 85.71429
## 134 8.8 78.57143
## 135 7.0 74.28571
## 136 6.5 91.42857
## 137 6.1 80.00000
## 138 7.1 72.85714
## 139 6.0 80.00000
## 140 5.8 80.00000
## 141 4.9 88.57143
## 142 6.7 80.00000
## 143 6.4 94.28571
## 144 6.4 82.85714
## 145 6.4 57.14286
## 146 5.8 70.00000
## 147 6.7 85.71429
## 148 5.7 94.28571
## 149 6.4 82.85714
## 150 6.2 88.57143
## 151 4.6 52.85714
## 152 5.4 88.57143
## 153 6.1 85.71429
## 154 6.2 91.42857
## 155 6.9 65.71429
## 156 6.2 87.14286
## 157 6.5 81.42857
## 158 6.4 100.00000
## 159 4.2 84.28571
## 160 6.1 58.57143
## 161 6.0 84.28571
## 162 6.3 77.14286
## 163 6.1 95.71429
## 164 7.4 88.57143
## 165 6.7 81.42857
## 166 7.4 100.00000
## 167 7.6 92.85714
## 168 5.9 75.71429
## 169 7.4 82.85714
## 170 6.8 92.85714
## 171 5.5 84.28571
## 172 6.8 91.42857
## 173 6.6 77.14286
## 174 7.6 70.00000
## 175 6.3 94.28571
## 176 8.2 94.28571
## 177 7.2 75.71429
## 178 4.6 77.14286
## 179 5.7 88.57143
## 180 6.9 71.42857
## 181 5.2 85.71429
## 182 6.9 70.00000
## 183 7.8 70.00000
## 184 6.3 88.57143
## 185 8.1 94.28571
## 186 7.9 98.57143
## 187 6.6 87.14286
## 188 6.5 74.28571
## 189 6.9 84.28571
## 190 7.1 87.14286
## 191 5.3 88.57143
## 192 6.7 94.28571
## 193 4.9 71.42857
## 194 6.0 65.71429
## 195 6.4 88.57143
## 196 5.0 91.42857
## 197 5.5 77.14286
## 198 6.2 78.57143
## 199 8.5 81.42857
## 200 5.4 78.57143data_baru_asc <- arrange(data_baru, persen_kehadiran)
head(data_baru_asc, n=63)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar Rata2_Jam_Tidur
## 1 MHS151 1.89 8 37 69 4.6
## 2 MHS078 2.43 8 38 103 6.6
## 3 MHS145 2.58 8 40 167 6.4
## 4 MHS160 2.16 6 41 40 6.1
## 5 MHS007 2.97 8 43 153 7.4
## 6 MHS103 2.58 5 43 116 5.9
## 7 MHS030 2.51 3 44 140 4.3
## 8 MHS063 2.87 5 44 156 8.3
## 9 MHS073 2.83 6 44 126 7.4
## 10 MHS087 2.31 5 44 142 7.6
## 11 MHS155 3.20 6 46 244 6.9
## 12 MHS194 1.99 5 46 74 6.0
## 13 MHS047 2.17 5 48 132 4.2
## 14 MHS131 2.71 8 48 154 7.1
## 15 MHS132 2.98 6 48 191 4.6
## 16 MHS056 2.87 6 49 84 7.0
## 17 MHS074 2.66 8 49 70 6.3
## 18 MHS105 3.24 6 49 194 7.5
## 19 MHS112 3.53 3 49 270 7.8
## 20 MHS146 2.40 3 49 92 5.8
## 21 MHS174 2.53 8 49 83 7.6
## 22 MHS182 2.74 6 49 163 6.9
## 23 MHS183 2.57 7 49 163 7.8
## 24 MHS022 2.70 4 50 148 7.6
## 25 MHS039 2.92 8 50 113 5.6
## 26 MHS060 3.21 7 50 187 5.3
## 27 MHS113 2.88 6 50 170 4.7
## 28 MHS180 2.33 3 50 171 6.9
## 29 MHS193 2.54 4 50 141 4.9
## 30 MHS015 3.41 5 51 221 7.2
## 31 MHS067 2.40 1 51 77 5.8
## 32 MHS095 3.15 5 51 159 6.9
## 33 MHS138 2.66 7 51 90 7.1
## 34 MHS032 2.31 7 52 95 6.2
## 35 MHS036 3.67 8 52 226 6.5
## 36 MHS055 2.62 4 52 132 7.7
## 37 MHS059 2.39 1 52 93 6.7
## 38 MHS124 2.77 3 52 169 7.3
## 39 MHS135 3.15 3 52 174 7.0
## 40 MHS188 2.95 1 52 169 6.5
## 41 MHS033 2.95 7 53 218 4.9
## 42 MHS071 2.43 5 53 102 7.2
## 43 MHS119 2.06 3 53 40 6.6
## 44 MHS168 2.53 1 53 160 5.9
## 45 MHS177 3.18 7 53 169 7.2
## 46 MHS002 3.13 5 54 246 7.1
## 47 MHS037 3.07 5 54 141 6.5
## 48 MHS038 2.95 4 54 161 5.6
## 49 MHS077 3.58 2 54 215 7.3
## 50 MHS127 2.64 6 54 136 6.4
## 51 MHS162 2.96 4 54 140 6.3
## 52 MHS173 2.94 5 54 183 6.6
## 53 MHS178 2.59 2 54 212 4.6
## 54 MHS197 3.15 8 54 212 5.5
## 55 MHS008 2.86 2 55 114 6.2
## 56 MHS040 2.46 3 55 166 4.7
## 57 MHS057 3.50 5 55 228 7.8
## 58 MHS072 3.65 4 55 268 4.8
## 59 MHS084 2.70 1 55 180 5.0
## 60 MHS099 2.88 3 55 249 6.3
## 61 MHS125 3.38 3 55 188 6.9
## 62 MHS129 3.53 4 55 210 7.4
## 63 MHS134 3.71 7 55 265 8.8
## persen_kehadiran
## 1 52.85714
## 2 54.28571
## 3 57.14286
## 4 58.57143
## 5 61.42857
## 6 61.42857
## 7 62.85714
## 8 62.85714
## 9 62.85714
## 10 62.85714
## 11 65.71429
## 12 65.71429
## 13 68.57143
## 14 68.57143
## 15 68.57143
## 16 70.00000
## 17 70.00000
## 18 70.00000
## 19 70.00000
## 20 70.00000
## 21 70.00000
## 22 70.00000
## 23 70.00000
## 24 71.42857
## 25 71.42857
## 26 71.42857
## 27 71.42857
## 28 71.42857
## 29 71.42857
## 30 72.85714
## 31 72.85714
## 32 72.85714
## 33 72.85714
## 34 74.28571
## 35 74.28571
## 36 74.28571
## 37 74.28571
## 38 74.28571
## 39 74.28571
## 40 74.28571
## 41 75.71429
## 42 75.71429
## 43 75.71429
## 44 75.71429
## 45 75.71429
## 46 77.14286
## 47 77.14286
## 48 77.14286
## 49 77.14286
## 50 77.14286
## 51 77.14286
## 52 77.14286
## 53 77.14286
## 54 77.14286
## 55 78.57143
## 56 78.57143
## 57 78.57143
## 58 78.57143
## 59 78.57143
## 60 78.57143
## 61 78.57143
## 62 78.57143
## 63 78.57143library(dplyr)
data_IPK <- df %>%
mutate(
kategori_IPK = case_when(
IPK < 2.50 ~ "Kurang",
IPK >= 2.50 & IPK <= 2.99 ~ "Cukup",
IPK >= 3.00 & IPK <= 3.49 ~ "Baik",
IPK >= 3.50 ~ "Sangat Baik",
TRUE ~ NA_character_
)
)
head(data_IPK)## ID_Mahasiswa IPK Semester Total_Kehadiran Total_Jam_Belajar Rata2_Jam_Tidur
## 1 MHS001 3.45 2 58 238 7.5
## 2 MHS002 3.13 5 54 246 7.1
## 3 MHS003 2.75 4 61 91 7.0
## 4 MHS004 3.52 4 67 162 6.6
## 5 MHS005 3.57 4 56 258 7.4
## 6 MHS006 3.52 7 56 222 7.5
## kategori_IPK
## 1 Baik
## 2 Baik
## 3 Cukup
## 4 Sangat Baik
## 5 Sangat Baik
## 6 Sangat Baikdata_IPK %>%
filter(IPK>=3.50) %>%
count()## n
## 1 49data_IPK %>%
filter(IPK >= 3.00 & IPK <= 3.49) %>%
count()## n
## 1 71library(dplyr)
hasil_ipk <- df %>%
group_by(Semester) %>%
summarise(rata_ipk = mean(IPK, na.rm = TRUE)) %>%
arrange(desc(rata_ipk))
print(hasil_ipk)## # A tibble: 8 × 2
## Semester rata_ipk
## <int> <dbl>
## 1 4 3.20
## 2 6 3.19
## 3 3 3.17
## 4 5 3.12
## 5 7 3.06
## 6 1 3.06
## 7 8 3.04
## 8 2 3.04med_jam <- median(df$Total_Jam_Belajar, na.rm = TRUE)
mean_ipk <- mean(df$IPK, na.rm = TRUE)
mahasiswa_rajin_ipk_rendah <- df %>%
filter(Total_Jam_Belajar > med_jam & IPK < mean_ipk)
nrow(mahasiswa_rajin_ipk_rendah)## [1] 17