Pengantar
Pada perkuliahan kali ini kita akan membahas tuntas tentang bagaimana kita memulai pekerjaan menggunakan data. Data yang digunakan dalam perkuliahan kali ini ialah data SAFI (Studying African Farmer-Led Irrigation), kita akan menggunakan data yang sudah bersih (SAFI_clean.csv).
key_idAdded to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)
villageVillage name
interview_dateDate of interview
no_membersHow many members in the household?
years_livHow many years have you been living in this village or neighboring village?
respondent_wall_typeWhat type of walls does their house have (from list)
roomsHow many rooms in the main house are used for sleeping?
memb_assocAre you a member of an irrigation association?
affect_conflictsHave you been affected by conflicts with other irrigators in the area?
liv_countNumber of livestock owned.
items_ownedWhich of the following items are owned by the household? (list)
no_mealsHow many meals do people in your household normally eat in a day?
months_lack_foodIndicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?
instanceIDUnique identifier for the form data submission
Importing Data
<- read.csv ("C: \\ Users \\ User \\ Documents \\ Data \\ SAFI_clean.csv" )head (safi)
key_ID village interview_date no_membrs years_liv respondent_wall_type
1 1 God 2016-11-17T00:00:00Z 3 4 muddaub
2 1 God 2016-11-17T00:00:00Z 7 9 muddaub
3 3 God 2016-11-17T00:00:00Z 10 15 burntbricks
4 4 God 2016-11-17T00:00:00Z 7 6 burntbricks
5 5 God 2016-11-17T00:00:00Z 7 40 burntbricks
6 6 God 2016-11-17T00:00:00Z 3 3 muddaub
rooms memb_assoc affect_conflicts liv_count
1 1 NULL NULL 1
2 1 yes once 3
3 1 NULL NULL 1
4 1 NULL NULL 2
5 1 NULL NULL 4
6 1 NULL NULL 1
items_owned
1 bicycle;television;solar_panel;table
2 cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
3 solar_torch
4 bicycle;radio;cow_plough;solar_panel;mobile_phone
5 motorcyle;radio;cow_plough;mobile_phone
6 NULL
no_meals months_lack_food instanceID
1 2 Jan uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
2 2 Jan;Sept;Oct;Nov;Dec uuid:099de9c9-3e5e-427b-8452-26250e840d6e
3 2 Jan;Feb;Mar;Oct;Nov;Dec uuid:193d7daf-9582-409b-bf09-027dd36f9007
4 2 Sept;Oct;Nov;Dec uuid:148d1105-778a-4755-aa71-281eadd4a973
5 2 Aug;Sept;Oct;Nov uuid:2c867811-9696-4966-9866-f35c3e97d02d
6 2 Aug;Sept;Oct uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70
[1] "C:/Users/User/Documents/Kerjaan/STA1517/Praktikum STA1517"
<- read.csv ("SAFI_clean.csv" )head (safii)
key_ID village interview_date no_membrs years_liv respondent_wall_type
1 1 God 2016-11-17T00:00:00Z 3 4 muddaub
2 1 God 2016-11-17T00:00:00Z 7 9 muddaub
3 3 God 2016-11-17T00:00:00Z 10 15 burntbricks
4 4 God 2016-11-17T00:00:00Z 7 6 burntbricks
5 5 God 2016-11-17T00:00:00Z 7 40 burntbricks
6 6 God 2016-11-17T00:00:00Z 3 3 muddaub
rooms memb_assoc affect_conflicts liv_count
1 1 NULL NULL 1
2 1 yes once 3
3 1 NULL NULL 1
4 1 NULL NULL 2
5 1 NULL NULL 4
6 1 NULL NULL 1
items_owned
1 bicycle;television;solar_panel;table
2 cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
3 solar_torch
4 bicycle;radio;cow_plough;solar_panel;mobile_phone
5 motorcyle;radio;cow_plough;mobile_phone
6 NULL
no_meals months_lack_food instanceID
1 2 Jan uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
2 2 Jan;Sept;Oct;Nov;Dec uuid:099de9c9-3e5e-427b-8452-26250e840d6e
3 2 Jan;Feb;Mar;Oct;Nov;Dec uuid:193d7daf-9582-409b-bf09-027dd36f9007
4 2 Sept;Oct;Nov;Dec uuid:148d1105-778a-4755-aa71-281eadd4a973
5 2 Aug;Sept;Oct;Nov uuid:2c867811-9696-4966-9866-f35c3e97d02d
6 2 Aug;Sept;Oct uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70
dari pengerjaan diatas kita mengetahui fungsi head() ialah menampilkan 6 data pengamatan awal. Sementara kalau ingin melihat 6 data terakhir kita bisa menggunakan fungsi tail().
key_ID village interview_date no_membrs
Min. : 1.00 Length:131 Length:131 Min. : 2.000
1st Qu.: 32.50 Class :character Class :character 1st Qu.: 5.000
Median : 66.00 Mode :character Mode :character Median : 7.000
Mean : 85.47 Mean : 7.191
3rd Qu.:138.00 3rd Qu.: 9.000
Max. :202.00 Max. :19.000
years_liv respondent_wall_type rooms memb_assoc
Min. : 1.00 Length:131 Min. :1.00 Length:131
1st Qu.:12.00 Class :character 1st Qu.:1.00 Class :character
Median :20.00 Mode :character Median :1.00 Mode :character
Mean :23.05 Mean :1.74
3rd Qu.:27.50 3rd Qu.:2.00
Max. :96.00 Max. :8.00
affect_conflicts liv_count items_owned no_meals
Length:131 Min. :1.000 Length:131 Min. :2.000
Class :character 1st Qu.:1.000 Class :character 1st Qu.:2.000
Mode :character Median :2.000 Mode :character Median :3.000
Mean :2.366 Mean :2.603
3rd Qu.:3.000 3rd Qu.:3.000
Max. :5.000 Max. :3.000
months_lack_food instanceID
Length:131 Length:131
Class :character Class :character
Mode :character Mode :character
'data.frame': 131 obs. of 14 variables:
$ key_ID : int 1 1 3 4 5 6 7 8 9 10 ...
$ village : chr "God" "God" "God" "God" ...
$ interview_date : chr "2016-11-17T00:00:00Z" "2016-11-17T00:00:00Z" "2016-11-17T00:00:00Z" "2016-11-17T00:00:00Z" ...
$ no_membrs : int 3 7 10 7 7 3 6 12 8 12 ...
$ years_liv : int 4 9 15 6 40 3 38 70 6 23 ...
$ respondent_wall_type: chr "muddaub" " muddaub" " burntbricks" " burntbricks" ...
$ rooms : int 1 1 1 1 1 1 1 3 1 5 ...
$ memb_assoc : chr "NULL" "yes" "NULL" "NULL" ...
$ affect_conflicts : chr "NULL" "once" "NULL" "NULL" ...
$ liv_count : int 1 3 1 2 4 1 1 2 3 2 ...
$ items_owned : chr "bicycle;television;solar_panel;table" "cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone" "solar_torch" "bicycle;radio;cow_plough;solar_panel;mobile_phone" ...
$ no_meals : int 2 2 2 2 2 2 3 2 3 3 ...
$ months_lack_food : chr "Jan" "Jan;Sept;Oct;Nov;Dec" "Jan;Feb;Mar;Oct;Nov;Dec" "Sept;Oct;Nov;Dec" ...
$ instanceID : chr "uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef" "uuid:099de9c9-3e5e-427b-8452-26250e840d6e" "uuid:193d7daf-9582-409b-bf09-027dd36f9007" "uuid:148d1105-778a-4755-aa71-281eadd4a973" ...
[1] "key_ID" "village" "interview_date"
[4] "no_membrs" "years_liv" "respondent_wall_type"
[7] "rooms" "memb_assoc" "affect_conflicts"
[10] "liv_count" "items_owned" "no_meals"
[13] "months_lack_food" "instanceID"
Subsetting Data Frames
## first element in the first column of the tibble 1 , 1 ]## first element in the 6th column of the tibble 1 , 6 ]## first column of the tibble (as a vector) 1 ]]
[1] 1 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
[37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 21 54
[55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 127
[73] 133 152 153 155 178 177 180 181 182 186 187 195 196 197 198 201 202 72
[91] 73 76 83 85 89 101 103 102 78 80 104 105 106 109 110 113 118 125
[109] 119 115 108 116 117 144 143 150 159 160 165 166 167 174 175 189 191 192
[127] 126 193 194 199 200
## first column of the tibble 1 ]
key_ID
1 1
2 1
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 21
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 127
73 133
74 152
75 153
76 155
77 178
78 177
79 180
80 181
81 182
82 186
83 187
84 195
85 196
86 197
87 198
88 201
89 202
90 72
91 73
92 76
93 83
94 85
95 89
96 101
97 103
98 102
99 78
100 80
101 104
102 105
103 106
104 109
105 110
106 113
107 118
108 125
109 119
110 115
111 108
112 116
113 117
114 144
115 143
116 150
117 159
118 160
119 165
120 166
121 167
122 174
123 175
124 189
125 191
126 192
127 126
128 193
129 194
130 199
131 200
- 1 ] # The whole tibble, except the first column
village interview_date no_membrs years_liv respondent_wall_type
1 God 2016-11-17T00:00:00Z 3 4 muddaub
2 God 2016-11-17T00:00:00Z 7 9 muddaub
3 God 2016-11-17T00:00:00Z 10 15 burntbricks
4 God 2016-11-17T00:00:00Z 7 6 burntbricks
5 God 2016-11-17T00:00:00Z 7 40 burntbricks
6 God 2016-11-17T00:00:00Z 3 3 muddaub
7 God 2016-11-17T00:00:00Z 6 38 muddaub
8 Chirodzo 2016-11-16T00:00:00Z 12 70 burntbricks
9 Chirodzo 2016-11-16T00:00:00Z 8 6 burntbricks
10 Chirodzo 2016-12-16T00:00:00Z 12 23 burntbricks
11 God 2016-11-21T00:00:00Z 6 20 sunbricks
12 God 2016-11-21T00:00:00Z 7 20 burntbricks
13 God 2016-11-21T00:00:00Z 6 8 burntbricks
14 God 2016-11-21T00:00:00Z 10 20 burntbricks
15 God 2016-11-21T00:00:00Z 5 30 sunbricks
16 God 2016-11-24T00:00:00Z 6 47 muddaub
17 God 2016-11-21T00:00:00Z 8 20 sunbricks
18 God 2016-11-21T00:00:00Z 4 20 muddaub
19 God 2016-11-21T00:00:00Z 9 23 burntbricks
20 God 2016-11-21T00:00:00Z 6 1 burntbricks
21 God 2016-11-21T00:00:00Z 8 20 burntbricks
22 God 2016-11-21T00:00:00Z 4 20 muddaub
23 Ruaca 2016-11-21T00:00:00Z 10 20 burntbricks
24 Ruaca 2016-11-21T00:00:00Z 6 4 burntbricks
25 Ruaca 2016-11-21T00:00:00Z 11 6 burntbricks
26 Ruaca 2016-11-21T00:00:00Z 3 20 burntbricks
27 Ruaca 2016-11-21T00:00:00Z 7 36 burntbricks
28 Ruaca 2016-11-21T00:00:00Z 2 2 muddaub
29 Ruaca 2016-11-21T00:00:00Z 7 10 burntbricks
30 Ruaca 2016-11-21T00:00:00Z 7 22 muddaub
31 Ruaca 2016-11-21T00:00:00Z 3 2 muddaub
32 Ruaca 2016-11-21T00:00:00Z 19 69 muddaub
33 Ruaca 2016-11-21T00:00:00Z 8 34 muddaub
34 Chirodzo 2016-11-17T00:00:00Z 8 18 burntbricks
35 Chirodzo 2016-11-17T00:00:00Z 5 45 muddaub
36 Chirodzo 2016-11-17T00:00:00Z 6 23 sunbricks
37 Chirodzo 2016-11-17T00:00:00Z 3 8 burntbricks
38 God 2016-11-17T00:00:00Z 10 19 muddaub
39 God 2016-11-17T00:00:00Z 6 22 muddaub
40 God 2016-11-17T00:00:00Z 9 23 burntbricks
41 God 2016-11-17T00:00:00Z 7 22 muddaub
42 God 2016-11-17T00:00:00Z 8 8 sunbricks
43 Chirodzo 2016-11-17T00:00:00Z 7 29 muddaub
44 Chirodzo 2016-11-17T00:00:00Z 2 6 muddaub
45 Chirodzo 2016-11-17T00:00:00Z 9 7 muddaub
46 Chirodzo 2016-11-17T00:00:00Z 10 42 burntbricks
47 Chirodzo 2016-11-17T00:00:00Z 2 2 muddaub
48 Chirodzo 2016-11-16T00:00:00Z 7 58 muddaub
49 Chirodzo 2016-11-16T00:00:00Z 6 26 burntbricks
50 Chirodzo 2016-11-16T00:00:00Z 6 7 muddaub
51 Chirodzo 2016-11-16T00:00:00Z 5 30 muddaub
52 Chirodzo 2016-11-16T00:00:00Z 11 15 burntbricks
53 Chirodzo 2016-11-16T00:00:00Z 8 16 burntbricks
54 Chirodzo 2016-11-16T00:00:00Z 7 15 muddaub
55 Chirodzo 2016-11-16T00:00:00Z 9 23 muddaub
56 Chirodzo 2016-11-16T00:00:00Z 12 23 burntbricks
57 Chirodzo 2016-11-16T00:00:00Z 4 27 burntbricks
58 Chirodzo 2016-11-16T00:00:00Z 11 45 burntbricks
59 Chirodzo 2016-11-16T00:00:00Z 2 60 muddaub
60 Chirodzo 2016-11-16T00:00:00Z 8 15 burntbricks
61 Chirodzo 2016-11-16T00:00:00Z 10 14 muddaub
62 Chirodzo 2016-11-16T00:00:00Z 5 5 muddaub
63 Chirodzo 2016-11-16T00:00:00Z 4 10 muddaub
64 Chirodzo 2016-11-16T00:00:00Z 6 1 muddaub
65 Chirodzo 2016-11-16T00:00:00Z 8 20 burntbricks
66 Chirodzo 2016-11-16T00:00:00Z 10 37 burntbricks
67 Chirodzo 2016-11-16T00:00:00Z 5 31 burntbricks
68 Chirodzo 2016-11-16T00:00:00Z 8 52 burntbricks
69 Chirodzo 2016-11-16T00:00:00Z 4 12 muddaub
70 Chirodzo 2016-11-16T00:00:00Z 8 25 burntbricks
71 Ruaca 2016-11-18T00:00:00Z 6 14 burntbricks
72 Chirodzo 2016-11-16T00:00:00Z 4 18 burntbricks
73 Ruaca 2016-11-23T00:00:00Z 5 25 burntbricks
74 Ruaca 2016-11-24T00:00:00Z 10 16 burntbricks
75 Ruaca 2016-11-24T00:00:00Z 5 41 burntbricks
76 God 2016-11-24T00:00:00Z 4 4 burntbricks
77 Ruaca 2016-11-25T00:00:00Z 5 79 burntbricks
78 God 2016-11-25T00:00:00Z 10 13 sunbricks
79 Ruaca 2016-11-25T00:00:00Z 7 50 muddaub
80 God 2016-11-25T00:00:00Z 11 25 sunbricks
81 God 2016-11-25T00:00:00Z 7 21 muddaub
82 God 2016-11-28T00:00:00Z 7 24 muddaub
83 God 2016-11-28T00:00:00Z 5 43 muddaub
84 God 2016-11-28T00:00:00Z 5 48 burntbricks
85 God 2016-11-28T00:00:00Z 7 49 burntbricks
86 God 2016-11-28T00:00:00Z 5 19 burntbricks
87 God 2016-11-28T00:00:00Z 3 49 burntbricks
88 God 2016-11-21T00:00:00Z 4 6 muddaub
89 God 2016-11-17T00:00:00Z 12 12 burntbricks
90 Ruaca 2017-04-26T00:00:00Z 6 24 muddaub
91 Ruaca 2017-04-26T00:00:00Z 7 9 burntbricks
92 Ruaca 2017-04-26T00:00:00Z 17 48 burntbricks
93 Ruaca 2017-04-27T00:00:00Z 5 22 burntbricks
94 Ruaca 2017-04-27T00:00:00Z 7 40 sunbricks
95 God 2017-04-27T00:00:00Z 5 10 burntbricks
96 God 2017-04-27T00:00:00Z 3 4 muddaub
97 Ruaca 2017-04-27T00:00:00Z 6 96 sunbricks
98 Ruaca 2017-04-28T00:00:00Z 12 15 burntbricks
99 Ruaca 2017-04-28T00:00:00Z 6 48 burntbricks
100 Ruaca 2017-04-28T00:00:00Z 5 12 muddaub
101 Ruaca 2017-04-28T00:00:00Z 14 52 sunbricks
102 Ruaca 2017-04-28T00:00:00Z 6 40 sunbricks
103 God 2017-04-30T00:00:00Z 15 22 sunbricks
104 God 2017-05-03T00:00:00Z 4 12 sunbricks
105 Ruaca 2017-05-03T00:00:00Z 6 22 sunbricks
106 Ruaca 2017-05-03T00:00:00Z 11 26 burntbricks
107 Ruaca 2017-05-04T00:00:00Z 5 25 muddaub
108 Ruaca 2017-05-04T00:00:00Z 5 14 burntbricks
109 Ruaca 2017-05-04T00:00:00Z 3 14 muddaub
110 Ruaca 2017-05-11T00:00:00Z 4 16 sunbricks
111 God 2017-05-11T00:00:00Z 15 22 burntbricks
112 Ruaca 2017-05-11T00:00:00Z 5 25 burntbricks
113 Ruaca 2017-05-11T00:00:00Z 10 28 muddaub
114 Ruaca 2017-05-18T00:00:00Z 7 5 burntbricks
115 Ruaca 2017-05-18T00:00:00Z 10 24 burntbricks
116 Ruaca 2017-05-18T00:00:00Z 7 8 muddaub
117 God 2017-05-18T00:00:00Z 4 24 sunbricks
118 God 2017-06-03T00:00:00Z 7 13 burntbricks
119 Ruaca 2017-06-03T00:00:00Z 9 14 burntbricks
120 Ruaca 2017-06-03T00:00:00Z 11 16 muddaub
121 Ruaca 2017-06-03T00:00:00Z 8 24 muddaub
122 Ruaca 2017-06-03T00:00:00Z 12 25 burntbricks
123 Ruaca 2017-06-03T00:00:00Z 7 36 burntbricks
124 Ruaca 2017-06-03T00:00:00Z 15 16 sunbricks
125 Ruaca 2017-06-03T00:00:00Z 10 5 burntbricks
126 Chirodzo 2017-06-03T00:00:00Z 9 20 burntbricks
127 Ruaca 2017-05-18T00:00:00Z 3 7 burntbricks
128 Ruaca 2017-06-04T00:00:00Z 7 10 cement
129 Ruaca 2017-06-04T00:00:00Z 4 5 muddaub
130 Chirodzo 2017-06-04T00:00:00Z 7 17 burntbricks
131 Chirodzo 2017-06-04T00:00:00Z 8 20 burntbricks
rooms memb_assoc affect_conflicts liv_count
1 1 NULL NULL 1
2 1 yes once 3
3 1 NULL NULL 1
4 1 NULL NULL 2
5 1 NULL NULL 4
6 1 NULL NULL 1
7 1 no never 1
8 3 yes never 2
9 1 no never 3
10 5 no never 2
11 1 NULL NULL 2
12 3 yes never 2
13 1 no never 3
14 3 NULL NULL 3
15 2 yes once 3
16 1 NULL NULL 4
17 1 NULL NULL 1
18 1 NULL NULL 3
19 2 NULL NULL 2
20 1 NULL NULL 1
21 1 no never 3
22 1 NULL NULL 1
23 4 NULL NULL 3
24 2 no never 3
25 3 no never 2
26 2 no never 2
27 2 NULL NULL 3
28 1 no more_once 1
29 2 yes frequently 1
30 2 NULL NULL 1
31 1 NULL NULL 1
32 2 yes more_once 5
33 1 no more_once 2
34 3 yes more_once 3
35 1 yes more_once 2
36 1 yes once 3
37 1 NULL NULL 2
38 1 yes never 3
39 1 NULL NULL 1
40 1 yes never 1
41 1 NULL NULL 2
42 1 no never 3
43 1 no never 2
44 1 NULL NULL 3
45 1 no never 4
46 2 no once 2
47 1 yes once 1
48 1 NULL NULL 3
49 2 NULL NULL 2
50 1 yes never 1
51 1 NULL NULL 1
52 3 no never 3
53 3 yes frequently 2
54 1 no never 1
55 2 NULL NULL 1
56 2 yes never 2
57 1 no never 1
58 3 no never 3
59 3 NULL NULL 3
60 2 no never 4
61 1 yes more_once 3
62 1 NULL NULL 1
63 1 NULL NULL 1
64 1 NULL NULL 1
65 3 no once 3
66 3 yes frequently 4
67 2 no more_once 4
68 3 no more_once 3
69 1 no more_once 1
70 2 no more_once 4
71 1 yes more_once 3
72 8 NULL NULL 1
73 2 no never 5
74 1 yes once 3
75 1 NULL NULL 1
76 1 NULL NULL 1
77 2 yes frequently 3
78 1 no more_once 2
79 1 no never 3
80 2 yes more_once 3
81 3 no more_once 2
82 1 no more_once 2
83 2 yes more_once 4
84 1 no never 3
85 2 yes more_once 3
86 2 no more_once 3
87 1 no never 1
88 2 NULL NULL 2
89 4 yes more_once 3
90 1 yes more_once 3
91 2 yes more_once 3
92 2 yes more_once 4
93 1 yes never 2
94 1 no never 2
95 2 no never 3
96 1 no never 1
97 1 no never 5
98 2 yes frequently 2
99 1 no more_once 2
100 1 no more_once 1
101 1 yes never 4
102 1 yes frequently 2
103 5 no never 2
104 1 NULL NULL 3
105 3 no never 3
106 3 no never 4
107 1 NULL NULL 1
108 1 no more_once 2
109 1 no never 4
110 2 NULL NULL 3
111 2 no never 4
112 3 NULL NULL 3
113 4 NULL NULL 1
114 4 no frequently 4
115 2 no frequently 3
116 1 no never 1
117 1 no never 1
118 2 yes frequently 2
119 1 no never 3
120 1 no never 1
121 1 no never 3
122 2 no never 3
123 1 no never 4
124 1 no never 3
125 4 no never 1
126 1 no once 1
127 1 no more_once 3
128 3 no more_once 3
129 1 no more_once 1
130 2 yes more_once 2
131 2 NULL NULL 3
items_owned
1 bicycle;television;solar_panel;table
2 cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
3 solar_torch
4 bicycle;radio;cow_plough;solar_panel;mobile_phone
5 motorcyle;radio;cow_plough;mobile_phone
6 NULL
7 motorcyle;cow_plough
8 motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;fridge
9 television;solar_panel;solar_torch
10 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table
11 radio;cow_plough
12 cow_cart;bicycle;radio;cow_plough;table
13 bicycle;radio;cow_plough;mobile_phone
14 bicycle;radio;cow_plough;solar_panel;table;mobile_phone
15 bicycle;radio;cow_plough;solar_panel;table
16 radio;cow_plough;solar_panel;solar_torch
17 mobile_phone
18 bicycle;mobile_phone
19 bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone
20 bicycle;cow_plough;solar_torch
21 NULL
22 radio
23 cow_cart;bicycle;television;radio;cow_plough;solar_panel;electricity;mobile_phone
24 radio;table;sofa_set;mobile_phone
25 cow_cart;motorcyle;television;radio;cow_plough;solar_panel;solar_torch;table;sofa_set;mobile_phone
26 radio;cow_plough;table;mobile_phone
27 bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone
28 NULL
29 motorcyle;bicycle;radio;table;mobile_phone
30 bicycle;radio;mobile_phone
31 NULL
32 cow_cart;motorcyle;radio;cow_plough;solar_panel;mobile_phone
33 cow_cart;lorry;motorcyle;sterio;cow_plough;solar_panel;mobile_phone
34 television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
35 bicycle;cow_plough
36 cow_cart;bicycle;radio;cow_plough;solar_panel;mobile_phone
37 bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone
38 bicycle;radio;cow_plough;solar_panel;table;mobile_phone
39 NULL
40 bicycle;radio;cow_plough;solar_panel;table;mobile_phone
41 motorcyle;bicycle;radio;cow_plough;table
42 mobile_phone
43 cow_plough;mobile_phone
44 radio;solar_torch
45 motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
46 motorcyle;computer;television;sterio;solar_panel;solar_torch;table;mobile_phone
47 solar_torch;mobile_phone
48 radio
49 bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
50 solar_torch
51 radio
52 motorcyle;television;radio;cow_plough;solar_panel;mobile_phone
53 bicycle;radio;mobile_phone
54 NULL
55 television;cow_plough;mobile_phone
56 motorcyle;bicycle;mobile_phone
57 radio
58 motorcyle;bicycle;television;radio;cow_plough;solar_panel;mobile_phone
59 NULL
60 cow_plough
61 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;table;mobile_phone
62 bicycle;radio;mobile_phone
63 NULL
64 bicycle;solar_torch;table;sofa_set;mobile_phone
65 motorcyle;radio;cow_plough;table
66 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone
67 motorcyle;radio;cow_plough;solar_panel;mobile_phone
68 motorcyle;television;sterio;solar_panel;mobile_phone
69 bicycle;radio;solar_torch;mobile_phone
70 cow_cart;bicycle;radio;cow_plough;solar_panel;mobile_phone
71 radio;cow_plough;mobile_phone
72 mobile_phone
73 cow_cart;car;lorry;motorcyle;bicycle;television;sterio;cow_plough;solar_panel;solar_torch;electricity;table;sofa_set;mobile_phone;fridge
74 motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;mobile_phone
75 NULL
76 electricity
77 radio;cow_plough;solar_panel;mobile_phone
78 motorcyle;television;cow_plough;solar_panel;mobile_phone
79 cow_plough;solar_panel
80 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;mobile_phone
81 solar_panel
82 cow_plough;mobile_phone
83 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;mobile_phone
84 cow_cart;bicycle;radio;cow_plough;solar_torch
85 radio;cow_plough;mobile_phone
86 bicycle;television;radio;cow_plough;solar_torch;table;mobile_phone
87 NULL
88 bicycle;radio;solar_torch;mobile_phone
89 cow_cart;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
90 bicycle;radio;cow_plough
91 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;table;mobile_phone
92 bicycle;radio;cow_plough;solar_panel;mobile_phone
93 radio;cow_plough;solar_torch
94 radio;cow_plough
95 bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
96 bicycle;solar_torch
97 cow_cart;cow_plough;solar_panel;sofa_set;mobile_phone
98 cow_plough;table;sofa_set;mobile_phone
99 cow_plough
100 cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch
101 cow_cart;bicycle;cow_plough
102 motorcyle;radio;cow_plough;solar_panel;mobile_phone
103 cow_cart;motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;solar_torch;table;mobile_phone
104 cow_cart;bicycle;radio;cow_plough;table
105 bicycle;radio;cow_plough;table;mobile_phone
106 cow_cart;motorcyle;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
107 radio;solar_torch;mobile_phone
108 bicycle;radio;cow_plough;solar_panel;solar_torch;mobile_phone
109 bicycle;cow_plough;solar_panel;mobile_phone
110 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
111 cow_cart;bicycle;radio;cow_plough;solar_panel;table;mobile_phone
112 motorcyle;bicycle;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
113 motorcyle;television;radio;solar_panel;solar_torch;table;mobile_phone
114 cow_cart;television;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
115 cow_cart;motorcyle;television;radio;cow_plough;solar_torch;table;mobile_phone
116 mobile_phone
117 radio;solar_panel;solar_torch
118 cow_cart;cow_plough;solar_torch;mobile_phone
119 cow_cart;motorcyle;bicycle;television;radio;cow_plough;solar_torch;electricity;table;sofa_set;mobile_phone;fridge
120 bicycle;solar_torch;mobile_phone
121 motorcyle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
122 car;lorry;motorcyle;radio;sterio;cow_plough;solar_panel;solar_torch;table;sofa_set;mobile_phone;fridge
123 motorcyle;bicycle;radio;sterio;cow_plough;solar_panel;table;mobile_phone
124 motorcyle;radio;sterio;cow_plough;solar_panel;table;mobile_phone
125 radio;cow_plough;solar_panel;solar_torch;mobile_phone
126 bicycle;television;radio;sterio;solar_panel;solar_torch;table;mobile_phone
127 motorcyle;radio;solar_panel
128 car;lorry;television;radio;sterio;cow_plough;solar_torch;electricity;table;sofa_set;mobile_phone;fridge
129 radio;solar_panel;solar_torch;mobile_phone
130 cow_cart;lorry;motorcyle;computer;television;radio;sterio;cow_plough;solar_panel;solar_torch;electricity;mobile_phone
131 radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
no_meals months_lack_food
1 2 Jan
2 2 Jan;Sept;Oct;Nov;Dec
3 2 Jan;Feb;Mar;Oct;Nov;Dec
4 2 Sept;Oct;Nov;Dec
5 2 Aug;Sept;Oct;Nov
6 2 Aug;Sept;Oct
7 3 Nov
8 2 Jan
9 3 Jan;Dec
10 3 Jan;Oct;Nov;Dec
11 2 Oct;Nov
12 3 Sept;Oct
13 2 Sept;Oct;Nov
14 3 June;July;Aug;Sept;Oct;Nov
15 2 Jan;Feb;Mar;Apr;May;June;July;Aug;Sept;Oct;Nov
16 3 Jan;Feb
17 2 Nov;Dec
18 2 Oct;Nov
19 3 Oct;Nov;Dec
20 2 Oct;Nov
21 2 Jan;Feb;Mar;Oct;Nov;Dec
22 2 Jan;Feb;Mar;Apr;Aug;Sept;Oct;Nov;Dec
23 3 none
24 2 Nov;Dec
25 2 Jan;Feb;Oct
26 2 none
27 3 none
28 3 Aug;Sept;Oct
29 3 Jan;Feb
30 2 Jan;Feb
31 3 none
32 2 none
33 2 none
34 2 Jan;Dec
35 3 Jan;Sept;Oct;Nov;Dec
36 3 none
37 3 Jan;Nov;Dec
38 3 Nov
39 3 Nov
40 3 Sept;Oct;Nov
41 3 Oct;Nov
42 3 Jan;Nov;Dec
43 2 Jan;Feb;Oct;Nov;Dec
44 2 Jan;Dec
45 3 none
46 2 Sept;Oct;Nov
47 3 none
48 3 June;July;Aug;Sept;Oct;Nov
49 3 Jan;Nov;Dec
50 2 June;July;Aug;Sept;Oct;Nov;Dec
51 3 Oct;Nov
52 3 Aug;Sept;Oct;Nov
53 2 Nov
54 2 Sept;Oct;Nov
55 2 Oct;Nov
56 3 none
57 2 none
58 2 none
59 2 none
60 2 none
61 3 Jan;Feb;Dec
62 3 Aug;Sept;Oct;Nov
63 3 Jan;Oct;Nov;Dec
64 3 Jan;Feb;Dec
65 3 Jan;Feb;Mar
66 3 none
67 3 none
68 3 none
69 3 none
70 2 none
71 2 Aug;Sept;Oct;Nov
72 2 Aug;Sept;Oct
73 3 Jan;Oct;Nov
74 3 none
75 2 Oct;Nov
76 2 Jan;Sept;Oct;Nov;Dec
77 3 none
78 3 Nov
79 3 Oct;Nov
80 3 none
81 3 Jan;Feb;Nov;Dec
82 3 none
83 3 none
84 2 Sept;Oct;Nov
85 3 none
86 2 Nov
87 3 Nov
88 2 Oct;Nov;Dec
89 3 Jan;Feb;Mar;Oct;Nov;Dec
90 2 Jan;Aug;Sept;Oct;Nov;Dec
91 3 Jan;Sept;Oct
92 3 none
93 2 Aug;Sept;Oct
94 2 Oct;Nov
95 3 Oct;Nov
96 3 Sept;Oct;Nov
97 3 Jan;Feb;Dec
98 3 Jan;Feb
99 2 Aug;Sept;Oct
100 3 none
101 3 Jan;Feb;Dec
102 3 Jan;Feb;Dec
103 3 Oct;Nov;Dec
104 3 July;Aug;Sept;Oct;Nov
105 2 none
106 3 none
107 3 Oct;Nov;Dec
108 3 Jan;Sept;Oct;Nov;Dec
109 3 none
110 3 none
111 3 Aug;Sept;Oct;Nov
112 3 Jan;Nov;Dec
113 3 Jan;Feb;Nov;Dec
114 2 none
115 3 Jan;Dec
116 3 Sept;Oct;Nov
117 3 Sept;Oct;Nov
118 2 Nov
119 3 none
120 2 Feb;Mar
121 2 Jan;Nov;Dec
122 3 Jan;Feb;Dec
123 2 Jan;Oct;Nov;Dec
124 3 Nov
125 2 Oct;Nov;Dec
126 3 Jan;Nov;Dec
127 3 Oct;Nov;Dec
128 3 none
129 3 Sept;Oct;Nov
130 3 Nov;Dec
131 3 Oct;Nov
instanceID
1 uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
2 uuid:099de9c9-3e5e-427b-8452-26250e840d6e
3 uuid:193d7daf-9582-409b-bf09-027dd36f9007
4 uuid:148d1105-778a-4755-aa71-281eadd4a973
5 uuid:2c867811-9696-4966-9866-f35c3e97d02d
6 uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70
7 uuid:ae20a58d-56f4-43d7-bafa-e7963d850844
8 uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a
9 uuid:846103d2-b1db-4055-b502-9cd510bb7b37
10 uuid:8f4e49bc-da81-4356-ae34-e0d794a23721
11 uuid:d29b44e3-3348-4afc-aa4d-9eb34c89d483
12 uuid:e6ee6269-b467-4e37-91fc-5e9eaf934557
13 uuid:6c00c145-ee3b-409c-8c02-2c8d743b6918
14 uuid:9b21467f-1116-4340-a3b1-1ab64f13c87d
15 uuid:a837e545-ff86-4a1c-a1a5-6186804b985f
16 uuid:d17db52f-4b87-4768-b534-ea8f9704c565
17 uuid:4707f3dc-df18-4348-9c2c-eec651e89b6b
18 uuid:7ffe7bd1-a15c-420c-a137-e1f006c317a3
19 uuid:e32f2dc0-0d05-42fb-8e21-605757ddf07d
20 uuid:d1005274-bf52-4e79-8380-3350dd7c2bac
21 uuid:6570a7d0-6a0b-452c-aa2e-922500e35749
22 uuid:a51c3006-8847-46ff-9d4e-d29919b8ecf9
23 uuid:58b37b6d-d6cd-4414-8790-b9c68bca98de
24 uuid:661457d3-7e61-45e8-a238-7415e7548f82
25 uuid:45ed84c4-114e-4df0-9f5d-c800806c2bee
26 uuid:1c54ee24-22c4-4ee9-b1ad-42d483c08e2e
27 uuid:3197cded-1fdc-4c0c-9b10-cfcc0bf49c4d
28 uuid:1de53318-a8cf-4736-99b1-8239f8822473
29 uuid:adcd7463-8943-4c67-b25f-f72311409476
30 uuid:59341ead-92be-45a9-8545-6edf9f94fdc6
31 uuid:cb06eb49-dd39-4150-8bbe-a599e074afe8
32 uuid:25597af3-cd79-449c-a48a-fb9aea6c48bf
33 uuid:0fbd2df1-2640-4550-9fbd-7317feaa4758
34 uuid:14c78c45-a7cc-4b2a-b765-17c82b43feb4
35 uuid:ff7496e7-984a-47d3-a8a1-13618b5683ce
36 uuid:c90eade0-1148-4a12-8c0e-6387a36f45b1
37 uuid:408c6c93-d723-45ef-8dee-1b1bd3fe20cd
38 uuid:81309594-ff58-4dc1-83a7-72af5952ee08
39 uuid:c0fb6310-55af-4831-ae3d-2729556c3285
40 uuid:c0b34854-eede-4e81-b183-ef58a45bfc34
41 uuid:b3ba34d8-eea1-453d-bc73-c141bcbbc5e5
42 uuid:e3a1dd8a-1bda-428c-a014-2b527f11ae64
43 uuid:b4dff49f-ef27-40e5-a9d1-acf287b47358
44 uuid:f9fadf44-d040-4fca-86c1-2835f79c4952
45 uuid:e3554d22-35b1-4fb9-b386-dd5866ad5792
46 uuid:35f297e0-aa5d-4149-9b7b-4965004cfc37
47 uuid:2d0b1936-4f82-4ec3-a3b5-7c3c8cd6cc2b
48 uuid:e180899c-7614-49eb-a97c-40ed013a38a2
49 uuid:2303ebc1-2b3c-475a-8916-b322ebf18440
50 uuid:4267c33c-53a7-46d9-8bd6-b96f58a4f92c
51 uuid:18ac8e77-bdaf-47ab-85a2-e4c947c9d3ce
52 uuid:6db55cb4-a853-4000-9555-757b7fae2bcf
53 uuid:cc7f75c5-d13e-43f3-97e5-4f4c03cb4b12
54 uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919
55 uuid:883c0433-9891-4121-bc63-744f082c1fa0
56 uuid:973c4ac6-f887-48e7-aeaf-4476f2cfab76
57 uuid:a7184e55-0615-492d-9835-8f44f3b03a71
58 uuid:a7a3451f-cd0d-4027-82d9-8dcd1234fcca
59 uuid:1936db62-5732-45dc-98ff-9b3ac7a22518
60 uuid:85465caf-23e4-4283-bb72-a0ef30e30176
61 uuid:2401cf50-8859-44d9-bd14-1bf9128766f2
62 uuid:c6597ecc-cc2a-4c35-a6dc-e62c71b345d6
63 uuid:86ed4328-7688-462f-aac7-d6518414526a
64 uuid:28cfd718-bf62-4d90-8100-55fafbe45d06
65 uuid:143f7478-0126-4fbc-86e0-5d324339206b
66 uuid:a457eab8-971b-4417-a971-2e55b8702816
67 uuid:6c15d667-2860-47e3-a5e7-7f679271e419
68 uuid:ef04b3eb-b47d-412e-9b09-4f5e08fc66f9
69 uuid:f86933a5-12b8-4427-b821-43c5b039401d
70 uuid:1feb0108-4599-4bf9-8a07-1f5e66a50a0a
71 uuid:761f9c49-ec93-4932-ba4c-cc7b78dfcef1
72 uuid:f6d04b41-b539-4e00-868a-0f62b427587d
73 uuid:429d279a-a519-4dcc-9f64-4673b0fd5d53
74 uuid:59738c17-1cda-49ee-a563-acd76f6bc487
75 uuid:7e7961ca-fa1c-4567-9bfa-a02f876e4e03
76 uuid:77b3021b-a9d6-4276-aaeb-5bfcfd413852
77 uuid:2186e2ec-f65a-47cc-9bc1-a0f36dd9591c
78 uuid:87998c33-c8d2-49ec-9dae-c123735957ec
79 uuid:ece89122-ea99-4378-b67e-a170127ec4e6
80 uuid:bf373763-dca5-4906-901b-d1bacb4f0286
81 uuid:394033e8-a6e2-4e39-bfac-458753a1ed78
82 uuid:268bfd97-991c-473f-bd51-bc80676c65c6
83 uuid:0a42c9ee-a840-4dda-8123-15c1bede5dfc
84 uuid:2c132929-9c8f-450a-81ff-367360ce2c19
85 uuid:44e427d1-a448-4bf2-b529-7d67b2266c06
86 uuid:85c99fd2-775f-40c9-8654-68223f59d091
87 uuid:28c64954-739c-444c-a6e0-355878e471c8
88 uuid:9e79a31c-3ea5-44f0-80f9-a32db49422e3
89 uuid:06d39051-38ef-4757-b68b-3327b1f16b9d
90 uuid:c4a2c982-244e-45a5-aa4b-71fa53f99e18
91 uuid:ac3da862-9e6c-4962-94b6-f4c31624f207
92 uuid:4178a296-903a-4a8e-9cfa-0cd6143476e8
93 uuid:a1e9df00-c8ae-411c-931c-c7df898c68d0
94 uuid:4d0f472b-f8ae-4026-87c9-6b5be14b0a70
95 uuid:b3b309c6-f234-4830-8b30-87d26a17ee1d
96 uuid:3c174acd-e431-4523-9ad6-eb14cddca805
97 uuid:e9d79844-ef14-493b-bbd6-d13691cc660e
98 uuid:76206b0b-af74-4344-b24f-81e839f0d7b0
99 uuid:da3fa7cc-5ce9-44fd-9a78-b8982b607515
100 uuid:a85df6df-0336-46fa-a9f4-522bf6f8b438
101 uuid:bb2bb365-7d7d-4fe9-9353-b21269676119
102 uuid:af0904ee-4fdb-4090-973f-599c81ddf022
103 uuid:468797c1-4a65-4f35-9c83-e28ce46972a2
104 uuid:602cd3f6-4a97-49c6-80e3-bcfd5c78dfa4
105 uuid:e7c51ac4-24e4-475e-88e7-f85e896945e3
106 uuid:01210861-aba1-4268-98d0-0260e05f5155
107 uuid:77335b2e-8812-4a35-b1e5-ca9ab626dfea
108 uuid:02b05c68-302e-4e7a-b229-81cb1377fd29
109 uuid:fa201fce-4e94-44b8-b435-c558c2e1ed55
110 uuid:628fe23d-188f-43e4-a203-a4bf3257d461
111 uuid:e4f4d6ba-e698-45a5-947f-ba6da88cc22b
112 uuid:cfee6297-2c0e-4f8a-94cc-9aaee0bd64cb
113 uuid:3fe626b3-c794-48e1-a80f-5bfe440c507b
114 uuid:0670cef6-d233-4852-89d8-36955261b0a3
115 uuid:9a096a12-b335-468c-b3cc-1191180d62de
116 uuid:92613d0d-e7b1-4d62-8ea4-451d7cd0a982
117 uuid:37577f91-d665-443e-8d70-b914954cef4b
118 uuid:f22831ec-6bc3-4b73-9197-4b01e01abb66
119 uuid:62f3f7af-f0f3-4f88-b9e0-acf8baa49ae4
120 uuid:40aac732-94df-496c-97ba-5b67f59bcc7a
121 uuid:a9d1a013-043b-475d-a71b-77ed80abe970
122 uuid:43ec6132-478c-4f87-878d-fb3c0c4d0c74
123 uuid:64fc743e-8176-40f6-8ae4-36ae97fac1d9
124 uuid:c17e374c-280b-4e78-bf21-74a7c1c73492
125 uuid:dad53aff-b520-4015-a9e3-f5fdf9168fe1
126 uuid:f94409a6-e461-4e4c-a6fb-0072d3d58b00
127 uuid:69caea81-a4e5-4e8d-83cd-9c18d8e8d965
128 uuid:5ccc2e5a-ea90-48b5-8542-69400d5334df
129 uuid:95c11a30-d44f-40c4-8ea8-ec34fca6bbbf
130 uuid:ffc83162-ff24-4a87-8709-eff17abc0b3b
131 uuid:aa77a0d7-7142-41c8-b494-483a5b68d8a7
[1] "God" "God" "God" "God" "God" "God"
[7] "God" "Chirodzo" "Chirodzo" "Chirodzo" "God" "God"
[13] "God" "God" "God" "God" "God" "God"
[19] "God" "God" "God" "God" "Ruaca" "Ruaca"
[25] "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Ruaca"
[31] "Ruaca" "Ruaca" "Ruaca" "Chirodzo" "Chirodzo" "Chirodzo"
[37] "Chirodzo" "God" "God" "God" "God" "God"
[43] "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo"
[49] "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo"
[55] "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo"
[61] "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo"
[67] "Chirodzo" "Chirodzo" "Chirodzo" "Chirodzo" "Ruaca" "Chirodzo"
[73] "Ruaca" "Ruaca" "Ruaca" "God" "Ruaca" "God"
[79] "Ruaca" "God" "God" "God" "God" "God"
[85] "God" "God" "God" "God" "God" "Ruaca"
[91] "Ruaca" "Ruaca" "Ruaca" "Ruaca" "God" "God"
[97] "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Ruaca"
[103] "God" "God" "Ruaca" "Ruaca" "Ruaca" "Ruaca"
[109] "Ruaca" "Ruaca" "God" "Ruaca" "Ruaca" "Ruaca"
[115] "Ruaca" "Ruaca" "God" "God" "Ruaca" "Ruaca"
[121] "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Ruaca" "Chirodzo"
[127] "Ruaca" "Ruaca" "Ruaca" "Chirodzo" "Chirodzo"
Factors
R has a special data class, called factor, to deal with categorical data that you may encounter when creating plots or doing statistical analyses. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.
Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:
<- factor (c ("earth" , "cement" , "cement" , "earth" ))levels (respondent_floor_type)nlevels (respondent_floor_type)
Jika anda membutuhkan konversi dari faktor ke karakter, anda bisa menggunakan as.character().
as.character (respondent_floor_type)
[1] "earth" "cement" "cement" "earth"
<- factor (c (1990 , 1983 , 1977 , 1998 , 1990 ))# coba jalankan perintah ini dengan menghilangkan tanda pagarnya # as.numeric(year_fct)
Maka akan keluar output yang salah. Karena trpe factor tidak bisa langsung dikonversi ke numerik. Sehingga kita perlu untuk mengubahnya ke type karakter terlebih dahulu. Maka perintah yang benar adalah sebagai berikut:
as.numeric (as.character (year_fct))
[1] 1990 1983 1977 1998 1990
atau bisa juga menggunakan perintah
as.numeric (levels (year_fct))[year_fct]
[1] 1990 1983 1977 1998 1990
Renaming Factors
[1] "NULL" "yes" "NULL" "NULL" "NULL" "NULL" "no" "yes" "no" "no"
[11] "NULL" "yes" "no" "NULL" "yes" "NULL" "NULL" "NULL" "NULL" "NULL"
[21] "no" "NULL" "NULL" "no" "no" "no" "NULL" "no" "yes" "NULL"
[31] "NULL" "yes" "no" "yes" "yes" "yes" "NULL" "yes" "NULL" "yes"
[41] "NULL" "no" "no" "NULL" "no" "no" "yes" "NULL" "NULL" "yes"
[51] "NULL" "no" "yes" "no" "NULL" "yes" "no" "no" "NULL" "no"
[61] "yes" "NULL" "NULL" "NULL" "no" "yes" "no" "no" "no" "no"
[71] "yes" "NULL" "no" "yes" "NULL" "NULL" "yes" "no" "no" "yes"
[81] "no" "no" "yes" "no" "yes" "no" "no" "NULL" "yes" "yes"
[91] "yes" "yes" "yes" "no" "no" "no" "no" "yes" "no" "no"
[101] "yes" "yes" "no" "NULL" "no" "no" "NULL" "no" "no" "NULL"
[111] "no" "NULL" "NULL" "no" "no" "no" "no" "yes" "no" "no"
[121] "no" "no" "no" "no" "no" "no" "no" "no" "no" "yes"
[131] "NULL"
<- safi$ memb_assoc<- as.factor (memb_assoc)
[1] NULL yes NULL NULL NULL NULL no yes no no NULL yes no NULL yes
[16] NULL NULL NULL NULL NULL no NULL NULL no no no NULL no yes NULL
[31] NULL yes no yes yes yes NULL yes NULL yes NULL no no NULL no
[46] no yes NULL NULL yes NULL no yes no NULL yes no no NULL no
[61] yes NULL NULL NULL no yes no no no no yes NULL no yes NULL
[76] NULL yes no no yes no no yes no yes no no NULL yes yes
[91] yes yes yes no no no no yes no no yes yes no NULL no
[106] no NULL no no NULL no NULL NULL no no no no yes no no
[121] no no no no no no no no no yes NULL
Levels: no NULL yes
Latihan
Import data SAFI_clean.csv ke dalam R dan tampilkan 10 baris pertama menggunakan fungsi yang sesuai.
Gunakan fungsi summary() untuk mendapatkan ringkasan statistik deskriptif dari data yang telah diimpor.
Tampilkan struktur data menggunakan str(). Apa tipe data dari kolom interview_date dan liv_count?
Hitung jumlah observasi dan variabel dalam data safi menggunakan fungsi yang sesuai.
Tampilkan elemen pertama dari kolom pertama dalam dataframe safi.
Ambil seluruh data kecuali kolom key_ID.
Tampilkan semua data untuk desa “Ruaca” saja.
Pilih hanya kolom village, no_membrs, dan liv_count dari dataset.
Konversi kolom memb_assoc menjadi tipe data faktor.
Berapa jumlah level unik pada faktor tersebut?
Ubah nama level NULL menjadi Unknown pada kolom memb_assoc.
Hitung frekuensi masing-masing level pada faktor memb_assoc.
Buat kolom baru bernama house_age yang merupakan hasil pengurangan tahun 2023 dengan years_liv.
Hitung rata-rata jumlah anggota keluarga (no_membrs) untuk setiap desa (village).
Berapa rata-rata liv_count untuk rumah yang memiliki lebih dari 2 kamar (rooms > 2)?
Filter data untuk rumah tangga yang memiliki lebih dari 5 anggota keluarga dan lebih dari 3 ternak (liv_count > 3).
Buat plot batang untuk melihat distribusi kepemilikan asosiasi (memb_assoc).
Buat histogram untuk variabel liv_count.
Buat scatter plot untuk melihat hubungan antara no_membrs dan liv_count.
Tambahkan warna berdasarkan desa (village) pada scatter plot yang telah dibuat.
Ekstrak tahun, bulan, dan hari dari kolom interview_date.
Hitung berapa banyak wawancara yang dilakukan pada tahun 2017.
Urutkan data berdasarkan tanggal wawancara (interview_date) dari yang paling lama ke yang terbaru.
Filter data untuk menampilkan rumah yang memiliki lebih dari 3 anggota keluarga dan lebih dari 2 kamar tidur.
Buat pivot table sederhana untuk menghitung rata-rata no_meals berdasarkan village.
Hitung persentase rumah tangga yang mengalami kekurangan makanan (months_lack_food tidak kosong) dibandingkan total data.
Gunakan ggplot2 untuk membuat boxplot yang menunjukkan distribusi liv_count berdasarkan village.
Buat heatmap untuk menganalisis korelasi antar variabel numerik di dataset.
