Type: | Package |
Title: | Desirable Dietary Pattern |
Version: | 0.0.3 |
Date: | 2021-05-08 |
Description: | The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weighted) score is calculated based on the type of food. This package is intended to calculate the DDP/ PPH score that is faster than traditional method via a manual calculation by BKP (2017) http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf and is simpler than the nutrition survey http://www.nutrisurvey.de. The database to create weights and baseline values is the Indonesia national survey in 2017. |
Depends: | R (≥ 2.10) |
License: | GPL-3 |
LazyData: | TRUE |
RoxygenNote: | 7.1.1 |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2021-05-08 14:46:09 UTC; Weksi Budiaji |
Author: | Weksi Budiaji [aut, cre] |
Maintainer: | Weksi Budiaji <budiaji@untirta.ac.id> |
Repository: | CRAN |
Date/Publication: | 2021-05-08 15:30:02 UTC |
Calory calculation
Description
This function calculates the total calory of each responden.
Usage
kalori(data, output = "all")
Arguments
data |
A data set of (n x 218) (see Details). |
output |
A desirable output, the default is "all" (see Details). |
Details
The data set is an n x 218 data frame. The first column is
the name of the respondent. The rest columns are types of food. The type of
food can be listed as in the data simulation (see in the data example
of simulasi
or vignette("ddp")
).
The output
argument has "all" as the
default, meaning that all of the calories are yielded. They are
energy, protein, fat, and carbohydrate. Single calory can be produced
by writing the output argument with "protein" for the calory of protein,
for example. The possible inputs for output
argument are
"all", "energi", "protein", "lemak" for fat, and "karbohidrat".
Value
Function returns a matrix of n x 4 for "all" and n x 1 for other "output" arguments.
Author(s)
Weksi Budiaji
Contact: budiaji@untirta.ac.id
References
BKP, Kementan. 2017. Aplikasi Harmonisasi Analisis PPH Data Susenas 2017. Badan Ketahanan Pangan Kementrian Pertanian.
Examples
#data simulation of 10 person
set.seed(2020)
n <- 10
matsim <- matrix(0, n, 218)
datsim <- as.data.frame(matsim)
datsim$V1 <- LETTERS[1:n]
#calory for boiled rice
datsim$V2 <- rnorm(n, 200, 50)
#calory for boiled egg
datsim$V73 <- rnorm(n, 60, 5)
#calory for fresh milk
datsim$V79 <- rnorm(n, 100, 10)
#calory for tomato
datsim$V93 <- rnorm(n, 19, 2)
#caloty for pineapple
datsim$V134 <- rnorm(n, 20, 2)
kalori(datsim)
Simulation data
Description
A dataset containing 218 columns and 5 rows. The first column is the name of the respondents, while the rest is the type of food. The type of food is expalined in Indonesian. The simulation data set is a family data set with 5 members. They eat rice (nasi) in a particular weight (in gram), cat fish, spinach (bayam), and banana (pisang lainnya). Three family members drink milk powder. Thus, the data have values in column 1, 28, 81, 85, and 135 only.
Usage
simulasi
Format
A data frame with 5 rows and 218 columns:
- Nama
The name of respondents
- X1
Beras:beras lokal, kualitas unggul, impor
- X2
Beras ketan
- X3
Jagung basah dengan kulit
- X4
Jagung pipilan/beras jagung
- X5
Tepung beras
- X6
Tepung jagung:maizena
- X7
Tepung terigu
- X8
Padi-padian lainnya
- X9
Ketela pohon/singkong
- X10
Ketela rambat/ubi jalar
- X11
Sagu:bukan dari ketela pohon
- X12
Talas/keladi
- X13
Kentang
- X14
Gaplek
- X15
Tepung Gaplek: tiwul
- X16
Tepung ketela pohon: tapioka/kanji
- X17
Umbi-umbian lainnya
- X18
Ekor kuning segar
- X19
Tongkol/tuna/cakalang segar
- X20
Tenggiri segar
- X21
Selar segar
- X22
Kembung segar
- X23
Teri segar
- X24
Bandeng segar
- X25
Gabus segar
- X26
Mujair/Nila segar
- X27
Mas segar
- X28
lele segar
- X29
Kakap segar
- X30
Baronang segar
- X31
Patin segar
- X32
Bawalsegar
- X33
Gurame segar
- X34
Ikan segar/basah lainnya
- X35
Udang segar
- X36
Cumi-cumi/sotong segar
- X37
Ketam/kepiting/rajungan segar
- X38
Kerang/siput segar
- X39
Udang dan hewan air lainnya yang segar lainnya
- X40
Kembung diawetkan/peda
- X41
Tenggiri diawetkan
- X42
Tongkol/tuna/cakalang diawetkan
- X43
Teri diawetkan
- X44
Selar diawetkan
- X45
Sepat diawetkan
- X46
Bandeng diawetkan
- X47
Gabus diawetkan
- X48
Ikan dalam kaleng
- X49
Ikan diawetkan lainnya
- X50
Udang: ebi, rebon diawetkan
- X51
Cumi-cumi/sotong diawetkan
- X52
Udang dan hewan air lainnya yang diawetkan
- X53
Daging sapi segar
- X54
Daging kerbau segar
- X55
Daging kambing segar
- X56
Daging babi segar
- X57
Daging ayam ras segar
- X58
Daging ayam kampung segar
- X59
Daging bebek/itik segar
- X60
Daging unggas segar lainnya
- X61
Daging segar lainnya
- X62
Dendeng
- X63
Abon: sapi, ayam, rusa, dsb
- X64
Daging dalam kaleng: kornet, dsb
- X65
Sosis, nuget, daging asap, bakso diawetkan
- X66
Daging diawetkan lainnya
- X67
Hati
- X68
Jeroan: usus, paru, limpa, babat, ampela, dsb
- X69
Tetelan
- X70
Tulang
- X71
Kategori daging lainnya selain dari 53 s.d 70
- X72
Telur ayam ras
- X73
Telur ayam kampung
- X74
Telur itik/manila
- X75
Telur puyuh
- X76
Telur lainnya
- X77
Telur asin
- X78
Susu murni
- X79
Susu cair pabrik
- X80
Susu kental manis
- X81
Susu bubuk
- X82
Susu bubuk bayi
- X83
Keju
- X84
Hasil lain dari susu
- X85
Bayam
- X86
Kangkung
- X87
Kol/kubis
- X88
Sawi putih/ petsai
- X89
Sawi hijau
- X90
Buncis
- X91
Kacang panjang
- X92
Tomat sayur
- X93
Wortel
- X94
Mentimun
- X95
Daun ketela pohon/ daun singkong
- X96
Terung
- X97
Tauge
- X98
Labu
- X99
Jagung muda
- X100
Bahan sayur sop/ cap cay
- X101
Bahan sayur asem/ lodeh
- X102
Nangka muda
- X103
Pepaya muda
- X104
Jamur
- X105
Petai
- X106
Jengkol
- X107
Bawang merah
- X108
Bawang putih
- X109
Cabe merah
- X110
Cabe hijau
- X111
Cabe rawit
- X112
Sayur dalam kaleng
- X113
Sayur-sayuran lainnya
- X114
Kacang tanah tanpa kulit
- X115
Kacang tanah dengan kulit
- X116
Kacang kedelai
- X117
Kacang hijau
- X118
Kacang mede
- X119
Kacang lainnya
- X120
Tahu
- X121
Tempe
- X122
Tauco
- X123
Oncom
- X124
Hasil lain dari kacang-kacangan
- X125
Jeruk
- X126
Mangga
- X127
Apel
- X128
Alpokat
- X129
Rambutan
- X130
Duku
- X131
Durian
- X132
Salak
- X133
Nanas
- X134
Pisang ambon
- X135
Pisang lainnya
- X136
Pepaya
- X137
Jambu
- X138
Sawo
- X139
Belimbing
- X140
Kedondong
- X141
Semangka
- X142
Melon
- X143
Nangka
- X144
Tomat buah
- X145
Buah dalam kaleng
- X146
Buah-buahan lainnya
- X147
Minyak kelapa
- X148
Minyak jagung
- X149
Minyak goreng
- X150
Kelapa
- X151
Margin
- X152
Minyak dan kelapa lainnya
- X153
Gula pasir
- X154
Gula merah/ gula cair
- X155
Teh bubuk
- X156
Teh celup: sachet
- X157
Kopi: bubuk, biji
- X158
Kopi instan: sachet
- X159
Coklat instan
- X160
Coklat bubuk
- X161
Sirup
- X162
Bahan minuman lainnya
- X163
Garam
- X164
Kemiri
- X165
Ketumbar/ jinten
- X166
Merica/ lada
- X167
Asam
- X168
Terasi/ petis
- X169
Kecap
- X170
Penyedap masakan/ vetsin
- X171
Sambal jadi
- X172
Saos tomat
- X173
Bumbu masak jadi/ kemasan
- X174
Bumbu dapur lainnya: pala, jahe, kunyit, dsb
- X175
Mie instan
- X176
Mie basah
- X177
Bihun
- X178
Makaroni/ mie kering
- X179
Kerupuk
- X180
Emping
- X181
Bahan agar-agar
- X182
Bubur bayi kemasan
- X183
Konsumsi lainnya selain nomor 175 s.d 182
- X184
Roti tawar
- X185
Roti manis/ lainnya
- X186
Kue kering/ biskuit
- X187
Kue basah
- X188
Makanan gorengan
- X189
Bubur kacang hijau
- X190
Gado-gado/ ketoprak/ pecel
- X191
Nasi campur/ rames
- X192
Nasi goreng
- X193
Nasi putih
- X194
Lontong/ ketupat sayur
- X195
Soto/ gulai/ sop/ rawon/ cincang
- X196
Sayur matang
- X197
Sate/ tongseng
- X198
Mie bakso/ rebus/ goreng
- X199
Mie instan makanan jadi
- X200
Makanan ringan anak-anak
- X201
Ikan matang
- X202
Ayam/ daging matang
- X203
Daging olahan matang
- X204
Bubur ayam
- X205
Siomay/ batagor
- X206
Makanan jadi lainnya
- X207
Air kemasan
- X208
Air kemasan galon
- X209
Air teh kemasan
- X210
Saribuah kemasan
- X211
Minuman ringan C02: soda
- X212
Minuman kesahatan/ energi
- X213
Minuman jadi: kopi, susu, teh, susu coklat, dsb
- X214
Es krim
- X215
Es lainnya
- X216
Bir
- X217
Minuman beralkohol lainnya
Desirable dietary pattern calculation
Description
This function calculates the desirable dietary pattern (DDP).
Usage
skorpph(data, wilayah = "Indonesia", baseline = 2000)
Arguments
data |
A data set of (n x 218) (see Details). |
wilayah |
An origin of the responden residence. (see Details). |
baseline |
A baseline value of personal calory required. |
Details
The data set is an n x 218 data frame. The first column is
the name of the respondent. wilayah
argument has "Indonesia" as the
default, meaning that the DPP are calculated based on the national (Indonesia)
baseline. The other possible inputs for wilayah
are "Aceh", "Sumut",
"Sumbar", "Riau", "KepRiau", "Jambi", "Sumsel", "Babel", "Bengkulu",
"Lampung", "Jakarta", "Jabar", "Banten", "Jateng", "DIY", "Jatim", "Bali",
"NTB", "NTT", "Kalbar", "Kalteng", "Kalsel", "Kaltim", "Kalut", "Sulut",
"Sulteng", "Sultra", "Sulsel", "Gorontalo", "Sulbar", "Maluku", "Malut",
"Papua", "Papbar". For baseline
argument, it is 2000 as the default
value because the minimal calory required in Indonesia is 2000 calory.
Value
Function returns a vector with n length indicates the index/ indices of the DDP per peson.
Author(s)
Weksi Budiaji
Contact: budiaji@untirta.ac.id
References
BKP, Kementan. 2017. Aplikasi Harmonisasi Analisis PPH Data Susenas 2017. Badan Ketahanan Pangan Kementrian Pertanian.
Examples
#data simulation of 10 person
set.seed(2020)
n <- 10
matsim <- matrix(0, n, 218)
datsim <- as.data.frame(matsim)
datsim$V1 <- LETTERS[1:n]
#calory for boiled rice
datsim$V2 <- rnorm(n, 200, 50)
#calory for boiled egg
datsim$V73 <- rnorm(n, 60, 5)
#calory for fresh milk
datsim$V79 <- rnorm(n, 100, 10)
#calory for tomato
datsim$V93 <- rnorm(n, 19, 2)
#caloty for pineapple
datsim$V134 <- rnorm(n, 20, 2)
skorpph(datsim)
Validity and Reliability check.
Description
This function calculates the item-rest correlation.
Usage
valid(data, alpha = 0.05, total = NULL)
Arguments
data |
A data set/ matrix (see Details). |
alpha |
An alpha value (see Details). |
total |
A single numeric value of the index column (see Details). |
Details
The data set is a data frame/ matrix n x k. The row is
the name of the respondent as many as n, while the column is
the variables (k). The alpha value is set between 0.0001 and
0.20, the default is 0.05. If the total
input is NULL
,
it means that the total score will be calculated first,
the column index of the total score can be also stated otherwise.
The index of the column is a numeric value with a length of one.
It has to be between 1 and (k).
Value
Function returns a data frame with k row and four columns. the columns indicate the item-rest correlation, correlation threshold, p value, and validity and reliability conclusion.
Author(s)
Weksi Budiaji
Contact: budiaji@untirta.ac.id
Examples
#data simulation of 10 person 5 variables
set.seed(1)
dat <- matrix(sample(1:7,10*5, replace = TRUE), 10,5)
valid(dat)