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Knowledge of Chikungunya Disease among Academic Population in Private Universities, Khartoum State, Sudan – 2019 | International Journal of TROPICAL DISEASE & Health

Introduction: Chikungunya is a viral disease that could lead to chronic symptoms. It has no approved treatment or vaccine to date.
Objective: To assess the level of knowledge about Chikungunya viral disease following an outbreak in Kassala Sudan among the academic population in private universities in Khartoum State.
Methods: A cross-sectional study was carried out in three private universities in Khartoum State during April-August 2019. A sample of 376 individuals (346 medical students and 30 teaching staff) was determined. A self-administered questionnaire was distributed to the target population. It included eleven variables about the information regarding Chikungunya disease. Data was imported into SPSS program version 20 and descriptive statistics were presented. Knowledge variables were categorized into scores as adequate, moderate and poor. Chi square test was used to test the knowledge levels among the study population at the confidence level of 95%.
Results: Out of 376 study population, 66 (17.6%) had never heard about the Chikungunya disease. Therefore, the knowledge variables were analyzed among 310 individuals who heard about the disease. Out of 310 individuals, 235 (75.8%) knew that the disease is viral and 245 (79.0%) knew that fever is the common symptom. Individuals who did not know the mode of transmission were 200 (64.5%). Individuals who did not know the diagnostic methods of the disease and management methods accounted for 228 (73.5%) and 174 (56.1%) respectively. One hundred seventy-five individuals (56.5%) did not know the prevention by vector control and 174 (56.1%) did not know if a vaccine is available or not. Out of 310 individuals, 60 (19.4%) had adequate knowledge about Chikungunya disease. Moderate to poor knowledge were significantly high among the study population, p value = 0.0002.
Conclusion: Most of the study population heard about Chikungunya disease but the majority had moderate to poor knowledge about the disease. Private universities should open channels with Ministries of Health to facilitate field training of students during outbreaks.
Please read full article –https://www.journalijtdh.com/index.php/IJTDH/
Keywords: Chikungunya, medical students, knowledge
submitted by sciencedomain to u/sciencedomain

Error generating a chi-square test with data that has been converted from wide to long

I am using Demographic and Health Survey data. My end goal is to run multivariate logistic regressions. Since I am using complex data, I converted the data from wide to long. I continued with analyses and ran a chi-square test. I successfully ran this using two variables. Because I want to run an analyses using four variables that describe the same undernutrition indicator for four children and cross it with the urban and rural variable, I ran another chi-square, considering my data is correctly converted from wide to long to do this. Unfortunately, I got an error this time. Please be aware I am running analyses adjusting for survey design. Below codes and data output. Can you support me with the correct coding to run an appropiate conversion from wide to long and a code to run a chi-square test appropiately?

`library(haven)`

`HNIR62FL_data_2 <- readta that has been converted from wide to long?_sav("~/DHS/HNIR62SV/HNIR62FL_data_2.SAV")`

`View(HNIR62FL_data_2)`

`obsHNIR62FL_data_2 <- subset(HNIR62FL_data_2, ![is.na](https://is.na)(V021) & ![is.na](https://is.na)(V022) & ![is.na](https://is.na)(D005))\`

`myvars <- c("CASEID", "V013", "V021", "V022", "V025", "V106", "V137", "V190", "V714", "D005", "D104", "D106", "D107", "D108","v1014", "v1016", "v1021", "v1023", "v1038", "v1039", "v1045", "v1113", "V701", "v1007_1", "v1007_2", "v1007_3", "v1007_4", "v1008_1", "v1008_2", "v1008_3", "v1008_4", "v1009_1", "v1009_2", "v1009_3", "v1009_4", "v1010_1", "v1010_2", "v1010_3", "v1010_4", "v1020_1", "v1020_2", "v1020_3", "v1020_4", "v1071_1", "v1071_2", "v1071_3", "v1071_4", "v1088_1", "v1088_2", "v1088_3", "v1088_4", "v1096_1", "v1096_2", "v1096_3", "v1096_4", "v1104_1", "v1104_2", "v1104_3", "v1104_4", "v1111_1", "v1111_2", "v1111_3", "v1111_4", "v1112_1", "v1112_2", "v1112_3", "v1112_4")`

`newobsHNIR62FL_data_2 <- obsHNIR62FL_data_2[myvars]`

`dhsdesign <- svydesign(newobsHNIR62FL_data_2$V021, strata = newobsHNIR62FL_data_2$V022, weights = newobsHNIR62FL_data_2$D005/1000000, data = newobsHNIR62FL_data_2)`

`newobsHNIR62FL_data_2 %>% mutate(across(starts_with(c("V", "v")), as.double)) %>% pivot_longer(cols=starts_with(c("V", "v")), names_to = c("name", "id"), values_to = "value", names_sep = "_")`

`A tibble: 379,237 x 9
CASEID D005 D104 D106 D107 D108 name id value

1 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V013 NA 2
2 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V021 NA 564
3 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V022 NA 16
4 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V025 NA 1
5 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V106 NA 1
6 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V137 NA 2
7 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V190 NA 3
8 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] V714 NA 0
9 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] v1014 NA 2
10 " 564 9~ 1758042 0 [No] 0 [No] 0 [No] 0 [No] v1016 NA 1
# ... with 379,227 more rows`

```
Warning message:
Expected 2 pieces. Missing pieces filled with `NA` in 17 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17].
```


`summary(svytable(~v1113+v1039, dhsdesign))`

```
v1039
v1113 1 2 3 4
0 1087 934 626 646
1 462 518 388 359
Pearson's X^2: Rao & Scott adjustment
data: svychisq(~v1113 + v1039, design = dhsdesign, statistic = "F")
F = 5.4626, ndf = 2.9836, ddf = 3243.1635, p-value = 0.0009852`
```

`summary(svytable(~v1007_1 + v1007_2+ v1007_3 + v1007_4 + V025, dhsdesign))`

```
Error in svychisq.survey.design(~v1007_1 + v1007_2 + v1007_3 + v1007_4 + :
Only 2-way tables at the moment
```

`dput(head(newobsHNIR62FL_data_2))`

```
`structure(list(CASEID = structure(c(" 564 91 2", " 198 61 2",
" 267 21 1", " 1089 81 2", " 583 61 8", " 989101 2"
), label = "Case Identification", format.spss = "A15", display_width = 17L),
V013 = structure(c(2, 4, 4, 4, 6, 3), label = "Age in 5-year groups", format.spss = "F1.0", display_width = 6L, labels = c(`15-19` = 1,
`20-24` = 2, `25-29` = 3, `30-34` = 4, `35-39` = 5, `40-44` = 6,
`45-49` = 7), class = c("haven_labelled", "vctrs_vctr", "double"
)), V021 = structure(c(564, 198, 267, 1089, 583, 989), label = "Primary sampling unit", format.spss = "F4.0", display_width = 6L),
V022 = structure(c(16, 8, 9, 37, 17, 33), label = "Sample strata for sampling errors", format.spss = "F4.0", display_width = 6L, labels = c(`Atlántida Urbano` = 1,
`Atlántida Rural` = 2, `Colón Urbano` = 3, `Colón Rural` = 4,
`Comayagua Urbano` = 5, `Comayagua Rural` = 6, `Copán Urbano` = 7,
`Copán Rural` = 8, `San Pedro Sula Urbano` = 9, `Cortés Resto Urbano` = 10,
`Cortés Resto Rural` = 11, `Choluteca Urbano` = 12, `Choluteca Rural` = 13,
`El Paraíso Urbano` = 14, `El Paraíso Rural` = 15, `Tegucigalpa Urbano` = 16,
`Morazán Resto Urbano` = 17, `Morazán Resto Rural` = 18,
`Gracias a Dios Urbano` = 19, `Gracias a Dios Rural` = 20,
`Intibucá Urbano` = 21, `Intibucá Rural` = 22, `Islas de Bahía Urbano` = 23,
`Islas de Bahía Rural` = 24, `La Paz Urbano` = 25, `La Paz Rural` = 26,
`Lempira Urbano` = 27, `Lempira Rural` = 28, `Ocotepeque Urbano` = 29,
`Ocotepeque Rural` = 30, `Olancho Urbano` = 31, `Olancho Rural` = 32,
`Santa Bárbara Urbano` = 33, `Santa Bárbara Rural` = 34,
`Valle Urbano` = 35, `Valle Rural` = 36, `Yoro Urbano` = 37,
`Yoro Rural` = 38), class = c("haven_labelled", "vctrs_vctr",
"double")), V025 = structure(c(1, 2, 1, 1, 1, 1), label = "Type of place of residence", format.spss = "F1.0", display_width = 6L, labels = c(Urban = 1,
Rural = 2), class = c("haven_labelled", "vctrs_vctr", "double"
)), V106 = structure(c(1, 0, 1, 1, 1, 2), label = "Highest educational level", format.spss = "F1.0", display_width = 6L, labels = c(`No education` = 0,
Primary = 1, Secondary = 2, Higher = 3), class = c("haven_labelled",
"vctrs_vctr", "double")), V137 = structure(c(2, 2, 2, 1,
0, 1), label = "Number of children 5 and under in household (de jure)", format.spss = "F2.0", display_width = 6L),
V190 = structure(c(3, 1, 2, 4, 2, 4), label = "Wealth index", format.spss = "F1.0", display_width = 6L, labels = c(Poorest = 1,
Poorer = 2, Middle = 3, Richer = 4, Richest = 5), class = c("haven_labelled",
"vctrs_vctr", "double")), V714 = structure(c(0, 0, 1, 1,
0, 0), label = "Respondent currently working", format.spss = "F1.0", display_width = 6L, labels = c(No = 0,
Yes = 1), class = c("haven_labelled", "vctrs_vctr", "double"
)), D005 = structure(c(1758042, 927099, 1296895, 1087346,
1005935, 1112882), label = "Weight for Domestic Violence (6 decimals)", format.spss = "F8.0", display_width = 10L),
D104 = structure(c(0, 0, 0, 0, 0, 0), label = "Experienced any emotional violence", format.spss = "F1.0", display_width = 6L, labels = c(No = 0,
Yes = 1), class = c("haven_labelled", "vctrs_vctr", "double"
)), D106 = structure(c(0, 0, 0, 0, 0, 0), label = "Experienced any less severe violence (D105A-C,J) by husband/partner", format.spss = "F1.0", display_width = 6L, labels = c(No = 0,
`Yes (D105A-D)` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), D107 = structure(c(0, 0, 0, 0, 0, 0), label = "Experienced any severe violence (D105D-F) by husband/partner", format.spss = "F1.0", display_width = 6L, labels = c(No = 0,
`Yes (D105E-G)` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), D108 = structure(c(0, 0, 0, 0, 0, 0), label = "Experienced any sexual violence (D105H-I,K) by husband/partner", format.spss = "F1.0", display_width = 6L, labels = c(No = 0,
`Yes (D105H-I)` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1014 = structure(c(2, 2, 2, 4, 2, 4), label = "women BMI category", format.spss = "F8.0", labels = c(underweight = 1,
`normal weight` = 2, overweight = 3, obese = 4), class = c("haven_labelled",
"vctrs_vctr", "double")), v1016 = structure(c(1, 0, 0, 1,
0, 1), label = "women height category", format.spss = "F8.0", labels = c(`woman height <150 cm` = 0,
`woman height 150 cm or more ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1021 = structure(c(4, 3, 1, 1,
4, 3), label = "region category", format.spss = "F8.0", labels = c(Northern = 1,
Southern = 2, Western = 3, Central = 4, Eastern = 5), class = c("haven_labelled",
"vctrs_vctr", "double")), v1023 = structure(c(2, 5, 3, 2,
4, 1), label = "parity", format.spss = "F8.0", labels = c(`0` = 0,
`1` = 1, `2` = 2, `3` = 3, `4` = 4, `5 or more` = 5), class = c("haven_labelled",
"vctrs_vctr", "double")), v1038 = structure(c(2, 1, 3, 2,
2, 2), label = "marital status", format.spss = "F8.0", labels = c(`Never in union` = 0,
Married = 1, `Living with partner ` = 2, `Divorced, widowed or separated/no longer living together` = 3
), class = c("haven_labelled", "vctrs_vctr", "double")),
v1039 = structure(c(2, 4, 3, 2, 3, 1), label = "marital duration", format.spss = "F8.0", labels = c(`Never in a union` = 0,
`0-4 years` = 1, `5-9 years` = 2, `10-14 years` = 3, `15 years or more` = 4
), class = c("haven_labelled", "vctrs_vctr", "double")),
v1045 = structure(c(4, 4, NA, 3, 1, 4), label = "Women decision making scale", format.spss = "F8.0", labels = c(`No decision making skills` = 0,
`Respondent alone/respondent and husband/partner decide on one issue` = 1,
`Respondent alone/respondent and husband/partner decide on two issues` = 2,
`Respondent alone/respondent and husband/partner decide on three issues` = 3,
`Respondent alone/respondent and husband/partner decide on four issues` = 4
), class = c("haven_labelled", "vctrs_vctr", "double")),
v1113 = structure(c(0, 0, 0, 0, 0, 0), label = "Any intimate partner violence", format.spss = "F8.0", labels = c(`Has not experienced any form of intimate partner violence` = 0,
`Has experienced any form of intimate partner violence` = 1
), class = c("haven_labelled", "vctrs_vctr", "double")),
V701 = structure(c(2, 1, 1, 2, 2, 2), label = "Husband/partner's education level", format.spss = "F1.0", display_width = 6L, labels = c(`No education` = 0,
Primary = 1, Secondary = 2, Higher = 3, `Don't know` = 8), class = c("haven_labelled",
"vctrs_vctr", "double")), v1007_1 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "youngest child stunting category", format.spss = "F8.0", labels = c(`stunted child ` = 0,
`not stunted child` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1007_2 = structure(c(1, 0, 0, NA, NA, NA), label = "stunting category (second to youngest child)", format.spss = "F8.0", labels = c(stunted = 0,
`not stunted` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1007_3 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "stunting category (third to youngest child)", format.spss = "F8.0", labels = c(stunted = 0,
`not stunted` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1007_4 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "stunting category (fourth to youngest child)", format.spss = "F8.0", labels = c(stunted = 0,
`not stunted` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1008_1 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "youngest child underweight category", format.spss = "F8.0", labels = c(`underweight child` = 0,
`not underweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1008_2 = structure(c(1, 1, 0,
NA, NA, NA), label = "underweight category (second to youngest child)", format.spss = "F8.0", labels = c(underweight = 0,
`not underweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1008_3 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "underweight category (third to youngest child)", format.spss = "F8.0", labels = c(underweight = 0,
`not underweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1008_4 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "underweight category (fourth to youngest child)", format.spss = "F8.0", labels = c(underweight = 0,
`not underweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1009_1 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "youngest child wasting category", format.spss = "F8.0", labels = c(`wasted child` = 0,
`not wasted child ` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1009_2 = structure(c(1, 1, 1, NA, NA, NA), label = "wasting category (second to youngest child)", format.spss = "F8.0", labels = c(wasted = 0,
`not wasted` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1009_3 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "wasting category (third to youngest child)", format.spss = "F8.0", labels = c(wasted = 0,
`not wasted ` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1009_4 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "wasting category (fourth to youngest child)", format.spss = "F8.0", labels = c(wasted = 0,
`not wasted` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1010_1 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "youngest child overweight category", format.spss = "F8.0", labels = c(`overweight child` = 0,
`not overweight child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1010_2 = structure(c(1, 1, 1,
NA, NA, NA), label = "overweight category (second to youngest child)", format.spss = "F8.0", labels = c(overweight = 0,
`not overweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1010_3 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "overweight category (third to youngest child)", format.spss = "F8.0", labels = c(overweight = 0,
`not overweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1010_4 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "overweight category (fourth to youngest child)", format.spss = "F8.0", labels = c(overweight = 0,
`not overweight` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1020_1 = structure(c(1, 0, 1, 0, 1, 0), label = "youngest child morbidity category", format.spss = "F8.0", labels = c(`youngest child with no morbidity` = 0,
`youngest child with morbidity ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1020_2 = structure(c(1, 0, 1,
NA, NA, NA), label = "Morbidity category (second to youngest child)", format.spss = "F8.0", labels = c(`youngest child with no morbidity` = 0,
`youngest child with morbidity` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1020_3 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Morbidity category (third to youngest child)", format.spss = "F8.0", labels = c(`youngest child with no morbidity` = 0,
`youngest child with morbidity` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1020_4 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Morbidity category (fourth to youngest child)", format.spss = "F8.0", labels = c(`youngest child with no morbidity` = 0,
`youngest child with morbidity` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1071_1 = structure(c(0, NA, 1,
1, 1, 1), label = "anemia category (youngest child)", format.spss = "F8.0", labels = c(anemic = 0,
`not anemic` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1071_2 = structure(c(1, 1, 0, NA, NA, NA), label = "anemia category (second to youngest child)", format.spss = "F8.0", labels = c(anemic = 0,
`not anemic` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1071_3 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "anemia category (third to youngest child)", format.spss = "F8.0", labels = c(anemic = 0,
`not anemic` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1071_4 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "anemia category (fourth to youngest child)", format.spss = "F8.0", labels = c(anemic = 0,
`not anemic` = 1), class = c("haven_labelled", "vctrs_vctr",
"double")), v1088_1 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), label = "youngest child stunting + overweight category", format.spss = "F8.0", labels = c(`stunted and overweight child` = 0,
`not stunted and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1088_2 = structure(c(1, 1, NA,
NA, NA, NA), label = "second to youngest child stunting + overweight category", format.spss = "F8.0", labels = c(`stunted and overweight child` = 0,
`not stunted and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1088_3 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "third to youngest child stunting + overweight category", format.spss = "F8.0", labels = c(`stunted and overweight child ` = 0,
`not stunted and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1088_4 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "fourth to youngest child stunting + overweight category", format.spss = "F8.0", labels = c(`stunted and overweight child ` = 0,
`not stunted and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1096_1 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "youngest child anemic + overweight category", format.spss = "F8.0", labels = c(`anemic and overweight child ` = 0,
`not anemic and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1096_2 = structure(c(1, 1, 1,
NA, NA, NA), label = "second to youngest child anemic + overweight category", format.spss = "F8.0", labels = c(`anemic and overweight child ` = 0,
`not anemic and overweight child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1096_3 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "third to youngest child anemic + overweight category", format.spss = "F8.0", labels = c(`anemic and overweight child ` = 0,
`not anemic and overweight child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1096_4 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "fourth to youngest child anemic + overweight category", format.spss = "F8.0", labels = c(`anemic and overweight child ` = 0,
`not anemic and overweight child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1104_1 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "youngest child anemic + stunted category", format.spss = "F8.0", labels = c(`anemic and stunted child` = 0,
`not anemic and not stunted child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1104_2 = structure(c(1, 1, NA,
NA, NA, NA), label = "second to youngest child anemic + stunted category", format.spss = "F8.0", labels = c(`anemic and stunted child ` = 0,
`not anemic and stunted child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1104_3 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "third to youngest child anemic + stunted category", format.spss = "F8.0", labels = c(`anemic and stunted child ` = 0,
`not anemic and stunted child ` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1104_4 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "fourth to youngest child anemic + stunted category", format.spss = "F8.0", labels = c(`stunted and anemic child` = 0,
`not stunted and anemic child` = 1), class = c("haven_labelled",
"vctrs_vctr", "double")), v1111_1 = structure(c(3, 1, 4,
4, 5, 6), label = "Child age (youngest child)", format.spss = "F8.0", labels = c(`0-5 months` = 1,
`6-11 months` = 2, `12-23 months` = 3, `24-35 months` = 4,
`36-47 months` = 5, `48-59 months` = 6), class = c("haven_labelled",
"vctrs_vctr", "double")), v1111_2 = structure(c(6, 5, 6,
NA, NA, NA), label = "Child age (second to youngest)", format.spss = "F8.0", labels = c(`0-5 months` = 1,
`6-11 months` = 2, `12-23 months` = 3, `24-35 months` = 4,
`36-47 months` = 5, `48-59 months` = 6), class = c("haven_labelled",
"vctrs_vctr", "double")), v1111_3 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Child age (third to youngest)", format.spss = "F8.0", labels = c(`0-5 months` = 1,
`6-11 months` = 2, `12-23 months` = 3, `24-35 months` = 4,
`36-47 months` = 5, `48-59 months` = 6), class = c("haven_labelled",
"vctrs_vctr", "double")), v1111_4 = structure(c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Child age (fourth to youngest)", format.spss = "F8.0", labels = c(`0-5 months` = 1,
`6-11 months` = 2, `12-23 months` = 3, `24-35 months` = 4,
`36-47 months` = 5, `48-59 months` = 6), class = c("haven_labelled",
"vctrs_vctr", "double")), v1112_1 = structure(c(2, 2, 1,
1, 1, 1), label = "Sex of child", format.spss = "F1.0", display_width = 7L, labels = c(Male = 1,
Female = 2), class = c("haven_labelled", "vctrs_vctr", "double"
)), v1112_2 = structure(c(1, 2, 1, 2, 1, NA), label = "Sex of child", format.spss = "F1.0", display_width = 7L, labels = c(Male = 1,
Female = 2), class = c("haven_labelled", "vctrs_vctr", "double"
)), v1112_3 = structure(c(NA, 2, 1, NA, 2, NA), label = "Sex of child", format.spss = "F1.0", display_width = 7L, labels = c(Male = 1,
Female = 2), class = c("haven_labelled", "vctrs_vctr", "double"
)), v1112_4 = structure(c(NA, 2, NA, NA, 1, NA), label = "Sex of child", format.spss = "F1.0", display_width = 7L, labels = c(Male = 1,
Female = 2), class = c("haven_labelled", "vctrs_vctr", "double"
))), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))`
```
submitted by Marielita2579 to rstats

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