How can I change haven_labelled variables to factor and numeric variables that can be adequate for R?

Question

I am using Demographic and Health Survey data and want to convert the data from wide to long.

However, I got an error when I used tidyr::pivot_longer.

I imported the data from PSPP (.sav) using read_sav from the haven package. The resulting columns have the class of haven_labelled. I am having difficulty converting the columns to be the same class.

I tried using the information in the linked duplicate, but it does not address this issue.

Below you can see my steps with codes and also the data output.

HNIR62FL_data_1 <- read_sav("~/DHS/HNIR62SV/HNIR62FL_data_1.SAV")
bsHNIR62FL_data_1 <- subset(HNIR62FL_data_1, !is.na(V021) & !is.na(V022) & !is.na(D005))
myvars <- c("CASEID", "V013", "V021", "V022", "V025", "V106", "V137", "V190", "V714", "D005", "D104", "D106", "D107", "D108","v1014", "v1016", "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_1 <- obsHNIR62FL_data_1[myvars]
tidyr::pivot_longer(newobsHNIR62FL_data_1, cols=starts_with(c("V", "v")), names_to = c("name", "id"), values_to = "value", names_sep = "_")

#Error: Can't convert from <labelled<double>> to <labelled<double>> due to #loss of precision.
#* Locations: 1, 3, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 26, ...
#Values are labelled in `` but not in ``.

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

Here is my data:

dput(head(newobsHNIR62FL_data_1))
structure(list(CASEID = structure(c("      372151  1", "      503201  2", 
"       76 81  1", "      603191  2", "      559 21  1", "      643131  1"
), label = "Case Identification", format.spss = "A15", display_width = 17L), 
    V013 = structure(c(3, 2, 3, 4, 3, 5), 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(372, 503, 76, 603, 559, 643), label = "Primary sampling unit", format.spss = "F4.0", display_width = 6L), 
    V022 = structure(c(11, 16, 3, 18, 16, 20), 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(2, 1, 1, 2, 1, 2), 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(3, 2, 2, 1, 2, 1), 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, 1, 0, 0, 
    1, 0), label = "Number of children 5 and under in household (de jure)", format.spss = "F2.0", display_width = 6L), 
    V190 = structure(c(5, 5, 4, 2, 4, 1), 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(1, 1, 1, 1, 
    1, 1), 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(1190085, 1726649, 607124, 1671912, 
    1102085, 118158), label = "Weight for Domestic Violence (6 decimals)", format.spss = "F8.0", display_width = 10L), 
    D104 = structure(c(0, 0, 0, 1, 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, 3, 3, 3, 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, 1, 1, 0, 
    1, 0), 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")), v1023 = structure(c(2, 1, 1, 2, 
    1, 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, 3, 2, 3, 
    3, 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, 2, 2, 3, 1, 4), 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, NA, 4, NA, NA, 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, 1, 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(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_), 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(1, 1, NA, 
    NA, 1, NA), 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(NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_), 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(1, 1, NA, NA, 1, NA), 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(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), 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(1, 1, NA, NA, 1, NA), 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(NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_), 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(1, 1, NA, NA, 1, NA), 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(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), 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, 1, NA, NA, 0, NA), 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(0, NA, NA, 
    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(NA, 1, NA, 
    NA, 1, NA), 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(NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_), 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(1, 1, NA, NA, 1, NA), 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(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), 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, 1, NA, 
    NA, 1, NA), 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(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), 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, 1, NA, 
    NA, 1, NA), 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(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), 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(2, 5, NA, 
    NA, 6, NA), 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(5, NA, NA, 
    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, 1, 2, 
    2, 2, 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(2, NA, NA, 2, NA, 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_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_), 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_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_), 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"))

How can I proceed?

Thank you!

Hi Mariela, welcome to SO. Please check out how to ask a question https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example. This will help the community be better in helping you. At the moment your question is not reproducible and very difficult to understand — hello_friend, Jun 18 '20 at 01:25
I tried to get this question reopened, but failed. The problem you are having is that your columns are of different classes. One way around that is to convert them all to the same type. Hopefully this will get you in the right direction: `library(dplyr); library(tidyr); library(labelled); newobsHNIR62FL_data_1 %>% 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 = "_",)` — Ian Campbell, Jun 19 '20 at 12:05
Sending a reproducible example to the package maintainer would help, too. — Thomas Lumley, Jun 21 '20 at 07:40
This is *not* the same issue as the linked duplicate. It is, however, the same issue as https://stackoverflow.com/questions/62485531/getting-a-subset-error-i-did-not-get-two-months-ago-when-running-logistic-regres — Thomas Lumley, Jun 22 '20 at 03:10
You can use `haven::as_factor` to turn labelled variables into factors, or `haven::zap_labels` to turn them into numeric variables with the labelled values becoming `NA`, or `as.numeric` to turn them into plain numeric variables. — Thomas Lumley, Jun 22 '20 at 06:26

How can I change haven_labelled variables to factor and numeric variables that can be adequate for R?

0 Answers0