Msc_Dataset_Eliza <- read_dta("thesis_database.dta")
new_column_names <- c("Cluster", "HHNumber", "sampleweight", "year","age","evermarried","psu","stratum","region","urbanrural","highestedu","hieghestyredu","edusingleyrs","eduattain","literacy","childevrborn", "birthfveyrs","birthpastyr","ageatfirstbirth","currPreg","termipreg","contraKnowl","currentcontraceptive","wealthindex", "maritualstatus")
Msc_Dataset_Eliza <- Msc_Dataset_Eliza %>%
rename_at(vars("v001","v002","v005","v007","v012","v020","v021","v022", "v024","v025","v106","v107","v133","v149","v155","v201","v208","v209","v212","v213","v228","v301a","v312","v190","v501"),
~new_column_names)
labels_res <- c("Urban", "Rural")
labels_educat <- c("No_education", "Primary", "Secondary", "higher")
labels_wealthcat <- c("Poorest", "Poorer", "Poor", "Rich", "Richest")
labels_marital <- c("Never_married", "Married", "Living_with_partner", "Widowed", "Divorced", "Not_living_together")
labels_region <- c("Northern", "Central", "Southern")
labels_yesno <- c("No", "Yes")
Msc_Dataset_Eliza $urbanrural <- factor(Msc_Dataset_Eliza $urbanrural, levels = c(1, 2),
labels = labels_res)
Msc_Dataset_Eliza $hieghestedu <- factor(Msc_Dataset_Eliza $highestedu, levels = c(0, 1, 2, 3),
labels = labels_educat)
Msc_Dataset_Eliza $wealthindex <- factor(Msc_Dataset_Eliza $wealthindex, levels = c(1, 2, 3, 4, 5),
labels = labels_wealthcat)
Msc_Dataset_Eliza $maritualstatus <- factor(Msc_Dataset_Eliza$maritualstatus, levels = c(0, 1, 2, 3, 4, 5),
labels = labels_marital)
Msc_Dataset_Eliza $region <- factor(Msc_Dataset_Eliza$region, levels = c(1, 2, 3),
labels = labels_region)
Msc_Dataset_Eliza $everpreg <- factor(Msc_Dataset_Eliza$everpreg, levels = c(0, 1),
labels = labels_yesno)
Msc_Dataset_Eliza $contraKnowl <- factor(Msc_Dataset_Eliza$contraKnowl, levels = c(0,1),
labels = labels_yesno)
Msc_Dataset_Eliza$psu <- as.factor(Msc_Dataset_Eliza$psu)
Msc_Dataset_Eliza <- Msc_Dataset_Eliza %>% mutate(wt= sampleweight/1000000)
options(survey.adjust.domain.lonely=TRUE)
options(survey.lonely.psu="adjust")
Datasetwt <- svydesign(
ids = ~psu, strata= ~stratum, weights= ~wt,
nest=TRUE, data=Msc_Dataset_Eliza
)
tab1_summary <- svyCreateTableOne2(
vars = c("region", "everpreg", "maritualstatus", "hieghestedu", "wealthindex", "contraKnowl", "age"),
strata = "urbanrural", data = Datasetwt,
factorVars = c("region", "everpreg", "maritualstatus", "hieghestedu", "wealthindex", "contraKnowl")
)
# view the table
tab1_summary
## level Urban Rural
## n "" " 2320.6" " 10339.0"
## region (%) "Northern" " 253.2 (10.9) " " 1325.4 (12.8) "
## "Central" " 950.8 (41.0) " " 4459.5 (43.1) "
## "Southern" " 1116.7 (48.1) " " 4554.1 (44.0) "
## everpreg (%) "No" " 1818.7 (78.4) " " 7216.8 (69.8) "
## "Yes" " 501.9 (21.6) " " 3122.2 (30.2) "
## maritualstatus (%) "Never_married" " 1886.2 (81.3) " " 7183.3 (69.5) "
## "Married" " 321.8 (13.9) " " 2499.4 (24.2) "
## "Living_with_partner" " 67.7 ( 2.9) " " 304.7 ( 2.9) "
## "Widowed" " 3.8 ( 0.2) " " 19.4 ( 0.2) "
## "Divorced" " 16.2 ( 0.7) " " 126.0 ( 1.2) "
## "Not_living_together" " 24.8 ( 1.1) " " 206.3 ( 2.0) "
## hieghestedu (%) "No_education" " 33.6 ( 1.4) " " 371.1 ( 3.6) "
## "Primary" " 1061.1 (45.7) " " 8098.1 (78.3) "
## "Secondary" " 1177.2 (50.7) " " 1854.5 (17.9) "
## "higher" " 48.7 ( 2.1) " " 15.2 ( 0.1) "
## wealthindex (%) "Poorest" " 63.5 ( 2.7) " " 2184.7 (21.1) "
## "Poorer" " 42.6 ( 1.8) " " 2263.1 (21.9) "
## "Poor" " 123.8 ( 5.3) " " 2352.5 (22.8) "
## "Rich" " 311.0 (13.4) " " 2202.0 (21.3) "
## "Richest" " 1779.7 (76.7) " " 1336.6 (12.9) "
## contraKnowl (%) "No" " 92.3 ( 4.0) " " 932.1 ( 9.0) "
## "Yes" " 2228.3 (96.0) " " 9407.0 (91.0) "
## age "" " 16.93 ± 1.43" " 16.90 ± 1.43"
## p test sig
## n "" "" NA
## region (%) " 0.323" "" ""
## "" "" NA
## "" "" NA
## everpreg (%) "<0.001" "" "**"
## "" "" NA
## maritualstatus (%) "<0.001" "" "**"
## "" "" NA
## "" "" NA
## "" "" NA
## "" "" NA
## "" "" NA
## hieghestedu (%) "<0.001" "" "**"
## "" "" NA
## "" "" NA
## "" "" NA
## wealthindex (%) "<0.001" "" "**"
## "" "" NA
## "" "" NA
## "" "" NA
## "" "" NA
## contraKnowl (%) "<0.001" "" "**"
## "" "" NA
## age " 0.465" "" ""
#to save the file
html_table <- as_huxtable(tab1_summary)
# Add some formatting
html_table <- set_all_borders(html_table, 1)
#html_table <- set_bold(html_table, where = "top-left-bottom-right", TRUE)
html_table <- set_font_size(html_table, 12)
# Print the formatted HTML table
print(html_table, output = "quarto")
## ┌────────────┬────────────┬────────────┬────────┬─────┬─────┐
## │ │ 2320.6 │ 10339.0 │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Northern │ 253.2 │ 1325.4 │ 0.323 │ │ │
## │ │ (10.9) │ (12.8) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Central │ 950.8 │ 4459.5 │ │ │ │
## │ │ (41.0) │ (43.1) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Southern │ 1116.7 │ 4554.1 │ │ │ │
## │ │ (48.1) │ (44.0) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ No │ 1818.7 │ 7216.8 │ <0.001 │ │ ** │
## │ │ (78.4) │ (69.8) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Yes │ 501.9 │ 3122.2 │ │ │ │
## │ │ (21.6) │ (30.2) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Never_marr │ 1886.2 │ 7183.3 │ <0.001 │ │ ** │
## │ ied │ (81.3) │ (69.5) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Married │ 321.8 │ 2499.4 │ │ │ │
## │ │ (13.9) │ (24.2) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Living_wit │ 67.7 ( │ 304.7 ( │ │ │ │
## │ h_partner │ 2.9) │ 2.9) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Widowed │ 3.8 ( │ 19.4 ( │ │ │ │
## │ │ 0.2) │ 0.2) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Divorced │ 16.2 ( │ 126.0 ( │ │ │ │
## │ │ 0.7) │ 1.2) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Not_living │ 24.8 ( │ 206.3 ( │ │ │ │
## │ _together │ 1.1) │ 2.0) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ No_educati │ 33.6 ( │ 371.1 ( │ <0.001 │ │ ** │
## │ on │ 1.4) │ 3.6) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Primary │ 1061.1 │ 8098.1 │ │ │ │
## │ │ (45.7) │ (78.3) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Secondary │ 1177.2 │ 1854.5 │ │ │ │
## │ │ (50.7) │ (17.9) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ higher │ 48.7 ( │ 15.2 ( │ │ │ │
## │ │ 2.1) │ 0.1) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Poorest │ 63.5 ( │ 2184.7 │ <0.001 │ │ ** │
## │ │ 2.7) │ (21.1) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Poorer │ 42.6 ( │ 2263.1 │ │ │ │
## │ │ 1.8) │ (21.9) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Poor │ 123.8 ( │ 2352.5 │ │ │ │
## │ │ 5.3) │ (22.8) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Rich │ 311.0 │ 2202.0 │ │ │ │
## │ │ (13.4) │ (21.3) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Richest │ 1779.7 │ 1336.6 │ │ │ │
## │ │ (76.7) │ (12.9) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ No │ 92.3 ( │ 932.1 ( │ <0.001 │ │ ** │
## │ │ 4.0) │ 9.0) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ Yes │ 2228.3 │ 9407.0 │ │ │ │
## │ │ (96.0) │ (91.0) │ │ │ │
## ├────────────┼────────────┼────────────┼────────┼─────┼─────┤
## │ │ 16.93 ± │ 16.90 ± │ 0.465 │ │ │
## │ │ 1.43 │ 1.43 │ │ │ │
## └────────────┴────────────┴────────────┴────────┴─────┴─────┘
##
## Column names: level, Urban, Rural, p, test, sig
write.csv(tab1_summary, "tab1_demographics.csv")