Nothing
## ----echo = FALSE-------------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, warning = FALSE, comment = "#>")
if (!requireNamespace("dplyr", quietly = TRUE) ||
!requireNamespace("tidyr", quietly = TRUE) ||
!requireNamespace("purrr", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
}
suppressPackageStartupMessages(library(sjmisc))
## ----message=FALSE------------------------------------------------------------
library(sjmisc)
library(dplyr)
data(efc)
## -----------------------------------------------------------------------------
frq(efc$c161sex)
## -----------------------------------------------------------------------------
# find all variables with "dependency" in name or label
find_var(efc, "dependency", out = "table")
## -----------------------------------------------------------------------------
flat_table(efc, e42dep, c161sex)
## -----------------------------------------------------------------------------
flat_table(efc, e42dep, c161sex, margin = "col")
## -----------------------------------------------------------------------------
efc$burden <- rec(
efc$neg_c_7,
rec = c("min:9=1 [low]; 10:12=2 [moderate]; 13:max=3 [high]; else=NA"),
var.label = "Subjective burden",
as.num = FALSE # we want a factor
)
# print frequencies
frq(efc$burden)
## -----------------------------------------------------------------------------
efc %>%
select(burden, c161sex) %>%
group_by(c161sex) %>%
frq()
## -----------------------------------------------------------------------------
# convert variable to labelled factor, because we then
# have the labels as factor levels in the output
efc$e42dep <- to_label(efc$e42dep, drop.levels = TRUE)
efc %>%
select(e42dep, burden, c161sex, quol_5) %>%
group_by(e42dep) %>%
tidyr::nest()
## -----------------------------------------------------------------------------
efc %>%
select(e42dep, burden, c161sex, quol_5) %>%
group_by(e42dep) %>%
tidyr::nest() %>%
na.omit() %>% # remove nested group for NA
arrange(e42dep) %>% # arrange by order of levels
mutate(models = purrr::map(
data, ~
lm(quol_5 ~ burden + c161sex, data = .))
) %>%
spread_coef(models)
## -----------------------------------------------------------------------------
efc %>%
select(e42dep, burden, c161sex, quol_5) %>%
group_by(e42dep) %>%
tidyr::nest() %>%
na.omit() %>% # remove nested group for NA
arrange(e42dep) %>% # arrange by order of levels
mutate(models = purrr::map(
data, ~
lm(quol_5 ~ burden + c161sex, data = .))
) %>%
spread_coef(models, burden3)
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