reporttoolDT
contains several "convenience"-functions that are made to make ones life easier when working with Survey data. This vignette attempts to provide a list with these functions and a small example to showcase their usage - we'll use the same dataset as in previous vignettes:
require(dplyr) require(reporttoolDT) sav <- read_data(system.file("extdata", "raw_data.sav", package = "reporttoolDT")) srv <- survey_tbl(sav) %>% set_association(.common = TRUE) %>% set_config(name = "Example", segment = "B2C", cutoff = .3) %>% set_marketshare(CompanyA = .3, CompanyB = .5, CompanyC = .2) %>% set_translation(.language = "english") %>% latents_mean()
add_latent_spread()
adds all spread variables to the data:
out <- srv %>% add_latent_spread() out %>% select(ends_with("_spread")) %>% head()
add_weight()
creates a weight ("w") based on marketshares for each entity:
out <- srv %>% add_weight() out %>% select(w) %>% head()
clean_scale()
helps you convert likert-scales stored as character vectors to numeric by extracting numbers from text:
clean_scale(c("1 Not happy", 2:9, "10 Very happy"))
rescale_100()
converts a 10-point scale to a 100-point scale:
rescale_100(1:10)
rescale_10()
does the reverse of rescale_100
:
rescale_10(rescale_100(1:10))
str_to_numeric()
lets you convert strings in general to numerics:
str_to_numeric("string 1 with many values 9", FUN = mean) # Output is 5 because there are two numbers: 1+9/2
recode()
let's you exchange one or more values for another in a vector:
recode(c("A", "B", "C"), GroupA = c("A", "C"), GroupB = "B", factor = TRUE)
spread_10()
helps you spread a 10-point likert as follows:
spread_10(1:10)
spread_100()
does the same for 100-point scales:
spread_100(rescale_100(1:10))
latent_table()
creates a table with latent scores:
srv %>% group_by(q1) %>% latent_table()
manifest_table()
does the same for manifest variables:
srv %>% group_by(q1) %>% manifest_table()
There is also impact_table()
for outer weights (sorted by weight), when you have read a survey with argument outer_weight = TRUE
.
latent_plot()
creates a plot with latent scores:
srv %>% group_by(q1) %>% latent_plot()
manifest_plot()
does the same for manifest scores:
srv %>% group_by(q1) %>% manifest_plot()
You can also use flow_chart()
after reading outer weights by specifying inner_weight = TRUE
in read_survey()
.
vignette("introduction", package = "reporttoolDT")
vignette("survey", package = "reporttoolDT")
vignette("prepare", package = "reporttoolDT")
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