In itsdalmo/reporttoolDT: Work with surveys to generate reports

Foreword

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() 

Adding variables

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()

Cleaning

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

Recoding

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))

Tables

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.

Charts

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().

More information

Introduction:

vignette("introduction", package = "reporttoolDT")

Survey-class:

vignette("survey", package = "reporttoolDT")

Preparing data:

vignette("prepare", package = "reporttoolDT")

itsdalmo/reporttoolDT documentation built on May 18, 2019, 7:11 a.m.