Setup

This is the setup of the document.

# define and show parameters
actor<-params$actor
actor

## [1] "Keanu Reeves"

st<-params$stereotype
st

## [1] "wom_warm"

Imports data

This imports data.

# create an object dataframe example `dfex` and assign to it the .sav file `sample.sav` that was introduced previously
dfex<-haven::read_sav("data/sample.sav")

# create an object movies metadata `dfmv` and assign to it the .xlsx file `movies.xlsx`
# note the different paths to these files
# note that we specify which sheet to read too; here only sheet 1 is imported
dfmv<-readxl::read_excel("mat/movies.xlsx",1)

# next, we check if the source material was imported successfully by observing the first lines in the tables
head(dfex)

## # A tibble: 6 × 9
##     ppn gen          age res       res_other men_warm men_comp wom_warm wom_comp
##   <dbl> <dbl+lbl>  <dbl> <dbl+lbl> <chr>     <dbl+lb> <dbl+lb> <dbl+lb> <dbl+lb>
## 1   459 1 [Female]    24  5 [Iasi] -99        3 [Und…  4 [Agr… 3 [Unde… 4 [Agre…
## 2   592 2 [Male]      21  5 [Iasi] -99        3 [Und…  4 [Agr… 3 [Unde… 3 [Unde…
## 3   634 2 [Male]      21 NA        petrosani  4 [Agr…  5 [Str… 4 [Agre… 4 [Agre…
## 4   369 1 [Female]    30  8 [Gala… -99       NA       NA       4 [Agre… 4 [Agre…
## 5   121 1 [Female]    21  4 [Timi… -99        4 [Agr…  3 [Und… 3 [Unde… 4 [Agre…
## 6   127 1 [Female]    20  4 [Timi… -99        4 [Agr…  4 [Agr… 4 [Agre… 2 [Disa…

head(dfmv)

## # A tibble: 4 × 6
##   Movie                       Actor                 Like  Why     Grade Wikilink
##   <chr>                       <chr>                 <chr> <chr>   <dbl> <chr>   
## 1 John Wick                   Keanu Reeves          Yes   Fight …    10 https:/…
## 2 Call me by your name        Timothee Chalamet     Yes   Beauti…    10 https:/…
## 3 Terminator                  Arnold Schwarzenegger Yes   Arnold      9 https:/…
## 4 4 months 3 weeks and 2 days <NA>                  Yes   Portra…     8 https:/…

Plain text vs. live text

This is an example of how automatization can be implemented in the work flow. My list of movies include 4 entries. The title of those movies are John Wick, Call me by your name, Terminator, 4 months 3 weeks and 2 days. Is there a movie that I actually don’t like on that list, well, the answer is that I dislike exactly 0 movies on that list.

Automated graphs

# 1 - imports dataset into object tempdf
tempdf<-haven::read_sav("data/tmpdf1.sav") %>% 
  sjlabelled::remove_all_labels() %>% 
  pivot_longer(contains("warm") | contains("comp")) %>% 
  filter(name %in% st)

# 2 - applies the ggplot to the dataset
ggplot(tempdf, aes(x=factor(gen), y=value)) + 
  labs(title=paste0("Evaluation based on ",st), 
       x="Gender",
       y="Stereotype") +
  geom_boxplot() + 
  theme_light()

Stereotipical evaluation

Automated tables

Movies graded 8 or more from liked and least like actors
like	name	movie	wiki
Yes	Keanu Reeves	John Wick	https://en.wikipedia.org/wiki/John_Wick_(film)

Another automated table

One other way to work with parameterized reports is to code the document such that it creates tables (or anything else for that matter) using a specific dataset. Basically, if the dataset format is identical but contains different N sizes or was collected by different teams or at different times, then parameterized reports can facilitate the creation of repeated reports at a button’s click.

# we assign the parameter sampledf to an object sampledf containing the dataset itself
sampledf<-paste0("data/",params$sampledf)
sampledf

## [1] "data/sample.sav"

abc<-haven::read_sav(sampledf)
head(abc)

## # A tibble: 6 × 9
##     ppn gen          age res       res_other men_warm men_comp wom_warm wom_comp
##   <dbl> <dbl+lbl>  <dbl> <dbl+lbl> <chr>     <dbl+lb> <dbl+lb> <dbl+lb> <dbl+lb>
## 1   459 1 [Female]    24  5 [Iasi] -99        3 [Und…  4 [Agr… 3 [Unde… 4 [Agre…
## 2   592 2 [Male]      21  5 [Iasi] -99        3 [Und…  4 [Agr… 3 [Unde… 3 [Unde…
## 3   634 2 [Male]      21 NA        petrosani  4 [Agr…  5 [Str… 4 [Agre… 4 [Agre…
## 4   369 1 [Female]    30  8 [Gala… -99       NA       NA       4 [Agre… 4 [Agre…
## 5   121 1 [Female]    21  4 [Timi… -99        4 [Agr…  3 [Und… 3 [Unde… 4 [Agre…
## 6   127 1 [Female]    20  4 [Timi… -99        4 [Agr…  4 [Agr… 4 [Agre… 2 [Disa…

nrow(abc)

## [1] 100

# assign parameterized data to an object dataframe
abc %>% 
  sjlabelled::remove_all_labels() %>% 
  pivot_longer(contains("warm") | contains("comp")) %>% 
  group_by(name) %>% 
  summarise(mean=mean(value, na.rm = TRUE), # we use missing remove TRUE (na.rm=TRUE) to make sure r gives an output
            sd=sd(value, na.rm = TRUE),
            min=min(value, na.rm = TRUE),
            max=max(value, na.rm = TRUE))

## # A tibble: 4 × 5
##   name      mean    sd   min   max
##   <chr>    <dbl> <dbl> <dbl> <dbl>
## 1 men_comp  3.91 0.755     2     5
## 2 men_warm  3.55 0.893     1     5
## 3 wom_comp  3.72 0.961     1     5
## 4 wom_warm  3.80 0.873     1     5

example