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improve vignette

genderequality-alternative
flavis 4 년 전
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d01cea9d52
3개의 변경된 파일38개의 추가작업 그리고 16개의 파일을 삭제
  1. +18
    -0
      R/analyze.R
  2. +5
    -0
      README.md
  3. +15
    -16
      vignettes/funwithdata.Rmd

+ 18
- 0
R/analyze.R 파일 보기

@@ -11,3 +11,21 @@ join_redner <- function(tb, res, fraktion_only = F) {
if (fraktion_only) select(joined, "fraktion") if (fraktion_only) select(joined, "fraktion")
else joined else joined
} }

party_colors <- c(
SPD="#DF0B25",
"CDU/CSU"="#000000",
AfD="#1A9FDD",
"AfD&Fraktionslos"="#1A9FDD",
"DIE LINKE"="#BC3475",
"BÜNDNIS 90 / DIE GRÜNEN"="#4A932B",
FDP="#FEEB34",
Fraktionslos="#FEEB34"
)

#' @export
bar_plot_fraktionen <- function(tb) {
ggplot(tb, aes(x = reorder(fraktion, -n), y = n, fill = fraktion)) +
scale_fill_manual(values = party_colors) +
geom_bar(stat = "identity")
}

+ 5
- 0
README.md 파일 보기

@@ -22,6 +22,11 @@ Um dokumentationen neu zu laden / zu erstellen (ruft roxgen auf)
document() document()
``` ```


Baue vignetten
```r
rmarkdown::render("vignettes/bla.Rmd")
```

# Herunterladen # Herunterladen


Bevor analysiert werden kann, muss fetch.R ausgeführt werden, um alle Protokolle herunterzuladen. Bevor analysiert werden kann, muss fetch.R ausgeführt werden, um alle Protokolle herunterzuladen.


+ 15
- 16
vignettes/funwithdata.Rmd 파일 보기

@@ -29,43 +29,42 @@ fetch_all("../records/") # path to directory where records should be stored
Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by: Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
```r ```r
read_all("../records/") %>% repair() -> res read_all("../records/") %>% repair() -> res

reden <- res$reden
redner <- res$redner
talks <- res$talks
``` ```
We also used `repair` to fix a bunch of formatting issues in the records and unpacked We also used `repair` to fix a bunch of formatting issues in the records and unpacked
the result into more descriptive variables. the result into more descriptive variables.


For development purposes, we load the tables from csv files. For development purposes, we load the tables from csv files.
```{r} ```{r}
tables <- read_from_csv('../csv/')

comments <- tables$comments
reden <- tables$reden
redner <- tables$redner
talks <- tables$talks
res <- read_from_csv('../csv/')
```
and unpack our tibbles
```{r}
comments <- res$comments
reden <- res$reden
redner <- res$redner
talks <- res$talks
``` ```


## Analysis ## Analysis


Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks: Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks:
```{r}
left_join(reden, redner, by=c("redner" = "id")) %>%
```{r, fig.width=10}
join_redner(reden, res) %>%
group_by(fraktion) %>% group_by(fraktion) %>%
summarize(n = n()) %>% summarize(n = n()) %>%
ggplot(aes(x = fraktion, y = n)) +
geom_bar(stat = "identity")
arrange(n) %>%
bar_plot_fraktionen()
``` ```


### Count a word occurence ### Count a word occurence


```{r}
```{r, fig.width=10}
find_word(res, "hitler") %>% find_word(res, "hitler") %>%
filter(occurences > 0) %>% filter(occurences > 0) %>%
join_redner(res) %>% join_redner(res) %>%
select(content, fraktion) %>% select(content, fraktion) %>%
group_by(fraktion) %>% group_by(fraktion) %>%
summarize(n = n()) %>% summarize(n = n()) %>%
arrange(desc(n))
arrange(desc(n)) %>%
bar_plot_fraktionen()
``` ```

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