remove funwithdata, add some text, improve some fig heights

vignettes/explicittopic.Rmd

@@ -32,20 +32,12 @@ Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomp
 ```r
 read_all("../inst/records/") %>% repair() -> res
 ```
-We also used `repair` to fix a bunch of formatting issues in the records and unpacked
-the result into more descriptive variables.
+We also used `repair` to fix a bunch of formatting issues in the records.
 
 For development purposes, we load the tables from csv files.
 ```{r}
 res <- read_from_csv('../inst/csv/')
 ```
-and unpack our tibbles
-```{r}
-comments <- res$comments
-speeches <- res$speeches
-speaker <- res$speaker
-talks <- res$talks
-```
 
 ## Analysis
 
@@ -53,7 +45,7 @@ Now we can start analysing our parsed dataset:
 
 ### Counting the occurences of a given word:
 
-```{r, fig.width=7}
+```{r, fig.width=7, fig.height=7}
 find_word(res, "Kohleausstieg") %>%
     filter(occurences > 0) %>%
     join_speaker(res) %>%
@@ -63,17 +55,22 @@ find_word(res, "Kohleausstieg") %>%
     summarize(n = n()) %>%
     arrange(desc(n)) %>%
     bar_plot_fractions(title = "Parties using the word 'Kohleausstieg' the most (absolutely)",
-                        ylab = "Number of uses of 'Kohleausstieg'",
-                        flipped = F)
+                       ylab = "Number of uses of 'Kohleausstieg'",
+                       flipped = F,
+                       rotatelab = T)
 ```
 
 ### When are which topics discussed the most?
 
-```{r, fig.width=7}
+First we define some search patterns, according to some common political topics.
+```{r}
 pandemic_pattern <- "(?i)virus|corona|covid|lockdown"
 climate_pattern <- "(?i)klimawandel|erderwärmung|co2|treibhaus|methan|kyoto-protokoll|klimaabkommen"
 pension_pattern <- "(?i)rente|pension|altersarmut"
-
+```
+Then we use the analysis helper `word_usage_by_date` to generate a tibble counting the
+occurences of our search patterns per date. We can then plot the results:
+```{r, fig.width=7, fig.height=6}
 word_usage_by_date(res, c(pandemic = pandemic_pattern,
                           climate = climate_pattern,
                           pension = pension_pattern)) %>%
@@ -83,3 +80,4 @@ word_usage_by_date(res, c(pandemic = pandemic_pattern,
     labs(color = "Topic") +
     geom_point()
 ```
+