This article compiles commonly requested alterations to ParseR output that have not been incorporated into the package’s functionality.

# Generate a sample
set.seed(1)
example <- ParseR::sprinklr_export %>%
dplyr::sample_n(1000)

## Tweaking the hexplot

Let’s make an example hexplot:

valence <- example %>%
ParseR::score_valence(text_var = Message,
valence_limits = c(-Inf, Inf),
remove_terms = NULL,
highlight = FALSE)

hexplot <- valence %>%
ParseR::hexplot_valence(valence_var = ave_sentiment,
x_var = Sender Followers Count,
log10_trans = TRUE,
theme = "viridis")

### Formatting the log10-scale labels

When we set log10_trans = TRUE the default labels for the x-axis are written as powers of 10:

hexplot

If you would prefer that they were written out in full:

hexplot +
ggplot2::scale_x_continuous(trans = scales::log10_trans(),
breaks = scales::trans_breaks("log10",
function(x) 10^x),
labels = scales::comma)

### Facetting

Because the hexplot_valence function outputs a ggplot object we can extend the graph with any of the usual functions from the ggplot2 package.

For example, if we wanted to compare different Twitter message types:

# Start with the dataframe produced using score_valence
valence %>%
# Filter so that we only have posts from Twitter
dplyr::filter(SocialNetwork == "TWITTER") %>%
# Create the default hexplot
ParseR::hexplot_valence(valence_var = ave_sentiment,
x_var = Sender Followers Count,
log10_trans = TRUE) +
# Add the facetting
facet_wrap(vars(MessageType))