Lab09 Plotly Graph

Part 1: Plotly

## Change this to read in whatever data you're using
covid = read_csv("covid_data.csv")

## Parsed with column specification:
## cols(
##   signal = col_character(),
##   geo_value = col_character(),
##   time_value = col_date(format = ""),
##   value = col_double(),
##   stderr = col_double(),
##   sample_size = col_double()
## )

covid %>%
  group_by(geo_value, signal) %>%
  summarize(
    avg = mean(value, na.rm = T)
  ) %>%
  pivot_wider(id_cols = geo_value, names_from = signal, values_from = avg) %>%
  ungroup() -> state_avg

## `summarise()` has grouped output by 'geo_value'. You can override using the `.groups` argument.

## Change this to make your plot

p1 = state_avg %>%
    mutate(state = str_to_upper(geo_value)) %>%
    ggplot(aes(x = smoothed_wearing_mask, y = smoothed_cli)) +
    geom_point() + 
    theme_minimal()

ggplotly(p1)

p2 = state_avg %>%
    mutate(state = str_to_upper(geo_value)) %>%
    ggplot(aes(x = smoothed_wearing_mask, y = smoothed_cli)) +
    geom_point(aes(text = toupper(geo_value))) + 
    theme_minimal()

## Warning: Ignoring unknown aesthetics: text

ggplotly(p2, tooltip = "text")

Part 2: Plotly on your own

brexit = read_csv("swarthmore.csv")

## Parsed with column specification:
## cols(
##   `10.2564` = col_double(),
##   `21.0817` = col_double()
## )

set.seed(011221)
samples = data.frame(matrix(rnorm(1000), ncol = 10))
colnames(samples) = paste0("sample", 1:10)
samples = tibble(samples)

p <- samples %>%
  pivot_longer(1:10) %>%
  ggplot(., aes(y = value, x = name, fill = name)) + 
  geom_boxplot(alpha = .7) + 
  scale_fill_viridis_d(end = .75, option = "C") + 
  scale_color_viridis_d(end = .75, option = "C") + 
  theme(legend.position = "none",
         axis.text.x = element_text(size = 7))

fig <- ggplotly(p)

fig