In matthewhirschey/bespokelearnr: Bespoke data science learning

Global vs Local

ggplot(df_input) +
  geom_jitter(aes(!!sym(df_char1_name), !!sym(df_numeric1_name))) +
  geom_boxplot(aes(!!sym(df_char1_name), !!sym(df_numeric1_name)))

Global vs Local

However, if we map our x and y values in the ggplot function we find that we generate the same graph

ggplot(r dataframe_name, aes(x=r df_char1_name, y=r df_numeric1_name) +
geom_jitter() +
geom_boxplot()

ggplot(df_input, aes(!!sym(df_char1_name), !!sym(df_numeric1_name))) +
  geom_jitter() +
  geom_boxplot()

Global vs Local {.build}

This is because when you set the aes mappings in the original ggplot function you are setting the aes globally.
This means all the functions afterwards will inherit that mapping. So in our example, this means that both the jitter and boxplot geoms know to graph the same information

You can also set aes values locally within the geom function. Doing so will only change the values in that geom

Global vs Local

ggplot(r dataframe_name, aes(x=r df_char1_name, y=r df_numeric1_name) +
geom_jitter() +
geom_boxplot(aes(color =r df_char1_name))

ggplot(df_input, aes(!!sym(df_char1_name), !!sym(df_numeric1_name))) +
  geom_jitter() +
  geom_boxplot(aes(color = !!sym(df_char1_name)))

Global vs Local {.build}

mean <- mean(df_numeric2_vec)
sd <- sd(df_numeric2_vec)

Data can also be set locally or globally. For this example, let's filter our original data first using the dplyr::filter function

df_filter <-r dataframe_name%>% filter(r df_numeric2_name>r round(mean + 2*sd))

*this number is two standard deviations above the mean value of r df_numeric2_name

Global vs Local {.build}

Now, let's identify only the r dataframe_about in our data that are outliers, more than 2SD above the mean, by setting data locally in a new geom

ggplot(r dataframe_name, aes(x=r df_char1_name, y=r df_numeric1_name) +
geom_jitter() +
geom_boxplot(aes(color =r df_char1_name)) +
geom_label(data=df_filter, aes(label=r df_id_name))

Global vs Local {.build}

df_filter <- df_input %>% filter(!!sym(df_numeric2_name) > round(mean + 2*sd))

ggplot(df_input, aes(!!sym(df_char1_name), !!sym(df_numeric1_name))) +
  geom_jitter() +
  geom_boxplot(aes(color = !!sym(df_char1_name))) +
  geom_label_repel(data = df_filter, aes(label = !!sym(df_id_name)))