ggplot(data = mpg ) + geom_boxplot(aes(x = class, y=hwy, fill=class)) + labs(y = "Highway miles per gallon")

ggplot(???)+ geom_??? + labs(x="Modified X label", y="Modified Y label")

theme( )

theme_modify( )theme_modify( )

theme_bw( )

theme_calc( )

pisa_au %>% group_by(state,gender) %>% summarise(avg_math = mean(math))%>% mutate(diff_math=female-male)

pisa_au %>% group_by(state,gender) %>% summarise(avg_math = mean(math)) %>% pivot_wider( names_from = "gender", values_from = "avg_math") %>% mutate(diff_math=female-male)

pisa_au %>% group_by(state,gender) %>% summarise(avg_math = mean(math)) %>% arrange(avg_math)

pisa_au %>% group_by(state,gender) %>% summarise(avg_math = mean(math)) %>% pivot_wider( names_from = "gender",values_from = "avg_math")

subject != 10

!is.na(subject)

subject  >= 10

subject > 10

This code will create a 5 by 1 tibble reporting the mean rating values for each individual type of rating (potato, buttery etc)

This code will create a 1 by 1 tibble reporting the mean rating value across all ratings

This will return a value of "NA". We need to include the command na.rm = TRUE to remove the NA values and return a value for the mean

This code will create a 5 by 1 tibble reporting the mean rating values for some of the individual type of ratings (potato, buttery etc), however we need to add na.rm = TRUE to get values for each type of rating