read_csv() read in comma separated values file

select() choose columns

filter() keep or drop rows

mutate() create new variable (column)

summarise() summarize data (sometimes used with group_by())

First, we use the read_csv() command to bring our data into RStudio, then we look at what the filter(), mutate(), and select() commands can do in a pipeline:

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We can also use the summarize() command to create new variables for later use. Here, we use it to identify the mean earnings of our sample population:

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ggplot(), aes(), labs(), group_by(), facet_wrap(), geom_histogram(), geom_point(), geom_smooth(), geom_line(), geom_boxplot()

labs(), scale_\*_\*(), theme_X()

geom_col(), geom_bar(), geom_dotplot(), group_by(), factor(), c()

These demos are not included in the Tidyverse Tutorial, but build on the extra ggplot2 uses mentioned at the end of the section. Additionally, the Dot Plot example utilizes 8gapminder* data different from that used in all previous examples.