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Data Manipulation with dplyr

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Run the hidden code cell below to import the data used in this course.

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Add notes about the concepts you've learned and code cells with code you want to keep.

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Add your code snippets here

library(dplyr)

glimpse(df) # allows to view the first few values from each variable, along with the data type

names_filtered <- names_normalized %>%
# Filter for the names Steven, Thomas, and Matthew
filter(name == "Steven"| name == "Thomas" | name == "Matthew")

# Visualize these names over time
ggplot(names_filtered, aes(x = year, y = fraction_max, color = name)) +
geom_line()

# Change the name of the unemployment column
counties %>%
rename(unemployment_rate = unemployment)

# Keep the state and county columns, and the columns containing poverty
counties %>%
select(state, county, contains("poverty"))

# Calculate the fraction_women column without dropping the other columns
counties %>%
mutate(fraction_women = women / population)

# Keep only the state, county, and employment_rate columns
counties %>%
transmute(state, county, employment_rate = employed / population)

counties_selected %>%
# Find the total population for each combination of state and metro
group_by(state, metro) %>%
summarize(total_pop = sum(population))

babynames %>%
# Add columns name_total and name_max for each name
group_by(name) %>%
mutate(name_total = sum(number),
name_max = max(number)) %>%
# Ungroup the table
ungroup() %>%
# Add the fraction_max column containing the number by the name maximum
mutate(fraction_max = number / name_max)``````
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