Data Manipulation with dplyr in R

目录

• select
• The filter and arrange verbs
• arrange
• filter
• fct_relevel {forcats}
  • Filtering and arranging
• Mutate
• The count verb
• Summarizing
• top_n
• Selecting
• rename
• transmute
• Grouped mutates
• Window functions

Data Manipulation with dplyr in R

select

select(data，变量名）

The filter and arrange verbs

arrange

counties_selected <- counties %>%
  select(state, county, population, private_work, public_work, self_employed)

# Add a verb to sort in descending order of public_work
counties_selected %>%arrange(desc(public_work))

filter

counties_selected <- counties %>%
  select(state, county, population)

# Filter for counties in the state of California that have a population above 1000000
counties_selected %>%
  filter(state == "California",
         population > 1000000)

#筛选多个变量
filter(id %in% c("a","b","c"...)) 存在
filter(id %in% c("a","b","c"...)) 不存在

fct_relevel {forcats}

Reorder factor levels by hand

排序，order不好使的时候

f <- factor(c("a", "b", "c", "d"), levels = c("b", "c", "d", "a"))
fct_relevel(f)
fct_relevel(f, "a")
fct_relevel(f, "b", "a")

# Move to the third position
fct_relevel(f, "a", after = 2)

# Relevel to the end
fct_relevel(f, "a", after = Inf)
fct_relevel(f, "a", after = 3)

# Revel with a function
fct_relevel(f, sort)
fct_relevel(f, sample)
fct_relevel(f, rev)

Filtering and arranging

 counties_selected <- counties %>%
    select(state, county, population, private_work, public_work, self_employed)
>
> # Filter for Texas and more than 10000 people; sort in descending order of private_work
> counties_selected %>%filter(state=='Texas',population>10000)%>%arrange(desc(private_work))
# A tibble: 169 x 6
   state county  population private_work public_work self_employed
   <chr> <chr>        <dbl>        <dbl>       <dbl>         <dbl>
 1 Texas Gregg       123178         84.7         9.8           5.4
 2 Texas Collin      862215         84.1        10             5.8
 3 Texas Dallas     2485003         83.9         9.5           6.4
 4 Texas Harris     4356362         83.4        10.1           6.3
 5 Texas Andrews      16775         83.1         9.6           6.8
 6 Texas Tarrant    1914526         83.1        11.4           5.4
 7 Texas Titus        32553         82.5        10             7.4
 8 Texas Denton      731851         82.2        11.9           5.7
 9 Texas Ector       149557         82          11.2           6.7
10 Texas Moore        22281         82          11.7           5.9
# ... with 159 more rows

Mutate

counties_selected <- counties %>%
  select(state, county, population, public_work)

# Sort in descending order of the public_workers column
counties_selected %>%
  mutate(public_workers = public_work * population / 100) %>%arrange(desc(public_workers))

counties %>%
  # Select the five columns
  select(state, county, population, men, women) %>%
  # Add the proportion_men variable
  mutate(proportion_men = men / population) %>%
  # Filter for population of at least 10,000
  filter(population >= 10000) %>%
  # Arrange proportion of men in descending order
  arrange(desc(proportion_men))

The count verb

counties_selected %>%count(region,sort=TRUE)

counties_selected %>%count(state,wt=citizens,sort=TRUE)

Summarizing

# Summarize to find minimum population, maximum unemployment, and average income
counties_selected %>%summarize(
min_population=min(population),
max_unemployment=max(unemployment),
average_income=mean(income)
)

# Add a density column, then sort in descending order
counties_selected %>%
  group_by(state) %>%
  summarize(total_area = sum(land_area),
            total_population = sum(population),
            density=total_population/total_area) %>%arrange(desc(density))

发现了，归根到底是一种函数关系，看看该怎样处理这个函数比较简单，如果写不出来，可能和小学的时候应用题写不出来有关系

top_n

按照优先级来筛选

# Extract the most populated row for each state
counties_selected %>%
  group_by(state, metro) %>%
  summarize(total_pop = sum(population)) %>%
  top_n(1, total_pop)

Selecting

Using the select verb, we can answer interesting questions about our dataset by focusing in on related groups of verbs.

The colon (