目录
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 (???? is useful for getting many columns at a time.
In the video you learned about the select helper starts_with(). Another select helper is ends_with(), which finds the columns that end with a particular string.
counties %>%
# Select the state, county, population, and those ending with "work"
select(state, county, population, ends_with("work")) %>%
# Filter for counties that have at least 50% of people engaged in public work
filter(public_work >= 50)
我觉得这种简单的逻辑关系不应该出错，但是老是出错。。是我真的不太适合做编程这一行嘛？
rename
rename()进行重命名
# Rename the n column to num_counties
counties %>%
count(state)%>%rename(num_counties=n)
也可以在select的时候直接重命名
# Select state, county, and poverty as poverty_rate
> counties %>%select(state,county,poverty_rate=poverty)
# A tibble: 3,138 x 3
state county poverty_rate
<chr> <chr> <dbl>
1 Alabama Autauga 12.9
2 Alabama Baldwin 13.4
3 Alabama Barbour 26.7
4 Alabama Bibb 16.8
5 Alabama Blount 16.7
6 Alabama Bullock 24.6
7 Alabama Butler 25.4
8 Alabama Calhoun 20.5
9 Alabama Chambers 21.6
10 Alabama Cherokee 19.2
# ... with 3,128 more rows
transmute
combination select & mutate
类似于mutate，添加新列但是只保留新列，删掉旧列
官方解释： use to calculate new columns while dropping other columns
counties %>%
# Keep the state, county, and populations columns, and add a density column
transmute(state, county, population, density = population / land_area) %>%
# Filter for counties with a population greater than one million
filter(population > 1000000) %>%
# Sort density in ascending order
arrange(density
这个解释挺好的
给出一个综合的例子
> # Change the name of the unemployment column
> counties %>%
rename(unemployment_rate = unemployment)
# A tibble: 3,138 x 40
census_id state county region metro population men women hispanic white
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1001 Alab~ Autau~ South Metro 55221 26745 28476 2.6 75.8
2 1003 Alab~ Baldw~ South Metro 195121 95314 99807 4.5 83.1
3 1005 Alab~ Barbo~ South Nonm~ 26932 14497 12435 4.6 46.2
4 1007 Alab~ Bibb South Metro 22604 12073 10531 2.2 74.5
5 1009 Alab~ Blount South Metro 57710 28512 29198 8.6 87.9
6 1011 Alab~ Bullo~ South Nonm~ 10678 5660 5018 4.4 22.2
7 1013 Alab~ Butler South Nonm~ 20354 9502 10852 1.2 53.3
8 1015 Alab~ Calho~ South Metro 116648 56274 60374 3.5 73
9 1017 Alab~ Chamb~ South Nonm~ 34079 16258 17821 0.4 57.3
10 1019 Alab~ Chero~ South Nonm~ 26008 12975 13033 1.5 91.7
# ... with 3,128 more rows, and 30 more variables: black <dbl>, native <dbl>,
# asian <dbl>, pacific <dbl>, citizens <dbl>, income <dbl>, income_err <dbl>,
# income_per_cap <dbl>, income_per_cap_err <dbl>, poverty <dbl>,
# child_poverty <dbl>, professional <dbl>, service <dbl>, office <dbl>,
# construction <dbl>, production <dbl>, drive <dbl>, carpool <dbl>,
# transit <dbl>, walk <dbl>, other_transp <dbl>, work_at_home <dbl>,
# mean_commute <dbl>, employed <dbl>, private_work <dbl>, public_work <dbl>,
# self_employed <dbl>, family_work <dbl>, unemployment_rate <dbl>,
# land_area <dbl>
>
> # Keep the state and county columns, and the columns containing poverty
> counties %>%
select(state, county, contains("poverty"))
# A tibble: 3,138 x 4
state county poverty child_poverty
<chr> <chr> <dbl> <dbl>
1 Alabama Autauga 12.9 18.6
2 Alabama Baldwin 13.4 19.2
3 Alabama Barbour 26.7 45.3
4 Alabama Bibb 16.8 27.9
5 Alabama Blount 16.7 27.2
6 Alabama Bullock 24.6 38.4
7 Alabama Butler 25.4 39.2
8 Alabama Calhoun 20.5 31.6
9 Alabama Chambers 21.6 37.2
10 Alabama Cherokee 19.2 30.1
# ... with 3,128 more rows
>
> # Calculate the fraction_women column without dropping the other columns
> counties %>%
mutate(fraction_women = women / population)
# A tibble: 3,138 x 41
census_id state county region metro population men women hispanic white
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1001 Alab~ Autau~ South Metro 55221 26745 28476 2.6 75.8
2 1003 Alab~ Baldw~ South Metro 195121 95314 99807 4.5 83.1
3 1005 Alab~ Barbo~ South Nonm~ 26932 14497 12435 4.6 46.2
4 1007 Alab~ Bibb South Metro 22604 12073 10531 2.2 74.5
5 1009 Alab~ Blount South Metro 57710 28512 29198 8.6 87.9
6 1011 Alab~ Bullo~ South Nonm~ 10678 5660 5018 4.4 22.2
7 1013 Alab~ Butler South Nonm~ 20354 9502 10852 1.2 53.3
8 1015 Alab~ Calho~ South Metro 116648 56274 60374 3.5 73
9 1017 Alab~ Chamb~ South Nonm~ 34079 16258 17821 0.4 57.3
10 1019 Alab~ Chero~ South Nonm~ 26008 12975 13033 1.5 91.7
# ... with 3,128 more rows, and 31 more variables: black <dbl>, native <dbl>,
# asian <dbl>, pacific <dbl>, citizens <dbl>, income <dbl>, income_err <dbl>,
# income_per_cap <dbl>, income_per_cap_err <dbl>, poverty <dbl>,
# child_poverty <dbl>, professional <dbl>, service <dbl>, office <dbl>,
# construction <dbl>, production <dbl>, drive <dbl>, carpool <dbl>,
# transit <dbl>, walk <dbl>, other_transp <dbl>, work_at_home <dbl>,
# mean_commute <dbl>, employed <dbl>, private_work <dbl>, public_work <dbl>,
# self_employed <dbl>, family_work <dbl>, unemployment <dbl>,
# land_area <dbl>, fraction_women <dbl>
>
> # Keep only the state, county, and employment_rate columns
> counties %>%
transmute(state, county, employment_rate = employed / population)
# A tibble: 3,138 x 3
state county employment_rate
<chr> <chr> <dbl>
1 Alabama Autauga 0.434
2 Alabama Baldwin 0.441
3 Alabama Barbour 0.319
4 Alabama Bibb 0.367
5 Alabama Blount 0.384
6 Alabama Bullock 0.362
7 Alabama Butler 0.384
8 Alabama Calhoun 0.406
9 Alabama Chambers 0.402
10 Alabama Cherokee 0.390
# ... with 3,128 more rows
貌似忘记%in%符号的使用了，复习一下啊
# Filter for the names Steven, Thomas, and Matthew
selected_names <- babynames %>%
filter(name %in% c("Steven", "Thomas", "Matthew"))
Grouped mutates
这个就是两两组合之前的例子中有的