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![> library("nycflights13")
> flights
Source: local data frame [336,776 x 16]
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight
1 2013 1 1 517 2 830 11 UA N14228 1545
2 2013 1 1 533 4 850 20 UA N24211 1714
3 2013 1 1 542 2 923 33 AA N619AA 1141
4 2013 1 1 544 -1 1004 -18 B6 N804JB 725
5 2013 1 1 554 -6 812 -25 DL N668DN 461
6 2013 1 1 554 -4 740 12 UA N39463 1696
7 2013 1 1 555 -5 913 19 B6 N516JB 507
8 2013 1 1 557 -3 709 -14 EV N829AS 5708
9 2013 1 1 557 -3 838 -8 B6 N593JB 79
10 2013 1 1 558 -2 753 8 AA N3ALAA 301
.. ... ... ... ... ... ... ... ... ... ...
Variables not shown: origin (chr), dest (chr), air_time (dbl), distance (dbl),
hour (dbl), minute (dbl)](https://image.slidesharecdn.com/dplyr-141120094124-conversion-gate02/75/Data-manipulation-with-dplyr-9-2048.jpg)
![tbl_df
A data frame that does print all of itself by default
> data <- tbl_df(mtcars)
> data
Source: local data frame [32 x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
.. ... ... ... ... ... ... ... .. .. ... ...](https://image.slidesharecdn.com/dplyr-141120094124-conversion-gate02/75/Data-manipulation-with-dplyr-10-2048.jpg)
Uploaded byRomain Francois
Data manipulation with dplyr
This document provides an overview of the dplyr package in R. It describes several key functions in dplyr for manipulating data frames, including verbs like filter(), select(), arrange(), mutate(), and summarise(). It also covers grouping data with group_by() and joining data with joins like inner_join(). Pipelines of dplyr operations can be chained together using the %>% operator from the magrittr package. The document concludes that dplyr provides simple yet powerful verbs for transforming data frames in a convenient way.
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Data manipulation with dplyr
- 1.
- 3. dplyr dplyr dplyrdplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr #rcatladies dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr dplyr
- 4. verb function thattakes a data frame as its first argument
- 5. Examples of Rverbs head, tail, … verb subject … > head( iris, n = 4 ) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa
- 6.
- 7. Classic R code mean( rnorm( 100, mean = 4, sd = 4), trim = .1 ) Pipeline R code with %>% 100 %>% rnorm( mean = 4, sd = 4) %>% mean( trim = .1 )
- 8. nycflights13: Data aboutflights departing NYC in 2013
- 9. > library("nycflights13") >flights Source: local data frame [336,776 x 16] year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight 1 2013 1 1 517 2 830 11 UA N14228 1545 2 2013 1 1 533 4 850 20 UA N24211 1714 3 2013 1 1 542 2 923 33 AA N619AA 1141 4 2013 1 1 544 -1 1004 -18 B6 N804JB 725 5 2013 1 1 554 -6 812 -25 DL N668DN 461 6 2013 1 1 554 -4 740 12 UA N39463 1696 7 2013 1 1 555 -5 913 19 B6 N516JB 507 8 2013 1 1 557 -3 709 -14 EV N829AS 5708 9 2013 1 1 557 -3 838 -8 B6 N593JB 79 10 2013 1 1 558 -2 753 8 AA N3ALAA 301 .. ... ... ... ... ... ... ... ... ... ... Variables not shown: origin (chr), dest (chr), air_time (dbl), distance (dbl), hour (dbl), minute (dbl)
- 10. tbl_df A dataframe that does print all of itself by default > data <- tbl_df(mtcars) > data Source: local data frame [32 x 11] mpg cyl disp hp drat wt qsec vs am gear carb 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 .. ... ... ... ... ... ... ... .. .. ... ...
- 11. filter A subsetof the rows of the data frame
- 12. filter(flights, month ==1, day == 1) flights %>% filter( dep_delay < 10 ) flights %>% filter( arr_delay < dep_delay ) flights %>% filter( hour < 12, arr_delay <= 0 )
- 13. arrange reorder adata frame
- 14. flights %>% filter(hour < 8 ) %>% arrange( year, month, day ) flights %>% arrange( desc(dep_delay) )
- 15. select select certaincolumns from the data frame
- 16. # Select columnsby name select(flights, year, month, day) # Select all columns between year and day select(flights, year:day) # Select all columns except those from year to # day (inclusive) select(flights, -(year:day))
- 17. mutate modify orcreate columns based on others
- 18. d <- flights%>% mutate( gain = arr_delay - dep_delay, speed = distance / air_time * 60 ) %>% filter( gain > 0 ) %>% arrange( desc(speed) ) d %>% select( year, month, day, dest, gain, speed )
- 19. summarise collapse adata frame into one row …
- 20. summarise(flights, delay =mean(dep_delay, na.rm = TRUE)) flights %>% filter( dep_delay > 0 ) %>% summarise(arr_delay = mean(arr_delay, na.rm = TRUE))
- 21. group_by Group observationsby one or more variables
- 22. flights %>% group_by(tailnum ) %>% summarise( count = n(), dist = mean(distance, na.rm = TRUE), delay = mean(arr_delay, na.rm = TRUE) ) %>% filter( is.finite(delay) ) %>% arrange( desc(count) )
- 23. flights %>% group_by(dest)%>% summarise( planes = n_distinct(tailnum), flights = n() ) %>% arrange( desc(flights) )
- 24. joins joining twodata frames
- 25. inner_join all rowsfrom x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. destinations <- flights %>% group_by(dest) %>% summarise( planes = n_distinct(tailnum), flights = n() ) %>% arrange( desc(flights) ) %>% rename( faa = dest ) inner_join( destinations, airports, by = "faa")
- 26. inner_join all rowsfrom x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned. destinations <- flights %>% group_by(dest) %>% summarise( planes = n_distinct(tailnum), flights = n() ) %>% arrange( desc(flights) ) inner_join( destinations, airports, by = c( "dest" = "faa" ) )
- 27. other joins See?join • left_join, right_join • inner_join, outer_join • semi_join • anti_join
- 28. dplyr %>% summary • Simple verbs: filter, mutate, select, summarise, arrange • Grouping with group_by • Joins with *_join • Convenient with %>% • F✈️ST
- 29. dplyr Romain François @romain_francois [email protected]