This R Markdown file contains examples on how to use various functions from dyplr package. I used flights data set from nycflights13 package, and mtcars data set to demonstrate the usage of different functions.
Load dplyr and nycflights13 packages.
library(dplyr)
library(nycflights13)Summary of the flightsdata set.
summary(flights)## year month day dep_time sched_dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906
## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359
## NA's :8255
## dep_delay arr_time sched_arr_time arr_delay
## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000
## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000
## Median : -2.00 Median :1535 Median :1556 Median : -5.000
## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895
## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000
## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000
## NA's :8255 NA's :8713 NA's :9430
## carrier flight tailnum origin
## Length:336776 Min. : 1 Length:336776 Length:336776
## Class :character 1st Qu.: 553 Class :character Class :character
## Mode :character Median :1496 Mode :character Mode :character
## Mean :1972
## 3rd Qu.:3465
## Max. :8500
##
## dest air_time distance hour
## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00
## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00
## Mode :character Median :129.0 Median : 872 Median :13.00
## Mean :150.7 Mean :1040 Mean :13.18
## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00
## Max. :695.0 Max. :4983 Max. :23.00
## NA's :9430
## minute time_hour
## Min. : 0.00 Min. :2013-01-01 05:00:00
## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00
## Median :29.00 Median :2013-07-03 10:00:00
## Mean :26.23 Mean :2013-07-03 05:22:54
## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00
## Max. :59.00 Max. :2013-12-31 23:00:00
## Summary of the mtcars data set.
summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000In the following section, you can found examples on how to use each functions with an brief explanation of the function, and the requirements for the inputs.
filter: select a subset of rows in a data frame. filter(data, conditions separated by commas)
Example 1: select flights where month = 11, day = 3, and carrier name is ‘AA’
fl <- flights %>%
filter(month == 11, day == 3, carrier == 'AA')
head(fl)Example 2: select cars where mpg > 20 and cyl = 6
mtcars %>%
filter(mpg > 20, cyl == 6)Example 3: use %in% to select multiple matches for an argument
flights %>%
filter(month %in% c(1,2,5), carrier == 'AA') %>%
distinct(month)slice: select rows by position slice(data, start_row_# : end_row_#)
Example 1: select the first 10 rows from flights
flights %>% slice(1:10)arrange: reorder the rows arrange(data, year, month,day, arr_time): order by year, month, day, arr_time Use desc(variable_name) if descending order is wanted
Example 1: arrange the data by cyl in ascending order
arr_1 <- mtcars %>% arrange(cyl)
head(arr_1)Example 2: arrange the data first by year, and then by month, by day, lastly by arr_time (in descending order)
arr <- flights %>% arrange(year, month,day,desc(arr_time))
head(arr)select: select the columns wanted select(data, column_names)
Example 1: select columns carrier and month
sele <- flights %>% select(carrier, month,day)
head(sele)Example 2: select columns mpg, hp
mtcars %>% select(mpg,hp)rename: rename the columns rename(data, new_column_name = old_column_name)
Example 1: change column name carrier to airline_carrier. Note: the order is new column name = old column name, not the other way around.
rename_fl <- flights %>% rename(airline_carrier = carrier) %>%
select(airline_carrier)
head(rename_fl)distinct: select distinct values or unique values in a column distinct(select(data, column_name))
Example 1: return all carrier names without repetition.
distinct(select(flights, carrier))# or
flights %>% distinct(carrier)mutate: add new columns that are functions of existing columns mutate(data, new_column_name = column_name - another_column_name)
transmute: similar to mutate but contains only the new column that has been created
Example 1: expected total travel time = schedule arrival time - schedule departure time.
mutate_fl <- flights %>%
mutate(total_time = sched_arr_time - sched_dep_time)
head(mutate_fl)Same as above but with only the newly created column total_time
transmute_fl <- flights%>%
transmute(total_time = sched_arr_time - sched_dep_time)
head(transmute_fl)Example 2: create a new column Performance by dividing hp by wt.
mutate_car <- mtcars %>%
mutate(Performance = hp/wt)
head(mutate_car)summarise: collapse data frames into single rows using some sort of function that aggregates a result. summarise(data, new_column_name = mean(column_name, na.rm = TRUE))
Example 1: calculate the mean air_time with summarise and mean.
Note: avg_air_time contains the average air_time with rows contain NA removed (that’s what ‘na.rm = TRUE’ do)
flights %>% summarise(avg_air_time = mean(air_time, na.rm = TRUE))Example 2: calculate the total air_time with summarise and sum
flights %>% summarise(total_air_time = sum(air_time, na.rm = TRUE))Example 3: calculate the average mpg across entire data set.
mtcars %>% summarise(avg_mpg = mean(mpg)) #the mean mpgExample 4: calculate the standard deviation pf hp value for cars with 6 cylinders.
mtcars %>% filter(cyl == 6) %>%
summarise(std_hp = sd(hp)) #the standard deviation pf hp value for cars with 6 cylinders.sample_n: select random number of rows sample_n(data, # rows to select)
sample_frac: select a fraction of the rows sample_frac(data, fraction of rows to select)
Example 1: randomly select 10 rows from the data frame
sample_n(flights, 10)Example 2: randomly select 10% of the rows from data frame
sample_frac(flights,0.1)To be continued…