Back to: Introduction to R
We have learned how to select the rows that we are interested in. The next logical step would be to select the columns we want as well. Many times we have so many columns that we are no interested in for a particular analysis. Instead of slowing down your analysis by continuing to run through extra data, we could just select the columns we care about.
Enter the select() Function
The select() function chooses columns that we specify. Again we can do this with base functions or with dplyr. We feel that as you continue on with your R usage that you will most likely want to go the route of dplyr functions instead.
Let’s say that we want to look at the flights data but we are really only interested in the arrival time, departure time and the particular flight number. This seems reasonable if we are a customer and wanted to only know these pieces of information. We could do this with indexing which we learned in dataframes:
flights[, c("dep_time", "arr_time", "flight")]## # A tibble: 336,776 × 3
## dep_time arr_time flight
## <int> <int> <int>
## 1 517 830 1545
## 2 533 850 1714
## 3 542 923 1141
## 4 544 1004 725
## 5 554 812 461
## 6 554 740 1696
## 7 555 913 507
## 8 557 709 5708
## 9 557 838 79
## 10 558 753 301
## # ... with 336,766 more rowsWe have indexed based on columns and picked the three columns which we wish to have information on. We could do this with piping in dplyr instead though:
select(.data, ...)where
.data is a tibble.
... are the columns that you wish to have in bare (no quotations)
We could then do the following
flights %>%
filter(dep_time, arr_time, flight)## # A tibble: 328,063 × 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # ... with 328,053 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>Further Uses of select() Function
Removing Columns
We may wish to pick certain columns that we wish to have but we also may want to remove certain columns. It is quite common to de-identify a dataset before actually distributing it to a research team. The select()function will also remove columns. Lets say that we wished to remove the month and day of the flights:
flights %>%
select(-month,-day)## # A tibble: 336,776 × 17
## year dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <dbl> <int> <int>
## 1 2013 517 515 2 830 819
## 2 2013 533 529 4 850 830
## 3 2013 542 540 2 923 850
## 4 2013 544 545 -1 1004 1022
## 5 2013 554 600 -6 812 837
## 6 2013 554 558 -4 740 728
## 7 2013 555 600 -5 913 854
## 8 2013 557 600 -3 709 723
## 9 2013 557 600 -3 838 846
## 10 2013 558 600 -2 753 745
## # ... with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>We also could use a vector for this:
cols <- c("month", "day")
flights %>%
select(-one_of(cols))Note: one_of is a helper function, it basically suggest that you take elements from a character vector.
We can also remove columns that contain a certain phrase in the name. If we were interested in removing any columns that had to do with time we could search for the word “time” in the data and remove them:
flights %>%
select(-contains("time"))## # A tibble: 336,776 × 13
## year month day dep_delay arr_delay carrier flight tailnum origin
## <int> <int> <int> <dbl> <dbl> <chr> <int> <chr> <chr>
## 1 2013 1 1 2 11 UA 1545 N14228 EWR
## 2 2013 1 1 4 20 UA 1714 N24211 LGA
## 3 2013 1 1 2 33 AA 1141 N619AA JFK
## 4 2013 1 1 -1 -18 B6 725 N804JB JFK
## 5 2013 1 1 -6 -25 DL 461 N668DN LGA
## 6 2013 1 1 -4 12 UA 1696 N39463 EWR
## 7 2013 1 1 -5 19 B6 507 N516JB EWR
## 8 2013 1 1 -3 -14 EV 5708 N829AS LGA
## 9 2013 1 1 -3 -8 B6 79 N593JB JFK
## 10 2013 1 1 -2 8 AA 301 N3ALAA LGA
## # ... with 336,766 more rows, and 4 more variables: dest <chr>,
## # distance <dbl>, hour <dbl>, minute <dbl>Note: contains() is another helper function, it suggests that whatever inside the function is a literal string. In other words it needs to match a name exactly.
Renaming Columns
Many times we may wish to rename a column so that it makes more sense to us. The select() function can rename things for us as well. For example, there is a variable called tailnum in the flights data. This actually refers to the tailnumber of the plane. We could rename this to be:
flights %>%
select(tail = tailnum)## # A tibble: 336,776 × 1
## tail
## <chr>
## 1 N14228
## 2 N24211
## 3 N619AA
## 4 N804JB
## 5 N668DN
## 6 N39463
## 7 N516JB
## 8 N829AS
## 9 N593JB
## 10 N3ALAA
## # ... with 336,766 more rowsflights %>% select(tail = tailnum)## # A tibble: 336,776 × 1
## tail
## <chr>
## 1 N14228
## 2 N24211
## 3 N619AA
## 4 N804JB
## 5 N668DN
## 6 N39463
## 7 N516JB
## 8 N829AS
## 9 N593JB
## 10 N3ALAA
## # ... with 336,766 more rowsNote: We only kept the column of data that we renamed.
Unique Observations
Many times we have a lot of repeats in our data. If we just would like to have an account of all things included then we can use the unique() command. Lets assume that we wish to know the origin of a flight and ist destination. We do not want to have every flight listed over and over again so we ask for unique values:
flights %>%
select(origin, dest) %>%
unique()## # A tibble: 224 × 2
## origin dest
## <chr> <chr>
## 1 EWR IAH
## 2 LGA IAH
## 3 JFK MIA
## 4 JFK BQN
## 5 LGA ATL
## 6 EWR ORD
## 7 EWR FLL
## 8 LGA IAD
## 9 JFK MCO
## 10 LGA ORD
## # ... with 214 more rowsOn Your Own: RStudio Practice
Consider the flights data: flights.
1. Select all but the year column.
2. Remove the month and day from them.
3. Select values which contain “time” in them.
4. Chain these together so that you run a command and it does all of these things.
Your answer should look like:
## # A tibble: 336,776 × 6
## dep_time sched_dep_time arr_time sched_arr_time air_time
## <int> <int> <int> <int> <dbl>
## 1 517 515 830 819 227
## 2 533 529 850 830 227
## 3 542 540 923 850 160
## 4 544 545 1004 1022 183
## 5 554 600 812 837 116
## 6 554 558 740 728 150
## 7 555 600 913 854 158
## 8 557 600 709 723 53
## 9 557 600 838 846 140
## 10 558 600 753 745 138
## # ... with 336,766 more rows, and 1 more variables: time_hour <dttm>