This is the Tidyverse course work material for Introduction to R Programming for Life Scientists Course, Uppsala, fall 2018.

1 Introduction

Welcome to the hands-on workshop “Tidy Work in Tidyverse”. Most of the things necessary to complete the tutorials and challenges were covered in the lecture. However, sometimes the tasks require that you check the docs or search online. Not all our solutions are optimal. Let us know if you can do better or solve things in a different way. If stuck, look at hints, next google and if still stuck, turn to TA. It is a lot of material, do not fee bad if you do not solve all tasks. Good luck!

2 General excercises

2.1 Pipes

2.1.1 Task 1

Rewrite the following code chunk as one pipe (magrittr):

my_cars <- mtcars[, c(1:4, 7)]
my_cars <- my_cars[my_cars$disp > mean(my_cars$disp), ]
print(my_cars)
my_cars <- colMeans(my_cars)

my_cars <- mtcars %>%
  select(c(1:4, 7)) %>%
  filter(disp > mean(disp)) %T>%
  print() %>%
  colMeans()

2.1.2 Task 2

Rewrite the correlations below using pipes.

cor(mtcars$gear, mtcars$mpg)

mtcars %$% cor(gear, mpg)

cor(mtcars)

mtcars %>% cor()

2.2 Tibbles

2.2.1 Task 1

Convert the mtcars dataset to a tibble vehicles.
Select the number of cylinders (cyl) variable using:
- the [[index]] accessor,
- the [[string]] accessor,
- the $ accessor.
Do the same selection as above, but using pipe and placeholders (use all three ways of accessing a variable).
Print the tibble.
Print the 30 first rows of the tibble.
Change the default behaviour of printing a tibble so that at least 15 and at most 30 rows are printed.
Convert vehicles back to a data.frame called automobiles.

# 1
vehicles <- mtcars %>% as_tibble()

# 2
vehicles[['cyl']]
vehicles[[2]]
vehicles$cyl

# 3
vehicles %T>%
  {print(.[['cyl']])} %T>%
  {print(.[[2]])} %>%
  .$cyl

# 4
vehicles

# 5
vehicles %>% head(n = 30)

# 6
options(tibble.print_min = 15, tibble.print_max = 30)

# 7
automobiles <- as.data.frame(vehicles)

2.2.2 Bonus Task

Do you think tibbles are lazy? Try to create a tibble that tests whether lazy evaluation applies to tibbles too.

tibble(x = sample(1:10, size = 10, replace = T), y = log10(x))

3 NYC flights Challenge

The nycflights13 package contains information about all flights that departed from NYC (i.e., EWR, JFK and LGA) in 2013: 336,776 flights with 16 variables. To help understand what causes delays, it also includes a number of other useful datasets: weather, planes, airports, airlines. We will use it to train working with tibbles and dplyr.

3.1 Task 1 – selecting column

load the nycflights13 package (install if necessary),
read about the data in the package docs,
inspect the flights tibble.
select all columns but carrier and arr_time,
select carrier, tailnum and origin,
hide columns from day through carrier,
select all columns that have to do with arrival (hint: ?tidyselect),
select columns based on a vector v <- c("arr_time", "sched_arr_time", "arr_delay"),
rename column dest to destination using:
- select() and
- rename()
  What is the difference between the two approaches?

install.packages('nycflights13')

library('nycflights13')

?nycflights13

flights

flights %>% select(-carrier, -arr_time)

flights %>% select(carrier, tailnum, origin)

flights %>% select(-(day:carrier))

flights %>% select(contains('arr_')) # or

v <- c("arr_time", "sched_arr_time", "arr_delay")
flights %>% select(v) # or
flights %>% select(one_of(v))

flights %>% select(destination = dest)
flights %>% rename(destination = dest)
# select keeps only the renamed column while rename returns the whole dataset
# with the column renamed

3.2 Task 2 – filtering rows

filter only the flights that arrived ahead of schedule,
filter the flights that had departure delay between 10 and 33,
fish out all flights with unknown arrival time,
retrieve rows 1234:1258 (hint: ?slice),
sample (?sample_n()) 3 random flights per day in March,
show 5 most departure-delayed flights in January per carrier,
retrieve all unique() routes and sort them by origin,
retrieve all distinct() routes and sort them by origin,
is unique() more efficient than distinct()?

flights %>% filter(arr_delay < 0)

flights %>% filter(dep_delay >= 10, dep_delay <= 33) # or
flights %>% filter(between(dep_delay, 10, 33))

flights %>% filter(is.na(arr_time))

flights %>% slice(1234:1258)

flights %>% filter(month == 3) %>%
  group_by(day) %>%
  sample_n(3)

flights %>%
  filter(month == 1) %>%
  group_by(carrier) %>%
  top_n(5, dep_delay)

3.3 Task 3 – Trans(mutations)

air_time is the amount of time in minutes spent in the air. Add a new column air_spd that will contain aircraft’s airspeed in mph,
as above, but keep only the new air_spd variable,
use rownames_to_column() on mtcars to add car model as an extra column,

flights %>% mutate(air_spd = distance/(air_time / 60))

flights %>% transmute(air_spd = distance/(air_time / 60))

mtcars %>% rownames_to_column('model')

3.4 Task 4 – groups and counts

use group_by(), summarise() and n() to see how many planes were delayed (departure) every month,

flights %>%
  filter(dep_delay > 0) %>%
  group_by(month) %>%
  summarise(num_dep_delayed = n())

what was the mean dep_delay per month?

flights %>%
  group_by(month) %>%
  summarise(mean_dep_delay = mean(dep_delay, na.rm = T))

count the number of incoming delayed flights from each unique origin and sort origins by this count (descending),

flights %>%
  filter(arr_delay > 0) %>%
  group_by(origin) %>%
  summarise(cnt = n()) %>%
  arrange(desc(cnt))

use summarise() to sum total dep_delay per month in hours,

 flights %>%
   group_by(month) %>%
   summarize(tot_dep_delay = sum(dep_delay/60, na.rm = T))

run group_size() on carrier what does it return?

flights %>%
    group_by(carrier) %>%
    group_size()

use n_groups() to check the number of unique origin-carrier pairs,

flights %>%
    group_by(carrier) %>%
    n_groups()

Note on ungroup Depending on the version of dplyr you may or may need to use the ungroup() if you want to group your data on some other variables. In the newer versions, summarise and mutate drop one aggregation level.

4 Session Info

This document has been created in RStudio using R Markdown and related packages.
For R Markdown, see http://rmarkdown.rstudio.com
For details about the OS, packages and versions, see detailed information below:

sessionInfo()

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] C
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] magrittr_1.5    forcats_0.3.0   stringr_1.3.1   dplyr_0.7.8    
##  [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.2     tibble_1.4.2   
##  [9] ggplot2_3.1.0   tidyverse_1.2.1 captioner_2.2.3 bookdown_0.7   
## [13] knitr_1.20     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.0  
##  [5] pillar_1.3.0     bindr_0.1.1      tools_3.5.0      digest_0.6.18   
##  [9] lubridate_1.7.4  jsonlite_1.5     evaluate_0.12    nlme_3.1-137    
## [13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.0.1   
## [17] cli_1.0.1        rstudioapi_0.8   yaml_2.2.0       haven_1.1.2     
## [21] xfun_0.4         bindrcpp_0.2.2   withr_2.1.2      xml2_1.2.0      
## [25] httr_1.3.1       hms_0.4.2        rprojroot_1.3-2  grid_3.5.0      
## [29] tidyselect_0.2.5 glue_1.3.0       R6_2.3.0         readxl_1.1.0    
## [33] rmarkdown_1.10   modelr_0.1.2     backports_1.1.2  scales_1.0.0    
## [37] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
## [41] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0     
## [45] crayon_1.3.4

Page built on: 14-Nov-2018 at 15:06:50.

2018 | SciLifeLab > NBIS)

Tidy Work in Tidyverse

Hands-on

Marcin Kierczak

14-Nov-2018