GCDI offers different types of support for digital scholarship such as our bi-weekly drop-in Python User’s Group (PUG), evening workshops, afternoon skillshare lunch conversations, one-on-one consultations for faculty and students, working groups based around common tools or data sources, special events such as our annual Digital Showcase and Sound Series, and online resources.

Whether you have participated in our workshops before or your idea of the perfect software is a paperback edition, there is something for you! Our offerings are open to scholars at all levels of digital experience and at all stages of graduate research. Whether you are digitally driven, curious, or defiant, we are prepared to help.

• Join us on the CUNY Academic Commons cuny.is/group-gcdi
• Drop-in to Python Users’ Group (PUG) in the Digital Scholarship Lab cuny.is/pug
• Check our Event Calendar for upcoming events and workshops cuny.is/workshops
• Request a one-on-one consultation at cuny.is/gcdfconsults (students) or cuny.is/gcfacultyconsults (faculty)
• Join the GIS Working Group cuny.is/group-gis-working-group
• Participate in the Humanidades Digitales/ DH in Spanish group cuny.is/dhspanish
• Apply for a Provost’s Digital Innovation Grant cuny.is/digitalgrants

• Follow the GCDI on Twitter @cunygcdi
• Follow the GC Digital Fellows on Twitter @digital_fellows
• Follow the #digitalGC hashtag on Twitter
• Follow the Digital Fellows blog, Tagging the Tower digitalfellows.commons.gc.cuny.edu
• Contact the Digital Fellows at digitalfellows.commons.gc.cuny.edu/contact-us/
• Drop-in to the Digital Scholarship Lab Open House hosted twice per semester, or attend PUG, Events, or Workshops!

• I am a doctoral student in Urban Education,
• began using R for data analysis in 2009,
• frustrated with R, began using Pandas and iPython instead,
• in 2016, fell in love with R again.

“Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.” (John W. Tukey, 1961)

• It is open source and free unlike Stata, SAS, SPSS, etc.
• Good documentation and large online community (e.g., Stack Overflow)
• Many available packages from Comprehensive R Archive Network and GitHub

Garrett Grolemund of RStudio has shared a few principles we will follow:

1. Do not teach R as if it were a programming language
2. Do not avoid the lecture
3. Do not let your workshop become a consulting clinic for installation bugs

library(tidyverse)

Use readr and readxl packages

readr::read_csv('path/to/file.csv')
readr::read_csv('http://host/file.csv')
readxl::read_excel('path/to/file.xlsx')

library(nycflights13)
flights %>% head(5)

## # A tibble: 5 x 19
##    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin dest  air_time distance  hour minute time_hour          
##   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>    <int> <chr>   <chr>  <chr>    <dbl>    <dbl> <dbl>  <dbl> <dttm>             
## 1  2013     1     1      517            515      2.00      830            819      11.0 UA        1545 N14228  EWR    IAH        227     1400  5.00   15.0 2013-01-01 05:00:00
## 2  2013     1     1      533            529      4.00      850            830      20.0 UA        1714 N24211  LGA    IAH        227     1416  5.00   29.0 2013-01-01 05:00:00
## 3  2013     1     1      542            540      2.00      923            850      33.0 AA        1141 N619AA  JFK    MIA        160     1089  5.00   40.0 2013-01-01 05:00:00
## 4  2013     1     1      544            545     -1.00     1004           1022     -18.0 B6         725 N804JB  JFK    BQN        183     1576  5.00   45.0 2013-01-01 05:00:00
## 5  2013     1     1      554            600     -6.00      812            837     -25.0 DL         461 N668DN  LGA    ATL        116      762  6.00    0   2013-01-01 06:00:00

flights %>%
  mutate(date = as.Date(sprintf('%d-%.2d-%.2d', year, month, day)),
         weekday = weekdays(date)) %>%
  select(date, weekday, air_time, distance) %>%
  head(5)

## # A tibble: 5 x 4
##   date       weekday air_time distance
##   <date>     <chr>      <dbl>    <dbl>
## 1 2013-01-01 Tuesday      227     1400
## 2 2013-01-01 Tuesday      227     1416
## 3 2013-01-01 Tuesday      160     1089
## 4 2013-01-01 Tuesday      183     1576
## 5 2013-01-01 Tuesday      116      762

flights %>%
  filter(day < 8) %>%
  arrange(-air_time,distance) %>%
  select(day, carrier, flight, air_time, distance) %>%
  head(5)

## # A tibble: 5 x 5
##     day carrier flight air_time distance
##   <int> <chr>    <int>    <dbl>    <dbl>
## 1     6 HA          51      691     4983
## 2     5 HA          51      679     4983
## 3     3 HA          51      671     4983
## 4     6 UA          15      665     4963
## 5     5 UA          15      664     4963

flights %>%
  summarise(maxairtime = max(air_time, na.rm=TRUE))

## # A tibble: 1 x 1
##   maxairtime
##        <dbl>
## 1        695

flights %>% 
  summarise(avg_time = mean(air_time, na.rm=TRUE),
            avg_speed = mean(distance/air_time, na.rm=TRUE))

## # A tibble: 1 x 2
##   avg_time avg_speed
##      <dbl>     <dbl>
## 1      151      6.57

weather %>%
  filter(origin == 'EWR') %>%
  head(5)

## # A tibble: 5 x 15
##   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed wind_gust precip pressure visib time_hour          
##   <chr>  <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>     <dbl>  <dbl>    <dbl> <dbl> <dttm>             
## 1 EWR     2013  1.00     1     0  37.0  21.9  54.0      230       10.4      11.9      0     1014  10.0 2012-12-31 19:00:00
## 2 EWR     2013  1.00     1     1  37.0  21.9  54.0      230       13.8      15.9      0     1013  10.0 2012-12-31 20:00:00
## 3 EWR     2013  1.00     1     2  37.9  21.9  52.1      230       12.7      14.6      0     1013  10.0 2012-12-31 21:00:00
## 4 EWR     2013  1.00     1     3  37.9  23.0  54.5      230       13.8      15.9      0     1013  10.0 2012-12-31 22:00:00
## 5 EWR     2013  1.00     1     4  37.9  24.1  57.0      240       15.0      17.2      0     1013  10.0 2012-12-31 23:00:00

flightsweather <- flights %>%
  left_join(weather, by = c("origin", "year", "month", "day", "hour")) %>%
  select(origin, dep_delay, wind_speed)
flightsweather %>% head(5)

## # A tibble: 5 x 3
##   origin dep_delay wind_speed
##   <chr>      <dbl>      <dbl>
## 1 EWR         2.00       NA  
## 2 LGA         4.00       NA  
## 3 JFK         2.00       NA  
## 4 JFK        -1.00       NA  
## 5 LGA        -6.00       13.8

flightsweather %>%
  na.omit() %>%
  ggplot(aes(x=dep_delay)) +
    geom_histogram(aes(y=..density..),
                   binwidth = 5)

flightsweather %>%
  filter(wind_speed < 250 & dep_delay < 500) %>%
  ggplot(aes(x=wind_speed, y=dep_delay)) +
    geom_point() +
    geom_smooth() +
    facet_grid(. ~ origin)

• Install R and RStudio Desktop
• Or, create an account on rstudio.cloud and copy the project for this workshop
• Read the RStudio Desktop documentation

Familiarize yourself with the Console

version
sessionInfo()
x <- date()
x

• A notebook is an rmarkdown document intended for recording your analyses
• Rather than comment your code, your documentation contains code
• Chunks are executed independently and interactively

• Notebooks are an example of literate programming
• Notebooks are reproducible documents
• Rmarkdown generates publication-quality output
  
• Share project as a git repository: do-the-right-thing

Data exercise notebook

• Fill out a workshop evaluation so we can improve our programming cuny.is/gcdievals
• Need more support about what you just learned? Request follow-up individual consultations at cuny.is/gcdfconsults (students) or cuny.is/gcfacultyconsults (faculty)
  
• Drop-in to PUG for support with Python or other programming questions. For current dates, visit cuny.is/pug
• Join the GCDI Group on the CUNY Academic Commons for all GCDI-related updates! cuny.is/group-gcdi
• Thank you for attending and being involved! The #DigitalGC is you!