This data set contains results from a survey conducted by Forwards of attendees at useR! 2016, the R user conference held at Stanford University, Stanford, California, June 27 - June 30 2016. Modifications made to anonymize the data are noted in Details.
useR2016A data frame with 449 records and 48 variables:
Q2A factor with 3 levels: "Men", "Non-Binary/Unknown", "Women".
Q3A factor with 2 levels: "> 35", "35 or under"
Q7A factor with 2 levels: "Doctorate/Professional", "Masters or lower"
Q8A factor with 2 levels: "Non-academic", "Academic"
Q11A factor with 4 levels: "< 2 years", "2-5 years", "5-10 years", "> 10 years"
Q12A factor with 2 levels: "Yes", "No"
Q13A character vector with values "I use functions from
existing R packages to analyze data" or NA
Q13_BA character vector with values "I write R code designed to
make my work easier, such as loops or conditionals or functions" or NA
Q13_CA character vector with values "I write R functions for
use by myself or my collaborators" or NA
Q13_DA character vector with values "I contribute to R
packages (on CRAN or elsewhere)" or NA
Q13_EA character vector with values "I have written my own R
package" or NA
Q13_FA character vector with values "I have written my own R
package and released it on CRAN or Bioconductor (or shared it on GitHub,
R-Forge or similar platforms)" or NA
Q14A factor with 3 levels: "Primarily as part of a job or educational course;", "Primarily as a recreational activity, in your free time;", "For both recreational and job/educational purposes."
Q15A factor with 5 levels: "Strongly disagree", "Disagree", "No opinion", "Agree", "Strongly agree"
Q15_BA factor with 5 levels: "Strongly disagree", "Disagree", "No opinion", "Agree", "Strongly agree"
Q15_CA factor with 5 levels: "Strongly disagree", "Disagree", "No opinion", "Agree", "Strongly agree"
Q15_DA factor with 5 levels: "Strongly disagree", "Disagree", "No opinion", "Agree", "Strongly agree"
Q16A factor with 2 levels: "Yes", "No"
Q17A factor with 21 levels: "Good for statistical analysis", "Good for working with biological data structures", ...
Q17_BA character vector of free text response for when
Q17 == "Other (please specify)"
Q18A factor with 2 levels: "Yes", "No"
Q19A character vector with values "The R mailing lists" or
NA
Q19_BA character vector with values "The #rstats hashtag on
Twitter" or NA
Q19_CA character vector with values "The R StackOverflow
queues" or NA
Q19_DA character vector with values "The R IRC channel" or
NA
Q19_EA character vector with values "The rOpenSci mailing
lists or chat forums" or NA
Q19_FA character vector with values "The Bioconductor support
site" or NA
Q19_GA character vector with values "Other (please specify)"
or NA
Q19_HA character vector of free text response for when
Q19_G == "Other (please specify)"
Q20A factor with 9 levels: "Twitter", "Facebook", "Google+", ...
Q20_BA character vector of free text response for when
Q20 == "Other (please specify)"
Q21A factor with 2 levels: "Yes", "No"
Q22A factor with 5 levels: "A general user group", "A user group for women in R", "A user group within a university", "A user group within a company", "Other (please specify)"
Q22_BA character vector of free text response for when
Q22 == "Other (please specify)"
Q23A factor with 6 levels: "There is no group nearby/the group is inactive", "I am too busy", ...
Q24A character vector with values "New R user group near me
(specify location in comments box)" or NA
Q24_BA character vector with values "New R user group near
me aimed at my demographic (specify relevant group in comments box)" or
NA
Q24_CA character vector with values "Free local
introductory R workshops" or NA
Q24_DA character vector with values "Paid local advanced R
workshops" or NA
Q24_EA character vector with values "R workshop at
conference in my domain (specify domain/conference in comments box)" or
NA
Q24_FA character vector with values "R workshop aimed at
my demographic (specify relevant group in comments box)" or NA
Q24_GA character vector with values "Mentoring (e.g. first
CRAN submission/useR! abstract submission/GitHub contribution)" or
NA
Q24_HA character vector with values "Training in
non-English language (specify language in comments box)" or NA
Q24_IA character vector with values "Training that
accommodates my disability (specify disability in comments box)"
or NA
Q24_JA character vector with values "Online forum to
discuss R-related issues" or NA
Q24_KA character vector with values "Online support group
for my demographic (specify relevant group in comments box)" or
NA
Q24_LA character vector with values "Special facilities at R conferences (give further detail in comments box)"
This data set contains responses to the following questions from the survey of useR! 2016 attendees:
What is your gender?
In what year were you born?
What is the highest level of education you have completed?
What is your current (primary) employment status?
How long have you been using R for?
Did you have previous programming experience before beginning to use R?
Which of the following do you do? Tick any that apply.
(Responses stored in Q13 to Q13_F.)
I use functions from existing R packages to analyze data
I write R code designed to make my work easier, such as loops or conditionals or functions
I write R functions for use by myself or my collaborators
I contribute to R packages (on CRAN or elsewhere)
I have written my own R package
I have written my own R package and released it on CRAN or Bioconductor (or shared it on GitHub, R-Forge or similar platforms)
Do you use R:
Primarily as part of a job or educational course;
Primarily as a recreational activity, in your free time;
For both recreational and job/educational purposes.
How much do you agree or disagree with the following statements?
(Responses stored in Q15 to Q15_D.)
Writing R is fun
Writing R is considered cool or interesting by my peers
Writing R is a monotonous task
Writing R is difficult
Would you recommend R to friends or colleagues as a programming language to learn?
What would be your number one argument for/against learning R?
(fixed responses in Q17, other specified responses in Q17_B)
Do you consider yourself part of the R community?
Which of the following resources do you use for support?
Select all that apply. (Fixed responses stored in Q19 to Q19_G,
other specified responses in Q19_H.)
The R mailing lists
The #rstats hashtag on Twitter
The R StackOverflow queues
The R IRC channel
The rOpenSci mailing lists or chat forums
The Bioconductor support site
Other (please specify)
What would be your preferred medium for R community news (e.g.
events, webinars, opportunities)? (Fixed responses in Q20, other
specified responses in Q20_B.)
Do you attend R user group meetings in your local area?
If you do: what type of user group is it? (Fixed responses
in Q22, other specified responses in Q22_B.)
If you do not: why not?
Which of the following would make you more likely to participate
in the R community, or improve your experience? Tick any that apply. (Fixed
responses stored in Q24 to Q24_L.)
Various measures were taken to protect anonymity of the respondents and avoid disclosure of sensitive information. In particular the following questions/variables are completely excluded:
What did you register as at useR! 2016?
To what racial or ethnic group(s) do you identify?
In what country do you currently reside?
Do you identify as LGBT (Lesbian, Gay, Bisexual, Asexual and/or Transgender)?
Is your current job:
Full-time
Part-time
I am not currently employed
Are you a caregiver for children or adult dependents on a regular basis?
Specific reason for not attending a user group
Specific location/demographic/domain/language etc for which the respondent would like a user group/workshop/other support
What other ideas do you have for improving the R community?
Do you have any feedback for the survey authors?
Summaries of all these variables have been presented in blog posts (see references). Q1, Q9 and Q10 were used in multivariate analyses (see references) but Q9 and Q10 did not feature in the interpretation and Q1 has inconsistencies with Q8. For the latter we give priority to Q8, the employment status of respondents at the time they completed the survey.
Of the remaining variables, we consider Q2, Q3, Q7, Q8, Q11, and Q13_F to be implicit identifiers (key variables). These variables were modified to achieve 3-anonymity, i.e. the smallest subgroup identifiable from combinations of these variables is at least of size 3. In particular, the following modifications were made
Non-binary grouped with missing; all other key variables for this group suppressed (set to NA).
Year of birth converted to approximate age groups: "> 35" and "35 and under"; age group suppressed for 14 individuals.
Highest education level aggregated to two groups: "Doctorate/Professional" and "Masters and under"; highest education level suppressed for 3 individuals.
Employment status aggregated to three groups: "Non-academic" (includes employment in industry, government, non-profit, self-employed) and "Academic" (includes retired, unemployed, student).
Length of R usage aggregated to four groups: combined groups corresponding to shortest times into "< 2 years" group.
Suppressed for two individuals.
In addition specific values containing personal/personally identifiable information were suppressed in Q19_H, Q22_B and Q23_B.
Bollmann, S., Cook, D., Debelak, R., Dumas, J., Fox, J., Josse, J., Keyes, O., Strobl, C. and Turner, H. (2017) Mapping useRs https://forwards.github.io/blog/2017/01/13/mapping-users/.
Bollmann, S., Cook, D., Debelak, R., Dumas, J., Fox, J., Josse, J., Keyes, O., Strobl, C. and Turner, H. (2017) useRs Relationship with R https://forwards.github.io/blog/2017/03/11/users-relationship-with-r/.
Josse, J. and Turner, H. (2017) useR! 2016 participants and R programming: a multivariate analysis https://forwards.github.io/docs/mca_programming_user2016_survey/.
Josse, J. and Turner, H. (2017) useR! 2016 participants and the R community: a multivariate analysis https://forwards.github.io/docs/mca_community_user2016_survey/.
# NOT RUN {
# cross-tabulate age and length of time using R
xtabs(~ Q3 + Q11, data = useR2016)
# fit a logistic regression with "contribute to or write packages" predicted by
# gender, length of R usage, employment status, and community belonging
response <- with(useR2016,
ifelse(!is.na(Q13_D) | !is.na(Q13_E) | !is.na(Q13_F), 1, 0))
glm(response ~ Q2 + Q11 + Q8 + Q18, data = useR2016)
# }
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