library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()raw_data <- read.csv("https://github.com/rstudio/learning-r-survey/raw/master/2019/data/2019%20English%20R%20Community%20Survey%20Responses.csv",
fileEncoding = "UTF-8")
dim(raw_data)## [1] 1838 52str(raw_data)## 'data.frame': 1838 obs. of 52 variables:
## $ Timestamp : chr "12/13/2019 9:50:30" "12/13/2019 9:50:38" "12/13/2019 9:51:19" "12/13/2019 9:53:51" ...
## $ How.would.you.rate.your.level.of.experience.using.R. : chr "Expert" "Beginner" "Intermediate" "Intermediate" ...
## $ Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.do.you.expect.learning.R.to.be. : int NA NA NA NA NA NA NA NA NA NA ...
## $ From.what.you.know.about.R..how.long.do.you.expect.that.it.will.take.for.you.to.learn.enough.to.use.R.productively. : chr "" "" "" "" ...
## $ How.do.you.think.you.would.go.about.the.process.of.learning.R. : chr "" "" "" "" ...
## $ Which.statement.most.closely.reflects.the.primary.reason.why.you.are.interested.in.learning.R. : chr "" "" "" "" ...
## $ If.you.were.to.learn.R..what.would.do.you.think.you.would.use.it.for...check.all.that.apply. : chr "" "" "" "" ...
## $ Which.analytical.tools.do.you.use.today.for.the.functions.that.you.might.learn.R.for...please.check.all.that.apply. : chr "" "" "" "" ...
## $ What.do.you.think.is.the.biggest.obstacle.you.must.overcome.in.trying.to.learn.R..The.choices.below.are.only.suggestions..if.we.haven.t.listed.your.obstacle..please.choose..Other..and.add.your.obstacle.in.the.text.. : chr "" "" "" "" ...
## $ What.year.did.you.first.start.learning.R. : int 2017 2018 2018 2007 2018 2014 2017 2012 2003 2016 ...
## $ How.did.you.learn.R..If.you.used.multiple.methods..please.select.the.one.you.used.the.most. : chr "On the job" "By myself using a book or online documentation that was not part of a formal course" "Online course (e.g., Coursera, edX, Datacamp)" "On the job" ...
## $ Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.has.it.been.for.you.to.learn.R. : int 5 3 3 4 3 3 3 2 2 2 ...
## $ Roughly.how.long.did.it.take.you.to.achieve.proficiency.in.R. : chr "Years" "I don't feel proficient in R yet." "Months" "Years" ...
## $ Which.statement.most.closely.reflects.the.primary.reason.why.you.learned.R. : chr "I was personally interested" "I thought it would open new career opportunities" "One or more members of the R community encouraged me to learn it" "It was required for my work" ...
## $ What.do.you.think.was.the.biggest.obstacle.you.had.to.overcome.in.learning.R..The.choices.below.are.only.suggestions..if.we.haven.t.listed.your.obstacle..please.choose..Other..and.add.your.obstacle.in.the.text.. : chr "Language irregularities" "Language irregularities" "Error messages" "Data types" ...
## $ How.often.do.you.use.R.today..either.for.professional.or.personal.projects. : chr "More than once a day" "Less than once a month" "More than once a day" "Between once a day and once a week" ...
## $ What.applications.do.you.use.R.for.most...check.all.that.apply. : chr "Statistical analysis, Data transformation, Modeling, Visualization, Machine learning, Text processing" "Statistical analysis, Data transformation, Visualization" "Statistical analysis, Data transformation, Visualization" "Data transformation" ...
## $ Please.rate.how.much.you.enjoy.using.R.on.a.scale.of.1.to.5..where.1.is.you.don.t.enjoy.it.at.all..and.5.is.that.you.enjoy.it.a.great.deal. : int 5 5 5 5 5 5 5 5 5 5 ...
## $ How.likely.are.you.to.recommend.R.to.a.colleague..friend..or.family.member. : int 10 10 10 9 10 10 9 10 10 10 ...
## $ Which.tools.do.you.use.with.your.R.applications...please.check.all.that.apply. : chr "Python, R Command Line (i.e., the native R terminal interface), RStudio Desktop (IDE), RStudio Server, Shinyapp"| __truncated__ "Microsoft Excel" "" "Microsoft Excel, Python, R Command Line (i.e., the native R terminal interface), RStudio.cloud, RStudio Connect"| __truncated__ ...
## $ Did.you.use.tidyverse.packages.such.as.ggplot2.or.dplyr.to.learn.R. : chr "Yes" "Yes" "Yes" "No" ...
## $ Do.you.use.tidyverse.packages.when.you.use.R.now. : chr "Occasionally" "Usually" "Usually" "Usually" ...
## $ What.do.you.like.best.about.using.R. : chr "The speed of experimentation and relative lack of dependency hell. CRAN! Rcpp! The community." "Great community, easy to get to results" "It is readable code that I can find mistakes in easily" "Data munging" ...
## $ What.do.you.like.least.about.using.R. : chr "Multiple inconsistent styles across all the packages. Lack of type hinting. The community." "Trying to access data in matrices" "" "Inconsistencies in base (naming, parameter order, etc.)" ...
## $ When.you.have.problems.in.R..where.do.you.go.for.help. : chr "Question and answer sites such as Stack Overflow and Quora, The repo, if available. " "General search websites such as Google and Yahoo, Social media such as blogs, R-bloggers, Twitter, Slack, or GitHub contacts" "General search websites such as Google and Yahoo, Your personal network, such as colleagues and professors, Que"| __truncated__ "General search websites such as Google and Yahoo, Your personal network, such as colleagues and professors, Que"| __truncated__ ...
## $ How.do.you.discover.new.packages.or.packages.that.are.unfamiliar.to.you. : chr "Email lists such as r-help, r-packages, or r-pkg-devel, Your personal network, such as colleagues and professor"| __truncated__ "General search websites such as Google and Yahoo, Social media such as blogs, R-bloggers, Twitter, Slack, or GitHub contacts" "General search websites such as Google and Yahoo, community.rstudio.com, Your personal network, such as colleag"| __truncated__ "General search websites such as Google and Yahoo, CRAN Task Views, Your personal network, such as colleagues and professors" ...
## $ How.do.you.share.the.results.that.you.create.in.R..Check.all.that.apply. : chr "Other internal web site or portal run by my organization (for example, Sharepoint), Public web sites such as G"| __truncated__ "Personal blog or web site owned by you" "Email, Other internal web site or portal run by my organization (for example, Sharepoint), Public web sites su"| __truncated__ "RStudio Connect site run by my organization" ...
## $ Looking.ahead..how.do.you.expect.your.use.of.R.to.change.in.2020. : chr "I expect it to stay the same" "I expect it to increase" "I expect it to increase" "I expect it to stay the same" ...
## $ To.help.us.ensure.that.you.are.not.a.robot..please.enter.the.number.of.characters.in.the.word..analysis..in.the.text.box.below..Please.type.your.answer.as.a.word..for.example.if.you.want.3.to.be.your.answer..type..three..: chr "Eight" "eight" "eight" "eight" ...
## $ Do.you.currently.use.R.Markdown..Choose.the.statement.that.most.closely.matches.your.use. : chr "Weekly -- Between once a day and once a week" "Occasionally -- less than once a month" "Occasionally -- less than once a month" "Weekly -- Between once a day and once a week" ...
## $ What.applications.do.you.use.R.Markdown.for..Check.all.that.apply. : chr "Creating publication-ready articles for submission, Building web sites via blogdown, Publishing online and offl"| __truncated__ "Publishing online and offline books via bookdown" "Creating R Notebooks" "Creating R Notebooks" ...
## $ Looking.forward..how.do.you.expect.your.use.of.R.Markdown.to.change.in.2020. : chr "I expect it to stay the same" "I expect it to increase" "I expect it to increase" "I expect it to stay the same" ...
## $ How.often.do.you.currently.use.Shiny..Choose.the.statement.that.most.closely.matches.your.use. : chr "Frequently -- I frequently use or build Shiny applications" "Never -- I don't use Shiny." "Never -- I don't use Shiny." "Sometimes -- I use or build Shiny applications occasionally" ...
## $ Looking.forward..how.do.you.expect.your.use.of.Shiny.to.change.in.2020. : chr "I expect it to stay the same" "I expect it to stay the same" "I expect it to increase" "I expect it to stay the same" ...
## $ Do.you.currently.use.Python..Choose.the.statement.that.most.closely.matches.your.use. : chr "Weekly -- Between once a day and once a week" "I don't use Python" "I don't use Python" "Monthly -- between once a week and once a month" ...
## $ What.applications.do.you.use.Python.for.most...check.all.that.apply. : chr "Machine learning, Text processing, General programming " "" "" "" ...
## $ Please.rate.how.much.you.enjoy.using.Python.on.a.scale.of.1.to.5..where.1.is.you.don.t.enjoy.it.at.all..and.5.is.that.you.enjoy.it.a.great.deal. : int 5 NA NA 3 NA 2 NA 2 5 3 ...
## $ How.likely.are.you.to.recommend.Python.to.a.colleague..friend..or.family.member. : int 10 NA NA 8 NA 8 NA 8 10 7 ...
## $ Looking.forward..how.do.you.expect.your.use.of.Python.to.change.in.2020. : chr "I expect it to increase" "I expect it to stay the same" "I expect it to stay the same" "I expect it to increase" ...
## $ What.computer.tools.and.or.languages.have.you.used.besides.R. : chr "C/C++, Python" "Excel, Tableau" "Excel, Tableau" "C/C++, Excel, Go, Java, Javascript, Matlab, PHP, Python, Ruby, Visual Basic" ...
## $ What.was.the.FIRST.computer.language.or.tool.that.you.learned. : chr "R" "TurboPascal" "HTML" "Java" ...
## $ What.year.were.you.born. : int 1987 1983 1992 NA 1990 1991 1994 1986 1964 1987 ...
## $ What.gender.do.you.identify.with. : chr "male" "Male" "female" "" ...
## $ I.identify.my.ethnicity.as..select.all.that.apply.. : chr "White" "White" "White" "" ...
## $ What.is.the.highest.degree.or.level.of.school.you.have.completed..If.currently.enrolled..please.use.the.highest.degree.received. : chr "Master’s degree (e.g. MA, MS, MEd)" "Master’s degree (e.g. MA, MS, MEd)" "Master’s degree (e.g. MA, MS, MEd)" "Master’s degree (e.g. MA, MS, MEd)" ...
## $ In.what.country.do.you.currently.reside. : chr "United States of America" "Netherlands" "United States of America" "United States of America" ...
## $ What.industry.do.you.work.or.participate.in. : chr "Education" "Sustainability" "Transportation" "Information Technologies" ...
## $ What.is.your.job.title..if.any. : chr "" "Data Specialist" "Operations Research Analyst" "Software Engineer" ...
## $ Which.category.best.describes.the.work.you.do. : chr "Data scientist or analyst" "Data scientist or analyst" "Data scientist or analyst" "Software developer" ...
## $ How.many.people.in.your.organization.or.work.group.do.you.feel.that.you.can.ask.for.help.or.support.when.working.with.R. : num 1 0 0 20 5 5 2 0 0 2 ...
## $ Which.of.the.following.events.have.you.attended..if.any..Check.all.that.apply. : chr "" "Local or regional R meetup, R Ladies meetups (local, regional, national, or international)" "Local or regional R meetup" "RStudio::conf, UseR conference, Local or regional R meetup, R Ladies meetups (local, regional, national, or int"| __truncated__ ...
## $ How.did.you.hear.about.this.survey. : chr "Twitter" "Twitter" "Twitter" "Email" ...str(raw_data[1:5])## 'data.frame': 1838 obs. of 5 variables:
## $ Timestamp : chr "12/13/2019 9:50:30" "12/13/2019 9:50:38" "12/13/2019 9:51:19" "12/13/2019 9:53:51" ...
## $ How.would.you.rate.your.level.of.experience.using.R. : chr "Expert" "Beginner" "Intermediate" "Intermediate" ...
## $ Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.do.you.expect.learning.R.to.be.: int NA NA NA NA NA NA NA NA NA NA ...
## $ From.what.you.know.about.R..how.long.do.you.expect.that.it.will.take.for.you.to.learn.enough.to.use.R.productively. : chr "" "" "" "" ...
## $ How.do.you.think.you.would.go.about.the.process.of.learning.R. : chr "" "" "" "" ...names(raw_data[1:5])## [1] "Timestamp"
## [2] "How.would.you.rate.your.level.of.experience.using.R."
## [3] "Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.do.you.expect.learning.R.to.be."
## [4] "From.what.you.know.about.R..how.long.do.you.expect.that.it.will.take.for.you.to.learn.enough.to.use.R.productively."
## [5] "How.do.you.think.you.would.go.about.the.process.of.learning.R."str(raw_data$In.what.country.do.you.currently.reside.)## chr [1:1838] "United States of America" "Netherlands" ...class(raw_data$What.year.did.you.first.start.learning.R.)## [1] "integer"names(raw_data[1:5])## [1] "Timestamp"
## [2] "How.would.you.rate.your.level.of.experience.using.R."
## [3] "Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.do.you.expect.learning.R.to.be."
## [4] "From.what.you.know.about.R..how.long.do.you.expect.that.it.will.take.for.you.to.learn.enough.to.use.R.productively."
## [5] "How.do.you.think.you.would.go.about.the.process.of.learning.R."head(raw_data[1:2])Timestamp <chr> | How.would.you.rate.your.level.of.experience.using.R. <chr> | ||
|---|---|---|---|
| 1 | 12/13/2019 9:50:30 | Expert | |
| 2 | 12/13/2019 9:50:38 | Beginner | |
| 3 | 12/13/2019 9:51:19 | Intermediate | |
| 4 | 12/13/2019 9:53:51 | Intermediate | |
| 5 | 12/13/2019 10:01:03 | Intermediate | |
| 6 | 12/13/2019 10:04:42 | Expert |
head(raw_data[1:2], n = 3)Timestamp <chr> | How.would.you.rate.your.level.of.experience.using.R. <chr> | ||
|---|---|---|---|
| 1 | 12/13/2019 9:50:30 | Expert | |
| 2 | 12/13/2019 9:50:38 | Beginner | |
| 3 | 12/13/2019 9:51:19 | Intermediate |
head(raw_data$How.likely.are.you.to.recommend.R.to.a.colleague..friend..or.family.member., n = 20)## [1] 10 10 10 9 10 10 9 10 10 10 10 10 10 10 10 10 9 9 9 9tail(raw_data$How.likely.are.you.to.recommend.R.to.a.colleague..friend..or.family.member., n = 20)## [1] 9 10 10 6 10 6 9 10 10 10 8 10 10 10 10 10 8 NA 10 9names(raw_data[2])## [1] "How.would.you.rate.your.level.of.experience.using.R."renamed <- raw_data %>%
rename("Qr_experience" = "How.would.you.rate.your.level.of.experience.using.R.")
# new name
names(renamed[1:2])## [1] "Timestamp" "Qr_experience"renamed <- raw_data %>%
rename("Qr_experience" = "How.would.you.rate.your.level.of.experience.using.R.",
"Qr_difficulty_experienced" = "Compared.with.other.technical.topics.you.ve.learned.in.school.and.on.the.job..on.a.scale.of.1.to.5..how.difficult.do.you.expect.learning.R.to.be.")
names(renamed[2:3])## [1] "Qr_experience" "Qr_difficulty_experienced"names(renamed[1:3])## [1] "Timestamp" "Qr_experience"
## [3] "Qr_difficulty_experienced"However, raw_data has 52 variables! This would take a long time to do. Instead of writing 52+ lines of code, we can use the names() function to set the names of our data frame (raw_data) equal to the names of another data frame.
The R Studio survey provides us with a .tsv file of proper variable names for the columns. Let’s load that first:
qnames <- read_tsv("https://raw.githubusercontent.com/rstudio/learning-r-survey/master/2019/data/2019-english-question-names-only.tsv")## Rows: 0 Columns: 52
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (52): Qtime, Qr_experience, Qr_difficulty, Qr_length_to_success, Qhow_to...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.# make a copy of the data frame
rsurvey <- raw_data
# rename the columns based on qnames
names(rsurvey) <- names(qnames)
names(rsurvey[1:4])## [1] "Qtime" "Qr_experience" "Qr_difficulty"
## [4] "Qr_length_to_success"Let’s make sure all the names are lowercase. This will make typing them in later analyses easier, as you don’t ever need to remember what is capital and what is not
library("janitor")##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testrsurvey <- rsurvey %>%
janitor::clean_names()
names(rsurvey[1:10])## [1] "qtime" "qr_experience" "qr_difficulty"
## [4] "qr_length_to_success" "qhow_to_learn_r" "qreason_to_learn"
## [7] "qr_use" "qtools" "qobstacles_to_starting"
## [10] "qr_year"library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphadescribe(rsurvey[1:5])vars <int> | n <dbl> | mean <dbl> | sd <dbl> | median <dbl> | trimmed <dbl> | mad <dbl> | min <dbl> | ||
|---|---|---|---|---|---|---|---|---|---|
| qtime* | 1 | 1838 | 913.236126 | 526.8858952 | 912.5 | 913.046196 | 676.0656 | 1 | |
| qr_experience* | 2 | 1838 | 3.412405 | 0.7785213 | 4.0 | 3.530571 | 0.0000 | 1 | |
| qr_difficulty | 3 | 8 | 3.500000 | 0.5345225 | 3.5 | 3.500000 | 0.7413 | 3 | |
| qr_length_to_success* | 4 | 1838 | 1.007073 | 0.1210282 | 1.0 | 1.000000 | 0.0000 | 1 | |
| qhow_to_learn_r* | 5 | 1838 | 1.007617 | 0.1275654 | 1.0 | 1.000000 | 0.0000 | 1 |
#create an age variable
rsurvey$age <- 2020-rsurvey$qyear_born
#Describe experience by age
describeBy(rsurvey$age, group=rsurvey$qr_experience, mat=TRUE)## Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning Inf## Warning in max(x, na.rm = na.rm): no non-missing arguments to max; returning
## -Infitem <chr> | group1 <chr> | vars <dbl> | n <dbl> | mean <dbl> | sd <dbl> | median <dbl> | trimmed <dbl> | mad <dbl> | ||
|---|---|---|---|---|---|---|---|---|---|---|
| X11 | 1 | 1 | 0 | NaN | NA | NA | NaN | NA | ||
| X12 | 2 | Beginner | 1 | 224 | 36.26786 | 12.968732 | 34 | 34.76111 | 11.8608 | |
| X13 | 3 | Expert | 1 | 506 | 36.62648 | 9.625914 | 35 | 35.35468 | 7.4130 | |
| X14 | 4 | Intermediate | 1 | 993 | 36.62034 | 10.948554 | 35 | 35.29560 | 8.8956 | |
| X15 | 5 | None | 1 | 8 | 44.12500 | 13.798939 | 42 | 44.12500 | 12.6021 |
summary(rsurvey[2:3])## qr_experience qr_difficulty
## Length:1838 Min. :3.0
## Class :character 1st Qu.:3.0
## Mode :character Median :3.5
## Mean :3.5
## 3rd Qu.:4.0
## Max. :4.0
## NA's :1830summary(as.factor(rsurvey$qr_experience))## Beginner Expert Intermediate None
## 31 233 529 1037 8summary(as.factor(rsurvey$qr_difficulty))## 3 4 NA's
## 4 4 1830library(skimr)
skim(rsurvey[2:3])| Name | rsurvey[2:3] |
| Number of rows | 1838 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| qr_experience | 0 | 1 | 0 | 12 | 31 | 5 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| qr_difficulty | 1830 | 0 | 3.5 | 0.53 | 3 | 3 | 3.5 | 4 | 4 | ▇▁▁▁▇ |
table(rsurvey$qcountry, rsurvey$qr_experience)[1:5,]##
## Beginner Expert Intermediate None
## 31 18 33 66 0
## Afghanistan 0 0 0 1 0
## Albania 0 0 0 1 0
## Algeria 0 7 5 13 1
## Andorra 0 0 1 1 0library("jmv")##
## Attaching package: 'jmv'## The following objects are masked from 'package:psych':
##
## pca, reliabilityrsurvey %>%
select(qr_experience, qr_year) %>%
descriptives(freq = TRUE)##
## DESCRIPTIVES
##
## Descriptives
## ---------------------------------------------------
## qr_experience qr_year
## ---------------------------------------------------
## N 1838 1676
## Missing 0 162
## Mean 2007.749
## Median 2015.000
## Standard deviation 107.3527
## Minimum 2
## Maximum 2019
## ---------------------------------------------------
##
##
## FREQUENCIES
##
## Frequencies of qr_experience
## --------------------------------------------------------
## Levels Counts % of Total Cumulative %
## --------------------------------------------------------
## 0.00000 0.00000
## Beginner 233 12.67682 0.00000
## Expert 529 28.78128 0.00000
## Intermediate 1037 56.42002 0.00000
## None 8 0.43526 0.00000
## --------------------------------------------------------rsurvey %>%
select(qr_year) %>%
arrange(qr_year) %>%
head(n = 10)qr_year <int> | ||||
|---|---|---|---|---|
| 1 | 2 | |||
| 2 | 6 | |||
| 3 | 13 | |||
| 4 | 18 | |||
| 5 | 207 | |||
| 6 | 1977 | |||
| 7 | 1985 | |||
| 8 | 1989 | |||
| 9 | 1989 | |||
| 10 | 1990 |
#run this to change the variable
rsurvey <- rsurvey %>%
mutate(qr_year2 = ifelse(qr_year < 1977, NA, qr_year))
#run this to check
rsurvey %>%
select(qr_year, qr_year2) %>%
arrange(qr_year) %>%
head(n=10)qr_year <int> | qr_year2 <int> | |||
|---|---|---|---|---|
| 1 | 2 | NA | ||
| 2 | 6 | NA | ||
| 3 | 13 | NA | ||
| 4 | 18 | NA | ||
| 5 | 207 | NA | ||
| 6 | 1977 | 1977 | ||
| 7 | 1985 | 1985 | ||
| 8 | 1989 | 1989 | ||
| 9 | 1989 | 1989 | ||
| 10 | 1990 | 1990 |
rsurvey %>% count(qr_experience)qr_experience <chr> | n <int> | |||
|---|---|---|---|---|
| 31 | ||||
| Beginner | 233 | |||
| Expert | 529 | |||
| Intermediate | 1037 | |||
| None | 8 |
recoded <- rsurvey %>%
select(qr_experience) %>%
mutate(qr_experience2 = factor(qr_experience,
levels=c("None","Beginner", "Intermediate", "Expert", NA ))
)
recoded %>% count(qr_experience2)qr_experience2 <fct> | n <int> | |||
|---|---|---|---|---|
| None | 8 | |||
| Beginner | 233 | |||
| Intermediate | 1037 | |||
| Expert | 529 | |||
| NA | 31 |
This works, so lets apply it to the real data set:
rsurvey <- rsurvey %>%
mutate(qr_experience = factor(qr_experience,
levels=c("None","Beginner", "Intermediate", "Expert", NA ))
)
recoded %>% count(qr_experience)qr_experience <chr> | n <int> | |||
|---|---|---|---|---|
| 31 | ||||
| Beginner | 233 | |||
| Expert | 529 | |||
| Intermediate | 1037 | |||
| None | 8 |