README.md
In favstats/openmindR:
openmindR
Install package like this:
devtools::install_github("favstats/openmindR")
Load package(s):
library(openmindR)
library(tidyverse)
OpenMind Cleaning Functions
om_filter_data
Filter down Assessment data from AirTable by AssessmentsDone,
AssessmentVersion and AccessCodes.
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = "Wilkes"
# "Wilkes" #try this out :)
)
## # A tibble: 653 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 2 rec0~ 1101~ ThomasWil~ 4 2 70 40
## 3 rec0~ 1430~ MaykWilke~ 4 2 50 50
## 4 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 5 rec0~ 4351~ WilliamsW~ 4 3 77 25
## 6 rec0~ 2247~ PradoWilk~ 4 2 19 100
## 7 rec0~ 3909~ VosikPeka~ 4 2 50 58
## 8 rec0~ 3873~ BalesterW~ 4 2 63 85
## 9 rec1~ 7625~ BalesterW~ 4 2 73 30
## 10 rec1~ 6771~ KarimiWil~ 4 3 90 20
## # ... with 643 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
This dataset was filtered down to only AccessCodes that include
“Wilkes”. The accesscode argument is not case-sensitive and can
both be used with vectors:
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = c("SuszkoWilkesUF18", "KarimiWilkesUF18")
)
## # A tibble: 31 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 2 rec1~ 6771~ KarimiWil~ 4 3 90 20
## 3 rec5~ 5923~ SuszkoWil~ 4 2 50 50
## 4 rec5~ 1915~ KarimiWil~ 4 2 35 5
## 5 rec7~ 1018~ SuszkoWil~ 4 2 30 85
## 6 recA~ 1326~ KarimiWil~ 4 2 70 45
## 7 recE~ 3793~ KarimiWil~ 4 2 100 30
## 8 recH~ 2939~ SuszkoWil~ 4 2 <NA> <NA>
## 9 recL~ 7456~ SuszkoWil~ 4 2 25 75
## 10 recT~ 3521~ KarimiWil~ 4 2 25 67
## # ... with 21 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
And individual strings:
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = c("suszko|karimi")
)
## # A tibble: 31 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 2 rec1~ 6771~ KarimiWil~ 4 3 90 20
## 3 rec5~ 5923~ SuszkoWil~ 4 2 50 50
## 4 rec5~ 1915~ KarimiWil~ 4 2 35 5
## 5 rec7~ 1018~ SuszkoWil~ 4 2 30 85
## 6 recA~ 1326~ KarimiWil~ 4 2 70 45
## 7 recE~ 3793~ KarimiWil~ 4 2 100 30
## 8 recH~ 2939~ SuszkoWil~ 4 2 <NA> <NA>
## 9 recL~ 7456~ SuszkoWil~ 4 2 25 75
## 10 recT~ 3521~ KarimiWil~ 4 2 25 67
## # ... with 21 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
om_clean_par
Cleans up ParticipantProgress data and creates several measures:
-
StepTimes1 to StepTimes5: Duration in minutes to complete a step
-
StepCorrect1 to StepCorrect5: Percentage of correct answers for
each step
-
FeedbackAnswers: Q1 to Q5 for each individual step
dat.par %>%
om_clean_par()
## # A tibble: 16,896 x 36
## OMID StepTimes StepsComplete StepCorrect1 StepCorrect2 StepCorrect3
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 7806~ <NA> 1, 1, 1, 1, 1 0.667 0.625 0.667
## 2 1000~ <NA> 1, 1, 1, 1, 1 0.75 1 0.833
## 3 1945~ <NA> 1, 1, 1, 1, 1 0.667 1 0.667
## 4 7858~ <NA> 1, 0, 0, 0, 0 0.833 NA NA
## 5 9277~ <NA> 1, 1, 1, 1, 1 1 1 1
## 6 6750~ <NA> 1, 1, 1, 1, 1 1 1 1
## 7 4023~ <NA> 1, 1, 1, 1, 1 0.833 1 0.667
## 8 1000~ <NA> 1, 1, 1, 1, 1 0.625 1 0.667
## 9 2458~ <NA> 1, 1, 1, 1, 0 1 1 1
## 10 9406~ <NA> 1, 1, 0, 0, 0 1 1 NA
## # ... with 16,886 more rows, and 30 more variables: StepCorrect4 <dbl>,
## # StepCorrect5 <dbl>, StepTimes1 <dbl>, StepTimes2 <dbl>,
## # StepTimes3 <dbl>, StepTimes4 <dbl>, StepTimes5 <dbl>, Step1 <chr>,
## # Step1_Q1 <chr>, Step1_Q2 <chr>, Step1_Q3 <chr>, Step1_Q4 <chr>,
## # Step1_Q5 <chr>, Step2 <chr>, Step2_Q1 <chr>, Step2_Q2 <chr>,
## # Step2_Q3 <chr>, Step2_Q4 <chr>, Step2_Q5 <chr>, Step5 <chr>,
## # Step5_Q1 <chr>, Step5_Q2 <chr>, Step5_Q3 <chr>, Step5_Q4 <chr>,
## # Step5_Q5 <chr>, FeedbackAnswers <chr>,
## # FeedbackAnswersVariableNames <chr>, AppRating <chr>,
## # AppRecommend <chr>, at_date <chr>
om_construct_measures
This is a higher-level function that uses both polar_measures and
calc_ih to constuct various measures.
Creates the following variables:
- Q14: Affective Polarization
- Q15: Ingroup
- Q16: Outgroup
- Q17: Ingroup vs. Outgroup Affective Polarization
- Q18: Intellectual Humility
Function automatically accounts for Assessment Version 4 and 5/5.1.
dat.ass %>%
om_construct_measures()
Error in polar_measures(., Q1Pre, Q2Pre) : Input data is missing
column ppol_cat. Please make sure to run om_clean_ppol before you
run om_construct_measures.
Uh oh! That didn’t work! om_construct_measures needs the column
ppol_cat to run which can be created with the function
om_clean_ppol.
om_clean_ppol
Creates the following measures of Political Orientation
- ppol_raw: a variable that merges Assessment V4 and V5.1
spelling of Political Orientation (D4)
- ppol: a factor variable ordered from “Very Progressive/left” to
“Very Conservative/right”. Excludes all other categories as NA
(classical liberal etc.)
- ppol_num: numeric variable ranging from 1 “Very
Progressive/left” to 7 “Very Conservative/right”
- ppol_cat: a factor variable which has two categories
“Progressive” and “Conservative”. The rest is NA.
dat.ass <- dat.ass %>%
om_clean_ppol()
dat.ass
## # A tibble: 16,805 x 82
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,795 more rows, and 75 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>
Now om_construct_measures will work!
dat.ass %>%
om_construct_measures()
## # A tibble: 16,805 x 97
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,795 more rows, and 90 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>,
## # Q14Pre <dbl>, Q15Pre <dbl>, Q16Pre <dbl>, Q17Pre <dbl>, Q18Pre <dbl>,
## # Q14Post <dbl>, Q15Post <dbl>, Q16Post <dbl>, Q17Post <dbl>,
## # Q18Post <dbl>, Q14FollowUp <dbl>, Q15FollowUp <dbl>,
## # Q16FollowUp <dbl>, Q17FollowUp <dbl>, Q18FollowUp <dbl>
remove_dups
This function is really important to clean up duplicated OMIDs that
occasionally occur within AirTable.
dat.ass %>%
remove_dups()
## Removing 256 duplicates...
## # A tibble: 16,293 x 83
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,283 more rows, and 76 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <dttm>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>,
## # count_na <dbl>
OpenMind ggplot2 theme
There are three functions for the ggplot2 theme:
theme_omscale_fill_omscale_color_om
Make sure you have the Poppins font installed!
windowsFonts(`Poppins` = windowsFont("Poppins"))
Good tutorial on how to install custom fonts in
R
Example
## Load tidyverse
library(tidyverse)
titanic_dat <- Titanic %>% as_tibble()
titanic_dat %>%
ggplot(aes(Sex, n)) +
geom_col(aes(fill = Class), position = position_dodge()) +
theme_om(legend_position = c(0.9, 0.75)) +
scale_fill_om("Class") +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Age and Class")
Adapt theme_om
legend_positionaxis_text_sizeaxis_title_sizelegend_text_sizetitle_size
titanic_dat %>%
ggplot(aes(Class, n, fill = Class)) +
geom_col() +
theme_om(legend_position = "bottom",
axis_text_size = 10,
axis_title_size = 15,
legend_text_size = 10,
title_size = 20) +
scale_fill_om() +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Class")
Or all text sizes at once
overall_text_size
titanic_dat %>%
ggplot(aes(Class, n, fill = Class)) +
geom_col() +
theme_om(legend_position = "top",
overall_text_size = 15) +
scale_fill_om() +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Class")
favstats/openmindR documentation built on May 23, 2019, 8:03 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
openmindR
Install package like this:
devtools::install_github("favstats/openmindR")
Load package(s):
library(openmindR)
library(tidyverse)
OpenMind Cleaning Functions
om_filter_data
Filter down Assessment data from AirTable by AssessmentsDone,
AssessmentVersion and AccessCodes.
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = "Wilkes"
# "Wilkes" #try this out :)
)
## # A tibble: 653 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 2 rec0~ 1101~ ThomasWil~ 4 2 70 40
## 3 rec0~ 1430~ MaykWilke~ 4 2 50 50
## 4 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 5 rec0~ 4351~ WilliamsW~ 4 3 77 25
## 6 rec0~ 2247~ PradoWilk~ 4 2 19 100
## 7 rec0~ 3909~ VosikPeka~ 4 2 50 58
## 8 rec0~ 3873~ BalesterW~ 4 2 63 85
## 9 rec1~ 7625~ BalesterW~ 4 2 73 30
## 10 rec1~ 6771~ KarimiWil~ 4 3 90 20
## # ... with 643 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
This dataset was filtered down to only AccessCodes that include
“Wilkes”. The accesscode argument is not case-sensitive and can
both be used with vectors:
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = c("SuszkoWilkesUF18", "KarimiWilkesUF18")
)
## # A tibble: 31 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 2 rec1~ 6771~ KarimiWil~ 4 3 90 20
## 3 rec5~ 5923~ SuszkoWil~ 4 2 50 50
## 4 rec5~ 1915~ KarimiWil~ 4 2 35 5
## 5 rec7~ 1018~ SuszkoWil~ 4 2 30 85
## 6 recA~ 1326~ KarimiWil~ 4 2 70 45
## 7 recE~ 3793~ KarimiWil~ 4 2 100 30
## 8 recH~ 2939~ SuszkoWil~ 4 2 <NA> <NA>
## 9 recL~ 7456~ SuszkoWil~ 4 2 25 75
## 10 recT~ 3521~ KarimiWil~ 4 2 25 67
## # ... with 21 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
And individual strings:
dat.ass %>%
# specify which number of assessment you want to have
om_filter_data(n_assessments = 1:3,
# assessment version?
version = 4,
# select Accesscode(s) to produce report for
accesscode = c("suszko|karimi")
)
## # A tibble: 31 x 78
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 rec0~ 1144~ SuszkoWil~ 4 2 31 65
## 2 rec1~ 6771~ KarimiWil~ 4 3 90 20
## 3 rec5~ 5923~ SuszkoWil~ 4 2 50 50
## 4 rec5~ 1915~ KarimiWil~ 4 2 35 5
## 5 rec7~ 1018~ SuszkoWil~ 4 2 30 85
## 6 recA~ 1326~ KarimiWil~ 4 2 70 45
## 7 recE~ 3793~ KarimiWil~ 4 2 100 30
## 8 recH~ 2939~ SuszkoWil~ 4 2 <NA> <NA>
## 9 recL~ 7456~ SuszkoWil~ 4 2 25 75
## 10 recT~ 3521~ KarimiWil~ 4 2 25 67
## # ... with 21 more rows, and 71 more variables: Q3Pre <chr>, Q4Pre <chr>,
## # Q5Pre <chr>, Q6Pre <chr>, Q7Pre <chr>, Q8Pre <chr>, Q9Pre <chr>,
## # Q10Pre <chr>, Q11Pre <chr>, Q12Pre <chr>, C1Pre <chr>, C2Pre <chr>,
## # C3Pre <chr>, D1 <chr>, D2 <chr>, D3 <chr>, D4 <chr>, D5 <chr>,
## # DatePre <chr>, Q1Post <chr>, Q2Post <chr>, Q3Post <chr>, Q4Post <chr>,
## # Q5Post <chr>, Q6Post <chr>, Q7Post <chr>, Q8Post <chr>, Q9Post <chr>,
## # Q10Post <chr>, Q11Post <chr>, Q12Post <chr>, DatePost <chr>,
## # B1Pre <chr>, B1Post <chr>, Q1FollowUp <chr>, Q2FollowUp <chr>,
## # Q3FollowUp <chr>, Q4FollowUp <chr>, Q5FollowUp <chr>,
## # Q6FollowUp <chr>, Q7FollowUp <chr>, Q8FollowUp <chr>,
## # Q9FollowUp <chr>, Q10FollowUp <chr>, Q11FollowUp <chr>,
## # Q12FollowUp <chr>, C1FollowUp <chr>, C2FollowUp <chr>,
## # C3FollowUp <chr>, DateFollowUp <chr>, W1FollowUp <chr>,
## # B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>, BTaskPre <chr>,
## # B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <chr>, C2Post <chr>,
## # C3Post <chr>, BTaskPost <chr>, B2Post <chr>, B3Post <chr>,
## # W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>
om_clean_par
Cleans up ParticipantProgress data and creates several measures:
-
StepTimes1 to StepTimes5: Duration in minutes to complete a step
-
StepCorrect1 to StepCorrect5: Percentage of correct answers for each step
-
FeedbackAnswers: Q1 to Q5 for each individual step
dat.par %>%
om_clean_par()
## # A tibble: 16,896 x 36
## OMID StepTimes StepsComplete StepCorrect1 StepCorrect2 StepCorrect3
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 7806~ <NA> 1, 1, 1, 1, 1 0.667 0.625 0.667
## 2 1000~ <NA> 1, 1, 1, 1, 1 0.75 1 0.833
## 3 1945~ <NA> 1, 1, 1, 1, 1 0.667 1 0.667
## 4 7858~ <NA> 1, 0, 0, 0, 0 0.833 NA NA
## 5 9277~ <NA> 1, 1, 1, 1, 1 1 1 1
## 6 6750~ <NA> 1, 1, 1, 1, 1 1 1 1
## 7 4023~ <NA> 1, 1, 1, 1, 1 0.833 1 0.667
## 8 1000~ <NA> 1, 1, 1, 1, 1 0.625 1 0.667
## 9 2458~ <NA> 1, 1, 1, 1, 0 1 1 1
## 10 9406~ <NA> 1, 1, 0, 0, 0 1 1 NA
## # ... with 16,886 more rows, and 30 more variables: StepCorrect4 <dbl>,
## # StepCorrect5 <dbl>, StepTimes1 <dbl>, StepTimes2 <dbl>,
## # StepTimes3 <dbl>, StepTimes4 <dbl>, StepTimes5 <dbl>, Step1 <chr>,
## # Step1_Q1 <chr>, Step1_Q2 <chr>, Step1_Q3 <chr>, Step1_Q4 <chr>,
## # Step1_Q5 <chr>, Step2 <chr>, Step2_Q1 <chr>, Step2_Q2 <chr>,
## # Step2_Q3 <chr>, Step2_Q4 <chr>, Step2_Q5 <chr>, Step5 <chr>,
## # Step5_Q1 <chr>, Step5_Q2 <chr>, Step5_Q3 <chr>, Step5_Q4 <chr>,
## # Step5_Q5 <chr>, FeedbackAnswers <chr>,
## # FeedbackAnswersVariableNames <chr>, AppRating <chr>,
## # AppRecommend <chr>, at_date <chr>
om_construct_measures
This is a higher-level function that uses both polar_measures and
calc_ih to constuct various measures.
Creates the following variables:
- Q14: Affective Polarization
- Q15: Ingroup
- Q16: Outgroup
- Q17: Ingroup vs. Outgroup Affective Polarization
- Q18: Intellectual Humility
Function automatically accounts for Assessment Version 4 and 5/5.1.
dat.ass %>%
om_construct_measures()
Error in polar_measures(., Q1Pre, Q2Pre) : Input data is missing column
ppol_cat. Please make sure to run om_clean_ppol before you run om_construct_measures.
Uh oh! That didn’t work! om_construct_measures needs the column
ppol_cat to run which can be created with the function
om_clean_ppol.
om_clean_ppol
Creates the following measures of Political Orientation
- ppol_raw: a variable that merges Assessment V4 and V5.1 spelling of Political Orientation (D4)
- ppol: a factor variable ordered from “Very Progressive/left” to “Very Conservative/right”. Excludes all other categories as NA (classical liberal etc.)
- ppol_num: numeric variable ranging from 1 “Very Progressive/left” to 7 “Very Conservative/right”
- ppol_cat: a factor variable which has two categories “Progressive” and “Conservative”. The rest is NA.
dat.ass <- dat.ass %>%
om_clean_ppol()
dat.ass
## # A tibble: 16,805 x 82
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,795 more rows, and 75 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>
Now om_construct_measures will work!
dat.ass %>%
om_construct_measures()
## # A tibble: 16,805 x 97
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,795 more rows, and 90 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <chr>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>,
## # Q14Pre <dbl>, Q15Pre <dbl>, Q16Pre <dbl>, Q17Pre <dbl>, Q18Pre <dbl>,
## # Q14Post <dbl>, Q15Post <dbl>, Q16Post <dbl>, Q17Post <dbl>,
## # Q18Post <dbl>, Q14FollowUp <dbl>, Q15FollowUp <dbl>,
## # Q16FollowUp <dbl>, Q17FollowUp <dbl>, Q18FollowUp <dbl>
remove_dups
This function is really important to clean up duplicated OMIDs that occasionally occur within AirTable.
dat.ass %>%
remove_dups()
## Removing 256 duplicates...
## # A tibble: 16,293 x 83
## id OMID AccessCode AssessmentVersi~ AssessmentsDone Q1Pre Q2Pre
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 rec0~ 4616~ GottlickU~ 4 2 40 60
## 2 rec0~ 4001~ PotterYCI~ 4 1 NA NA
## 3 rec0~ 3465~ LittleUGA~ 4 2 30 36
## 4 rec0~ 8406~ Jayawickr~ 4 2 50 50
## 5 rec0~ 8721~ BurmanCha~ 4 2 NA NA
## 6 rec0~ 7194~ Jayawickr~ 4 2 80 45
## 7 rec0~ 5743~ ZipayUOre~ 4 2 78 34
## 8 rec0~ 5717~ TalpashWi~ 4 3 56 54
## 9 rec0~ 5370~ Individua~ 4 2 50 37
## 10 rec0~ 6459~ BursonInd~ 4 2 50 50
## # ... with 16,283 more rows, and 76 more variables: Q3Pre <dbl>,
## # Q4Pre <dbl>, Q5Pre <dbl>, Q6Pre <dbl>, Q7Pre <dbl>, Q8Pre <dbl>,
## # Q9Pre <dbl>, Q10Pre <dbl>, Q11Pre <dbl>, Q12Pre <dbl>, C1Pre <dbl>,
## # C2Pre <dbl>, C3Pre <dbl>, D1 <dbl>, D2 <chr>, D3 <chr>, D4 <chr>,
## # D5 <chr>, DatePre <chr>, Q1Post <dbl>, Q2Post <dbl>, Q3Post <dbl>,
## # Q4Post <dbl>, Q5Post <dbl>, Q6Post <dbl>, Q7Post <dbl>, Q8Post <dbl>,
## # Q9Post <dbl>, Q10Post <dbl>, Q11Post <dbl>, Q12Post <dbl>,
## # DatePost <chr>, B1Pre <chr>, B1Post <chr>, Q1FollowUp <dbl>,
## # Q2FollowUp <dbl>, Q3FollowUp <dbl>, Q4FollowUp <dbl>,
## # Q5FollowUp <dbl>, Q6FollowUp <dbl>, Q7FollowUp <dbl>,
## # Q8FollowUp <dbl>, Q9FollowUp <dbl>, Q10FollowUp <dbl>,
## # Q11FollowUp <dbl>, Q12FollowUp <dbl>, C1FollowUp <dbl>,
## # C2FollowUp <dbl>, C3FollowUp <dbl>, DateFollowUp <chr>,
## # W1FollowUp <chr>, B1FollowUp <chr>, createdTime <dttm>, S1Pre <chr>,
## # BTaskPre <chr>, B2Pre <chr>, B3Pre <chr>, S1Post <chr>, C1Post <dbl>,
## # C2Post <dbl>, C3Post <dbl>, BTaskPost <chr>, B2Post <chr>,
## # B3Post <chr>, W1Post <chr>, W1Pre <chr>, D6 <chr>, S1FollowUp <chr>,
## # BTaskFollowUp <chr>, B2FollowUp <chr>, B3FollowUp <chr>,
## # ppol_raw <chr>, ppol <fct>, ppol_num <dbl>, ppol_cat <fct>,
## # count_na <dbl>
OpenMind ggplot2 theme
There are three functions for the ggplot2 theme:
theme_omscale_fill_omscale_color_om
Make sure you have the Poppins font installed!
windowsFonts(`Poppins` = windowsFont("Poppins"))
Good tutorial on how to install custom fonts in R
Example
## Load tidyverse
library(tidyverse)
titanic_dat <- Titanic %>% as_tibble()
titanic_dat %>%
ggplot(aes(Sex, n)) +
geom_col(aes(fill = Class), position = position_dodge()) +
theme_om(legend_position = c(0.9, 0.75)) +
scale_fill_om("Class") +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Age and Class")
Adapt theme_om
legend_positionaxis_text_sizeaxis_title_sizelegend_text_sizetitle_size
titanic_dat %>%
ggplot(aes(Class, n, fill = Class)) +
geom_col() +
theme_om(legend_position = "bottom",
axis_text_size = 10,
axis_title_size = 15,
legend_text_size = 10,
title_size = 20) +
scale_fill_om() +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Class")
Or all text sizes at once
overall_text_size
titanic_dat %>%
ggplot(aes(Class, n, fill = Class)) +
geom_col() +
theme_om(legend_position = "top",
overall_text_size = 15) +
scale_fill_om() +
facet_wrap(~Survived) +
labs(title = "Titanic Survival by Class")
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