SCH6245 Clinical Bioinformatics in R Markdown
Artika Kirby
26/02/2022
0.1 Load dependencies (provided in session information)
0.2 Data preparation
0.2.1 Data retrieved from https://github.com/AlyceRussell/DataManagement_ReproducibleResearch/tree/master/data
#load Excel spreadsheet dataset
#first list the sheet names - had to use full directory address
excel_sheets("../data/surveys_data.xlsx")## [1] "surveys" "plot_info" "species_info"getwd()## [1] "C:/Users/ladki/Desktop/a SCH6245 Clinical Bio/ClinicalBioinformaticsInR/documents"#load survey_data located on sheet 1
survey_data<-read_excel("../data/surveys_data.xlsx", sheet=1)
#load plot_info located on sheet 2
plot_info<-read_excel("../data/surveys_data.xlsx", sheet=2)
#load species_info located on sheet 3
species_info<-read_excel("../data/surveys_data.xlsx", sheet=3)#check the structure of survey_data to determine the steps required (variable names are shown next to '$'),
#variable formats
#survey_data has 10 variables including sex (characters)
str(survey_data)## tibble [35,549 x 10] (S3: tbl_df/tbl/data.frame)
## $ record_id : num [1:35549] 1 2 3 4 5 6 7 8 9 10 ...
## $ month : num [1:35549] 7 7 7 7 7 7 7 7 7 7 ...
## $ day : num [1:35549] 16 16 16 16 16 16 16 16 16 16 ...
## $ year : num [1:35549] 1977 1977 1977 1977 1977 ...
## $ plot_id : num [1:35549] 2 3 2 7 3 1 2 1 1 6 ...
## $ species_id : chr [1:35549] "NL" "NL" "DM" "DM" ...
## $ sex : chr [1:35549] "M" "M" "F" "M" ...
## $ hindfoot_length: num [1:35549] 32 33 37 36 35 14 NA 37 34 20 ...
## $ weight : num [1:35549] NA NA NA NA NA NA NA NA NA NA ...
## $ date : POSIXct[1:35549], format: "1977-07-16" "1977-07-16" ...#recode sex (m,f) to numeric - set value labels - (0,1)
survey_data$sex<-ifelse(survey_data$sex=="M", 0,
ifelse(survey_data$sex=="F", 1, NA))
survey_data$sex<-factor(survey_data$sex,
levels=c(0,1),
labels=c("Male", "Female"))#to label plot_ID, first explore plot_info data structure to determine the steps required
#plot_info has 2 variables: plot_id (numeric) and plot_type (character)
str(plot_info)## tibble [24 x 2] (S3: tbl_df/tbl/data.frame)
## $ plot_id : num [1:24] 1 2 3 4 5 6 7 8 9 10 ...
## $ plot_type: chr [1:24] "Spectab exclosure" "Control" "Long-term Krat Exclosure" "Control" ...#view the first few lines
head(plot_info)## # A tibble: 6 x 2
## plot_id plot_type
## <dbl> <chr>
## 1 1 Spectab exclosure
## 2 2 Control
## 3 3 Long-term Krat Exclosure
## 4 4 Control
## 5 5 Rodent Exclosure
## 6 6 Short-term Krat Exclosure#label plot_id in surveys excel sheet according to plot_type info in the plot_info dataset (sheet =2)
survey_data$plot_id<-factor(survey_data$plot_id,
levels=plot_info$plot_id, labels=plot_info$plot_type)#first explore the species_info data structure to determine the steps required
#species_info has 4 variables: species_id, genus, species, taxa (all variables are characters)
str(species_info)## tibble [54 x 4] (S3: tbl_df/tbl/data.frame)
## $ species_id: chr [1:54] "AB" "AH" "AS" "BA" ...
## $ genus : chr [1:54] "Amphispiza" "Ammospermophilus" "Ammodramus" "Baiomys" ...
## $ species : chr [1:54] "bilineata" "harrisi" "savannarum" "taylori" ...
## $ taxa : chr [1:54] "Bird" "Rodent" "Bird" "Rodent" ...#merge genus and species into one variable called species_lab (creating a new column in the species info excel sheet)
#NOTE: " " adds a space between genus and species
species_info$species_lab<-paste(species_info$genus, species_info$species, sep=" ")
#check first 10 rows head(species_info) will give the first 6 rows
head(species_info[1:10,])## # A tibble: 6 x 5
## species_id genus species taxa species_lab
## <chr> <chr> <chr> <chr> <chr>
## 1 AB Amphispiza bilineata Bird Amphispiza bilineata
## 2 AH Ammospermophilus harrisi Rodent Ammospermophilus harrisi
## 3 AS Ammodramus savannarum Bird Ammodramus savannarum
## 4 BA Baiomys taylori Rodent Baiomys taylori
## 5 CB Campylorhynchus brunneicapillus Bird Campylorhynchus brunneicap~
## 6 CM Calamospiza melanocorys Bird Calamospiza melanocorys#check for and remove white space - lagging, forward, extra in-between. Then check Data in Global Environment
species_info$species_lab<-trimws(species_info$species_lab, which = "right")
#check first 10 rows
head(species_info[1:10,])## # A tibble: 6 x 5
## species_id genus species taxa species_lab
## <chr> <chr> <chr> <chr> <chr>
## 1 AB Amphispiza bilineata Bird Amphispiza bilineata
## 2 AH Ammospermophilus harrisi Rodent Ammospermophilus harrisi
## 3 AS Ammodramus savannarum Bird Ammodramus savannarum
## 4 BA Baiomys taylori Rodent Baiomys taylori
## 5 CB Campylorhynchus brunneicapillus Bird Campylorhynchus brunneicap~
## 6 CM Calamospiza melanocorys Bird Calamospiza melanocorys#label species_id values in survey_data
#by using species_id (levels) and corresponding species_lab(labels) from species_info
survey_data$species_id<-factor(survey_data$species_id,
levels= species_info$species_id,
labels=species_info$species_lab)
#add genus and taxa from species_info to survey_data, x=survey_data, and y=species_info. Using primary key to add genus and taxa to survey_data
survey_data<-merge(survey_data, species_info[,c(2,4,5)],
by.x="species_id", by.y="species_lab")
#check main data
str(survey_data)## 'data.frame': 34786 obs. of 12 variables:
## $ species_id : Factor w/ 54 levels "Amphispiza bilineata",..: 3 3 2 2 2 2 2 2 2 2 ...
## $ record_id : num 18932 20588 27074 12824 22059 ...
## $ month : num 8 1 10 5 2 4 5 10 10 12 ...
## $ day : num 7 24 26 28 4 21 15 24 25 14 ...
## $ year : num 1991 1993 1997 1987 1995 ...
## $ plot_id : Factor w/ 5 levels "Spectab exclosure",..: 3 2 2 5 2 1 4 3 5 4 ...
## $ sex : Factor w/ 2 levels "Male","Female": NA NA NA NA NA NA NA NA NA NA ...
## $ hindfoot_length: num NA NA NA NA NA NA NA NA NA NA ...
## $ weight : num NA NA NA NA NA NA NA NA NA NA ...
## $ date : POSIXct, format: "1991-08-07" "1993-01-24" ...
## $ genus : chr "Ammodramus" "Ammodramus" "Ammospermophilus" "Ammospermophilus" ...
## $ taxa : chr "Bird" "Bird" "Rodent" "Rodent" ...head(survey_data)## species_id record_id month day year plot_id
## 1 Ammodramus savannarum 18932 8 7 1991 Long-term Krat Exclosure
## 2 Ammodramus savannarum 20588 1 24 1993 Control
## 3 Ammospermophilus harrisi 27074 10 26 1997 Control
## 4 Ammospermophilus harrisi 12824 5 28 1987 Short-term Krat Exclosure
## 5 Ammospermophilus harrisi 22059 2 4 1995 Control
## 6 Ammospermophilus harrisi 18618 4 21 1991 Spectab exclosure
## sex hindfoot_length weight date genus taxa
## 1 <NA> NA NA 1991-08-07 Ammodramus Bird
## 2 <NA> NA NA 1993-01-24 Ammodramus Bird
## 3 <NA> NA NA 1997-10-26 Ammospermophilus Rodent
## 4 <NA> NA NA 1987-05-28 Ammospermophilus Rodent
## 5 <NA> NA NA 1995-02-04 Ammospermophilus Rodent
## 6 <NA> NA NA 1991-04-21 Ammospermophilus Rodent#remove labels with missing, for species_id - that do not exist, overwriting survey_data$species_id
survey_data$species_id<-droplevels(survey_data$species_id)#remove missing values/data for sex and weight
#create index number to indicate missing sex
survey_data$sexmiss<-ifelse(is.na(survey_data$sex),1,0) #if survey_data$sex is.na assign 1, else (not na) 0
survey_data$weightmiss<-ifelse(is.na(survey_data$weight),1,0) #if survey_data$weight is.na assign 1, else 0
survey_data$anymiss<-ifelse(survey_data$sexmiss==1 | survey_data$weightmiss==1,1,0) #in the new variable ..$anymiss to understand (1,1,0): if sexmiss ==1 or weightmiss==1 then assign 1, else assign 0
#check all sexmiss is counted - missing by sexmiss 0r both (sexmiss and weightmiss) = a total of 2604 samples of interest
table(survey_data$sexmiss, survey_data$anymiss)##
## 0 1
## 0 32182 856
## 1 0 1748#identify total of 1 meaning atleast 1 of the 2 values is missing so either sex or weight. If it is 0, it means both values are identified.
table(survey_data$anymiss)##
## 0 1
## 32182 2604#new data excludng missing by indicator, ! means not equal to for a logical type comparison e.g. ..anymiss is !=1, so in this case, this code saves only 0 values into survey_data2
survey_data2<-survey_data[!survey_data$anymiss==1, ]
#read as row x column
dim(survey_data2)## [1] 32182 15#information about the data oncluding type: factor, number etc
str(survey_data2)## 'data.frame': 32182 obs. of 15 variables:
## $ species_id : Factor w/ 48 levels "Amphispiza bilineata",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ record_id : num 17486 19121 19249 17374 18252 ...
## $ month : num 4 10 11 4 1 3 12 11 3 5 ...
## $ day : num 26 10 13 24 12 7 15 14 14 25 ...
## $ year : num 1990 1991 1991 1990 1991 ...
## $ plot_id : Factor w/ 5 levels "Spectab exclosure",..: 3 2 3 1 3 3 3 3 3 3 ...
## $ sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 2 2 2 2 2 ...
## $ hindfoot_length: num 14 13 12 14 14 14 14 16 14 14 ...
## $ weight : num 7 6 8 7 9 9 8 9 8 10 ...
## $ date : POSIXct, format: "1990-04-26" "1991-10-10" ...
## $ genus : chr "Baiomys" "Baiomys" "Baiomys" "Baiomys" ...
## $ taxa : chr "Rodent" "Rodent" "Rodent" "Rodent" ...
## $ sexmiss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weightmiss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ anymiss : num 0 0 0 0 0 0 0 0 0 0 ...#different format to visualise information
head(survey_data2)## species_id record_id month day year plot_id sex
## 743 Baiomys taylori 17486 4 26 1990 Long-term Krat Exclosure Male
## 744 Baiomys taylori 19121 10 10 1991 Control Male
## 745 Baiomys taylori 19249 11 13 1991 Long-term Krat Exclosure Male
## 746 Baiomys taylori 17374 4 24 1990 Spectab exclosure Male
## 747 Baiomys taylori 18252 1 12 1991 Long-term Krat Exclosure Female
## 748 Baiomys taylori 19775 3 7 1992 Long-term Krat Exclosure Female
## hindfoot_length weight date genus taxa sexmiss weightmiss anymiss
## 743 14 7 1990-04-26 Baiomys Rodent 0 0 0
## 744 13 6 1991-10-10 Baiomys Rodent 0 0 0
## 745 12 8 1991-11-13 Baiomys Rodent 0 0 0
## 746 14 7 1990-04-24 Baiomys Rodent 0 0 0
## 747 14 9 1991-01-12 Baiomys Rodent 0 0 0
## 748 14 9 1992-03-07 Baiomys Rodent 0 0 0#print name and column number displays the columns in the table-- use to exclude unwanted cols
names(survey_data2)## [1] "species_id" "record_id" "month" "day"
## [5] "year" "plot_id" "sex" "hindfoot_length"
## [9] "weight" "date" "genus" "taxa"
## [13] "sexmiss" "weightmiss" "anymiss"#removes columns 13-15 which is the ..sexmiss, weightmiss, and anymiss columns
survey_data2<-survey_data2[, c(1:12)]0.3 Manipulate and summarise the data using dplyr
# create season variable with a nested ifelse function -- this is intermediate level but USEFUL to know
#if Dec to Feb, code '0', else..
survey_data2$season <- ifelse(survey_data2$month==12 | survey_data2$month==1 | survey_data2$month==2, 0,
ifelse(survey_data2$month==3 | survey_data2$month==4 | survey_data2$month==5, 1,
ifelse(survey_data2$month==6 | survey_data2$month==7 | survey_data2$month==8, 2,
ifelse(survey_data2$month==9 | survey_data2$month==10 | survey_data2$month==11, 3, NA))))
# check
table(survey_data2$season)##
## 0 1 2 3
## 7401 8947 7779 8055# label seasons variable
survey_data2$season <- factor(survey_data2$season,
#telling it these are the levels
levels=c(0,1,2,3),
#labelling the levels
labels=c("Summer", "Autumn", "Winter", "Spring"))
# check that labels have worked
table(survey_data2$season) ##
## Summer Autumn Winter Spring
## 7401 8947 7779 80550.4 Exploratory Data Analysis
#summary table
# counts
summSeason <- survey_data2 %>%
group_by(season) %>%
#count by season
count() %>%
#output as data.frame not tibble
as.data.frame()
#create percentages, using nrow from survey_data2 rather than summSeason$p to account for if there were any NAs
summSeason$p <- round(summSeason$n/nrow(survey_data2)*100,1)
summSex <- survey_data2 %>%
select(season, sex) %>%
group_by(season) %>%
count(sex) %>%
as.data.frame()
summSex$p <- c(round(summSex$n[1:2]/sum(summSex$n[1:2])*100,1), #[1:2] row numbers in summSex (*100,1) means x100 to 1 d-pl.
round(summSex$n[3:4]/sum(summSex$n[3:4])*100,1),
round(summSex$n[5:6]/sum(summSex$n[5:6])*100,1),
round(summSex$n[7:8]/sum(summSex$n[7:8])*100,1)) #create percentages by columns
#pipeline for summarise species in each season and find percenrtages accordingly
summSpecies <- survey_data2 %>%
select(season, species_id) %>%
group_by(season) %>%
count(species_id) %>%
as.data.frame()
summSpecies$p <- c(round(summSpecies$n[1:22]/sum(summSpecies$n[1:22])*100,3), #to 1 d-pl
round(summSpecies$n[23:44]/sum(summSpecies$n[23:44])*100,3),
round(summSpecies$n[45:67]/sum(summSpecies$n[45:67])*100,3),
round(summSpecies$n[68:89]/sum(summSpecies$n[68:89])*100,3))
# means - for the numerical values
summMeans <- survey_data2 %>%
select(season, hindfoot_length, weight) %>%
group_by(season) %>%
summarise(across(.cols = everything(), ~mean(., na.rm = TRUE))) %>%
as.data.frame()
#round to 3 decimal places - exploring
summMeans <- c(summMeans[1], round(summMeans[2:3], 3))
# sd
summSD <- survey_data2 %>%
select(season, hindfoot_length, weight) %>%
group_by(season) %>%
summarise(across(.cols = everything(), ~sd(., na.rm = TRUE))) %>%
as.data.frame()
#round to 3 decimal places - exploring
summSD <- c(summSD[1], round(summSD[2:3], 3))
# plot (meaning exclosure, i.e. location) by season - do they differ over the year??
summPlots <- survey_data2 %>%
select(season, plot_id) %>%
group_by(season) %>%
count(plot_id) %>%
as.data.frame()
summPlots$p <- c(round(summPlots$n[1:5]/sum(summPlots$n[1:5])*100,1),
round(summPlots$n[6:10]/sum(summPlots$n[6:10])*100,1),
round(summPlots$n[11:15]/sum(summPlots$n[11:15])*100,1),
round(summPlots$n[16:20]/sum(summPlots$n[16:20])*100,1))#create dataframe
t1 <- data.frame(vars = c(paste0("**Overall Count** "," *n (%)*"),
paste0("**Sex** "," *n (%)*"),
"*Female*", "*Male*",
paste0("**Hindfoot Length** ", "mm", " *mean (SD)*"),
paste0("**Weight** ", "g", " *mean (SD)*"),
paste0("**Plot Information** ", "*n (%)*"),
"*Spectab Exclusure*", "*Control*", "*Long-term Krat Exclosure*",
"*Rodent Exclosure*", "*Short-term Krat Exclusure*"),
summer = c(paste0(summSeason$n[1], " (", summSeason$p[1], "%)"),
"", # add space for sex title
paste0(summSex$n[1:2], " (", summSex$p[1:2], "%)"),
paste0(round(summMeans$hindfoot_length[1],2), " (",
round(summSD$hindfoot_length[1], 2), ")"),
paste0(round(summMeans$weight[1], 2), " (",
round(summSD$weight[1], 2), ")"),
"", # space for Plot Info title
paste0(summPlots$n[1:5], " (", summPlots$p[1:5], "%)")),
autumn = c(paste0(summSeason$n[2], " (", summSeason$p[2], "%)"),
"",
paste0(summSex$n[3:4], " (", summSex$p[3:4], "%)"),
paste0(round(summMeans$hindfoot_length[2],2), " (",
round(summSD$hindfoot_length[2], 2), ")"),
paste0(round(summMeans$weight[2], 2), " (",
round(summSD$weight[2], 2), ")"),
"",
paste0(summPlots$n[6:10], " (", summPlots$p[6:10], "%)")),
winter = c(paste0(summSeason$n[3], " (", summSeason$p[3], "%)"),
"",
paste0(summSex$n[5:6], " (", summSex$p[5:6], "%)"),
paste0(round(summMeans$hindfoot_length[3],2), " (",
round(summSD$hindfoot_length[3], 2), ")"),
paste0(round(summMeans$weight[3], 2), " (",
round(summSD$weight[3], 2), ")"),
"",
paste0(summPlots$n[11:15], " (", summPlots$p[11:15], "%)")),
spring = c(paste0(summSeason$n[4], " (", summSeason$p[4], "%)"),
"",
paste0(summSex$n[7:8], " (", summSex$p[7:8], "%)"),
paste0(round(summMeans$hindfoot_length[4],2), " (",
round(summSD$hindfoot_length[4], 2), ")"),
paste0(round(summMeans$weight[4], 2), " (",
round(summSD$weight[4], 2), ")"),
"",
paste0(summPlots$n[16:20], " (", summPlots$p[16:20], "%)")),
stringsAsFactors = FALSE)0.5 Tabulate with kable and kableExtra
#create table using kable
kable(t1,
caption = "**Summary of study variables by season**", # adds table caption
col.names = c("", "Summer", "Autumn", "Winter", "Spring"), # ("") means no title to first column
align="lcccc",
type="") %>%
column_spec(1, width_min="5cm", border_right = TRUE) %>%
column_spec(c(2,3,4,5), width_min="3cm") %>%
row_spec(0, bold=T, color="ivory", background="#666666") %>%
row_spec(1:12, color="black", background="white") %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
add_indent(c(3,4,8:12)) %>% # indent rows for measures with levels
footnote(general = c("*% are within groups for all non-missing values*")) %>% # add footnote
add_header_above(c(" " = 1, "Season" = 4), bold=T, color="black", background="white", include_empty = T)| Summer | Autumn | Winter | Spring | |
|---|---|---|---|---|
| Overall Count n (%) | 7401 (23%) | 8947 (27.8%) | 7779 (24.2%) | 8055 (25%) |
| Sex n (%) | ||||
| Female | 3981 (53.8%) | 4833 (54%) | 3897 (50.1%) | 4168 (51.7%) |
| Male | 3420 (46.2%) | 4114 (46%) | 3882 (49.9%) | 3887 (48.3%) |
| Hindfoot Length mm mean (SD) | 29.03 (9.56) | 29.74 (9.89) | 28.91 (9.2) | 29.12 (9.43) |
| Weight g mean (SD) | 40.77 (34.39) | 44.72 (36.74) | 40.93 (35.95) | 43.55 (38.45) |
| Plot Information n (%) | ||||
| Spectab Exclusure | 885 (12%) | 1092 (12.2%) | 840 (10.8%) | 888 (11%) |
| Control | 3329 (45%) | 4070 (45.5%) | 3494 (44.9%) | 3718 (46.2%) |
| Long-term Krat Exclosure | 1057 (14.3%) | 1247 (13.9%) | 1215 (15.6%) | 1157 (14.4%) |
| Rodent Exclosure | 986 (13.3%) | 1048 (11.7%) | 869 (11.2%) | 894 (11.1%) |
| Short-term Krat Exclusure | 1144 (15.5%) | 1490 (16.7%) | 1361 (17.5%) | 1398 (17.4%) |
| Note: | ||||
| % are within groups for all non-missing values |
0.6 Data visualisation with ggplot2
# remove levels where species_id has 0 records in survey_data2
survey_data2$species_id <- droplevels(survey_data2$species_id)
survey_data2$genus <- as.factor(survey_data2$genus)
# number samples collected each year, group by year, counting the genus
p1 <- survey_data2 %>%
select(year, genus) %>%
group_by(year) %>%
count(genus) %>%
ggplot( aes(x=year, y=n, colour=genus)) +
geom_line(size=1.2) +
theme_bw() +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90)) +
xlab("Year") + ylab("Number of Samples Collected (by genus)")
# weight vs length by plot_id colour=species_id
p2 <- ggplot(survey_data2, aes(x=hindfoot_length, y=weight, colour=as.factor(genus))) +
geom_point(na.rm = TRUE, alpha=0.5) + theme_bw() +
xlab("Hindfoot Length (mm)") + ylab("Weight (g)") +
facet_grid(~ plot_id) +
scale_colour_discrete(name = "Genus") + theme(legend.position = "none")
# length by sex
p3 <- ggplot(survey_data2, aes(x=hindfoot_length, fill=sex)) +
geom_histogram(aes(y = ..density..), colour="black", bins=50, alpha=0.5) +
geom_density(size=1, alpha=0.4) +
theme_bw() + facet_grid(sex ~ .) + theme(legend.position = "none") +
xlab("Hindfoot Length (mm)") + ylab("Density")
p3a <- ggplot(survey_data2, aes(x=as.factor(genus), y=hindfoot_length, colour = as.factor(genus))) +
geom_boxplot(fill="white", weight=1.5) +
geom_point(#fill="white",
position="jitter",
alpha=0.1) +
xlab("Genus") + ylab("Hindfoot Length (mm)") +
theme_bw() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90)) +
coord_flip()
p4 <- ggplot(survey_data2, aes(x=weight, fill=sex)) +
geom_histogram(aes(y = ..density..), colour="black", bins=50, alpha=0.5) +
geom_density(size=1, alpha=0.4) +
theme_bw() + facet_grid(sex ~ .) + theme(legend.position = "none") +
xlab("Weight (g)") + ylab("Density")
p4a <- ggplot(survey_data2, aes(x=as.factor(genus), y=weight, colour = as.factor(genus))) +
geom_boxplot(fill="white", weight=1.5) +
geom_point(#fill="white",
position="jitter",
alpha=0.1) +
xlab("Genus") + ylab("Weight (g)") +
theme_bw() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90)) +
coord_flip()ggarrange(
p2, p1,
labels = c("a", "b"),
nrow = 2 #, heights = c(1,1.5)
)
ggplot(survey_data2, aes(x=as.factor(sex), y=weight, colour = as.factor(sex))) +
geom_boxplot(fill="white", weight=1.5) +
geom_point(#fill="white",
position="jitter",
alpha=0.1) +
xlab("Sex") + ylab("Weight (g)") +
theme_bw() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90)) +
coord_flip()
ggplot(survey_data2, aes(x=year)) +
labs(title = "Density per Year")+
geom_histogram(aes(y=..density..), bins = 50, color = "palegreen3", fill = "palegreen3") +
geom_density(alpha = .2, fill = "antiquewhite3", color= "black") +
theme_bw()
0.7 Session information
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.4.0 kableExtra_1.3.4 readxl_1.3.1 forcats_0.5.1
## [5] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
## [9] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 svglite_2.1.0 lubridate_1.7.10 assertthat_0.2.1
## [5] digest_0.6.27 utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
## [9] backports_1.2.1 reprex_2.0.1 evaluate_0.14 highr_0.9
## [13] httr_1.4.2 pillar_1.6.2 rlang_0.4.11 rstudioapi_0.13
## [17] car_3.0-12 jquerylib_0.1.4 rmarkdown_2.11 labeling_0.4.2
## [21] webshot_0.5.2 munsell_0.5.0 broom_0.7.9 compiler_4.1.2
## [25] modelr_0.1.8 xfun_0.25 pkgconfig_2.0.3 systemfonts_1.0.2
## [29] htmltools_0.5.2 tidyselect_1.1.1 fansi_0.5.0 viridisLite_0.4.0
## [33] crayon_1.4.1 tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.3
## [37] grid_4.1.2 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0
## [41] DBI_1.1.1 magrittr_2.0.1 scales_1.1.1 carData_3.0-5
## [45] cli_3.0.1 stringi_1.7.4 farver_2.1.0 ggsignif_0.6.3
## [49] fs_1.5.0 xml2_1.3.2 bslib_0.3.0 ellipsis_0.3.2
## [53] generics_0.1.0 vctrs_0.3.8 cowplot_1.1.1 tools_4.1.2
## [57] glue_1.4.2 hms_1.1.0 abind_1.4-5 fastmap_1.1.0
## [61] yaml_2.2.1 colorspace_2.0-2 rstatix_0.7.0 rvest_1.0.1
## [65] knitr_1.34 haven_2.4.3 sass_0.4.0