2  Data Visualisation

2.1 Import/ Export data

Data can be imported in R in different ways. In the Environment tab, there is an import-data button, in which it is possible to select the type of data (e.g. Excel). From that tab it is possible to receive the command needed if one wants to have it in a script.

To import .csv data, the read.csv() (or read_csv()) function can be used:

csv_data <- read.csv("csv file location")

It is also possible to easily export data to a csv-file by using the function write.csv() :

write.csv("name of data", "name of the file to export to (and location if other than working directory)", row.names = FALSE) row.names = FALSE to remove the row names to be written in the output.

For importing data from an Excel-file, the package readxl package can be used. In the package, the function read_excel() can be used which requires the location of the file and to import a specific sheet, the argument sheet= "name of the sheet" can be used.

library(readxl)
#Will import the sheet "Ovwerview" from the Excel-file "Statistics_Kumla_20200408" and save it in the object "Data_Kumla"
Data_Kumla <- read_excel("C:/Users/vicer06/OneDrive - Linköpings universitet/Documents/03_Courses/R_Course/Statistics_Kumla_20200408.xlsx", sheet = "Overview")

The imported data will be a data frame.

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2.2 Basic Plotting

Plots can be made by using the Base R functions.

#Scatter plot of the data in column "PFOS" and "sum_PFOS"
plot(x=Data_Kumla$PFOS, y=Data_Kumla$sum_PFOS,
     xlab = "PFOS concentration [ng/L]", #adds x-axis title
     ylab = "sum_PFOS [ng/L]", #adds y-axis title
     pch = 16, #solid circles, pch=1 unfilled cicles
     cex.lab=0.75, #changes font size of axis titels, 1 is default size
     cex.axis=0.75, #changes font size of axis values
     cex = 0.6, #changes point size
     col="darkred") 

grid(lty=1, col="lightgrey", lwd=1) #adds a grid

lines(lowess(Data_Kumla$PFOS, Data_Kumla$sum_PFOS),
      col="black", lwd=2, lty=2) #adds a local regression line as a black colour dashed (lty=2) line with line width 2

abline(lm(Data_Kumla$sum_PFOS ~ Data_Kumla$PFOS, data=Data_Kumla), col="darkgrey", lwd=2, lty=3) #adds regression line in darkgrey colour

legend(x="topleft", legend=c("Scatter plot", "LOWESS", "Linear regression"), fill=c("darkred","black","darkgrey"),
       cex=0.5)

Other plot types:

Data_Kumla$`Sampling site`<- factor(Data_Kumla$`Sampling site`)

barplot(height=Data_Kumla$PFOS,
        ylab="Sampling site",
        xlab="PFOS concentraion [ng/L]",
        col= c("darkred","darkgreen","darkblue"),
        cex.axis = 0.75,
        cex.names = 0.75,
        legend.text=c("K1", "KS", "KRef"),
        args.legend=list(x="bottomright", cex=0.5),
        horiz = TRUE,
        xlim = c(0, max(Data_Kumla$PFOS)+75))

box() #adds box around the plot

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2.3 Tidyverse

Tidyverse contains a collection of packages. All the functions from the packages follow the same type of structure, making them easier to use. The core packages included in Tidyverse are ggplot2, dplyr, tidyr, readr, purr, tibble, stringr, and forcats.

readr	for reading rectangular data from e.g. .csv by using read_csv()
tidyr	for organising tabular data e.g. pivot_longer() to combine multiple columns to a long table
purr	can be used to exchange For Loops with code e.g. by using map() to apply a function to all elements in a list and returns a list
dplyr	can be used for data manipulations e.g. by piping

Tidy data: Each variable is a column, each column is a variable. Each observation is a row, each row is an observation. Each value is a cell, each cell is a single value.

2.3.1 Pipes

Is a way to simplify R code. By using %>% (or |>, shortcut: ctrl+shift+m) code can be rewritten to become easier interpretated. The symbol can be thought of as a “+” symbol.

For example, if you would like to only have the concentrations of PFOS in Data_Kumla above 100 ng/L. Instead of having to use For loops and/or if statements or other type of commands, it can simply be written using pipe operations:

library(tidyverse)
PFOS_above100 <- Data_Kumla |> select(PFOS)|> filter(PFOS>100)
#or use %>% instead of |>, first requires paranthesis when calling functions, second does not
PFOS_above100

# A tibble: 65 × 1
    PFOS
   <dbl>
 1  229.
 2  140.
 3 1045.
 4  442.
 5  540.
 6  223.
 7  976.
 8  559.
 9  830.
10  162.
# ℹ 55 more rows

Or in situation when nested function i used. E.g. want to the mean of the PFOS concentrations and round it to 1 decimal.

mean_PFOS <- round(mean(Data_Kumla$PFOS),1)
mean_PFOS

[1] 321.5

However, by using pipe operation it becomes more readable:

mean_PFOS_pipe <- Data_Kumla$PFOS %>% mean %>% round(1)
mean_PFOS_pipe

[1] 321.5

2.3.2 ggplot2

ggplot2 can be used to make plots in R instead of using the base R functions.

require(ggpubr) #reqiured to add regression equation

Loading required package: ggpubr

mean_data <- data.frame(a=mean(Data_Kumla$PFOS), b=mean(Data_Kumla$sum_PFOS))
p <- ggplot(data=Data_Kumla, mapping=aes(x=PFOS, y=sum_PFOS))+
  geom_point(size=0.6, aes(colour="Scatter plot"))+
  geom_smooth(method="loess", size=0.6, aes(colour="LOWESS"), se=FALSE,linetype="longdash")+
  geom_smooth(method="lm", size=0.6, aes(colour="Linear regression"), se=FALSE, linetype="dashed")+
  stat_regline_equation(label.x=1300, label.y=580, colour="darkgrey", size=3)+ #adds regression equation
  stat_cor(aes(label=..rr.label..), label.x=1300, label.y=420, size=3, colour="darkgrey", r.digits = 4)+ #adds R^2
  scale_color_manual(values = c("Scatter plot" = "darkred", "LOWESS" = "black", "Linear regression" = "darkgrey")) + #colours of points
  
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 0.4, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=9),
    legend.position = c(0.25, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(), #removes the title of the legend
    legend.margin = margin(0.001, 1, 0.5, 0.5))+
  geom_point(data=mean_data, aes(x=a,y=b), colour="black",size=3, alpha=0.9, shape=21, fill="salmon") #highlights the mean point in red

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.

p+ geom_text(aes(mean(PFOS)+350, mean(sum_PFOS)+10,label=paste("Mean point")), size=3, colour="red4") #adds a text for the mean point

Warning: The dot-dot notation (`..rr.label..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(rr.label)` instead.

Warning in geom_text(aes(mean(PFOS) + 350, mean(sum_PFOS) + 10, label = paste("Mean point")), : All aesthetics have length 1, but the data has 108 rows.
ℹ Did you mean to use `annotate()`?

`geom_smooth()` using formula = 'y ~ x'

`geom_smooth()` using formula = 'y ~ x'

#p+ geom_point(data=Data_Kumla, aes(x=PFOSA, y=sum_PFOS), colour="pink")

ggscatter(data=Data_Kumla,x="PFOS", y="sum_PFOS", add="reg.line")+
  stat_cor(label.x=1300, label.y=750)+
  stat_regline_equation(label.x=1300, label.y=480)

sampling <- as.factor(Data_Kumla$`Sampling site`)
b <- ggplot(data=Data_Kumla, aes(x=sampling, y=PFOS, fill=sampling))+
  geom_bar(stat = "identity", width = 0.5, colour="black",position=position_dodge())+
  scale_fill_manual(values = c("K1" = "darkblue", "KS" = "darkgreen", "KRef" = "darkred"))+
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid.major = element_blank(),  # Remove grid lines
    panel.grid.minor = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    #aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 0.4, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=9),
    #legend.position = c(0.85, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(),
    #legend.margin = margin(0.001, 1, 0.5, 0.5)
)

b

b2 <- ggplot(data=Data_Kumla, aes(x=PFOS,fill=sampling))+
  geom_bar(stat = "bin", binwidth = 108, colour="black",position=position_dodge())+
  scale_fill_manual(values = c("K1" = "darkblue", "KS" = "darkgreen", "KRef" = "darkred"))+
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid.major = element_blank(),  # Remove grid lines
    panel.grid.minor = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    #aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 0.4, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=9),
    #legend.position = c(0.85, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(),
    #legend.margin = margin(0.001, 1, 0.5, 0.5)
)

b2

---

#http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization 

#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables

data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

#another way to calculate mean and sd, notice data_pfas created below with pivot_longer(). Could use Data_Kumla and summarize on selected columns instead.
data_pfas |> group_by(Type_of_PFAS) |> summarize(Conc_mean = mean(Conc_of_PFAS), Cond_SD = sd(Conc_of_PFAS)) 

data_pfas <- Data_Kumla |> pivot_longer(cols = 13:31,
                            names_to = "Type_of_PFAS",
                            values_to = "Conc_of_PFAS") |>
                        select(c(`Sampling site`, Type_of_PFAS, Conc_of_PFAS))

data_pfas_sd <- data_pfas |> data_summary(varname="Conc_of_PFAS", groupnames="Type_of_PFAS") |> filter(Conc_of_PFAS <100) #exclude PFAS above 100 in mean concentration

Loading required package: plyr

------------------------------------------------------------------------------

You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)

------------------------------------------------------------------------------

Attaching package: 'plyr'

The following object is masked from 'package:ggpubr':

    mutate

The following objects are masked from 'package:dplyr':

    arrange, count, desc, failwith, id, mutate, rename, summarise,
    summarize

The following object is masked from 'package:purrr':

    compact

ggplot(data=data_pfas_sd, aes(y=Conc_of_PFAS, x=Type_of_PFAS, fill=Type_of_PFAS))+
  geom_bar(stat = "identity",position=position_dodge())+
  geom_errorbar(aes(ymin=Conc_of_PFAS-sd, ymax=Conc_of_PFAS+sd), width=0.2, position=position_dodge(0.9))+ #add errorbars
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid.major = element_blank(),  # Remove grid lines
    panel.grid.minor = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9, angle=45, hjust=1),  # Make x-axis text bold and in an angle
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    #aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 1, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=7),
    #legend.position = c(0.85, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(),
    #legend.margin = margin(0.001, 1, 0.5, 0.5)'
)

#excludes PFOS that has higher concentrations
data_pfas |> filter(Type_of_PFAS != c("PFOS")) |>
ggplot(mapping=aes(x=Type_of_PFAS, y=Conc_of_PFAS, fill=Type_of_PFAS))+
  geom_boxplot(outlier.color = "darkred", outlier.shape=16, outlier.size = .6, notch=FALSE)+ #log of conc. values
  geom_line(data=data_pfas_sd, aes(x = Type_of_PFAS, y = Conc_of_PFAS), size = 1, color="red", group = 1)+ #log of mean values
  
  stat_boxplot(geom = "errorbar",
                   width = 0.25)+
  stat_summary(fun.y=mean, geom="point", shape=20, size=4, color="red", fill="red", alpha=0.7) +
  
  scale_y_log10()+
  
  labs(x = "Type of PFAS", y = "log(Concentration of PFAS)", color= "") +

  
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid.major = element_blank(),  # Remove grid lines
    panel.grid.minor = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9, angle=45, hjust=1),  # Make x-axis text bold and in an angle
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    #aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 1, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=7),
    #legend.position = c(0.85, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(),
    #legend.margin = margin(0.001, 1, 0.5, 0.5)'
)

Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
ℹ Please use the `fun` argument instead.

sampling_pfos <- Data_Kumla |> select(c(`Sampling site`, PFOS))

ggplot(data=sampling_pfos, aes(x=`Sampling site`,y=PFOS, fill=`Sampling site`))+
  geom_boxplot(outlier.color = "red", outlier.shape=16, outlier.size = .8, notch=TRUE, aes(y=log(PFOS)))+
  
  scale_fill_manual(values=c("darkred", "darkgreen", "darkblue"))+
  labs(x = "Sampling site", y = "log(Concentration of PFOS)", color= "") +
  
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid.major = element_blank(),  # Remove grid lines
    panel.grid.minor = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9, angle=45, hjust=1),  # Make x-axis text bold and in an angle
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    #aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 1, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=7),
    #legend.position = c(0.85, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(),
    #legend.margin = margin(0.001, 1, 0.5, 0.5)'
)

ggplot(data=Data_Kumla)+
  geom_point(size=0.6, aes(colour="Scatter plot", x=PFOS,y=log(sum_PFOS)))+
  geom_smooth(method="loess", size=0.8, aes(colour="LOWESS",x=PFOS,y=log(sum_PFOS)), se=TRUE,linetype="longdash")+
  scale_color_manual(values = c("Scatter plot" = "darkred", "LOWESS" = "black")) + #colours of points
  
  theme_minimal()+
  theme(
    panel.background = element_rect(fill = "white"),  # Set white background
    panel.grid = element_blank(),  # Remove grid lines
    axis.text = element_text(size = 9),  # Increase font size of axis text
    axis.title.y = element_text(size = 9,face = "bold"),  # Make y-axis label bold and italic
    axis.text.y = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.title.x = element_text(size = 10,face = "bold"),
    axis.text.x = element_text(color = "black", size = 9),  # Make x-axis text bold
    axis.ticks.x = element_line(color = "black"),  # Add ticks to x-axis in black color
    axis.ticks.y = element_line(color = "black"),  # Add ticks to y-axis in black color
    aspect.ratio = 1,  # Set aspect ratio
    plot.margin = margin(0.5, 0.5, 0.4, 0.5, "cm"),  # Set plot margins
    legend.text=element_text(size=9),
    #legend.position = c(0.25, 0.85),
    legend.background = element_rect(fill="white",
                                   size=0.5, linetype="solid", 
                                   colour ="black"),
    legend.title = element_blank(), #removes the title of the legend
    legend.margin = margin(0.001, 1, 0.5, 0.5))

`geom_smooth()` using formula = 'y ~ x'

---