useful-commands.Rmd

---
title: "Useful Commands"
output: 
  html_document :
    toc: TRUE
    theme: cerulean
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Data formats
## Creating dataframes
### Making a df from files in folder
```{r dir to df,eval=FALSE}
library(tidyverse)

#creating the dir_to_df function
dir_to_df <- function(files) {
  files <- gsub("^./","",files)
  df <- data.frame(column= unlist(files)) %>% 
    separate(column, sep="/", into=c("site","image"))
}
my_df <- dir_to_df(files)
```
### Making a df from vectors
```{r vec to df}
# vectors
employee <- c('John Doe','Peter Gynn','Jolie Hope') #character vector
salary <- c(21000, 23400, 26800) #numeric vector
startdate <- as.Date(c('2010-11-1','2008-3-25','2007-3-14')) #date vector
#combine into df
employ.data<-data.frame(employee,salary,startdate)
str(employ.data)
employ.data2<-data.frame(employee,salary,startdate,stringsAsFactors=TRUE) #employee names become factors
```

## Dataframe manipulations
### Accessing data
```{r access}
#like a list
employ.data["employee"] #returns a df
employ.data$employee #returns a vector
employ.data[["employee"]] #returns a vector
employ.data[[1]] #returns a vector
#like a matrix 
head(employ.data,n=2) #display two rows
employ.data[2:3,] #display row 2 & 3
employ.data[employ.data$salary > 21000,]
employ.data[1:2,2] #as vector
employ.data[1:2,2, drop = FALSE] #as df
```

### Modifying 1 value
```{r modif}
employ.data[1,"salary"] <- 30; employ.data
```

### Turn NAs into 0
```{r NA,eval=FALSE}
mydata[is.na(mydata)] = 0
```

### Adding columns/rows
```{r adding}
employ.data2<-rbind(employ.data,list("Lea Katz",35000,"2021-10-01")) #adds a row
employ.data3<-cbind(employ.data2,age=c(35,45,27,26)) #adds a column
employ_data3<-employ.data2$age<-c(35,45,27,26) #another way to add a column
```

### Removing columns/rows
```{r removing}
employ.data3$age <- NULL #removes column
employ_data5 <- employ.data2[-1,] #removes first row
```

### Change colnames/rownames
```{r names,eval=FALSE}
#baseR
"start" -> colnames(employ.data)[3]
#dplyr
iris %>% rename(petal_length = Petal.Length)
#from a csv... sometimes not well read
area<-header.true(area) #makes first column into title and removes the auto-generated ones
```




### Creating subsets
```{r subset}
library(dplyr)
#filter with a condition
employ.subset<-subset(employ.data,subset = salary==21000 )
#select only some variables
employ.data %>% select(salary,employee)
```

### Find value and change it
```{r find and change,eval=FALSE}
data<-as.matrix(data)
m <- data[,-1]
rownames(m) <- data[,1]

for (x in 1:nrow(sum)) {
  i <- sum$image_filename[x]
  j <- sum$label_names[x]
  m[i,j]<-sum$cover[x]
}
```

### Group by a variable and only keep last value
```{r group & last,eval=FALSE}
tlog<-aggregate(tlog,by=list(as.character(tlog$unix)), FUN=last)

```

### Group by a variable and make a sum
```{r group & sum,eval=FALSE}
# for 1 variable
sum<- area %>% 
  group_by(image_filename, label_names) %>% 
  summarize(cover = sum(cover))
# for all variables (if numeric)
summed<-mydata %>% 
  group_by(region.code) %>% 
  summarise(across(everything(),sum,na.rm=TRUE))
```

### not sure what this does anymore...
```{r notsure,eval=FALSE}
names(images)<-as.character(unlist(images[1,]))
```

### Create new columns with operations
```{r operations}
library(dplyr)
employ.data %>% mutate(double_salary = salary*2)
```

## Merging 2 dataframes
```{r second employee,include=FALSE}
age<-c(25,38,27)
married<-c("yes","yes","no")
spouse<-c("Marie","Joanne",NA)
personal.info<-data.frame(age,married,spouse)
```

### Binding columns (rows match)
```{r cbind}
employ.info<-cbind(employ.data,personal.info)
#OR
employ.info<-bind_cols(employ.data,personal.info)
```

### Matching values for common variables
```{r innerjoin,eval=FALSE}
mydata<-inner_join(mydata,metadata, by="transect")
```

## Creating Lists
### Making a list from df and vectors
```{r list}
age_vec<-c(35,45,27,26)
mylist<-list(employees = employ.data, ages = age_vec)
```

## Vectors
### Change "numerical vector" into "named numerical vector"
```{r namedvec}
ages<-c(35,45,27,26)
names<-employ.data$employee
names(ages)<-names #gives names to numerical values of the vector
```

# Simple things
## Paste
```{r paste,eval=FALSE}
#in a string
dive<-"20220601-DIV1"
campaign<-"zeeland"
my_path = paste0("data/",campaign,"/",dive,"/images")

#in a function 
file<-read.csv(paste0("tlog/csv-files/",dive,"-gps.csv"))
```

# Plots
```{r grahamdata,include=FALSE}
library(ggplot2)
library(tidyverse)
graham <- readr::read_csv("caribous_graham.csv",
          col_types = cols(season=col_factor(),sex=col_character()))
tibble::glimpse(graham)
##for just 1 individual
graham_ind <- graham %>%
  dplyr::filter(animal_id == "GR_C12") %>%
  dplyr::select(-animal_id,-sex)
tibble::glimpse(graham_ind)
```

## Spatial data
### Position points
```{r geompoint}
ggplot(graham_ind,aes(date,tree_cover)) +
  geom_point() +
  theme_light()
```

### Tracked position
```{r geompath}
ggplot(graham_ind,aes(longitude,latitude)) +
  geom_path() #in order of dataset, use geom_line for ascending order
```

### Full positioning plot with all the labels and titles
```{r full position}
ggplot(graham_ind,aes(longitude,latitude)) +
  #geom_point() + # order is important, best if points are on top of path
  geom_path(color="grey67") +
  geom_point(color="pink") +
  geom_rug(sides="rb", alpha = .05) + #rb=right+bottom, alpha=transparency
  #xlab("Longitude") +
  #ylab("Latitude")  + #labels
  #ggtitle("Trajectory of Caribou C12") but "labs()" is better
  labs(
    x = "Longitude", #use NULL if you don't want any label
    y = "Latitude",
    title = "Trajectory of Caribou C12",
    subtitle = "Map of 2,562 locations of female C12 tracked between 2001 and 2003 in Graham, Canada",
    caption = "Data: B.C. Ministry of Environment & Climate Change",
    tag = "Fig. 1"
  )
```

### Coordinate system
```{r coordinates}
#linear and non-linear
ggplot(graham_ind,aes(longitude, latitude)) +
  geom_path() +
coord_cartesian(      
    ylim = c(56.8, NA) #you can set the min and max values of both axis
  )
#THIS WILL NOT REMOVE POINTS OUTSIDE THE PLOT AREA, just zoom
#if you want to really remove the points do like this :
ggplot(graham_ind,aes(longitude, latitude)) +
  geom_path() +
  scale_y_continuous(limits = c(56.8, NA))

#another features
ggplot(graham_ind,aes(longitude, latitude)) +
  geom_point() +
  coord_cartesian(
    expand = FALSE, #to have à (0,0) origin
    clip = "off",         #so points/labels don't get cut on the axis lines
    ylim = c(56.8, NA) 
  )

#coord_fixed
ggplot(graham_ind,aes(longitude, latitude)) +
  geom_point() +
  coord_fixed(
    ratio = 5  #default is 1 if you put nothing
  ) +
  scale_y_continuous(
    breaks = seq(56,58, by = .04)
  )

#coord_polar : makes straight lines into curves! 
ggplot(graham_ind,aes(longitude, latitude)) +
  geom_path() +
  coord_polar()
```




## Statistics
### Boxplot
```{r geomboxplot}
ggplot(graham_ind,aes(season,tree_cover)) +
  geom_boxplot() +
  theme_linedraw()
```

## Assigning, saving, rendering, etc.
### Assign plot to object
```{r assign,eval=FALSE}
#g<-ggplot(graham_ind, aes(longitude,latitude)) +
  #geom_point()
#g
(g<-ggplot(graham_ind, aes(longitude,latitude)) +
    geom_point()) #to print it directly !! use parentheses
g +
  geom_path(color="purple") +
  geom_rug(side="rb",alpha= .08) # you can add layers to your plot object later... can aboid typos if you cant to change things?
```

### Save
```{r save,eval=FALSE}
ggsave(filename="myggplot.png", # raster graphic
       width = 10, height = 7,
       dpi = 700) # pixel intensity to 600 or higher!

ggsave(filename="myggplot.pdf", #vector graphic => best
       width = 10, height = 7,
       device=cairo_pdf) # doesn't work?
```

## Different types of plots
### Discrete variable
```{r aes discrete}
ggplot(graham_ind,aes(
  longitude, latitude,
  color = season, #if color is a variable in the dataset, put inside "aes()"
  shape = season
  )) + 
  geom_point()
```

### Continuous variable
```{r aes cont}
ggplot(graham_ind,aes(
  longitude, latitude,
  color = tree_cover)) + 
  geom_point()

ggplot(graham_ind,aes(
  longitude, latitude,
  size = tree_cover
  )) + 
  geom_point(
    color = "darkgreen", #should not go in aesthetics because it is for the geom_points
    shape = 1 
  )
```

### Scales
```{r scales}
ggplot(graham_ind,aes(
  date, tree_cover,
  color = season)) + 
  geom_point() +
  scale_x_date() + #tells ggplot that the x axis is a date
  scale_y_continuous() +   #tells ggplot that the y axis is continuous
  scale_color_discrete()
```
```{r scales2}
ggplot(graham_ind,aes(
  date, tree_cover,
  color = season)) + 
  geom_point() +
  scale_x_date(
    expand = c(0,0), #removes the "padding" on the sides)
    date_breaks = "3 months", #can use english to specify date breaks
    date_labels = "%m/%y", #to shorten the dates to desired abbreviation
    name = NULL
  ) +
  scale_y_continuous(
    labels = scales::percent_format(scale=1), #our numbers were already 75, but if it was 0.75, we could do scale = 100
    sec.axis = dup_axis(name = NULL), #duplicate the y axis for easier reading
    name = "Tree Cover"
  ) +   
  scale_color_discrete(
    type = c("#AE79F0","#EEAF5C"), #either use normal color names or hexcodes
    name = "Season:"
  )
```

### Color gradients
```{r color gradients}
ggplot(graham_ind,
       aes(longitude,latitude,
           color = yday)) +
  geom_point () +
  scale_color_continuous (
    type = "viridis" # with existing palette, install more palette packages :)
  )
ggplot(graham_ind,
       aes(longitude,latitude,
           color = yday)) +
  geom_point () +
  scale_color_gradient (
    low = "#AE79F0", high = "#EEAF5C" # custom gradient
  )
```

### heatmap
```{r heat}
ggplot(graham_ind,aes(longitude,latitude,)) +
  geom_hex (
    aes(fill=..count..), # is useful to put inside the geom if there are several geoms
    bins = 15,
    color = "white", #white outlines draws your eye to global extremes
    size = 0.5 
  ) +
  scale_fill_continuous(type = "viridis") +
  scale_color_continuous(type = "viridis", guide = "none") +
  labs (
    x = "Longitude",
    y = "Latitude",
    title = "Heatmap of C12",
    caption = "Data source : blablabla"
  )
```
 
### heatmap without the outlines
```{r heat2}
ggplot(graham_ind,aes(longitude,latitude,)) +
  geom_hex (aes(color =..count..), bins = 15) +
  scale_fill_continuous(type = "viridis",
                        name = "Count:",
                        #breaks = c(50,100,150,200,250)) 
                        #breaks = seq(50, 250, by = 50))
                        breaks = 1:5*50 #from 1 to 5 and multiply each by 50
                        ) +
  scale_color_continuous(type = "viridis", guide = "none") +
  labs (
    x = "Longitude",
    y = "Latitude",
    title = "Heatmap of C12",
    caption = "Data source : blablabla"
  )
#this is another way to do it  
ggplot(graham_ind,aes(longitude,latitude,)) +
  geom_hex (bins = 15, color = "white", size = 1) +  #bins = resolution => always try several
  rcartocolor::scale_fill_carto_c(palette="SunsetDark",
                                  name = "Observations:",
                                  breaks = 1:5*50)
```  

### Mutliple plots together (facets)
```{r facets}
g <- ggplot(graham_ind,aes(longitude,latitude)) +
  geom_path(color="grey67") +
  geom_point(aes(color=factor(year))) + #aes here only colors the points!
  scale_color_discrete(
    name = "Year",
    #labels = c("year 2001", "year 2002", "year 2003")
    labels= function(x) paste("Year", x))
g + facet_wrap(~ season) #tilde ~ is option + N
g + facet_wrap(~factor(year)) #to add ylabs to mini-plots again => lemon package
```

### to arrange the plots differently
```{r arrange}
g + 
  facet_wrap(
    ~ factor(year),
    ncol = 2
    )

#two pariables => GRID
g + 
  facet_grid(
    factor(year) ~ season
  )
```

## Aestethics 
```{r aes libraries,include=FALSE}
library(RColorBrewer)
```

### Color palettes
```{r rbrewerpalettes,eval=FALSE}
display.brewer.all(type="seq") # "Sequential" : from light to dark
display.brewer.all(type="div") # "Diverging" : dark-light-dark
display.brewer.all(type="qual") # "Qualitative" : visual differences between groups
display.brewer.all(colorblindFriendly=TRUE)
```

### Using a palette in ggplot
```{r use the palette}
ggplot(graham_ind,aes(longitude,latitude,color=factor(year))) +
  geom_path(color="grey67") +
  geom_point() + #aes here only colors the points!
  scale_color_brewer(
    palette="YlOrRd",
    name = "Year",
    #labels = c("year 2001", "year 2002", "year 2003"),
    labels = function(x) paste("Year", x)
  )
```

# Maps
```{r libraries,include=FALSE}
library(sf)
library(mapview)
```
## Mapview
```{r mapview}
caribou <- st_as_sf(graham_ind, coords = c("longitude", "latitude"), crs = 4326, remove = FALSE)
mapview(caribou)
```