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examples improved (done for fun_gg_boxplot and fun_gg_scatter)

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README.md
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......@@ -46,9 +46,9 @@ The present repository of Cute Little R functions is for beta testing. Ultimatel
**examples.R** examples of all the functions, except fun_gg_boxplot() and fun_gg_scatter(), that can be sourced or copied-pasted
**boxplot_examples.R** examples of fun_gg_boxplot() that can be sourced or copied-pasted
**examples_gg_boxplot.R** examples of fun_gg_boxplot() that can be sourced or copied-pasted
**scatter_examples.R** examples of fun_gg_scatter() that can be sourced or copied-pasted
**examples_gg_scatter.R** examples of fun_gg_scatter() that can be sourced or copied-pasted
**other** folder containing avorted developments
......
examples.R 0 → 100644
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######## fun_check() #### check class, type, length, etc., of objects
### Datasets
vec1 <- -1:3 # vector of integers
vec2 <- 1:3 / 3 # vector of proportions
vec3 <- c(1, 2, 3) # vector of integers but stored as "double"
vec4 <- "pearson"
vec5 <- c("a", "b","a", "b")
mat1 <- matrix(vec1) # matrix of integers
mat2 <- matrix(c(1:3 / 3, NA)) # matrix of proportions with NA
### Datasets info
vec1
vec2
vec3
vec4
vec5
mat1
mat2
### Simple examples
# Check that vec1 is of class integer (means that it is also a vector) -> okdensity(x, bw = bandwidth)
fun_check(data = vec1, class = "integer")
# Check that vec1 is a numeric vector -> error because vec1 is a vector of integers
fun_check(data = vec1, class = "numeric")
# Check that vec1 is an integer vector of length 3 without negative values and without NA -> error because of length 5 and negative values inside vec1
fun_check(data = vec1, class = "vector", typeof = "integer", length = 3, neg.values = FALSE, na.contain = FALSE)
# No result displayed because the output list is assigned into res (see below the print argument)
res <- fun_check(data = vec1, class = "integer")
res
### Argument class, typeof, mode and length are the same as the corresponding R function, except class which 1) has also "vector" and 2) remains "matrix" for matrices
# Example
fun_check(data = vec1,
class = "vector",
typeof = "integer",
mode = "numeric",
length = 5,
)
# Warning: the function odes not check for inconsistencies between arguments. It just checks if everything is ok between arguments values and data
fun_check(data = vec1,
typeof = "integer",
mode = "character", # the mode "character" exists but is inconsistant with typeof "integer". However, this aspect is not signaled by the function
)
# Error message due to wrong value in the class and length arguments
fun_check(data = vec1,
mode = "integer", # the mode "integer" does not exist in the mode() function of R
)
### Argument prop
fun_check(data = mat2,
prop = TRUE # Check for values between 0 and 1 only
)
### Argument double.as.integer.allowed
fun_check(data = vec3, typeof = "integer",
double.as.integer.allowed = TRUE # with TRUE, integers stored as double are accepted
)
### Argument options
fun_check(data = vec4,
options = c("pearson", "spearman", "kendall")
)
### Argument all.options.in.data
# No error
fun_check(data = vec5,
options = c("a", "b"),
all.options.in.data = TRUE
)
# No error
fun_check(data = vec5,
options = c("a", "b", "c"),
all.options.in.data = FALSE
)
# Error
fun_check(data = vec5,
options = c("a", "b", "c"),
all.options.in.data = TRUE
)
### Argument na.contain
fun_check(data = mat2, class = "matrix", prop = TRUE,
na.contain = FALSE # with TRUE, integers stored as double are accepted
)
### Argument neg.values
# Warning: only considered if set to FALSE, to check for non negative values when class is set to "vector", "numeric", "matrix", "array", "data.frame", "table", or typeof is set to "double", "integer", or mode is set to "numeric"
fun_check(data = mat1, class = "matrix",
neg.values = FALSE # with TRUE, integers stored as double are accepted
)
### Argument print
# No error message because print is FALSE
res <- fun_check(data = mat1, class = "data.frame",
print = FALSE
)
# Error message
res <- fun_check(data = mat1, class = "data.frame",
print = TRUE
)
# No error message because no error
res <- fun_check(data = mat1, class = "matrix",
print = TRUE
)
### Arguments data.name and fun.name
# Example
tempo <- fun_check(data = vec1, class = "integer",
data.name = "OBSERVATION_1",
fun.name = "FUNCTION_1"
)
tempo$text
# In fact, these two arguments are interesting when fun_check() is used inside functions
fun1 <- function(arg1){
tempo <- fun_check(data = arg1, class = "integer",
data.name = NULL, # if NULL, the name displayed is arg1
fun.name = NULL # if NULL, no name displayed
)
if(tempo$problem == TRUE){
cat(paste0("\n\n================\n\n", tempo$text, "\n\n================\n\n"))
}
}
fun1(arg1 = vec4)
### All the arguments
fun_check(
data = vec1,
class = "integer",
typeof = NULL,
mode = NULL,
length = NULL,
prop = FALSE,
double.as.integer.allowed = FALSE,
options = NULL,
all.options.in.data = FALSE,
na.contain = FALSE,
neg.values = TRUE,
print = FALSE,
data.name = NULL,
fun.name = NULL
)
other/qsdqd.Rmd deleted 100644 → 0
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---
title: "Untitled"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#### NICE REPRESENTATION
```{r, echo=TRUE}
source("C:\\Users\\Gael\\Documents\\Git_projects\\cute_little_R_functions\\cute_little_R_functions.R")
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 10, 3), time = rnorm(1000, 10, 3), group1 = rep(c("A1", "A2"), 500)) ; obs2 <-data.frame(km = rnorm(1000, 15, 3), time = rnorm(1000, 15, 3), group2 = rep(c("G1", "G2"), 500)) ; set.seed(NULL) ; obs1$L1$km[2:3] <- NA ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), categ = list(L1 = "group1", L2 = "group2"), legend.name = NULL, color = list(L1 = 4:5, L2 = 7:8), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), dot.size = 3, line.size = 0.5, xlim = c(1, 25), xlab = "KM", xlog = "no", x.tick.nb = 10, x.inter.tick.nb = 1, x.left.extra.margin = 0, x.right.extra.margin = 0, ylim = c(1, 25), ylab = expression(paste("TIME (", 10^-20, " s)")), ylog = "log10", y.tick.nb = 5, y.top.extra.margin = 0, y.bottom.extra.margin = 0, xy.include.zero = TRUE, classic = TRUE)
```
#### SINGLE GEOMETRIC LAYER
# simple example (1) of scatter plot using the classical writting
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time")
```
# simple example (2) of scatter plot, identical to (1) but using the list writting. Here, a list of one compartment, systematically named L1, is provided to the data1, x, y, categ, geom and alpha. Contrary to example (1), the geom and alpha argument have to be included because the default value are not lists (if data1 is a list, all the x, y, categ, legend.name, color, geom and alpha must also be list if non NULL)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = list(L1 = obs1), x = list(L1 = "km"), y = list(L1 = "time"), geom = list(L1 = "geom_point"), alpha = list(L1 = 0.5))
```
# color of dots. Example (1) using the classical writting
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", color = "blue")
```
# color of dots. Example (2) using the list writting
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = list(L1 = obs1), x = list(L1 = "km"), y = list(L1 = "time"), color = list(L1 = "blue"), geom = list(L1 = "geom_point"), alpha = list(L1 = 1))
```
# From here, classical writting is use for single element in data1 and list writting otherwise
# color of dots. Example (3) when dots are in different categories. Note that categ argument controls the legend display
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group")
```
# color of dots. Example (4) when dots are in different categories. A single color mentionned is applied to all the dots
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", color = "coral")
```
# color of dots. Example (5) when dots are in different categories. Numbers can be used if ggplot colors are desired
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", color = 2)
```
# color of dots. Example (6) when dots are in different categories, with one color per category (try also color = 2:1)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", color = c("coral", "green"))
```
# color of dots. Example (7) when dots are in different categories, with colors as a data frame column. BEWARE: one color per category must be respected (try also numbers)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B"), col = rep(c("coral", "green"), each = 3)) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", color = obs1$col)
```
# color of dots. Example (8) when dots are in different categories, with colors as a data frame column. Easiest way (ggplot colors)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", color = as.numeric(obs1$group))
```
# legend name
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", categ = "group", legend.name = "CLASSES")
```
# different geom features. Example (1) with geom_line kind of lines
```{r, echo=TRUE}
obs1 <- data.frame(km = c(1, 3, 2, 6, 4, 5), time = c(1, 3, 2, 6, 4, 5)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", geom = "geom_line", categ = "group")
```
# different geom features. Example (2) with geom_path kind of lines (see the difference with (1))
```{r, echo=TRUE}
obs1 <- data.frame(km = c(1, 3, 2, 6, 4, 5), time = c(1, 3, 2, 6, 4, 5)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", geom = "geom_path", categ = "group")
```
# different geom features. Example (3) with geom_hline kind of lines. Fake_y y-axis name by default because y argument must be NULL (see ylab argument below to change this)
```{r, echo=TRUE}
obs1 <- data.frame(km = 1:2, time = (1:2)^2, group = c("A", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = NULL, y = "km", geom = "geom_hline", categ = "group", xlim = c(1,10))
```
# different geom features. Example (4) with geom_vline kind of lines. Fake_y y-axis name by default because y argument must be NULL (see ylab argument below to change this)
```{r, echo=TRUE}
obs1 <- data.frame(km = 1:2, time = (1:2)^2, group = c("A", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = NULL, geom = "geom_vline", categ = "group", ylim = c(1,10))
```
#### MULTI GEOMETRIC LAYERS
# Note that in subsequent examples, names of list compartments are systematically referred to as L1, L2, etc., to show the correspondence between the arguments data1, x, y, categ, etc.
# single layer (as examples above)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1), x = list(L1 = "km"), y = list(L1 = "time"), geom = list(L1 = "geom_point"), alpha = list(L1 = 0.5))
```
# simple example of two layers
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5))
```
# color of dots. Example (1)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = "coral", L2 = "green"))
```
# color of dots. Example (2) of the legend display. The categ argument must be supplied. Make a fake categorical colum in the data frame if necessary (as in this example). The categ argument triggers the legend display. The legend.name argument is used to remove the legend title of each layer
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = "GROUP1") ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = "GROUP2") ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), categ = list(L1 = "group1", L2 = "group2"), legend.name = list(L1 = NULL, L2 = NULL), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = "coral", L2 = "green"))
```
# color of dots. Example (3) when dots are in different categories (default colors)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5))
```
# color of dots. Example (3) when dots are in different categories. A single color mentionned per layer is applied to all the dots of the layer
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = "coral", L2 = "green"))
```
# color of dots. Example (5) when dots are in different categories, with one color per category in each layer
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = c("coral", "blue"), L2 = c("green", "black")))
```
# color of dots. Example (4) when dots are in different categories. Numbers can be used if ggplot colors are desired
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = 1:2, L2 = c(4, 7)))
```
# color of dots. Example (7) when dots are in different categories, with colors as a data frame column. BEWARE: one color per category must be respected (try also numbers). BEWARE: in color argument, if the column of the data frame does not exist, color can be still displayed (L2 = obs2$notgood is equivalent to L2 = NULL). Such situation is reported in the warning messages (see below)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500), col1 = rep(c("coral", "blue"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500), col2 = rep(c("green", "black"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = obs1$col1, L2 = obs2$col2))
```
# color of dots. Example (8) when dots are in different categories, with colors as a data frame column. Easiest way is not recommended with mutiple layers
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500), col1 = rep(c("coral", "blue"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500), col2 = rep(c("green", "black"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5), color = list(L1 = as.numeric(obs1$group1), L2 = as.numeric(obs2$group2)))
```
# legend name
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), legend.name = list(L1 = "CLASS A", L2 = "CLASS G"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 0.5, L2 = 0.5))
```
# different geom features. Example (1) with 5 layers. Note that order in data1 defines the overlay order (from below to above) and the order in the legend (from top to bottom)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; obs3 <- data.frame(time = c(29, 31), group3 = c("HORIZ.THRESHOLD.1", "HORIZ.THRESHOLD.2")) ; obs4 <- data.frame(km = 26, group4 = "VERTIC.THRESHOLD") ; obs5 <- data.frame(km = seq(1, 100, 0.1), time = 7*seq(1, 100, 0.1)^0.5, group5 = "FUNCTION") ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2, L3 = obs3, L4 = obs4, L5 = obs5), x = list(L1 = "km", L2 = "km", L3 = NULL, L4 = "km", L5 = "km"), y = list(L1 = "time", L2 = "time", L3 = "time", L4 = NULL, L5 = "time"), categ = list(L1 = "group1", L2 = "group2", L3 = "group3", L4 = "group4", L5 = "group5"), geom = list(L1 = "geom_point", L2 = "geom_point", L3 = "geom_hline", L4 = "geom_vline", L5 = "geom_line"), alpha = list(L1 = 0.5, L2 = 0.5, L3 = 0.5, L4 = 0.5, L5 = 0.5), xlim = c(10, 40), ylim = c(10, 40), classic = TRUE, line.size = 0.75)
```
# layer transparency. One transparency defined by layer (from 0 invisible to 1 opaque). Note that for lines, transparency in not applied in the legend to prevent a ggplot2 bug (https://github.com/tidyverse/ggplot2/issues/2452)
```{r, echo=TRUE}
set.seed(1) ; obs1 <- data.frame(km = rnorm(1000, 22, 3), time = rnorm(1000, 22, 3), group1 = rep(c("A1", "A2"), each = 500)) ; obs2 <-data.frame(km = rnorm(1000, 30, 3), time = rnorm(1000, 30, 3), group2 = rep(c("G1", "G2"), each = 500)) ; set.seed(NULL) ; fun_gg_scatter(data1 = list(L1 = obs1, L2 = obs2), x = list(L1 = "km", L2 = "km"), y = list(L1 = "time", L2 = "time"), , categ = list(L1 = "group1", L2 = "group2"), geom = list(L1 = "geom_point", L2 = "geom_point"), alpha = list(L1 = 1, L2 = 0.1))
```
# other different example of mutiple geom features are shown in the fun_segmentation function
#### OTHER GRAPHIC ARGUMENTS
# dot size (line.size argument controls size of lines)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", dot.size = 5)
```
# axis management: examples are shown for x-axis but are identical for y-axis
# x-axis limits. Example (1)
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", xlim = c(-1, 25))
```
# x-axis limits. Example (2) showing that order matters in ylim argument
```{r, echo=TRUE}
obs1 <- data.frame(km = 2:7, time = (2:7)^2, group = c("A", "A", "A", "B", "B", "B")) ; obs1 ; fun_gg_scatter(data1 = obs1, x = "km", y = "time", xlim = c(25, -1))
```
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