Class 1

Data import and view

In order to see all the code click on Code in upper right and choose “Show all code”.

getwd() - This command return the current working directory of R session. all files read, save etc will happen in that directory by default.

setwd("path/to/the/right/directory") - Change (set) working directory. Windows users should use backslash/ to seperate directories and not normal slash (\) as default in windows.

read.csv("filename.csv") - read CSV (Comma Seperated Values) file. File name should be between qutotaion mark. by default it take the first line as header (or names) to the columns.

View(variable name) - Command that show nicely the variable in RStudio dedicated window.

vector[5] - return the 5 th element in a vector.

data.table[1,] - Return the first row and all columns from a a table.

data.table[,1] - Return the first columns and all rows from a a table.

names(variable) - Return the names (or columns) of the table.

Data

Data file: Iris1A.csv


getwd()

file1 = read.csv("Iris1A.csv")
pl8reader = file1
# View(pl8reader)

pl8reader[1,]
pl8reader[,1]
 
names(pl8reader)[1]

Plot with plotly

Install packages - you can use command install.packages("package-name"). Or you can use package tab (lower right pane) and clock install.

To use the package (or library) you can click on square near package name in the right window. Or better use the command library(package-name).

plotly gReat cheatsheet

# install.packages("plotly")
library(plotly)

plot_ly( data = pl8reader
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ) 
plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ) 

Google: plotly add line to plot plotly add name to legend

add_trace() - Command to add new trace to a graph. more information in the documentation page.

Another way to get the documentation is type in the console ?function-name anc click enter. or ??package-name for package help.

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ) 
add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          )

layout() - documentation page Google: plotly r add main title

In order to show correct time in hours I just duvude the seconds value with 60*60=3600.


p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s. /3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ) 
p = add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          )
layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )

Class 2

Error bars

Google: plotly r error bars

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ,error_y = ~list(value = pl8reader[,5])
        
        ) 
p = add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          ,error_y = ~list(value = pl8reader[,6])
          )
layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )

Error area

Google: plotly r area

upper_errorB4 = pl8reader[,3] + pl8reader[,5]
lower_errorB4 = pl8reader[,3] - pl8reader[,5]
upper_errorCa = pl8reader[,4] + pl8reader[,6]
lower_errorCa = pl8reader[,4] - pl8reader[,6]

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4)
p = add_trace(p, y = upper_errorB4, fill = "tonexty")

p = add_trace(p, y = pl8reader[,4], name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa)
p = add_trace(p, y = upper_errorCa, fill = "tonexty")

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )
edge = list(color = 'rgba(0,0,0,0)')

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,4],name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )

No grid no zeroline.

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,4],name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F)
       )

Loops

View(pl8reader)
seq(from = 1, to = 35, by = 2.3)
for(i in seq(from = 3, to = 29, by = 4))
{print(names(pl8reader)[i])}
p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 3, to = 29, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i], name = colnames(pl8reader)[i], color = bbb[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j], color = bbb[i])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))

Data file: Iris2A.csv


file2 = read.csv("Iris2A.csv")
pl8reader = file2

dim(pl8reader)

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 2, to = 25, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i],name = colnames(pl8reader)[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))

Data file: Iris3A.csv

Google: r read csv skip empty lines There is some problem about std


# file3 = read.csv("Iris3A.csv")
# file3 = read.csv(file = "Iris3A.csv" ,skip = as.numeric(rownames(file3[which(file3[,1]!=''),])[1]))
# pl8reader = file3[3:length(file3),]
# 
# View(pl8reader)

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 2, to = 29, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i],name = colnames(pl8reader)[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))
---
title: "Plotly workshop"
author: "Rotem Hadar"
date: "4th Tamuz"
output: html_notebook

---
# Class 1
## Data import and view

In order to see all the code click on `Code` in upper right and choose "Show all code".

`getwd()` - This command return the current working directory of R session. all files read, save etc will happen in that directory by default.

`setwd("path/to/the/right/directory")` - Change (set) working directory. Windows users should use backslash`/` to seperate directories and not normal slash (`\`) as default in windows.

`read.csv("filename.csv")` - read CSV (Comma Seperated Values) file. File name should be between qutotaion mark. by default it take the first line as header (or names) to the columns.

`View(variable name)` - Command that show nicely the variable in RStudio dedicated window. 

`vector[5]` - return the 5 th element in a vector.

`data.table[1,]` - Return the first row and all columns from a a table.

`data.table[,1]` - Return the first columns and all rows from a a table.

`names(variable)` - Return the names (or columns) of the table.

### Data
 Data file: [Iris1A.csv](https://bitbucket.org/benzzz/plotly-workshop/src/master/Iris1A.csv)
```{r data-import, echo = TRUE}

getwd()

file1 = read.csv("Iris1A.csv")
pl8reader = file1
# View(pl8reader)

pl8reader[1,]
pl8reader[,1]
 
names(pl8reader)[1]

```
## Plot with plotly

Install packages - you can use command `install.packages("package-name")`. Or you can use package tab (lower right pane) and clock install.

To use the package (or library) you can click on square near package name in the right window. Or better use the command `library(package-name)`.

[plotly gReat cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf)
```{r start-plotly, echo=TRUE}
# install.packages("plotly")
library(plotly)

plot_ly( data = pl8reader
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ) 

```


```{r 1, echo=TRUE}
plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ) 
```

Google: [plotly add line to plot]( https://www.google.co.il/search?q=plotly+add+line+to+plot)
        [plotly add name to legend]( https://www.google.co.il/search?q=plotly+add+name+to+legend)
        
`add_trace()` - Command to add new trace to a graph. more information in the [documentation page](https://www.rdocumentation.org/packages/plotly/versions/4.7.1/topics/add_trace).

Another way to get the documentation is type in the console `?function-name` anc click enter. or `??package-name` for package help.
```{r 2, echo=TRUE}
p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s.
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ) 
add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          )
```



`layout()` - [documentation page](https://www.rdocumentation.org/packages/plotly/versions/4.7.1/topics/layout)
Google: [plotly r add main title]( https://www.google.co.il/search?q=plotly+r+add+main+title)

In order to show correct time in hours I just duvude the seconds value with `60*60=3600`.
```{r 3, echo=TRUE}

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s. /3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ) 
p = add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          )
layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )
```
# Class 2
## Error bars
Google: [plotly r error bars]( https://www.google.co.il/search?q=plotly+r+error+bars)
```{r 4, echo=TRUE}
p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
        ,error_y = ~list(value = pl8reader[,5])
        
        ) 
p = add_trace(p
          ,y = pl8reader[,4]
          ,name = colnames(pl8reader)[4]
          ,error_y = ~list(value = pl8reader[,6])
          )
layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )

```
### Error area
Google: [plotly r area]( https://www.google.co.il/search?q=plotly+r+area)
```{r 5, echo=TRUE}
upper_errorB4 = pl8reader[,3] + pl8reader[,5]
lower_errorB4 = pl8reader[,3] - pl8reader[,5]
upper_errorCa = pl8reader[,4] + pl8reader[,6]
lower_errorCa = pl8reader[,4] - pl8reader[,6]

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4)
p = add_trace(p, y = upper_errorB4, fill = "tonexty")

p = add_trace(p, y = pl8reader[,4], name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa)
p = add_trace(p, y = upper_errorCa, fill = "tonexty")

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )
```

```{r 6, echo=TRUE}
edge = list(color = 'rgba(0,0,0,0)')

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,4],name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm")
       ,xaxis = list(title = "Time (H)")
       )
```

No grid no zeroline.
```{r 7, echo=TRUE}
p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
        ,x = pl8reader$Time..s./3600
        ,y = pl8reader[,3]
        ,name = colnames(pl8reader)[3]
)
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,4],name = colnames(pl8reader)[4])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F)
       )

```
## Loops
```{r}
View(pl8reader)
```


```{r}
seq(from = 1, to = 35, by = 2.3)
```

```{r}
for(i in seq(from = 3, to = 29, by = 4))
{print(names(pl8reader)[i])}
```


```{r 8, echo=TRUE}
p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 3, to = 29, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i], name = colnames(pl8reader)[i], color = bbb[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j], color = bbb[i])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))

```
Data file: [Iris2A.csv](https://bitbucket.org/benzzz/plotly-workshop/src/master/Iris2A.csv)
```{r 9, echo=TRUE}

file2 = read.csv("Iris2A.csv")
pl8reader = file2

dim(pl8reader)

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 2, to = 25, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i],name = colnames(pl8reader)[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))

```

Data file: [Iris3A.csv](https://bitbucket.org/benzzz/plotly-workshop/src/master/Iris3A.csv)

Google: [r read csv skip empty lines](https://www.google.co.il/search?q=r+read+csv+skip+empty+lines)
There is some problem about std
```{r 10, echo=TRUE}

# file3 = read.csv("Iris3A.csv")
# file3 = read.csv(file = "Iris3A.csv" ,skip = as.numeric(rownames(file3[which(file3[,1]!=''),])[1]))
# pl8reader = file3[3:length(file3),]
# 
# View(pl8reader)

p = plot_ly( data = pl8reader
        ,type = "scatter"
        ,mode = "lines"
)

for(i in seq(from = 2, to = 29, by = 4)){
j=i+1
upper_errorB4 = pl8reader[,i] + pl8reader[,i+2]
lower_errorB4 = pl8reader[,i] - pl8reader[,i+2]
lower_errorB4[lower_errorB4<0] = 0
upper_errorCa = pl8reader[,j] + pl8reader[,j+2]
lower_errorCa = pl8reader[,j] - pl8reader[,j+2]
lower_errorCa[lower_errorCa<0] = 0


p = add_trace(p, y = pl8reader[,i],name = colnames(pl8reader)[i])
p = add_trace(p, y = lower_errorB4, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorB4, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')

p = add_trace(p, y = pl8reader[,j],name = colnames(pl8reader)[j])
p = add_trace(p, y = lower_errorCa, showlegend = F, line = edge)
p = add_trace(p, y = upper_errorCa, fill = "tonexty",showlegend = F, line = edge, fillcolor = 'rgba(1,1,1,0.1)')
}

layout(p
       ,title = "Growth curve"
       ,yaxis = list(title = "OD<sub>600</sub> nm", showgrid = FALSE)
       ,xaxis = list(title = "Time (H)", showgrid = FALSE, zeroline = F))

```
