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# Efficient use of R-Instat and R
## Introduction

In this guide Chapters 2 and 3 largely made use of the general
facilities in R-Instat, shown in Fig. 8.1a. They were dialogues from the
File, Prepare and Describe menus. Chapters 4 to 7 used the climatic menu
shown in Fig. 8.1b

-----------------------------------------------------------------------------------------------------------
***Fig. 8.1a The R-Instat menus*** ***Fig. 8.1b The Climatic menu***
----------------------------------------------------- -----------------------------------------------------
![](media/image237.png){width="3.070092957130359in" ![](media/image230.png){width="2.949284776902887in"
height="1.693844050743657in"} height="3.438086176727909in"}

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

The Climatic menu, Fig. 8.1b, mirrors the general menus, Fig. 8.1a in
that parts of this menu correspond to the facilities in the File,
Prepare, Describe and Model menus. Thus you start by getting the File
with the data. Then there is usually a Prepare stage, where the data are
organised and checked, ready for analysis. This stage often includes a
"reshaping" of the data, where daily records are summarised to a monthly
or yearly basis.

Then the initial analyses are usually descriptive, so use the Describe
section of the Climatic menu or the Describe menu itself. The materials
in Chapters 4 to 7 were all devoted to descriptive analyses.

When descriptive methods are not enough there is the Model menu to fit
and examine statistical models.

For users who are starting their climatic analyses with R-Instat we
distinguish between four or five "levels". These different "levels" are
discussed in this chapter.

1. If your analyses are "standard", then you may find all you need is
in the climatic menu. That is the idea of the special menu.

2. If you need more, then the general R-Instat menus may be used. The
Prepare menu is sometimes needed for more of the initial data
manipulation than is in the climatic menu. The powerful ggplot2
graphics system is also available through the Describe menu.

3. R-Instat includes some "halfway" dialogues, that we discuss in
Section 8.3. These are dialogues where you have essentially to write
a single R command. That's quite easy and can be a stepping-stone to
using R directly.

4. Sometimes a dialogue does not do quite what is needed for an
analysis. The ***To Script*** button, is on each dialogue and copies
the relevant R command to a special script window. You can then
"tweak" the resulting command(s) to produce the appropriate
analysis. This is described in Section 8.4.

5. Finally, you may be ready to use R "properly"! This is either
because the analysis you need is not available in R-Instat, or
because the click and point method is becoming tedious and you would
like to work more efficiently. One option is then still to start in
R-Instat. Then produce the log file, which has a record of all the
commands you have used. This may be transferred and should run just
the same in RStudio. Then you can continue the analyses using R
directly. This process is described in Section 8.5.

Most of the ideas in this chapter and also discussed in more detail in
the R-Instat guide called "Reading, Tweaking and Using R Commands".

[Solving problems rather than learning to use R-Instat.]{.mark}

Possibly discuss loops for successive analysis of data for multiple
stations

## Using the "ordinary" R-Instat

[Could show smoothing with loess and splines]{.mark}, though a bit of
that in Chapter 7. Could refer back

[Mention export of graphs for an editor: \"I\'d suggest exporting the
figure from R as a vector graphic file (.svg) then adding your labels in
a vector graphic software. I use **Inkscape**
[[software]{.underline}](https://inkscape.org/about/) because it can be
downloaded for free and its fairly intuitive to learn.\"]{.mark}

[Also discuss data sheet and data book -- though also in Chapter
3.]{.mark}

[And the metadata windows, including changing names and also altering
precision.]{.mark}

[Could perhaps be a good place to discuss the Tools \> Options dialogue
-- though maybe that deserves its own section?]{.mark}

On tasks in this section include summary of hourly to daily data
probably with the example from the openair package?

## The halfway dialogues

[Mention the risks that using these commands brings. Can make mistakes.
Good to make some mistakes intentionally so that you are ready for
them.]{.mark}

[Use an example of infilling data and the calculate dialogue -
transform. Could use infilling of temperatures to work towards a
complete record.Could install chillR partly because they have an
interesting data set where they have introduced missing values. Also
because their ideas on infilling will be generally useful for R-Instat
in the future.]{.mark}

[Then also the model and use model menu. This could include modelling
extremes.]{.mark}

## The script window

[Add an R package:]{.mark}

[install.packages('packagename')]{.mark}

[Tried Install.packages("finalfit") -- which gives an error? Wrong
quotes! Use
Install.packages(]{.mark}***\"***[finalfit]{.mark}***\"***[)]{.mark}

[To use data without needing to give the full name include
attach("dataframename")]{.mark}

library() lists all available packages

library(dplyr) makes the package available, so can give the commands
without dplyr:: at the start.

Move the example here from Chapter 3 of adding a skew boxplot.

## The log window and R

+-----------------------------------------------------------------------+
| ***Code to add date to an x-variable*** |
+=======================================================================+
| \# Code generated by the dialog, Line Plot |
| |
| Moorings_by_s_doy \<- |
| data_book\$get_data_frame(data_name=\"Moorings_by_s_doy\", |
| stack_data=TRUE, id.vars=\"s_doy\", |
| measure.vars=c(\"prop120.lt.600\",\"prop120.lt.450\")) |
| |
| [Moorings_by_s_doy \<- Moorings_by_s_doy %\>% |
| mutate(s_doy=as.Date(s_doy, origin = \"2015-07-31\"))]{.mark} |
| |
| last_graph \<- ggplot2::ggplot(data=Moorings_by_s_doy, |
| mapping=ggplot2::aes(x=s_doy, y=value, colour=variable)) + |
| ggplot2::geom_line() + theme_grey() + |
| ggplot2::theme(axis.text.x=ggplot2::element_text()) + |
| ggplot2::scale_y_continuous(limits=c(0, 1))[+scale_x_date(date_labels |
| = \"%d %b\", date_breaks=\"1 month\")]{.mark} |
| |
| data_book\$add_graph(graph_name=\"last_graph\", graph=last_graph, |
| data_name=\"Moorings_by_s_doy\") |
| |
| data_book\$get_graphs(data_name=\"Moorings_by_s_doy\", |
| graph_name=\"last_graph\") |
| |
| rm(list=c(\"last_graph\", \"Moorings_by_s_doy\")) |
# Efficient use of R-Instat and R
## Introduction

In this guide Chapters 2 and 3 largely made use of the general
facilities in R-Instat, shown in Fig. 8.1a. They were dialogues from the
File, Prepare and Describe menus. Chapters 4 to 7 used the climatic menu
shown in Fig. 8.1b

-----------------------------------------------------------------------------------------------------------
***Fig. 8.1a The R-Instat menus*** ***Fig. 8.1b The Climatic menu***
----------------------------------------------------- -----------------------------------------------------
![](figures/Fig8.1a.png){width="3.070092957130359in" ![](figures/Fig8.1b.png){width="2.949284776902887in"
height="1.693844050743657in"} height="3.438086176727909in"}

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

The Climatic menu, Fig. 8.1b, mirrors the general menus, Fig. 8.1a in
that parts of this menu correspond to the facilities in the File,
Prepare, Describe and Model menus. Thus you start by getting the File
with the data. Then there is usually a Prepare stage, where the data are
organised and checked, ready for analysis. This stage often includes a
"reshaping" of the data, where daily records are summarised to a monthly
or yearly basis.

Then the initial analyses are usually descriptive, so use the Describe
section of the Climatic menu or the Describe menu itself. The materials
in Chapters 4 to 7 were all devoted to descriptive analyses.

When descriptive methods are not enough there is the Model menu to fit
and examine statistical models.

For users who are starting their climatic analyses with R-Instat we
distinguish between four or five "levels". These different "levels" are
discussed in this chapter.

1. If your analyses are "standard", then you may find all you need is
in the climatic menu. That is the idea of the special menu.

2. If you need more, then the general R-Instat menus may be used. The
Prepare menu is sometimes needed for more of the initial data
manipulation than is in the climatic menu. The powerful ggplot2
graphics system is also available through the Describe menu.

3. R-Instat includes some "halfway" dialogues, that we discuss in
Section 8.3. These are dialogues where you have essentially to write
a single R command. That's quite easy and can be a stepping-stone to
using R directly.

4. Sometimes a dialogue does not do quite what is needed for an
analysis. The ***To Script*** button, is on each dialogue and copies
the relevant R command to a special script window. You can then
"tweak" the resulting command(s) to produce the appropriate
analysis. This is described in Section 8.4.

5. Finally, you may be ready to use R "properly"! This is either
because the analysis you need is not available in R-Instat, or
because the click and point method is becoming tedious and you would
like to work more efficiently. One option is then still to start in
R-Instat. Then produce the log file, which has a record of all the
commands you have used. This may be transferred and should run just
the same in RStudio. Then you can continue the analyses using R
directly. This process is described in Section 8.5.

Most of the ideas in this chapter and also discussed in more detail in
the R-Instat guide called "Reading, Tweaking and Using R Commands".

[Solving problems rather than learning to use R-Instat.]{.mark}

Possibly discuss loops for successive analysis of data for multiple
stations

## Using the "ordinary" R-Instat

[Could show smoothing with loess and splines]{.mark}, though a bit of
that in Chapter 7. Could refer back

[Mention export of graphs for an editor: \"I\'d suggest exporting the
figure from R as a vector graphic file (.svg) then adding your labels in
a vector graphic software. I use **Inkscape**
[[software]{.underline}](https://inkscape.org/about/) because it can be
downloaded for free and its fairly intuitive to learn.\"]{.mark}

[Also discuss data sheet and data book -- though also in Chapter
3.]{.mark}

[And the metadata windows, including changing names and also altering
precision.]{.mark}

[Could perhaps be a good place to discuss the Tools \> Options dialogue
-- though maybe that deserves its own section?]{.mark}

On tasks in this section include summary of hourly to daily data
probably with the example from the openair package?

## The halfway dialogues

[Mention the risks that using these commands brings. Can make mistakes.
Good to make some mistakes intentionally so that you are ready for
them.]{.mark}

[Use an example of infilling data and the calculate dialogue -
transform. Could use infilling of temperatures to work towards a
complete record.Could install chillR partly because they have an
interesting data set where they have introduced missing values. Also
because their ideas on infilling will be generally useful for R-Instat
in the future.]{.mark}

[Then also the model and use model menu. This could include modelling
extremes.]{.mark}

## The script window

[Add an R package:]{.mark}

[install.packages('packagename')]{.mark}

[Tried Install.packages("finalfit") -- which gives an error? Wrong
quotes! Use
Install.packages(]{.mark}***\"***[finalfit]{.mark}***\"***[)]{.mark}

[To use data without needing to give the full name include
attach("dataframename")]{.mark}

library() lists all available packages

library(dplyr) makes the package available, so can give the commands
without dplyr:: at the start.

Move the example here from Chapter 3 of adding a skew boxplot.

## The log window and R

+-----------------------------------------------------------------------+
| ***Code to add date to an x-variable*** |
+=======================================================================+
| \# Code generated by the dialog, Line Plot |
| |
| Moorings_by_s_doy \<- |
| data_book\$get_data_frame(data_name=\"Moorings_by_s_doy\", |
| stack_data=TRUE, id.vars=\"s_doy\", |
| measure.vars=c(\"prop120.lt.600\",\"prop120.lt.450\")) |
| |
| [Moorings_by_s_doy \<- Moorings_by_s_doy %\>% |
| mutate(s_doy=as.Date(s_doy, origin = \"2015-07-31\"))]{.mark} |
| |
| last_graph \<- ggplot2::ggplot(data=Moorings_by_s_doy, |
| mapping=ggplot2::aes(x=s_doy, y=value, colour=variable)) + |
| ggplot2::geom_line() + theme_grey() + |
| ggplot2::theme(axis.text.x=ggplot2::element_text()) + |
| ggplot2::scale_y_continuous(limits=c(0, 1))[+scale_x_date(date_labels |
| = \"%d %b\", date_breaks=\"1 month\")]{.mark} |
| |
| data_book\$add_graph(graph_name=\"last_graph\", graph=last_graph, |
| data_name=\"Moorings_by_s_doy\") |
| |
| data_book\$get_graphs(data_name=\"Moorings_by_s_doy\", |
| graph_name=\"last_graph\") |
| |
| rm(list=c(\"last_graph\", \"Moorings_by_s_doy\")) |
+-----------------------------------------------------------------------+

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