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Introduction to `R` and `RStudio`

Figure 1

Screenshot of RStudio showing the 4 "panes".

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Screenshot of RStudio Global Options with Restore .RData into workspace at startup unchecked and Save workspace to .RData on exit set to Never

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Introduction to `R Packages`, `Markdown` and `Notebooks`

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Starting with Data

Figure 1

A 3 by 3 data frame with columns showing numeric, character and logical values.

Figure 2

Yes/no bar graph showing number of individuals who are members of irrigation association

Figure 3

Bar plot of association membership, showing missing responses.

Figure 4

bar graph showing number of individuals who are members of irrigation association, including undetermined option

Manipulating Data with `dplyr` and `tidyr`

Figure 1

Screenshot of RStudio showing the empty global environment.

Figure 2

R for Data Science, Wickham H and Grolemund G (https://r4ds.had.co.nz/index.html) © Wickham, Grolemund 2017 This image is licenced under Attribution-NonCommercial-NoDerivs 3.0 United States (CC-BY-NC-ND 3.0 US)

Figure 3

Figure 4

Image 1 of 1: ‘Two tables shown side-by-side. The first row of the left table is highlighted in blue, and the first four rows of the right table are also highlighted in blue to show how each of the values of 'items owned' are given their own row with the separate longer delim function. The 'items owned logical' column is highlighted in yellow on the right table to show how the mutate function adds a new column.’

Two tables shown side-by-side. The first row of the left table is highlighted in blue, and the first four rows of the right table are also highlighted in blue to show how each of the values of 'items owned' are given their own row with the separate longer delim function. The 'items owned logical' column is highlighted in yellow on the right table to show how the mutate function adds a new column.

Figure 5

Image 1 of 1: ‘Two tables shown side-by-side. The 'items owned' column is highlighted in blue on the left table, and the column names are highlighted in blue on the right table to show how the values of the 'items owned' become the column names in the output of the pivot wider function. The 'items owned logical' column is highlighted in yellow on the left table, and the values of the bicycle, television, and solar panel columns are highlighted in yellow on the right table to show how the values of the 'items owned logical' column became the values of all three of the aforementioned columns.’

Two tables shown side-by-side. The 'items owned' column is highlighted in blue on the left table, and the column names are highlighted in blue on the right table to show how the values of the 'items owned' become the column names in the output of the pivot wider function. The 'items owned logical' column is highlighted in yellow on the left table, and the values of the bicycle, television, and solar panel columns are highlighted in yellow on the right table to show how the values of the 'items owned logical' column became the values of all three of the aforementioned columns.

All Images

Introduction to `R` and `RStudio`

Figure 1

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Figure 3

Introduction to `R Packages`, `Markdown` and `Notebooks`

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Figure 2

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Figure 4

Starting with Data

Figure 1

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Figure 4

Manipulating Data with `dplyr` and `tidyr`

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Quantitative Data Analysis in R

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All Images

Introduction to R and RStudio

Figure 1

Figure 2

Figure 3

Introduction to R Packages, Markdown and Notebooks

Figure 1

Figure 2

Figure 3

Figure 4

Starting with Data

Figure 1

Figure 2

Figure 3

Figure 4

Manipulating Data with dplyr and tidyr

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Quantitative Data Analysis in R

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

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Introduction to `R` and `RStudio`

Introduction to `R Packages`, `Markdown` and `Notebooks`

Manipulating Data with `dplyr` and `tidyr`