category
stringclasses 2
values | function
stringclasses 71
values | instruction_en
stringlengths 21
489
| response_en
stringlengths 3
748
⌀ |
|---|---|---|---|
R for Data Science
|
A field guide to base R
|
Name the function in base R used for numeric summaries.
|
`sum()` and `mean()` are used for numeric summaries.
|
R for Data Science
|
A field guide to base R
|
How can you subset a vector using positive integers in base R?
|
Using `x[c(3, 2, 5)]` where `x` is a vector, and `c(3, 2, 5)` are positive integers to keep elements at those positions.
|
R for Data Science
|
A field guide to base R
|
What does subsetting with negative integers do in base R?
|
Subsetting with negative integers like `x[-c(1, 3)]` drops the elements at those positions.
|
R for Data Science
|
A field guide to base R
|
How can you subset a vector with a logical vector in base R?
|
Using `x[!is.na(x) & x > 0]` keeps all values corresponding to a TRUE value in the logical vector.
|
R for Data Science
|
A field guide to base R
|
Explain how to subset a vector using a character vector by name.
|
If you have a named vector `x`, you can subset it with a character vector `x[c('name1', 'name2')]`.
|
R for Data Science
|
A field guide to base R
|
Describe the result of subsetting a vector with nothing (`x[]`).
|
`x[]` returns the complete vector `x` without removing any elements.
|
R for Data Science
|
A field guide to base R
|
How do you use the `[` operator to select multiple rows and columns in a data frame?
|
Use `df[rows, cols]` where `rows` and `cols` specify row and column indices.
|
R for Data Science
|
A field guide to base R
|
What is the base R equivalent of `filter()` for subsetting rows based on conditions?
|
Using logical vectors within `[`, e.g., `df[df$x > 5, ]`.
|
R for Data Science
|
A field guide to base R
|
How can you create a new column in a data frame in base R?
|
Use `df$new_col <- values` to assign new values to a column.
|
R for Data Science
|
A field guide to base R
|
Compare the use of `[[` and `$` for extracting a single element or column from a data frame.
|
`[[` can access by position or name, `$` is used for access by name.
|
R for Data Science
|
A field guide to base R
|
How does subsetting with `[[` differ when used with lists?
|
`[[` extracts elements or components of a list, allowing access to deeper levels.
|
R for Data Science
|
A field guide to base R
|
Give an example of using the `apply` family of functions on a matrix.
|
Use `apply(matrix, 1, sum)` to apply `sum` to each row of a matrix.
|
R for Data Science
|
A field guide to base R
|
Describe how to write a basic `for` loop in base R.
|
Use `for(variable in sequence) { expressions }` to execute expressions for each value in sequence.
|
R for Data Science
|
A field guide to base R
|
Explain the purpose of the `plot()` function in base R.
|
`plot()` is used to create basic types of graphs including scatter plots and line plots.
|
R for Data Science
|
A field guide to base R
|
How do you access the help documentation for a specific function in base R?
|
Use `?function_name` or `help(function_name)` to access documentation.
|
R for Data Science
|
A field guide to base R
|
Describe the functionality of `str()` in base R.
|
`str()` function displays the internal structure of an R object.
|
R for Data Science
|
A field guide to base R
|
Explain the use of `seq()` function in base R.
|
`seq()` generates sequences of numbers in various formats.
|
R for Data Science
|
A field guide to base R
|
How can `split()` function be used in base R?
|
`split()` divides the data into groups based on some criteria.
|
R for Data Science
|
A field guide to base R
|
What is the purpose of the `cut()` function?
|
`cut()` converts a numeric vector into a factor by categorizing it into intervals.
|
R for Data Science
|
A field guide to base R
|
How to perform matrix multiplication in base R?
|
Use `%*%` for matrix multiplication in base R.
|
R for Data Science
|
A field guide to base R
|
What is the base R method for generating random samples?
|
Use `sample()` to generate random samples from a given vector.
|
R for Data Science
|
A field guide to base R
|
How to calculate the sum of elements in a vector conditionally?
|
Use `sum(x[x > threshold])` where `x` is a vector and `threshold` is the condition.
|
R for Data Science
|
A field guide to base R
|
Describe the process of converting a character vector to a date object.
|
Use `as.Date()` to convert a character vector to a date object.
|
R for Data Science
|
A field guide to base R
|
What is the use of the `aggregate()` function?
|
`aggregate()` applies a function over subsets of a dataframe or a list.
|
R for Data Science
|
A field guide to base R
|
Explain how `tapply()` works in base R.
|
`tapply()` applies a function over subsets of a vector and optionally groups them by some other vector.
|
R for Data Science
|
A field guide to base R
|
Describe how to merge two data frames by a common column.
|
Use `merge(df1, df2, by='common_column')` to merge data frames on a common column.
|
R for Data Science
|
A field guide to base R
|
How to check for NA values in a vector?
|
Use `is.na(x)` to check for NA values in the vector `x`.
|
R for Data Science
|
A field guide to base R
|
Explain the use of the `diff()` function.
|
`diff()` calculates the difference between adjacent elements in a vector.
|
R for Data Science
|
A field guide to base R
|
How to reshape data from long to wide format?
|
Use `reshape()` with direction='wide' to convert data from long to wide format.
|
R for Data Science
|
A field guide to base R
|
How to reverse a vector in base R?
|
Use `rev(x)` to reverse the elements in vector `x`.
|
R for Data Science
|
A field guide to base R
|
What is the purpose of the `which.max()` function?
|
`which.max(x)` returns the index of the first maximum value of `x`.
|
R for Data Science
|
A field guide to base R
|
Explain the difference between `rnorm()` and `runif()`.
|
`rnorm()` generates random numbers from a Normal distribution, while `runif()` generates random numbers from a Uniform distribution.
|
R for Data Science
|
A field guide to base R
|
How to create a factorial function in base R?
|
Use `factorial(x)` or define a custom function using recursion for calculating factorial of `x`.
|
R for Data Science
|
A field guide to base R
|
What does the `lapply()` function return?
|
`lapply()` returns a list of the same length as the input, each element being the result of applying a function.
|
R for Data Science
|
A field guide to base R
|
How to convert a list to a vector in base R?
|
Use `unlist(l)` to convert list `l` into a vector, concatenating all elements.
|
R for Data Science
|
A field guide to base R
|
Describe the `order()` function's purpose and usage.
|
`order()` returns a permutation which rearranges its first argument into ascending or descending order.
|
R for Data Science
|
A field guide to base R
|
What is the functionality of the `merge()` function when applied to data frames?
|
`merge()` combines data frames by columns or rows, based on common key columns.
|
R for Data Science
|
A field guide to base R
|
How can you generate a sequence of dates in base R?
|
Use `seq.Date()` or `seq(as.Date('start_date'), as.Date('end_date'), by='interval')`.
|
R for Data Science
|
A field guide to base R
|
Explain the use and benefits of the `RColorBrewer` package in plotting.
|
`RColorBrewer` provides color schemes for maps and other graphics, enhancing data visualization.
|
R for Data Science
|
A field guide to base R
|
How to calculate row sums and column sums in a matrix?
|
Use `rowSums(mat)` for row sums and `colSums(mat)` for column sums where `mat` is a matrix.
|
R for Data Science
|
A field guide to base R
|
Describe the `paste()` and `paste0()` functions.
|
`paste()` concatenates strings with a separator, `paste0()` does the same but without any separator.
|
R for Data Science
|
A field guide to base R
|
What does setting `stringsAsFactors = FALSE` in `data.frame()` do?
|
It prevents automatic conversion of character vectors to factors in the data frame.
|
R for Data Science
|
A field guide to base R
|
How to calculate the mode of a numeric vector?
|
Define a custom function using `table()` and `which.max()` to find the mode, as base R does not have a direct mode function.
|
R for Data Science
|
A field guide to base R
|
Explain the concept and usage of `environments` in R.
|
Environments are R objects for managing bindings of variables to values, useful in scoping and functions.
|
R for Data Science
|
A field guide to base R
|
Create a vector of numbers from 1 to 20. Subset this vector to include only numbers divisible by 3.
|
```r
x <- 1:20
x[x %% 3 == 0]
```
|
R for Data Science
|
A field guide to base R
|
Given a data frame with columns 'A', 'B', and 'C', write a command to select and print columns 'A' and 'C'.
|
```r
df <- data.frame(A = 1:3, B = 4:6, C = 7:9)
df[, c('A', 'C')]
```
|
R for Data Science
|
A field guide to base R
|
Using the mtcars dataset, write a dplyr command to filter cars with an mpg value greater than 20 and arrange them in descending order of hp.
|
```r
library(dplyr)
mtcars %>% filter(mpg > 20) %>% arrange(desc(hp))
```
|
R for Data Science
|
A field guide to base R
|
Write a command to replace all NA values in the vector c(1, NA, 3, NA, 5) with the mean of non-NA values of this vector.
|
```r
x <- c(1, NA, 3, NA, 5)
x[is.na(x)] <- mean(x, na.rm = TRUE)
x
```
|
R for Data Science
|
A field guide to base R
|
Use lapply to calculate the mean of each list element in the list list(c(1,2,3), c(4,5,6), c(7,8,9)).
|
```r
lapply(list(c(1,2,3), c(4,5,6), c(7,8,9)), mean)
```
|
R for Data Science
|
A field guide to base R
|
Write a for loop that prints numbers from 1 to 10, but skips numbers that are divisible by 3.
|
```r
for (i in 1:10) {
if (i %% 3 != 0) print(i)
}
```
|
R for Data Science
|
A field guide to base R
|
Using base R functions, create a scatter plot of mtcars with 'mpg' on the x-axis and 'hp' on the y-axis.
|
```r
plot(mtcars$mpg, mtcars$hp, xlab = 'MPG', ylab = 'HP', main = 'MPG vs HP')
```
|
R for Data Science
|
A field guide to base R
|
Perform a left join on two data frames, df1 and df2, by a common column 'id'.
|
```r
merge(df1, df2, by = 'id', all.x = TRUE)
```
|
R for Data Science
|
A field guide to base R
|
Calculate the summary statistics (mean, median, standard deviation) for the `mpg` column in the `mtcars` dataset.
|
```r
summary(mtcars$mpg)
```
|
R for Data Science
|
A field guide to base R
|
Given a vector of strings, write a command to convert all strings to lowercase and remove spaces.
|
```r
str <- c('Hello World', 'R Programming')
tolower(gsub(' ', '', str))
```
|
R for Data Science
|
A field guide to base R
|
Create a ggplot2 scatter plot of the `mtcars` dataset with `mpg` on the x-axis, `hp` on the y-axis, colored by `cyl`.
|
```r
library(ggplot2)
ggplot(mtcars, aes(x=mpg, y=hp, color=factor(cyl))) + geom_point()
```
|
R for Data Science
|
A field guide to base R
|
Given a time series object ts, write a command to plot this time series data.
|
```r
plot(ts)
```
|
R for Data Science
|
A field guide to base R
|
Perform PCA on the `mtcars` dataset and plot the first two principal components.
|
```r
pca_result <- prcomp(mtcars[, -1], center = TRUE, scale. = TRUE)
plot(pca_result$x[, 1:2])
```
|
R for Data Science
|
A field guide to base R
|
Fit a linear regression model predicting `mpg` from `hp` and `wt` in the `mtcars` dataset. Summarize the model.
|
```r
model <- lm(mpg ~ hp + wt, data = mtcars)
summary(model)
```
|
R for Data Science
|
A field guide to base R
|
Use `stringr` to extract the first word from each element in a character vector.
|
```r
library(stringr)
str_vector <- c('Hello World', 'R Programming')
str_extract(str_vector, '\\w+')
```
|
R for Data Science
|
A field guide to base R
|
Identify and remove rows with any missing values in a given data frame.
|
```r
df <- na.omit(df)
```
|
R for Data Science
|
A field guide to base R
|
Group the `mtcars` dataset by `cyl` and calculate the average `mpg` for each group.
|
```r
library(dplyr)
mtcars %>% group_by(cyl) %>% summarise(avg_mpg = mean(mpg))
```
|
R for Data Science
|
A field guide to base R
|
Use `ggplot2` and `geom_sf` to plot a simple map.
|
```r
library(ggplot2); library(sf)
# Assuming 'map' is a simple features object
ggplot(data = map) + geom_sf()
```
|
R for Data Science
|
A field guide to base R
|
Find the frequency of words in a character vector using the `tm` package.
|
```r
library(tm)
# Assuming 'texts' is a character vector
corpus <- Corpus(VectorSource(texts))
tdm <- TermDocumentMatrix(corpus)
findFreqTerms(tdm, lowfreq = 2)
```
|
R for Data Science
|
A field guide to base R
|
Split the `iris` dataset into training and test sets, and fit a k-nearest neighbors model.
|
```r
library(class)
set.seed(123)
train_indices <- sample(1:nrow(iris), nrow(iris) * 0.7)
train <- iris[train_indices, ]
test <- iris[-train_indices, ]
pred <- knn(train[, 1:4], test[, 1:4], train$Species, k = 3)
```
|
R for Data Science
|
A field guide to base R
|
Generate a report that dynamically lists the top 5 rows of the `mtcars` dataset using R Markdown.
|
```r
# In an R Markdown document, use:
```{r}
head(mtcars, 5)
```
```
|
R for Data Science
|
A field guide to base R
|
Use regular expressions to extract all email addresses from a text.
|
```r
emails <- regmatches(text, gregexpr('[[:alnum:]._-]+@[[:alnum:]._-]+', text))
```
|
R for Data Science
|
A field guide to base R
|
Create a basic Shiny app that displays a histogram of the `mpg` column from the `mtcars` dataset.
|
```r
# Shiny app code skeleton
library(shiny)
ui <- fluidPage(plotOutput('mpgHist'))
server <- function(input, output) {
output$mpgHist <- renderPlot({hist(mtcars$mpg)})
}
shinyApp(ui, server)
```
|
R for Data Science
|
A field guide to base R
|
Use `purrr` to apply a function over a list and return a list of summaries.
|
```r
library(purrr)
list_data <- list(rnorm(10), runif(10), rnorm(10, 1))
map(list_data, ~ summary(.x))
```
|
R for Data Science
|
Quarto
|
What command is used to start a new Quarto document in RStudio?
|
To start a new Quarto document, select File > New File > Quarto Document in the RStudio menu.
|
R for Data Science
|
Quarto
|
What is the keyboard shortcut to insert a new chunk in a Quarto document?
|
The keyboard shortcut to insert a new chunk in a Quarto document is Cmd + Option + I (Mac) or Ctrl + Alt + I (Windows/Linux).
|
R for Data Science
|
Quarto
|
How can you run all code in a Quarto document?
|
To run all code in a Quarto document, click the 'Render' button or press Cmd/Ctrl + Shift + K.
|
R for Data Science
|
Quarto
|
What is the purpose of the setup chunk in a Quarto document?
|
The setup chunk is run automatically once before any other code, used for initial setup like loading libraries or setting options.
|
R for Data Science
|
Quarto
|
How do you specify that a chunk's code should not be evaluated?
|
To specify that a chunk's code should not be evaluated, use the chunk option `eval: false`.
|
R for Data Science
|
Quarto
|
Which chunk option is used to hide the code but show its results?
|
To hide the code but show its results, use the chunk option `include: false`.
|
R for Data Science
|
Quarto
|
What does the `echo: false` chunk option do?
|
`echo: false` hides the chunk's code from the final document output but shows the results.
|
R for Data Science
|
Quarto
|
How can you prevent messages from being displayed in a Quarto document's output?
|
To prevent messages, use the chunk option `message: false`.
|
R for Data Science
|
Quarto
|
What is the effect of the `error: true` chunk option?
|
`error: true` allows the rendering process to continue even if the chunk generates an error.
|
R for Data Science
|
Quarto
|
How do you add a caption to a figure in a Quarto document?
|
Add a caption to a figure by using the syntax `` within the chunk's code.
|
R for Data Science
|
Quarto
|
What is the difference between the YAML header in Quarto and Markdown documents?
|
The YAML header in Quarto documents configures document-wide settings and metadata, similar to Markdown but with Quarto-specific options.
|
R for Data Science
|
Quarto
|
How can you change the default chunk option for all chunks in a Quarto document?
|
Change default chunk options by adding them under the `execute` field in the document's YAML header.
|
R for Data Science
|
Quarto
|
What is the command to render a Quarto document to PDF format?
|
To render a Quarto document to PDF format, use the command `quarto render your-document.qmd --to pdf`.
|
R for Data Science
|
Quarto
|
How can inline code be used within the narrative of a Quarto document?
|
Inline code can be included using the syntax `` `r CODE` `` where `CODE` is the R code to be executed.
|
R for Data Science
|
Quarto
|
What is the function of the `results: 'hide'` option in a code chunk?
|
The `results: 'hide'` option hides the output of the chunk from the final document.
|
R for Data Science
|
Quarto
|
How do you embed a YouTube video in a Quarto document?
|
Embed a YouTube video using the HTML `<iframe>` tag within a Quarto document.
|
R for Data Science
|
Quarto
|
What syntax is used to create a table in a Quarto document using Markdown?
|
Tables in Markdown are created using pipes (`|`) and dashes (`-`) to define columns and headers.
|
R for Data Science
|
Quarto
|
How can you include external R scripts in a Quarto document?
|
Include external R scripts using the `source()` function within a code chunk.
|
R for Data Science
|
Quarto
|
What is the purpose of the `fig.cap` chunk option in Quarto?
|
The `fig.cap` option is used to add captions to figures generated by code chunks.
|
R for Data Science
|
Quarto
|
How can you create a cross-reference to a figure in a Quarto document?
|
Create a cross-reference to a figure by using the syntax `{#fig:label}` in the figure caption and referring to it with `@ref(fig:label)`.
|
R for Data Science
|
Quarto
|
Explain the process of adding a bibliography in Quarto. What file formats are supported for the bibliography?
|
Add a bibliography by including a `bibliography` field in the YAML header and specifying a `.bib`, `.json`, or `.yaml` file containing references.
|
R for Data Science
|
Quarto
|
How can you customize the appearance of HTML output using CSS in Quarto?
|
Customize HTML output with CSS by including a `css` field in the YAML header or using `<style>` tags within the document.
|
R for Data Science
|
Quarto
|
What is the difference between the `fig.width` and `fig.height` chunk options?
|
The `fig.width` and `fig.height` options control the size of figures in code output, while `out.width` and `out.height` control the display size in the final document.
|
R for Data Science
|
Quarto
|
How do you perform spell check in a Quarto document within RStudio?
|
Perform spell check in RStudio by clicking the 'ABC' button in the editor toolbar or using the `Tools -> Spell Check Document` menu.
|
R for Data Science
|
Quarto
|
Describe how you can use parameters in Quarto to create dynamic reports.
|
Use parameters in Quarto by defining them in the YAML header under `params` and accessing them in the document with `params$parameter_name`.
|
R for Data Science
|
Quarto
|
What are the benefits of using the `cache: true` chunk option? When should it be used?
|
`cache: true` saves computation time by caching chunk results. It should be used for chunks with time-consuming calculations.
|
R for Data Science
|
Quarto
|
How can you produce a presentation slide deck using Quarto?
|
Produce presentation slides by setting the `format` field in the YAML header to a presentation format, such as `revealjs` or `powerpoint`.
|
R for Data Science
|
Quarto
|
Explain how to create a multi-language (e.g., R and Python) Quarto document.
|
Create multi-language documents by specifying the language in each code chunk header, e.g., `{r}` for R and `{python}` for Python.
|
R for Data Science
|
Quarto
|
What is the function of the `out.width` and `out.height` options for controlling figure output size?
|
The `out.width` and `out.height` options control the displayed size of figures in the final document, overriding `fig.width` and `fig.height`.
|
R for Data Science
|
Quarto
|
How do you include footnotes in a Quarto document?
|
Include footnotes by using the syntax `[^1]` in the text and defining the footnote with `[^1]: Footnote text.`
|
R for Data Science
|
Quarto
|
How can you adjust the size of text in Quarto for HTML output?
|
Adjust the size of text for HTML output using CSS or the `css` field in the YAML header.
|
R for Data Science
|
Quarto
|
What chunk option allows you to specify the engine (R, Python, etc.) for code execution?
|
The engine for code execution is specified directly in the chunk header, e.g., `{python}` for Python.
|
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