Data Visualization in R - Midterm Study Guide

These flashcards encompass essential concepts and functions related to data visualization and manipulation in R, aiming to help the student review for their midterm exam.

The function to import comma-separated data in R is __.

read_csv("file.csv")

To export a tibble as CSV, you would use __.

write_csv(df, "out.csv")

The tidy data principle states that each __ corresponds to one column.

variable

Use the pipe %>% to __ multiple steps together.

chain

To check for any missing values in a data frame, you can use __.

any(is.na(df))

In data inspection, the function __ provides a compact structure view.

glimpse(df)

To convert a character variable to a factor, the function used is __.

as_factor(x)

In dplyr, the function __ is used to remove duplicate rows.

distinct()

To sort rows in a data frame, you would use __.

arrange()

The function __ summarizes multiple columns at once.

summarize(across())

To create a new variable based on an existing one, you can use __.

mutate()

The core function to count frequency of values in dplyr is __.

count(var)

The command __ reshapes data from wide format to long format.

pivot_longer()

To fill missing values in a variable, use __.

replace_na(list(var = value))

The function __ allows joining two data frames using a common key.

left_join()

A rectangular shape storing both geometry and attributes in spatial data is called __.

a shapefile.

In ggplot2, to add a regression line, you use __.

geom_smooth(method = "lm")

Shapefile projection can be changed using __.

st_transform(object, crs = 4326)

To visualize distribution by category, you would typically use __.

geom_boxplot()

The function __ is used to handle multiple conditions in data transformation.

case_when()

The function to import comma-separated data in R is __.\n\n

read_csv("file.csv")\n\n

To export a tibble as CSV, you would use __.\n\n

write_csv(df, "out.csv")\n\n

The tidy data principle states that each __ corresponds to one column.\n\n

variable\n\n

Use the pipe %>% to __ multiple steps together.\n\n

chain\n\n

To check for any missing values in a data frame, you can use __.\n\n

any(is.na(df))\n\n

In data inspection, the function __ provides a compact structure view.\n\n

glimpse(df)\n\n

To convert a character variable to a factor, the function used is __.\n\n

as_factor(x)\n\n

In dplyr, the function __ is used to remove duplicate rows.\n\n

distinct()\n\n

To sort rows in a data frame, you would use __.\n\n

arrange()\n\n

The function __ summarizes multiple columns at once.\n\n

summarize(across())\n\n

To create a new variable based on an existing one, you can use __.\n\n

mutate()\n\n

The core function to count frequency of values in dplyr is __.\n\n

count(var)\n\n

The command __ reshapes data from wide format to long format.\n\n

pivot_longer()\n\n

To fill missing values in a variable, use __.\n\n

replace_na(list(var = value))\n\n

The function __ allows joining two data frames using a common key.\n\n

left_join()\n\n

A rectangular shape storing both geometry and attributes in spatial data is called __.\n\n

a shapefile.\n\n

In ggplot2, to add a regression line, you use __.\n\n

geom_smooth(method = "lm")\n\n

Shapefile projection can be changed using __.\n\n

st_transform(object, crs = 4326)\n\n

To visualize distribution by category, you would typically use __.\n\n

geom_boxplot()\n\n

The function __ is used to handle multiple conditions in data transformation.\n\n

case_when()\n\n

To select a subset of rows based on conditions, you would use __.\n\n

filter()\n\n

The function to choose specific columns in a data frame is __.\n\n

select()\n\n

To perform grouped operations, first use __.\n\n

group_by()\n\n

To change the name of a column, use __.\n\n

rename()\n\n

To create a scatter plot in ggplot2, you would use __.\n\n

geom_point()\n\n

The command __ reshapes data from long format to wide format.\n\n

pivot_wider()\n\n

To remove rows containing any missing values, use __.\n\n

drop_na()\n\n

The function to import comma-separated data in R is ****\\.\n\n

read\_csv(\"file.csv\")\n\n

To export a tibble as CSV, you would use ****\\.\n\n

write\_csv(df, \"out.csv\")\n\n

The tidy data principle states that each ****\\ corresponds to one column.\n\n

variable\n\n

Use the pipe %>% to ****\\ multiple steps together.\n\n

chain\n\n

To check for any missing values in a data frame, you can use ****\\.\n\n

any(is.na(df))\n\n

In data inspection, the function ****\\ provides a compact structure view.\n\n

glimpse(df)\n\n

To convert a character variable to a factor, the function used is ****\\.\n\n

as\_factor(x)\n\n

In dplyr, the function ****\\ is used to remove duplicate rows.\n\n

distinct()\n\n

To sort rows in a data frame, you would use ****\\.\n\n

arrange()\n\n

The function ****\\ summarizes multiple columns at once.\n\n

summarize(across())\n\n

To create a new variable based on an existing one, you can use ****\\.\n\n

mutate()\n\n

The core function to count frequency of values in dplyr is ****\\.\n\n

count(var)\n\n

The command ****\\ reshapes data from wide format to long format.\n\n

pivot\_longer()\n\n

To fill missing values in a variable, use ****\\.\n\n

replace\_na(list(var = value))\n\n

The function ****\\ allows joining two data frames using a common key.\n\n

left\_join()\n\n

A rectangular shape storing both geometry and attributes in spatial data is called ****\\.\n\n

a shapefile.\n\n

In ggplot2, to add a regression line, you use ****\\.\n\n

geom\_smooth(method = \"lm\")\n\n

Shapefile projection can be changed using ****\\.\n\n

st\_transform(object, crs = 4326)\n\n

To visualize distribution by category, you would typically use ****\\.\n\n

geom\_boxplot()\n\n

The function ****\\ is used to handle multiple conditions in data transformation.\n\n

case\_when()\n\n

To select a subset of rows based on conditions, you would use ****\\.\n\n

filter()\n\n

The function to choose specific columns in a data frame is ****\\.\n\n

select()\n\n

To perform grouped operations, first use ****\\.\n\n

group\_by()\n\n

To change the name of a column, use ****\\.\n\n

rename()\n\n

To create a scatter plot in ggplot2, you would use ****\\.\n\n

geom\_point()\n\n

The command ****\\ reshapes data from long format to wide format.\n\n

pivot\_wider()\n\n

To remove rows containing any missing values, use ****\\.\n\n

drop\_na()\n\n

To initialize a ggplot object, defining the default dataset and aesthetic mappings, you would use ****\\.\n\n

ggplot()\n\n

Inside ggplot2, to map variables to visual aesthetics (like x, y, color), the function used is ****\\.\n\n

aes()\n\n

To separate a plot into subplots based on one or more discrete variables in ggplot2, you can use ****\\ or ****\\.\n\n

facet\wrap() or facet\grid()\n\n

To add a main title, subtitle, captions, or axis labels to a ggplot visualization, you would use ****\\.\n\n

labs()\n\n

In dplyr, to select rows by their integer position, you would use the function ****\\.\n\n

slice()\n\n