This page will show you how you can analyze and visualize single variables using R.

A variable can be described with statistics like the mode, mean, median, variance and standard deviation. Also, it is interesting to visualize a variable using a plot.

Below you will see several examples of obtaining descriptive statistics for items in a data set called `data_new`

.

```
# To summarise some main characteristics of one numerical item (called Item1):
data_new %>%
summarise(mean = mean(Item1), sd = sd(Item1), var = var(Item1), minimum = min(Item1),
maximum = max(Item1))
# You can also ask for only one of the statistics.
# To summarize the main characteristics (mean, median, minimum, maximum, Q1, Q3
# and number of missings) of multiple variables (in this example: the first 10
# variables) in the data set:
data_new %>%
select(1:10) %>%
summary()
# To compute a certain statistic for the first 10 variables in the data set,
# you can also use the map() function:
data_new %>%
select(1:10) %>%
map(var)
# instead of var, you can also use mean, sd, or other functions
```

The tabyl() function from the `janitor`

package is a way to get frequency tables. tabyl() is tidyverse-aligned and is primarily built upon the dplyr and tidyr packages.

```
# Make sure 'janitor' in loaded into the library
# Create a frequency table
gss_cat %>%
tabyl(race)
# Create frequency tables for all variables of a data set:
gss_cat %>%
map(tabyl)
# If the values are in alphabetical, rather than in a meaningful order, make
# sure the variable is stored as an 'ordered factor' (see above).
# The frequency table can be made nicer using the adorn commands, for example:
gss_cat %>%
tabyl(marital) %>%
adorn_totals("row") %>%
adorn_pct_formatting()
```

We will use the package `ggplot2`

(part of the core tidyverse) for creating visualizations. The function **ggplot** is extremely flexible and there is an almost infinite number of ways to display your data.

The basic idea of ggplot commands is that you (1) have a data frame, pipe (%>%) that into a (2) ggplot(), add a (3) geom_…() to select the type of display you want to have, and (4) use aesthetics (aes()) to select the variable(s) you will use from that data frame.

There are more ‘layers’ in a ggplot, but these are the most important.

For example, you can use a bar chart to visualize a categorical (= nominal) variable.

```
# creating a basic bar chart with the nominal variable item 1
gss_cat %>%
ggplot() +
geom_bar(aes(x = marital))
# to create a box plot for the ratio variable tvhours in gss_cat
# (please note that x = and y = in the aesthetics tilt the picture)
gss_cat %>%
ggplot() +
geom_boxplot(aes(y = tvhours))
# to create a histogram for the ratio variable tvhours in gss_cat
gss_cat %>%
ggplot() +
geom_histogram(aes(x = tvhours))
# there are many more geom's to use, but we will focus on these in univariate analysis. But this is definitely a topic you can explore further. Think for example about changing the colors of the bars in a barchart, adding better titles or adding annotations.
# Note 1: although we generally recommend using the pipe operator like this, if you start visualizing data from different objects in one plot, it may be wise to put the data inside the ggplot:
ggplot(gss_cat) +
geom_histogram(aes(x = tvhours))
# Note 2: the aesthetics are put in the geom. However, if you combine different geom's (scatter and line) displaying the same variables, it is more efficient, to but the aesthetics in the general ggplot command too:
ggplot(gss_cat, aes(x = tvhours)) +
geom_histogram()
```

*No examples available yet*

`adorn_pct_formatting()`

*package: janitor*

Change numeric columns to percentages in a frequency table (tabyl).`adorn_totals()`

*package: janitor*

Add a row or column with totals to a frequency table (tabyl).`aes()`

*package: ggplot2 (tidyverse)*

Create an aestetic map, which explains which visual properties (aesthetics) are linked (mapped) to which variables in the dataframe.`ggplot()`

*package: ggplot2 (tidyverse)*

Initialize any type of graph.`geom_bar()`

*package: ggplot2 (tidyverse)*

Create a bar chart using the initialized ggplot.`geom_boxplot()`

*package: ggplot2 (tidyverse)*

Create a boxplot using the initialized ggplot.`geom_histogram()`

*package: ggplot2 (tidyverse)*

Create a histogram using the initialized ggplot.`map()`

*package: purrr (tidyverse)*

Apply a transformation to every variable in the dataframe.`max()`

*package: base*

Find the highest number from a list of numbers.`mean()`

*package: utils (base)*

Find the mean of a list of numbers.`median()`

*package: stats (base)*

Find the median of a list of numbers.`min()`

*package: base*

Find the lowest number from a list of numbers.`sd()`

*package: stats (base)*

Find the standard deviation of a list of numbers.`summarise()`

*package: dplyr (tidyverse)*

Create a new dataframe summarizing the values in another dataframe.`tabyl()`

*package: janitor*

Generate a frequency table.`var()`

*package: stats (base)*

Find the variation of a list of numbers.

*When creating a graph, i get the message Did you use %>% instead of +?* When using the

`ggplot()`

command together with another command like `geom_bar()`

or `geom_boxplot()`

you don’t use the pipe (`%>%`

) between them, but instead you use the `+`

. That is because these two commands need to be executed together, and instead of that the result of one needs to be used in the next.- Data visualisation cheatsheet with ggplot2

An overview of all the different graph types and how to make them using the`ggplot()`

function.