To get a descriptive statistic of a single variable in a tibble, we can
use that variable as an argument to a relevant function (using $ to
refer to a variable in a tibble).
mean(data$age)
## [1] 19.68276
median(data$age)
## [1] 19
sd(data$grade)
## [1] 1.281705
To get the frequencies of a categorical variable, we can use the
count() function, with the sort = TRUE argument returning the values
in descending frequency. count() is a tidy function that works well
with pipe workflows and can count the joint frequencies of multiple
variables.
# frequencies of a single variable
count(data, reading)
## # A tibble: 2 × 2
## reading n
## <lgl> <int>
## 1 FALSE 76
## 2 TRUE 69
# joint frequency distribution
count(data, reading, listening, notes)
## # A tibble: 8 × 4
## reading listening notes n
## <lgl> <lgl> <lgl> <int>
## 1 FALSE FALSE FALSE 14
## 2 FALSE FALSE TRUE 23
## 3 FALSE TRUE FALSE 20
## 4 FALSE TRUE TRUE 19
## 5 TRUE FALSE FALSE 19
## 6 TRUE FALSE TRUE 14
## 7 TRUE TRUE FALSE 15
## 8 TRUE TRUE TRUE 21
To get the correlation coefficient between two variables, we can use the
cor() function in the same way we used other descriptives such as
mean().
cor(data$age, data$grade)
## [1] 0.1856025
The easiest way to get summary statistics of all variables in a tibble
is with the summary() function: this function shows the distribution
of numeric variables, the frequencies of categorical variables, and the
number of missing values for each variable.
summary(data)
## id age sex scholarship
## Min. :5001 Min. :18.00 Length:145 Min. : 25.00
## 1st Qu.:5037 1st Qu.:18.00 Class :character 1st Qu.: 50.00
## Median :5073 Median :19.00 Mode :character Median : 50.00
## Mean :5073 Mean :19.68 Mean : 64.76
## 3rd Qu.:5109 3rd Qu.:21.00 3rd Qu.: 75.00
## Max. :5145 Max. :26.00 Max. :100.00
## NA's :1
## additional_work reading notes listening
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:96 FALSE:76 FALSE:68 FALSE:70
## TRUE :49 TRUE :69 TRUE :77 TRUE :75
##
##
##
##
## grade
## Min. :0.000
## 1st Qu.:1.500
## Median :3.000
## Mean :2.755
## 3rd Qu.:4.000
## Max. :4.000
##
The summary() function is useful for viewing the data in the Console,
but doesn’t export to outside of R nicely. There are a few packages
available for generating simple summary statistics tables that contain
information about the central tendencies and dispersion of the data,
such as vtable or stargazer (with many more available). These
packages all have different default settings, output types, and
customization options.
library(vtable)
library(stargazer)
# vtable
data |>
# by default creates HTML table; out = "csv" returns a dataframe
# can change which descriptives to keep
# can report group-level descriptives
sumtable(out = "csv", group = "reading")
## Variable N Mean SD N Mean SD
## 1 reading No Yes
## 2 id 76 5080 44 69 5066 39
## 3 age 76 20 2.3 69 20 1.7
## 4 sex 76 69
## 5 ... Female 32 42% 26 38%
## 6 ... Male 44 58% 43 62%
## 7 scholarship 75 65 18 69 64 21
## 8 additional_work 76 69
## 9 ... No 47 62% 49 71%
## 10 ... Yes 29 38% 20 29%
## 11 notes 76 69
## 12 ... No 34 45% 34 49%
## 13 ... Yes 42 55% 35 51%
## 14 listening 76 69
## 15 ... No 37 49% 33 48%
## 16 ... Yes 39 51% 36 52%
## 17 grade 76 2.5 1.3 69 3 1.2
# stargazer
data |>
# input needs to be a data.frame, not tibble
as.data.frame() |>
# default output is LaTeX table
# can be exported with the out argument or a following write() function
# can change which descriptives to keep or omit
# limited to numeric variables
stargazer(type = "text")
##
## ==================================================
## Statistic N Mean St. Dev. Min Max
## --------------------------------------------------
## id 145 5,073.000 42.002 5,001 5,145
## age 145 19.683 1.992 18 26
## scholarship 144 64.757 19.480 25 100
## additional_work 145 0.338 0.475 0 1
## reading 145 0.476 0.501 0 1
## notes 145 0.531 0.501 0 1
## listening 145 0.517 0.501 0 1
## grade 145 2.755 1.282 0.000 4.000
## --------------------------------------------------
Alternatively, we can define our own summary statistics with the dplyr
functions group_by() and summarize(), which also easily allows the
calculation of more complex descriptive statistics, including grouped
statistics based on categorical variables. The across() helper
function in the summarize() function can be used to apply the same
calculation to multiple variables at once: it requires the first
argument as the list of variables (potentially with the help of selector
functions) and the function we’d like to apply.
# tibble of mean and sd for a single variable
data |>
summarize(mean_grade = mean(grade),
sd_grade = sd(grade))
## # A tibble: 1 × 2
## mean_grade sd_grade
## <dbl> <dbl>
## 1 2.76 1.28
# mean and sd of age and grade variables, grouped by reading
data |>
group_by(reading) |>
# .names allows overriding default option to reuse original column names
summarize(across(c(age, grade), mean, .names = "mean_{.col}"),
across(c(age, grade), sd, .names = "sd_{.col}"))
## # A tibble: 2 × 5
## reading mean_age mean_grade sd_age sd_grade
## <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 19.7 2.54 2.27 1.34
## 2 TRUE 19.7 2.99 1.66 1.18
# mean of all numeric variables, grouped by reading
data |>
group_by(reading) |>
# where() is a helper function evaluating the contents of variables
# specify full function call with ~ at the start and .x replacing the variable name
summarize(across(where(is.numeric), ~mean(.x, na.rm = TRUE)))
## # A tibble: 2 × 5
## reading id age scholarship grade
## <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 5080. 19.7 65 2.54
## 2 TRUE 5066. 19.7 64.5 2.99
# mean of all variables with names containing the letter a
data |>
summarize(across(contains("a"), ~mean(.x, na.rm = TRUE)))
## # A tibble: 1 × 5
## age scholarship additional_work reading grade
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 19.7 64.8 0.338 0.476 2.76
# sample size of each group and correlation between age and grade per group
data |>
group_by(reading, listening) |>
summarize(age_grade_correlation = cor(age, grade),
n = n())
## # A tibble: 4 × 4
## # Groups: reading [2]
## reading listening age_grade_correlation n
## <lgl> <lgl> <dbl> <int>
## 1 FALSE FALSE 0.267 37
## 2 FALSE TRUE 0.334 39
## 3 TRUE FALSE 0.0720 33
## 4 TRUE TRUE -0.0777 36
The list of helper functions that can be used instead of listing which
variables to include/exclude is in the help file accessible with
?dplyr_tidy_select.
To export a descriptive statistics table, we can use the relevant
write...() function shown in the data importing section
(e.g. write_csv() for tibbles, general write() for HTML, plain text,
LaTeX, other general types). CSV tables already copy nicely into e.g. MS
Word. If using LaTeX or RMarkdown, the knitr package contains the
kable() function that directly improves on the design of the table
without needing formatting afterwards.
data |>
count(reading, listening) |>
write_csv("table1.csv")
data |>
count(reading, listening) |>
# knitr:: allows using function from the package without library(knitr)
knitr::kable()
| reading | listening | n |
|---|---|---|
| FALSE | FALSE | 37 |
| FALSE | TRUE | 39 |
| TRUE | FALSE | 33 |
| TRUE | TRUE | 36 |