Description Usage Arguments Details Value Examples
Generate automatic data summaries for different types of data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data_summary(input, ...)
## S3 method for class 'data.frame'
data_summary(input, at, show = TRUE)
## S3 method for class 'grouped_df'
data_summary(input, at, show = TRUE)
## S3 method for class 'numeric'
data_summary(input, show = TRUE)
## S3 method for class 'logical'
data_summary(input, show = TRUE)
## S3 method for class 'character'
data_summary(input, show = TRUE)
## S3 method for class 'factor'
data_summary(input, show = TRUE)
|
input |
Data that should be summarized, can be of class |
at |
|
show |
Indicator whether output should be returned or passed to |
The function data_summary
provides 11 columns of output for each variable selected in at
:
Variable - variable name
Cluster - cluster for grouping
Type - variable type
n - number of non-missing observations
SD - standard deviation
Min. - minimum
1st Qu. - .25 percentile
Mean - mean
Median - median, .50 percentile
3rd Qu. - .75 percentile
Max. - maximum
Groups - number of distinct groups represented in the variable
The exact output for the function depends on the data provided as input
:
data.frame
- The summary is provided for each variables selected in at
in a separate row, the summary statistics depend on the variable type
grouped_df
- As for data.frame
but the summary is provided for each group separately, the respective group is shown in the variable Cluster
numeric
- All output columns except "Groups"
logical
- All output columns
character
- Only output columns Variable, Type, n, Groups
factor
- As for character
Summary statistics for the data provided as "input"
and the variables selected in at
. When the option show
is set to TRUE
the output is printed to the console. If the package knitr
is available, the output is printed using knitr::kable
. When show
is set to FALSE
the output is returned as data.frame
or tibble
(when package tibble
is available).
1 2 3 4 5 | data <- supportR::create_data(n = 50)
data$country <- as.factor(data$country)
data_summary(data, at = c("firm_value", "female_ceo", "industry", "country"))
data_summary(data$firm_value, show = FALSE)
data_summary(dplyr::group_by(data, female_ceo), at = c("firm_value", "country"))
|
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