Grow a decision tree
Grow a decision tree with one line of code:
iris %>% select(-Species) %>% explain_tree(target = is_versicolor)
You can control the growth of the tree using the parameters maxdepth, minsplit and cp.
Interactive data exploration with one line of code or use a easy to remember set of tidy functions for exploratory data analysis. Introduces two main verbs. describe() to describe a variable or table, explore() to grafically explore a variable or table.
explore package on Github: https://github.com/rolkra/explore
As the explore-functions fits well into the tidyverse, we load the dplyr-package as well.
library(dplyr)
library(explore)Explore your dataset (in this case the iris dataset) in one line of code:
explore(iris)A shiny app is launched, you can inspect individual variable, explore their relation to a binary target, grow a decision tree or create a fully automated report of all variables with a few “mouseclicks”.
You can choose each variable containng 0/1, FALSE/TRUE or “no”/“yes” as a target. As the iris dataset doesn’t contain a binary target, we create one:
iris$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0)
iris %>% explore()Create a rich HTML report of all variables with one line of code:
# report of all variables
iris %>% report(output_file = "report.html", output_dir = tempdir())Or you can simply add a target and create the report:
# report of all variables and their relationship with a binary target
iris$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0)
iris %>%
report(output_file = "report.html",
output_dir = tempdir(),
target = is_versicolor)Grow a decision tree with one line of code:
iris %>% select(-Species) %>% explain_tree(target = is_versicolor)
You can control the growth of the tree using the parameters maxdepth, minsplit and cp.
Explore a variable with one line of code. You don’t have to care if a variable is numerical or categorical.
iris %>% explore(Species)
iris %>% explore(Sepal.Length)
Explore a variable and its relationship with a binary target with one line of code. You don’t have to care if a variable is numerical or categorical.
iris %>% explore(Species, target = is_versicolor)
iris %>% explore(Sepal.Length, target = is_versicolor)
iris %>%
select(Sepal.Length, Sepal.Width) %>%
explore_all()
iris %>%
select(Sepal.Length, Sepal.Width, is_versicolor) %>%
explore_all(target = is_versicolor)
iris %>%
select(Sepal.Length, Sepal.Width, is_versicolor) %>%
explore_all(target = is_versicolor, density = FALSE)
Explore correlation between two variables with one line of code:
iris %>% explore(Sepal.Length, Petal.Length)
You can add a target too:
iris %>% explore(Sepal.Length, Petal.Length, target = is_versicolor)
If you use explore to explore a variable and want to set lower and upper limits for values, you can use the min_val and max_val parameters. All values below min_val will be set to min_val. All values above max_val will be set to max_val.
iris %>% explore(Sepal.Length, min_val = 4.5, max_val = 7)
explore uses auto-scale by default. To deactivate it use the parameter auto_scale = FALSE
You can force the explore-function to use bars with the parameter density = FALSE
iris %>% explore(Sepal.Length, density = FALSE, auto_scale = FALSE)
Describe your data in one line of code:
iris %>% describe()
#> variable type na na_pct unique min mean max
#> 1 Sepal.Length dou 0 0 35 4.3 5.84 7.9
#> 2 Sepal.Width dou 0 0 23 2.0 3.06 4.4
#> 3 Petal.Length dou 0 0 43 1.0 3.76 6.9
#> 4 Petal.Width dou 0 0 22 0.1 1.20 2.5
#> 5 Species fct 0 0 3 NA NA NA
#> 6 is_versicolor dou 0 0 2 0.0 0.33 1.0The result is a data-frame, where each row is a variable of your data. You can use filter from dplyr for quick checks:
# show all variables that contain less than 5 unique values
iris %>% describe() %>% filter(unique < 5)
#> variable type na na_pct unique min mean max
#> 1 Species fct 0 0 3 NA NA NA
#> 2 is_versicolor dou 0 0 2 0 0.33 1# show all variables contain NA values
iris %>% describe() %>% filter(na > 0)
#> [1] variable type na na_pct unique min mean max
#> <0 rows> (or 0-length row.names)You can use describe for describing variables too. You don’t need to care if a variale is numerical or categorical. The output is a text.
# describe a numerical variable
iris %>% describe(Species)
#> variable = Species
#> type = factor
#> na = 0 of 150 (0%)
#> unique = 3
#> setosa = 50 (33.3%)
#> versicolor = 50 (33.3%)
#> virginica = 50 (33.3%)# describe a categorical variable
iris %>% describe(Sepal.Length)
#> variable = Sepal.Length
#> type = double
#> na = 0 of 150 (0%)
#> unique = 35
#> min|max = 4.3 | 7.9
#> q05|q95 = 4.6 | 7.3
#> q25|q75 = 5.1 | 6.4
#> median = 5.8
#> mean = 5.8Create a Data Dictionary of a dataset (Markdown File data_dict.md)
iris %>% data_dict_md(output_dir = tempdir())Add title, detailed descriptions and change default filename
description <- data.frame(
variable = c("Species"),
description = c("Species of Iris flower"))
data_dict_md(iris,
title = "iris flower data set",
description = description,
output_file = "data_dict_iris.md",
output_dir = tempdir())To clean a variable you can use clean_var. With one line of code you can rename a variable, replace NA-values and set a minimum and maximum for the value.
iris %>%
clean_var(Sepal.Length,
min_val = 4.5,
max_val = 7.0,
na = 5.8,
name = "sepal_length") %>%
describe()
#> variable type na na_pct unique min mean max
#> 1 sepal_length dou 0 0 26 4.5 5.81 7.0
#> 2 Sepal.Width dou 0 0 23 2.0 3.06 4.4
#> 3 Petal.Length dou 0 0 43 1.0 3.76 6.9
#> 4 Petal.Width dou 0 0 22 0.1 1.20 2.5
#> 5 Species fct 0 0 3 NA NA NA
#> 6 is_versicolor dou 0 0 2 0.0 0.33 1.0The explore package comes with a set easy to remember function to connect, read and write from/to a datawarehouse (dwh) using odbc.
# connect to a dwh(odbc DSN must be defined)
dwh <- dwh_connect("DWH_DSN")
# if you need to pass user and password
dwh <- dwh_connect("DWH_DSN",
user = "myuser",
pwd = rstudioapi::askForPassword()
)
# read table from a dwh
data <- dwh_read_table(dwh, "db.tablename")
# read data from a dwh using sql
data <- dwh_read_data(dwh, sql = "select * from db.tablename")
# disconnect from dwh
dwh_disconnect(dwh)To write large data to a dwh you can use dwh_fastload(). It connects to a dwh, writes the data and disconnects.
# connect to a dwh(odbc DSN must be defined)
data %>% dwh_fastload("DWH_DSN", "db.tablename")