Description Usage Arguments Details See Also Examples
The plot_correlate() visualize correlation plot for find relationship between two numerical variables.
1 2 3 4 5 6 7 | plot_correlate(.data, ...)
## S3 method for class 'data.frame'
plot_correlate(.data, ..., method = c("pearson", "kendall", "spearman"))
## S3 method for class 'grouped_df'
plot_correlate(.data, ..., method = c("pearson", "kendall", "spearman"))
|
.data |
a data.frame or a |
method |
a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated. |
... |
one or more unquoted expressions separated by commas. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. If the first expression is negative, plot_correlate() will automatically start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing. See vignette("EDA") for an introduction to these concepts. |
The scope of the visualization is the provide a correlation information. Since the plot is drawn for each variable, if you specify more than one variable in the ... argument, the specified number of plots are drawn.
plot_correlate.tbl_dbi
, plot_outlier.data.frame
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # Visualize correlation plot of all numerical variables
plot_correlate(heartfailure)
# Select the variable to compute
plot_correlate(heartfailure, creatinine, sodium)
plot_correlate(heartfailure, -creatinine, -sodium)
plot_correlate(heartfailure, "creatinine", "sodium")
plot_correlate(heartfailure, 1)
plot_correlate(heartfailure, creatinine, sodium, method = "spearman")
# Using dplyr::grouped_dt
library(dplyr)
gdata <- group_by(heartfailure, smoking, death_event)
plot_correlate(gdata, "creatinine")
plot_correlate(gdata)
# Using pipes ---------------------------------
# Visualize correlation plot of all numerical variables
heartfailure %>%
plot_correlate()
# Positive values select variables
heartfailure %>%
plot_correlate(creatinine, sodium)
# Negative values to drop variables
heartfailure %>%
plot_correlate(-creatinine, -sodium)
# Positions values select variables
heartfailure %>%
plot_correlate(1)
# Positions values select variables
heartfailure %>%
plot_correlate(-1, -3, -5, -7)
# Using pipes & dplyr -------------------------
# Visualize correlation plot of 'creatinine' variable by 'smoking'
# and 'death_event' variables.
heartfailure %>%
group_by(smoking, death_event) %>%
plot_correlate(creatinine)
# Extract only those with 'smoking' variable level is "Yes",
# and visualize correlation plot of 'creatinine' variable by 'hblood_pressure'
# and 'death_event' variables.
heartfailure %>%
filter(smoking == "Yes") %>%
group_by(hblood_pressure, death_event) %>%
plot_correlate(creatinine)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.