Description Usage Arguments Details See Also Examples
The plot_correlate() visualize correlation plot for find relationship between two numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi.
1 2 3 4 5 6 7 8 | ## S3 method for class 'tbl_dbi'
plot_correlate(
.data,
...,
in_database = FALSE,
collect_size = Inf,
method = c("pearson", "kendall", "spearman")
)
|
.data |
a tbl_dbi. |
... |
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. |
in_database |
Specifies whether to perform in-database operations. If TRUE, most operations are performed in the DBMS. if FALSE, table data is taken in R and operated in-memory. Not yet supported in_database = TRUE. |
collect_size |
a integer. The number of data samples from the DBMS to R. Applies only if in_database = FALSE. |
method |
a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated. 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.data.frame
, plot_outlier.tbl_dbi
.
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 50 51 52 53 | library(dplyr)
# connect DBMS
con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
# copy heartfailure to the DBMS with a table named TB_HEARTFAILURE
copy_to(con_sqlite, heartfailure, name = "TB_HEARTFAILURE", overwrite = TRUE)
# Using pipes ---------------------------------
# Visualize correlation plot of all numerical variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
plot_correlate()
# Positive values select variables, and In-memory mode and collect size is 200
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
plot_correlate(platelets, sodium, collect_size = 200)
# Negative values to drop variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
plot_correlate(-platelets, -sodium)
# Positions values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
plot_correlate(1)
# Positions values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
plot_correlate(-1, -2, -3, -5, -6)
# Using pipes & dplyr -------------------------
# Visualize correlation plot of 'sodiumsodium' variable by 'smoking'
# and 'death_event' variables.
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
group_by(smoking, death_event) %>%
plot_correlate(sodium)
# Extract only those with 'smoking' variable level is "Yes",
# and visualize correlation plot of 'sodium' variable by 'sex'
# and 'death_event' variables.
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
filter(smoking == "Yes") %>%
group_by(sex, death_event) %>%
plot_correlate(sodium)
# Disconnect DBMS
DBI::dbDisconnect(con_sqlite)
|
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