Description Usage Arguments Details Correlation coefficient information See Also Examples
The correlate() compute Pearson's the correlation coefficient of the numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi.
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.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, 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. |
This function is useful when used with the group_by() function of the dplyr package.
If you want to compute by level of the categorical data you are interested in,
rather than the whole observation, you can use grouped_df
as the group_by() function.
This function is computed stats::cor() function by use = "pairwise.complete.obs" option.
The information derived from the numerical data compute is as follows.
var1 : names of numerical variable
var2 : name of the corresponding numeric variable
coef_corr : Pearson's correlation coefficient
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# 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 ---------------------------------
# Correlation coefficients of all numerical variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate()
# Positive values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate(platelets, sodium)
# Negative values to drop variables, and In-memory mode and collect size is 200
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate(-platelets, -sodium, collect_size = 200)
# Positions values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate(1)
# Positions values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate(-1, -2, -3, -5, -6)
# ---------------------------------------------
# Correlation coefficient
# that eliminates redundant combination of variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate() %>%
filter(as.integer(var1) > as.integer(var2))
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
correlate(platelets, sodium) %>%
filter(as.integer(var1) > as.integer(var2))
# Using pipes & dplyr -------------------------
# Compute the correlation coefficient of creatinine variable by 'hblood_pressure'
# and 'death_event' variables. And extract only those with absolute
# value of correlation coefficient is greater than 0.2
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
group_by(hblood_pressure, death_event) %>%
correlate(creatinine) %>%
filter(abs(coef_corr) >= 0.2)
# extract only those with 'hblood_pressure' variable level is "Yes",
# and compute the correlation coefficient of 'creatinine' variable
# by 'sex' and 'death_event' variables.
# And the correlation coefficient is negative and smaller than -0.3
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
filter(hblood_pressure == "Yes") %>%
group_by(sex, death_event) %>%
correlate(creatinine) %>%
filter(coef_corr < 0) %>%
filter(abs(coef_corr) > 0.3)
# Disconnect DBMS
DBI::dbDisconnect(con_sqlite)
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