README.md
In rebelrebel04/xplor: Data Exploration Utilities
xplor
A package of lightweight utility functions for assisting with interactive data exploration, particularly geared toward understanding record layouts of relational data (e.g., the grain of a table, mappings between primary and affiliate dimensions, extent of normalization/denormalization, etc.). NSE and SE versions of core functions are provided to facilitate interactive and programming use. Functions are designed to work well with dplyr.
Installation
Install xplor from github with:
# install.packages("devtools")
devtools::install_github("xplor/rebelrebel04")
Core Functions
cpf
Create cumulative percentile/frequency tables with cpf. The most common use case is interactive crosstabs (though note that the output frequency table is always in long format).
data(mtcars)
cpf(mtcars, gear)
## gear n cumsum pct cumpct
## 1 3 15 15 47% 47%
## 2 4 12 27 38% 84%
## 3 5 5 32 16% 100%
## 4 ==== 32 NA 100% NA
You can also request a lightly-formatted kable when running cpf within an Rmarkdown file:
cpf(mtcars, gear, kable = TRUE)
| gear | n| cumsum| pct| cumpct|
|:-----|----:|-------:|-----:|-------:|
| 3 | 15| 15| 47%| 47%|
| 4 | 12| 27| 38%| 84%|
| 5 | 5| 32| 16%| 100%|
| ==== | 32| NA| 100%| NA|
A nice feature is that you can pass cpf computed variables using bare variable names; optionally provide descriptive names for the table columns:
cpf(mtcars, carb >= 4)
## carb >= 4 n cumsum pct cumpct
## 1 FALSE 20 20 62% 62%
## 2 TRUE 12 32 38% 100%
## 3 ==== 32 NA 100% NA
cpf(mtcars, has_cyl = !is.na(cyl))
## has_cyl n cumsum pct cumpct
## 1 TRUE 32 32 100% 100%
## 2 ==== 32 NA 100% NA
Optionally specify a weighting variable for frequencies:
cpf(mtcars, carb, wt = "cyl")
## carb n cumsum pct cumpct
## 1 4 72 72 36% 36%
## 2 2 56 128 28% 65%
## 3 1 32 160 16% 81%
## 4 3 24 184 12% 93%
## 5 8 8 192 4% 97%
## 6 6 6 198 3% 100%
## 7 ==== 198 NA 100% NA
A chi-square test can be requested for frequency tables that have two or more dimensions:
cpf(mtcars, cyl > 4, hp > 123, chi_square = TRUE)
## Test for independence of all factors:
## Chisq = 15, df = 1, p-value = 0.00012
## cyl > 4 hp > 123 n cumsum pct cumpct
## 1 TRUE TRUE 15 15 47% 47%
## 2 FALSE FALSE 11 26 34% 81%
## 3 TRUE FALSE 6 32 19% 100%
## 4 ==== ==== 32 NA 100% NA
cpf returns a dataframe containing the frequency table, which can be useful for downstream processing or plotting:
cpf(mtcars, gear, cyl, margin = FALSE) %>%
ggplot2::ggplot(ggplot2::aes(x = gear, y = cyl, fill = pct)) +
ggplot2::geom_tile() +
ggplot2::scale_fill_gradient(label = scales::percent)
The SE version of cpf_ is provided in the event you have variable names built up through some other means:
vars <- names(mtcars)[grepl("^c", names(mtcars))]
cpf_(mtcars, .dots = vars)
## cyl carb n cumsum pct cumpct
## 1 4 2 6 6 19% 19%
## 2 8 4 6 12 19% 38%
## 3 4 1 5 17 16% 53%
## 4 6 4 4 21 12% 66%
## 5 8 2 4 25 12% 78%
## 6 8 3 3 28 9% 88%
## 7 6 1 2 30 6% 94%
## 8 6 6 1 31 3% 97%
## 9 8 8 1 32 3% 100%
## 10 ==== ==== 32 NA 100% NA
has
has is a wrapper around the common pattern of using cpf to cross-tab missing value (NA) counts across multiple dimensions:
mtcars.na <- mtcars
set.seed(1234)
mtcars.na[runif(10, 1, nrow(mtcars.na)), "gear"] <- NA
mtcars.na[runif(5, 1, nrow(mtcars.na)), "cyl"] <- NA
has(mtcars.na, gear, cyl, carb, kable = TRUE)
| has_gear | has_cyl | has_carb | n| cumsum| pct| cumpct|
|:----------|:---------|:----------|----:|-------:|-----:|-------:|
| TRUE | TRUE | TRUE | 19| 19| 59%| 59%|
| FALSE | TRUE | TRUE | 8| 27| 25%| 84%|
| TRUE | FALSE | TRUE | 5| 32| 16%| 100%|
| ==== | ==== | ==== | 32| NA| 100%| NA|
# This is equivalent to:
# cpf(mtcars.na, has_gear = is.na(gear), has_cyl = is.na(cyl), has_carb = is.na(carb), kable = TRUE)
dup
Check for duplicate cases in a dataframe with dup:
dup(mtcars, gear, cyl)
##
##
## isUnique n cumsum pct cumpct
## --------- --- ------- ----- -------
## FALSE 30 30 94% 94%
## TRUE 2 32 6% 100%
## ==== 32 NA 100% NA
dup divides cases into those with unique keys (which appear only once in the dataset) and those with non-unique keys. This varies from the duplicated function in base R, which only counts cases as duplicates after the first (or last) occurrence. This is why dup reports the number of unique vs. non-unique cases by the specified keys.
dup invisibly returns the original dataframe filtered to rows with non-unique keys, with a variable .n attached indicating the total number of cases with each key combination:
print(dup(mtcars, disp))
##
##
## isUnique n cumsum pct cumpct
## --------- --- ------- ----- -------
## TRUE 23 23 72% 72%
## FALSE 9 32 28% 100%
## ==== 32 NA 100% NA
## # A tibble: 9 × 12
## mpg cyl disp hp drat wt qsec vs am gear carb .n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 3
## 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 3
## 3 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 3
## 4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2
## 5 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
## 6 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 2
## 7 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 2
## 8 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2
## 9 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 2
A weight can be supplied to generate weighted duplication rates. This can be useful to determine if duplication is limited to low-frequency cases when weighting by a separate frequency variable:
dup(mtcars, disp, wt = "mpg")
##
##
## isUnique n cumsum pct cumpct
## --------- ---- ------- ----- -------
## TRUE 482 482 75% 75%
## FALSE 161 643 25% 100%
## ==== 643 NA 100% NA
ss
Produce a quick table of summary statistics with ss. The function accepts a named list of functions (specified as formulas) via the funs argument, with the default list covering the basics. If no variables are specified the table will summarize all numeric columns in the dataset. If plot = TRUE a facet-wrapped plot of histograms will be produced as a side-effect.
ss(mtcars, plot = TRUE, kable = TRUE)
| Variable | N| NAs| Min| P10| Mean| Median| P90| Max| SD| CV|
|:---------|----:|----:|------:|------:|-------:|-------:|-------:|-------:|-------:|-----:|
| mpg | 32| 0| 10.40| 14.34| 20.09| 19.20| 30.09| 33.90| 6.03| 0.30|
| cyl | 32| 0| 4.00| 4.00| 6.19| 6.00| 8.00| 8.00| 1.79| 0.29|
| disp | 32| 0| 71.10| 80.61| 230.72| 196.30| 396.00| 472.00| 123.94| 0.54|
| hp | 32| 0| 52.00| 66.00| 146.69| 123.00| 243.50| 335.00| 68.56| 0.47|
| drat | 32| 0| 2.76| 3.01| 3.60| 3.70| 4.21| 4.93| 0.53| 0.15|
| wt | 32| 0| 1.51| 1.96| 3.22| 3.33| 4.05| 5.42| 0.98| 0.30|
| qsec | 32| 0| 14.50| 15.53| 17.85| 17.71| 19.99| 22.90| 1.79| 0.10|
| vs | 32| 0| 0.00| 0.00| 0.44| 0.00| 1.00| 1.00| 0.50| 1.15|
| am | 32| 0| 0.00| 0.00| 0.41| 0.00| 1.00| 1.00| 0.50| 1.23|
| gear | 32| 0| 3.00| 3.00| 3.69| 4.00| 5.00| 5.00| 0.74| 0.20|
| carb | 32| 0| 1.00| 1.00| 2.81| 2.00| 4.00| 8.00| 1.62| 0.57|
A dplyr-grouped dataframe can be passed to produce a dimensional summary table:
library(dplyr)
mtcars %>%
group_by(cyl) %>%
ss(disp, mpg, kable = TRUE)
| cyl| Variable | N| NAs| Min| P10| Mean| Median| P90| Max| SD| CV|
|----:|:---------|----:|----:|------:|-------:|-------:|-------:|-------:|------:|------:|-----:|
| 4| disp | 11| 0| 71.1| 75.70| 105.14| 108.0| 140.80| 146.7| 26.87| 0.26|
| 4| mpg | 11| 0| 21.4| 21.50| 26.66| 26.0| 32.40| 33.9| 4.51| 0.17|
| 6| disp | 7| 0| 145.0| 154.00| 183.31| 167.6| 238.20| 258.0| 41.56| 0.23|
| 6| mpg | 7| 0| 17.8| 17.98| 19.74| 19.7| 21.16| 21.4| 1.45| 0.07|
| 8| disp | 14| 0| 275.8| 275.80| 353.10| 350.5| 454.00| 472.0| 67.77| 0.19|
| 8| mpg | 14| 0| 10.4| 11.27| 15.10| 15.2| 18.28| 19.2| 2.56| 0.17|
Pass a custom list of summary functions as formulas on x:
mtcars %>%
ss(
funs = list(
"variance" = ~var(x, na.rm = TRUE),
"skewness" = ~moments::skewness(x, na.rm = TRUE),
"kurtosis" = ~moments::kurtosis(x, na.rm = TRUE)
),
kable = TRUE
)
| Variable | variance| skewness| kurtosis|
|:---------|---------:|---------:|---------:|
| mpg | 36.32| 0.64| 2.80|
| cyl | 3.19| -0.18| 1.32|
| disp | 15360.80| 0.40| 1.91|
| hp | 4700.87| 0.76| 3.05|
| drat | 0.29| 0.28| 2.44|
| wt | 0.96| 0.44| 3.17|
| qsec | 3.19| 0.39| 3.55|
| vs | 0.25| 0.25| 1.06|
| am | 0.25| 0.38| 1.15|
| gear | 0.54| 0.55| 2.06|
| carb | 2.61| 1.10| 4.54|
mapping
For dimensions x and y in a dataset, there may be only one unique value of y associated with each unique value of x, or there may be multiple unique values of y associated with each unique value of x. mapping summarizes the association between x and y in a dataset, specified as a formula x ~ y:
mapping(cyl ~ gear, mtcars)
## unique_ys n_xs pct_xs
## 1 1 1 33%
## 2 2 2 67%
In this example, there is one value of the "x" variable cyl that has a single unique "y" (gear) value associated with it, and two values of cyl that each have two unique gear values associated with them.
findGrain
Often with a new dataset, you may not know the grain of the table (i.e., the combination of dimensions at which cases are unique). This function searches through candidate dimensions and ranks the top combinations in descending degree of de-duplication (aka normalization). In theory, the true grain of a table would be defined by the smallest combination of dimensions that yields 0 duplicates.
data(sleep)
findGrain(sleep, group, ID)
## n_keys keys duplicates
## 1 2 group * ID 0
## 2 1 ID 10
## 3 1 group 18
vjoin
Verbose join wrapper around dplyr join functions to provide diagnostic output. Joined result is returned invisibly.
a <- mtcars[1:10, c("hp", "mpg", "disp")]
b <- mtcars[11:15, c("hp", "cyl")]
vjoin(a, b, "hp")
## a LEFT JOIN b ON hp
## SET a: 10 obs 3 variables 8 unique keys 1 keys with duplicates
## SET b: 5 obs 2 variables 3 unique keys 1 keys with duplicates
## KEYS IN a AND b: 1
## KEYS IN a NOT b: 7
## KEYS IN b NOT a: 2
## Result: 10 obs 6 variables 8 unique keys 1 keys with duplicates
## Match Table (unweighted):
## .in_a .in_b n cumsum pct cumpct
## 1 TRUE FALSE 9 9 90% 90%
## 2 TRUE TRUE 1 10 10% 100%
## 3 ==== ==== 10 NA 100% NA
Match rates can be weighted:
vjoin(a, b, "hp", wt = "disp")
## a LEFT JOIN b ON hp
## SET a: 10 obs 3 variables 8 unique keys 1 keys with duplicates
## SET b: 5 obs 2 variables 3 unique keys 1 keys with duplicates
## KEYS IN a AND b: 1
## KEYS IN a NOT b: 7
## KEYS IN b NOT a: 2
## Result: 10 obs 6 variables 8 unique keys 1 keys with duplicates
## Match Table (weighted by disp):
## .in_a .in_b n cumsum pct cumpct
## 1 TRUE FALSE 1,918 1,918 92% 92%
## 2 TRUE TRUE 168 2,086 8% 100%
## 3 ==== ==== 2,086 NA 100% NA
rebelrebel04/xplor documentation built on May 27, 2019, 4:01 a.m.
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xplor
A package of lightweight utility functions for assisting with interactive data exploration, particularly geared toward understanding record layouts of relational data (e.g., the grain of a table, mappings between primary and affiliate dimensions, extent of normalization/denormalization, etc.). NSE and SE versions of core functions are provided to facilitate interactive and programming use. Functions are designed to work well with dplyr.
Installation
Install xplor from github with:
# install.packages("devtools")
devtools::install_github("xplor/rebelrebel04")
Core Functions
cpf
Create cumulative percentile/frequency tables with cpf. The most common use case is interactive crosstabs (though note that the output frequency table is always in long format).
data(mtcars)
cpf(mtcars, gear)
## gear n cumsum pct cumpct
## 1 3 15 15 47% 47%
## 2 4 12 27 38% 84%
## 3 5 5 32 16% 100%
## 4 ==== 32 NA 100% NA
You can also request a lightly-formatted kable when running cpf within an Rmarkdown file:
cpf(mtcars, gear, kable = TRUE)
| gear | n| cumsum| pct| cumpct| |:-----|----:|-------:|-----:|-------:| | 3 | 15| 15| 47%| 47%| | 4 | 12| 27| 38%| 84%| | 5 | 5| 32| 16%| 100%| | ==== | 32| NA| 100%| NA|
A nice feature is that you can pass cpf computed variables using bare variable names; optionally provide descriptive names for the table columns:
cpf(mtcars, carb >= 4)
## carb >= 4 n cumsum pct cumpct
## 1 FALSE 20 20 62% 62%
## 2 TRUE 12 32 38% 100%
## 3 ==== 32 NA 100% NA
cpf(mtcars, has_cyl = !is.na(cyl))
## has_cyl n cumsum pct cumpct
## 1 TRUE 32 32 100% 100%
## 2 ==== 32 NA 100% NA
Optionally specify a weighting variable for frequencies:
cpf(mtcars, carb, wt = "cyl")
## carb n cumsum pct cumpct
## 1 4 72 72 36% 36%
## 2 2 56 128 28% 65%
## 3 1 32 160 16% 81%
## 4 3 24 184 12% 93%
## 5 8 8 192 4% 97%
## 6 6 6 198 3% 100%
## 7 ==== 198 NA 100% NA
A chi-square test can be requested for frequency tables that have two or more dimensions:
cpf(mtcars, cyl > 4, hp > 123, chi_square = TRUE)
## Test for independence of all factors:
## Chisq = 15, df = 1, p-value = 0.00012
## cyl > 4 hp > 123 n cumsum pct cumpct
## 1 TRUE TRUE 15 15 47% 47%
## 2 FALSE FALSE 11 26 34% 81%
## 3 TRUE FALSE 6 32 19% 100%
## 4 ==== ==== 32 NA 100% NA
cpf returns a dataframe containing the frequency table, which can be useful for downstream processing or plotting:
cpf(mtcars, gear, cyl, margin = FALSE) %>%
ggplot2::ggplot(ggplot2::aes(x = gear, y = cyl, fill = pct)) +
ggplot2::geom_tile() +
ggplot2::scale_fill_gradient(label = scales::percent)
The SE version of cpf_ is provided in the event you have variable names built up through some other means:
vars <- names(mtcars)[grepl("^c", names(mtcars))]
cpf_(mtcars, .dots = vars)
## cyl carb n cumsum pct cumpct
## 1 4 2 6 6 19% 19%
## 2 8 4 6 12 19% 38%
## 3 4 1 5 17 16% 53%
## 4 6 4 4 21 12% 66%
## 5 8 2 4 25 12% 78%
## 6 8 3 3 28 9% 88%
## 7 6 1 2 30 6% 94%
## 8 6 6 1 31 3% 97%
## 9 8 8 1 32 3% 100%
## 10 ==== ==== 32 NA 100% NA
has
has is a wrapper around the common pattern of using cpf to cross-tab missing value (NA) counts across multiple dimensions:
mtcars.na <- mtcars
set.seed(1234)
mtcars.na[runif(10, 1, nrow(mtcars.na)), "gear"] <- NA
mtcars.na[runif(5, 1, nrow(mtcars.na)), "cyl"] <- NA
has(mtcars.na, gear, cyl, carb, kable = TRUE)
| has_gear | has_cyl | has_carb | n| cumsum| pct| cumpct| |:----------|:---------|:----------|----:|-------:|-----:|-------:| | TRUE | TRUE | TRUE | 19| 19| 59%| 59%| | FALSE | TRUE | TRUE | 8| 27| 25%| 84%| | TRUE | FALSE | TRUE | 5| 32| 16%| 100%| | ==== | ==== | ==== | 32| NA| 100%| NA|
# This is equivalent to:
# cpf(mtcars.na, has_gear = is.na(gear), has_cyl = is.na(cyl), has_carb = is.na(carb), kable = TRUE)
dup
Check for duplicate cases in a dataframe with dup:
dup(mtcars, gear, cyl)
##
##
## isUnique n cumsum pct cumpct
## --------- --- ------- ----- -------
## FALSE 30 30 94% 94%
## TRUE 2 32 6% 100%
## ==== 32 NA 100% NA
dup divides cases into those with unique keys (which appear only once in the dataset) and those with non-unique keys. This varies from the duplicated function in base R, which only counts cases as duplicates after the first (or last) occurrence. This is why dup reports the number of unique vs. non-unique cases by the specified keys.
dup invisibly returns the original dataframe filtered to rows with non-unique keys, with a variable .n attached indicating the total number of cases with each key combination:
print(dup(mtcars, disp))
##
##
## isUnique n cumsum pct cumpct
## --------- --- ------- ----- -------
## TRUE 23 23 72% 72%
## FALSE 9 32 28% 100%
## ==== 32 NA 100% NA
## # A tibble: 9 × 12
## mpg cyl disp hp drat wt qsec vs am gear carb .n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 3
## 2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 3
## 3 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 3
## 4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2
## 5 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
## 6 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 2
## 7 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 2
## 8 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2
## 9 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 2
A weight can be supplied to generate weighted duplication rates. This can be useful to determine if duplication is limited to low-frequency cases when weighting by a separate frequency variable:
dup(mtcars, disp, wt = "mpg")
##
##
## isUnique n cumsum pct cumpct
## --------- ---- ------- ----- -------
## TRUE 482 482 75% 75%
## FALSE 161 643 25% 100%
## ==== 643 NA 100% NA
ss
Produce a quick table of summary statistics with ss. The function accepts a named list of functions (specified as formulas) via the funs argument, with the default list covering the basics. If no variables are specified the table will summarize all numeric columns in the dataset. If plot = TRUE a facet-wrapped plot of histograms will be produced as a side-effect.
ss(mtcars, plot = TRUE, kable = TRUE)
| Variable | N| NAs| Min| P10| Mean| Median| P90| Max| SD| CV| |:---------|----:|----:|------:|------:|-------:|-------:|-------:|-------:|-------:|-----:| | mpg | 32| 0| 10.40| 14.34| 20.09| 19.20| 30.09| 33.90| 6.03| 0.30| | cyl | 32| 0| 4.00| 4.00| 6.19| 6.00| 8.00| 8.00| 1.79| 0.29| | disp | 32| 0| 71.10| 80.61| 230.72| 196.30| 396.00| 472.00| 123.94| 0.54| | hp | 32| 0| 52.00| 66.00| 146.69| 123.00| 243.50| 335.00| 68.56| 0.47| | drat | 32| 0| 2.76| 3.01| 3.60| 3.70| 4.21| 4.93| 0.53| 0.15| | wt | 32| 0| 1.51| 1.96| 3.22| 3.33| 4.05| 5.42| 0.98| 0.30| | qsec | 32| 0| 14.50| 15.53| 17.85| 17.71| 19.99| 22.90| 1.79| 0.10| | vs | 32| 0| 0.00| 0.00| 0.44| 0.00| 1.00| 1.00| 0.50| 1.15| | am | 32| 0| 0.00| 0.00| 0.41| 0.00| 1.00| 1.00| 0.50| 1.23| | gear | 32| 0| 3.00| 3.00| 3.69| 4.00| 5.00| 5.00| 0.74| 0.20| | carb | 32| 0| 1.00| 1.00| 2.81| 2.00| 4.00| 8.00| 1.62| 0.57|
A dplyr-grouped dataframe can be passed to produce a dimensional summary table:
library(dplyr)
mtcars %>%
group_by(cyl) %>%
ss(disp, mpg, kable = TRUE)
| cyl| Variable | N| NAs| Min| P10| Mean| Median| P90| Max| SD| CV| |----:|:---------|----:|----:|------:|-------:|-------:|-------:|-------:|------:|------:|-----:| | 4| disp | 11| 0| 71.1| 75.70| 105.14| 108.0| 140.80| 146.7| 26.87| 0.26| | 4| mpg | 11| 0| 21.4| 21.50| 26.66| 26.0| 32.40| 33.9| 4.51| 0.17| | 6| disp | 7| 0| 145.0| 154.00| 183.31| 167.6| 238.20| 258.0| 41.56| 0.23| | 6| mpg | 7| 0| 17.8| 17.98| 19.74| 19.7| 21.16| 21.4| 1.45| 0.07| | 8| disp | 14| 0| 275.8| 275.80| 353.10| 350.5| 454.00| 472.0| 67.77| 0.19| | 8| mpg | 14| 0| 10.4| 11.27| 15.10| 15.2| 18.28| 19.2| 2.56| 0.17|
Pass a custom list of summary functions as formulas on x:
mtcars %>%
ss(
funs = list(
"variance" = ~var(x, na.rm = TRUE),
"skewness" = ~moments::skewness(x, na.rm = TRUE),
"kurtosis" = ~moments::kurtosis(x, na.rm = TRUE)
),
kable = TRUE
)
| Variable | variance| skewness| kurtosis| |:---------|---------:|---------:|---------:| | mpg | 36.32| 0.64| 2.80| | cyl | 3.19| -0.18| 1.32| | disp | 15360.80| 0.40| 1.91| | hp | 4700.87| 0.76| 3.05| | drat | 0.29| 0.28| 2.44| | wt | 0.96| 0.44| 3.17| | qsec | 3.19| 0.39| 3.55| | vs | 0.25| 0.25| 1.06| | am | 0.25| 0.38| 1.15| | gear | 0.54| 0.55| 2.06| | carb | 2.61| 1.10| 4.54|
mapping
For dimensions x and y in a dataset, there may be only one unique value of y associated with each unique value of x, or there may be multiple unique values of y associated with each unique value of x. mapping summarizes the association between x and y in a dataset, specified as a formula x ~ y:
mapping(cyl ~ gear, mtcars)
## unique_ys n_xs pct_xs
## 1 1 1 33%
## 2 2 2 67%
In this example, there is one value of the "x" variable cyl that has a single unique "y" (gear) value associated with it, and two values of cyl that each have two unique gear values associated with them.
findGrain
Often with a new dataset, you may not know the grain of the table (i.e., the combination of dimensions at which cases are unique). This function searches through candidate dimensions and ranks the top combinations in descending degree of de-duplication (aka normalization). In theory, the true grain of a table would be defined by the smallest combination of dimensions that yields 0 duplicates.
data(sleep)
findGrain(sleep, group, ID)
## n_keys keys duplicates
## 1 2 group * ID 0
## 2 1 ID 10
## 3 1 group 18
vjoin
Verbose join wrapper around dplyr join functions to provide diagnostic output. Joined result is returned invisibly.
a <- mtcars[1:10, c("hp", "mpg", "disp")]
b <- mtcars[11:15, c("hp", "cyl")]
vjoin(a, b, "hp")
## a LEFT JOIN b ON hp
## SET a: 10 obs 3 variables 8 unique keys 1 keys with duplicates
## SET b: 5 obs 2 variables 3 unique keys 1 keys with duplicates
## KEYS IN a AND b: 1
## KEYS IN a NOT b: 7
## KEYS IN b NOT a: 2
## Result: 10 obs 6 variables 8 unique keys 1 keys with duplicates
## Match Table (unweighted):
## .in_a .in_b n cumsum pct cumpct
## 1 TRUE FALSE 9 9 90% 90%
## 2 TRUE TRUE 1 10 10% 100%
## 3 ==== ==== 10 NA 100% NA
Match rates can be weighted:
vjoin(a, b, "hp", wt = "disp")
## a LEFT JOIN b ON hp
## SET a: 10 obs 3 variables 8 unique keys 1 keys with duplicates
## SET b: 5 obs 2 variables 3 unique keys 1 keys with duplicates
## KEYS IN a AND b: 1
## KEYS IN a NOT b: 7
## KEYS IN b NOT a: 2
## Result: 10 obs 6 variables 8 unique keys 1 keys with duplicates
## Match Table (weighted by disp):
## .in_a .in_b n cumsum pct cumpct
## 1 TRUE FALSE 1,918 1,918 92% 92%
## 2 TRUE TRUE 168 2,086 8% 100%
## 3 ==== ==== 2,086 NA 100% NA
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