library(ggplot2) library(pander)
ggAggHist <- getFromNamespace("ggAggHist", "dataMaid") ggAggBarplot <- getFromNamespace("ggAggBarplot", "dataMaid")
The dataset examined has the following dimensions:
Feature Result
Number of observations 500
The following variable checks were performed, depending on the data type of each variable:
character factor labelled numeric integer logical Date
Identify miscoded missing values $\times$ $\times$ $\times$ $\times$ $\times$ $\times$
Identify prefixed and suffixed whitespace $\times$ $\times$ $\times$
Identify levels with < 6 obs. $\times$ $\times$ $\times$
Identify case issues $\times$ $\times$ $\times$
Identify misclassified numeric or integer variables $\times$ $\times$ $\times$
Identify outliers (Turkish Boxplot style) $\times$ $\times$
Please note that all numerical values in the following have been rounded to 2 decimals.
Variable class # unique values Missing observations Any problems?
\bminione
Feature Result
Variable type numeric
Number of missing obs. 1 (0.2 %)
Number of unique values 414
Median 10.22
1st and 3rd quartiles 9.91; 10.47
\emini \bminitwo
ggAggHist(data = structure(list(factorV = structure(1:20, .Label = c("[3.17,3.71]", "(3.71,4.25]", "(4.25,4.79]", "(4.79,5.34]", "(5.34,5.88]", "(5.88,6.42]", "(6.42,6.96]", "(6.96,7.5]", "(7.5,8.04]", "(8.04,8.58]", "(8.58,9.12]", "(9.12,9.67]", "(9.67,10.2]", "(10.2,10.7]", "(10.7,11.3]", "(11.3,11.8]", "(11.8,12.4]", "(12.4,12.9]", "(12.9,13.5]", "(13.5,14]"), class = "factor"), Freq = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 58L, 184L, 190L, 35L, 16L, 7L, 2L, 0L, 1L), xmin = c(3.16992500144231, 3.71127890213383, 4.25263280282536, 4.79398670351688, 5.33534060420841, 5.87669450489993, 6.41804840559146, 6.95940230628298, 7.50075620697451, 8.04211010766603, 8.58346400835755, 9.12481790904908, 9.6661718097406, 10.2075257104321, 10.7488796111237, 11.2902335118152, 11.8315874125067, 12.3729413131982, 12.9142952138897, 13.4556491145813), xmax = c(3.71127890213383, 4.25263280282536, 4.79398670351688, 5.33534060420841, 5.87669450489993, 6.41804840559146, 6.95940230628298, 7.50075620697451, 8.04211010766603, 8.58346400835755, 9.12481790904908, 9.6661718097406, 10.2075257104321, 10.7488796111237, 11.2902335118152, 11.8315874125067, 12.3729413131982, 12.9142952138897, 13.4556491145813, 13.9970030152728), ymin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ymax = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 58L, 184L, 190L, 35L, 16L, 7L, 2L, 0L, 1L)), .Names = c("factorV", "Freq", "xmin", "xmax", "ymin", "ymax"), row.names = c(NA, -20L ), class = "data.frame"), vnam = "x")
\emini
The following suspected missing value codes enter as regular values: \"-Inf\".
Note that a check function found the following problematic values: \"-Inf\", \"3.17\", \"8.84\", \"8.94\", \"9.02\", \"11.35\", \"11.4\", \"11.41\", \"11.41\", \"11.47\" (21 additional values omitted).
\fullline
Report generation information:
Created by Anne Helby Petersen (username: zms499
).
Report creation time: fr maj 18 2018 12:11:15
Report Was run from directory: P:/PCADSC/R
dataMaid v1.1.2 [Pkg: 2018-05-03 from CRAN (R 3.4.4)]
R version 3.4.2 (2017-09-28).
Platform: x86_64-w64-mingw32/x64 (64-bit)(Windows 7 x64 (build 7601) Service Pack 1).
Function call: makeDataReport(data = test, checks = setChecks(numeric = defaultNumericChecks(add = "identifyOutliersTBStyle",
remove = "identifyOutliers"), integer = defaultIntegerChecks(add = "identifyOutliersTBStyle",
remove = "identifyOutliers")))
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