Introduction

dataquieR provides many outputs ready to be integrated with a quality report. However, users’ requirements are usually more specific. This tutorial explains how to adjust dataquieR’s main result types (data frames and ggplot2-graphics) to meet particular needs.

Example output of `dataquieR`

The basic example used in this documentation requires two objects, which are mandatory for all dataquieR functions:

study data, and
metadata.

These are loaded from the dataquieR package:

sd1 <- prep_get_data_frame("https://dataquality.qihs.uni-greifswald.de/extdata/study_data.RData")
md1 <- prep_get_data_frame("https://dataquality.qihs.uni-greifswald.de/extdata/meta_data.RData")
#load(system.file("extdata", "meta_data.RData", package = "dataquieR"))
#md1 <- meta_data

The example output is generated using the dataquieR function: com_item_missingness().

tab_ex1 <- com_item_missingness(study_data = sd1,
                                meta_data = md1,
                                threshold_value = 90,
                                include_sysmiss = TRUE)

This function generates four objects: SummaryTable, SummaryData, SummaryPlot, ReportSummaryTable. The first two are data frames, and the last two are ggplots. The following steps show how to edit these objects.

Data frames

For the use of data frames in data quality reporting, there are two important aspects.

they should be displayed in a neat and comprehensible way. For this aspect, many packages exist, e.g. xtable, kableExtra, pixiedust, huxtable and DT, each of which integrates with some of the most output formats supported by rmarkdown/pandoc, namely html, docx, pdf, and flexdashbaord. For using these package, we ask the reader to refer to these packages’ documentation, please.
Given the size of data frames there must be ways to filter or sort them, to add or remove columns, and to rename columns. For these issues a good choice is the dplyr package from the tidyverse. The related package tidyr is useful for transforming tables from long to wide format.

The most simple output of the tab_ex1$SummaryTable data frame is (only the 10 first lines are shown to reduce the file size):

Variables	Expected observations N	Sysmiss N	Datavalues N	Missing codes N	Measurements N	Missing_expected_obs	PCT_com_crm_mv	GRADING
CENTER_0	2940	0	2940	0	2940	0	0.00	0
PSEUDO_ID	2940	0	2940	0	2940	0	0.00	0
SEX_0	2940	0	2940	0	2940	0	0.00	0
AGE_0	2940	0	2940	0	2940	0	0.00	0
AGE_GROUP_0	2940	0	2940	0	2940	0	0.00	0
AGE_1	2940	0	2940	0	2940	0	0.00	0
SEX_1	2940	0	2940	0	2940	0	0.00	0
PART_STUDY	3000	60	2940	0	2940	60	2.00	0
SBP_0	2940	239	2701	140	2561	379	12.63	1
DBP_0	2940	233	2707	163	2544	396	13.20	1

Styling

The table above comprises information regarding missing values of all variables in the study data. Nevertheless, its readability can be improved using functionalities from the kableExtra and dplyr packages.

library(dplyr)
library(kableExtra)
kable(tab_ex1$SummaryTable, "html") %>%
  kable_styling(bootstrap_options = c("hover"))

Variables	Expected observations N	Sysmiss N	Datavalues N	Missing codes N	Jumps N	Measurements N	Missing_expected_obs	PCT_com_crm_mv	GRADING
CENTER_0	2940	0	2940	0	0	2940	0	0.00	0
PSEUDO_ID	2940	0	2940	0	0	2940	0	0.00	0
SEX_0	2940	0	2940	0	0	2940	0	0.00	0
AGE_0	2940	0	2940	0	0	2940	0	0.00	0
AGE_GROUP_0	2940	0	2940	0	0	2940	0	0.00	0
AGE_1	2940	0	2940	0	0	2940	0	0.00	0
SEX_1	2940	0	2940	0	0	2940	0	0.00	0
PART_STUDY	3000	60	2940	0	0	2940	60	2.00	0
SBP_0	2940	239	2701	140	0	2561	379	12.63	1
DBP_0	2940	233	2707	163	0	2544	396	13.20	1
GLOBAL_HEALTH_VAS_0	2940	246	2694	76	0	2618	322	10.73	1
ASTHMA_0	2940	227	2713	72	0	2641	299	9.97	1
VO2_CAPCAT_0	2940	225	2715	120	0	2595	345	11.50	1
ARM_CIRC_0	2940	220	2720	63	0	2657	283	9.43	0
ARM_CIRC_DISC_0	2940	238	2702	69	0	2633	307	10.23	1
ARM_CUFF_0	2940	236	2704	81	0	2623	317	10.57	1
USR_VO2_0	2940	89	2851	69	0	2782	158	5.27	0
USR_BP_0	2940	80	2860	85	0	2775	165	5.50	0
EXAM_DT_0	2940	0	2940	0	0	2940	0	0.00	0
PART_PHYS_EXAM	2940	0	2940	0	0	2940	0	0.00	0
CRP_0	2940	172	2768	69	0	2699	241	8.03	0
BSG_0	2940	182	2758	72	0	2686	254	8.47	0
DEV_NO_0	2940	248	2692	0	0	2692	248	8.27	0
LAB_DT_0	2940	0	2940	0	0	2940	0	0.00	0
PART_LAB	2940	0	2940	0	0	2940	0	0.00	0
EDUCATION_0	2924	72	2852	380	0	2472	452	15.07	1
EDUCATION_1	2924	83	2841	416	0	2425	499	16.63	1
FAM_STAT_0	2924	122	2802	413	0	2389	535	17.83	1
MARRIED_0	2924	126	2798	432	0	2366	558	18.60	1
N_CHILD_0	2924	160	2764	428	0	2336	588	19.60	1
EATING_PREFS_0	2924	148	2776	448	0	2328	596	19.87	1
MEAT_CONS_0	2924	171	2753	451	0	2302	622	20.73	1
SMOKING_0	2924	183	2741	449	0	2292	632	21.07	1
SMOKE_SHOP_0	2924	1605	1319	513	0	806	2118	70.60	1
N_INJURIES_0	2924	244	2680	481	0	2199	725	24.17	1
N_BIRTH_0	2924	213	2711	499	1113	1099	1825	60.83	1
INCOME_GROUP_0	2924	235	2689	515	0	2174	750	25.00	1
PREGNANT_0	2924	274	2650	519	1066	1065	1859	61.97	1
MEDICATION_0	2924	1733	1191	550	0	641	2283	76.10	1
N_ATC_CODES_0	2924	310	2614	556	0	2058	866	28.87	1
USR_SOCDEM_0	2924	306	2618	332	0	2286	638	21.27	1
INT_DT_0	2924	0	2924	0	0	2924	0	0.00	0
PART_INTERVIEW	2940	0	2940	0	0	2940	0	0.00	0
ITEM_1_0	2864	317	2547	299	0	2248	616	20.53	1
ITEM_2_0	2864	343	2521	324	0	2197	667	22.23	1
ITEM_3_0	2864	355	2509	325	0	2184	680	22.67	1
ITEM_4_0	2864	347	2517	374	0	2143	721	24.03	1
ITEM_5_0	2864	416	2448	374	0	2074	790	26.33	1
ITEM_6_0	2864	427	2437	389	0	2048	816	27.20	1
ITEM_7_0	2864	395	2469	401	0	2068	796	26.53	1
ITEM_8_0	2864	424	2440	427	0	2013	851	28.37	1
QUEST_DT_0	2864	0	2864	0	0	2864	0	0.00	0
PART_QUESTIONNAIRE	2924	0	2924	0	0	2924	0	0.00	0

Paging (in R Markdown)

The table above is getting very long. Another possibility is to use paged output for data frames. For this, under output in the YAML-header, the line df_print: paged must be added. A simple call of the data frame allows then browsing rows and columns.

Alternatively, you may use the DT package, For this, you have to add a chunk like the following to your R-Markdown file:

library(knitr),
library(DT),
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE),
knit_print.data.frame = function(x, ...) { knit_print(DT::datatable(x), ...) },
registerS3method("knit_print", "data.frame", knit_print.data.frame)

Remove columns

In some instances, removing a column is needed. For example, we could remove the Expected observations N column via the $-$ operator:

tab_ex1$SummaryTable %>%
  select(-'Expected observations N')

Variables	Sysmiss N	Datavalues N	Missing codes N	Jumps N	Measurements N	Missing_expected_obs	PCT_com_crm_mv	GRADING
CENTER_0	0	2940	0	0	2940	0	0.00	0
PSEUDO_ID	0	2940	0	0	2940	0	0.00	0
SEX_0	0	2940	0	0	2940	0	0.00	0
AGE_0	0	2940	0	0	2940	0	0.00	0
AGE_GROUP_0	0	2940	0	0	2940	0	0.00	0
AGE_1	0	2940	0	0	2940	0	0.00	0
SEX_1	0	2940	0	0	2940	0	0.00	0
PART_STUDY	60	2940	0	0	2940	60	2.00	0
SBP_0	239	2701	140	0	2561	379	12.63	1
DBP_0	233	2707	163	0	2544	396	13.20	1
GLOBAL_HEALTH_VAS_0	246	2694	76	0	2618	322	10.73	1
ASTHMA_0	227	2713	72	0	2641	299	9.97	1
VO2_CAPCAT_0	225	2715	120	0	2595	345	11.50	1
ARM_CIRC_0	220	2720	63	0	2657	283	9.43	0
ARM_CIRC_DISC_0	238	2702	69	0	2633	307	10.23	1
ARM_CUFF_0	236	2704	81	0	2623	317	10.57	1
USR_VO2_0	89	2851	69	0	2782	158	5.27	0
USR_BP_0	80	2860	85	0	2775	165	5.50	0
EXAM_DT_0	0	2940	0	0	2940	0	0.00	0
PART_PHYS_EXAM	0	2940	0	0	2940	0	0.00	0
CRP_0	172	2768	69	0	2699	241	8.03	0
BSG_0	182	2758	72	0	2686	254	8.47	0
DEV_NO_0	248	2692	0	0	2692	248	8.27	0
LAB_DT_0	0	2940	0	0	2940	0	0.00	0
PART_LAB	0	2940	0	0	2940	0	0.00	0
EDUCATION_0	72	2852	380	0	2472	452	15.07	1
EDUCATION_1	83	2841	416	0	2425	499	16.63	1
FAM_STAT_0	122	2802	413	0	2389	535	17.83	1
MARRIED_0	126	2798	432	0	2366	558	18.60	1
N_CHILD_0	160	2764	428	0	2336	588	19.60	1
EATING_PREFS_0	148	2776	448	0	2328	596	19.87	1
MEAT_CONS_0	171	2753	451	0	2302	622	20.73	1
SMOKING_0	183	2741	449	0	2292	632	21.07	1
SMOKE_SHOP_0	1605	1319	513	0	806	2118	70.60	1
N_INJURIES_0	244	2680	481	0	2199	725	24.17	1
N_BIRTH_0	213	2711	499	1113	1099	1825	60.83	1
INCOME_GROUP_0	235	2689	515	0	2174	750	25.00	1
PREGNANT_0	274	2650	519	1066	1065	1859	61.97	1
MEDICATION_0	1733	1191	550	0	641	2283	76.10	1
N_ATC_CODES_0	310	2614	556	0	2058	866	28.87	1
USR_SOCDEM_0	306	2618	332	0	2286	638	21.27	1
INT_DT_0	0	2924	0	0	2924	0	0.00	0
PART_INTERVIEW	0	2940	0	0	2940	0	0.00	0
ITEM_1_0	317	2547	299	0	2248	616	20.53	1
ITEM_2_0	343	2521	324	0	2197	667	22.23	1
ITEM_3_0	355	2509	325	0	2184	680	22.67	1
ITEM_4_0	347	2517	374	0	2143	721	24.03	1
ITEM_5_0	416	2448	374	0	2074	790	26.33	1
ITEM_6_0	427	2437	389	0	2048	816	27.20	1
ITEM_7_0	395	2469	401	0	2068	796	26.53	1
ITEM_8_0	424	2440	427	0	2013	851	28.37	1
QUEST_DT_0	0	2864	0	0	2864	0	0.00	0
PART_QUESTIONNAIRE	0	2924	0	0	2924	0	0.00	0

The column Variables contains rather technical names of variables not enabling for interpretation of the content. For this reason, all dataquieR functions have an option called label_col. The selected label can be any column in the meta data, but our schema suggests to name that column LABEL. For the time being, the labels must be valid in R formulas, which means, they should not contain characters other than letters or numbers.

tab_ex2 <- com_item_missingness(study_data = sd1,
                                meta_data = md1,
                                threshold_value = 90,
                                label_col = "LABEL",
                                include_sysmiss = TRUE,
                                show_causes = FALSE)

tab_ex2$SummaryTable %>%
  select(-'Expected observations N')

Variables	Sysmiss N	Datavalues N	Missing codes N	Jumps N	Measurements N	Missing_expected_obs	PCT_com_crm_mv	GRADING
CENTER_0	0	2940	0	0	2940	0	0.00	0
PSEUDO_ID	0	2940	0	0	2940	0	0.00	0
SEX_0	0	2940	0	0	2940	0	0.00	0
AGE_0	0	2940	0	0	2940	0	0.00	0
AGE_GROUP_0	0	2940	0	0	2940	0	0.00	0
AGE_1	0	2940	0	0	2940	0	0.00	0
SEX_1	0	2940	0	0	2940	0	0.00	0
PART_STUDY	60	2940	0	0	2940	60	2.00	0
SBP_0	239	2701	140	0	2561	379	12.63	1
DBP_0	233	2707	163	0	2544	396	13.20	1
GLOBAL_HEALTH_VAS_0	246	2694	76	0	2618	322	10.73	1
ASTHMA_0	227	2713	72	0	2641	299	9.97	1
VO2_CAPCAT_0	225	2715	120	0	2595	345	11.50	1
ARM_CIRC_0	220	2720	63	0	2657	283	9.43	0
ARM_CIRC_DISC_0	238	2702	69	0	2633	307	10.23	1
ARM_CUFF_0	236	2704	81	0	2623	317	10.57	1
USR_VO2_0	89	2851	69	0	2782	158	5.27	0
USR_BP_0	80	2860	85	0	2775	165	5.50	0
EXAM_DT_0	0	2940	0	0	2940	0	0.00	0
PART_PHYS_EXAM	0	2940	0	0	2940	0	0.00	0
CRP_0	172	2768	69	0	2699	241	8.03	0
BSG_0	182	2758	72	0	2686	254	8.47	0
DEV_NO_0	248	2692	0	0	2692	248	8.27	0
LAB_DT_0	0	2940	0	0	2940	0	0.00	0
PART_LAB	0	2940	0	0	2940	0	0.00	0
EDUCATION_0	72	2852	380	0	2472	452	15.07	1
EDUCATION_1	83	2841	416	0	2425	499	16.63	1
FAM_STAT_0	122	2802	413	0	2389	535	17.83	1
MARRIED_0	126	2798	432	0	2366	558	18.60	1
N_CHILD_0	160	2764	428	0	2336	588	19.60	1
EATING_PREFS_0	148	2776	448	0	2328	596	19.87	1
MEAT_CONS_0	171	2753	451	0	2302	622	20.73	1
SMOKING_0	183	2741	449	0	2292	632	21.07	1
SMOKE_SHOP_0	1605	1319	513	0	806	2118	70.60	1
N_INJURIES_0	244	2680	481	0	2199	725	24.17	1
N_BIRTH_0	213	2711	499	1113	1099	1825	60.83	1
INCOME_GROUP_0	235	2689	515	0	2174	750	25.00	1
PREGNANT_0	274	2650	519	1066	1065	1859	61.97	1
MEDICATION_0	1733	1191	550	0	641	2283	76.10	1
N_ATC_CODES_0	310	2614	556	0	2058	866	28.87	1
USR_SOCDEM_0	306	2618	332	0	2286	638	21.27	1
INT_DT_0	0	2924	0	0	2924	0	0.00	0
PART_INTERVIEW	0	2940	0	0	2940	0	0.00	0
ITEM_1_0	317	2547	299	0	2248	616	20.53	1
ITEM_2_0	343	2521	324	0	2197	667	22.23	1
ITEM_3_0	355	2509	325	0	2184	680	22.67	1
ITEM_4_0	347	2517	374	0	2143	721	24.03	1
ITEM_5_0	416	2448	374	0	2074	790	26.33	1
ITEM_6_0	427	2437	389	0	2048	816	27.20	1
ITEM_7_0	395	2469	401	0	2068	796	26.53	1
ITEM_8_0	424	2440	427	0	2013	851	28.37	1
QUEST_DT_0	0	2864	0	0	2864	0	0.00	0
PART_QUESTIONNAIRE	0	2924	0	0	2924	0	0.00	0

Order rows

We may want to sort columns or rows. This can also be achieved by dplyr functions:

tab_ex2$SummaryData %>%
  select(-'Expected observations N') %>%
  arrange(desc(`Measurements N (%)`))

Variables	Sysmiss N (%)	Datavalues N (%)	Missing codes N (%)	Jumps N (%)	Measurements N (%)	Missing expected obs. N (%)	Crude missingness N (%)
SMOKE_SHOP_0	1605 (54.89)	1319 (45.11)	513 (17.54)	0 (0)	806 (27.56)	2118 (72.44)	2118 (70.6)
MEDICATION_0	1733 (59.27)	1191 (40.73)	550 (18.81)	0 (0)	641 (21.92)	2283 (78.08)	2283 (76.1)
PART_STUDY	60 (2)	2940 (98)	0 (0)	0 (0)	2940 (98)	60 (2)	60 (2)
CENTER_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
PSEUDO_ID	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
SEX_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
AGE_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
AGE_GROUP_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
AGE_1	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
SEX_1	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
EXAM_DT_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
PART_PHYS_EXAM	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
LAB_DT_0	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
PART_LAB	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
PART_INTERVIEW	0 (0)	2940 (100)	0 (0)	0 (0)	2940 (100)	0 (0)	0 (0)
INT_DT_0	0 (0)	2924 (100)	0 (0)	0 (0)	2924 (100)	0 (0)	0 (0)
PART_QUESTIONNAIRE	0 (0)	2924 (100)	0 (0)	0 (0)	2924 (100)	0 (0)	0 (0)
QUEST_DT_0	0 (0)	2864 (100)	0 (0)	0 (0)	2864 (100)	0 (0)	0 (0)
USR_VO2_0	89 (3.03)	2851 (96.97)	69 (2.35)	0 (0)	2782 (94.63)	158 (5.37)	158 (5.27)
USR_BP_0	80 (2.72)	2860 (97.28)	85 (2.89)	0 (0)	2775 (94.39)	165 (5.61)	165 (5.5)
CRP_0	172 (5.85)	2768 (94.15)	69 (2.35)	0 (0)	2699 (91.8)	241 (8.2)	241 (8.03)
DEV_NO_0	248 (8.44)	2692 (91.56)	0 (0)	0 (0)	2692 (91.56)	248 (8.44)	248 (8.27)
BSG_0	182 (6.19)	2758 (93.81)	72 (2.45)	0 (0)	2686 (91.36)	254 (8.64)	254 (8.47)
ARM_CIRC_0	220 (7.48)	2720 (92.52)	63 (2.14)	0 (0)	2657 (90.37)	283 (9.63)	283 (9.43)
ASTHMA_0	227 (7.72)	2713 (92.28)	72 (2.45)	0 (0)	2641 (89.83)	299 (10.17)	299 (9.97)
ARM_CIRC_DISC_0	238 (8.1)	2702 (91.9)	69 (2.35)	0 (0)	2633 (89.56)	307 (10.44)	307 (10.23)
ARM_CUFF_0	236 (8.03)	2704 (91.97)	81 (2.76)	0 (0)	2623 (89.22)	317 (10.78)	317 (10.57)
GLOBAL_HEALTH_VAS_0	246 (8.37)	2694 (91.63)	76 (2.59)	0 (0)	2618 (89.05)	322 (10.95)	322 (10.73)
VO2_CAPCAT_0	225 (7.65)	2715 (92.35)	120 (4.08)	0 (0)	2595 (88.27)	345 (11.73)	345 (11.5)
SBP_0	239 (8.13)	2701 (91.87)	140 (4.76)	0 (0)	2561 (87.11)	379 (12.89)	379 (12.63)
DBP_0	233 (7.93)	2707 (92.07)	163 (5.54)	0 (0)	2544 (86.53)	396 (13.47)	396 (13.2)
EDUCATION_0	72 (2.46)	2852 (97.54)	380 (13)	0 (0)	2472 (84.54)	452 (15.46)	452 (15.07)
EDUCATION_1	83 (2.84)	2841 (97.16)	416 (14.23)	0 (0)	2425 (82.93)	499 (17.07)	499 (16.63)
FAM_STAT_0	122 (4.17)	2802 (95.83)	413 (14.12)	0 (0)	2389 (81.7)	535 (18.3)	535 (17.83)
MARRIED_0	126 (4.31)	2798 (95.69)	432 (14.77)	0 (0)	2366 (80.92)	558 (19.08)	558 (18.6)
N_CHILD_0	160 (5.47)	2764 (94.53)	428 (14.64)	0 (0)	2336 (79.89)	588 (20.11)	588 (19.6)
EATING_PREFS_0	148 (5.06)	2776 (94.94)	448 (15.32)	0 (0)	2328 (79.62)	596 (20.38)	596 (19.87)
MEAT_CONS_0	171 (5.85)	2753 (94.15)	451 (15.42)	0 (0)	2302 (78.73)	622 (21.27)	622 (20.73)
SMOKING_0	183 (6.26)	2741 (93.74)	449 (15.36)	0 (0)	2292 (78.39)	632 (21.61)	632 (21.07)
USR_SOCDEM_0	306 (10.47)	2618 (89.53)	332 (11.35)	0 (0)	2286 (78.18)	638 (21.82)	638 (21.27)
ITEM_1_0	317 (11.07)	2547 (88.93)	299 (10.44)	0 (0)	2248 (78.49)	616 (21.51)	616 (20.53)
N_INJURIES_0	244 (8.34)	2680 (91.66)	481 (16.45)	0 (0)	2199 (75.21)	725 (24.79)	725 (24.17)
ITEM_2_0	343 (11.98)	2521 (88.02)	324 (11.31)	0 (0)	2197 (76.71)	667 (23.29)	667 (22.23)
ITEM_3_0	355 (12.4)	2509 (87.6)	325 (11.35)	0 (0)	2184 (76.26)	680 (23.74)	680 (22.67)
INCOME_GROUP_0	235 (8.04)	2689 (91.96)	515 (17.61)	0 (0)	2174 (74.35)	750 (25.65)	750 (25)
ITEM_4_0	347 (12.12)	2517 (87.88)	374 (13.06)	0 (0)	2143 (74.83)	721 (25.17)	721 (24.03)
ITEM_5_0	416 (14.53)	2448 (85.47)	374 (13.06)	0 (0)	2074 (72.42)	790 (27.58)	790 (26.33)
ITEM_7_0	395 (13.79)	2469 (86.21)	401 (14)	0 (0)	2068 (72.21)	796 (27.79)	796 (26.53)
N_ATC_CODES_0	310 (10.6)	2614 (89.4)	556 (19.02)	0 (0)	2058 (70.38)	866 (29.62)	866 (28.87)
ITEM_6_0	427 (14.91)	2437 (85.09)	389 (13.58)	0 (0)	2048 (71.51)	816 (28.49)	816 (27.2)
ITEM_8_0	424 (14.8)	2440 (85.2)	427 (14.91)	0 (0)	2013 (70.29)	851 (29.71)	851 (28.37)
N_BIRTH_0	213 (7.28)	2711 (92.72)	499 (17.07)	1113 (38.06)	1099 (60.68)	1825 (62.41)	1825 (60.83)
PREGNANT_0	274 (9.37)	2650 (90.63)	519 (17.75)	1066 (36.46)	1065 (57.32)	1859 (63.58)	1859 (61.97)

The text in the columns for sorting can be extracted using:

splitted_measurements_col <- # this will be a list of character vectors of length 2 (part before and part after the '(' character for each row)
  strsplit(tab_ex2$SummaryData$`Measurements N (%)`, # the measurement count column
           '(', # splited at the opening bracket
           fixed = TRUE # fixed string match, no pattern match
           )
percent_part_in_col <- # this will be a character vector of the percentages
  unlist( # we don't want to have a list but a vector of percentages as usually for data frame columns
    lapply(splitted_measurements_col, `[[`, 2) # select the second entry of each entry in the list
  )
sort_order <- as.numeric(sub(')', '', percent_part_in_col, fixed = TRUE)) # remove the closing bracket and convert the characters to numbers
tab_ex2$SummaryTable %>%
  select(-'Expected observations N') %>%
  arrange(desc(sort_order))

Variables	Sysmiss N	Datavalues N	Missing codes N	Jumps N	Measurements N	Missing_expected_obs	PCT_com_crm_mv	GRADING
CENTER_0	0	2940	0	0	2940	0	0.00	0
PSEUDO_ID	0	2940	0	0	2940	0	0.00	0
SEX_0	0	2940	0	0	2940	0	0.00	0
AGE_0	0	2940	0	0	2940	0	0.00	0
AGE_GROUP_0	0	2940	0	0	2940	0	0.00	0
AGE_1	0	2940	0	0	2940	0	0.00	0
SEX_1	0	2940	0	0	2940	0	0.00	0
EXAM_DT_0	0	2940	0	0	2940	0	0.00	0
PART_PHYS_EXAM	0	2940	0	0	2940	0	0.00	0
LAB_DT_0	0	2940	0	0	2940	0	0.00	0
PART_LAB	0	2940	0	0	2940	0	0.00	0
INT_DT_0	0	2924	0	0	2924	0	0.00	0
PART_INTERVIEW	0	2940	0	0	2940	0	0.00	0
QUEST_DT_0	0	2864	0	0	2864	0	0.00	0
PART_QUESTIONNAIRE	0	2924	0	0	2924	0	0.00	0
PART_STUDY	60	2940	0	0	2940	60	2.00	0
USR_VO2_0	89	2851	69	0	2782	158	5.27	0
USR_BP_0	80	2860	85	0	2775	165	5.50	0
CRP_0	172	2768	69	0	2699	241	8.03	0
DEV_NO_0	248	2692	0	0	2692	248	8.27	0
BSG_0	182	2758	72	0	2686	254	8.47	0
ARM_CIRC_0	220	2720	63	0	2657	283	9.43	0
ASTHMA_0	227	2713	72	0	2641	299	9.97	1
ARM_CIRC_DISC_0	238	2702	69	0	2633	307	10.23	1
ARM_CUFF_0	236	2704	81	0	2623	317	10.57	1
GLOBAL_HEALTH_VAS_0	246	2694	76	0	2618	322	10.73	1
VO2_CAPCAT_0	225	2715	120	0	2595	345	11.50	1
SBP_0	239	2701	140	0	2561	379	12.63	1
DBP_0	233	2707	163	0	2544	396	13.20	1
EDUCATION_0	72	2852	380	0	2472	452	15.07	1
EDUCATION_1	83	2841	416	0	2425	499	16.63	1
FAM_STAT_0	122	2802	413	0	2389	535	17.83	1
MARRIED_0	126	2798	432	0	2366	558	18.60	1
N_CHILD_0	160	2764	428	0	2336	588	19.60	1
EATING_PREFS_0	148	2776	448	0	2328	596	19.87	1
MEAT_CONS_0	171	2753	451	0	2302	622	20.73	1
ITEM_1_0	317	2547	299	0	2248	616	20.53	1
SMOKING_0	183	2741	449	0	2292	632	21.07	1
USR_SOCDEM_0	306	2618	332	0	2286	638	21.27	1
ITEM_2_0	343	2521	324	0	2197	667	22.23	1
ITEM_3_0	355	2509	325	0	2184	680	22.67	1
N_INJURIES_0	244	2680	481	0	2199	725	24.17	1
ITEM_4_0	347	2517	374	0	2143	721	24.03	1
INCOME_GROUP_0	235	2689	515	0	2174	750	25.00	1
ITEM_5_0	416	2448	374	0	2074	790	26.33	1
ITEM_7_0	395	2469	401	0	2068	796	26.53	1
ITEM_6_0	427	2437	389	0	2048	816	27.20	1
N_ATC_CODES_0	310	2614	556	0	2058	866	28.87	1
ITEM_8_0	424	2440	427	0	2013	851	28.37	1
N_BIRTH_0	213	2711	499	1113	1099	1825	60.83	1
PREGNANT_0	274	2650	519	1066	1065	1859	61.97	1
SMOKE_SHOP_0	1605	1319	513	0	806	2118	70.60	1
MEDICATION_0	1733	1191	550	0	641	2283	76.10	1

Reorder columns

Maybe the columns should be in another order too:

tab_ex2$SummaryTable %>%
  select(-'Expected observations N') %>% # the GRADING column must be removed without using the everyting() in the next row, so we keep to lines.
  select(`Variables`, `Measurements N (%)`, everything()) # everything adds all columns not yet available.

Variables	Measurements N (%)	Sysmiss N (%)	Datavalues N (%)	Missing codes N (%)	Jumps N (%)	Missing expected obs. N (%)	Crude missingness N (%)
CENTER_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PSEUDO_ID	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
SEX_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
AGE_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
AGE_GROUP_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
AGE_1	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
SEX_1	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PART_STUDY	2940 (98)	60 (2)	2940 (98)	0 (0)	0 (0)	60 (2)	60 (2)
SBP_0	2561 (87.11)	239 (8.13)	2701 (91.87)	140 (4.76)	0 (0)	379 (12.89)	379 (12.63)
DBP_0	2544 (86.53)	233 (7.93)	2707 (92.07)	163 (5.54)	0 (0)	396 (13.47)	396 (13.2)
GLOBAL_HEALTH_VAS_0	2618 (89.05)	246 (8.37)	2694 (91.63)	76 (2.59)	0 (0)	322 (10.95)	322 (10.73)
ASTHMA_0	2641 (89.83)	227 (7.72)	2713 (92.28)	72 (2.45)	0 (0)	299 (10.17)	299 (9.97)
VO2_CAPCAT_0	2595 (88.27)	225 (7.65)	2715 (92.35)	120 (4.08)	0 (0)	345 (11.73)	345 (11.5)
ARM_CIRC_0	2657 (90.37)	220 (7.48)	2720 (92.52)	63 (2.14)	0 (0)	283 (9.63)	283 (9.43)
ARM_CIRC_DISC_0	2633 (89.56)	238 (8.1)	2702 (91.9)	69 (2.35)	0 (0)	307 (10.44)	307 (10.23)
ARM_CUFF_0	2623 (89.22)	236 (8.03)	2704 (91.97)	81 (2.76)	0 (0)	317 (10.78)	317 (10.57)
USR_VO2_0	2782 (94.63)	89 (3.03)	2851 (96.97)	69 (2.35)	0 (0)	158 (5.37)	158 (5.27)
USR_BP_0	2775 (94.39)	80 (2.72)	2860 (97.28)	85 (2.89)	0 (0)	165 (5.61)	165 (5.5)
EXAM_DT_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PART_PHYS_EXAM	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
CRP_0	2699 (91.8)	172 (5.85)	2768 (94.15)	69 (2.35)	0 (0)	241 (8.2)	241 (8.03)
BSG_0	2686 (91.36)	182 (6.19)	2758 (93.81)	72 (2.45)	0 (0)	254 (8.64)	254 (8.47)
DEV_NO_0	2692 (91.56)	248 (8.44)	2692 (91.56)	0 (0)	0 (0)	248 (8.44)	248 (8.27)
LAB_DT_0	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PART_LAB	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
EDUCATION_0	2472 (84.54)	72 (2.46)	2852 (97.54)	380 (13)	0 (0)	452 (15.46)	452 (15.07)
EDUCATION_1	2425 (82.93)	83 (2.84)	2841 (97.16)	416 (14.23)	0 (0)	499 (17.07)	499 (16.63)
FAM_STAT_0	2389 (81.7)	122 (4.17)	2802 (95.83)	413 (14.12)	0 (0)	535 (18.3)	535 (17.83)
MARRIED_0	2366 (80.92)	126 (4.31)	2798 (95.69)	432 (14.77)	0 (0)	558 (19.08)	558 (18.6)
N_CHILD_0	2336 (79.89)	160 (5.47)	2764 (94.53)	428 (14.64)	0 (0)	588 (20.11)	588 (19.6)
EATING_PREFS_0	2328 (79.62)	148 (5.06)	2776 (94.94)	448 (15.32)	0 (0)	596 (20.38)	596 (19.87)
MEAT_CONS_0	2302 (78.73)	171 (5.85)	2753 (94.15)	451 (15.42)	0 (0)	622 (21.27)	622 (20.73)
SMOKING_0	2292 (78.39)	183 (6.26)	2741 (93.74)	449 (15.36)	0 (0)	632 (21.61)	632 (21.07)
SMOKE_SHOP_0	806 (27.56)	1605 (54.89)	1319 (45.11)	513 (17.54)	0 (0)	2118 (72.44)	2118 (70.6)
N_INJURIES_0	2199 (75.21)	244 (8.34)	2680 (91.66)	481 (16.45)	0 (0)	725 (24.79)	725 (24.17)
N_BIRTH_0	1099 (60.68)	213 (7.28)	2711 (92.72)	499 (17.07)	1113 (38.06)	1825 (62.41)	1825 (60.83)
INCOME_GROUP_0	2174 (74.35)	235 (8.04)	2689 (91.96)	515 (17.61)	0 (0)	750 (25.65)	750 (25)
PREGNANT_0	1065 (57.32)	274 (9.37)	2650 (90.63)	519 (17.75)	1066 (36.46)	1859 (63.58)	1859 (61.97)
MEDICATION_0	641 (21.92)	1733 (59.27)	1191 (40.73)	550 (18.81)	0 (0)	2283 (78.08)	2283 (76.1)
N_ATC_CODES_0	2058 (70.38)	310 (10.6)	2614 (89.4)	556 (19.02)	0 (0)	866 (29.62)	866 (28.87)
USR_SOCDEM_0	2286 (78.18)	306 (10.47)	2618 (89.53)	332 (11.35)	0 (0)	638 (21.82)	638 (21.27)
INT_DT_0	2924 (100)	0 (0)	2924 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PART_INTERVIEW	2940 (100)	0 (0)	2940 (100)	0 (0)	0 (0)	0 (0)	0 (0)
ITEM_1_0	2248 (78.49)	317 (11.07)	2547 (88.93)	299 (10.44)	0 (0)	616 (21.51)	616 (20.53)
ITEM_2_0	2197 (76.71)	343 (11.98)	2521 (88.02)	324 (11.31)	0 (0)	667 (23.29)	667 (22.23)
ITEM_3_0	2184 (76.26)	355 (12.4)	2509 (87.6)	325 (11.35)	0 (0)	680 (23.74)	680 (22.67)
ITEM_4_0	2143 (74.83)	347 (12.12)	2517 (87.88)	374 (13.06)	0 (0)	721 (25.17)	721 (24.03)
ITEM_5_0	2074 (72.42)	416 (14.53)	2448 (85.47)	374 (13.06)	0 (0)	790 (27.58)	790 (26.33)
ITEM_6_0	2048 (71.51)	427 (14.91)	2437 (85.09)	389 (13.58)	0 (0)	816 (28.49)	816 (27.2)
ITEM_7_0	2068 (72.21)	395 (13.79)	2469 (86.21)	401 (14)	0 (0)	796 (27.79)	796 (26.53)
ITEM_8_0	2013 (70.29)	424 (14.8)	2440 (85.2)	427 (14.91)	0 (0)	851 (29.71)	851 (28.37)
QUEST_DT_0	2864 (100)	0 (0)	2864 (100)	0 (0)	0 (0)	0 (0)	0 (0)
PART_QUESTIONNAIRE	2924 (100)	0 (0)	2924 (100)	0 (0)	0 (0)	0 (0)	0 (0)

Plots from `ggplot2`

The versatile ggplot2 package provides possibilities to modify graphics after they have been created, to render them in vector formats and even to extract the underlying data. It is handy for interfacing with user code. Also, ggplot2 has a comprehensive concept behind, a graphics grammar, which makes it highly structured and using its code easy to understand. For more advice about the ggplot2 package, we refer kindly to the vignettes of that package:

browseVignettes(package = "ggplot2")

The package dataquieR generates two types of ggplot-objects.

Either a single summary plot called SummaryPlot or
a list of plots called SummaryPlotList.

The latter is used if several plots are generated, typically for each variable of the study data. As the handling and manipulation of a single SummaryPlot is more straightforward we exemplify a plot list using the dataquieR function acc_distributions:

ex1 <- acc_distributions(resp_vars      = NULL, 
                         label_col      = "LABEL",
                         study_data     = sd1, 
                         meta_data      = md1)

This yields a set of 39 figures! All of which are ggplot2 objects:

unique(unlist(lapply(ex1$SummaryPlotList, class)))
#> [1] "gg"     "ggplot"

There is a package named ggedit for editing ggplot2-objects easily. Nevertheless, in the following the basics to do so are discussed. For more complex adjustments, we recommend now ggedit.

Lists of plots

To list them all, a simple print of the ex1$SummaryPlotList can be used, but this will also print the “normal” output of printing a list, i.e. the names or numbers of all its elements. To avoid this, you can simply print each element of the list separately:

# for (i in 1:length(ex1$SummaryPlotList)) # substituted by the next row to shorten the output of this vignette:
for (i in head(seq_along(ex1$SummaryPlotList), 4)) {
  print(ex1$SummaryPlotList[[i]])
}

Of course, an apply-iteration would be possible too, but for the means of plotting figures, the for loop perfectly fits.

Using this code, all figures are printed one below the other. To have them in columns, the chunk-option out.width can be handy. rmarkdown plots figures aside, if the current row is not yet filled, so something like out.width=c('50%', '50%') can be used to achieve a two-column image list.

Arrange plots

Another possibility to arrange list of plots is the ggpubr package which handles a specific formal for lists of ggplot2 objects.

ggpubr::ggarrange(plotlist = ex1$SummaryPlotList[1:4])

An alternative to ggpubris the patchwork-package, which provides a very intuitive way of aligning ggplot2 graphics:

library(patchwork)
p1 <- ex1$SummaryPlotList[[1]]
p2 <- ex1$SummaryPlotList[[2]]
p3 <- ex1$SummaryPlotList[[3]]

p1 | (p2 / p3)

See the patchwork vignette for more details.

Plot rotation

The following example rotates the plot so that the counts appear in the x-axis. For this, we can use the +-operator in combination with the function coord_flip:

library(ggplot2)
print(
  ex1$SummaryPlotList[[3]] +
    coord_flip()
)
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.

To add a red line, we use the annotate function to draw objects not directly mapped (by aes) to specific data points/samples (which avoids redundant plotting):

library(ggplot2)
print(
  ex1$SummaryPlotList[[3]] +
    coord_flip() +
    annotate("segment", x = -Inf, xend = Inf, y = 0, yend = 0, colour = "red") 
)
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.

Highlighting

Then, we may like to highlight the largest bin in red. For this, we need to access the bins calculated by geom_histogram which the ggplot_build function makes accessible for ggplot2-objects:

p <- ex1$SummaryPlotList[[3]] # choose the third figure generated by dataquieR.
x <- ggplot_build(p) # make its graphical properties accessible.
largest_bin <- which.max(x[["data"]][[1]][["count"]]) # find the largest bin.
print(x[["data"]][[1]][largest_bin, c("xmin", "xmax", "ymin", "ymax")]) # this would print out the cartesian coordinates of the largest bin.
#>    xmin xmax ymin ymax
#> 18   50   51    0  264
# see also the helpful contribution there: https://community.rstudio.com/t/geom-histogram-max-bin-height/10026
print( # print
  p +  # the plot
    annotate("segment", x = -Inf, xend = Inf, y = 0, yend = 0, colour = "red") + # annotate it with the red line again
    annotate("rect", # and highlight the largest bin by overplotting it with red framed black rectangle.
             xmin = x[["data"]][[1]]$xmin[[largest_bin]], 
             xmax = x[["data"]][[1]]$xmax[[largest_bin]], 
             ymin = x[["data"]][[1]]$ymin[[largest_bin]], 
             ymax = x[["data"]][[1]]$ymax[[largest_bin]], color = "red")
)

Annotation

Unfortunately, the annotate function’s documentation is sparse. The geom-parameter refers to existing implementations of graphics in ggplot2 all of which are prefixed with geom_. Usually they extract their coordinates from the data using the mapping given in the aes-parameter of the whole ggplot2 object or for the specific geom. text is a useful geom for annotating the plot:

print( # print
  p +  # the plot
    scale_y_continuous(limits = c(0, 300)) +  # expand y-axis to make annotations visible
    annotate("segment", x = -Inf, xend = Inf, y = 0, yend = 0, colour = "red") + # annotate it with the red line again
    annotate("rect", # and highlight the largest bin by overplotting it with red framed black rectangle.
             xmin = x[["data"]][[1]]$xmin[[largest_bin]], 
             xmax = x[["data"]][[1]]$xmax[[largest_bin]], 
             ymin = x[["data"]][[1]]$ymin[[largest_bin]], 
             ymax = x[["data"]][[1]]$ymax[[largest_bin]], color = "red") +
    annotate("text", label = "Largest bin", x = x[["data"]][[1]]$xmax[[largest_bin]], y = x[["data"]][[1]]$ymax[[largest_bin]], angle = 270, vjust = -.5)
)

You may see the documentation of ggplot2::annotate for some examples.

Coordinates are given in the same coordinate system that is shown in the plot, so drawing a line at 100 observations is as easy as directly choosing 100 as y coordinate.

print( # print
  p + # the plot
    scale_y_continuous(limits = c(0, 300)) +  # expand y-axis to make annotations visible
    annotate("segment", x = -Inf, xend = Inf, y = 100, yend = 100, colour = "red") + # annotate it with the red line again
    annotate("segment", x = -Inf, xend = Inf, y = 0, yend = 0, colour = "red") + # annotate it with the red line again
    annotate("rect", # and highlight the largest bin by overplotting it with red framed black rectangle.
             xmin = x[["data"]][[1]]$xmin[[largest_bin]], 
             xmax = x[["data"]][[1]]$xmax[[largest_bin]], 
             ymin = x[["data"]][[1]]$ymin[[largest_bin]], 
             ymax = x[["data"]][[1]]$ymax[[largest_bin]], color = "red") +
    annotate("text", label = "Largest bin", x = x[["data"]][[1]]$xmax[[largest_bin]], y = x[["data"]][[1]]$ymax[[largest_bin]], angle = 270, vjust = -.5) 
)

We will now rotate the annotation:

p2 <-  p +  # the plot
    annotate("segment", x = -Inf, xend = Inf, y = 100, yend = 100, colour = "red") + # annotate it with the red line again
    annotate("segment", x = -Inf, xend = Inf, y = 0, yend = 0, colour = "red") + # annotate it with the red line again
    annotate("rect", # and highlight the largest bin by overplotting it with red framed black rectangle.
             xmin = x[["data"]][[1]]$xmin[[largest_bin]], 
             xmax = x[["data"]][[1]]$xmax[[largest_bin]], 
             ymin = x[["data"]][[1]]$ymin[[largest_bin]], 
             ymax = x[["data"]][[1]]$ymax[[largest_bin]], color = "red") +
    annotate("text", label = "Largest bin", x = x[["data"]][[1]]$xmax[[largest_bin]], y = x[["data"]][[1]]$ymax[[largest_bin]], angle = 0, vjust = -.5)
suppressMessages(p2 + coord_cartesian()) # this restores the original cartesian coordinate system replacing the flipped one introduced by acc_distributions However, it emits a message about replacing the coordinate system, which we can suppress here with suppressMessages.

# Note, that neither `ggplot2::coord_flip` nor `ggpubr::rotate` can solve this 
# issue. These functions are not aware of already-rotated plots, so the following 
# will *not* rotate the plot back:
# 
# ```{r}
# p2 + coord_flip()     # does not rotate the plot but prints
#                       # Coordinate system already present. Adding new coordinate
#                       # system, which will replace the existing one.
# 
# p2 + ggpubr::rotate() # does not rotate the plot but prints
#                       # Coordinate system already present. Adding new coordinate
#                       # system, which will replace the existing one.
# ```

Add new data

All functions of the dataquieR use the data as they are imported, i.e. variables of the study data can be examined and used for grouping/stratification of results. All information for these variables must be attached to the metadata. In some situations, particularly during data quality reporting, it is necessary to use new calculated/transformed variable. Naturally, respective information is not defined in the metadata. This peculiarity would preclude the use of such calculated or transformed variables in data quality reporting.

To illustrate the need of a helper function is shown with the following example from com_segment_missingness():

MissSegs <- com_segment_missingness(study_data = sd1, 
                                    meta_data = md1, 
                                    threshold_value = 1, 
                                    color_gradient_direction = "above",
                                    exclude_roles = c("secondary", "process"))

The SummaryPlot shows the frequency of observations in which all measurements of respective study segments are missing.

MissSegs$SummaryPlot

Exploring the segment missingness over time would require another variable in the study data. We will generate such a variable using the lubridate package.

sd1$exq <- as.integer(lubridate::quarter(sd1$v00013))
table(sd1$exq)
#> 
#>   1   2   3   4 
#> 724 713 778 725

Information regarding this variable is then added to a copy of the metadata (md2) using the dataquieR function prep_add_to_meta():

md2 <- dataquieR::prep_add_to_meta(VAR_NAMES = "exq", 
                                   DATA_TYPE = "integer",
                                   LABEL = "EX_QUARTER_0",
                                   VALUE_LABELS = "1 = 1st | 2 = 2nd | 3 = 3rd | 4 = 4th",
                                   VARIABLE_ROLE = "process",
                                   MISSING_LIST = NA,
                                   meta_data = md1)

MissSegs <- com_segment_missingness(study_data = sd1, 
                                    meta_data = md2, 
                                    threshold_value = 1, 
                                    label_col = LABEL,
                                    group_vars = "EX_QUARTER_0",
                                    color_gradient_direction = "above",
                                    exclude_roles = "process")

MissSegs$SummaryPlot

Output Settings

Introduction

Example output of `dataquieR`

Data frames

Styling

Paging (in R Markdown)

Remove columns

Order rows

Reorder columns

Plots from `ggplot2`

Lists of plots

Arrange plots

Plot rotation

Highlighting

Annotation

Add new data

Back to Overview

Output Settings

Introduction

Example output of dataquieR

Data frames

Styling

Paging (in R Markdown)

Remove columns

Order rows

Reorder columns

Plots from ggplot2

Lists of plots

Arrange plots

Plot rotation

Highlighting

Annotation

Add new data

Back to Overview

Example output of `dataquieR`

Plots from `ggplot2`