\([![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3635430.svg)](https://doi.org/10.5281/zenodo.3635430)\)
https://doi.org/10.5281/zenodo.3635430
Histopathology Research Template 🔬
Describe Materials and Methods as highlighted in (Knijn, Simmer, and Nagtegaal 2015).2
Describe patient characteristics, and inclusion and exclusion criteria
Describe treatment details
Describe the type of material used
Specify how expression of the biomarker was assessed
Describe the number of independent (blinded) scorers and how they scored
State the method of case selection, study design, origin of the cases, and time frame
Describe the end of the follow-up period and median follow-up time
Define all clinical endpoints examined
Specify all applied statistical methods
Describe how interactions with other clinical/pathological factors were analyzed
Codes for general settings.3
Setup global chunk settings4
knitr::opts_chunk$set(
eval = TRUE,
echo = TRUE,
fig.path = here::here("figs/"),
message = FALSE,
warning = FALSE,
error = TRUE,
cache = TRUE,
comment = NA,
tidy = TRUE,
fig.width = 6,
fig.height = 4
)
library(knitr)
hook_output = knit_hooks$get("output")
knit_hooks$set(output = function(x, options) {
# this hook is used only when the linewidth option is not NULL
if (!is.null(n <- options$linewidth)) {
x = knitr:::split_lines(x)
# any lines wider than n should be wrapped
if (any(nchar(x) > n))
x = strwrap(x, width = n)
x = paste(x, collapse = "\n")
}
hook_output(x, options)
})
jtable <- function(jobject, digits = 3) {
snames <- sapply(jobject$columns, function(a) a$title)
asDF <- jobject$asDF
tnames <- unlist(lapply(names(asDF), function(n) snames[[n]]))
names(asDF) <- tnames
kableExtra::kable(asDF, "html", table.attr = "class=\"jmv-results-table-table\"",
row.names = F, digits = 3)
}
Block rmdnote
Block rmdtip
Block warning
Load Library
see R/loadLibrary.R
for the libraries loaded.
Codes for generating fake data.5
Generate Fake Data
This code generates a fake histopathological data. Some sources for fake data generation here6 , here7 , here8 , here9 , here10 , here11 , here12 , here13 , and here14 .
Use this code to generate fake clinicopathologic data
Codes for importing data.15
Read the data
library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importing
Add code for import multiple data purrr reduce
Codes for reporting general features.16
Dataframe Report
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others (0 missing)
- Name: 249 entries: Aceyn, n = 1; Adalaide, n = 1; Adidas, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 127; Female, n = 122 (1 missing)
- Age: Mean = 49.54, SD = 14.16, Median = , MAD = 17.79, range: [25, 73], Skewness = 0.00, Kurtosis = -1.15, 1 missing
- Race: 7 entries: White, n = 158; Hispanic, n = 38; Black, n = 30 and 4 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 203; Present, n = 46 (1 missing)
- LVI: 2 entries: Absent, n = 147; Present, n = 102 (1 missing)
- PNI: 2 entries: Absent, n = 171; Present, n = 78 (1 missing)
- Death: 2 levels: FALSE (n = 83, 33.20%); TRUE (n = 166, 66.40%) and missing (n = 1, 0.40%)
- Group: 2 entries: Treatment, n = 131; Control, n = 118 (1 missing)
- Grade: 3 entries: 3, n = 109; 1, n = 78; 2, n = 62 (1 missing)
- TStage: 4 entries: 4, n = 118; 3, n = 65; 2, n = 43 and 1 other (0 missing)
- AntiX_intensity: Mean = 2.39, SD = 0.66, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.63, Kurtosis = -0.65, 1 missing
- AntiY_intensity: Mean = 2.02, SD = 0.80, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.03, Kurtosis = -1.42, 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 144; Present, n = 105 (1 missing)
- Valid: 2 levels: FALSE (n = 116, 46.40%); TRUE (n = 133, 53.20%) and missing (n = 1, 0.40%)
- Smoker: 2 levels: FALSE (n = 130, 52.00%); TRUE (n = 119, 47.60%) and missing (n = 1, 0.40%)
- Grade_Level: 3 entries: high, n = 109; low, n = 77; moderate, n = 63 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101 (0 missing)
250 observations with 21 variables
18 variables containing missings (NA)
0 variables with no variance
Codes for defining variable types.19
print column names as vector
c("ID", "Name", "Sex", "Age", "Race", "PreinvasiveComponent",
"LVI", "PNI", "LastFollowUpDate", "Death", "Group", "Grade",
"TStage", "AntiX_intensity", "AntiY_intensity", "LymphNodeMetastasis",
"Valid", "Smoker", "Grade_Level", "SurgeryDate", "DeathTime")
vctrs::vec_assert()
dplyr::all_equal()
arsenal::compare()
visdat::vis_compare()
See the code as function in R/find_key.R
.
keycolumns <- mydata %>% sapply(., FUN = dataMaid::isKey) %>% tibble::as_tibble() %>%
dplyr::select(which(.[1, ] == TRUE)) %>% names()
keycolumns
[1] "ID" "Name"
Get variable types
# A tibble: 4 x 4
type cnt pcnt col_name
<chr> <int> <dbl> <list>
1 character 11 57.9 <chr [11]>
2 logical 3 15.8 <chr [3]>
3 numeric 3 15.8 <chr [3]>
4 POSIXct POSIXt 2 10.5 <chr [2]>
mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>% describer::describe() %>%
knitr::kable(format = "markdown")
.column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
---|---|---|---|---|---|---|---|---|---|---|
Sex | character | character | 250 | NA | NA | Female | NA | NA | NA | Male |
Age | numeric | double | 250 | 49.538153 | 14.1595015 | 25 | 37 | 49 | 61 | 73 |
Race | character | character | 250 | NA | NA | Asian | NA | NA | NA | White |
PreinvasiveComponent | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
LVI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
PNI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
Death | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
Group | character | character | 250 | NA | NA | Control | NA | NA | NA | Treatment |
Grade | character | character | 250 | NA | NA | 1 | NA | NA | NA | 3 |
TStage | character | character | 250 | NA | NA | 1 | NA | NA | NA | 4 |
AntiX_intensity | numeric | double | 250 | 2.389558 | 0.6636071 | 1 | 2 | 2 | 3 | 3 |
AntiY_intensity | numeric | double | 250 | 2.016064 | 0.7980211 | 1 | 1 | 2 | 3 | 3 |
LymphNodeMetastasis | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
Valid | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
Smoker | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
Grade_Level | character | character | 250 | NA | NA | high | NA | NA | NA | moderate |
DeathTime | character | character | 250 | NA | NA | MoreThan1Year | NA | NA | NA | Within1Year |
Plot variable types
character
variablescharacterVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "character") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
characterVariables
[1] "Sex" "Race" "PreinvasiveComponent"
[4] "LVI" "PNI" "Group"
[7] "Grade" "TStage" "LymphNodeMetastasis"
[10] "Grade_Level" "DeathTime"
categorical
variablescategoricalVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"factor") %>% dplyr::select(column_name) %>% dplyr::pull()
categoricalVariables
character(0)
continious
variablescontiniousVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"numeric" | column_type == "double") %>% dplyr::select(column_name) %>% dplyr::pull()
continiousVariables
[1] "Age" "AntiX_intensity" "AntiY_intensity"
numeric
variablesnumericVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "numeric") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
numericVariables
[1] "Age" "AntiX_intensity" "AntiY_intensity"
integer
variablesintegerVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "integer") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
integerVariables
NULL
Codes for overviewing the data.20
reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE,
searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE,
highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE,
showSortIcon = TRUE, showSortable = TRUE)
Summary of Data via summarytools 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
summarytools::view(x = summarytools::dfSummary(mydata %>% dplyr::select(-keycolumns)),
file = here::here("out", "mydata_summary.html"))
Summary via dataMaid 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
dataMaid::makeDataReport(data = mydata, file = here::here("out", "dataMaid_mydata.Rmd"),
replace = TRUE, openResult = FALSE, render = FALSE, quiet = TRUE)
Summary via explore 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
mydata %>% dplyr::select(-dateVariables) %>% explore::report(output_file = "mydata_report.html",
output_dir = here::here("out"))
Glimpse of Data
Observations: 250
Variables: 17
$ Sex <chr> "Female", "Female", "Female", "Female", "Male", …
$ Age <dbl> 30, 32, 53, 57, 47, 58, 59, 54, 35, 27, 53, 55, …
$ Race <chr> "White", "White", "White", "Hispanic", "White", …
$ PreinvasiveComponent <chr> "Absent", "Absent", "Absent", "Absent", "Absent"…
$ LVI <chr> "Present", "Absent", "Absent", "Present", "Absen…
$ PNI <chr> "Absent", "Absent", "Absent", "Present", "Absent…
$ Death <lgl> FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRU…
$ Group <chr> "Control", "Control", "Control", "Control", "Con…
$ Grade <chr> "1", "1", "2", "1", "2", "2", "3", "1", "2", "1"…
$ TStage <chr> "4", "4", "3", "3", "1", "3", "3", "3", "4", "4"…
$ AntiX_intensity <dbl> 2, 2, 2, 2, 3, 1, 1, 3, 2, 3, 2, 3, 1, 3, 1, 2, …
$ AntiY_intensity <dbl> 2, 2, 2, 3, 2, 1, 2, 3, 3, 1, 1, 2, 1, 3, 1, 2, …
$ LymphNodeMetastasis <chr> "Present", "Absent", "Present", "Present", "Pres…
$ Valid <lgl> TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRU…
$ Smoker <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TR…
$ Grade_Level <chr> "moderate", "moderate", "high", "low", "high", "…
$ DeathTime <chr> "Within1Year", "Within1Year", "Within1Year", "Wi…
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 49.5 73
5 Race chr 1 0.4 8 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 1 0.4 3 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.67 1
# … with 11 more rows
Explore
Control Data if matching expectations
visdat::vis_expect(data = mydata, expectation = ~.x == -1, show_perc = TRUE)
visdat::vis_expect(mydata, ~.x >= 25)
See missing values
$variables
Variable q qNA pNA qZero pZero qBlank pBlank qInf pInf
1 Smoker 250 1 0.4% 130 52% 0 - 0 -
2 Valid 250 1 0.4% 116 46.4% 0 - 0 -
3 Death 250 1 0.4% 83 33.2% 0 - 0 -
4 Sex 250 1 0.4% 0 - 0 - 0 -
5 PreinvasiveComponent 250 1 0.4% 0 - 0 - 0 -
6 LVI 250 1 0.4% 0 - 0 - 0 -
7 PNI 250 1 0.4% 0 - 0 - 0 -
8 Group 250 1 0.4% 0 - 0 - 0 -
9 LymphNodeMetastasis 250 1 0.4% 0 - 0 - 0 -
10 Grade 250 1 0.4% 0 - 0 - 0 -
11 AntiX_intensity 250 1 0.4% 0 - 0 - 0 -
12 AntiY_intensity 250 1 0.4% 0 - 0 - 0 -
13 Grade_Level 250 1 0.4% 0 - 0 - 0 -
14 Race 250 1 0.4% 0 - 0 - 0 -
15 LastFollowUpDate 250 1 0.4% 0 - 0 - 0 -
16 Age 250 1 0.4% 0 - 0 - 0 -
17 SurgeryDate 250 1 0.4% 0 - 0 - 0 -
18 Name 250 1 0.4% 0 - 0 - 0 -
19 DeathTime 250 0 - 0 - 0 - 0 -
20 TStage 250 0 - 0 - 0 - 0 -
21 ID 250 0 - 0 - 0 - 0 -
qDistinct type anomalous_percent
1 3 Logical 52.4%
2 3 Logical 46.8%
3 3 Logical 33.6%
4 3 Character 0.4%
5 3 Character 0.4%
6 3 Character 0.4%
7 3 Character 0.4%
8 3 Character 0.4%
9 3 Character 0.4%
10 4 Character 0.4%
11 4 Numeric 0.4%
12 4 Numeric 0.4%
13 4 Character 0.4%
14 8 Character 0.4%
15 13 Timestamp 0.4%
16 50 Numeric 0.4%
17 233 Timestamp 0.4%
18 250 Character 0.4%
19 2 Character -
20 4 Character -
21 250 Character -
$problem_variables
[1] Variable q qNA pNA
[5] qZero pZero qBlank pBlank
[9] qInf pInf qDistinct type
[13] anomalous_percent problems
<0 rows> (or 0-length row.names)
================================================================================
[1] "Ignoring variable LastFollowUpDate: Unsupported type for visualization."
[1] "Ignoring variable SurgeryDate: Unsupported type for visualization."
Variable p_1 p_10 p_25 p_50 p_75 p_90 p_99
1 AntiX_intensity 1 1.8 2 2 3 3 3
2 AntiY_intensity 1 1 1 2 3 3 3
3 Age 25 30.8 37 49 61 70 73
Summary of Data via DataExplorer 📦
# A tibble: 1 x 9
rows columns discrete_columns continuous_colu… all_missing_col…
<int> <int> <int> <int> <int>
1 250 21 18 3 0
# … with 4 more variables: total_missing_values <int>, complete_rows <int>,
# total_observations <int>, memory_usage <dbl>
Drop columns
Write results as described in (Knijn, Simmer, and Nagtegaal 2015)22
Describe the number of patients included in the analysis and reason for dropout
Report patient/disease characteristics (including the biomarker of interest) with the number of missing values
Describe the interaction of the biomarker of interest with established prognostic variables
Include at least 90 % of initial cases included in univariate and multivariate analyses
Report the estimated effect (relative risk/odds ratio, confidence interval, and p value) in univariate analysis
Report the estimated effect (hazard rate/odds ratio, confidence interval, and p value) in multivariate analysis
Report the estimated effects (hazard ratio/odds ratio, confidence interval, and p value) of other prognostic factors included in multivariate analysis
Codes for clean and recode data.24
questionr::irec()
questionr::iorder()
questionr::icut()
iris %>% mutate(sumVar = rowSums(.[1:4]))
iris %>% mutate(sumVar = rowSums(select(., contains(“Sepal”)))) %>% head
iris %>% mutate(sumVar = select(., contains(“Sepal”)) %>% rowSums()) %>% head
iRenameColumn.R
iSelectColumn.R
<= 22 Low
>= 23 & <= 41 Average
>=42 High
Codes for missing data and impute.25
Multiple imputation support in Finalfit
https://www.datasurg.net/2019/09/25/multiple-imputation-support-in-finalfit/
Missing data
https://finalfit.org/articles/missing.html
Codes for Descriptive Statistics.26
Report Data properties via report 📦
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others (0 missing)
- Name: 249 entries: Aceyn, n = 1; Adalaide, n = 1; Adidas, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 127; Female, n = 122 (1 missing)
- Age: Mean = 49.54, SD = 14.16, Median = , MAD = 17.79, range: [25, 73], Skewness = 0.00, Kurtosis = -1.15, 1 missing
- Race: 7 entries: White, n = 158; Hispanic, n = 38; Black, n = 30 and 4 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 203; Present, n = 46 (1 missing)
- LVI: 2 entries: Absent, n = 147; Present, n = 102 (1 missing)
- PNI: 2 entries: Absent, n = 171; Present, n = 78 (1 missing)
- Death: 2 levels: FALSE (n = 83, 33.20%); TRUE (n = 166, 66.40%) and missing (n = 1, 0.40%)
- Group: 2 entries: Treatment, n = 131; Control, n = 118 (1 missing)
- Grade: 3 entries: 3, n = 109; 1, n = 78; 2, n = 62 (1 missing)
- TStage: 4 entries: 4, n = 118; 3, n = 65; 2, n = 43 and 1 other (0 missing)
- AntiX_intensity: Mean = 2.39, SD = 0.66, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.63, Kurtosis = -0.65, 1 missing
- AntiY_intensity: Mean = 2.02, SD = 0.80, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.03, Kurtosis = -1.42, 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 144; Present, n = 105 (1 missing)
- Valid: 2 levels: FALSE (n = 116, 46.40%); TRUE (n = 133, 53.20%) and missing (n = 1, 0.40%)
- Smoker: 2 levels: FALSE (n = 130, 52.00%); TRUE (n = 119, 47.60%) and missing (n = 1, 0.40%)
- Grade_Level: 3 entries: high, n = 109; low, n = 77; moderate, n = 63 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101 (0 missing)
Table 1 via arsenal 📦
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- arsenal::tableby(
~ Sex +
Age +
Race +
PreinvasiveComponent +
LVI +
PNI +
Death +
Group +
Grade +
TStage +
# `Anti-X-intensity` +
# `Anti-Y-intensity` +
LymphNodeMetastasis +
Valid +
Smoker +
Grade_Level
,
data = mydata
)
summary(tab1)
Overall (N=250) | |
---|---|
Sex | |
N-Miss | 1 |
Female | 122 (49.0%) |
Male | 127 (51.0%) |
Age | |
N-Miss | 1 |
Mean (SD) | 49.538 (14.160) |
Range | 25.000 - 73.000 |
Race | |
N-Miss | 1 |
Asian | 15 (6.0%) |
Bi-Racial | 5 (2.0%) |
Black | 30 (12.0%) |
Hispanic | 38 (15.3%) |
Native | 2 (0.8%) |
Other | 1 (0.4%) |
White | 158 (63.5%) |
PreinvasiveComponent | |
N-Miss | 1 |
Absent | 203 (81.5%) |
Present | 46 (18.5%) |
LVI | |
N-Miss | 1 |
Absent | 147 (59.0%) |
Present | 102 (41.0%) |
PNI | |
N-Miss | 1 |
Absent | 171 (68.7%) |
Present | 78 (31.3%) |
Death | |
N-Miss | 1 |
FALSE | 83 (33.3%) |
TRUE | 166 (66.7%) |
Group | |
N-Miss | 1 |
Control | 118 (47.4%) |
Treatment | 131 (52.6%) |
Grade | |
N-Miss | 1 |
1 | 78 (31.3%) |
2 | 62 (24.9%) |
3 | 109 (43.8%) |
TStage | |
1 | 24 (9.6%) |
2 | 43 (17.2%) |
3 | 65 (26.0%) |
4 | 118 (47.2%) |
LymphNodeMetastasis | |
N-Miss | 1 |
Absent | 144 (57.8%) |
Present | 105 (42.2%) |
Valid | |
N-Miss | 1 |
FALSE | 116 (46.6%) |
TRUE | 133 (53.4%) |
Smoker | |
N-Miss | 1 |
FALSE | 130 (52.2%) |
TRUE | 119 (47.8%) |
Grade_Level | |
N-Miss | 1 |
high | 109 (43.8%) |
low | 77 (30.9%) |
moderate | 63 (25.3%) |
Table 1 via tableone 📦
library(tableone)
mydata %>% dplyr::select(-keycolumns, -dateVariables) %>% tableone::CreateTableOne(data = .)
Overall
n 250
Sex = Male (%) 127 (51.0)
Age (mean (SD)) 49.54 (14.16)
Race (%)
Asian 15 ( 6.0)
Bi-Racial 5 ( 2.0)
Black 30 (12.0)
Hispanic 38 (15.3)
Native 2 ( 0.8)
Other 1 ( 0.4)
White 158 (63.5)
PreinvasiveComponent = Present (%) 46 (18.5)
LVI = Present (%) 102 (41.0)
PNI = Present (%) 78 (31.3)
Death = TRUE (%) 166 (66.7)
Group = Treatment (%) 131 (52.6)
Grade (%)
1 78 (31.3)
2 62 (24.9)
3 109 (43.8)
TStage (%)
1 24 ( 9.6)
2 43 (17.2)
3 65 (26.0)
4 118 (47.2)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.80)
LymphNodeMetastasis = Present (%) 105 (42.2)
Valid = TRUE (%) 133 (53.4)
Smoker = TRUE (%) 119 (47.8)
Grade_Level (%)
high 109 (43.8)
low 77 (30.9)
moderate 63 (25.3)
DeathTime = Within1Year (%) 149 (59.6)
Descriptive Statistics of Continuous Variables
mydata %>% dplyr::select(continiousVariables, numericVariables, integerVariables) %>%
summarytools::descr(., style = "rmarkdown")
# A tibble: 15 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Sex chr 1 0.4 3 NA NA NA
2 PreinvasiveComponent chr 1 0.4 3 NA NA NA
3 LVI chr 1 0.4 3 NA NA NA
4 PNI chr 1 0.4 3 NA NA NA
5 Death lgl 1 0.4 3 0 0.67 1
6 Group chr 1 0.4 3 NA NA NA
7 Grade chr 1 0.4 4 NA NA NA
8 TStage chr 0 0 4 NA NA NA
9 AntiX_intensity dbl 1 0.4 4 1 2.39 3
10 AntiY_intensity dbl 1 0.4 4 1 2.02 3
11 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
12 Valid lgl 1 0.4 3 0 0.53 1
13 Smoker lgl 1 0.4 3 0 0.48 1
14 Grade_Level chr 1 0.4 4 NA NA NA
15 DeathTime chr 0 0 2 NA NA NA
# A tibble: 18 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Name chr 1 0.4 250 NA NA NA
2 Sex chr 1 0.4 3 NA NA NA
3 Age dbl 1 0.4 50 25 49.5 73
4 Race chr 1 0.4 8 NA NA NA
5 PreinvasiveComponent chr 1 0.4 3 NA NA NA
6 LVI chr 1 0.4 3 NA NA NA
7 PNI chr 1 0.4 3 NA NA NA
8 LastFollowUpDate dat 1 0.4 13 NA NA NA
9 Death lgl 1 0.4 3 0 0.67 1
10 Group chr 1 0.4 3 NA NA NA
11 Grade chr 1 0.4 4 NA NA NA
12 AntiX_intensity dbl 1 0.4 4 1 2.39 3
13 AntiY_intensity dbl 1 0.4 4 1 2.02 3
14 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
15 Valid lgl 1 0.4 3 0 0.53 1
16 Smoker lgl 1 0.4 3 0 0.48 1
17 Grade_Level chr 1 0.4 4 NA NA NA
18 SurgeryDate dat 1 0.4 233 NA NA NA
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 49.5 73
5 Race chr 1 0.4 8 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 1 0.4 3 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.67 1
# … with 11 more rows
Use R/gc_desc_cat.R
to generate gc_desc_cat.Rmd
containing descriptive statistics for categorical variables
mydata %>% janitor::tabyl(Sex) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
Sex | n | percent | valid_percent |
---|---|---|---|
Female | 122 | 48.8% | 49.0% |
Male | 127 | 50.8% | 51.0% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Race) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
Race | n | percent | valid_percent |
---|---|---|---|
Asian | 15 | 6.0% | 6.0% |
Bi-Racial | 5 | 2.0% | 2.0% |
Black | 30 | 12.0% | 12.0% |
Hispanic | 38 | 15.2% | 15.3% |
Native | 2 | 0.8% | 0.8% |
Other | 1 | 0.4% | 0.4% |
White | 158 | 63.2% | 63.5% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PreinvasiveComponent) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
PreinvasiveComponent | n | percent | valid_percent |
---|---|---|---|
Absent | 203 | 81.2% | 81.5% |
Present | 46 | 18.4% | 18.5% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(LVI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
LVI | n | percent | valid_percent |
---|---|---|---|
Absent | 147 | 58.8% | 59.0% |
Present | 102 | 40.8% | 41.0% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PNI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
PNI | n | percent | valid_percent |
---|---|---|---|
Absent | 171 | 68.4% | 68.7% |
Present | 78 | 31.2% | 31.3% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Group) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
Group | n | percent | valid_percent |
---|---|---|---|
Control | 118 | 47.2% | 47.4% |
Treatment | 131 | 52.4% | 52.6% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
Grade | n | percent | valid_percent |
---|---|---|---|
1 | 78 | 31.2% | 31.3% |
2 | 62 | 24.8% | 24.9% |
3 | 109 | 43.6% | 43.8% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(TStage) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
TStage | n | percent |
---|---|---|
1 | 24 | 9.6% |
2 | 43 | 17.2% |
3 | 65 | 26.0% |
4 | 118 | 47.2% |
mydata %>% janitor::tabyl(LymphNodeMetastasis) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
LymphNodeMetastasis | n | percent | valid_percent |
---|---|---|---|
Absent | 144 | 57.6% | 57.8% |
Present | 105 | 42.0% | 42.2% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade_Level) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
Grade_Level | n | percent | valid_percent |
---|---|---|---|
high | 109 | 43.6% | 43.8% |
low | 77 | 30.8% | 30.9% |
moderate | 63 | 25.2% | 25.3% |
NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(DeathTime) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()
DeathTime | n | percent |
---|---|---|
MoreThan1Year | 101 | 40.4% |
Within1Year | 149 | 59.6% |
race_stats <- summarytools::freq(mydata$Race)
print(race_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Race Group")
variable = PreinvasiveComponent
type = character
na = 1 of 250 (0.4%)
unique = 3
Absent = 203 (81.2%)
Present = 46 (18.4%)
NA = 1 (0.4%)
## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(mydata, Target = NULL, margin = 1, clim = 10, nlim = 5, round = 2,
bin = NULL, per = T)
Variable Valid Frequency Percent CumPercent
1 Sex Female 122 48.8 48.8
2 Sex Male 127 50.8 99.6
3 Sex NA 1 0.4 100.0
4 Sex TOTAL 250 NA NA
5 Race Asian 15 6.0 6.0
6 Race Bi-Racial 5 2.0 8.0
7 Race Black 30 12.0 20.0
8 Race Hispanic 38 15.2 35.2
9 Race NA 1 0.4 35.6
10 Race Native 2 0.8 36.4
11 Race Other 1 0.4 36.8
12 Race White 158 63.2 100.0
13 Race TOTAL 250 NA NA
14 PreinvasiveComponent Absent 203 81.2 81.2
15 PreinvasiveComponent NA 1 0.4 81.6
16 PreinvasiveComponent Present 46 18.4 100.0
17 PreinvasiveComponent TOTAL 250 NA NA
18 LVI Absent 147 58.8 58.8
19 LVI NA 1 0.4 59.2
20 LVI Present 102 40.8 100.0
21 LVI TOTAL 250 NA NA
22 PNI Absent 171 68.4 68.4
23 PNI NA 1 0.4 68.8
24 PNI Present 78 31.2 100.0
25 PNI TOTAL 250 NA NA
26 Group Control 118 47.2 47.2
27 Group NA 1 0.4 47.6
28 Group Treatment 131 52.4 100.0
29 Group TOTAL 250 NA NA
30 Grade 1 78 31.2 31.2
31 Grade 2 62 24.8 56.0
32 Grade 3 109 43.6 99.6
33 Grade NA 1 0.4 100.0
34 Grade TOTAL 250 NA NA
35 TStage 1 24 9.6 9.6
36 TStage 2 43 17.2 26.8
37 TStage 3 65 26.0 52.8
38 TStage 4 118 47.2 100.0
39 TStage TOTAL 250 NA NA
40 LymphNodeMetastasis Absent 144 57.6 57.6
41 LymphNodeMetastasis NA 1 0.4 58.0
42 LymphNodeMetastasis Present 105 42.0 100.0
43 LymphNodeMetastasis TOTAL 250 NA NA
44 Grade_Level high 109 43.6 43.6
45 Grade_Level low 77 30.8 74.4
46 Grade_Level moderate 63 25.2 99.6
47 Grade_Level NA 1 0.4 100.0
48 Grade_Level TOTAL 250 NA NA
49 DeathTime MoreThan1Year 101 40.4 40.4
50 DeathTime Within1Year 149 59.6 100.0
51 DeathTime TOTAL 250 NA NA
52 AntiX_intensity 1 25 10.0 10.0
53 AntiX_intensity 2 102 40.8 50.8
54 AntiX_intensity 3 122 48.8 99.6
55 AntiX_intensity NA 1 0.4 100.0
56 AntiX_intensity TOTAL 250 NA NA
57 AntiY_intensity 1 77 30.8 30.8
58 AntiY_intensity 2 91 36.4 67.2
59 AntiY_intensity 3 81 32.4 99.6
60 AntiY_intensity NA 1 0.4 100.0
61 AntiY_intensity TOTAL 250 NA NA
# A tibble: 16 x 5
col_name cnt common common_pcnt levels
<chr> <int> <chr> <dbl> <named list>
1 Death 3 TRUE 66.4 <tibble [3 × 3]>
2 DeathTime 2 Within1Year 59.6 <tibble [2 × 3]>
3 Grade 4 3 43.6 <tibble [4 × 3]>
4 Grade_Level 4 high 43.6 <tibble [4 × 3]>
5 Group 3 Treatment 52.4 <tibble [3 × 3]>
6 ID 250 001 0.4 <tibble [250 × 3]>
7 LVI 3 Absent 58.8 <tibble [3 × 3]>
8 LymphNodeMetastasis 3 Absent 57.6 <tibble [3 × 3]>
9 Name 250 Aceyn 0.4 <tibble [250 × 3]>
10 PNI 3 Absent 68.4 <tibble [3 × 3]>
11 PreinvasiveComponent 3 Absent 81.2 <tibble [3 × 3]>
12 Race 8 White 63.2 <tibble [8 × 3]>
13 Sex 3 Male 50.8 <tibble [3 × 3]>
14 Smoker 3 FALSE 52 <tibble [3 × 3]>
15 TStage 4 4 47.2 <tibble [4 × 3]>
16 Valid 3 TRUE 53.2 <tibble [3 × 3]>
# A tibble: 3 x 3
value prop cnt
<chr> <dbl> <int>
1 Treatment 0.524 131
2 Control 0.472 118
3 <NA> 0.004 1
summarytools::stby(list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), mydata$PNI,
summarytools::ctable)
mydata %>% dplyr::select(characterVariables) %>% dplyr::select(PreinvasiveComponent,
PNI, LVI) %>% reactable::reactable(data = ., groupBy = c("PreinvasiveComponent",
"PNI"), columns = list(LVI = reactable::colDef(aggregate = "count")))
Descriptive Statistics Age
mydata %>% jmv::descriptives(data = ., vars = "Age", hist = TRUE, dens = TRUE, box = TRUE,
violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, skew = TRUE,
kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────
Age
──────────────────────────────────
N 249
Missing 1
Mean 49.5
Median 49.0
Mode 72.0
Standard deviation 14.2
Variance 200
Minimum 25.0
Maximum 73.0
Skewness 0.00389
Std. error skewness 0.154
Kurtosis -1.15
Std. error kurtosis 0.307
25th percentile 37.0
50th percentile 49.0
75th percentile 61.0
──────────────────────────────────
Descriptive Statistics AntiX_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiX_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiX_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 2.39
Median 2.00
Mode 3.00
Standard deviation 0.664
Variance 0.440
Minimum 1.00
Maximum 3.00
Skewness -0.631
Std. error skewness 0.154
Kurtosis -0.640
Std. error kurtosis 0.307
25th percentile 2.00
50th percentile 2.00
75th percentile 3.00
──────────────────────────────────────────
Descriptive Statistics AntiY_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiY_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiY_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 2.02
Median 2.00
Mode 2.00
Standard deviation 0.798
Variance 0.637
Minimum 1.00
Maximum 3.00
Skewness -0.0289
Std. error skewness 0.154
Kurtosis -1.43
Std. error kurtosis 0.307
25th percentile 1.00
50th percentile 2.00
75th percentile 3.00
──────────────────────────────────────────
Overall
n 250
Age (mean (SD)) 49.54 (14.16)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.80)
Overall
n 250
Age (mean (SD)) 49.54 (14.16)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.80)
variable = Age
type = double
na = 1 of 250 (0.4%)
unique = 50
min|max = 25 | 73
q05|q95 = 28 | 72
q25|q75 = 37 | 61
median = 49
mean = 49.53815
mydata %>% dplyr::select(continiousVariables) %>% SmartEDA::ExpNumStat(data = .,
by = "A", gp = NULL, Qnt = seq(0, 1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)
# A tibble: 3 x 10
col_name min q1 median mean q3 max sd pcnt_na hist
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <named list>
1 Age 25 37 49 49.5 61 73 14.2 0.4 <tibble [12…
2 AntiX_intens… 1 2 2 2.39 3 3 0.664 0.4 <tibble [12…
3 AntiY_intens… 1 1 2 2.02 3 3 0.798 0.4 <tibble [12…
# A tibble: 27 x 2
value prop
<chr> <dbl>
1 [-Inf, 24) 0
2 [24, 26) 0.0201
3 [26, 28) 0.0281
4 [28, 30) 0.0361
5 [30, 32) 0.0361
6 [32, 34) 0.0602
7 [34, 36) 0.0482
8 [36, 38) 0.0241
9 [38, 40) 0.0161
10 [40, 42) 0.0602
# … with 17 more rows
summarytools::stby(data = mydata, INDICES = mydata$PreinvasiveComponent, FUN = summarytools::descr,
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)
with(mydata, summarytools::stby(Age, PreinvasiveComponent, summarytools::descr),
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)
## Summary statistics by – category
SmartEDA::ExpNumStat(mydata, by = "GA", gp = "PreinvasiveComponent", Qnt = seq(0,
1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)
Vname Group TN nNeg nZero nPos NegInf PosInf NA_Value
1 Age PreinvasiveComponent:All 250 0 0 249 0 0 1
2 Age PreinvasiveComponent:Absent 203 0 0 203 0 0 0
3 Age PreinvasiveComponent:Present 46 0 0 45 0 0 1
4 Age PreinvasiveComponent:NA 0 0 0 0 0 0 0
Per_of_Missing sum min max mean median SD CV IQR Skewness Kurtosis
1 0.40 12335 25 73 49.54 49 14.16 0.29 24.0 0.00 -1.16
2 0.00 10117 25 73 49.84 51 14.34 0.29 23.5 -0.02 -1.20
3 2.17 2170 25 72 48.22 49 13.55 0.28 22.0 0.08 -0.98
4 NaN 0 Inf -Inf NaN NA NA NA NA NaN NaN
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% LB.25% UB.75% nOutliers
1 25 30.8 34.0 40.4 45.0 49 54.0 59.0 64 70.0 73 1.00 97.00 0
2 25 31.0 34.0 40.6 45.0 51 54.0 59.0 65 70.8 73 2.25 96.25 0
3 25 30.8 34.8 40.2 43.6 49 51.8 56.8 59 68.6 72 3.00 91.00 0
4 NA NA NA NA NA NA NA NA NA NA NA NA NA 0
Codes for cross tables.27
# dependent <- c('dependent1', 'dependent2' )
# explanatory <- c('explanatory1', 'explanatory2' )
dependent <- "PreinvasiveComponent"
explanatory <- c("Sex", "Age", "Grade", "TStage")
Change column = TRUE
argument to get row or column percentages.
Cross Table PreinvasiveComponent
mydata %>%
summary_factorlist(dependent = 'PreinvasiveComponent',
explanatory = explanatory,
# column = TRUE,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE,
na_include=FALSE
# catTest = catTestfisher
) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))
Dependent: PreinvasiveComponent | Absent | Present | Total | p | |
---|---|---|---|---|---|
Sex | Female | 104 (51.2) | 17 (37.8) | 121 (48.8) | 0.102 |
Male | 99 (48.8) | 28 (62.2) | 127 (51.2) | ||
Age | Mean (SD) | 49.8 (14.3) | 48.2 (13.6) | 49.5 (14.2) | 0.492 |
Grade | 1 | 68 (33.7) | 9 (19.6) | 77 (31.0) | 0.100 |
2 | 46 (22.8) | 16 (34.8) | 62 (25.0) | ||
3 | 88 (43.6) | 21 (45.7) | 109 (44.0) | ||
TStage | 1 | 18 (8.9) | 6 (13.0) | 24 (9.6) | 0.117 |
2 | 38 (18.7) | 4 (8.7) | 42 (16.9) | ||
3 | 48 (23.6) | 17 (37.0) | 65 (26.1) | ||
4 | 99 (48.8) | 19 (41.3) | 118 (47.4) |
library(DT)
datatable(mtcars, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
Codes for generating Plots.28
R allows to build any type of interactive graphic. My favourite library is plotly that will turn any of your ggplot2 graphic interactive in one supplementary line of code. Try to hover points, to select a zone, to click on the legend.
library(ggplot2)
library(plotly)
library(gapminder)
p <- gapminder %>% filter(year == 1977) %>% ggplot(aes(gdpPercap, lifeExp, size = pop,
color = continent)) + geom_point() + scale_x_log10() + theme_bw()
ggplotly(p)
embedgist <- gistr::gist("https://gist.github.com/sbalci/834ebc154c0ffcb7d5899c42dd3ab75e") %>%
gistr::embed()
# https://stackoverflow.com/questions/43053375/weighted-sankey-alluvial-diagram-for-visualizing-discrete-and-continuous-panel/48133004
library(tidyr)
library(dplyr)
library(alluvial)
library(ggplot2)
library(forcats)
set.seed(42)
individual <- rep(LETTERS[1:10], each = 2)
timeperiod <- paste0("time_", rep(1:2, 10))
discretechoice <- factor(paste0("choice_", sample(letters[1:3], 20, replace = T)))
continuouschoice <- ceiling(runif(20, 0, 100))
d <- data.frame(individual, timeperiod, discretechoice, continuouschoice)
# stacked bar diagram of discrete choice by individual
g <- ggplot(data = d, aes(timeperiod, fill = fct_rev(discretechoice)))
g + geom_bar(position = "stack") + guides(fill = guide_legend(title = NULL))
# alluvial diagram of discrete choice by individual
d_alluvial <- d %>% select(individual, timeperiod, discretechoice) %>% spread(timeperiod,
discretechoice) %>% group_by(time_1, time_2) %>% summarize(count = n()) %>% ungroup()
Error in UseMethod("ungroup"): no applicable method for 'ungroup' applied to an object of class "list"
Error in log_select(.data, .fun = dplyr::select, .funname = "select", : object 'd_alluvial' not found
# stacked bar diagram of discrete choice, weighting by continuous choice
g + geom_bar(position = "stack", aes(weight = continuouschoice))
library(ggalluvial)
ggplot(data = d, aes(x = timeperiod, stratum = discretechoice, alluvium = individual,
y = continuouschoice)) + geom_stratum(aes(fill = discretechoice)) + geom_flow()
CD44changes <- mydata %>% dplyr::select(TumorCD44, TomurcukCD44, PeritumoralTomurcukGr4) %>%
dplyr::filter(complete.cases(.)) %>% dplyr::group_by(TumorCD44, TomurcukCD44,
PeritumoralTomurcukGr4) %>% dplyr::tally()
Error: Can't subset columns that don't exist.
[31mx[39m The column `TumorCD44` doesn't exist.
library(ggalluvial)
ggplot(data = CD44changes, aes(axis1 = TumorCD44, axis2 = TomurcukCD44, y = n)) +
scale_x_discrete(limits = c("TumorCD44", "TomurcukCD44"), expand = c(0.1, 0.05)) +
xlab("Tumor Tomurcuk") + geom_alluvium(aes(fill = PeritumoralTomurcukGr4, colour = PeritumoralTomurcukGr4)) +
geom_stratum(alpha = 0.5) + geom_text(stat = "stratum", infer.label = TRUE) +
# geom_text(stat = 'alluvium', infer.label = TRUE) +
theme_minimal() + ggtitle("Changes in CD44")
Error in ggplot(data = CD44changes, aes(axis1 = TumorCD44, axis2 = TomurcukCD44, : object 'CD44changes' not found
Codes for generating paired tests.29
Codes for generating hypothesis tests.30
mytable <- jmv::ttestIS(formula = HindexCTLA4 ~ PeritumoralTomurcukGr4, data = mydata,
vars = HindexCTLA4, students = FALSE, mann = TRUE, norm = TRUE, meanDiff = TRUE,
desc = TRUE, plots = TRUE)
Error: Argument 'vars' contains 'HindexCTLA4' which is not present in the dataset
```r print(jtable(mytable$ttest)) ``` ``` Error in lapply(X = X, FUN = FUN, ...): object 'mytable' not found ``` ```r cat("
")
</pre>
## Categorical
### Chi-Square Cramer Association Predictive Power
## Continious
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/t.test.html
t.test(mtcars$mpg ~ mtcars$am) %>%
report::report()
report(t.test(iris$Sepal.Length, iris$Petal.Length))
## Odds
# Frequently Used Statistical Tests By Pathologists
Frequently Used Statistical Tests^[Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA] by [@Schmidt2017]
- Student t test
- Regression/ANOVA
- Chi-square test
- Mann-Whitney test (rank sum)
- Fisher exact test
- Survival analysis
- Kaplan-Meier/log-rank
- Cox regression
- Multiple comparison adjustment
- Tukey
- Bonferroni
- Newman-Keuls
- Kappa Statistic
- ROC analysis
- Logistic regression
- Spearman rank correlation
- Kruskal-Wallis test
- Pearson correlation statistic
- Normality test
- McNemar test
# Consider Adding:
- https://cran.r-project.org/web/packages/sm/sm.pdf
- https://cran.r-project.org/web/packages/Rfit/Rfit.pdf
\newpage
\blandscape
**Codes for ROC**.^[See [`childRmd/_16ROC.Rmd`](https://github.com/sbalci/histopathology-template/blob/master/childRmd/_16ROC.Rmd) file for other codes]
# ROC
**Codes for Decision Tree**.^[See [`childRmd/_17decisionTree.Rmd`](https://github.com/sbalci/histopathology-template/blob/master/childRmd/_17decisionTree.Rmd)]
# Decision Tree
**Explore**
```r
explore::explore(mydata)
Codes for Survival Analysis31
https://link.springer.com/article/10.1007/s00701-019-04096-9
Calculate survival time
mydata$int <- lubridate::interval(lubridate::ymd(mydata$SurgeryDate), lubridate::ymd(mydata$LastFollowUpDate))
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
recode death status outcome as numbers for survival analysis
## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death), `1` = "TRUE", `0` = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))
it is always a good practice to double-check after recoding32
0 1
FALSE 83 0
TRUE 0 166
library(survival)
# data(lung) km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km, 80)
[1] 4.5+ 7.8 7.1 7.9 10.6 6.9+ 8.4+ 11.0 3.5 7.6 8.4 6.0
[13] NA 9.5 11.2 11.7 9.2 7.6? 4.1 4.7 9.7+ 8.3+ 6.0+ 5.5+
[25] 6.4 11.4 3.8+ 10.2 3.0 6.4 11.3 6.5+ 9.7 6.7 3.3+ 11.2+
[37] 7.8 7.0 6.3 10.2 7.0 11.2 9.7+ 6.8 3.1 3.6 7.8 9.5+
[49] 6.0 10.4+ 11.2+ 3.3+ 7.4 9.2+ 9.9 11.2+ 10.0 5.4 9.5 5.4
[61] 5.9 8.4 4.1 9.2 7.3+ 6.6 7.0+ 8.6+ 4.0 4.1 10.7 4.7
[73] 6.9 6.6 5.3 8.0 9.3 8.4+ 8.6+ 8.8
Kaplan-Meier Plot Log-Rank Test
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
mydata %>%
finalfit::surv_plot(.data = .,
dependent = "Surv(OverallTime, Outcome)",
explanatory = "LVI",
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)
library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit::finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni[, 1:4], row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))
Dependent: Surv(OverallTime, Outcome) | all | HR (univariable) | |
---|---|---|---|
LVI | Absent | 147 (100.0) | - |
Present | 102 (100.0) | 1.59 (1.15-2.20, p=0.005) |
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names()
tUni_df_descr <- paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1],
" is ", tUni_df$x[2], ", there is ", tUni_df$hr_univariable[2], " times risk than ",
"when ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1],
".")
When LVI is Present, there is 1.59 (1.15-2.20, p=0.005) times risk than when LVI is Absent.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 144 100 22.0 14.3 31.0
LVI=Present 102 64 10.5 9.9 13.8
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names() %>%
tibble::rownames_to_column()
km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::select(description) %>% dplyr::pull()
When LVI=Absent, median survival is 22 [14.3 - 31, 95% CI] months., When LVI=Present, median survival is 10.5 [9.9 - 13.8, 95% CI] months.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 75 52 0.617 0.0421 0.539 0.705
36 19 35 0.252 0.0452 0.177 0.358
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 49 0.383 0.0566 0.2870 0.512
36 4 12 0.134 0.0488 0.0657 0.274
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])
km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% dplyr::pull()
When LVI=Absent, 12 month survival is 62% [54%-70.5%, 95% CI]., When LVI=Absent, 36 month survival is 25% [18%-35.8%, 95% CI]., When LVI=Present, 12 month survival is 38% [29%-51.2%, 95% CI]., When LVI=Present, 36 month survival is 13% [7%-27.4%, 95% CI].
Kaplan-Meier Plot Log-Rank Test
library(survival)
library(survminer)
library(finalfit)
mydata %>%
finalfit::surv_plot('Surv(OverallTime, Outcome)', 'LVI',
xlab='Time (months)', pval=TRUE, legend = 'none',
break.time.by = 12, xlim = c(0,60)
# legend.labs = c('a','b')
)
Univariate Cox-Regression
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit(dependentUni, explanatoryUni, metrics = TRUE)
knitr::kable(tUni[, 1:4], row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))
Error in tUni[, 1:4]: incorrect number of dimensions
Univariate Cox-Regression Summary
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names(dat = .,
case = "snake")
n_level <- dim(tUni_df)[1]
tUni_df_descr <- function(n) {
paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[n +
1], ", there is ", tUni_df$hr_univariable[n + 1], " times risk than ", "when ",
tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1], ".")
}
results5 <- purrr::map(.x = c(2:n_level - 1), .f = tUni_df_descr)
print(unlist(results5))
[1] "When is c(\"Absent\", \"Present\"), there is times risk than when is c(\"LVI\", \"\")."
Median Survival
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 144 100 22.0 14.3 31.0
LVI=Present 102 64 10.5 9.9 13.8
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names(dat = .,
case = "snake") %>% tibble::rownames_to_column(.data = ., var = "LVI")
km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When, LVI, {LVI}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::mutate(description = gsub(pattern = "thefactor=", replacement = " is ",
x = description)) %>% dplyr::select(description) %>% dplyr::pull()
km_fit_median_definition
When, LVI, LVI=Absent, median survival is 22 [14.3 - 31, 95% CI] months.
When, LVI, LVI=Present, median survival is 10.5 [9.9 - 13.8, 95% CI] months.
1-3-5-yr survival
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 75 52 0.617 0.0421 0.539 0.705
36 19 35 0.252 0.0452 0.177 0.358
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 49 0.383 0.0566 0.2870 0.512
36 4 12 0.134 0.0488 0.0657 0.274
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])
km_fit_df
strata time n.risk n.event surv std.err lower upper
1 LVI=Absent 12 75 52 0.6165782 0.04211739 0.53931696 0.7049078
2 LVI=Absent 36 19 35 0.2520087 0.04515881 0.17737163 0.3580528
3 LVI=Present 12 23 49 0.3833784 0.05662684 0.28701265 0.5120993
4 LVI=Present 36 4 12 0.1340646 0.04881983 0.06566707 0.2737036
km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% dplyr::pull()
km_fit_definition
When LVI=Absent, 12 month survival is 62% [54%-70.5%, 95% CI].
When LVI=Absent, 36 month survival is 25% [18%-35.8%, 95% CI].
When LVI=Present, 12 month survival is 38% [29%-51.2%, 95% CI].
When LVI=Present, 36 month survival is 13% [7%-27.4%, 95% CI].
records n.max n.start events *rmean *se(rmean) median 0.95LCL
LVI=Absent 144 144 144 100 24.71341 1.571856 22.0 14.3
LVI=Present 102 102 102 64 17.48672 1.904576 10.5 9.9
0.95UCL
LVI=Absent 31.0
LVI=Present 13.8
km_fit_median_df <- summary(km_fit)
results1html <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names(dat = .,
case = "snake") %>% tibble::rownames_to_column(.data = ., var = "LVI")
results1html[, 1] <- gsub(pattern = "thefactor=", replacement = "", x = results1html[,
1])
knitr::kable(results1html, row.names = FALSE, align = c("l", rep("r", 9)), format = "html",
digits = 1)
LVI | records | n_max | n_start | events | rmean | se_rmean | median | x0_95lcl | x0_95ucl |
---|---|---|---|---|---|---|---|---|---|
LVI=Absent | 144 | 144 | 144 | 100 | 24.7 | 1.6 | 22.0 | 14.3 | 31.0 |
LVI=Present | 102 | 102 | 102 | 64 | 17.5 | 1.9 | 10.5 | 9.9 | 13.8 |
Pairwise Comparisons
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 144 100 22.0 14.3 31.0
LVI=Present 102 64 10.5 9.9 13.8
print(km_fit,
scale=1,
digits = max(options()$digits - 4,3),
print.rmean=getOption("survfit.print.rmean"),
rmean = getOption('survfit.rmean'),
print.median=getOption("survfit.print.median"),
median = getOption('survfit.median')
)
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 144 100 22.0 14.3 31.0
LVI=Present 102 64 10.5 9.9 13.8
explanatory = c(“age.factor”, “sex.factor”, “obstruct.factor”, “perfor.factor”) dependent = ‘mort_5yr’ colon_s %>% hr_plot(dependent, explanatory)
library(survival)
library(survminer)
library(finalfit)
mb_followup %>%
finalfit::surv_plot('Surv(OverallTime, Outcome)', 'Operation',
xlab='Time (months)', pval=TRUE, legend = 'none',
# pval.coord
break.time.by = 12, xlim = c(0,60), ylim = c(0.8, 1)
# legend.labs = c('a','b')
)
Univariate Cox-Regression
explanatoryUni <- "Operation"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mb_followup %>% finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni[, 1:4], row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))
Univariate Cox-Regression Summary
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names(dat = .,
case = "snake")
n_level <- dim(tUni_df)[1]
tUni_df_descr <- function(n) {
paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[n +
1], ", there is ", tUni_df$hr_univariable[n + 1], " times risk than ", "when ",
tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1], ".")
}
results5 <- purrr::map(.x = c(2:n_level - 1), .f = tUni_df_descr)
print(unlist(results5))
Median Survival
km_fit <- survfit(Surv(OverallTime, Outcome) ~ Operation, data = mb_followup)
# km_fit
# summary(km_fit)
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names(dat = .,
case = "snake") %>% tibble::rownames_to_column(.data = ., var = "Derece")
km_fit_median_df
# km_fit_median_df %>% knitr::kable(format = 'latex') %>%
# kableExtra::kable_styling(latex_options='scale_down')
km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When, Derece, {Derece}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::mutate(description = gsub(pattern = "thefactor=", replacement = " is ",
x = description)) %>% dplyr::select(description) %>% dplyr::pull()
# km_fit_median_definition
1-3-5-yr survival
summary(km_fit, times = c(12, 36, 60))
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])
km_fit_df %>% knitr::kable(format = "latex") %>% kableExtra::kable_styling(latex_options = "scale_down")
km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% dplyr::pull()
km_fit_definition
Pairwise Comparisons
survminer::pairwise_survdiff(formula = Surv(OverallTime, Outcome) ~ Operation, data = mb_followup,
p.adjust.method = "BH")
library(gt)
library(gtsummary)
library(survival)
fit1 <- survfit(Surv(ttdeath, death) ~ trt, trial)
tbl_strata_ex1 <- tbl_survival(fit1, times = c(12, 24), label = "{time} Months")
fit2 <- survfit(Surv(ttdeath, death) ~ 1, trial)
tbl_nostrata_ex2 <- tbl_survival(fit2, probs = c(0.1, 0.2, 0.5), header_estimate = "**Months**")
Codes for generating Survival Analysis.33
Codes for generating Shiny Survival Analysis.34
Codes for generating correlation analysis.35
https://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.test.html
https://neuropsychology.github.io/psycho.R/2018/05/20/correlation.html
devtools::install_github("neuropsychology/psycho.R") # Install the newest version
remove.packages("psycho")
renv::install("neuropsychology/psycho.R@0.4.0")
# devtools::install_github("neuropsychology/psycho.R@0.4.0")
library(psycho)
<!-- library(tidyverse) -->
cor <- psycho::affective %>%
correlation()
summary(cor)
plot(cor)
print(cor)
Codes used in models36
Use these descriptions to add autoreporting of new models
generate automatic reporting of model via easystats/report 📦
We fitted a linear model (estimated using OLS) to predict Sepal.Length with Species (formula = Sepal.Length ~ Species). Standardized parameters were obtained by fitting the model on a standardized version of the dataset. Effect sizes were labelled following Funder's (2019) recommendations.
The model explains a significant and substantial proportion of variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61). The model's intercept, corresponding to Sepal.Length = 0 and Species = setosa, is at 5.01 (SE = 0.07, 95% CI [4.86, 5.15], p < .001). Within this model:
- The effect of Speciesversicolor is positive and can be considered as very large and significant (beta = 1.12, SE = 0.12, 95% CI [0.88, 1.37], std. beta = 1.12, p < .001).
- The effect of Speciesvirginica is positive and can be considered as very large and significant (beta = 1.91, SE = 0.12, 95% CI [1.66, 2.16], std. beta = 1.91, p < .001).
Table report for a linear model
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/glm.html
model <- glm(vs ~ mpg + cyl, data=mtcars, family="binomial")
r <- report(model)
to_fulltext(r)
to_fulltable(r)
Where a multivariable model contains a subset of the variables specified in the full univariable set, this can be specified.
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
dependent = 'mort_5yr'
colon_s %>%
summarizer(dependent, explanatory, explanatory.multi)
Random effects.
e.g. lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
random.effect = "hospital"
dependent = 'mort_5yr'
colon_s %>%
summarizer(dependent, explanatory, explanatory.multi, random.effect)
metrics=TRUE provides common model metrics.
colon_s %>%
summarizer(dependent, explanatory, explanatory.multi, metrics=TRUE)
Cox proportional hazards
e.g. survival::coxph(dependent ~ explanatory)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
summarizer(dependent, explanatory)
Rather than going all-in-one, any number of subset models can be manually added on to a summary.factorlist() table using summarizer.merge(). This is particularly useful when models take a long-time to run or are complicated.
Note requirement for glm.id=TRUE. fit2df is a subfunction extracting most common models to a dataframe.
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
random.effect = "hospital"
dependent = 'mort_5yr'
# Separate tables
colon_s %>%
summary.factorlist(dependent, explanatory, glm.id=TRUE) -> example.summary
colon_s %>%
glmuni(dependent, explanatory) %>%
fit2df(estimate.suffix=" (univariable)") -> example.univariable
colon_s %>%
glmmulti(dependent, explanatory) %>%
fit2df(estimate.suffix=" (multivariable)") -> example.multivariable
colon_s %>%
glmmixed(dependent, explanatory, random.effect) %>%
fit2df(estimate.suffix=" (multilevel") -> example.multilevel
# Pipe together
example.summary %>%
summarizer.merge(example.univariable) %>%
summarizer.merge(example.multivariable) %>%
summarizer.merge(example.multilevel) %>%
select(-c(glm.id, index)) -> example.final
example.final
Cox Proportional Hazards example with separate tables merged together.
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
dependent = "Surv(time, status)"
# Separate tables
colon_s %>%
summary.factorlist(dependent, explanatory, glm.id=TRUE) -> example2.summary
colon_s %>%
coxphuni(dependent, explanatory) %>%
fit2df(estimate.suffix=" (univariable)") -> example2.univariable
colon_s %>%
coxphmulti(dependent, explanatory.multi) %>%
fit2df(estimate.suffix=" (multivariable)") -> example2.multivariable
# Pipe together
example2.summary %>%
summarizer.merge(example2.univariable) %>%
summarizer.merge(example2.multivariable) %>%
select(-c(glm.id, index)) -> example2.final
example2.final
# OR plot
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
or.plot(dependent, explanatory)
# Previously fitted models (`glmmulti()` or `glmmixed()`) can be provided directly to `glmfit`
# HR plot (not fully tested)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
hr.plot(dependent, explanatory, dependent_label = "Survival")
# Previously fitted models (`coxphmulti`) can be provided directly using `coxfit`
Test if your model is a good model
https://easystats.github.io/performance/
Some Text ile sağkalım açısından bir ilişki bulunmamıştır (p = 0.22).
Text Here
Since R Markdown use the bootstrap framework under the hood. It is possible to benefit its powerful grid system. Basically, you can consider that your row is divided in 12 subunits of same width. You can then choose to use only a few of this subunits.
Here, I use 3 subunits of size 4 (4x3=12). The last column is used for a plot. You can read more about the grid system here. I got this result showing the following code in my R Markdown document.
Interpret the results in context of the working hypothesis elaborated in the introduction and other relevant studies; include a discussion of limitations of the study.
Discuss potential clinical applications and implications for future research
Knijn, N., F. Simmer, and I. D. Nagtegaal. 2015. “Recommendations for Reporting Histopathology Studies: A Proposal.” Virchows Archiv 466 (6): 611–15. https://doi.org/10.1007/s00428-015-1762-3.
Schmidt, Robert L., Deborah J. Chute, Jorie M. Colbert-Getz, Adolfo Firpo-Betancourt, Daniel S. James, Julie K. Karp, Douglas C. Miller, et al. 2017. “Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty.” Archives of Pathology & Laboratory Medicine 141 (2): 279–87. https://doi.org/10.5858/arpa.2016-0200-OA.
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_01header.Rmd
file for other general settings↩︎
Change echo = FALSE
to hide codes after knitting and Change cache = TRUE
to knit quickly. Change error=TRUE
to continue rendering while errors are present.↩︎
See childRmd/_02fakeData.Rmd
file for other codes↩︎
Synthea The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 19, 44 (2019) doi:10.1186/s12911-019-0793-0↩︎
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0793-0↩︎
https://medium.com/free-code-camp/how-our-test-data-generator-makes-fake-data-look-real-ace01c5bde4a↩︎
lung, cancer, breast datası ile birleştir↩︎
See childRmd/_03importData.Rmd
file for other codes↩︎
See childRmd/_04briefSummary.Rmd
file for other codes↩︎
Kişisel verilerin kaydedilmesi ve kişisel verileri hukuka aykırı olarak verme veya ele geçirme Türk Ceza Kanunu’nun 135. ve 136. maddesi kapsamında bizim hukuk sistemimizde suç olarak tanımlanmıştır. Kişisel verilerin kaydedilmesi suçunun cezası 1 ila 3 yıl hapis cezasıdır. Suçun nitelikli hali ise, kamu görevlisi tarafından görevin verdiği yetkinin kötüye kullanılarak veya belirli bir meslek veya sanatın sağladığı kolaylıktan yararlanılarak işlenmesidir ki bu durumda suçun cezası 1.5 ile 4.5 yıl hapis cezası olacaktır.↩︎
See childRmd/_06variableTypes.Rmd
file for other codes↩︎
See childRmd/_07overView.Rmd
file for other codes↩︎
Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_08dataDictionary.Rmd
file for other codes↩︎
See childRmd/_09cleanRecode.Rmd
file for other codes↩︎
See childRmd/_10impute.Rmd
file for other codes↩︎
See childRmd/_11descriptives.Rmd
file for other codes↩︎
See childRmd/_12crossTables.Rmd
file for other codes↩︎
See childRmd/_13plots.Rmd
file for other codes↩︎
See childRmd/_14pairedTests.Rmd
file for other codes↩︎
See childRmd/_15hypothesisTests.Rmd
file for other codes↩︎
See childRmd/_18survival.Rmd
file for other codes, and childRmd/_19shinySurvival.Rmd
for shiny
application↩︎
JAMA retraction after miscoding – new Finalfit function to check recoding↩︎
See childRmd/_18survival.Rmd
file for other codes↩︎
See childRmd/_19shinySurvival.Rmd
file for other codes↩︎
See childRmd/_20correlation.Rmd
file for other codes↩︎
See childRmd/_21models.Rmd
file for other codes↩︎
See childRmd/_23footer.Rmd
file for other codes↩︎
Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group. (2016) Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86 https://www.force11.org/software-citation-principles↩︎
A work by Serdar Balci
drserdarbalci@gmail.com