This project contains a pipeline for analysis of The Cancer Genome Atlas Kidney - Renal Clear Cell Carcinoma (TCGA-KIRC) clinical data, from Genomic Data Commons Data Portal and cBioPortal.
In this section, the initial preprocessing is applied to clean the data and arrange following the Tidyverse philosophy. Exploratory Data Analysis summarizes their main characteristics.
## This chunk automatically generates a text .R version of this script when running within knitr.
input = knitr::current_input() # filename of input document
output = paste(tools::file_path_sans_ext(input), 'R', sep = '.')
try(knitr::purl(input,output,documentation=2,quiet=T), silent=T)
# Avoid duplicate label error of knitr::purl
options(knitr.duplicate.label = 'allow')
# Code to browse the markdown file with renderized images.
knitr::opts_chunk$set(
fig.path = "figs/1-prep_"
)
- Data importing and visualizing
kirc_clin_raw <- read_delim("data/kirc_tcga_clinical_data.tsv", "\t",
escape_double = FALSE,
trim_ws = TRUE)
- Cleaning data
Select variables based on NA count (> 50% complete is a good choice!).
NA_fifty <- dim(kirc_clin_raw)[1]/2
NA_sum <- colSums(is.na(kirc_clin_raw))
NA_sum <- as.data.frame(NA_sum)
NA_sum <- tibble::rownames_to_column(NA_sum, "variables")
NA_sum <- NA_sum %>%
filter(NA_sum < NA_fifty)
kirc_clean <- kirc_clin_raw %>%
select(one_of(NA_sum$variables))
Remove duplicate observations:
kirc_clean0 <- kirc_clean %>%
distinct_at('Patient ID', .keep_all = TRUE)
Remove nuneric variables with unique observations:
kirc_clean0 %>%
select_if(is.numeric) %>%
skim()
| Name | Piped data |
| Number of rows | 537 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Diagnosis Age | 0 | 1.00 | 60.59 | 12.15 | 26.00 | 52.00 | 61.00 | 70.00 | 90.00 | ▁▅▇▆▂ |
| Last Alive Less Initial Pathologic Diagnosis Date Calculated Day Value | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| Disease Free (Months) | 99 | 0.82 | 40.24 | 31.66 | -11.79 | 13.43 | 36.20 | 60.51 | 133.84 | ▇▇▇▂▂ |
| Fraction Genome Altered | 9 | 0.98 | 0.17 | 0.17 | 0.00 | 0.06 | 0.12 | 0.21 | 0.95 | ▇▂▁▁▁ |
| Year Cancer Initial Diagnosis | 0 | 1.00 | 2006.02 | 2.76 | 1998.00 | 2004.00 | 2006.00 | 2007.00 | 2013.00 | ▁▆▇▃▁ |
| Longest Dimension | 35 | 0.93 | 1.66 | 0.66 | 0.40 | 1.20 | 1.50 | 2.00 | 4.00 | ▃▇▃▂▁ |
| Mutation Count | 86 | 0.84 | 73.85 | 127.76 | 1.00 | 34.00 | 48.00 | 65.50 | 1392.00 | ▇▁▁▁▁ |
| Overall Survival (Months) | 0 | 1.00 | 44.26 | 32.25 | 0.00 | 18.10 | 38.96 | 63.21 | 149.05 | ▇▇▃▂▁ |
| Number of Samples Per Patient | 0 | 1.00 | 1.00 | 0.04 | 1.00 | 1.00 | 1.00 | 1.00 | 2.00 | ▇▁▁▁▁ |
| Sample type id | 0 | 1.00 | 1.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▇▁▁ |
| Shortest Dimension | 35 | 0.93 | 0.38 | 0.21 | 0.10 | 0.20 | 0.30 | 0.50 | 1.00 | ▆▇▂▁▁ |
| Specimen Second Longest Dimension | 35 | 0.93 | 0.94 | 0.31 | 0.30 | 0.70 | 0.90 | 1.10 | 2.00 | ▃▇▆▂▁ |
kirc_clean1 <- kirc_clean0 %>%
select(!c('Last Alive Less Initial Pathologic Diagnosis Date Calculated Day Value',
'Number of Samples Per Patient',
'Sample type id'))
Remove character variables with unique observations:
kirc_clean1 %>%
select_if(is.character) %>%
skim()
| Name | Piped data |
| Number of rows | 537 |
| Number of columns | 43 |
| _______________________ | |
| Column type frequency: | |
| character | 43 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Study ID | 0 | 1.00 | 9 | 9 | 0 | 1 | 0 |
| Patient ID | 0 | 1.00 | 12 | 12 | 0 | 537 | 0 |
| Sample ID | 0 | 1.00 | 15 | 15 | 0 | 537 | 0 |
| American Joint Committee on Cancer Metastasis Stage Code | 2 | 1.00 | 2 | 2 | 0 | 3 | 0 |
| Neoplasm Disease Lymph Node Stage American Joint Committee on Cancer Code | 0 | 1.00 | 2 | 2 | 0 | 3 | 0 |
| Neoplasm Disease Stage American Joint Committee on Cancer Code | 3 | 0.99 | 7 | 9 | 0 | 4 | 0 |
| American Joint Committee on Cancer Tumor Stage Code | 0 | 1.00 | 2 | 3 | 0 | 11 | 0 |
| Cancer Type | 0 | 1.00 | 20 | 20 | 0 | 1 | 0 |
| Cancer Type Detailed | 0 | 1.00 | 26 | 26 | 0 | 1 | 0 |
| Disease Free Status | 99 | 0.82 | 11 | 19 | 0 | 2 | 0 |
| Ethnicity Category | 152 | 0.72 | 18 | 22 | 0 | 2 | 0 |
| Form completion date | 0 | 1.00 | 6 | 10 | 0 | 114 | 0 |
| Neoplasm Histologic Grade | 3 | 0.99 | 2 | 2 | 0 | 5 | 0 |
| Hemoglobin level | 83 | 0.85 | 3 | 8 | 0 | 3 | 0 |
| Neoplasm Histologic Type Name | 0 | 1.00 | 33 | 33 | 0 | 1 | 0 |
| Neoadjuvant Therapy Type Administered Prior To Resection Text | 0 | 1.00 | 2 | 3 | 0 | 2 | 0 |
| Prior Cancer Diagnosis Occurence | 0 | 1.00 | 2 | 48 | 0 | 4 | 0 |
| ICD-10 Classification | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| International Classification of Diseases for Oncology, Third Edition ICD-O-3 Histology Code | 0 | 1.00 | 6 | 6 | 0 | 2 | 0 |
| International Classification of Diseases for Oncology, Third Edition ICD-O-3 Site Code | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| Informed consent verified | 0 | 1.00 | 3 | 3 | 0 | 1 | 0 |
| Is FFPE | 0 | 1.00 | 2 | 2 | 0 | 1 | 0 |
| Primary Tumor Laterality | 0 | 1.00 | 4 | 9 | 0 | 3 | 0 |
| Primary Lymph Node Presentation Assessment Ind-3 | 7 | 0.99 | 2 | 3 | 0 | 2 | 0 |
| Oncotree Code | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| Overall Survival Status | 0 | 1.00 | 6 | 8 | 0 | 2 | 0 |
| Other Patient ID | 0 | 1.00 | 36 | 36 | 0 | 537 | 0 |
| Other Sample ID | 0 | 1.00 | 36 | 36 | 0 | 537 | 0 |
| Pathology Report File Name | 0 | 1.00 | 53 | 53 | 0 | 537 | 0 |
| Pathology report uuid | 0 | 1.00 | 36 | 36 | 0 | 537 | 0 |
| Platelet count | 93 | 0.83 | 3 | 8 | 0 | 3 | 0 |
| Tissue Prospective Collection Indicator | 20 | 0.96 | 2 | 3 | 0 | 2 | 0 |
| Race Category | 7 | 0.99 | 5 | 25 | 0 | 3 | 0 |
| Tissue Retrospective Collection Indicator | 18 | 0.97 | 2 | 3 | 0 | 2 | 0 |
| Sample Type | 0 | 1.00 | 7 | 7 | 0 | 1 | 0 |
| Serum calcium level | 172 | 0.68 | 3 | 8 | 0 | 3 | 0 |
| Sex | 0 | 1.00 | 4 | 6 | 0 | 3 | 0 |
| Tumor Tissue Site | 0 | 1.00 | 6 | 6 | 0 | 1 | 0 |
| Tissue Source Site | 0 | 1.00 | 2 | 2 | 0 | 20 | 0 |
| Person Neoplasm Status | 35 | 0.93 | 10 | 10 | 0 | 2 | 0 |
| Vial number | 0 | 1.00 | 1 | 1 | 0 | 2 | 0 |
| Patient’s Vital Status | 3 | 0.99 | 4 | 5 | 0 | 2 | 0 |
| WBC | 96 | 0.82 | 3 | 8 | 0 | 3 | 0 |
kirc_clean2 <- kirc_clean1 %>%
select(!c('Study ID', 'Cancer Type', 'Cancer Type Detailed',
'Neoplasm Histologic Type Name', 'ICD-10 Classification',
'International Classification of Diseases for Oncology, Third Edition ICD-O-3 Site Code',
'Informed consent verified', 'Is FFPE', 'Oncotree Code', 'Sample Type', 'Tumor Tissue Site'))
Remove character variables with similar information - check each one!
table(kirc_clean2$`Overall Survival Status`, exclude = NULL)
##
## DECEASED LIVING
## 177 360
table(kirc_clean2$`Patient's Vital Status`, exclude = NULL)
##
## Alive Dead <NA>
## 360 174 3
kirc_clean3 <- kirc_clean2 %>%
select(!c('Sample ID', 'Other Patient ID', 'Other Sample ID', 'Pathology Report File Name', 'Pathology report uuid', "Patient's Vital Status"))
Remove other variables not directly related to patient - check each one!
kirc_clean4 <- kirc_clean3 %>%
select(!c('Form completion date','International Classification of Diseases for Oncology, Third Edition ICD-O-3 Histology Code','Primary Lymph Node Presentation Assessment Ind-3', 'Tissue Prospective Collection Indicator', 'Tissue Retrospective Collection Indicator','Vial number'))
- Changing variables names
Using snake_style
kirc_clean4 <- kirc_clean4 %>%
rename(patient_id = 'Patient ID',
age = 'Diagnosis Age',
metastasis_stg = 'American Joint Committee on Cancer Metastasis Stage Code',
lymph_stg = 'Neoplasm Disease Lymph Node Stage American Joint Committee on Cancer Code',
neoplasm_stg = 'Neoplasm Disease Stage American Joint Committee on Cancer Code',
tumor_stg = 'American Joint Committee on Cancer Tumor Stage Code',
disease_free_mth = 'Disease Free (Months)',
disease_free_stt = 'Disease Free Status',
ethnicity = 'Ethnicity Category',
frac_genome_alter = 'Fraction Genome Altered',
histology_grd = 'Neoplasm Histologic Grade',
hemoglobin = 'Hemoglobin level',
neoadj_therapy = 'Neoadjuvant Therapy Type Administered Prior To Resection Text',
prior_cancer = 'Prior Cancer Diagnosis Occurence',
year_diagnose = 'Year Cancer Initial Diagnosis',
tumor_lateral = 'Primary Tumor Laterality',
long_dim = 'Longest Dimension',
mutation_cnt = 'Mutation Count',
over_surv_mth = 'Overall Survival (Months)',
over_surv_stt = 'Overall Survival Status',
platelet = 'Platelet count',
race = 'Race Category',
serum_ca = 'Serum calcium level',
gender = 'Sex',
short_dim = 'Shortest Dimension',
second_long_dim = 'Specimen Second Longest Dimension',
tissue_site = 'Tissue Source Site',
person_neoplasm_stt = 'Person Neoplasm Status',
wbc = 'WBC')
- Taming data
Use lubridate for dates
kirc_clean4 <- kirc_clean4 %>%
mutate_if(is.character, as.factor) %>%
mutate(patient_id = as.character(patient_id),
age = as.integer(age),
year_diagnose = as.integer(year_diagnose))
- Checking NA patterns
Check distincts types of NAs: MCAR, MAR, MNAR
kirc_clean4 %>%
missing_plot()
missing_glimpse(kirc_clean4) %>% knitr::kable(.)
| label | var_type | n | missing_n | missing_percent | |
|---|---|---|---|---|---|
| patient_id | patient_id | 537 | 0 | 0.0 | |
| age | age | 537 | 0 | 0.0 | |
| metastasis_stg | metastasis_stg | 535 | 2 | 0.4 | |
| lymph_stg | lymph_stg | 537 | 0 | 0.0 | |
| neoplasm_stg | neoplasm_stg | 534 | 3 | 0.6 | |
| tumor_stg | tumor_stg | 537 | 0 | 0.0 | |
| disease_free_mth | disease_free_mth | 438 | 99 | 18.4 | |
| disease_free_stt | disease_free_stt | 438 | 99 | 18.4 | |
| ethnicity | ethnicity | 385 | 152 | 28.3 | |
| frac_genome_alter | frac_genome_alter | 528 | 9 | 1.7 | |
| histology_grd | histology_grd | 534 | 3 | 0.6 | |
| hemoglobin | hemoglobin | 454 | 83 | 15.5 | |
| neoadj_therapy | neoadj_therapy | 537 | 0 | 0.0 | |
| prior_cancer | prior_cancer | 537 | 0 | 0.0 | |
| year_diagnose | year_diagnose | 537 | 0 | 0.0 | |
| tumor_lateral | tumor_lateral | 537 | 0 | 0.0 | |
| long_dim | long_dim | 502 | 35 | 6.5 | |
| mutation_cnt | mutation_cnt | 451 | 86 | 16.0 | |
| over_surv_mth | over_surv_mth | 537 | 0 | 0.0 | |
| over_surv_stt | over_surv_stt | 537 | 0 | 0.0 | |
| platelet | platelet | 444 | 93 | 17.3 | |
| race | race | 530 | 7 | 1.3 | |
| serum_ca | serum_ca | 365 | 172 | 32.0 | |
| gender | gender | 537 | 0 | 0.0 | |
| short_dim | short_dim | 502 | 35 | 6.5 | |
| second_long_dim | second_long_dim | 502 | 35 | 6.5 | |
| tissue_site | tissue_site | 537 | 0 | 0.0 | |
| person_neoplasm_stt | person_neoplasm_stt | 502 | 35 | 6.5 | |
| wbc | wbc | 441 | 96 | 17.9 |
- Checking numeric variables
Check data distribution, plausible ranges, outliers; Thinking about deleting outliers from dataset? Need to evaluate carefully each one!
kirc_clean4 %>%
select_if(is.numeric) %>%
summary()
## age disease_free_mth frac_genome_alter year_diagnose
## Min. :26.00 Min. :-11.79 Min. :0.00000 Min. :1998
## 1st Qu.:52.00 1st Qu.: 13.43 1st Qu.:0.06295 1st Qu.:2004
## Median :61.00 Median : 36.20 Median :0.12065 Median :2006
## Mean :60.59 Mean : 40.24 Mean :0.17016 Mean :2006
## 3rd Qu.:70.00 3rd Qu.: 60.51 3rd Qu.:0.20885 3rd Qu.:2007
## Max. :90.00 Max. :133.84 Max. :0.94770 Max. :2013
## NA's :99 NA's :9
## long_dim mutation_cnt over_surv_mth short_dim
## Min. :0.400 Min. : 1.00 Min. : 0.00 Min. :0.1000
## 1st Qu.:1.200 1st Qu.: 34.00 1st Qu.: 18.10 1st Qu.:0.2000
## Median :1.500 Median : 48.00 Median : 38.96 Median :0.3000
## Mean :1.662 Mean : 73.85 Mean : 44.26 Mean :0.3759
## 3rd Qu.:2.000 3rd Qu.: 65.50 3rd Qu.: 63.21 3rd Qu.:0.5000
## Max. :4.000 Max. :1392.00 Max. :149.05 Max. :1.0000
## NA's :35 NA's :86 NA's :35
## second_long_dim
## Min. :0.3000
## 1st Qu.:0.7000
## Median :0.9000
## Mean :0.9368
## 3rd Qu.:1.1000
## Max. :2.0000
## NA's :35
ggplot(kirc_clean4, aes(age)) +
geom_histogram(bins = 20, alpha = 0.8, color = "red")
ggplot(kirc_clean4, aes(year_diagnose)) +
geom_histogram(bins = 20, alpha = 0.8, color = "red")
ggplot(kirc_clean4, aes(x ='', y=disease_free_mth)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 99 rows containing non-finite values (stat_boxplot).
## Warning: Removed 99 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$disease_free_mth)
## $stats
## [1] -11.79 13.40 36.20 60.55 130.98
##
## $n
## [1] 438
##
## $conf
## [1] 32.6404 39.7596
##
## $out
## [1] 133.84
# error: disease_free_mth < 0
ggplot(kirc_clean4, aes(x ='', y=frac_genome_alter)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$frac_genome_alter)
## $stats
## [1] 0.00000 0.06290 0.12065 0.20920 0.42800
##
## $n
## [1] 528
##
## $conf
## [1] 0.1105903 0.1307097
##
## $out
## [1] 0.8213 0.6552 0.4608 0.9477 0.5888 0.9208 0.7741 0.4837 0.9477 0.4610
## [11] 0.6549 0.6511 0.5180 0.8910 0.8893 0.9477 0.5246 0.4568 0.4937 0.9477
## [21] 0.4438 0.6947 0.5218 0.4768 0.4593 0.4447 0.9452 0.6347 0.5311 0.4562
## [31] 0.4617 0.5256 0.6318 0.5430 0.4506 0.5764 0.7102 0.4641 0.5894 0.4976
## [41] 0.4513 0.6818
ggplot(kirc_clean4, aes(x ='', y=long_dim)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 35 rows containing non-finite values (stat_boxplot).
## Warning: Removed 35 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$long_dim)
## $stats
## [1] 0.4 1.2 1.5 2.0 3.2
##
## $n
## [1] 502
##
## $conf
## [1] 1.443585 1.556415
##
## $out
## [1] 3.3 4.0 3.3 3.5 3.4 3.5 3.5 3.4 3.4 3.5 3.6
ggplot(kirc_clean4, aes(x ='', y=mutation_cnt)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 86 rows containing non-finite values (stat_boxplot).
## Warning: Removed 86 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$mutation_cnt)
## $stats
## [1] 1.0 34.0 48.0 65.5 109.0
##
## $n
## [1] 451
##
## $conf
## [1] 45.65642 50.34358
##
## $out
## [1] 514 656 577 537 477 150 137 708 1392 460 327 934 409 383 804
## [16] 319 524 426 227 553 400 350 410 430 708 649 126 116 115
ggplot(kirc_clean4, aes(x ='', y=over_surv_mth)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
boxplot.stats(kirc_clean4$over_surv_mth)
## $stats
## [1] 0.00 18.10 38.96 63.21 130.55
##
## $n
## [1] 537
##
## $conf
## [1] 35.88431 42.03569
##
## $out
## [1] 133.84 149.05 131.04 130.98 133.61
ggplot(kirc_clean4, aes(x ='', y=short_dim)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 35 rows containing non-finite values (stat_boxplot).
## Warning: Removed 35 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$short_dim)
## $stats
## [1] 0.1 0.2 0.3 0.5 0.9
##
## $n
## [1] 502
##
## $conf
## [1] 0.2788443 0.3211557
##
## $out
## [1] 1 1
ggplot(kirc_clean4, aes(x ='', y=second_long_dim)) +
geom_boxplot(width = .5) +
geom_jitter(width = 0.05, alpha = 0.2, color = "orange")
## Warning: Removed 35 rows containing non-finite values (stat_boxplot).
## Warning: Removed 35 rows containing missing values (geom_point).
boxplot.stats(kirc_clean4$second_long_dim)
## $stats
## [1] 0.3 0.7 0.9 1.1 1.7
##
## $n
## [1] 502
##
## $conf
## [1] 0.8717925 0.9282075
##
## $out
## [1] 1.8 2.0 1.8 1.9 1.8 2.0 2.0 1.8 1.8 1.8 1.8
- Checking categorical variables
Check frequency, lables and levels
kirc_clean4 %>%
select_if(is.factor) %>%
summary()
## metastasis_stg lymph_stg neoplasm_stg tumor_stg
## M0 :426 N0:240 Stage I :269 T1a :142
## M1 : 79 N1: 17 Stage II : 57 T3a :122
## MX : 30 NX:280 Stage III:125 T1b :111
## NA's: 2 Stage IV : 83 T2 : 55
## NA's : 3 T3b : 53
## T1 : 22
## (Other): 32
## disease_free_stt ethnicity histology_grd
## DiseaseFree :311 HISPANIC OR LATINO : 26 G1 : 14
## Recurred/Progressed:127 NOT HISPANIC OR LATINO:359 G2 :230
## NA's : 99 NA's :152 G3 :207
## G4 : 78
## GX : 5
## NA's: 3
##
## hemoglobin neoadj_therapy
## Elevated: 5 No :519
## Low :263 Yes: 18
## Normal :186
## NA's : 83
##
##
##
## prior_cancer tumor_lateral
## No :459 Bilateral: 1
## Yes : 72 Left :253
## Yes, History of Prior Malignancy : 2 Right :283
## Yes, History of Synchronous/Bilateral Malignancy: 4
##
##
##
## over_surv_stt platelet race serum_ca
## DECEASED:177 Elevated: 38 ASIAN : 8 Elevated: 10
## LIVING :360 Low : 46 BLACK OR AFRICAN AMERICAN: 56 Low :204
## Normal :360 WHITE :466 Normal :151
## NA's : 93 NA's : 7 NA's :172
##
##
##
## gender tissue_site person_neoplasm_stt wbc
## Female:191 BP :142 TUMOR FREE:361 Elevated:164
## Male :345 B0 :107 WITH TUMOR:141 Low : 9
## MALE : 1 CJ : 71 NA's : 35 Normal :268
## A3 : 52 NA's : 96
## CZ : 40
## B8 : 33
## (Other): 92
# agregating levels
kirc_clin <- kirc_clean4 %>%
mutate(tumor_stg = fct_collapse(tumor_stg,
T1 = c('T1', 'T1a', 'T1b'),
T2 = c('T2', 'T2a', 'T2b'),
T3 = c('T3', 'T3a', 'T3b', 'T3c')))
kirc_clin <- kirc_clin %>%
mutate(prior_cancer = fct_collapse(prior_cancer,
Yes = c('Yes', 'Yes, History of Prior Malignancy', 'Yes, History of Synchronous/Bilateral Malignancy')))
kirc_clin <- kirc_clin %>%
mutate(gender = fct_collapse(gender, Male = c('MALE', 'Male')))
kirc_clin <- kirc_clin %>%
mutate(tissue_site = fct_collapse(tissue_site,
A = c('A3', 'AK', 'AS'),
B = c('B0', 'B2', 'B4', 'B8', 'BP'),
C = c('CJ', 'CW', 'CZ'),
OTHERS = c('G6', 'GK', 'MM', 'MW',
'3Z', '6D', 'DV', 'EU', 'T7')))
# changing level names
kirc_clin <- kirc_clin %>%
mutate(ethnicity = fct_recode(ethnicity, 'hispanic/latino'='HISPANIC OR LATINO', 'not hispanic/latino'='NOT HISPANIC OR LATINO'),
race = fct_recode(race, Asian='ASIAN', 'Black/African.american'='BLACK OR AFRICAN AMERICAN', White='WHITE'),
person_neoplasm_stt = fct_recode(person_neoplasm_stt, Tumor.Free='TUMOR FREE', With.Tumor='WITH TUMOR'))
kirc_clin %>%
select_if(is.factor) %>%
summary()
## metastasis_stg lymph_stg neoplasm_stg tumor_stg disease_free_stt
## M0 :426 N0:240 Stage I :269 T1:275 DiseaseFree :311
## M1 : 79 N1: 17 Stage II : 57 T2: 69 Recurred/Progressed:127
## MX : 30 NX:280 Stage III:125 T3:182 NA's : 99
## NA's: 2 Stage IV : 83 T4: 11
## NA's : 3
##
## ethnicity histology_grd hemoglobin neoadj_therapy
## hispanic/latino : 26 G1 : 14 Elevated: 5 No :519
## not hispanic/latino:359 G2 :230 Low :263 Yes: 18
## NA's :152 G3 :207 Normal :186
## G4 : 78 NA's : 83
## GX : 5
## NA's: 3
## prior_cancer tumor_lateral over_surv_stt platelet
## No :459 Bilateral: 1 DECEASED:177 Elevated: 38
## Yes: 78 Left :253 LIVING :360 Low : 46
## Right :283 Normal :360
## NA's : 93
##
##
## race serum_ca gender tissue_site
## Asian : 8 Elevated: 10 Female:191 OTHERS: 28
## Black/African.american: 56 Low :204 Male :346 A : 79
## White :466 Normal :151 B :303
## NA's : 7 NA's :172 C :127
##
##
## person_neoplasm_stt wbc
## Tumor.Free:361 Elevated:164
## With.Tumor:141 Low : 9
## NA's : 35 Normal :268
## NA's : 96
##
##
- Correcting and checking again
# month values < 0
kirc_clin$disease_free_mth[kirc_clin$disease_free_mth == -11.79] <- 11.79
kirc_clin$disease_free_mth[kirc_clin$disease_free_mth == -0.62] <- 0.62
skim(kirc_clin)
| Name | kirc_clin |
| Number of rows | 537 |
| Number of columns | 29 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 19 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patient_id | 0 | 1 | 12 | 12 | 0 | 537 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| metastasis_stg | 2 | 1.00 | FALSE | 3 | M0: 426, M1: 79, MX: 30 |
| lymph_stg | 0 | 1.00 | FALSE | 3 | NX: 280, N0: 240, N1: 17 |
| neoplasm_stg | 3 | 0.99 | FALSE | 4 | Sta: 269, Sta: 125, Sta: 83, Sta: 57 |
| tumor_stg | 0 | 1.00 | FALSE | 4 | T1: 275, T3: 182, T2: 69, T4: 11 |
| disease_free_stt | 99 | 0.82 | FALSE | 2 | Dis: 311, Rec: 127 |
| ethnicity | 152 | 0.72 | FALSE | 2 | not: 359, his: 26 |
| histology_grd | 3 | 0.99 | FALSE | 5 | G2: 230, G3: 207, G4: 78, G1: 14 |
| hemoglobin | 83 | 0.85 | FALSE | 3 | Low: 263, Nor: 186, Ele: 5 |
| neoadj_therapy | 0 | 1.00 | FALSE | 2 | No: 519, Yes: 18 |
| prior_cancer | 0 | 1.00 | FALSE | 2 | No: 459, Yes: 78 |
| tumor_lateral | 0 | 1.00 | FALSE | 3 | Rig: 283, Lef: 253, Bil: 1 |
| over_surv_stt | 0 | 1.00 | FALSE | 2 | LIV: 360, DEC: 177 |
| platelet | 93 | 0.83 | FALSE | 3 | Nor: 360, Low: 46, Ele: 38 |
| race | 7 | 0.99 | FALSE | 3 | Whi: 466, Bla: 56, Asi: 8 |
| serum_ca | 172 | 0.68 | FALSE | 3 | Low: 204, Nor: 151, Ele: 10 |
| gender | 0 | 1.00 | FALSE | 2 | Mal: 346, Fem: 191 |
| tissue_site | 0 | 1.00 | FALSE | 4 | B: 303, C: 127, A: 79, OTH: 28 |
| person_neoplasm_stt | 35 | 0.93 | FALSE | 2 | Tum: 361, Wit: 141 |
| wbc | 96 | 0.82 | FALSE | 3 | Nor: 268, Ele: 164, Low: 9 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1.00 | 60.59 | 12.15 | 26.0 | 52.00 | 61.00 | 70.00 | 90.00 | ▁▅▇▆▂ |
| disease_free_mth | 99 | 0.82 | 40.30 | 31.58 | 0.0 | 13.43 | 36.20 | 60.51 | 133.84 | ▇▅▃▁▁ |
| frac_genome_alter | 9 | 0.98 | 0.17 | 0.17 | 0.0 | 0.06 | 0.12 | 0.21 | 0.95 | ▇▂▁▁▁ |
| year_diagnose | 0 | 1.00 | 2006.02 | 2.76 | 1998.0 | 2004.00 | 2006.00 | 2007.00 | 2013.00 | ▁▆▇▃▁ |
| long_dim | 35 | 0.93 | 1.66 | 0.66 | 0.4 | 1.20 | 1.50 | 2.00 | 4.00 | ▃▇▃▂▁ |
| mutation_cnt | 86 | 0.84 | 73.85 | 127.76 | 1.0 | 34.00 | 48.00 | 65.50 | 1392.00 | ▇▁▁▁▁ |
| over_surv_mth | 0 | 1.00 | 44.26 | 32.25 | 0.0 | 18.10 | 38.96 | 63.21 | 149.05 | ▇▇▃▂▁ |
| short_dim | 35 | 0.93 | 0.38 | 0.21 | 0.1 | 0.20 | 0.30 | 0.50 | 1.00 | ▆▇▂▁▁ |
| second_long_dim | 35 | 0.93 | 0.94 | 0.31 | 0.3 | 0.70 | 0.90 | 1.10 | 2.00 | ▃▇▆▂▁ |
- Saving dataset
write_csv(kirc_clin, path = "data/kirc_clin.csv")
rm(kirc_clean4, kirc_clean3, kirc_clean2, kirc_clean1, kirc_clean0, kirc_clean, NA_sum, NA_fifty)
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
##
## locale:
## [1] LC_CTYPE=pt_BR.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=pt_BR.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=pt_BR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=pt_BR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] finalfit_1.0.2 skimr_2.1.2 forcats_0.5.0 stringr_1.4.0
## [5] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
## [9] tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 lubridate_1.7.9 lattice_0.20-41 assertthat_0.2.1
## [5] digest_0.6.25 utf8_1.1.4 R6_2.4.1 cellranger_1.1.0
## [9] repr_1.1.0 backports_1.1.10 reprex_0.3.0 evaluate_0.14
## [13] highr_0.8 httr_1.4.2 pillar_1.4.6 rlang_0.4.7
## [17] readxl_1.3.1 rstudioapi_0.11 blob_1.2.1 Matrix_1.2-18
## [21] rmarkdown_2.4 labeling_0.3 splines_4.0.3 munsell_0.5.0
## [25] broom_0.7.1 compiler_4.0.3 modelr_0.1.8 xfun_0.18
## [29] pkgconfig_2.0.3 base64enc_0.1-3 htmltools_0.5.0 tidyselect_1.1.0
## [33] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4 withr_2.3.0
## [37] grid_4.0.3 jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
## [41] DBI_1.1.0 magrittr_1.5 scales_1.1.1 cli_2.0.2
## [45] stringi_1.5.3 farver_2.0.3 fs_1.5.0 mice_3.11.0
## [49] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.4
## [53] boot_1.3-25 tools_4.0.3 glue_1.4.2 hms_0.5.3
## [57] survival_3.2-7 yaml_2.2.1 colorspace_1.4-1 rvest_0.3.6
## [61] knitr_1.30 haven_2.3.1