class3_solutions.md

Introduction to R, Class 3: Solutions

Challenge-columns

race_disease <- select(clinical, race, ethnicity, disease)

Challenge-rows

race_BRCA <- filter(race_disease, disease == "BRCA")

Challenge-pipes

clinical %>%
  filter(vital_status == "alive") %>%
  filter(cigarettes_per_day < 1) %>%
  select(gender, years_smoked, year_of_birth) %>%
  head() # output only the first few lines

##   gender years_smoked year_of_birth
## 1 female           NA          1940
## 2 female           NA          1928
## 3 female           NA          1942
## 4 female           28          1924
## 5   male           NA            NA
## 6   male           31          1941

Challenge-lung

clinical %>%
  filter(disease == "LUSC") %>%
  mutate(total_cig = years_smoked * cigarettes_per_day * 365) %>%
  head() # output only the first few lines

##   primary_diagnosis tumor_stage age_at_diagnosis vital_status morphology
## 1             C34.1    stage ia            24477         dead     8070/3
## 2             C34.1    stage ib            26615         dead     8070/3
## 3             C34.3    stage ib            28171         dead     8070/3
## 4             C34.1    stage ia            27154        alive     8083/3
## 5             C34.3   stage iib            29827         dead     8070/3
## 6             C34.1  stage iiia            23370        alive     8070/3
##   days_to_death state tissue_or_organ_of_origin days_to_birth
## 1           371  live                     C34.1        -24477
## 2           136  live                     C34.1        -26615
## 3          2304  live                     C34.3        -28171
## 4            NA  live                     C34.1        -27154
## 5           146  live                     C34.3        -29827
## 6            NA  live                     C34.1        -23370
##   site_of_resection_or_biopsy days_to_last_follow_up cigarettes_per_day
## 1                       C34.1                     NA          10.958904
## 2                       C34.1                     NA           2.191781
## 3                       C34.3                   2099           1.643836
## 4                       C34.1                   3747           1.095890
## 5                       C34.3                     NA                 NA
## 6                       C34.1                   3576           2.739726
##   years_smoked gender year_of_birth         race              ethnicity
## 1           NA   male          1936        white not hispanic or latino
## 2           NA   male          1931        asian not hispanic or latino
## 3           NA female          1927        white not hispanic or latino
## 4           NA   male          1930        white not hispanic or latino
## 5           NA   male          1923 not reported           not reported
## 6           NA female          1942 not reported           not reported
##   year_of_death bcr_patient_barcode disease total_cig
## 1          2004        TCGA-18-3406    LUSC        NA
## 2          2003        TCGA-18-3407    LUSC        NA
## 3            NA        TCGA-18-3408    LUSC        NA
## 4            NA        TCGA-18-3409    LUSC        NA
## 5          2004        TCGA-18-3410    LUSC        NA
## 6            NA        TCGA-18-3411    LUSC        NA

Challenge-smoke-complete

smoke_complete <- clinical %>%
  filter(!is.na(age_at_diagnosis)) %>%
  filter(!is.na(cigarettes_per_day))

Challenge-save

write.csv(smoke_complete, "data/smoke_complete.csv", row.names = FALSE)

Google search for “save file R”.

Challenge-birth-complete

birth_complete <- clinical %>%
  filter(!is.na(year_of_birth)) %>%
  filter(!is.na(vital_status)) %>%
  filter(vital_status != "not reported")

Challenge-tumor

# counting number of records for each tumor stage
tumor_counts <- clinical %>%
  count(tumor_stage)
# get names of tumors with many cases
frequent_tumors <- tumor_counts %>%
  filter(n > 200)
# extract data from cancers to keep
tumor_reduced <- clinical %>%
  filter(tumor_stage %in% frequent_tumors$tumor_stage)

Extra exercises

Challenge-ethnicity

clinical %>%
  filter(race != "white") %>% # only non-white individuals
  filter(ethnicity == "hispanic or latino") %>% # only hispanic/latino
  group_by(race) %>% # group by other races
  tally() # count total for each race

## # A tibble: 5 x 2
##   race                                          n
##   <chr>                                     <int>
## 1 american indian or alaska native              7
## 2 asian                                         1
## 3 black or african american                    12
## 4 native hawaiian or other pacific islander     1
## 5 not reported                                 24

Challenge-years

clinical <- clinical %>%
  mutate(age = year_of_death - year_of_birth)

Challenge-helpers

# preferred way
clinical %>%
  select(contains("diagnosis")) %>%
  head() # output only the first few lines

##   primary_diagnosis age_at_diagnosis
## 1             C34.1            24477
## 2             C34.1            26615
## 3             C34.3            28171
## 4             C34.1            27154
## 5             C34.3            29827
## 6             C34.1            23370

# also works, but only because in this example both columns end in "diagnosis"
clinical %>%
  select(ends_with("diagnosis")) %>%
  head() # output only the first few lines

##   primary_diagnosis age_at_diagnosis
## 1             C34.1            24477
## 2             C34.1            26615
## 3             C34.3            28171
## 4             C34.1            27154
## 5             C34.3            29827
## 6             C34.1            23370

Challenge-combine

clinical %>%
  filter(ethnicity == "hispanic or latino") %>%
  filter(year_of_death > 2000) %>%
  filter(!is.na(cigarettes_per_day)) %>%
  count()

##    n
## 1 11