Data Querying and Selection
Topics: query, select, filter, dplyr
When analyzing clinical or research data, we often need to filter patients, extract specific variables, or identify subgroups (e.g., diabetic patients with high glucose).
This section teaches you how to efficiently query and select relevant medical data using dplyr.
Selecting Columns
Single column selection
💡 Use case: Selecting only the patient names for anonymization or reporting.
library(dplyr)
names_col <- df |> select(Name)
cat("Names (data frame):", class(names_col), "\n")
# As a vector
names_vec <- df$Name
[1] "Patient_1" "Patient_2" "Patient_3" "Patient_4" "Patient_5" "Patient_6" "Patient_7" "Patient_8" "Patient_9" "Patient_10" "Patient_11" "Patient_12" "Patient_13" "Patient_14"
[15] "Patient_15" "Patient_16" "Patient_17" "Patient_18" "Patient_19" "Patient_20" "Patient_21" "Patient_22" "Patient_23" "Patient_24" "Patient_25" "Patient_26" "Patient_27" "Patient_28"
[29] "Patient_29" "Patient_30" "Patient_31" "Patient_32" "Patient_33" "Patient_34" "Patient_35" "Patient_36" "Patient_37" "Patient_38" "Patient_39" "Patient_40" "Patient_41" "Patient_42"
[43] "Patient_43" "Patient_44" "Patient_45" "Patient_46" "Patient_47" "Patient_48" "Patient_49" "Patient_50" "Patient_51" "Patient_52" "Patient_53" "Patient_54" "Patient_55" "Patient_56"
[57] "Patient_57" "Patient_58" "Patient_59" "Patient_60" "Patient_61" "Patient_62" "Patient_63" "Patient_64" "Patient_65" "Patient_66" "Patient_67" "Patient_68" "Patient_69" "Patient_70"
[71] "Patient_71" "Patient_72" "Patient_73" "Patient_74" "Patient_75" "Patient_76" "Patient_77" "Patient_78" "Patient_79" "Patient_80" "Patient_81" "Patient_82" "Patient_83" "Patient_84"
[85] "Patient_85" "Patient_86" "Patient_87" "Patient_88" "Patient_89" "Patient_90" "Patient_91" "Patient_92" "Patient_93" "Patient_94" "Patient_95" "Patient_96" "Patient_97" "Patient_98"
[99] "Patient_99" "Patient_100"
Multiple column selection
💡 Use case: Extracting basic demographic and diagnosis data for a summary table.
basic_info <- df |> select(Name, Age, Diagnosis)
print(basic_info)
Output:
# A tibble: 100 × 3
Name Age Diagnosis
<chr> <dbl> <chr>
1 Patient_1 66 Diabetes
2 Patient_2 54 Hypertension
3 Patient_3 18 Diabetes
4 Patient_4 42 Hypertension
5 Patient_5 27 Healthy
6 Patient_6 53 Diabetes
7 Patient_7 35 Diabetes
8 Patient_8 75 Healthy
9 Patient_9 66 Hypertension
10 Patient_10 64 Hypertension
# ℹ 90 more rows
# ℹ Use `print(n = ...)` to see more rows
Column selection by type
💡 Use case: Identifying all numeric variables (e.g., blood pressure, glucose) for statistical analysis.
numeric_columns <- df |> select(where(is.numeric))
cat("Numeric columns:", paste(names(numeric_columns), collapse = ", "), "\n")
print(numeric_columns)
Output:
# A tibble: 100 × 4
Patient_ID Age Blood_Pressure Glucose
<dbl> <dbl> <dbl> <dbl>
1 1 66 153 120
2 2 54 130 79
3 3 18 142 190
4 4 42 151 158
5 5 27 180 106
6 6 53 158 96
7 7 35 126 115
8 8 75 129 100
9 9 66 151 160
10 10 64 174 169
# ℹ 90 more rows
# ℹ Use `print(n = ...)` to see more rows
Selecting Rows
Row selection by position
💡 Use case: Quickly view the first few patient records.
first_patient <- df[1, ] # First row
first_five_patients <- df[1:5, ] # First 5 rows
print(first_patient)
print(first_five_patients)
Results:
# A tibble: 1 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
# A tibble: 5 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
2 2 Patient_2 54 Hypertension 130 79 2024-01-08
3 3 Patient_3 18 Diabetes 142 190 2024-01-15
4 4 Patient_4 42 Hypertension 151 158 2024-01-22
5 5 Patient_5 27 Healthy 180 106 2024-01-29
Row selection by condition
💡 Use case: Filtering patients with hyperglycemia or a specific medical condition.
high_glucose <- df |> filter(Glucose > 150)
hypertensive_patients <- df |> filter(Diagnosis == "Hypertension")
print(high_glucose)
print(hypertensive_patients)
cat("High glucose patients:", nrow(high_glucose), "\n") # High glucose patients: 43
cat("Hypertensive patients:", nrow(hypertensive_patients), "\n") # Hypertensive patients: 41
High-glucose patients:
# A tibble: 43 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 3 Patient_3 18 Diabetes 142 190 2024-01-15
2 4 Patient_4 42 Hypertension 151 158 2024-01-22
3 9 Patient_9 66 Hypertension 151 160 2024-02-26
4 10 Patient_10 64 Hypertension 174 169 2024-03-04
5 11 Patient_11 41 Healthy 180 189 2024-03-11
6 14 Patient_14 42 Hypertension 161 192 2024-04-01
7 15 Patient_15 54 Healthy 112 161 2024-04-08
8 17 Patient_17 37 Diabetes 148 162 2024-04-22
9 20 Patient_20 64 Diabetes 116 165 2024-05-13
10 21 Patient_21 20 Diabetes 132 171 2024-05-20
# ℹ 33 more rows
# ℹ Use `print(n = ...)` to see more rows
Hypertensive patients:
# A tibble: 41 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 2 Patient_2 54 Hypertension 130 79 2024-01-08
2 4 Patient_4 42 Hypertension 151 158 2024-01-22
3 9 Patient_9 66 Hypertension 151 160 2024-02-26
4 10 Patient_10 64 Hypertension 174 169 2024-03-04
5 14 Patient_14 42 Hypertension 161 192 2024-04-01
6 16 Patient_16 63 Hypertension 162 102 2024-04-15
7 22 Patient_22 58 Hypertension 127 177 2024-05-27
8 25 Patient_25 53 Hypertension 107 99 2024-06-17
9 28 Patient_28 62 Hypertension 112 97 2024-07-08
10 31 Patient_31 51 Hypertension 129 78 2024-07-29
# ℹ 31 more rows
# ℹ Use `print(n = ...)` to see more rows
Multiple conditions
💡 Use case: Identify high-risk patient groups for focused analysis.
elderly_diabetics <- df |> filter(Age > 60, Diagnosis == "Diabetes")
young_healthy <- df |> filter(Age < 30, Diagnosis == "Healthy")
print(elderly_diabetics)
print(young_healthy)
cat("Elderly diabetics:", nrow(elderly_diabetics), "\n") # Elderly diabetics: 8
cat("Young healthy individuals:", nrow(young_healthy), "\n") # Young healthy individuals: 9
Elderly diabetics:
# A tibble: 8 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
2 19 Patient_19 67 Diabetes 100 114 2024-05-06
3 20 Patient_20 64 Diabetes 116 165 2024-05-13
4 43 Patient_43 75 Diabetes 102 86 2024-10-21
5 54 Patient_54 73 Diabetes 153 185 2025-01-06
6 55 Patient_55 67 Diabetes 154 177 2025-01-13
7 73 Patient_73 75 Diabetes 119 128 2025-05-19
8 98 Patient_98 71 Diabetes 106 79 2025-11-10
Early diabetics:
# A tibble: 9 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 5 Patient_5 27 Healthy 180 106 2024-01-29
2 34 Patient_34 20 Healthy 136 151 2024-08-19
3 44 Patient_44 25 Healthy 152 151 2024-10-28
4 51 Patient_51 22 Healthy 121 159 2024-12-16
5 52 Patient_52 21 Healthy 125 130 2024-12-23
6 65 Patient_65 19 Healthy 113 116 2025-03-24
7 67 Patient_67 20 Healthy 110 91 2025-04-07
8 70 Patient_70 19 Healthy 154 136 2025-04-28
9 85 Patient_85 29 Healthy 110 179 2025-08-11
Advanced Querying
Using filter() with complex conditions
💡 Use case: Query diabetic patients with high blood pressure.
high_bp_diabetics <- df |> filter(Diagnosis == "Diabetes", Blood_Pressure > 140)
recent_admissions <- df |> filter(Admission_Date > as.Date("2024-06-01"))
print(high_bp_diabetics)
print(recent_admissions)
cat("High BP diabetics:", nrow(high_bp_diabetics), "\n") # High BP diabetics: 18
cat("Recent admissions:", nrow(recent_admissions), "\n") # Recent admissions: 78
High BP diabetics:
# A tibble: 18 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
2 3 Patient_3 18 Diabetes 142 190 2024-01-15
3 6 Patient_6 53 Diabetes 158 96 2024-02-05
4 12 Patient_12 24 Diabetes 172 122 2024-03-18
5 17 Patient_17 37 Diabetes 148 162 2024-04-22
6 18 Patient_18 43 Diabetes 160 105 2024-04-29
7 26 Patient_26 54 Diabetes 179 123 2024-06-24
8 30 Patient_30 37 Diabetes 164 180 2024-07-22
9 32 Patient_32 45 Diabetes 150 123 2024-08-05
10 33 Patient_33 57 Diabetes 159 184 2024-08-12
11 41 Patient_41 32 Diabetes 172 89 2024-10-07
12 46 Patient_46 21 Diabetes 178 163 2024-11-11
13 54 Patient_54 73 Diabetes 153 185 2025-01-06
14 55 Patient_55 67 Diabetes 154 177 2025-01-13
15 57 Patient_57 41 Diabetes 157 158 2025-01-27
16 74 Patient_74 27 Diabetes 178 138 2025-05-26
17 89 Patient_89 52 Diabetes 153 126 2025-09-08
18 92 Patient_92 54 Diabetes 158 179 2025-09-29
Recent admissions:
# A tibble: 78 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 23 Patient_23 42 Healthy 101 74 2024-06-03
2 24 Patient_24 44 Diabetes 130 191 2024-06-10
3 25 Patient_25 53 Hypertension 107 99 2024-06-17
4 26 Patient_26 54 Diabetes 179 123 2024-06-24
5 27 Patient_27 48 Healthy 102 71 2024-07-01
6 28 Patient_28 62 Hypertension 112 97 2024-07-08
7 29 Patient_29 22 Diabetes 111 200 2024-07-15
8 30 Patient_30 37 Diabetes 164 180 2024-07-22
9 31 Patient_31 51 Hypertension 129 78 2024-07-29
10 32 Patient_32 45 Diabetes 150 123 2024-08-05
# ℹ 68 more rows
# ℹ Use `print(n = ...)` to see more rows
Using %in% for multiple values
💡 Use case: Selecting patients with comorbidities.
target_conditions <- df |> filter(Diagnosis %in% c("Diabetes", "Hypertension"))
cat("Patients with Diabetes or Hypertension:", nrow(target_conditions), "\n")
# Output
# Patients with Diabetes or Hypertension: 77
print(target_conditions)
# A tibble: 77 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
2 2 Patient_2 54 Hypertension 130 79 2024-01-08
3 3 Patient_3 18 Diabetes 142 190 2024-01-15
4 4 Patient_4 42 Hypertension 151 158 2024-01-22
5 6 Patient_6 53 Diabetes 158 96 2024-02-05
6 7 Patient_7 35 Diabetes 126 115 2024-02-12
7 9 Patient_9 66 Hypertension 151 160 2024-02-26
8 10 Patient_10 64 Hypertension 174 169 2024-03-04
9 12 Patient_12 24 Diabetes 172 122 2024-03-18
10 13 Patient_13 53 Diabetes 116 93 2024-03-25
# ℹ 67 more rows
# ℹ Use `print(n = ...)` to see more rows
String operations
💡 Use case: Useful for data cleaning or searching names.
patients_with_a <- df |> filter(grepl("a", Name, ignore.case = TRUE))
cat("Patients with 'a' in name:", nrow(patients_with_a), "\n") # Patients with 'a' in name: 100
print(patients_with_a)
# A tibble: 100 × 7
Patient_ID Name Age Diagnosis Blood_Pressure Glucose Admission_Date
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <date>
1 1 Patient_1 66 Diabetes 153 120 2024-01-01
2 2 Patient_2 54 Hypertension 130 79 2024-01-08
3 3 Patient_3 18 Diabetes 142 190 2024-01-15
4 4 Patient_4 42 Hypertension 151 158 2024-01-22
5 5 Patient_5 27 Healthy 180 106 2024-01-29
6 6 Patient_6 53 Diabetes 158 96 2024-02-05
7 7 Patient_7 35 Diabetes 126 115 2024-02-12
8 8 Patient_8 75 Healthy 129 100 2024-02-19
9 9 Patient_9 66 Hypertension 151 160 2024-02-26
10 10 Patient_10 64 Hypertension 174 169 2024-03-04
# ℹ 90 more rows
# ℹ Use `print(n = ...)` to see more rows
Key Takeaways
- You can select and filter medical data easily using
dplyr. - Use column and row selection to extract specific patient subsets.
- Conditional filtering allows targeted analysis (e.g., high-risk patients, comorbidities).
- The
|>pipe operator makes complex filters more readable and natural. - These skills are essential for clinical research, public health analytics, and hospital record management.