library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'dplyr' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors?tidyverse## starting httpd help server ... donelibrary(datasets)data(CO2)
CO2 <- tibble::as.tibble(CO2)## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.class(CO2)## [1] "tbl_df" "tbl" "data.frame"head(CO2)## # A tibble: 6 × 5
## Plant Type Treatment conc uptake
## <ord> <fct> <fct> <dbl> <dbl>
## 1 Qn1 Quebec nonchilled 95 16
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2glimpse(CO2)## Rows: 84
## Columns: 5
## $ Plant <ord> Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn1, Qn2, Qn2, Qn2, Qn2, Qn2, …
## $ Type <fct> Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Quebec, Queb…
## $ Treatment <fct> nonchilled, nonchilled, nonchilled, nonchilled, nonchilled, …
## $ conc <dbl> 95, 175, 250, 350, 500, 675, 1000, 95, 175, 250, 350, 500, 6…
## $ uptake <dbl> 16.0, 30.4, 34.8, 37.2, 35.3, 39.2, 39.7, 13.6, 27.3, 37.1, …#summarise() # Menghitung rata-rata conc dan uptake
summary_stats <- CO2 %>%
summarise(mean_conc = mean(conc, na.rm = TRUE),
mean_uptake = mean(uptake, na.rm = TRUE))
print(summary_stats)## # A tibble: 1 × 2
## mean_conc mean_uptake
## <dbl> <dbl>
## 1 435 27.2#arrange() # Mengurutkan data berdasarkan conc secara menaik
sorted_CO2 <- CO2 %>%
arrange(conc)
print(head(sorted_CO2))## # A tibble: 6 × 5
## Plant Type Treatment conc uptake
## <ord> <fct> <fct> <dbl> <dbl>
## 1 Qn1 Quebec nonchilled 95 16
## 2 Qn2 Quebec nonchilled 95 13.6
## 3 Qn3 Quebec nonchilled 95 16.2
## 4 Qc1 Quebec chilled 95 14.2
## 5 Qc2 Quebec chilled 95 9.3
## 6 Qc3 Quebec chilled 95 15.1#filter() # Menyaring data dengan uptake di atas rata-rata
filtered_CO2 <- CO2 %>%
filter(uptake > mean(uptake, na.rm = TRUE))
print(filtered_CO2)## # A tibble: 47 × 5
## Plant Type Treatment conc uptake
## <ord> <fct> <fct> <dbl> <dbl>
## 1 Qn1 Quebec nonchilled 175 30.4
## 2 Qn1 Quebec nonchilled 250 34.8
## 3 Qn1 Quebec nonchilled 350 37.2
## 4 Qn1 Quebec nonchilled 500 35.3
## 5 Qn1 Quebec nonchilled 675 39.2
## 6 Qn1 Quebec nonchilled 1000 39.7
## 7 Qn2 Quebec nonchilled 175 27.3
## 8 Qn2 Quebec nonchilled 250 37.1
## 9 Qn2 Quebec nonchilled 350 41.8
## 10 Qn2 Quebec nonchilled 500 40.6
## # ℹ 37 more rows#mutate() # Menambahkan kolom baru ‘adjusted_conc’ dengan nilai conc dikali 2 (mutate())
mutated_CO2 <- CO2 %>%
mutate(adjusted_conc = conc * 2)
print(head(mutated_CO2))## # A tibble: 6 × 6
## Plant Type Treatment conc uptake adjusted_conc
## <ord> <fct> <fct> <dbl> <dbl> <dbl>
## 1 Qn1 Quebec nonchilled 95 16 190
## 2 Qn1 Quebec nonchilled 175 30.4 350
## 3 Qn1 Quebec nonchilled 250 34.8 500
## 4 Qn1 Quebec nonchilled 350 37.2 700
## 5 Qn1 Quebec nonchilled 500 35.3 1000
## 6 Qn1 Quebec nonchilled 675 39.2 1350#select() # Memilih kolom tertentu
selected_CO2 <- CO2 %>%
select(Plant, Type, conc, uptake)
print(head(selected_CO2))## # A tibble: 6 × 4
## Plant Type conc uptake
## <ord> <fct> <dbl> <dbl>
## 1 Qn1 Quebec 95 16
## 2 Qn1 Quebec 175 30.4
## 3 Qn1 Quebec 250 34.8
## 4 Qn1 Quebec 350 37.2
## 5 Qn1 Quebec 500 35.3
## 6 Qn1 Quebec 675 39.2#Penggunaan secara bersama-sama (Chained) untuk Analisis Sederhana:
result <- CO2 %>%
filter(Treatment == "chilled") %>%
arrange(conc) %>%
mutate(adjusted_uptake = uptake * 1.5) %>%
select(Plant, Type, conc, uptake, adjusted_uptake) %>%
summarise(mean_uptake = mean(uptake, na.rm = TRUE),
max_adj_uptake = max(adjusted_uptake))
print(result)## # A tibble: 1 × 2
## mean_uptake max_adj_uptake
## <dbl> <dbl>
## 1 23.8 63.6#Menggabungkan beberapa fungsi secara bersama-sama
combined_operations <- CO2 %>%
filter(Treatment == "nonchilled") %>%
arrange(desc(uptake)) %>%
mutate(adjusted_conc = conc * 2) %>%
select(Plant, Type, conc, uptake, adjusted_conc) %>%
summarise(mean_uptake = mean(uptake, na.rm = TRUE),
max_adj_conc = max(adjusted_conc))
print(combined_operations)## # A tibble: 1 × 2
## mean_uptake max_adj_conc
## <dbl> <dbl>
## 1 30.6 2000#Menggabungkan beberapa fungsi secara bersama-sama
advanced_result <- CO2 %>%
filter(Treatment %in% c("chilled", "nonchilled")) %>%
arrange(Treatment, conc) %>%
mutate(adjusted_uptake = ifelse(Treatment == "chilled", uptake * 1.5, uptake * 2)) %>%
select(Plant, Type, Treatment, conc, uptake, adjusted_uptake) %>%
group_by(Treatment) %>%
summarise(mean_uptake = mean(uptake, na.rm = TRUE),
max_adj_uptake = max(adjusted_uptake),
total_plants = n())
print(advanced_result)## # A tibble: 2 × 4
## Treatment mean_uptake max_adj_uptake total_plants
## <fct> <dbl> <dbl> <int>
## 1 nonchilled 30.6 91 42
## 2 chilled 23.8 63.6 42