STA1261_K2_P2
Meringkas data: menghitung rata-rata semua kategori dengan fungsi
summarise()
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
rataan_data <- Persepsi_Costumer %>%
summarise(across(everything(), ~ mean(.x, na.rm = TRUE)))
rataan_data #menampilkan rata-rata dari semua kategori
## # A tibble: 1 × 13
## Product_Quality E_Commerce Technical_Support ...4 Complaint_Resolution ...6
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.81 3.67 5.36 5.44 4.01 5.80
## # ℹ 7 more variables: Advertising <dbl>, Product <dbl>, Salesforce <dbl>,
## # Competitive <dbl>, Warranty <dbl>, Order <dbl>, Delivery <dbl>
Mengurutkan berdasarkan peubah Product_Quality dari nilai terbesar
dengan fungsi arrange()
data_terurut <- Persepsi_Costumer %>%
arrange(desc(Product_Quality))
head(data_terurut) #menampilkan 6 data teratas yang telah diurutkan dari nilai terbesar berdasarkan peubah Product_Quality
## # A tibble: 6 × 13
## Product_Quality E_Commerce Technical_Support ...4 Complaint_Resolution ...6
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 10 4.3 7.1 6.3 2.9 5.4
## 2 10 4.3 5.3 3.7 4.2 5.4
## 3 9.9 3.7 3.7 6.1 4.2 7
## 4 9.9 2.8 7.2 6.9 2.6 5.8
## 5 9.9 3.7 7.5 4.7 5.6 7
## 6 9.9 3 6.8 5 5.4 5.9
## # ℹ 7 more variables: Advertising <dbl>, Product <dbl>, Salesforce <dbl>,
## # Competitive <dbl>, Warranty <dbl>, Order <dbl>, Delivery <dbl>
Memfilter data Product_Quality dan Order berdasarkan kondisi
tertentu dengan fungsi filter()
data_filter <- Persepsi_Costumer %>%
filter(
Product_Quality > 7.5, # Kondisi untuk Product_Quality
Order >= 5.5 # Kondisi untuk Order
)
data_filter
## # A tibble: 8 × 13
## Product_Quality E_Commerce Technical_Support ...4 Complaint_Resolution ...6
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 9.1 4.5 3.6 6.4 5.3 5.3
## 2 7.6 3.6 3 4 5.1 4.2
## 3 8 3.3 6.1 5.7 5.5 4.6
## 4 8.2 3.6 3.9 6.2 5.8 4.9
## 5 8.3 3.4 3.3 5.5 3.1 4.6
## 6 8 3.3 3.8 5.8 3.2 4.6
## 7 9.1 4.5 6.1 5.9 6.3 5.3
## 8 7.6 3.6 7.6 4.6 4.7 4.6
## # ℹ 7 more variables: Advertising <dbl>, Product <dbl>, Salesforce <dbl>,
## # Competitive <dbl>, Warranty <dbl>, Order <dbl>, Delivery <dbl>
Menambahkan kolom baru dengan fungsi mutate()
data_baru <- Persepsi_Costumer %>%
mutate(
Avg_Support = (Technical_Support + Complaint_Resolution) / 2,
Product_Performance_Ratio = Product_Quality / Competitive
)
head(data_baru)
## # A tibble: 6 × 15
## Product_Quality E_Commerce Technical_Support ...4 Complaint_Resolution ...6
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 8.5 3.9 2.5 5.9 4.8 4.9
## 2 8.2 2.7 5.1 7.2 3.4 7.9
## 3 9.2 3.4 5.6 5.6 5.4 7.4
## 4 6.4 3.3 7 3.7 4.7 4.7
## 5 9 3.4 5.2 4.6 2.2 6
## 6 6.5 2.8 3.1 4.1 4 4.3
## # ℹ 9 more variables: Advertising <dbl>, Product <dbl>, Salesforce <dbl>,
## # Competitive <dbl>, Warranty <dbl>, Order <dbl>, Delivery <dbl>,
## # Avg_Support <dbl>, Product_Performance_Ratio <dbl>
Memilih kolom-kolom tertentu menggunakan fungsi select()
data_terpilih <- Persepsi_Costumer %>%
select(Technical_Support, Complaint_Resolution, Product_Quality, Competitive)
data_terpilih
## # A tibble: 100 × 4
## Technical_Support Complaint_Resolution Product_Quality Competitive
## <dbl> <dbl> <dbl> <dbl>
## 1 2.5 4.8 8.5 4.3
## 2 5.1 3.4 8.2 4
## 3 5.6 5.4 9.2 4.6
## 4 7 4.7 6.4 3.6
## 5 5.2 2.2 9 4.5
## 6 3.1 4 6.5 9.5
## 7 5 2.1 6.9 2.5
## 8 3.9 4.6 6.2 4.8
## 9 5.1 3.7 5.8 4.4
## 10 5.1 4.7 6.4 5.3
## # ℹ 90 more rows
Menggunakan 2 fungsi secara bersamaan: fungsi select() dan
filter()
data_2 <- Persepsi_Costumer %>%
select(Product_Quality, Order, Delivery) %>% # Pilih kolom yang diinginkan
filter(Product_Quality > 7.81, Order > 4.61, Delivery > 3.8)
data_2
## # A tibble: 7 × 3
## Product_Quality Order Delivery
## <dbl> <dbl> <dbl>
## 1 9.1 6.1 4.4
## 2 8 6.6 4.2
## 3 8.2 6.9 4.5
## 4 8.3 5.8 3.9
## 5 8 6.6 4.2
## 6 9.9 4.9 4
## 7 9.1 6.1 4.4