library(dplyr)
##
## 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
df_mahasiswa <- read.csv("df_mahasiswa.csv")
head(df_mahasiswa)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## 1 1 MHS001 L 4 1
## 2 2 MHS002 P 4 2
## 3 3 MHS003 P 2 6
## 4 4 MHS004 P 5 3
## 5 5 MHS005 L 3 2
## 6 6 MHS006 L 3 7
## motivasi_belajar ipk
## 1 82 3.12
## 2 73 3.45
## 3 71 3.07
## 4 98 3.43
## 5 81 2.83
## 6 61 3.10
head(df_mahasiswa)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## 1 1 MHS001 L 4 1
## 2 2 MHS002 P 4 2
## 3 3 MHS003 P 2 6
## 4 4 MHS004 P 5 3
## 5 5 MHS005 L 3 2
## 6 6 MHS006 L 3 7
## motivasi_belajar ipk
## 1 82 3.12
## 2 73 3.45
## 3 71 3.07
## 4 98 3.43
## 5 81 2.83
## 6 61 3.10
str(df_mahasiswa)
## 'data.frame': 55 obs. of 7 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ id_mahasiswa : chr "MHS001" "MHS002" "MHS003" "MHS004" ...
## $ jenis_kelamin : chr "L" "P" "P" "P" ...
## $ jam_belajar_per_hari: int 4 4 2 5 3 3 1 3 2 1 ...
## $ frekuensi_login_lms : int 1 2 6 3 2 7 7 4 6 5 ...
## $ motivasi_belajar : int 82 73 71 98 81 61 44 69 44 46 ...
## $ ipk : num 3.12 3.45 3.07 3.43 2.83 3.1 2.98 3.08 2.82 2.93 ...
is.na(df_mahasiswa)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
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## motivasi_belajar ipk
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any(is.na(df_mahasiswa))
## [1] FALSE
sum(is.na(df_mahasiswa))
## [1] 0
df_mahasiswa %>%
group_by(jenis_kelamin) %>%
summarise(Jumlah = n())
## # A tibble: 2 × 2
## jenis_kelamin Jumlah
## <chr> <int>
## 1 L 32
## 2 P 23
df_mahasiswa %>%
group_by(jenis_kelamin) %>%
summarise(rata_rata_ipk = mean(ipk))
## # A tibble: 2 × 2
## jenis_kelamin rata_rata_ipk
## <chr> <dbl>
## 1 L 3.16
## 2 P 3.18
df_mahasiswa %>%
arrange(desc(motivasi_belajar)) %>%
slice(1)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## 1 54 MHS054 L 5 4
## motivasi_belajar ipk
## 1 100 3.41
hasil_analisis <- df_mahasiswa %>%
filter(frekuensi_login_lms == max(frekuensi_login_lms)) %>%
mutate(ipk_tinggi = ifelse(ipk > 3.5, "YA (>3.5)", "TIDAK (<=3.5)"))
print(hasil_analisis)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## 1 6 MHS006 L 3 7
## 2 7 MHS007 P 1 7
## 3 17 MHS017 L 2 7
## 4 18 MHS018 L 5 7
## 5 22 MHS022 P 4 7
## 6 31 MHS031 P 2 7
## 7 33 MHS033 P 1 7
## 8 41 MHS041 L 5 7
## 9 44 MHS044 P 2 7
## 10 55 MHS055 P 2 7
## motivasi_belajar ipk ipk_tinggi
## 1 61 3.10 TIDAK (<=3.5)
## 2 44 2.98 TIDAK (<=3.5)
## 3 52 3.06 TIDAK (<=3.5)
## 4 92 3.89 YA (>3.5)
## 5 72 3.69 YA (>3.5)
## 6 71 3.22 TIDAK (<=3.5)
## 7 46 3.11 TIDAK (<=3.5)
## 8 90 3.73 YA (>3.5)
## 9 63 3.15 TIDAK (<=3.5)
## 10 71 3.21 TIDAK (<=3.5)
df_mahasiswa %>%
group_by(jam_belajar_per_hari) %>%
summarise(rata_rata_ipk = mean(ipk))
## # A tibble: 5 × 2
## jam_belajar_per_hari rata_rata_ipk
## <int> <dbl>
## 1 1 2.79
## 2 2 3.06
## 3 3 2.95
## 4 4 3.33
## 5 5 3.51
df_mahasiswa %>%
arrange(desc(ipk)) %>%
slice(1)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## 1 18 MHS018 L 5 7
## motivasi_belajar ipk
## 1 92 3.89
hasil <- df_mahasiswa %>%
filter(ipk > 3.5, motivasi_belajar > 85)
table(hasil$jenis_kelamin)
##
## L P
## 4 1