data_mahasiswa <- read.csv("df_mahasiswa.csv")
head(data_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(data_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(data_mahasiswa)
## X id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## [1,] FALSE FALSE FALSE FALSE FALSE
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## motivasi_belajar ipk
## [1,] FALSE FALSE
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table(data_mahasiswa$jenis_kelamin)
##
## L P
## 32 23
aggregate(ipk ~ jenis_kelamin, data=data_mahasiswa, mean)
## jenis_kelamin ipk
## 1 L 3.158125
## 2 P 3.176957
data_mahasiswa[order(-data_mahasiswa$motivasi_belajar), c("id_mahasiswa", "motivasi_belajar")]
## id_mahasiswa motivasi_belajar
## 54 MHS054 100
## 4 MHS004 98
## 45 MHS045 96
## 18 MHS018 92
## 48 MHS048 92
## 51 MHS051 92
## 35 MHS035 91
## 43 MHS043 91
## 16 MHS016 90
## 38 MHS038 90
## 41 MHS041 90
## 26 MHS026 89
## 52 MHS052 88
## 30 MHS030 87
## 34 MHS034 87
## 19 MHS019 84
## 23 MHS023 83
## 1 MHS001 82
## 47 MHS047 82
## 5 MHS005 81
## 14 MHS014 81
## 24 MHS024 81
## 40 MHS040 80
## 53 MHS053 80
## 2 MHS002 73
## 20 MHS020 73
## 42 MHS042 73
## 49 MHS049 73
## 22 MHS022 72
## 3 MHS003 71
## 31 MHS031 71
## 55 MHS055 71
## 25 MHS025 70
## 8 MHS008 69
## 37 MHS037 68
## 36 MHS036 66
## 44 MHS044 63
## 6 MHS006 61
## 13 MHS013 59
## 27 MHS027 59
## 50 MHS050 57
## 11 MHS011 56
## 28 MHS028 53
## 17 MHS017 52
## 39 MHS039 52
## 29 MHS029 48
## 21 MHS021 47
## 10 MHS010 46
## 33 MHS033 46
## 7 MHS007 44
## 9 MHS009 44
## 15 MHS015 44
## 46 MHS046 43
## 32 MHS032 39
## 12 MHS012 35
data_mahasiswa[order(-data_mahasiswa$frekuensi_login_lms), c("id_mahasiswa", "frekuensi_login_lms", "ipk")]
## id_mahasiswa frekuensi_login_lms ipk
## 6 MHS006 7 3.10
## 7 MHS007 7 2.98
## 17 MHS017 7 3.06
## 18 MHS018 7 3.89
## 22 MHS022 7 3.69
## 31 MHS031 7 3.22
## 33 MHS033 7 3.11
## 41 MHS041 7 3.73
## 44 MHS044 7 3.15
## 55 MHS055 7 3.21
## 3 MHS003 6 3.07
## 9 MHS009 6 2.82
## 13 MHS013 6 3.11
## 21 MHS021 6 2.96
## 26 MHS026 6 3.32
## 28 MHS028 6 3.14
## 37 MHS037 6 3.67
## 42 MHS042 6 3.05
## 51 MHS051 6 3.65
## 10 MHS010 5 2.93
## 43 MHS043 5 3.29
## 47 MHS047 5 3.13
## 49 MHS049 5 3.22
## 8 MHS008 4 3.08
## 23 MHS023 4 3.54
## 24 MHS024 4 3.61
## 34 MHS034 4 3.56
## 48 MHS048 4 3.70
## 50 MHS050 4 3.05
## 52 MHS052 4 3.30
## 53 MHS053 4 3.29
## 54 MHS054 4 3.41
## 4 MHS004 3 3.43
## 27 MHS027 3 2.94
## 30 MHS030 3 3.40
## 46 MHS046 3 2.49
## 2 MHS002 2 3.45
## 5 MHS005 2 2.83
## 12 MHS012 2 2.71
## 14 MHS014 2 3.31
## 15 MHS015 2 2.90
## 19 MHS019 2 3.09
## 29 MHS029 2 2.89
## 32 MHS032 2 2.66
## 40 MHS040 2 3.17
## 45 MHS045 2 3.33
## 1 MHS001 1 3.12
## 11 MHS011 1 2.64
## 16 MHS016 1 3.46
## 20 MHS020 1 2.73
## 25 MHS025 1 2.65
## 35 MHS035 1 3.13
## 36 MHS036 1 2.95
## 38 MHS038 1 3.29
## 39 MHS039 1 2.52
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data_mahasiswa %>%
mutate(jam_belajar_per_hari = ifelse (jam_belajar_per_hari >=4, ">=4 jam", "<4 jam")) %>%
group_by(jam_belajar_per_hari) %>%
summarise(ipk = mean(ipk, na.rm = TRUE))
## # A tibble: 2 × 2
## jam_belajar_per_hari ipk
## <chr> <dbl>
## 1 <4 jam 2.94
## 2 >=4 jam 3.41
mahasiswa_ipk_tinggi <- data_mahasiswa %>%
arrange(desc(ipk)) %>%
slice(1)
id_tertinggi <- mahasiswa_ipk_tinggi$id_mahasiswa
motivasi_nilai <- mahasiswa_ipk_tinggi$motivasi_belajar
ipk_nilai <- mahasiswa_ipk_tinggi
mahasiswa_ipk_tinggi
## 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
cerdas <- data_mahasiswa %>%
filter(ipk >= 3.5) %>%
filter(motivasi_belajar >=85) %>%
select(jenis_kelamin, motivasi_belajar, ipk) %>%
group_by(jenis_kelamin) %>%
summarise(sum=n())
cerdas
## # A tibble: 2 × 2
## jenis_kelamin sum
## <chr> <int>
## 1 L 4
## 2 P 1