# siapkan data dan library
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, uniondf = as.data.frame(read.csv("mahasiswa.csv"))df## 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
## 7 7 MHS007 P 1 7
## 8 8 MHS008 L 3 4
## 9 9 MHS009 P 2 6
## 10 10 MHS010 L 1 5
## 11 11 MHS011 L 1 1
## 12 12 MHS012 P 1 2
## 13 13 MHS013 P 2 6
## 14 14 MHS014 L 4 2
## 15 15 MHS015 L 1 2
## 16 16 MHS016 L 5 1
## 17 17 MHS017 L 2 7
## 18 18 MHS018 L 5 7
## 19 19 MHS019 L 5 2
## 20 20 MHS020 L 3 1
## 21 21 MHS021 L 1 6
## 22 22 MHS022 P 4 7
## 23 23 MHS023 L 4 4
## 24 24 MHS024 P 5 4
## 25 25 MHS025 L 3 1
## 26 26 MHS026 L 4 6
## 27 27 MHS027 L 2 3
## 28 28 MHS028 P 2 6
## 29 29 MHS029 L 2 2
## 30 30 MHS030 P 5 3
## 31 31 MHS031 P 2 7
## 32 32 MHS032 L 1 2
## 33 33 MHS033 P 1 7
## 34 34 MHS034 P 5 4
## 35 35 MHS035 L 4 1
## 36 36 MHS036 P 3 1
## 37 37 MHS037 L 4 6
## 38 38 MHS038 L 4 1
## 39 39 MHS039 P 1 1
## 40 40 MHS040 L 4 2
## 41 41 MHS041 L 5 7
## 42 42 MHS042 P 4 6
## 43 43 MHS043 P 4 5
## 44 44 MHS044 P 2 7
## 45 45 MHS045 P 5 2
## 46 46 MHS046 L 1 3
## 47 47 MHS047 L 4 5
## 48 48 MHS048 L 5 4
## 49 49 MHS049 P 4 5
## 50 50 MHS050 P 2 4
## 51 51 MHS051 L 4 6
## 52 52 MHS052 L 4 4
## 53 53 MHS053 L 3 4
## 54 54 MHS054 L 5 4
## 55 55 MHS055 P 2 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
## 7 44 2.98
## 8 69 3.08
## 9 44 2.82
## 10 46 2.93
## 11 56 2.64
## 12 35 2.71
## 13 59 3.11
## 14 81 3.31
## 15 44 2.90
## 16 90 3.46
## 17 52 3.06
## 18 92 3.89
## 19 84 3.09
## 20 73 2.73
## 21 47 2.96
## 22 72 3.69
## 23 83 3.54
## 24 81 3.61
## 25 70 2.65
## 26 89 3.32
## 27 59 2.94
## 28 53 3.14
## 29 48 2.89
## 30 87 3.40
## 31 71 3.22
## 32 39 2.66
## 33 46 3.11
## 34 87 3.56
## 35 91 3.13
## 36 66 2.95
## 37 68 3.67
## 38 90 3.29
## 39 52 2.52
## 40 80 3.17
## 41 90 3.73
## 42 73 3.05
## 43 91 3.29
## 44 63 3.15
## 45 96 3.33
## 46 43 2.49
## 47 82 3.13
## 48 92 3.70
## 49 73 3.22
## 50 57 3.05
## 51 92 3.65
## 52 88 3.30
## 53 80 3.29
## 54 100 3.41
## 55 71 3.21# cek missing value
sum(is.na(df))## [1] 0# cek laki laki perempuan
kelamin <- df %>%
group_by(jenis_kelamin) %>%
summarise(jumlah = n())
kelamin## # A tibble: 2 × 2
## jenis_kelamin jumlah
## <chr> <int>
## 1 L 32
## 2 P 23# rata rata ipk tinggi berdasrkan kelamin
ipk <- df %>%
group_by(jenis_kelamin) %>%
summarise(mean = mean(ipk))
ipk## # A tibble: 2 × 2
## jenis_kelamin mean
## <chr> <dbl>
## 1 L 3.16
## 2 P 3.18# motivasi belajae tinggi
motivasi <- df %>%
select(id_mahasiswa, motivasi_belajar) %>%
arrange(desc(motivasi_belajar))
motivasi## id_mahasiswa motivasi_belajar
## 1 MHS054 100
## 2 MHS004 98
## 3 MHS045 96
## 4 MHS018 92
## 5 MHS048 92
## 6 MHS051 92
## 7 MHS035 91
## 8 MHS043 91
## 9 MHS016 90
## 10 MHS038 90
## 11 MHS041 90
## 12 MHS026 89
## 13 MHS052 88
## 14 MHS030 87
## 15 MHS034 87
## 16 MHS019 84
## 17 MHS023 83
## 18 MHS001 82
## 19 MHS047 82
## 20 MHS005 81
## 21 MHS014 81
## 22 MHS024 81
## 23 MHS040 80
## 24 MHS053 80
## 25 MHS002 73
## 26 MHS020 73
## 27 MHS042 73
## 28 MHS049 73
## 29 MHS022 72
## 30 MHS003 71
## 31 MHS031 71
## 32 MHS055 71
## 33 MHS025 70
## 34 MHS008 69
## 35 MHS037 68
## 36 MHS036 66
## 37 MHS044 63
## 38 MHS006 61
## 39 MHS013 59
## 40 MHS027 59
## 41 MHS050 57
## 42 MHS011 56
## 43 MHS028 53
## 44 MHS017 52
## 45 MHS039 52
## 46 MHS029 48
## 47 MHS021 47
## 48 MHS010 46
## 49 MHS033 46
## 50 MHS007 44
## 51 MHS009 44
## 52 MHS015 44
## 53 MHS046 43
## 54 MHS032 39
## 55 MHS012 35# lms dan ipk
lms_ipkTinggi <- df %>%
arrange(desc(frekuensi_login_lms)) %>%
filter(ipk > 3.5) %>%
select(id_mahasiswa, frekuensi_login_lms, ipk)
lms_ipkRendah <- df %>%
arrange(desc(frekuensi_login_lms)) %>%
filter(ipk < 3.5) %>%
select(id_mahasiswa, frekuensi_login_lms, ipk)
lms_ipkTinggi## id_mahasiswa frekuensi_login_lms ipk
## 1 MHS018 7 3.89
## 2 MHS022 7 3.69
## 3 MHS041 7 3.73
## 4 MHS037 6 3.67
## 5 MHS051 6 3.65
## 6 MHS023 4 3.54
## 7 MHS024 4 3.61
## 8 MHS034 4 3.56
## 9 MHS048 4 3.70lms_ipkRendah## id_mahasiswa frekuensi_login_lms ipk
## 1 MHS006 7 3.10
## 2 MHS007 7 2.98
## 3 MHS017 7 3.06
## 4 MHS031 7 3.22
## 5 MHS033 7 3.11
## 6 MHS044 7 3.15
## 7 MHS055 7 3.21
## 8 MHS003 6 3.07
## 9 MHS009 6 2.82
## 10 MHS013 6 3.11
## 11 MHS021 6 2.96
## 12 MHS026 6 3.32
## 13 MHS028 6 3.14
## 14 MHS042 6 3.05
## 15 MHS010 5 2.93
## 16 MHS043 5 3.29
## 17 MHS047 5 3.13
## 18 MHS049 5 3.22
## 19 MHS008 4 3.08
## 20 MHS050 4 3.05
## 21 MHS052 4 3.30
## 22 MHS053 4 3.29
## 23 MHS054 4 3.41
## 24 MHS004 3 3.43
## 25 MHS027 3 2.94
## 26 MHS030 3 3.40
## 27 MHS046 3 2.49
## 28 MHS002 2 3.45
## 29 MHS005 2 2.83
## 30 MHS012 2 2.71
## 31 MHS014 2 3.31
## 32 MHS015 2 2.90
## 33 MHS019 2 3.09
## 34 MHS029 2 2.89
## 35 MHS032 2 2.66
## 36 MHS040 2 3.17
## 37 MHS045 2 3.33
## 38 MHS001 1 3.12
## 39 MHS011 1 2.64
## 40 MHS016 1 3.46
## 41 MHS020 1 2.73
## 42 MHS025 1 2.65
## 43 MHS035 1 3.13
## 44 MHS036 1 2.95
## 45 MHS038 1 3.29
## 46 MHS039 1 2.52# jam terbang tinggi = ipk tinggi
jam_tinggi <- df %>%
select(jam_belajar_per_hari, ipk) %>%
filter(jam_belajar_per_hari >= 4) %>%
summarise(mean = mean(ipk))
jam_rendah <- df %>%
select(jam_belajar_per_hari, ipk) %>%
filter(jam_belajar_per_hari < 4) %>%
summarise(mean = mean(ipk))
jam_rendah## mean
## 1 2.935357jam_tinggi## mean
## 1 3.405185# ipk tinggi dan motivasi
ipk_motivasi <- df %>%
select(id_mahasiswa, motivasi_belajar, ipk) %>%
arrange(desc(ipk))
ipk_motivasi## id_mahasiswa motivasi_belajar ipk
## 1 MHS018 92 3.89
## 2 MHS041 90 3.73
## 3 MHS048 92 3.70
## 4 MHS022 72 3.69
## 5 MHS037 68 3.67
## 6 MHS051 92 3.65
## 7 MHS024 81 3.61
## 8 MHS034 87 3.56
## 9 MHS023 83 3.54
## 10 MHS016 90 3.46
## 11 MHS002 73 3.45
## 12 MHS004 98 3.43
## 13 MHS054 100 3.41
## 14 MHS030 87 3.40
## 15 MHS045 96 3.33
## 16 MHS026 89 3.32
## 17 MHS014 81 3.31
## 18 MHS052 88 3.30
## 19 MHS038 90 3.29
## 20 MHS043 91 3.29
## 21 MHS053 80 3.29
## 22 MHS031 71 3.22
## 23 MHS049 73 3.22
## 24 MHS055 71 3.21
## 25 MHS040 80 3.17
## 26 MHS044 63 3.15
## 27 MHS028 53 3.14
## 28 MHS035 91 3.13
## 29 MHS047 82 3.13
## 30 MHS001 82 3.12
## 31 MHS013 59 3.11
## 32 MHS033 46 3.11
## 33 MHS006 61 3.10
## 34 MHS019 84 3.09
## 35 MHS008 69 3.08
## 36 MHS003 71 3.07
## 37 MHS017 52 3.06
## 38 MHS042 73 3.05
## 39 MHS050 57 3.05
## 40 MHS007 44 2.98
## 41 MHS021 47 2.96
## 42 MHS036 66 2.95
## 43 MHS027 59 2.94
## 44 MHS010 46 2.93
## 45 MHS015 44 2.90
## 46 MHS029 48 2.89
## 47 MHS005 81 2.83
## 48 MHS009 44 2.82
## 49 MHS020 73 2.73
## 50 MHS012 35 2.71
## 51 MHS032 39 2.66
## 52 MHS025 70 2.65
## 53 MHS011 56 2.64
## 54 MHS039 52 2.52
## 55 MHS046 43 2.49ipk_mot_85 <- df %>%
filter(ipk >= 3.5) %>%
filter(motivasi_belajar >= 55) %>%
group_by(jenis_kelamin) %>%
summarise(sum = n())
ipk_mot_85## # A tibble: 2 × 2
## jenis_kelamin sum
## <chr> <int>
## 1 L 6
## 2 P 3