``

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

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 ...

sum(is.na(df_mahasiswa))

## [1] 0

table(df_mahasiswa$jenis_kelamin)

## 
##  L  P 
## 32 23

aggregate (ipk ~ jenis_kelamin, data = df_mahasiswa,mean)

##   jenis_kelamin      ipk
## 1             L 3.158125
## 2             P 3.176957

df_mahasiswa[order(-df_mahasiswa$motivasi_belajar), c("id_mahasiswa","motivasi_belajar"),] |> head(1)

##    id_mahasiswa motivasi_belajar
## 54       MHS054              100

df_mahasiswa[order(-df_mahasiswa$motivasi_belajar), c("id_mahasiswa",
"frekuensi_login_lms", "ipk")] |> head(5)

##    id_mahasiswa frekuensi_login_lms  ipk
## 54       MHS054                   4 3.41
## 4        MHS004                   3 3.43
## 45       MHS045                   2 3.33
## 18       MHS018                   7 3.89
## 48       MHS048                   4 3.70

#jam terbang tinngi = ipk tinggi
jam_tinggi <-df_mahasiswa %>%
  select(jam_belajar_per_hari, ipk) %>%
  filter(jam_belajar_per_hari >= 4)%>%
  summarise(mean= mean(ipk))

jam_rendah <-df_mahasiswa %>%
  select(jam_belajar_per_hari, ipk) %>%
  filter(jam_belajar_per_hari < 4) %>%
  summarise(mean= mean(ipk))

jam_rendah

##       mean
## 1 2.935357

jam_tinggi

##       mean
## 1 3.405185

df_mahasiswa[order(-df_mahasiswa$ipk),c("id_mahasiswa","ipk","motivasi_belajar")] |>head(1)

##    id_mahasiswa  ipk motivasi_belajar
## 18       MHS018 3.89               92

pintar <- df_mahasiswa %>%
  filter(ipk >= 3.5) %>%
  filter(motivasi_belajar >= 85) %>%
  select(jenis_kelamin, motivasi_belajar, ipk) %>%
  group_by(jenis_kelamin) %>%
  summarise(sum =n())
pintar

## # A tibble: 2 × 2
##   jenis_kelamin   sum
##   <chr>         <int>
## 1 L                 4
## 2 P                 1