kuis 1

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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 ...

colSums(is.na(df_mahasiswa))

##                    X         id_mahasiswa        jenis_kelamin 
##                    0                    0                    0 
## jam_belajar_per_hari  frekuensi_login_lms     motivasi_belajar 
##                    0                    0                    0 
##                  ipk 
##                    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 %>%
  group_by(jenis_kelamin) %>%
  summarize(rata_ipk=mean(ipk, na.rm = TRUE))

## # A tibble: 2 × 2
##   jenis_kelamin rata_ipk
##   <chr>            <dbl>
## 1 L                 3.16
## 2 P                 3.18

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

##    id_mahasiswa motivasi_belajar
## 54       MHS054              100
## 4        MHS004               98
## 45       MHS045               96
## 18       MHS018               92
## 48       MHS048               92

library(dplyr)

df_mahasiswa %>%
  filter(frekuensi_login_lms == max(frekuensi_login_lms,  na.rm = TRUE)) %>%
  select(id_mahasiswa, frekuensi_login_lms, ipk)

##    id_mahasiswa frekuensi_login_lms  ipk
## 1        MHS006                   7 3.10
## 2        MHS007                   7 2.98
## 3        MHS017                   7 3.06
## 4        MHS018                   7 3.89
## 5        MHS022                   7 3.69
## 6        MHS031                   7 3.22
## 7        MHS033                   7 3.11
## 8        MHS041                   7 3.73
## 9        MHS044                   7 3.15
## 10       MHS055                   7 3.21

df_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

q11 <- df_mahasiswa %>%
  arrange(desc(ipk))%>%
  slice(1)%>%
  select(id_mahasiswa,ipk,motivasi_belajar)
q11

##   id_mahasiswa  ipk motivasi_belajar
## 1       MHS018 3.89               92

q22 <- df_mahasiswa %>%
  filter(ipk>3.5,motivasi_belajar>85) %>%
  group_by(jenis_kelamin)%>%
  summarise(jumlah=n())
q22

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