Kuis 1

library(readxl)
df_mahasiswa <- read_excel("df_mahasiswa.xlsx")
df_mahasiswa

## # A tibble: 55 × 6
##    id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
##    <chr>        <chr>                        <dbl>               <dbl>
##  1 MHS001       L                                4                   1
##  2 MHS002       P                                4                   2
##  3 MHS003       P                                2                   6
##  4 MHS004       P                                5                   3
##  5 MHS005       L                                3                   2
##  6 MHS006       L                                3                   7
##  7 MHS007       P                                1                   7
##  8 MHS008       L                                3                   4
##  9 MHS009       P                                2                   6
## 10 MHS010       L                                1                   5
## # ℹ 45 more rows
## # ℹ 2 more variables: motivasi_belajar <dbl>, ipk <dbl>

head ("df_mahasiswa.xlsx")

## [1] "df_mahasiswa.xlsx"

any(is.na("df_mahasiswa.xlsx"))

## [1] FALSE

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 %>%
  count(jenis_kelamin)

## # A tibble: 2 × 2
##   jenis_kelamin     n
##   <chr>         <int>
## 1 L                32
## 2 P                23

library (dplyr)
df_mahasiswa %>%
  group_by (jenis_kelamin) %>%
  summarise (rata_rata_ipk = mean (ipk, na.rm = TRUE))

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

library (dplyr)
df_mahasiswa %>%
  select (id_mahasiswa, motivasi_belajar) %>% 
  arrange (desc (motivasi_belajar)) %>%
  head (n=1)

## # A tibble: 1 × 2
##   id_mahasiswa motivasi_belajar
##   <chr>                   <dbl>
## 1 MHS054                    100

library (dplyr)
df_mahasiswa %>%
  arrange (desc (frekuensi_login_lms)) %>%
  select (id_mahasiswa, ipk) %>%
  head (n=1)

## # A tibble: 1 × 2
##   id_mahasiswa   ipk
##   <chr>        <dbl>
## 1 MHS006         3.1

library (dplyr)
df_mahasiswa %>%
  filter(jam_belajar_per_hari >= 4) %>%
  arrange (desc (jam_belajar_per_hari)) %>%
  select (id_mahasiswa, ipk) %>%
  head (n=1)

## # A tibble: 1 × 2
##   id_mahasiswa   ipk
##   <chr>        <dbl>
## 1 MHS004        3.43

library (dplyr)
df_mahasiswa %>%
  filter(jam_belajar_per_hari < 4) %>%
  arrange (desc (jam_belajar_per_hari)) %>%
  select (id_mahasiswa, ipk) %>%
  head (n=1)

## # A tibble: 1 × 2
##   id_mahasiswa   ipk
##   <chr>        <dbl>
## 1 MHS005        2.83

library (dplyr)
df_mahasiswa %>%
  arrange (desc (ipk)) %>%
  select (id_mahasiswa, ipk, motivasi_belajar) %>%
  head (n=1)

## # A tibble: 1 × 3
##   id_mahasiswa   ipk motivasi_belajar
##   <chr>        <dbl>            <dbl>
## 1 MHS018        3.89               92

library (dplyr)
df_mahasiswa %>%
  filter (ipk > 3.5, motivasi_belajar > 85) %>%
  count (jenis_kelamin)

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