library(readxl)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
df_mahasiswa <- read_excel("df_mahasiswa.xlsx")
head(df_mahasiswa)
## # A tibble: 6 × 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
## # ℹ 2 more variables: motivasi_belajar <dbl>, ipk <dbl>
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>
library(readxl)
str(df_mahasiswa)
## tibble [55 × 6] (S3: tbl_df/tbl/data.frame)
## $ id_mahasiswa : chr [1:55] "MHS001" "MHS002" "MHS003" "MHS004" ...
## $ jenis_kelamin : chr [1:55] "L" "P" "P" "P" ...
## $ jam_belajar_per_hari: num [1:55] 4 4 2 5 3 3 1 3 2 1 ...
## $ frekuensi_login_lms : num [1:55] 1 2 6 3 2 7 7 4 6 5 ...
## $ motivasi_belajar : num [1:55] 82 73 71 98 81 61 44 69 44 46 ...
## $ ipk : num [1:55] 3.12 3.45 3.07 3.43 2.83 3.1 2.98 3.08 2.82 2.93 ...
library(readxl)
any(is.na(df_mahasiswa))
## [1] FALSE
library(readxl)
is.na(df_mahasiswa)
## id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
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## motivasi_belajar ipk
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library(dplyr)
q1<-df_mahasiswa %>%
count(jenis_kelamin)
q1
## # A tibble: 2 × 2
## jenis_kelamin n
## <chr> <int>
## 1 L 32
## 2 P 23
library(dplyr)
df_mahasiswa %>%
group_by(jenis_kelamin) %>%
summarise(ipk = mean(ipk, na.rm = TRUE)) %>%
arrange(desc(ipk))
## # A tibble: 2 × 2
## jenis_kelamin ipk
## <chr> <dbl>
## 1 P 3.18
## 2 L 3.16
library(dplyr)
q2 <- df_mahasiswa %>%
filter(motivasi_belajar == max(motivasi_belajar, na.rm = TRUE))%>%
count(id_mahasiswa)
q2
## # A tibble: 1 × 2
## id_mahasiswa n
## <chr> <int>
## 1 MHS054 1
library(dplyr)
q3 <- max(df_mahasiswa$frekuensi_login_lms, na.rm = TRUE)
q4 <- df_mahasiswa %>%
filter(frekuensi_login_lms == q3) %>%
mutate(ipk_tinggi = ipk > 3.5)
q3
## [1] 7
q4
## # A tibble: 10 × 7
## id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## <chr> <chr> <dbl> <dbl>
## 1 MHS006 L 3 7
## 2 MHS007 P 1 7
## 3 MHS017 L 2 7
## 4 MHS018 L 5 7
## 5 MHS022 P 4 7
## 6 MHS031 P 2 7
## 7 MHS033 P 1 7
## 8 MHS041 L 5 7
## 9 MHS044 P 2 7
## 10 MHS055 P 2 7
## # ℹ 3 more variables: motivasi_belajar <dbl>, ipk <dbl>, ipk_tinggi <lgl>
library(dplyr)
q2 <- df_mahasiswa %>%
filter(frekuensi_login_lms == max(frekuensi_login_lms, na.rm = TRUE))%>%
count(id_mahasiswa)
q2
## # A tibble: 10 × 2
## id_mahasiswa n
## <chr> <int>
## 1 MHS006 1
## 2 MHS007 1
## 3 MHS017 1
## 4 MHS018 1
## 5 MHS022 1
## 6 MHS031 1
## 7 MHS033 1
## 8 MHS041 1
## 9 MHS044 1
## 10 MHS055 1
aggregate(ipk ~ jam_belajar_per_hari >= 4, data = df_mahasiswa, FUN = mean)
## jam_belajar_per_hari >= 4 ipk
## 1 FALSE 2.935357
## 2 TRUE 3.405185
library(dplyr)
q1 <- df_mahasiswa %>%
filter(ipk == max(ipk, na.rm = TRUE)) %>%
select(jenis_kelamin, ipk, motivasi_belajar, id_mahasiswa)
q1
## # A tibble: 1 × 4
## jenis_kelamin ipk motivasi_belajar id_mahasiswa
## <chr> <dbl> <dbl> <chr>
## 1 L 3.89 92 MHS018
library(dplyr)
q1 <- df_mahasiswa %>%
filter(ipk >= 3.5, motivasi_belajar >85) %>%
count(jenis_kelamin)
q1
## # A tibble: 2 × 2
## jenis_kelamin n
## <chr> <int>
## 1 L 4
## 2 P 1