Praktik 1

Silahkan buat RMarkdown baru dan jawab setiap pertanyaan menggunakan fungsi-fungsi yang telah dipelajari pada RMarkdown tersebut dengan menampilkan kode dan outputnya. RMarkdown yang telah selesai lalu publikasi pada RPubs, dan link publikasi nya dapat dikirimkan di SIMARI pada tanggal 28 Mei 2026 dalam rentang 18.00-20.00 WITA.

memanggil library

library(tidyverse)

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Persiapkan File Data

# Menyimpan URL raw data GitHub ke dalam variabel
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv"

# Membaca data read_csv
df_crop <- read_csv(url)

## Rows: 13075 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Entity, Code
## dbl (12): Year, Wheat (tonnes per hectare), Rice (tonnes per hectare), Maize...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Melihat data

glimpse(df_crop)

## Rows: 13,075
## Columns: 14
## $ Entity                             <chr> "Afghanistan", "Afghanistan", "Afgh…
## $ Code                               <chr> "AFG", "AFG", "AFG", "AFG", "AFG", …
## $ Year                               <dbl> 1961, 1962, 1963, 1964, 1965, 1966,…
## $ `Wheat (tonnes per hectare)`       <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.9…
## $ `Rice (tonnes per hectare)`        <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.7…
## $ `Maize (tonnes per hectare)`       <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.4…
## $ `Soybeans (tonnes per hectare)`    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Potatoes (tonnes per hectare)`    <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.8…
## $ `Beans (tonnes per hectare)`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Peas (tonnes per hectare)`        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Cassava (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Barley (tonnes per hectare)`      <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.0…
## $ `Cocoa beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Bananas (tonnes per hectare)`     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.

data_select <- select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)`, `Cassava (tonnes per hectare)`)
data_select

## # A tibble: 13,075 × 4
##    Entity       Year `Potatoes (tonnes per hectare)` Cassava (tonnes per hecta…¹
##    <chr>       <dbl>                           <dbl>                       <dbl>
##  1 Afghanistan  1961                            8.67                          NA
##  2 Afghanistan  1962                            7.67                          NA
##  3 Afghanistan  1963                            8.13                          NA
##  4 Afghanistan  1964                            8.6                           NA
##  5 Afghanistan  1965                            8.8                           NA
##  6 Afghanistan  1966                            9.07                          NA
##  7 Afghanistan  1967                            9.8                           NA
##  8 Afghanistan  1968                           10                             NA
##  9 Afghanistan  1969                           10.2                           NA
## 10 Afghanistan  1970                            9.54                          NA
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Cassava (tonnes per hectare)`

Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.

select(df_crop, -c(`Soybeans (tonnes per hectare)`, `Beans (tonnes per hectare)`, `Peas (tonnes per hectare)`))

## # A tibble: 13,075 × 11
##    Entity      Code   Year `Wheat (tonnes per hectare)` Rice (tonnes per hecta…¹
##    <chr>       <chr> <dbl>                        <dbl>                    <dbl>
##  1 Afghanistan AFG    1961                        1.02                      1.52
##  2 Afghanistan AFG    1962                        0.974                     1.52
##  3 Afghanistan AFG    1963                        0.832                     1.52
##  4 Afghanistan AFG    1964                        0.951                     1.73
##  5 Afghanistan AFG    1965                        0.972                     1.73
##  6 Afghanistan AFG    1966                        0.867                     1.52
##  7 Afghanistan AFG    1967                        1.12                      1.92
##  8 Afghanistan AFG    1968                        1.16                      1.95
##  9 Afghanistan AFG    1969                        1.19                      1.98
## 10 Afghanistan AFG    1970                        0.956                     1.81
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # ℹ 6 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>,
## #   `Cassava (tonnes per hectare)` <dbl>, `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>,
## #   `Bananas (tonnes per hectare)` <dbl>

Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?

df_crop %>% 
  filter(Entity == "Indonesia") %>% 
  filter(`Rice (tonnes per hectare)` < 2) %>% 
  select(Year, `Rice (tonnes per hectare)`)

## # A tibble: 7 × 2
##    Year `Rice (tonnes per hectare)`
##   <dbl>                       <dbl>
## 1  1961                        1.76
## 2  1962                        1.79
## 3  1963                        1.72
## 4  1964                        1.76
## 5  1965                        1.77
## 6  1966                        1.77
## 7  1967                        1.76

Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?

df_crop %>% 
  filter(Year >= 2000) %>% 
  filter(`Wheat (tonnes per hectare)` > 5) %>% 
  select(Entity, Year, `Wheat (tonnes per hectare)`)

## # A tibble: 424 × 3
##    Entity   Year `Wheat (tonnes per hectare)`
##    <chr>   <dbl>                        <dbl>
##  1 Austria  2001                         5.24
##  2 Austria  2004                         5.92
##  3 Austria  2005                         5.03
##  4 Austria  2008                         5.69
##  5 Austria  2010                         5.01
##  6 Austria  2011                         5.85
##  7 Austria  2013                         5.37
##  8 Austria  2014                         5.92
##  9 Austria  2015                         5.70
## 10 Austria  2016                         6.25
## # ℹ 414 more rows

Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?

df_crop %>% 
  filter(Entity %in% c("Indonesia", "Malaysia")) %>% 
  filter(Year == 2015)

## # A tibble: 2 × 14
##   Entity    Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##   <chr>     <chr> <dbl>                        <dbl>                       <dbl>
## 1 Indonesia IDN    2015                           NA                        5.34
## 2 Malaysia  MYS    2015                           NA                        4.02
## # ℹ 9 more variables: `Maize (tonnes per hectare)` <dbl>,
## #   `Soybeans (tonnes per hectare)` <dbl>,
## #   `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## #   `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## #   `Barley (tonnes per hectare)` <dbl>,
## #   `Cocoa beans (tonnes per hectare)` <dbl>,
## #   `Bananas (tonnes per hectare)` <dbl>

Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?

df_crop %>% 
  filter(Year == 2020) %>% 
  filter(!is.na(`Maize (tonnes per hectare)`)) %>% 
  arrange(`Maize (tonnes per hectare)`) %>% 
  slice(1) %>% 
  select(Entity, Year, `Maize (tonnes per hectare)`)

## # A tibble: 0 × 3
## # ℹ 3 variables: Entity <chr>, Year <dbl>, Maize (tonnes per hectare) <dbl>

Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.

df_crop %>% 
  filter(Entity == "Indonesia") %>% 
  arrange(desc(`Potatoes (tonnes per hectare)`)) %>% 
  select(Year, `Potatoes (tonnes per hectare)`)

## # A tibble: 58 × 2
##     Year `Potatoes (tonnes per hectare)`
##    <dbl>                           <dbl>
##  1  2018                            18.7
##  2  2016                            18.3
##  3  2015                            18.2
##  4  2014                            17.7
##  5  2006                            16.9
##  6  2008                            16.7
##  7  1995                            16.6
##  8  2012                            16.6
##  9  2009                            16.5
## 10  2005                            16.4
## # ℹ 48 more rows

Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.

df_crop <- df_crop %>% 
  mutate(Rice_Status = if_else(`Rice (tonnes per hectare)` > 4, "Tinggi", "Rendah"))
df_crop %>% select(Entity, Year, `Rice (tonnes per hectare)`, Rice_Status)

## # A tibble: 13,075 × 4
##    Entity       Year `Rice (tonnes per hectare)` Rice_Status
##    <chr>       <dbl>                       <dbl> <chr>      
##  1 Afghanistan  1961                        1.52 Rendah     
##  2 Afghanistan  1962                        1.52 Rendah     
##  3 Afghanistan  1963                        1.52 Rendah     
##  4 Afghanistan  1964                        1.73 Rendah     
##  5 Afghanistan  1965                        1.73 Rendah     
##  6 Afghanistan  1966                        1.52 Rendah     
##  7 Afghanistan  1967                        1.92 Rendah     
##  8 Afghanistan  1968                        1.95 Rendah     
##  9 Afghanistan  1969                        1.98 Rendah     
## 10 Afghanistan  1970                        1.81 Rendah     
## # ℹ 13,065 more rows

Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?

df_crop %>% 
  filter(Entity == "Indonesia") %>% 
  summarise(Rata_Rata_Pisang = mean(`Bananas (tonnes per hectare)`, na.rm = TRUE))

## # A tibble: 1 × 1
##   Rata_Rata_Pisang
##              <dbl>
## 1             30.5

10.Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar

df_crop %>% 
  filter(Year >= 2010) %>% 
  group_by(Entity) %>% 
  summarise(`SD Maize` = sd(`Maize (tonnes per hectare)`, na.rm = TRUE)) %>% 
  arrange(desc(`SD Maize`))

## # A tibble: 242 × 2
##    Entity                           `SD Maize`
##    <chr>                                 <dbl>
##  1 Kuwait                                 9.24
##  2 United Arab Emirates                   9.19
##  3 Jordan                                 7.03
##  4 Israel                                 4.80
##  5 Saint Vincent and the Grenadines       2.89
##  6 Qatar                                  2.74
##  7 French Guiana                          2.50
##  8 New Caledonia                          2.29
##  9 Slovakia                               1.68
## 10 Oman                                   1.61
## # ℹ 232 more rows

Praktik 1

Nadya Salsabila

2026-05-21

Praktik 1