Praktik 1

#install.packages("tidyverse")
library(tidyverse)

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# 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.

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,…

df_crop$Entity <- as.factor(df_crop$Entity)
df_crop$Code <- as.factor(df_crop$Code)
#Periksa apakah ada perubahan
glimpse(df_crop)

## Rows: 13,075
## Columns: 14
## $ Entity                             <fct> "Afghanistan", "Afghanistan", "Afgh…
## $ Code                               <fct> AFG, AFG, 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,…

NO 1. 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
##    <fct>       <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)`

NO 2. 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
##    <fct>       <fct> <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>

NO 3. Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?

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

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

NO 4. Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?

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

## # A tibble: 406 × 3
##    Entity   Year `Wheat (tonnes per hectare)`
##    <fct>   <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 396 more rows

NO 5. Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?

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

## # A tibble: 2 × 14
##   Entity    Code   Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
##   <fct>     <fct> <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>

NO 6. Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?

filter(df_crop, Year == 2020)%>%
arrange(`Maize (tonnes per hectare)`)%>%
select(Entity, Year, `Maize (tonnes per hectare)`)

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

NO 7. Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.

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

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

NO 8. Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.

df_crop %>% 
  mutate(Rice_Status = ifelse(`Rice (tonnes per hectare)` > 4, "Tinggi", "Rendah")) %>% select(Code,Year,`Rice (tonnes per hectare)`,Rice_Status)

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

NO 9. Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?

df_crop%>% 
  filter(Entity == "Indonesia")%>% 
  summarise(
    Entity = first(Entity),
    `Mean Bananas (tonnes per hectare)` = mean(`Bananas (tonnes per hectare)`))

## # A tibble: 1 × 2
##   Entity    `Mean Bananas (tonnes per hectare)`
##   <fct>                                   <dbl>
## 1 Indonesia                                30.5

NO 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 (tonnes per hectare)` = sd(`Maize (tonnes per hectare)`)) %>%
  arrange(desc(`SD Maize (tonnes per hectare)`))

## # A tibble: 242 × 2
##    Entity                           `SD Maize (tonnes per hectare)`
##    <fct>                                                      <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

Ni Luh Nyoman Jennyta Az-zahra

2026-05-22

NO 1. Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.

NO 2. Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.

NO 3. Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?

NO 4. Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?

NO 5. Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?

NO 6. Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?

NO 7. Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.

NO 8. Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.

NO 9. Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?

NO 10. Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar