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
## <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)`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
## <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>NO. 3 : Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?
filter(df_crop, Code == "IDN", `Rice (tonnes per hectare)` < 2) %>%
select(Year)## # A tibble: 7 × 1
## Year
## <dbl>
## 1 1961
## 2 1962
## 3 1963
## 4 1964
## 5 1965
## 6 1966
## 7 1967NO. 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: 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 rowsNO. 5 : Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?
filter(df_crop, Code %in% c("IDN", "MYS"), 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>NO. 6 : Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?
df_crop %>%
filter(Year == 2020) %>%
arrange(`Maize (tonnes per hectare)`) %>%
select(Entity, Code, Year, `Maize (tonnes per hectare)`)## # A tibble: 0 × 4
## # ℹ 4 variables: Entity <chr>, Code <chr>, Year <dbl>,
## # Maize (tonnes per hectare) <dbl>max(df_crop$Year)## [1] 2018Karena data maksimal tahun hanya sampai 2018, maka tidak ada data yang difilter di tahun 2020
NO. 7 : Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.
df_idn <- filter(df_crop, Code == "IDN")
arrange(df_idn, desc(`Potatoes (tonnes per hectare)`)) %>%
select(Entity, Year, `Potatoes (tonnes per hectare)`)## # A tibble: 58 × 3
## Entity Year `Potatoes (tonnes per hectare)`
## <chr> <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 rowsNO. 8 : Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.
df_rice <- mutate(df_crop, Rice_Status = ifelse(`Rice (tonnes per hectare)` > 4, "Tinggi", "Rendah"))
select(df_rice, `Rice (tonnes per hectare)`, Rice_Status)## # A tibble: 13,075 × 2
## `Rice (tonnes per hectare)` Rice_Status
## <dbl> <chr>
## 1 1.52 Rendah
## 2 1.52 Rendah
## 3 1.52 Rendah
## 4 1.73 Rendah
## 5 1.73 Rendah
## 6 1.52 Rendah
## 7 1.92 Rendah
## 8 1.95 Rendah
## 9 1.98 Rendah
## 10 1.81 Rendah
## # ℹ 13,065 more rowsNO. 9 : Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?
summarise(filter(df_crop, Code == "IDN"), Rata_Rata_Pisang = mean(`Bananas (tonnes per hectare)`,
na.rm = TRUE))## # A tibble: 1 × 1
## Rata_Rata_Pisang
## <dbl>
## 1 30.5NO. 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(Simpangan_Baku = sd(`Maize (tonnes per hectare)`, na.rm = TRUE)) %>%
arrange(desc(Simpangan_Baku))## # A tibble: 242 × 2
## Entity Simpangan_Baku
## <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
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