library(tidyverse)
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url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.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.
- Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.
select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)` , `Cassava (tonnes per hectare)`)
## # 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", `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 1967
- Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada
tahun 2000 ke atas?
df_crop %>%
filter(`Wheat (tonnes per hectare)` > 5,
Year >= 2000) %>%
select(Entity)
## # A tibble: 424 × 1
## Entity
## <chr>
## 1 Austria
## 2 Austria
## 3 Austria
## 4 Austria
## 5 Austria
## 6 Austria
## 7 Austria
## 8 Austria
## 9 Austria
## 10 Austria
## # ℹ 414 more rows
- Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus
untuk tahun 2015 saja?
df_crop %>%
filter((Entity == "Indonesia" | Entity == "Malaysia"),
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? Jawab: Ini ga muncul karena di datasetnya tidak ada tahun 2020,
adanya maksimal di 2018
df_crop %>%
filter(Year == 2020) %>%
arrange(`Maize (tonnes per hectare)`) %>%
select(Entity)
## # A tibble: 0 × 1
## # ℹ 1 variable: Entity <chr>
- Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling
tinggi.
df_crop %>%
filter(Entity == "Indonesia") %>%
arrange(desc(`Potatoes (tonnes per hectare)`))
## # A tibble: 58 × 14
## Entity Code Year `Wheat (tonnes per hectare)` Rice (tonnes per hectare…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Indonesia IDN 2018 NA 5.19
## 2 Indonesia IDN 2016 NA 5.24
## 3 Indonesia IDN 2015 NA 5.34
## 4 Indonesia IDN 2014 NA 5.13
## 5 Indonesia IDN 2006 NA 4.62
## 6 Indonesia IDN 2008 NA 4.89
## 7 Indonesia IDN 1995 NA 4.35
## 8 Indonesia IDN 2012 NA 5.14
## 9 Indonesia IDN 2009 NA 5.00
## 10 Indonesia IDN 2005 NA 4.57
## # ℹ 48 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # 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>, …
- 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"))
## # A tibble: 13,075 × 15
## 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 10 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>, …
- Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari
seluruh tahun yang ada?
df_crop %>%
filter(Entity == "Indonesia") %>%
summarise(`Rata-rata Bananas` =
mean(`Bananas (tonnes per hectare)`,
na.rm = TRUE))
## # A tibble: 1 × 1
## `Rata-rata Bananas`
## <dbl>
## 1 30.5
- 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