#install.packages("tidyverse")
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.
df_crop$Entity <- as.factor(df_crop$Entity)
df_crop$Code <- as.factor(df_crop$Code)
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,…
- 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)`
- Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.
data_select <- select(df_crop, -c(`Soybeans (tonnes per hectare)`, `Beans (tonnes per hectare)`, `Peas (tonnes per hectare)`))
data_select
## # 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>
- Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya
di bawah 2 ton?
data_filter <- filter(df_crop, Entity == "Indonesia", `Rice (tonnes per hectare)` < 2) %>%
select(Year, `Rice (tonnes per hectare)`)
data_filter
## # 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?
data_filter <- filter(df_crop, Year >= 2000, `Wheat (tonnes per hectare)` > 5) %>%
select(Entity, Year, `Wheat (tonnes per hectare)`)
data_filter
## # A tibble: 424 × 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 414 more rows
- 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>
- Negara mana yang punya hasil jagung (Maize) paling rendah di tahun
2020?
df_crop %>%
filter(Year == 2020,) %>%
arrange((`Maize (tonnes per hectare)`))
## # A tibble: 0 × 14
## # ℹ 14 variables: Entity <fct>, Code <fct>, Year <dbl>,
## # Wheat (tonnes per hectare) <dbl>, Rice (tonnes per hectare) <dbl>,
## # 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>
- Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling
tinggi.
df_crop %>%
filter(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
- 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(Entity, Code, Year, `Rice (tonnes per hectare)`, Rice_Status)
## # A tibble: 13,075 × 5
## Entity Code Year `Rice (tonnes per hectare)` Rice_Status
## <fct> <fct> <dbl> <dbl> <chr>
## 1 Afghanistan AFG 1961 1.52 Rendah
## 2 Afghanistan AFG 1962 1.52 Rendah
## 3 Afghanistan AFG 1963 1.52 Rendah
## 4 Afghanistan AFG 1964 1.73 Rendah
## 5 Afghanistan AFG 1965 1.73 Rendah
## 6 Afghanistan AFG 1966 1.52 Rendah
## 7 Afghanistan AFG 1967 1.92 Rendah
## 8 Afghanistan AFG 1968 1.95 Rendah
## 9 Afghanistan AFG 1969 1.98 Rendah
## 10 Afghanistan AFG 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 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
## <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