library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'ggplot2' was built under R version 4.5.3## Warning: package 'tidyr' was built under R version 4.5.3## Warning: package 'purrr' was built under R version 4.5.3## Warning: package 'dplyr' was built under R version 4.5.3## Warning: package 'stringr' was built under R version 4.5.3## Warning: package 'forcats' was built under R version 4.5.3## Warning: package 'lubridate' was built under R version 4.5.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#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.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)`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>3.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 1.764.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 rows5.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>6.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>7.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 rows8.Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.
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 rowsdf_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 30.510.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