praktik1
naela amelya salsabila
2026-05-27
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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 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsurl <- "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.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 %>%
select(
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)`df_crop %>%
select(
-`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>df_crop %>%
filter(
Entity == "Indonesia",
`Rice (tonnes per hectare)` < 2
) %>%
select(
Entity,
Year,
`Rice (tonnes per hectare)`
)## # A tibble: 7 × 3
## Entity Year `Rice (tonnes per hectare)`
## <chr> <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 1.76df_crop %>%
filter(
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 rowsdf_crop %>%
filter(
Entity %in% c("Indonesia", "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>df_crop %>%
filter(Year == 2020) %>%
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>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>, …df_crop %>%
mutate(
Rice_Status = if_else(
`Rice (tonnes per hectare)` > 4,
"Tinggi",
"Rendah"
)
) %>%
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_Bananas =
mean(`Bananas (tonnes per hectare)`, na.rm = TRUE)
)## # A tibble: 1 × 1
## Rata_rata_Bananas
## <dbl>
## 1 30.5df_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